Reviewerly/exHarmony huggingface dataset:
Linear Last-iterate Convergence for Matrix Games and Stochastic Games.

Optimistic Gradient Descent Ascent (OGDA) algorithm for saddle-point optimization has received growing attention due to its favorable last-iterate convergence. However, its behavior for simple two-player matrix games is still not fully understood -- previous analysis lacks explicit convergence rates, only applies to exponentially small learning rate, or requires additional conditions such as uniqueness of the optimal solution. In this work, we significantly expand the understanding of OGDA, introducing a set of sufficient conditions under which OGDA exhibits concrete last-iterate convergence rates with a constant learning rate. Specifically, we show that matrix games satisfy these conditions and OGDA converges exponentially fast without any additional assumptions. More generally, our conditions hold for smooth bilinear functions and strongly-convex-strongly-concave functions over a constrained set. We provide experimental results to further support our theory. To further demonstrate the significance of our results for matrix games, we greatly generalize the ideas to finite-horizon stochastic/Markov games and provide the first algorithm that simultaneously ensures 1) linear last-iterate convergence when playing against itself and 2) low regret when playing against an arbitrary slowly-changing opponent.
Applying and developing semantic web technologies for exploiting a corpus in history of science: The case study of the Henri Poincaré correspondence

The Henri Poincaré correspondence is a corpus of letters sent and received by this mathematician. The edition of this correspondence is a long-term project begun during the 1990s. Since 1999, a website is devoted to publish online this correspondence with digitized letters. In 2017, it has been decided to reforge this website using Omeka S. This content management system offers useful services but some user needs have led to the development of an RDFS infrastructure associated to it. Approximate and explained searches are managed thanks to SPARQL query transformations. A prototype for efficient RDF annotation of this corpus (and similar corpora) has been designed and implemented. This article deals with these three research issues and how they are addressed.
Integrating Graph-Based and Transition-Based Dependency Parsers in the Deep Contextualized Era

Graph-based and transition-based dependency parsers used to have different strengths and weaknesses.Therefore, combining the outputs of parsers from both paradigms used to be the standard approach to improve or analyze their performance.However, with the recent adoption of deep contextualized word representations, the chief weakness of graph-based models, i.e., their limited scope of features, has been mitigated.Through two popular combination techniques -blending and stackingwe demonstrate that the remaining diversity in the parsing models is reduced below the level of models trained with different random seeds.Thus, an integration no longer leads to increased accuracy.When both parsers depend on BiLSTMs, the graph-based architecture has a consistent advantage.This advantage stems from globally-trained BiLSTM representations, which capture more distant look-ahead syntactic relations.Such representations can be exploited through multi-task learning, which improves the transition-based parser, especially on treebanks with a high ratio of right-headed dependencies.
Reading the 21st Century World: Self-Assessment of Digital Media Literacy among Secondary School Students in Malaysia

Students today participate in digital media cultures in numerous ways and it is important to consider their level of confidence in understanding their digital skills. The focus on digital media literacy is important to ensure that students have sufficient knowledge and skills to participate in society and in the economy as productive citizens. This study aimed to examine the digital media skills of Malaysian students in selected schools in Malaysia. The study presents the findings of a self-assessment tool, consisting of 10 core domains that was constructed to respond appropriately to the needs of students in secondary schools in Malaysia. The self-reporting by the students indicated that in general, they were quite self-assured about their capacities and many of them have rated their digital media skills at fairly high confidence levels. The findings suggest that many of students considered that they were competent in the areas of mobile, social and problem solving skills when working within digital platforms. However, the respondents deemed that the most challenging domain was creative as they felt less competent when it came to doing creative tasks using digital tools. By way of conclusion, this paper urges education authorities and schools to develop digital media literacy programmes that enhance students’ capacities in confronting challenges as well as exercising rights as active and responsible digital citizens.
The Uncertainty of Side-channel Analysis: A Way to Leverage from Heuristics

Performing a comprehensive side-channel analysis evaluation of small embedded devices is a process known for its variability and complexity. In real-world experimental setups, the results are largely influenced by a huge amount of parameters, some of which are not easily adjusted without trial and error and are heavily relying on the experience of professional security analysts. In this article, we advocate the usage of an existing statistical methodology called Six Sigma (6 <?TeX $\sigma$?> ) for side-channel analysis optimization. This well-known methodology is commonly used in other industrial fields, such as production and quality engineering, to reduce the variability of industrial processes. We propose a customized Six Sigma methodology, which allows even a less-experienced security analysis to select optimal values for the different variables that are critical for the side-channel analysis procedure. Moreover, we show how our methodology helps in improving different phases in the side-channel analysis process.
A survey on analyzing encrypted network traffic of mobile devices

