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Basu Roy Chowdhury, Somnath, and Snigdha Chaturvedi. "Sustaining Fairness via Incremental Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (June 26, 2023): 6797–805. http://dx.doi.org/10.1609/aaai.v37i6.25833.
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Machine learning systems are often deployed for making critical decisions like credit lending, hiring, etc. While making decisions, such systems often encode the user's demographic information (like gender, age) in their intermediate representations. This can lead to decisions that are biased towards specific demographics. Prior work has focused on debiasing intermediate representations to ensure fair decisions. However, these approaches fail to remain fair with changes in the task or demographic distribution. To ensure fairness in the wild, it is important for a system to adapt to such changes as it accesses new data in an incremental fashion. In this work, we propose to address this issue by introducing the problem of learning fair representations in an incremental learning setting. To this end, we present Fairness-aware Incremental Representation Learning (FaIRL), a representation learning system that can sustain fairness while incrementally learning new tasks. FaIRL is able to achieve fairness and learn new tasks by controlling the rate-distortion function of the learned representations. Our empirical evaluations show that FaIRL is able to make fair decisions while achieving high performance on the target task, outperforming several baselines.
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Li, Yanying, Xiuling Wang, Yue Ning, and Hui Wang. "FairLP: Towards Fair Link Prediction on Social Network Graphs." Proceedings of the International AAAI Conference on Web and Social Media 16 (May 31, 2022): 628–39. http://dx.doi.org/10.1609/icwsm.v16i1.19321.
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Link prediction has been widely applied in social network analysis. Despite its importance, link prediction algorithms can be biased by disfavoring the links between individuals in particular demographic groups. In this paper, we study one particular type of bias, namely, the bias in predicting inter-group links (i.e., links across different demographic groups). First, we formalize the definition of bias in link prediction by providing quantitative measurements of accuracy disparity, which measures the difference in prediction accuracy of inter-group and intra-group links. Second, we unveil the existence of bias in six existing state-of-the-art link prediction algorithms through extensive empirical studies over real world datasets. Third, we identify the imbalanced density across intra-group and inter-group links in training graphs as one of the underlying causes of bias in link prediction. Based on the identified cause, fourth, we design a pre-processing bias mitigation method named FairLP to modify the training graph, aiming to balance the distribution of intra-group and inter-group links while preserving the network characteristics of the graph. FairLP is model-agnostic and thus is compatible with any existing link prediction algorithm. Our experimental results on real-world social network graphs demonstrate that FairLP achieves better trade-off between fairness and prediction accuracy than the existing fairness-enhancing link prediction methods.
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Swain, Phillip A. "Procedurally fair? Fairly procedural?: … ethics, fairness and welfare practice." Children Australia 24, no. 3 (1999): 34–38. http://dx.doi.org/10.1017/s103507720000924x.
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This paper examines the principles of procedural fairness and their application to welfare practice. The paper considers whether social workers ought to measure the adequacy of their practice, not just against those requirements ususally set out in the professional Codes of Ethics, but also against the procedural fairness expectations of decision-making more usually the province of courts and like bodies. The paper concludes that these expectations are not only in keeping with the Code of Ethics, but that competent practice demands no less of practitioners.
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Sweeney, James A. "Scrutiny: Alconbury and the Right to a Fairly Fair Hearing." European Public Law 8, Issue 1 (March 1, 2002): 35–44. http://dx.doi.org/10.54648/395961.
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Murphy, Andrew, and Ben Jenner‐Leuthart. "Fairly sold? Adding value with fair trade coffee in cafes." Journal of Consumer Marketing 28, no. 7 (November 2011): 508–15. http://dx.doi.org/10.1108/07363761111181491.
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Kunz. "The Fair Rubber Association: where fairly traded rubber hits the road." Journal of Fair Trade 2, no. 2 (2021): 13. http://dx.doi.org/10.13169/jfairtrade.2.2.0013.
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Xu, Xiaolin, Song Wu, Hai Jin, and Chuxiong Yan. "Fairly Sharing the Network for Multitier Applications in Clouds." International Journal of Web Services Research 12, no. 4 (October 2015): 29–47. http://dx.doi.org/10.4018/ijwsr.2015100103.
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A significant trend caused by cloud computing is to aggregate applications for sharing resources. Thus, it is necessary to provide fair resources and performance among applications, especially for the network, which is provided in the best-effort manner in current clouds. Although many studies have made efforts for provisioning fair bandwidth, they are not sufficient for network fairness. In fact, for interactive applications, response time is more sensitive than bandwidth, and users expect a fair response time not just bandwidth. In this study, the authors want to investigate whether the traditional methods of sharing bandwidth can help the fairness of response time. They show that: (1) bandwidth has little relationship to response time, and adjusting bandwidth hardly affects response time in most cases. Thus, the traditional methods cannot help the fairness of response time much; and (2) the fairness between components is different from the one between transactions, and many prior studies only consider the former while ignoring the latter. Thus, the authors cannot help much for multitier applications consisting of multiple transactions either. As a result, they construct a model with two metrics to evaluate the fairness status of the network sharing, while considering the applications' characteristics on both the response time and throughput. Based on the model, they also propose a mechanism to improve the fairness status. The evaluation results show that the authors' mechanism improves the fairness status by 26.5%–52.8%, and avoids performance degradation compared to some practical mechanisms.
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Han, Jiatong, and Warut Suksompong. "Fast & Fair: A Collaborative Platform for Fair Division Applications." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 21 (March 24, 2024): 23796–98. http://dx.doi.org/10.1609/aaai.v38i21.30568.
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Fair division, the study of how to fairly allocate resources among agents, has received substantial interest in the areas of artificial intelligence and multiagent systems. While there is an extensive theoretical literature on fair division by now, the developed algorithms are still mostly confined to research papers and inaccessible to the public. We attempt to bridge this gap by developing Fast & Fair, an open-source web application that hosts a number of fair allocation algorithms with user-friendly interfaces and explainable outcomes. In contrast to existing implementations, Fast & Fair is a collaborative platform that is open to community contributions and thereby facilitates the deployment of additional algorithms.
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Li, Shu, Jiong Yu, Xusheng Du, Yi Lu, and Rui Qiu. "Fair Outlier Detection Based on Adversarial Representation Learning." Symmetry 14, no. 2 (February 9, 2022): 347. http://dx.doi.org/10.3390/sym14020347.
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Outlier detection aims to identify rare, minority objects in a dataset that are significantly different from the majority. When a minority group (defined by sensitive attributes, such as gender, race, age, etc.) does not represent the target group for outlier detection, outlier detection methods are likely to propagate statistical biases in the data and generate unfair results. Our work focuses on studying the fairness of outlier detection. We characterize the properties of fair outlier detection and propose an appropriate outlier detection method that combines adversarial representation learning and the LOF algorithm (AFLOF). Unlike the FairLOF method that adds fairness constraints to the LOF algorithm, AFLOF uses adversarial networks to learn the optimal representation of the original data while hiding the sensitive attribute in the data. We introduce a dynamic weighting module that assigns lower weight values to data objects with higher local outlier factors to eliminate the influence of outliers on representation learning. Lastly, we conduct comparative experiments on six publicly available datasets. The results demonstrate that compared to the density-based LOF method and the recently proposed FairLOF method, our proposed AFLOF method has a significant advantage in both the outlier detection performance and fairness.
