Reviewerly/exHarmony huggingface dataset:
Mitigating Stragglers in the Decentralized Training on Heterogeneous Clusters

Decentralized algorithms, e.g., AllReduce, have been widely applied as the synchronization strategy for data-parallel distributed deep learning due to its superior performance over centralized ones. The synchronous Stochastic Gradient Descent (SGD) approach guarantees accuracy for various deep learning models, but its performance suffers from stragglers, i.e., "long-tail effects." The straggler can be caused by the inherent load imbalance from workloads or system heterogeneity. Despite existing optimizations to support centralized algorithms against stragglers, little effort has been explored in decentralized training algorithms.
Bayesian Quickest Detection of Credit Card Fraud

This paper addresses the risk of fraud in credit card transactions by developing a probabilistic model for the quickest detection of illegitimate purchases. Using optimal stopping theory, the goal is to determine the moment, known as disorder or fraud time, at which the continuously monitored process of a consumer's transactions exhibits a disorder due to fraud, in order to return the best trade-off between two sources of cost: on the one hand, the disorder time should be detected as soon as possible to counteract illegal activities and minimize the loss that banks, merchants and consumers suffer; on the other hand, the frequency of false alarms should be minimized to avoid generating adverse effects for cardholders and to limit the operational and process costs for the card issuers. The proposed approach allows us to score consumers' transactions and to determine, in a rigorous, personalized and optimal manner, the threshold with which scores are compared to establish whether a purchase is fraudulent.
Affinity Learning Via Self-Supervised Diffusion for Spectral Clustering

Spectral clustering makes use of the spectrum of an input affinity matrix to segment data into disjoint clusters.The performance of spectral clustering depends heavily on the quality of the affinity matrix.Commonly used affinity matrices are constructed by either the Gaussian kernel or the self-expressive model with sparse or low-rank constraints.A technique called diffusion which acts as a post-process has recently shown to improve the quality of the affinity matrix significantly, by taking advantage of the contextual information.In this paper, we propose a variant of the diffusion process, named Self-Supervised Diffusion, which incorporates clustering result as feedback to provide supervisory signals for the diffusion process.The proposed method contains two stages, namely affinity learning with diffusion and spectral clustering.It works in an iterative fashion, where in each iteration the clustering result is utilized to calculate a pseudo-label similarity so that it can aid the affinity learning stage in the next iteration.Extensive experiments on both synthetic and real-world data have demonstrated that the proposed method can learn accurate and robust affinity, and thus achieves superior clustering performance.
OTHR multitarget tracking with a GMRF model of ionospheric parameters

The ionosphere is the propagation medium for radio waves transmitted by an over-the-horizon radar (OTHR). Ionospheric parameters, typically, virtual ionospheric heights (VIHs), are required to perform coordinate registration for OTHR multitarget tracking and localization. The inaccuracy of ionospheric parameters has a significant deleterious effect on the target localization of OTHR. Therefore, to improve the localization accuracy of OTHR, it is important to develop accurate models and estimation methods of ionospheric parameters and the corresponding target tracking algorithms. In this paper, we consider the variation of the ionosphere with location and the spatial correlation of the ionosphere. We use a Gaussian Markov random field (GMRF) to model the VIHs, providing a more accurate representation of the VIHs for OTHR target tracking. Based on expectation-conditional maximization and GMRF modeling of the VIHs, we propose a novel joint optimization solution, namely ECM-GMRF, to perform target state estimation, multipath data association and VIHs estimation simultaneously. In ECM-GMRF, the measurements from both ionosondes and OTHR are exploited to estimate the VIHs, leading to a better estimation of the VIHs which improves the accuracy of data association and target state estimation, and vice versa. The simulation indicates the effectiveness of the proposed algorithm.
A flexible robust Student's t-based multimodel approach with maximum Versoria criterion

The performance of the state estimation for Gaussian state space models can be degraded if the models are affected by the non-Gaussian process and measurement noises with uncertain degree of non-Gaussianity. In this paper, we propose a flexible robust Student's t-based multimodel approach. More specifically, the degrees of freedom parameter from the Student's t-distribution is assumed unknown and modelled by a Markov chain of state values. In order to capture more information of the Student's t-distributions propagated through multiple models, we establish a model-based Versoria cost function in the form of a weighted mixture rather than the original form, and maximize the function to interact and fuse the multiple models. Simulated results prove the flexibility of the robustness of the proposed Student's t-basedmultimodel approach when the existence probability of the outliers is uncertain.
Improving Frequency Estimation under Local Differential Privacy