Over the years, use of smartphones has come to dominate several areas, improving our lives, offering us convenience, and reshaping our daily work circumstances. Beyond traditional use for communication, they are used for many peripheral tasks such as gaming, browsing, and shopping. A significant amount of traffic over the Internet belongs to the applications running over mobile devices. Applications encrypt their communication to ensure the privacy and security of the user’s data. However, it has been found that the amount and nature of incoming and outgoing traffic to a mobile device could reveal a significant amount of information that can be used to identify the activities performed and to profile the user. To that end, researchers are trying to develop techniques to classify encrypted mobile traffic at different levels of granularity, with the objectives of performing mobile user profiling, network performance optimization, etc. This paper proposes a framework to categorize the research works on analyzing encrypted network traffic related to mobile devices. After that, we provide an extensive review of the state of the art based on the proposed framework.
Sequential no-Substitution k-Median-Clustering

We study the sample-based k-median clustering objective under a sequential setting without substitutions. In this setting, an i.i.d. sequence of examples is observed. An example can be selected as a center only immediately after it is observed, and it cannot be substituted later. The goal is to select a set of centers with a good k-median cost on the distribution which generated the sequence. We provide an efficient algorithm for this setting, and show that its multiplicative approximation factor is twice the approximation factor of an efficient offline algorithm. In addition, we show that if efficiency requirements are removed, there is an algorithm that can obtain the same approximation factor as the best offline algorithm. We demonstrate in experiments the performance of the efficient algorithm on real data sets. Our code is available at this https URL.
Contextual Online False Discovery Rate Control

Multiple hypothesis testing, a situation when we wish to consider many hypotheses, is a core problem in statistical inference that arises in almost every scientific field. In this setting, controlling the false discovery rate (FDR), which is the expected proportion of type I error, is an important challenge for making meaningful inferences. In this paper, we consider the problem of controlling FDR in an online manner. Concretely, we consider an ordered, possibly infinite, sequence of hypotheses, arriving one at each timestep, and for each hypothesis we observe a p-value along with a set of features specific to that hypothesis. The decision whether or not to reject the current hypothesis must be made immediately at each timestep, before the next hypothesis is observed. The model of multi-dimensional feature set provides a very general way of leveraging the auxiliary information in the data which helps in maximizing the number of discoveries. We propose a new class of powerful online testing procedures, where the rejections thresholds (significance levels) are learnt sequentially by incorporating contextual information and previous results. We prove that any rule in this class controls online FDR under some standard assumptions. We then focus on a subclass of these procedures, based on weighting significance levels, to derive a practical algorithm that learns a parametric weight function in an online fashion to gain more discoveries. We also theoretically prove, in a stylized setting, that our proposed procedures would lead to an increase in the achieved statistical power over a popular online testing procedure proposed by Javanmard & Montanari (2018). Finally, we demonstrate the favorable performance of our procedure, by comparing it to state-of-the-art online multiple testing procedures, on both synthetic data and real data generated from different applications.
3D Data Repository Features, Best Practices, and Implications for Preservation Models: Findings from a National Forum

This study identifies challenges and directions for 3D/VR repository standards and practices. As 3D technologies become more affordable and accessible, academic libraries need to implement workflows, standards, and practices that support the full lifecycle of 3D data. This study invited experts across several disciplines to analyze current national repository and preservation efforts. Outlined models provide frameworks to identify features, examine workflows, and determine implications of 3D data on current preservation models. Participants identified challenges for supporting 3D data, including intellectual property and fair use; providing repository system management beyond academic libraries; seeking guidance outside of academia for workflows to model.
Comparing the inversion statistic for distribution-biased and distribution-shifted permutations with the geometric and the GEM distributions

%PDF-1.5 % 44 0 obj << /Length 3792 /Filter /FlateDecode >> stream x[Ý"Û ¶ Byf, Inâ¸-Mb_IxïŽµ$EÅ‚dbzf//gO fY.fVÌOE1arv<{Ù"B(Ì›tP\%6Ç"~Ueh|77Q&oXÍOE:í€ qC^xfR'YrElgß˜BdoL3_G{~q63iXjpR\rsL=wß…>:E;b02y~"sT2SÜ®=l*,Tpp06[( im+×°mV&M*)sÉ'_jK5lkY7å'¨qW^\\y]gip)"Î¹8]$ae †< 4Mds~]È•É‹~ .;>3Ü¥Ø°}Û¹pI?@Ö¼PÉ¤X"6./v7>]yy?o`0^Û™M/DM^&jkuMá‡ BT~#^| 4f MeZZ VLÂ®'R''Òˆ] s4Oh]cC/$[3_zI$|]l s[ Af36 )=T@iXÊ¸mÇŽhG[T:U5ffÌ²eÑ©MS.f@C\gTÒR8}$ LR[Å ä'ª.s×€kp>UjÃ‰Ô‚^Etw7]GV1G9wWSKSZ>Ðº6ZË¨ AM+7I6m&(lF: 9u%cwC6ÝŽ=ÊƒÕ®K{%@Ai)K_Fey3 g?m/m)
Confrustion and Gaming While Learning with Erroneous Examples in a Decimals Game