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Pickering, Jonathan, Frank Jotzo, and Peter J. Wood. "Sharing the Global Climate Finance Effort Fairly with Limited Coordination." Global Environmental Politics 15, no. 4 (November 2015): 39–62. http://dx.doi.org/10.1162/glep_a_00325.
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Mobilizing climate finance for developing countries is crucial for achieving a fair and effective global climate regime. To date, developed countries retain wide discretion over their national contributions. We explore how different degrees of international coordination may influence the fairness of the global financing effort, and we present quantitative scenarios, for both the metrics used to distribute the collective effort among countries contributing funding, and the number of contributing countries. We find that an intermediate degree of coordination—combining nationally determined financing pledges with a robust international review mechanism—may reduce distortions in relative efforts as well as shortfalls in overall funding, while reflecting reasonable differences over what constitutes a fair share. A broader group of contributors may do little to improve adequacy or equity unless it can converge on credible measures of responsibility and capacity. Our analysis highlights the importance of building common understandings about effort sharing.
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Maccheroni, Fabio, Fabio Maccheroni, Massimo Marinacci, and Massimo Marinacci. "How to cut a pizza fairly: Fair division with decreasing marginal evaluations." Social Choice and Welfare 20, no. 3 (June 1, 2003): 457–65. http://dx.doi.org/10.1007/s003550200192.
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Mateen, Muhammad Haris, Ibrahim Al-Dayel, and Turki Alsuraiheed. "Fermatean Fuzzy Fairly Aggregation Operators with Multi-Criteria Decision-Making." Axioms 12, no. 9 (September 7, 2023): 865. http://dx.doi.org/10.3390/axioms12090865.
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A Fermatean fuzzy set (FRFS) is the extension of a fuzzy set, an intuitionistic fuzzy set, and a Pythagorean fuzzy set, and is used in different fields. Unlike other fuzzy structures, the sum of cubes of membership grades in FRFSs approximates a unit interval, increasing uncertainty. In this study, we intend to provide unique operational rules and aggregation operators (AOs) inside a Fermatean fuzzy environment. To develop a fair remedy for the membership degree and non-membership degree features of “Fermatean fuzzy numbers (FRFNs)”, our solution introduces new neutral or fair operating principles, which include the concept of proportional distribution. Based on the suggested operating principles, we provide the “Fermatean fuzzy fairly weighted average operator and the Fermatean fuzzy fairly ordered weighted averaging operator”. Our suggested AOs provide more generalized, reliable, and exact data than previous techniques. Combining the recommended AOs with multiple decision-makers and partial weight information under FRFSs, we also devised a technique for “multi-criteria decision-making”. To illustrate the application of our novel method, we provide an example of the algorithm’s effectiveness in addressing decision-making challenges.
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Oh, Hoon, Ariel D. Procaccia, and Warut Suksompong. "Fairly Allocating Many Goods with Few Queries." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2141–48. http://dx.doi.org/10.1609/aaai.v33i01.33012141.
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We investigate the query complexity of the fair allocation of indivisible goods. For two agents with arbitrary monotonic valuations, we design an algorithm that computes an allocation satisfying envy-freeness up to one good (EF1), a relaxation of envy-freeness, using a logarithmic number of queries. We show that the logarithmic query complexity bound also holds for three agents with additive valuations. These results suggest that it is possible to fairly allocate goods in practice even when the number of goods is extremely large. By contrast, we prove that computing an allocation satisfying envyfreeness and another of its relaxations, envy-freeness up to any good (EFX), requires a linear number of queries even when there are only two agents with identical additive valuations.
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He, Liyu, Carl Hsin-han Shen, and Cheng-Yi Shiu. "Is fair value information fairly priced? Evidence from IPOs in global capital markets✰." Journal of Banking & Finance 135 (February 2022): 106368. http://dx.doi.org/10.1016/j.jbankfin.2021.106368.
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Nevalainen, Terttu, and Matti Rissanen. "Fairly pretty or pretty fair? On the development and grammaticalization of English downtoners." Language Sciences 24, no. 3-4 (May 2002): 359–80. http://dx.doi.org/10.1016/s0388-0001(01)00038-9.
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Li, Dandan, Ofir Turel, Shuyue Zhang, and Qinghua He. "Self-Serving Dishonesty Partially Substitutes Fairness in Motivating Cooperation When People Are Treated Fairly." International Journal of Environmental Research and Public Health 19, no. 10 (May 23, 2022): 6326. http://dx.doi.org/10.3390/ijerph19106326.
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Fairness is a key expectation in social interactions. Its violation leads to adverse reactions, including non-cooperation and dishonesty. The present study aimed to examine how (1) fair (unfair) treatment may drive cooperation (defection) and honesty (self-serving dishonesty), (2) dishonesty primes further moral disengagement and reduced cooperation, and (3) dishonesty weakens (substitutes) the effect of fairness on cooperation. The prisoner’s dilemma (Experiment 1 and 2) and die-rolling task (Experiment 2) were employed for capturing cooperation and dishonest behaviors, respectively. To manipulate perceived unfairness, participants were randomly assigned to play the prisoner’s dilemma game, where players either choose more cooperation (fair condition) or defection (unfair condition). Results of Experiment 1 (n = 102) suggested that participants perceive higher unfairness and behave less cooperatively when the other player primarily chooses defection. Results of Exp. 2 (n = 240) (a) confirmed Exp. 1 results, (b) showed that players in the unfair condition also show more self-serving dishonest behavior, and (c) that dishonest behavior weakens the effect of fairness on cooperation. Together, these results extended previous work by highlighting the self-serving lies when the opponent is fair trigger higher cooperation, presumably as a means to alleviate self-reflective moral emotions or restore justice.
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Driskell, Shannon O. S. "Students' Strategies for Fair Shares." Mathematics Teaching in the Middle School 10, no. 3 (October 2004): 132–35. http://dx.doi.org/10.5951/mtms.10.3.132.
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Children often begin to construct an informal understanding of fractions before entering school as they learn to share their crayons or snacks fairly with friends. NCTM (2000) recommends that teachers recognize and build on each child's informal knowledge of fractions during grades K–2. In grades 3–5, children should be actively engaged in constructing conceptual knowledge about fraction concepts, with an emphasis on computational fluency as they progress into grades 6–8. The NCTM (2000) further suggests that “The study of rational numbers in the middle grades should build on students' prior knowledge of whole-number concepts and skills and their encounter with fractions, decimals, and percents in lower grades and in everyday life” (p. 215).
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Almeida, Bruno José Machado de. "True and Fair View." Review of Business and Legal Sciences, no. 22 (July 26, 2017): 175. http://dx.doi.org/10.26537/rebules.v0i22.1021.