Local Differential Privacy protocols are stochastic protocols used in data aggregation when individual users do not trust the data aggregator with their private data. In such protocols there is a fundamental tradeoff between user privacy and aggregator utility. In the setting of frequency estimation, established bounds on this tradeoff are either nonquantitative, or far from what is known to be attainable. In this paper, we use information-theoretical methods to significantly improve established bounds. We also show that the new bounds are attainable for binary inputs. Furthermore, our methods lead to improved frequency estimators, which we experimentally show to outperform state-of-the-art methods.
DEALER: decentralized incentives for threat intelligence reporting and exchange

Abstract The exchange of threat intelligence information can make a significant contribution to improving IT security in companies and has become increasingly important in recent years. However, such an exchange also entails costs and risks, preventing many companies from participating. In addition, since legal reporting requirements were introduced in various countries, certain requirements must be taken into account in the exchange process. However, existing exchange platforms neither offer incentives to participate in the exchange process, nor fulfill requirements resulting from reporting obligations. With this work, we present a decentralized platform for the exchange of threat intelligence information. The platform supports the fulfillment of legal reporting obligations for security incidents and provides additional incentives for information exchange between the parties involved. We evaluate the platform by implementing it based on the EOS blockchain and IPFS distributed hash table. The prototype and cost measurements demonstrate the feasibility and cost-efficiency of our concept.
When stakes are high: Balancing accuracy and transparency with Model-Agnostic Interpretable Data-driven suRRogates

Technological advancements allow to develop high-performance black box predictive models. However, strictly regulated industries (like banking and insurance) ask for transparent decision-making algorithms. We therefore present a procedure to develop a Model-Agnostic Interpretable Data-driven suRRogate (maidrr) suited for structured tabular data. Knowledge is extracted from a black box via partial dependence effects. These are used to perform smart feature engineering by grouping variable values. This results in a segmentation of the feature space with automatic variable selection. A transparent generalized linear model (GLM) is fit to the features in categorical format and their relevant interactions. This GLM serves as a global surrogate to the original black box and replaces it in production. We demonstrate our R package maidrr with a case study on general insurance claim frequency modeling for six publicly available datasets. Our maidrr GLM closely approximates a gradient boosting machine (GBM) black box and outperforms both a linear and tree surrogate as benchmarks. • Procedure to develop an interpretable global surrogate for a complex system. • Surrogate closely approximates a black box model regarding accuracy and fidelity. • Automatic feature selection, segmentation and both global and local explanations. • Satisfy transparency needs of a strictly regulated industry or high-stakes decision. • Case study on insurance claim frequency prediction for six public datasets.
Non-interactive Composition of Sigma-Protocols via Share-then-Hash

Proofs of partial knowledge demonstrate the possession of certain subsets of witnesses for a given collection of statements $$x_1,\dots ,x_n$$ . Cramer, Damgård, and Schoenmakers (CDS), built proofs of partial knowledge, given "atomic" protocols for individual statements $$x_i$$ , by having the prover randomly secret share the verifier's challenge and using the shares as challenges for the atomic protocols. This simple and highly-influential transformation has been used in numerous applications, ranging from anonymous credentials to ring signatures. We consider what happens if, instead of using the shares directly as challenges, the prover first hashes them. We show that this elementary enhancement can result in significant benefits: None of the above features is satisfied by the CDS transformation.
Face Commands - User-Defined Facial Gestures for Smart Glasses

We propose the use of face-related gestures involving the movement of the face, eyes, and head for augmented reality (AR). This technique allows us to use computer systems via hands-free, discreet interactions. In this paper, we present an elicitation study to explore the proper use of facial gestures for daily tasks in the context of a smart home. We used Amazon Mechanical Turk to conduct this study (N=37). Based on the proposed gestures, we report usage scenarios and complexity, proposed associations between gestures/tasks, a user-defined gesture set, and insights from the participants. We also conducted a technical feasibility study (N=13) with participants using smart eyewear to consider their uses in daily life. The device has 16 optical sensors and an inertial measurement unit (IMU). We can potentially integrate the system into optical see-through displays or other smart glasses. The results demonstrate that the device can detect eight temporal face-related gestures with a mean F1 score of 0.911 using a convolutional neural network (CNN). We also report the results of user-independent training and a one-hour recording of the experimenter testing two of the gestures.
A fuzzy matrix factor recommendation method with forgetting function and user features

