Reviewerly/exHarmony huggingface dataset:
GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized Personalization

Previous chapter Next chapter Full AccessProceedings Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)GRP-FED: Addressing Client Imbalance in Federated Learning via Global-Regularized PersonalizationYen-Hsiu Chou, Shenda Hong, Chenxi Sun, Derun Cai, Moxian Song, and Hongyan LiYen-Hsiu Chou, Shenda Hong, Chenxi Sun, Derun Cai, Moxian Song, and Hongyan Lipp.451 - 458Chapter DOI:https://doi.org/10.1137/1.9781611977172.51PDFBibTexSections ToolsAdd to favoritesExport CitationTrack CitationsEmail SectionsAboutAbstract Since data is presented long-tailed in reality, it is challenging for Federated Learning (FL) to train across decentralized clients as practical applications. We present Global-Regularized Personalization (GRP-FED) to tackle the data imbalanced issue by considering a single global model and multiple local models for each client. With adaptive aggregation, the global model treats multiple clients fairly and mitigates the global long-tailed issue. Each local model is learned from the local data and aligns with its distribution for customization. To prevent the local model from just overfitting, GRP-FED applies an adversarial discriminator to regularize between the learned global-local features. Extensive results show that our GRP-FED improves under both global and local scenarios on real-world MIT-BIH and synthesis CIFAR-10 datasets, achieving comparable performance and addressing client imbalance. Previous chapter Next chapter RelatedDetails Published:2022eISBN:978-1-61197-717-2 https://doi.org/10.1137/1.9781611977172Book Series Name:ProceedingsBook Code:PRDT22Book Pages:1-737
Evaluation of Task Scheduling Algorithms in Heterogeneous Computing Environments

This work establishes a set of methodologies to evaluate the performance of any task scheduling policy in heterogeneous computing contexts. We formally state a scheduling model for hybrid edge–cloud computing ecosystems and conduct simulation-based experiments on large workloads. In addition to the conventional cloud datacenters, we consider edge datacenters comprising smartphone and Raspberry Pi edge devices, which are battery powered. We define realistic capacities of the computational resources. Once a schedule is found, the various task demands can or cannot be fulfilled by the resource capacities. We build a scheduling and evaluation framework and measure typical scheduling metrics such as mean waiting time, mean turnaround time, makespan, throughput on the Round-Robin, Shortest Job First, Min-Min and Max-Min scheduling schemes. Our analysis and results show that the state-of-the-art independent task scheduling algorithms suffer from performance degradation in terms of significant task failures and nonoptimal resource utilization of datacenters in heterogeneous edge–cloud mediums in comparison to cloud-only mediums. In particular, for large sets of tasks, due to low battery or limited memory, more than 25% of tasks fail to execute for each scheduling scheme.
Phygitally Yours: Examination of Virtual Reality Experiences in Digital Sports and Recreational Games

&lt;em&gt;&lt;span lang="EN-GB"&gt;The demand for, and investment in digital virtual reality (VR) games, both by companies and consumers, are increasing day by day. Accordingly, understanding the experiences created by VR within the scope of phygital marketing is important in terms of contributing to virtual experiential marketing, marketing communication, recreation and similar fields. The concept of phygital, expresses the integral wholeness of the digital and physical. And ‘Phygitally Yours’ refers to individuals who can live in their own phygital worlds in today's technologies. The aim of this study was to examine the experiences created by digital games involving sports and recreational content within the scope of phygital marketing. Eight individuals who did not have any prior VR experience played digital games with sports (Grand Turismo Sport) and recreational (VR Worlds) content using VR glasses. Subsequently, in-depth interviews were conducted. The data were analysed using the content analysis method, and thematic codes and concepts were revealed and interpreted. As a result of the research, it was determined that the participants' first VR experience of digital games involved the dimensions of sense, interaction, pleasure and flow, with the following codes emerging: perfect holistic effect, showroom feeling, understandable, competition, magic, gorgeous, dilemma, and place independent behaviour.&lt;/span&gt;&lt;/em&gt;
Quantum outage probability for time-varying quantum channels

Recent experimental studies have shown that the relaxation time ${T}_{1}$ and the dephasing time ${T}_{2}$ of superconducting qubits fluctuate considerably over time. Time-varying quantum channel (TVQC) models have been proposed in order to consider the time-varying nature of the parameters that define qubit decoherence. This dynamic nature of quantum channels causes a degradation of the performance of quantum error correction codes (QECCs) that is portrayed as a flattening of their error rate curves. In this article we introduce the concepts of quantum outage probability and quantum hashing outage probability as asymptotically achievable error rates by a QECC with the quantum rate ${R}_{Q}$ operating over a TVQC. We derive closed-form expressions for the family of time-varying amplitude damping channels and study their behavior for different scenarios. We quantify the impact of time variation as a function of the relative variation of ${T}_{1}$ around its mean. We conclude that the performance of QECCs is limited in many cases by the inherent fluctuations of their decoherence parameters and corroborate that parameter stability is crucial to maintain the excellent performance observed over static quantum channels.
A Classification of Essential Factors for the Development and Implementation of Cyber Security Strategy in Public Sector Organizations