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De acordo com o parágrafo 46 da estrutura concetual do SNC as demonstrações financeiras devem mostrar uma imagem verdadeira e apropriada, ou apresentar apropriadamente, a posição financeira, o desempenho e as alterações da posição financeira de uma entidade. Todavia, a estrutura concetual do SNC não interpreta diretamente tais conceitos, situação que se pode refletir na qualidade do reporting financeiro apresentado e divulgado. O referencial contabilístico português apresenta, como equivalentes, as noções de true and fair view, presentfairly, fairly reflect ou fair reflection, e silenciando toda a polémica envolvente às expressões apresentadas. Com efeito, a primeira expressão é identificada, com a União Europeia, onde a apresentação e divulgação da informação financeira é baseada num conjunto de princípios — principies — based standards — e pressupõe o exercício de um julgamento, enquanto que as três expressões seguintes traduzem o standard de reporting financeiro, para os Estados Unidos, tendo subjacente a abordagem designado por — rule based standards — A não abordagem do SNC levou-nos a refletir sobre o assunto apresentado, apresentando uma análise multifacetada do conceito.
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Holz, Christian, Sivan Kartha, and Tom Athanasiou. "Fairly sharing 1.5: national fair shares of a 1.5 °C-compliant global mitigation effort." International Environmental Agreements: Politics, Law and Economics 18, no. 1 (August 19, 2017): 117–34. http://dx.doi.org/10.1007/s10784-017-9371-z.
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Hershkowitz, D. Ellis, Anson Kahng, Dominik Peters, and Ariel D. Procaccia. "District-Fair Participatory Budgeting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 6 (May 18, 2021): 5464–71. http://dx.doi.org/10.1609/aaai.v35i6.16688.
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Participatory budgeting is a method used by city governments to select public projects to fund based on residents' votes. Many cities use participatory budgeting at a district level. Typically, a budget is divided among districts proportionally to their population, and each district holds an election over local projects and then uses its budget to fund the projects most preferred by its voters. However, district-level participatory budgeting can yield poor social welfare because it does not necessarily fund projects supported across multiple districts. On the other hand, decision making that only takes global social welfare into account can be unfair to districts: A social-welfare-maximizing solution might not fund any of the projects preferred by a district, despite the fact that its constituents pay taxes to the city. Thus, we study how to fairly maximize social welfare in a participatory budgeting setting with a single city-wide election. We propose a notion of fairness that guarantees each district at least as much welfare as it would have received in a district-level election. We show that, although optimizing social welfare subject to this notion of fairness is NP-hard, we can efficiently construct a lottery over welfare-optimal outcomes that is fair in expectation. Moreover, we show that, when we are allowed to slightly relax fairness, we can efficiently compute a fair solution that is welfare-maximizing, but which may overspend the budget.
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Lahav, Ori, Egor Namakonov, Jonas Oberhauser, Anton Podkopaev, and Viktor Vafeiadis. "Making weak memory models fair." Proceedings of the ACM on Programming Languages 5, OOPSLA (October 20, 2021): 1–27. http://dx.doi.org/10.1145/3485475.
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Liveness properties, such as termination, of even the simplest shared-memory concurrent programs under sequential consistency typically require some fairness assumptions about the scheduler. Under weak memory models, we observe that the standard notions of thread fairness are insufficient, and an additional fairness property, which we call memory fairness, is needed. In this paper, we propose a uniform definition for memory fairness that can be integrated into any declarative memory model enforcing acyclicity of the union of the program order and the reads-from relation. For the well-known models, SC, x86-TSO, RA, and StrongCOH, that have equivalent operational and declarative presentations, we show that our declarative memory fairness condition is equivalent to an intuitive model-specific operational notion of memory fairness, which requires the memory system to fairly execute its internal propagation steps. Our fairness condition preserves the correctness of local transformations and the compilation scheme from RC11 to x86-TSO, and also enables the first formal proofs of termination of mutual exclusion lock implementations under declarative weak memory models.
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Ciccone, Luca, and Luca Padovani. "Fair termination of binary sessions." Proceedings of the ACM on Programming Languages 6, POPL (January 16, 2022): 1–30. http://dx.doi.org/10.1145/3498666.
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A binary session is a private communication channel that connects two processes, each adhering to a protocol description called session type . In this work, we study the first type system that ensures the fair termination of binary sessions. A session fairly terminates if all of the infinite executions admitted by its protocol are deemed unrealistic because they violate certain fairness assumptions . Fair termination entails the eventual completion of all pending input/output actions, including those that depend on the completion of an unbounded number of other actions in possibly different sessions. This form of lock freedom allows us to address a large family of natural communication patterns that fall outside the scope of existing type systems. Our type system is also the first to adopt fair subtyping , a liveness-preserving refinement of the standard subtyping relation for session types that so far has only been studied theoretically. Fair subtyping is surprisingly subtle not only to characterize concisely but also to use appropriately, to the point that the type system must carefully account for all usages of fair subtyping to avoid compromising its liveness-preserving properties.
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Sunaryo, Sunaryo. "Konsep Fairness John Rawls, Kritik dan Relevansinya." Jurnal Konstitusi 19, no. 1 (March 28, 2022): 001. http://dx.doi.org/10.31078/jk1911.
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Keadilan merupakan hal yang sangat fundamental dalam masyarakat majemuk. Pandangan Rawls mengenai keadilan yang dipahami sebagai Justice as Fairness mencoba menawarkan bagaimana kita menata masyarakat yang majemuk secara fair. Konsep fairness dipahami sebagai kemampuan untuk bersikap dan bertindak yang dapat diterima dan didukung secara timbal balik. Dalam pandangan Rawls, adanya penerimaan dan dukungan secara timbal balik dari para subjek yang bebas dan setara, tanpa intimidasi dan tekanan adalah tanda bahwa kesepakatan tersebut fair. Ia menyebut sikap semacam ini sebagai kewarasan publik (reasonableness). Untuk membangun masyarakat majemuk yang fair, kelompok-kelompok yang ada di dalam masyarakat harus mampu bertindak secara waras (reasonable). Dengan kapasitas ini mereka akan mampu menyepakati prinsip-prinsip keadilan yang akan menjadi basis kesatuan sosial (basis of social unity) mereka. Konsep mengenai fairness ini sangat penting untuk masyarakat majemuk seperti Indonesia. Jika kita gagal menghadirkan keadilan dalam masyarakat majemuk, maka kesatuan kita sebagai sebuah masyarakat akan berada dalam ancaman serius.Justice is very fundamental in a pluralistic society. Rawls's view of justice, which is understood as fairness, tries to offer how we organize a pluralistic society fairly. This view is crucial to be considered. The concept of fairness is understood as the ability to act that is acceptable and supported reciprocally. In Rawls's view, the existence of mutual acceptance and support from free and equal citizens without intimidation and pressure signifies that the agreement is fair. He called this kind of attitude reasonableness. In a just society, people must be able to act reasonably. With this capacity, they will agree on the principles of justice, which will be the basis of their social unity. The concept of fairness is essential for a pluralistic society like Indonesia. If we fail to bring justice in a pluralistic society, then our unity as a society will be under serious threat.