In recent years, a variety of recommendation algorithms have been proposed. However, there are still some issues such as cold start, sparsity and timeliness. On the basis of traditional collaborative filtering, matrix decomposition technologies can greatly reduce the computational complexity and address the issues of cold start and sparsity. Based on forgetting functions and users’ multi-attribute features, in this paper, a fuzzy matrix decomposition and trace norm minimization method is proposed. Firstly, a forgetting function is incorporated into the historical scores, which makes existing scoring information more efficient. Then the user features are applied to find trusted users, which will give some valuable suggestions and possible scores to target items. The user features are also applied to list the relationships among users. In this paper, the relations between users and items are represented by the scores between them. Secondly, both time and feature information is integrated into the proposed optimization model. Then a fuzzy matrix decomposition and trace norm minimization model is established and further solved by our proposed algorithm to give better recommendations. Moreover, the convergence of the proposed algorithm is also proved in theoretically. Finally, we investigate the empirical recoverability properties of our model and analyze its advantages over classical norm minimization models. Extensive experimental results on synthetic and real-world data sets verify the efficiency and effectiveness of our method compared with the state-of-the-art algorithms.
A Workflow for the Performance of the Differential Ovarian Follicle Count Using Deep Neuronal Networks

In order to automate the counting of ovarian follicles required in multigeneration reproductive studies performed in the rat according to Organization for Economic Co-operation and Development guidelines 443 and 416, the application of deep neural networks was tested. The manual evaluation of the differential ovarian follicle count is a tedious and time-consuming task that requires highly trained personnel. In this regard, deep learning outputs provide overlay pictures for a more detailed documentation, together with an increased reproducibility of the counts. To facilitate the planned good laboratory practice (GLP) validation a workflow was set up using MLFlow to make all steps from generating of scans, training of the neural network, uploading of study images to the neural network, generation and storage of the results in a compliant manner controllable and reproducible. PyTorch was used as main framework to build the Faster region-based convolutional neural network for the training. We compared the performances of different depths of ResNet models with specific regard to the sensitivity, specificity, accuracy of the models. In this paper, we describe all steps from data labeling, training of networks, and the performance metrics chosen to evaluate different network architectures. We also make recommendation on steps, which should be taken into consideration when GLP validation is aimed for.
Reliable Collaborative Learning with Commensurate Incentive Schemes

Collaborative learning techniques allow numerous clients conjointly to improve artificial intelligence models using their private datasets. The clients carry out the training locally and periodically exchanging gradient values through devices. Unlike conventional training approaches, the training data in the collaborative techniques are not revealed publicly. Regardless of privacy merits, clients are often less motivated to improve the model due to inadequate incentives procedural. In short, the resources owned are not maximally utilized. To tackle the issue, we design a collaborative learning model with a secure, fair, and immutable incentive mechanism by leveraging blockchain technology. Incentives are distributed proportionately to clients according to their respective contributions. We implement our incentive schemes on Ethereum. We also evaluate the performance of collaborative learning in a different setting. The results indicate that the design objectives are met.
A novel evolutionary row class entropy based optimal multi-level thresholding technique for brain MR images

The local averaging technique adopted for the construction of 2D histogram in Otsu's method fails to preserve the edge information. Further, the consideration of the diagonal pixels only results in the loss of information. These make the 2D Otsu method of multi-level thresholding inefficient to retain the spatial correlation information. Although the computation of 2D histogram based on gray gradient information is a better way to threshold an image, it faces a backlash due to the high magnitude peaks. To solve these problems, we suggest a new normalized local variance (NLV) method for constructing 2D histogram using the local variance followed by a novel evolutionary row class entropy (ERCE) method for optimal multi-level image thresholding, which tries to preserve maximum spatial information through normalization of the local variance. A new optimization technique called hybrid Adaptive Cuckoo Search-Squirrel Search Algorithm (ACS-SSA) is also introduced. A new fitness function is suggested. The standard CEC 2005 benchmark test functions are used to validate the performance of our proposed ACS-SSA technique. The optimum threshold values obtained are used to segment 100 slices of T-2 weighted axial brain MR images (taken from the Harvard Medical School database). Several performance evaluation metrics are computed to compare the performance of our method with the state-of-the-art methods. The analysis of the results shows that ERCE method outperforms other methods. This method may set a new direction in the multilevel image thresholding research.
Accelerating Large-Scale Heterogeneous Interaction Graph Embedding Learning via Importance Sampling