To ensure the achievement of quality security to safeguard business objectives, implementing, and maintaining an effective Cyber Security Strategy (CSS) is crucial.Inevitably, we need to recognize and evaluate the essential factors, such as technological, cultural, regulatory, economic, and others, that may hinder the efficacy of a CSS development and implementation.From the literature review, it is evident that such factors are either abstractly stated, or only assessed from singular viewpoint and are scattered across the literature.Moreover, there is a lack of holistic studies that could assist us in comprehending the critical factors affecting a CSS.In this paper, we present a systematic classification of distinct, structured, and comprehensive list of key factors covering multiple aspects of an organization's CSS, including organizational, cultural, economic, legal and political, and security, to provide a more complete view of understanding the essentials and analyzing the aptitude of a planned or given CSS.The proposed classification is further evaluated to examine the critical factors verified by conducting semi-structured interviews from security experts in different public sector organizations.Furthermore, we present a comparison of our work with the recent attempts that reflects that a significant accumulation of essential factors have been holistically identified in this study.
Efficient Real-time Earliest Deadline First based scheduling for Apache Spark

Apache Spark is a distributed computing framework for fast in-memory data analysis, Machine Learning jobs, and SQL queries that employs Resilient Distributed Dataset for distributing data and Directed Acyclic Graph for scheduling computations. Currently, Spark provides two scheduling policies for tasks pending execution: a First In First Out policy and a FAIR policy, providing no support for deadline-based real-time scheduling. In this paper, we present a new system designed to accept deadlines for heterogeneous Spark Jobs and perform a real-time scheduling policy based on Earliest Deadline First (EDF). To showcase the efficiency of our scheduling policy, we compare and analyze the performance of our solution with the current Spark execution policies in terms of job lateness. We empirically prove that our real-time policy provides much lower lateness given suitable constraints.
SEDIQA: Sound Emitting Document Image Quality Assessment in a Reading Aid for the Visually Impaired

For visually impaired people (VIPs), the ability to convert text to sound can mean a new level of independence or the simple joy of a good book. With significant advances in optical character recognition (OCR) in recent years, a number of reading aids are appearing on the market. These reading aids convert images captured by a camera to text which can then be read aloud. However, all of these reading aids suffer from a key issue-the user must be able to visually target the text and capture an image of sufficient quality for the OCR algorithm to function-no small task for VIPs. In this work, a sound-emitting document image quality assessment metric (SEDIQA) is proposed which allows the user to hear the quality of the text image and automatically captures the best image for OCR accuracy. This work also includes testing of OCR performance against image degradations, to identify the most significant contributors to accuracy reduction. The proposed no-reference image quality assessor (NR-IQA) is validated alongside established NR-IQAs and this work includes insights into the performance of these NR-IQAs on document images. SEDIQA is found to consistently select the best image for OCR accuracy. The full system includes a document image enhancement technique which introduces improvements in OCR accuracy with an average increase of 22% and a maximum increase of 68%.
Forecasting potential yields under uncertainty using fuzzy cognitive maps

Abstract Background The aim of the study is identification of factors influencing the reduction of the potential maximum yield of winter wheat in weather conditions of dry farming in European part of Russia, Volgograd region. The novelty of the work is forecasting potential yields under uncertainty that allows to assess the risks and potential threats that can influence and maximize the potential yield. To solve this problem, the tool for formalization, analysis and modeling of semi-structured systems and processes Fuzzy Cognitive Maps (FCM) is used. Results Based on disparate and heterogeneous information about the multitude of external influences on crop formation during plant photosynthesis, a model for analyzing the level of influencing factors on the target factor is constructed and an effective control impact scenario is developed. This model is used to identify the factors, where each one of them iteratively passes from the initial value to the stable one according to the chosen formula, based on which, the influence of the factors on each other are determined. Conclusions The conclusions obtained as a result of the work confirm the concept of precision farming: the quantity and quality of innovation in agriculture depends on the ability to apply it effectively in the field. Developed method of predicting potential yield levels can be used not only to model future agricultural performance, but also to estimate harvested yields.
Lookup-Table Recurrent Language Models for Long Tail Speech Recognition