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Thorkildsen, Theresa A. "Defining Social Goods and Distributing Them Fairly: The Development of Conceptions of Fair Testing Practices." Child Development 62, no. 4 (August 1991): 852. http://dx.doi.org/10.2307/1131182.
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Sumartinah, Sumartinah. "Ketrampilan Komunikasi Interpersonal, Penguasaan Teori Praktik Fisioterapi, dan Kompetensi Profesi Fisioterapis Mahasiswa yang Belajar Praktik Pengalaman Klinis di Rumah Sakit Ortopedi Prof. R. Soeharso Surakarta." SOTIRIA (Jurnal Theologia dan Pendidikan Agama Kristen) 2, no. 2 (December 28, 2019): 109–20. http://dx.doi.org/10.47166/sot.v2i2.14.
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The research is aimed at describing the interpersonal communication skills, the mastery of physiotherapy theory and practice, and the competence of physiotherapist profession of the students of the physiotherapy students conducting clinical practice at the Orthopedics Hospital Prof. Dr. R. Soeharso Surakarta. The research used a qualitative-etnomethodology by describing the process of the students’ clinical practice. Subject of the research were 82 Physiotherapy students who conducted clinical practice at the Orthopedics Hospital Prof. Dr. R. Soeharso Surakarta. Data of the research were taken by collecting the clinical status records made by the students, and the use of the documents. The results of the research shows that (1) The interpersonal communication skills of the students of Diploma 3 in Physiotherapy range from Poor, Fair, Fairly Good, to Good (dominated by Fairly Good, and Good categories). Meanwhile, the interpersonal communication skills of the students of Diploma 4 in Physiotherapy range from Poor, Fair, Fairly Good, to Good (dominated by Poor and Good categories). (2) The mastery of physiotherapy theory and practice of the students of Diploma 3 in Physiotherapy range from Fair, Fairly Good, to Good (dominated by Good categories). Meanwhile, the mastery of physiotherapy theory and practice of the students of Diploma 4 in Physiotherapy range from Fair, Fairly Good, Good, to Very Good (dominated by Fairly Good categories). (3) The Competence of Physiotherapist Profession the students of Diploma 3 in Physiotherapy range from Fair, Fairly Good, Good, to Very Good (dominated by Fairly Good, and Good categories). Meanwhile, the Competence of Physiotherapist Profession the students of Diploma 4 in Physiotherapy range from Fair, Fairly Good, Good, to Very Good (dominated by Fair categories).
Abstrak: Penelitian ini bertujuan untuk mendeskripsikan keterampilan komunikasi interpersonal, penguasaan teori dan praktik fisioterapi, dan kompetensi profesi fisioterapis mahasiswa mahasiswa fisioterapi yang melakukan praktik klinis di Rumah Sakit Ortopedi Prof. Dr. R. Soeharso Surakarta. Penelitian ini menggunakan kualitatif-etnometodologi dengan menggambarkan proses praktik klinis siswa. Subjek penelitian adalah 82 mahasiswa Fisioterapi yang melakukan praktik klinis di Rumah Sakit Ortopedi Prof. Dr. R. Soeharso Surakarta. Data penelitian diambil dengan mengum-pulkan catatan status klinis yang dibuat oleh siswa, dan penggunaan dokumen. Hasil penelitian menunjukkan bahwa (1) Keterampilan komunikasi interpersonal dari siswa Diploma 3 dalam Fisioterapi berkisar dari kategori Buruk, Adil, Cukup Baik, hingga Baik (didominasi oleh kategori Cukup Baik, dan Baik). Sementara itu, keterampilan komunikasi interpersonal siswa Diploma 4 dalam Fisioterapi berkisar dari Buruk, Adil, Cukup Baik, hingga Baik (didominasi oleh kategori Buruk dan Baik). (2) Penguasaan teori fisioterapi dan praktik mahasiswa Diploma 3 dalam Fisioterapi berkisar dari Adil, Cukup Baik, hingga Baik (didominasi oleh kategori Baik). Sementara itu, penguasaan teori fisioterapi dan praktik para mahasiswa Diploma 4 dalam Fisioterapi berkisar dari Adil, Cukup Baik, Baik, hingga Sangat Baik (didominasi oleh kategori Cukup Baik). (3) Kompetensi Profesi Fisioterapis mahasiswa Diploma 3 dalam Fisioterapi berkisar dari Cukup, Cukup Baik, Bagus, hingga Sangat Bagus (didominasi oleh kategori Cukup Bagus, dan Baik). Sementara itu, Kompetensi Profesi Fisioterapis mahasiswa Diploma 4 dalam Fisioterapi berkisar dari Wajar, Cukup Baik, Bagus, hingga Sangat Bagus (didominasi oleh kategori Wajar).
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Bei, Xiaohui, Guangda Huzhang, and Warut Suksompong. "Truthful fair division without free disposal." Social Choice and Welfare 55, no. 3 (April 25, 2020): 523–45. http://dx.doi.org/10.1007/s00355-020-01256-0.
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Abstract We study the problem of fairly dividing a heterogeneous resource, commonly known as cake cutting and chore division, in the presence of strategic agents. While a number of results in this setting have been established in previous works, they rely crucially on the free disposal assumption, meaning that the mechanism is allowed to throw away part of the resource at no cost. In the present work, we remove this assumption and focus on mechanisms that always allocate the entire resource. We exhibit a truthful and envy-free mechanism for cake cutting and chore division for two agents with piecewise uniform valuations, and we complement our result by showing that such a mechanism does not exist when certain additional constraints are imposed on the mechanisms. Moreover, we provide bounds on the efficiency of mechanisms satisfying various properties, and give truthful mechanisms for multiple agents with restricted classes of valuations.
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Anjaria, Kushal Ajaybhai. "Computational implementation and formalism of FAIR data stewardship principles." Data Technologies and Applications 54, no. 2 (April 19, 2020): 193–214. http://dx.doi.org/10.1108/dta-09-2019-0164.
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PurposeThe progress of life science and social science research is contingent on effective modes of data storage, data sharing and data reproducibility. In the present digital era, data storage and data sharing play a vital role. For productive data-centric tasks, findable, accessible, interoperable and reusable (FAIR) principles have been developed as a standard convention. However, FAIR principles have specific challenges from computational implementation perspectives. The purpose of this paper is to identify the challenges related to computational implementations of FAIR principles. After identification of challenges, this paper aims to solve the identified challenges.Design/methodology/approachThis paper deploys Petri net-based formal model and Petri net algebra to implement and analyze FAIR principles. The proposed Petri net-based model, theorems and corollaries may assist computer system architects in implementing and analyzing FAIR principles.FindingsTo demonstrate the use of derived petri net-based theorems and corollaries, existing data stewardship platforms – FAIRDOM and Dataverse – have been analyzed in this paper. Moreover, a data stewardship model – “Datalection” has been developed and conversed about in the present paper. Datalection has been designed based on the petri net-based theorems and corollaries.Originality/valueThis paper aims to bridge information science and life science using the formalism of data stewardship principles. This paper not only provides new dimensions to data stewardship but also systematically analyzes two existing data stewardship platforms FAIRDOM and Dataverse.