Prior studies have explored the potential of erroneous examples in helping students learn more effectively by correcting errors in solutions to decimal problems. One recent study found that while students experience more confusion and frustration (confrustion) when working with erroneous examples, they demonstrate better retention of decimal concepts. In this study, we investigated whether this finding could be replicated in a digital learning game. In the erroneous examples (ErrEx) version of the game, students saw a character play the games and make mistakes, and then they corrected the characters’ errors. In the problem solving (PS) version, students played the games by themselves. We found that confrustion was significantly, negatively correlated with performance in both pretest (r = −.62, p < .001) and posttest (r = −.68, p < .001) and so was gaming the system (pretest r = −.58, p < .001, posttest r = −.66, p < .001). Posthoc (Tukey) tests indicated that students who did not see any erroneous examples (PS-only) experienced significantly lower levels of confrustion (p < .001) and gaming (p < .001). While we did not find significant differences in post-test performance across conditions, our findings show that students working with erroneous examples experience consistently higher levels of confrustion in both game and non-game contexts.
AI Enabled Tutor for Accessible Training

A significant number of jobs require highly skilled labor which necessitate training on pre-requisite knowledge. Examples include jobs in military, technical field such computer science, large scale fulfillment centers such as Amazon. Moreover, making such jobs accessible to the disabled population requires even more pre-requisite training such as knowledge of sign language. An artificial intelligent (AI) agent can potentially act as a tutor for such pre-requisite training. This will not only reduce resource requirements for such training but also decrease the time taken for making personnel job ready. In this paper, we develop an AI tutor that can teach users gestures that are required on the field as a pre-requisite. The AI tutor uses a model learning technique that learns the gestures performed by experts. It then uses a model comparison technique to compare a learner with the expert gesture and provides feedback for the learner to improve.
FEATURE CLUSTERING FOR PSO-BASED FEATURE CONSTRUCTION ON HIGH-DIMENSIONAL DATA

Feature construction (FC) refers to a process that uses the original features to construct new features with better discrimination ability. Particle Swarm Optimisation (PSO) is an effective search technique that has been successfully utilised in FC. However, the application of PSO for feature construction using high dimensional data has been a challenge due to its large search space and high computational cost. Moreover, unnecessary features that were irrelevant, redundant and contained noise were constructed when PSO was applied to the whole feature. The feature clustering methods were used to aggregate similar features into clusters, whereby the dimensionality of the data was lowered by choosing representative features from every cluster to form the final feature subset. The clustering of each features are proven to be accurate in feature selection (FS), however, only one study investigated its application in FC for classification. The study identified some limitations, such as the implementation of only two binary classes and the decreasing accuracy of the data. This paper proposes a cluster based PSO feature construction approach called ClusPSOFC. The Redundancy-Based Feature Clustering (RFC) algorithm was applied to choose the most informative features from the original data, while PSO was used to construct new features from those selected by RFC. Experimental results were obtained by using six UCI data sets and six high-dimensional data to demonstrate the efficiency of the proposed method when compared to the original full features, other PSO based FC methods, and standard genetic programming based feature construction (GPFC). Hence, the ClusPSOFC method is effective for feature construction in the classification of high dimensional data.
A Morphable Physically Unclonable Function and True Random Number Generator using a Commercial Magnetic Memory

In this work, a morphable security primitive using commercial magnetic memory is proposed which can be utilized as a Physically Unclonable Function (PUF) and as a True Random Number Generator (TRNG) by manipulating the write time and the number of write pulses respectively. The intra-HD, inter-HD, energy, bandwidth and area of the proposed PUF is found to be 0, 46.25%, 0.14pJ/bit, 0.34Gbit/s and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0.385}\mu\mathbf{m}^{\mathbf{2}}/\mathbf{bit}$</tex> (including peripherals) respectively. The proposed TRNG provides all possible outcomes with a standard deviation of 0.0062, correlation coefficient of 0.05 and an entropy of 0.95. The energy, bandwidth and area of the proposed TRNG is found to be 0.41pJ/bit, 0.12Gbit/s and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{0.769}\mu\mathbf{m}^{\mathbf{2}}/\mathbf{bit}$</tex> (including peripherals). The proposed TRNG has also been tested with NIST test suite.
Implementation of a Strongly Robust Identity-Based Encryption Scheme over Type-3 Pairings