We introduce Lookup-Table Language Models (LookupLM), a method for scaling up the size of RNN language models with only a constant increase in the floating point operations, by increasing the expressivity of the embedding table. In particular, we instantiate an (additional) embedding table which embeds the previous n-gram token sequence, rather than a single token. This allows the embedding table to be scaled up arbitrarily -- with a commensurate increase in performance -- without changing the token vocabulary. Since embeddings are sparsely retrieved from the table via a lookup; increasing the size of the table adds neither extra operations to each forward pass nor extra parameters that need to be stored on limited GPU/TPU memory. We explore scaling n-gram embedding tables up to nearly a billion parameters. When trained on a 3-billion sentence corpus, we find that LookupLM improves long tail log perplexity by 2.44 and long tail WER by 23.4% on a downstream speech recognition task over a standard RNN language model baseline, an improvement comparable to a scaling up the baseline by 6.2x the number of floating point operations.
Abstraction, validation<scp>,</scp> and generalization for explainable artificial intelligence

Abstract Neural network architectures are achieving superhuman performance on an expanding range of tasks. To effectively and safely deploy these systems, their decision‐making must be understandable to a wide range of stakeholders. Methods to explain artificial intelligence (AI) have been proposed to answer this challenge, but a lack of theory impedes the development of systematic abstractions, which are necessary for cumulative knowledge gains. We propose Bayesian Teaching as a framework for unifying explainable AI (XAI) by integrating machine learning and human learning. Bayesian Teaching formalizes explanation as a communication act of an explainer to shift the beliefs of an explainee. This formalization decomposes a wide range of XAI methods into four components: (a) the target inference, (b) the explanation, (c) the explainee model, and (d) the explainer model. The abstraction afforded by Bayesian Teaching to decompose XAI methods elucidates the invariances among them. The decomposition of XAI systems enables modular validation, as each of the first three components listed can be tested semi‐independently. This decomposition also promotes generalization through recombination of components from different XAI systems, which facilitates the generation of novel variants. These new variants need not be evaluated one by one provided that each component has been validated, leading to an exponential decrease in development time. Finally, by making the goal of explanation explicit, Bayesian Teaching helps developers to assess how suitable an XAI system is for its intended real‐world use case. Thus, Bayesian Teaching provides a theoretical framework that encourages systematic, scientific investigation of XAI.
RoleSim*: Scaling axiomatic role-based similarity ranking on large graphs

Abstract RoleSim and SimRank are among the popular graph-theoretic similarity measures with many applications in, e.g., web search, collaborative filtering, and sociometry. While RoleSim addresses the automorphic (role) equivalence of pairwise similarity which SimRank lacks, it ignores the neighboring similarity information out of the automorphically equivalent set. Consequently, two pairs of nodes, which are not automorphically equivalent by nature, cannot be well distinguished by RoleSim if the averages of their neighboring similarities over the automorphically equivalent set are the same. To alleviate this problem: 1) We propose a novel similarity model, namely RoleSim*, which accurately evaluates pairwise role similarities in a more comprehensive manner. RoleSim* not only guarantees the automorphic equivalence that SimRank lacks, but also takes into account the neighboring similarity information outside the automorphically equivalent sets that are overlooked by RoleSim. 2) We prove the existence and uniqueness of the RoleSim* solution, and show its three axiomatic properties ( i.e., symmetry, boundedness, and non-increasing monotonicity). 3) We provide a concise bound for iteratively computing RoleSim* formula, and estimate the number of iterations required to attain a desired accuracy. 4) We induce a distance metric based on RoleSim* similarity, and show that the RoleSim* metric fulfills the triangular inequality, which implies the sum-transitivity of its similarity scores. 5) We present a threshold-based RoleSim* model that reduces the computational time further with provable accuracy guarantee. 6) We propose a single-source RoleSim* model, which scales well for sizable graphs. 7) We also devise methods to scale RoleSim* based search by incorporating its triangular inequality property with partitioning techniques. Our experimental results on real datasets demonstrate that RoleSim* achieves higher accuracy than its competitors while scaling well on sizable graphs with billions of edges.
New Approximation Algorithms for Forest Closeness Centrality – for Individual Vertices and Vertex Groups

The emergence of massive graph data sets requires fast mining algorithms.Centrality measures to identify important vertices belong to the most popular analysis methods in graph mining.A measure that is gaining attention is forest closeness centrality; it is closely related to electrical measures using current flow but can also handle disconnected graphs.Recently, [Jin et al., ICDM'19] proposed an algorithm to approximate this measure probabilistically.Their algorithm processes small inputs quickly, but does not scale well beyond hundreds of thousands of vertices.In this paper, we first propose a different approximation algorithm; it is up to two orders of magnitude faster and more accurate in practice.Our method exploits the strong connection between uniform spanning trees and forest distances by adapting and extending recent approximation algorithms for related single-vertex problems.This results in a nearly-linear time algorithm with an absolute probabilistic error guarantee.In addition, we are the first to consider the problem of finding an optimal group of vertices w. r. t. forest closeness.We prove that this latter problem is NP-hard; to approximate it, we adapt a greedy algorithm by [Li et al., WWW'19], which is based on (partial) matrix inversion.Moreover, our experiments show that on disconnected graphs, group forest closeness outperforms existing centrality measures in the context of semi-supervised vertex classification.
EDUCATIONAL PLANNING IN NIGERIA DURING COVID-19: PROBLEMS AND WAY FORWARD