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Aslan, Fatih, George Iosifidis, Jose A. Ayala-Romero, Andres Garcia-Saavedra, and Xavier Costa-Perez. "Fair Resource Allocation in Virtualized O-RAN Platforms." ACM SIGMETRICS Performance Evaluation Review 52, no. 1 (June 11, 2024): 59–60. http://dx.doi.org/10.1145/3673660.3655054.
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O-RAN systems in virtualized platforms (O-Cloud) offer performance boosts but also raise energy concerns. This paper assesses O-Cloud's energy costs and proposes energy-efficient policies for base station (BS) data loads and transport block (TB) sizes. These policies balance energy savings and performance fairly across servers and users. To handle the unknown and time-varying parameters affecting the policies, we develop a novel online learning framework with fairness guarantees that apply to the entire operation horizon of the system (long-term fairness).
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Begeny, Christopher T., Yuen J. Huo, Heather J. Smith, and Michelle K. Ryan. "Being treated fairly in groups is important, but not sufficient: The role of distinctive treatment in groups, and its implications for mental health." PLOS ONE 16, no. 5 (May 14, 2021): e0251871. http://dx.doi.org/10.1371/journal.pone.0251871.
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Organizations and other groups often recognize the importance of members treating each other in a fair (dignified, unbiased) manner. This type of treatment is key to fostering individuals’ sense of belonging in the group. However, while a sense of belonging is important, individuals also need to be shown that they have some distinct value to the group–enabling them to not only “fit in” but also “stand out.” Building from research on fair treatment, we explicate another form, distinctive treatment, whereby others show interest and appreciation for an individual’s more distinguishing, group-relevant qualities. In six studies using multiple methods (e.g., experimental, longitudinal) and in multiple types of groups (work organizations, student communities, racial/ethnic minority groups), we show that fair and distinctive treatment play fundamentally different roles–shaping individuals’ perceived belonging versus intragroup standing, respectively–and with downstream benefits for mental health (less anxiety, fewer depressive symptoms). Overall, this illustrates that promoting fair treatment in groups is important, but not sufficient. Experiencing distinctive treatment is also key. Each type of treatment provides unique social evaluative information that fosters a healthy sense of self. This research further indicates that distinctive treatment may be a vital yet overlooked element to promoting diversity and inclusion in groups, as it provides a path for recognizing and appreciating, and thus encouraging, a diversity of ideas, insights, knowledge and skills that individuals bring to the group.
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Leimona, Beria, Meine van Noordwijk, Rudolf de Groot, and Rik Leemans. "Fairly efficient, efficiently fair: Lessons from designing and testing payment schemes for ecosystem services in Asia." Ecosystem Services 12 (April 2015): 16–28. http://dx.doi.org/10.1016/j.ecoser.2014.12.012.
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Watson, Jane M. "Children's Construction of ‘Fair’ Representations of One-Third." Australasian Journal of Early Childhood 22, no. 2 (June 1997): 34–38. http://dx.doi.org/10.1177/183693919702200208.
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This article discusses the development of the concept of the fraction one-third by children from pre-Grade 1 to Grade 4 in terms of their ideas of fairness and equality of sharing. Twenty-four children were interviewed and asked to share a pancake fairly among three dolls. The context was chosen to allow children to use out-of-school intuition and understanding if preferred. Four levels of development are identified leading to the idea of fair fractions as those where each part contains the same amount of pancake. The identification of intermediate strategies for sharing should assist teachers in planning activities to achieve a mathematical understanding of fair fractions.
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FEDRIZZI, MICHELE, and MATTEO BRUNELLI. "FAIR CONSISTENCY EVALUATION FOR RECIPROCAL RELATIONS AND IN GROUP DECISION MAKING." New Mathematics and Natural Computation 05, no. 02 (July 2009): 407–20. http://dx.doi.org/10.1142/s1793005709001398.
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In decision-making processes, it often occurs that the decision maker is asked to pairwise compare alternatives. His/her judgements over a set of pairs of alternatives can be collected into a matrix and some relevant properties, for instance, consistency, can be estimated. Consistency is a desirable property which implies that all the pairwise comparisons respect a principle of transitivity. So far, many indices have been proposed to estimate consistency. Nevertheless, in this paper we argue that most of these indices do not fairly evaluate this property. Then, we introduce a new consistency evaluation method and we propose to use it in group decision making problems in order to fairly weigh the decision maker's preferences according to their consistency. In our analysis, we consider two families of pairwise comparison matrices: additively reciprocal pairwise comparison matrices and multiplicatively reciprocal pairwise comparison matrices.
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Siachiwena, Hangala, and Chris Saunders. "Elections, Legitimacy, and Democratic Consolidation in Southern Africa Lessons from Zambia, Zimbabwe and Malawi." Journal of African Elections 20, no. 1 (June 1, 2021): 67–89. http://dx.doi.org/10.20940/jae/2021/v20i1a4.
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Regular elections are now the norm across most of sub-Saharan Africa, but repeated elections have not guaranteed the consolidation of democracy. Election legitimacy is crucial for democratisation. When losing political actors and their supporters are not satisfied with the electoral process, there is potential for growing political tensions. Fraudulent or controversial elections fail to confer legitimacy on the winners, and undermine the integrity of elections and democracy. Drawing on Afrobarometer data and media accounts, this paper focuses on the most recent elections held in three southern African countries: Zambia, Zimbabwe and Malawi. We show that when citizens believe that elections were not free and fair, there is a decline in their satisfaction with democracy and the trust they have in institutions such as electoral commissions and courts of law. The absence of political reforms to address disputed election outcomes increases the likelihood that future elections will not be contested fairly. This sets countries on a path of democratic decline rather than consolidation.
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Mao, Runze, Wenqi Fan, and Qing Li. "GCARe: Mitigating Subgroup Unfairness in Graph Condensation through Adversarial Regularization." Applied Sciences 13, no. 16 (August 11, 2023): 9166. http://dx.doi.org/10.3390/app13169166.
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Training Graph Neural Networks (GNNs) on large-scale graphs in the deep learning era can be expensive. While graph condensation has recently emerged as a promising approach through which to reduce training cost by compressing large graphs into smaller ones and for preserving most knowledge, its capability in treating different node subgroups fairly during compression remains unexplored. In this paper, we investigate current graph condensation techniques from a perspective of fairness, and show that they bear severe disparate impact toward node subgroups. Specifically, GNNs trained on condensed graphs become more biased than those trained on original graphs. Since the condensed graphs comprise synthetic nodes, which are absent of explicit group IDs, the current algorithms used to train fair GNNs fail in this case. To address this issue, we propose Graph Condensation with Adversarial Regularization (GCARe), which is a method that directly regularizes the condensation process to distill the knowledge of different subgroups fairly into resulting graphs. A comprehensive series of experiments substantiated that our method enhances the fairness in condensed graphs without compromising accuracy, thus yielding more equitable GNN models. Additionally, our discoveries underscore the significance of incorporating fairness considerations in data condensation, and offer invaluable guidance for future inquiries in this domain.
