Reviewerly/exHarmony huggingface dataset:
Multi-expert human action recognition with hierarchical super-class learning

In still image human action recognition, existing studies have mainly leveraged extra bounding box information along with class labels to mitigate the lack of temporal information in still images. However, preparing additional annotations such as human and object bounding boxes is time-consuming and also prone to human errors because these annotations are prepared manually. In this paper, we propose a two-phase multi-expert classification method for human action recognition by means of super-class learning and without any extra information. Specifically, a coarse-grained phase selects the most relevant fine-grained experts. Then, the fine-grained experts encode the intricate details within each super-class so that the inter-class variation increases. In the proposed approach, to choose the best configuration for each super-class and characterize inter-class dependency between different action classes, we propose a novel Graph-Based Class Selection (GCS) algorithm. Moreover, the proposed method copes with long-tailed distribution, which the existing studies have not addressed in action recognition. Extensive experimental evaluations are conducted on various public human action recognition datasets, including Stanford40, Pascal VOC 2012 Action, BU101+, and IHAR datasets. The experimental results demonstrate that the proposed method yields promising improvements. To be more specific, in IHAR, Sanford40, Pascal VOC 2012 Action, and BU101+ benchmarks, the proposed approach outperforms the state-of-the-art studies by 8.92%, 0.41%, 0.66%, and 2.11% with much less computational cost and without any auxiliary annotation information. Besides, it is proven that in addressing action recognition with long-tailed distribution, the proposed method outperforms its counterparts by a significant margin. • Still image action recognition is cast as a fine-grained classification. • The proposed SCLAR method relaxes the requirement of auxiliary supervision. • The proposed GCS algorithm promotes inter-class variation among different classes. • Data imbalance issue in still image action recognition is addressed. • The IHAR dataset with long-tailed distribution is introduced in action recognition.
Continuous–discrete unscented Kalman filtering framework by MATLAB ODE solvers and square-root methods

This paper addresses the problem of designing the continuous–discrete unscented Kalman filter (UKF) implementation methods. More precisely, the aim is to propose the MATLAB-based UKF algorithms for accurate and robust state estimation of stochastic dynamic systems. The accuracy of the continuous–discrete nonlinear filters heavily depends on how the implementation method manages the discretization error arisen at the filter prediction step. We suggest the elegant and accurate implementation framework for tracking the hidden states by utilizing the MATLAB built-in numerical integration schemes developed for solving ordinary differential equations (ODEs). The accuracy is boosted by the discretization error control involved in all MATLAB ODE solvers. This keeps the discretization error below the tolerance value provided by users, automatically. Meanwhile, the robustness of the UKF filtering methods is examined in terms of the stability to roundoff. In contrast to the pseudo-square-root UKF implementations established in engineering literature, which are based on the one-rank Cholesky updates, we derive the stable square-root methods by utilizing the J-orthogonal transformations for calculating the Cholesky square-root factors.
Smart contract based digital evidence management framework over blockchain for vehicle accident investigation in IoV era

Digital collection and maintenance of evidence corresponding to an accident is of utmost relevance as vehicles become connected and part of Internet of things ecosystem. A conceptual evidence management framework is proposed examining the future of accident investigation forensics in the era of connected vehicles. The framework discusses how evidence generated from vehicles involved in incident along with supporting evidence from nearby vehicles CCTVs and road users can be collected and managed over blockchain using smart contracts in a vehicle to everything connected environment. The management of evidence over blockchain serves as an immutable and auditable means to investigate and settle cases post an accident among the stakeholders. Dynamic access control over evidence data and reports based on location of incident and vehicle involved is implemented with the help of smart contracts. The cost of deploying and carrying out transactions using the smart contracts is evaluated over both public and private Ethereum blockchain. Mechanism and cost involved in storing evidence on chain versus off chain using Inter Planetary File System in most optimised manner saving memory and execution cost is compared and presented. The smart contract codes are deployed on Rinkeby test network and can be accessed using address mentioned.
Differentially private distributed algorithms for stochastic aggregative games