Identity-based encryption (IBE) is a powerful mechanism for maintaining security. However, systems based on IBE are unpopular when compared with those of the public-key encryption (PKE). In our opinion, one of the reasons is a gap between theory and practice. For example, a generic transformation of weakly/strongly robust IBE from any IBE has been proposed by Abdalla et al., no robust IBE scheme is explicitly given. This means that, theoretically, anyone can construct a weakly/strongly robust IBE scheme by employing this transformation. However, this seems not easily applicable to non-cryptographers. In this paper, we first introduce the Gentry IBE scheme constructed over Type-3 pairings by employing the transformation proposed by Abe et al., and second we explicitly give strongly/weakly robust Gentry IBE schemes by employing the Abdalla et al. transformation. Finally, we show its implementation result and show that we can add strong robustness to the Gentry IBE scheme with a very few additional costs. We employ the mcl library to support a Barreto-Naehrig curve defined over the 462-bit prime. The encryption requires about 5 ms, whereas the decryption requires about 9 ms.
Research and Implementation of Railway Technical Specification Question Answering System Based on Deep Learning

In recent years, the significant progress in deep learning and natural language processing technology has provided high efficient technical solutions for the two key problems of user intension recognition, and question and answer semantic matching in the intelligent question and answer field. The improvement of the accuracy of the intelligent question answering system enables its application in special business fields possible. The railway technical specifications have the characteristics such as many knowledge points, wide coverage and many digital specifications, making it difficult for railway employees to learn and master them, and making it difficult to meet the work requirements on technical specifications. This paper proposes a solution for the railway technical specification intelligent question answering system based on deep learning. The experimental test shows that the accuracy rate of question and answer matching of this scheme is 96.85%, with high accuracy, which can be used to provide real-time, accurate and convenient intelligent consultation service of technical management specifications for railway employees and promote the improvement of work efficiency.
Effectively Prefetching Remote Memory with Leap

Memory disaggregation over RDMA can improve the performance of memory-constrained applications by replacing disk swapping with remote memory accesses. However, state-of-the-art memory disaggregation solutions still use data path components designed for slow disks. As a result, applications experience remote memory access latency significantly higher than that of the underlying low-latency network, which itself is too high for many applications. In this paper, we propose Leap, a prefetching solution for remote memory accesses due to memory disaggregation. At its core, Leap employs an online, majority-based prefetching algorithm, which increases the page cache hit rate. We complement it with a lightweight and efficient data path in the kernel that isolates each application's data path to the disaggregated memory and mitigates latency bottlenecks arising from legacy throughput-optimizing operations. Integration of Leap in the Linux kernel improves the median and tail remote page access latencies of memory-bound applications by up to 104.04x and 22.62x, respectively, over the default data path. This leads to up to 10.16x performance improvements for applications using disaggregated memory in comparison to the state-of-the-art solutions.
The Improved Equilibrium Optimization Algorithm with Best Candidates

Abstract The best candidates play the important role during the exploration and exploitation of individuals in almost all of the swarm-based algorithms. More best candidates were involved in such procedure of the grey wolf optimization algorithm and the newly raised equilibrium optimization (EO) algorithm we called here. The EO algorithm introduced four best candidates besides their average and constructs an equilibrium pool. However, the best candidates would still perform the guiding role and a novel improvement was introduced. Experiments on some classical benchmark functions were carried out and results show the better performance than the original one. Consequently, the EO algorithm should be improved and focused more on the best candidates furthermore.
A Novel GLS Consensus Algorithm for Alliance Chain in Edge Computing Environment

Edge computing devices are widely deployed. An important issue that arises is in that these devices suffer from security attacks. To deal with it, we turn to the blockchain technologies. The note in the alliance chain need rules to limit write permissions. Alliance chain can provide security management functions, using these functions to meet the management between the members, certification, authorization, monitoring and auditing. This article mainly analyzes some requirements realization which applies to the alliance chain, and introduces a new consensus algorithm, generalized Legendre sequence (GLS) consensus algorithm, for alliance chain. GLS algorithms inherit the recognition and verification efficiency of binary sequence ciphers in computer communication and can solve a large number of nodes verification of key distribution issues. In the alliance chain, GLS consensus algorithm can complete node address hiding, automatic task sorting, task automatic grouping, task node scope confirmation, task address binding and stamp timestamp. Moreover, the GLS consensus algorithm increases the difficulty of network malicious attack.