Educational planning is the foundation of education. The development of education depend on effective planning. Educational planning is a continuous process and it covers all aspects of education. The COVID-19 outbreak disrupted the activities of educational planning in Nigeria. This article is aimed to discuss how COVID-19 clock down affected the planning of education in Nigeria. The researchers used secondary date sourced from both print materials and online to support some of the points raised in the article. The COVID-19 lockdown affected the educational planning in Nigeria in the following ways: suspension of implementation of planned programme, suspension of school planning, suspension of lesson planning at school level and suspension of data collection for educational planning. To prepare for the future and to ensure that even in the mist of pandemic planning of education continues, this article hereby suggests the following: Educational planners in all ministries and agencies of education should be train on how to use ICT facilities to carry out planning functions and ICT facilities should be provided for all the educational planners in the country e.tc.
Garnierite characterization for open mineral databases

The nickel laterite mining industry faces lower recovery rates related to low ore grades, multiple nickel carrying minerals, as well as lateral and vertical heterogeneities at ore deposit scale. Garnierite, a complex mixture of nickel-bearing phyllosilicates, occurs at the base of nickel laterite profiles. Samples of Ni-rich and Ni-poor phyllosilicates from garnierite and host saprolite, from New Caledonia and the Dominican Republic, were analyzed by laboratory X-ray diffractometry (XRD), Fourier Transform Infrared (FTIR) and Raman spectroscopies. These phyllosilicates are mainly composed of serpentine-, talc- and/or sepiolite-like phases. XRD allows clearly distinguishing the different phyllosilicate phases. Moreover, it shows a difference between Ni-rich and Ni-poor phases after refinement of the diffractograms, in particular the (060) reflection, little exploited at present. When Ni is present, the peaks are broader and the spacing corresponding to the (060) reflection tends to be less than 1.534 Å. Mg substitution by Ni also cause shifts of hydroxyl bands in FTIR (3500–3800 cm−1 and 500–800 cm−1 spectral range), and Raman spectra (3500–3700 cm−1 spectral range). Moreover, the intensities of the bands can be correlated with the Ni content. Both are reliable indicators to define the nature of garnierite. In Raman spectroscopy, the substitution of Mg by Ni generates shifts on most bands, and there is a variation in intensity ratios between several bands which are correlated with Ni content. Our results can be applied to spectra obtained by online and handheld analytical devices, in-field core scanners or XRD/X-Ray fluorescence (XRF) combined expert systems.
Overview of Motion-to-Photon Latency Reduction for Mitigating VR Sickness

For several years, virtual reality (VR) and augmented reality (AR) technologies have been improving.However, some hurdles remain that slow down the distribution of VR and AR devices, such as head-mounted display (HMD), and related consumer content.One issue is VR motion sickness, which has been experienced by users using 360 degree VR content via HMD.This paper discusses the related international standardization work that classifies the factors causing VR sickness, and proposes the process for VR sickness level evaluation.Among the factors causing VR sickness, many research institutes regard minimizing MTP (Motion-to-Photon) latency as the key enabler to mitigate VR sickness.Thus, this paper introduces research trends of MTP latency measurement and MTP latency mitigation.This paper categorizes the research on MTP latency measurement into 2 categories of hardware-based approach and software code-level approach.The 2 approaches have different pros and cons depending on use-case, purpose, and architecture of each multimedia system.The pros and cons are addressed in this paper.Additionally, the research on mitigating MTP latency with diverse strategies such as proactive computing, caching, and edge server technology is explained, and compared to conventional technologies, shows improved performance.
Data leakage detection in Wi-Fi networks

Most users secure their phones from physical access, but other security practices are mostly poor. Smartphones contain a significant amount of personal data. Additionally, they are always in the user’s possession, which allows them to be abused for tracking. The applications we install on smartphones generate numerous patterns of network traffic, periodic updates, background activities, etc. These applications leak information about side-channel data such as volume, transfer timing, and packet size. An intruder may use and exploit this information to steal confidential information about the users. This chapter focuses upon several vulnerabilities to the confidential undertakings of the user, with an upsurge in commercial fascination to trace wireless data that is publicly broadcast. The chapter shows mechanisms to infer consumer demeanor from encoded wireless network activities and besides depicts the ways in which information is transmitted over the network can be detected by an absolute inert eavesdropper.