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Igarashi, Ayumi, Martin Lackner, Oliviero Nardi, and Arianna Novaro. "Repeated Fair Allocation of Indivisible Items." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 9 (March 24, 2024): 9781–89. http://dx.doi.org/10.1609/aaai.v38i9.28837.
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The problem of fairly allocating a set of indivisible items is a well-known challenge in the field of (computational) social choice. In this scenario, there is a fundamental incompatibility between notions of fairness (such as envy-freeness and proportionality) and economic efficiency (such as Pareto-optimality). However, in the real world, items are not always allocated once and for all, but often repeatedly. For example, the items may be recurring chores to distribute in a household. Motivated by this, we initiate the study of the repeated fair division of indivisible goods and chores, and propose a formal model for this scenario. In this paper, we show that, if the number of repetitions is a multiple of the number of agents, there always exists a sequence of allocations that is proportional and Pareto-optimal. On the other hand, irrespective of the number of repetitions, an envy-free and Pareto-optimal sequence of allocations may not exist. For the case of two agents, we show that if the number of repetitions is even, it is always possible to find a sequence of allocations that is overall envy-free and Pareto-optimal. We then prove even stronger fairness guarantees, showing that every allocation in such a sequence satisfies some relaxation of envy-freeness. Finally, in case that the number of repetitions can be chosen freely, we show that envy-free and Pareto-optimal allocations are achievable for any number of agents.
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Saputro, Arnaz Anggoro, and Ossa Viantina. "Understanding fair play basketball club players in perbasi jombang." PHEDHERAL 19, no. 1 (June 18, 2022): 29. http://dx.doi.org/10.20961/phduns.v19i1.61480.
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<p>Basketball is a sport in which body contact occurs between players, which often causes chaos during matches so that it can become a source of commotion that can injure the spirit of sportsmanship in sport (fair play). This research is a population study, so the entire population is used as a research sample, namely all basketball clubs sheltered by Perbasi Jombang, totaling 102 players. The research variable is the single variable, namely the understanding of fair play of basketball club players in Perbasi Jombang. The data collection method used a questionnaire technique, the data were analyzed using descriptive percentage analysis. Based on the results of the study, it is known that as many as 13 respondents (12.75%) have a very good level of understanding of fair play, 79 respondents (77.45%) have a good level of understanding of fair play and as many as 10 respondents (9.80%) have a good level of understanding of fair play. fairly good understanding of fair play. That understanding includes the meaning of the word fair play which is actually 82.23%, understanding the purpose of fair play by 77.78%, understanding the moral values contained in fair play by 83.74%, understanding the rules or fair play exponents. in basketball games by 72.84%, and the form of self-esteem reflected in fair play by 79.36%. It can be concluded that most of the basketball club players in Perbasi Jombang are good at understanding fair play in a basketball game</p>
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Varma, Tanvee, Michelle Mello, Joseph S. Ross, Cary Gross, and Jennifer Miller. "Metrics, baseline scores, and a tool to improve sponsor performance on clinical trial diversity: retrospective cross sectional study." BMJ Medicine 2, no. 1 (January 2023): e000395. http://dx.doi.org/10.1136/bmjmed-2022-000395.
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ObjectiveTo develop a measure for fair inclusion in pivotal trials by assessing transparency and representation of enrolled women, older adults (aged 65 years and older), and racially and ethnically minoritized patients.DesignRetrospective cross sectional study.PopulationSponsors of novel oncology therapeutics that were approved by the US Food and Drug Administration over 1 January 2012 to 31 December 2017.Data sourcesTrial data from Drugs@FDA, ClinicalTrials.gov, and corresponding publications; cancer incidence demographics from US Cancer Statistics and the American Cancer Society.Main outcome measuresTransparency measures assess whether trials publicly report participant sex, age, and racial and ethnic identity. Representation measures assess whether trial participant demographics represent more than 80% of the US patient population for studied conditions, calculated by dividing the percentage of study participants in each demographic subgroup by the percentage of the US cancer population with the studied condition per group. Composite fair inclusion measures assess average transparency and representation scores, overall and for each demographic group. Results are reported at the trial, product, and sponsor levels.ResultsBetween 1 January 2012 and 31 December 2017, the FDA approved 59 novel cancer therapeutics, submitted by 25 sponsors (all industry companies) on the basis of 64 pivotal trials. All 25 sponsors (100%) reported participant sex, 10 (40%) reported age, and six (24%) reported race and ethnicity. Although 14 (56%) sponsors had adequate representation of women in trials, only six (24%) adequately represented older adults, and four (16%) adequately represented racially and ethnically minoritized patients (black, Asian, Hispanic or Latinx). On overall fair inclusion, one sponsor scored 100% and the median sponsor score was 81% (interquartile range 75-87%). More than half of sponsors (13 (56%) of 25) fairly included women, 20% (n=5) fairly included older adults, and 4% (n=1) fairly included racially and ethnically minoritized patients in trials. 80% of product had pivotal trials that fairly included women, 24% fairly included older adults, and 5% fairly included racially and ethnically minoritized patients.ConclusionsThis novel approach evaluates trials, products, and sponsors on their fair inclusion of demographic groups in research. For oncology trials, substantial room was noted for improved inclusion of older adults and patients who identify as black or Latinx and transparency around the number of participants identifying as Native Hawaiian, Pacific Islander, American Indian, and Alaska Native. These measures can be used by sponsors, ethics committees, among others, to set and evaluate trial diversity goals to help spur progress toward greater research equity in the US.
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Osler, Turner M., Laurent G. Glance, Frederick B. Rogers, and Steven R. Shackford. "TRISS METHODOLOGY AND THE Z SCORE FAIL TO FAIRLY COMPARE TRAUMA CENTER RESULTS." Journal of Trauma: Injury, Infection, and Critical Care 45, no. 6 (December 1998): 1119. http://dx.doi.org/10.1097/00005373-199812000-00078.
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Luo, Xi, Ran Yan, Shuaian Wang, and Lu Zhen. "A fair evaluation of the potential of machine learning in maritime transportation." Electronic Research Archive 31, no. 8 (2023): 4753–72. http://dx.doi.org/10.3934/era.2023243.
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<abstract> <p>Machine learning (ML) techniques are extensively applied to practical maritime transportation issues. Due to the difficulty and high cost of collecting large volumes of data in the maritime industry, in many maritime studies, ML models are trained with small training datasets. The relative predictive performances of these trained ML models are then compared with each other and with the conventional model using the same test set. The ML model that performs the best out of the ML models and better than the conventional model on the test set is regarded as the most effective in terms of this prediction task. However, in scenarios with small datasets, this common process may lead to an unfair comparison between the ML and the conventional model. Therefore, we propose a novel process to fairly compare multiple ML models and the conventional model. We first select the best ML model in terms of predictive performance for the validation set. Then, we combine the training and the validation sets to retrain the best ML model and compare it with the conventional model on the same test set. Based on historical port state control (PSC) inspection data, we examine both the common process and the novel process in terms of their ability to fairly compare ML models and the conventional model. The results show that the novel process is more effective at fairly comparing the ML models with the conventional model on different test sets. Therefore, the novel process enables a fair assessment of ML models' ability to predict key performance indicators in the context of limited data availability in the maritime industry, such as predicting the ship fuel consumption and port traffic volume, thereby enhancing their reliability for real-world applications.</p> </abstract>
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McGlaughlin, Peter, and Jugal Garg. "Improving Nash Social Welfare Approximations." Journal of Artificial Intelligence Research 68 (May 14, 2020): 225–45. http://dx.doi.org/10.1613/jair.1.11618.