In real-world problems, heterogeneous entities are often related to each other through multiple interactions, forming a Heterogeneous Interaction Graph (HIG). While modeling HIGs to deal with fundamental tasks, graph neural networks present an attractive opportunity that can make full use of the heterogeneity and rich semantic information by aggregating and propagating information from different types of neighborhoods. However, learning on such complex graphs, often with millions or billions of nodes, edges, and various attributes, could suffer from expensive time cost and high memory consumption. In this article, we attempt to accelerate representation learning on large-scale HIGs by adopting the importance sampling of heterogeneous neighborhoods in a batch-wise manner, which naturally fits with most batch-based optimizations. Distinct from traditional homogeneous strategies neglecting semantic types of nodes and edges, to handle the rich heterogeneous semantics within HIGs, we devise both type-dependent and type-fusion samplers where the former respectively samples neighborhoods of each type and the latter jointly samples from candidates of all types. Furthermore, to overcome the imbalance between the down-sampled and the original information, we respectively propose heterogeneous estimators including the self-normalized and the adaptive estimators to improve the robustness of our sampling strategies. Finally, we evaluate the performance of our models for node classification and link prediction on five real-world datasets, respectively. The empirical results demonstrate that our approach performs significantly better than other state-of-the-art alternatives, and is able to reduce the number of edges in computation by up to 93%, the memory cost by up to 92% and the time cost by up to 86%.
Digital education: VKontakte social network as a means of organizing the educational process

Abstract The article reveals the topic of the place and role of the Vkontakte social network in the organization of the educational process, the relevance of which was confirmed during the Covid-19 pandemic. The object of the research is digital education at Russian universities. The subject of the research is the VKontakte social network as a means of organizing the educational process. The authors share the experience of using social networks, accumulated in the process of teaching social disciplines at the Timiryazev Academy. The data proving the popularity of this network among students in Russia are presented. The main scientific result: not only the high efficiency of using the VKontakte social network for organizing the educational process has been proven, but also a proposal is formulated to consider digital literacy and knowledge of modern digital technologies as one of the qualification requirements for higher school teachers.
Natural language processing for learner corpus research

Preview this article: Natural language processing for learner corpus research, Page 1 of 1 < Previous page | Next page > /docserver/preview/fulltext/ijlcr.00019.int-1.gif
Research on business English autonomous learning based on artificial intelligence and improved BP network model

Due to various factors, the learning process of business English is mostly autonomous learning. However, the traditional autonomous learning model is difficult to effectively improve the learning effect of business English. In order to improve the business English learning model, based on artificial intelligence and improved BP network model, this paper builds a business intelligence autonomous learning system with certain intelligence. Moreover, this paper designs functional modules for the characteristics of business English learners, and combines the self-learning needs to facilitate the processing of structural functions, so that students can complete the operation independently. The system sets up multiple functional modules, conducts guided recommendation learning according to the characteristics of the self-learning process, and combines the feedback system to correct the shortcomings in students’ autonomous learning. Through this system, teachers can perform a variety of operations offline and eliminate restrictions on location and teaching time. In addition, in order to verify the performance of the model, the experimental study was conducted by setting up a control group and an experimental group. The research results show that the model constructed in this paper has good performance.
Moody Learners - Explaining Competitive Behaviour of Reinforcement Learning Agents

Designing the decision-making processes of artificial agents that are involved in competitive interactions is a challenging task. In a competitive scenario, the agent does not only have a dynamic environment but also is directly affected by the opponents' actions. Observing the Q-values of the agent is usually a way of explaining its behavior, however, it does not show the temporal-relation between the selected actions. We address this problem by proposing the Moody framework that creates an intrinsic representation for each agent based on the Pleasure/Arousal model. We evaluate our model by performing a series of experiments using the competitive multiplayer Chef's Hat card game and discuss how by observing the intrinsic state generated by our model allows us to obtain a holistic representation of the competitive dynamics within the game.
Classification of Ethereum Vulnerabilities and their Propagations

Blockchain technology is having an ever-increasing impact on distributed applications domain, since the adoption of Blockchain 2.0 led to the spread of smart contracts. In such a context, Ethereum is the framework with the highest diffusion in terms of smart contract's development, with a consequent rise of exploitation of code vulnerabilities, some of which causing bad financial losses. For this reason, this paper focuses on the issues of Ethereum smart contracts implementation (made with the Turing-complete language Solidity), providing a comprehensive systematization of such vulnerabilities basing on a slice of the Common Weakness Enumeration (CWE). Moreover, some relevant propagation cases among different vulnerabilities and CWE groups, observed in exploited contracts, are highlighted.