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We consider the problem of fairly allocating a set of indivisible goods among n agents. Various fairness notions have been proposed within the rapidly growing field of fair division, but the Nash social welfare (NSW) serves as a focal point. In part, this follows from the ‘unreasonable’ fairness guarantees provided, in the sense that a max NSW allocation meets multiple other fairness metrics simultaneously, all while satisfying a standard economic concept of efficiency, Pareto optimality. However, existing approximation algorithms fail to satisfy all of the remarkable fairness guarantees offered by a max NSW allocation, instead targeting only the specific NSW objective. We address this issue by presenting a 2 max NSW, Prop-1, 1/(2n) MMS, and Pareto optimal allocation in strongly polynomial time. Our techniques are based on a market interpretation of a fractional max NSW allocation. We present novel definitions of fairness concepts in terms of market prices, and design a new scheme to round a market equilibrium into an integral allocation in a way that provides most of the fairness properties of an integral max NSW allocation.
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Martyna, Jerzy. "Quasi-Offline Fair Scheduling in Third Generation Wireless Data Networks." Journal of Telecommunications and Information Technology, no. 3 (September 30, 2013): 112–14. http://dx.doi.org/10.26636/jtit.2013.3.1231.
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In 3G wireless data networks, network operators would like to balance system throughput while serving users fairly. This is achieved through the use of fair scheduling. However, this approach provides non-Pareto optimal bandwidth allocation when considering a network as a whole. In this paper an optimal offline algorithm that is based on the decomposition result for a double stochastic matrix by Birkhoff and von Neumann is proposed. A utility max-min fairness is suggested for the derivation of a double stochastic matrix. Using a numerical experiment, new approach improves the fairness objective and is close to the optimal solution.
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Briones, Alan, Adrià Mallorquí, Agustín Zaballos, and Ramon Martin de Pozuelo. "Wireless Loss Detection over Fairly Shared Heterogeneous Long Fat Networks." Electronics 10, no. 9 (April 21, 2021): 987. http://dx.doi.org/10.3390/electronics10090987.
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The quality of inter-network communication is often detrimentally affected by the large deployment of heterogeneous networks, including Long Fat Networks, as a result of wireless media introduction. Legacy transport protocols assume an independent wired connection to the network. When a loss occurs, the protocol considers it as a congestion loss, decreasing its throughput in order to reduce the network congestion without evaluating a possible channel failure. Distinct wireless transport protocols and their reference metrics are analyzed in order to design a mechanism that improves the Aggressive and Adaptative Transport Protocol (AATP) performance over Heterogeneous Long Fat Networks (HLFNs). In this paper, we present the Enhanced-AATP, which introduces the designed Loss Threshold Decision maker mechanism for the detection of different types of losses in the AATP operation. The degree to which the protocol can maintain throughput levels during channel losses or decrease production while congestion losses occur depends on the evolution of the smooth Jitter Ratio metric value. Moreover, the defined Weighted Fairness index enables the modification of protocol behavior and hence the prioritized fair use of the node’s resources. Different experiments are simulated over a network simulator to demonstrate the operation and performance improvement of the Enhanced-AATP. To conclude, the Enhanced-AATP performance is compared with other modern protocols.
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Nivette, Amy, Ingrid Obsuth, Denis Ribeaud, and Manuel Eisner. "Fair Teachers, Fair Police? Assessing the Pathways between Perceptions of Teacher and Police Authority in Childhood and Adolescence." Journal of Youth and Adolescence 51, no. 2 (November 16, 2021): 193–207. http://dx.doi.org/10.1007/s10964-021-01537-6.
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AbstractExisting research has shown that the quality of the relationship between teacher and child is associated with more positive perceptions of school authorities. There has been relatively less attention to the processes that connect attitudes towards different sources of authority, such as between teachers and police. The current study uses a counterfactual approach to estimate the direct and indirect effects of teacher–child relationships on children’s later perceptions of police legitimacy. Using data from a longitudinal study of youth in Zurich, Switzerland, this study applies non-bipartite propensity score matching to identify matched pairs (n = 232 pairs, 55% male) of children with better versus worse relationships with their teacher at age 11 following a teacher change. Matched pairs were then compared on potential mediators (moral norms about deviant behavior and low self-control) at age 13 and perceptions of police legitimacy at age 15. The results demonstrate the importance of the quality of the relationships between students and teachers in shaping young people’s interpersonal characteristics as well as perceptions of the world around them. Namely, if young people feel that they are being treated fairly by their teachers, they are more likely to distinguish behaviors that are right or wrong (moral norms) and control their actions (self-control). Moreover, as a result they are also more likely to perceive authorities such as police as legitimate agents that facilitate societal order.
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Vasquez Trespalacios, Elsa Maria, and Carolina Aranda Beltran. "Fair organizations: is it possible to build them?" Visión de Futuro, no. 24, No 2 (Julio - Diciembre) (July 1, 2020): 184–93. http://dx.doi.org/10.36995/j.visiondefuturo.2020.24.02.005.en.
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The well-being of workers and the productivity of companies have been subjects of great interest in the scientific literature and in the daily life of organizations. Organizational justice is one of the factors that contributes to achieving these results. Theories that attempt to explain the effects of a perception of justice at work emphasize the relationship of reciprocity that must exist between the employee and the employer. The perception that the employer acts fairly gives rise to feelings of trust, commitment and loyalty to the employer, which has an impact on better job performance and positive attitudes in the workplace. Each of the procedures carried out in the work environment, such as the selection of personnel, performance evaluation, conflict resolution, distribution of rewards and layoffs, are at high risk of generating perceptions of injustice due to the parties involved, this is why they must be rethought in the light of the dimensions of organizational justice. The objective then is to know how the concept of justice can be applied in the most important procedures at the organizational level.
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Biswas, Arpita, Gourab K. Patro, Niloy Ganguly, Krishna P. Gummadi, and Abhijnan Chakraborty. "Toward Fair Recommendation in Two-sided Platforms." ACM Transactions on the Web 16, no. 2 (May 31, 2022): 1–34. http://dx.doi.org/10.1145/3503624.
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Many online platforms today (such as Amazon, Netflix, Spotify, LinkedIn, and AirBnB) can be thought of as two-sided markets with producers and customers of goods and services. Traditionally, recommendation services in these platforms have focused on maximizing customer satisfaction by tailoring the results according to the personalized preferences of individual customers. However, our investigation reinforces the fact that such customer-centric design of these services may lead to unfair distribution of exposure to the producers, which may adversely impact their well-being. However, a pure producer-centric design might become unfair to the customers. As more and more people are depending on such platforms to earn a living, it is important to ensure fairness to both producers and customers. In this work, by mapping a fair personalized recommendation problem to a constrained version of the problem of fairly allocating indivisible goods, we propose to provide fairness guarantees for both sides. Formally, our proposed FairRec algorithm guarantees Maxi-Min Share of exposure for the producers, and Envy-Free up to One Item fairness for the customers. Extensive evaluations over multiple real-world datasets show the effectiveness of FairRec in ensuring two-sided fairness while incurring a marginal loss in overall recommendation quality. Finally, we present a modification of FairRec (named as FairRecPlus ) that at the cost of additional computation time, improves the recommendation performance for the customers, while maintaining the same fairness guarantees.
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Odokuma-Alonge, O., N. S. Chibuzor, and A. Ogbamikhumi. "Geochemical Approach and Industrial Utilization of Some Marble Bodies from Ubo River Area and Environs, Southwestern Nigeria." Journal of Applied Sciences and Environmental Management 24, no. 4 (May 22, 2020): 653–58. http://dx.doi.org/10.4314/jasem.v24i4.17.
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Marble bodies occur in Ubo River Area as lenses of rocks. Seven (07) marble samples were obtained, analyzed and subjected to X-Ray Flourescence (XRF) analysis to determine both the major and trace elemental composition of the samples. The analysis revealed high CaO values (52.23-58.10 wt. %) and moderate values for SiO2 (7.50-10.43wt. %), fairly low values for Al2O3 (2.30 – 4.21 wt. %), Fe2O3 (2.62 – 5.15 wt. %), MgO (0.42 – 0.7 wt. %), MnO (0.01 – 0.06 wt. %), K2O (0.32 – 1.55 wt. %) and Na2O (0.01 – 0.03 wt. %), respectively. Variation plots involving Na2O/Al2O3 vs K2O/Al2O3 reveal sedimentary progenitors for the rocks. The fairly high SiO2 and low K2O content is an indication of the environment of deposition and may have been in a shallow marine environment with fair input of classic sediments into the system. The relatively high CaO content, moderate SiO2content and fairly low Al2O3 and Fe2O3 values indicates a fair degree of purity of the parent rock, hence its suitability as a raw material in the industrial sector.
Keywords: Clastic, Deposition, Environment, Progenitor, Marble, Chemical
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Georgakoudis, Giorgis, Charles Gillan, Ahmed Sayed, Ivor Spence, Richard Faloon, and Dimitrios S. Nikolopoulos. "Iso-Quality of Service: Fairly Ranking Servers for Real-Time Data Analytics." Parallel Processing Letters 25, no. 03 (September 2015): 1541004. http://dx.doi.org/10.1142/s0129626415410042.
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We present a mathematically rigorous iso-Quality-of-Service (QoS) metric which relates the achievable quality of service (QoS) for a real-time analytics service with workload specific and use case specific performance and output quality requirements to the energy cost of offering the service by different server architectures. Using a new iso-QoS evaluation methodology, we scale server resources to meet QoS targets and directly rank the servers in terms of their energy-efficiency and by extension cost of ownership. Our metric and method are platform-independent and enable fair comparison of datacenter compute servers with significant architectural diversity, including micro-servers. We deploy our metric and methodology to compare three servers running financial option pricing workloads on real-life market data. We find that server ranking is sensitive to data inputs and desired QoS level and that although scale-out micro-servers can be up to two times more energy-efficient than conventional heavyweight servers for the same target QoS, they are still six times less energy efficient than high-performance computational accelerators.
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Aslan, Fatih, George Iosifidis, Jose A. Ayala-Romero, Andres Garcia-Saavedra, and Xavier Costa-Perez. "Fair Resource Allocation in Virtualized O-RAN Platforms." Proceedings of the ACM on Measurement and Analysis of Computing Systems 8, no. 1 (February 16, 2024): 1–34. http://dx.doi.org/10.1145/3639043.
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O-RAN systems and their deployment in virtualized general-purpose computing platforms (O-Cloud) constitute a paradigm shift expected to bring unprecedented performance gains. However, these architectures raise new implementation challenges and threaten to worsen the already-high energy consumption of mobile networks. This paper presents first a series of experiments which assess the O-Cloud's energy costs and their dependency on the servers' hardware, capacity and data traffic properties which, typically, change over time. Next, it proposes a compute policy for assigning the base station data loads to O-Cloud servers in an energy-efficient fashion; and a radio policy that determines at near-real-time the minimum transmission block size for each user so as to avoid unnecessary energy costs. The policies balance energy savings with performance, and ensure that both of them are dispersed fairly across the servers and users, respectively. To cater for the unknown and time-varying parameters affecting the policies, we develop a novel online learning framework with fairness guarantees that apply to the entire operation horizon of the system (long-term fairness). The policies are evaluated using trace-driven simulations and are fully implemented in an O-RAN compatible system where we measure the energy costs and throughput in realistic scenarios.
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Jones, Michael A., and Stanley F. Cohen. "Fairness: How to Achieve It and How to Optimize in a Fair-Division Procedure." Mathematics Teacher 97, no. 3 (March 2004): 170–74. http://dx.doi.org/10.5951/mt.97.3.0170.
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Many disputes all over the world–on international, national, local, and personal levels–involve the division of items for which two parties have equal claim. We describe a simple yet practical application of mathematics for fairly resolving disputes. It requires no more mathematical sophistication than algebra. But what does “fairly” mean? And what does it have to do with mathematics? We explain the basic properties of fair divisions and show how to determine mathematically whether these properties are met. We introduce the adjusted-winner procedure of dividing disputed goods between two parties or players by dividing the prizes won in a fictitious contest. We also consider how one player can use information about his or her opponent's preferences for the disputed goods to maximize his or her return under the adjusted-winner procedure. Through this exercise, students learn concepts about optimization, without introducing calculus.
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Sun, Fengjie, Xianchang Wang, and Rui Zhang. "Fair Task Allocation When Cost of Task Is Multidimensional." Applied Sciences 10, no. 8 (April 17, 2020): 2798. http://dx.doi.org/10.3390/app10082798.
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We consider the problem of fairly allocating indivisible tasks, focusing on a recently introduced notion of fairness called Minmax share guarantee. Minmax share (MMS) is a term of fairness guarantees that is defined to be the minimum cost that an agent can ensure for herself, if she were to partition the tasks into n bundles, and then receive the maximum cost bundle of tasks. However, the cost of tasks considered in previous work is single dimensional, and multidimensional situations have not been researched. In this work, we proposed an allocation algorithm that allocates tasks with multidimensional cost to agents under ordinal model. We prove the approximation ratio of MMS of the algorithm proposed can be guaranteed under 2 + m · α i · ( 1 + n ) - n n 2 , in addition the time complexity of the algorithm is O ( m log m ) . This proposed method is implemented and tested on datasets generated based on a real environment, and the experimental result shows that our algorithm has better performance than existing task allocation algorithms when cost of tasks is multidimensional.