Designing privacy-preserving distributed algorithms for stochastic aggregative games is urgent due to the privacy issues caused by information exchange between players. This paper proposes two differentially private distributed algorithms seeking the Nash equilibrium in stochastic aggregative games. By adding time-varying random noises, the input and output-perturbation methods are given to protect each player’s sensitive information. For the case of output-perturbation, utilizing mini-batch methods, the algorithm’s mean square error is inversely proportional to the privacy level ε and the number of samples. For the case of input-perturbation, a differentially private distributed stochastic approximation-type algorithm is developed to achieve almost sure convergence and (ε,δ)-differential privacy. Under suitable consensus time conditions, the algorithm’s convergence rate is rigorously presented for the first time, where the optimal convergence rate O(1/k) in a mean square sense is obtained. Then, utilizing mini-batch methods, the influence of added privacy noise on the algorithm’s performance is reduced, and the convergence rate of the algorithm is improved. Specifically, when the batch sizes and the number of consensus times at each iteration grow at a suitable rate, an exponential rate of convergence can be achieved with the same privacy level. Finally, a simulation example demonstrates the algorithms’ effectiveness.
Introducing SourceXplorer, an open-source statistical tool for guided lithic sourcing

Archaeologists' access to analytical infrastructure has grown exponentially over the last two decades. This is especially the case for benchtop X-ray fluorescence (XRF) and portable XRF (pXRF) instruments, which are now practically commonplace in archaeological laboratories and provide users with a non-destructive and rapid means to analyze the elemental compositions of archaeological specimens. As XRF has become more accessible, the volume of analytical measurements available in archaeological datasets as well as the number and diversity of researchers participating in data collection have inherently increased. Those researchers, who have various levels of experience with the nuances of lithic sourcing procedures, are also often the ones attempting to interpret the elemental data they produce. While standardized analytical procedures have enabled inexperienced analysts to take accurate and reproducible XRF measurements, interpreting the resulting data is more difficult to convert and standardize with the same degree of user-friendliness. To address this challenge, we have bundled a series of statistical approaches and data exploration tools into an intuitive open-source graphical user interface designed to facilitate reproducible and robust outcomes during lithic sourcing studies. Our application, SourceXplorer, permits easy access to and exploration of numeric baseline data using a map interface while facilitating a guided interpretation of source affiliations for archaeological specimens (e.g., lithics) within any natural context using multivariate statistical analyses. We demonstrate SourceXplorer's functionality in relation to a complex archaeological challenge by examining evidence for the procurement and use of lithic material from previously undocumented toolstone source locations in southwestern British Columbia, Canada. We also provide open access to SourceXplorer, including both a deployed version of the application that can be used with any Internet browser and the packaged script, which can be run locally in the open-source R statistical programming environment.
Computing correctly with inductive relations

Inductive relations are the predominant way of writing specifications in mechanized proof developments. Compared to purely functional specifications, they enjoy increased expressive power and facilitate more compositional reasoning. However, inductive relations also come with a significant drawback: they can't be used for computation.
Time enhanced graph neural networks for session-based recommendation

Session-based recommendation (SBR) is a challenging task, aiming at recommending items according to the behavior of anonymous users. Previous research efforts mainly focus on capturing sequential transitions between consecutive items via recurrent neural networks (RNN) or modeling the complex transitions between non-adjacent items based on graph neural networks (GNN). Although these works have achieved encouraging performance on solving the session-based recommendation problem, few efforts have been dedicated to exploring the rich information related to the shifts of user interests within the transition relationships, which is the research gap we attempt to bridge in this work. In this paper, we propose a novel model, named Time Enhanced Graph Neural Networks (TE-GNN), which attempts to capture the complex user interest shift patterns within sessions. In TE-GNN, we construct a Time Enhanced Session Graph (TES-Graph) where transition relationships between items are treated adaptively with respect to the degree of user interest drift. In addition, a novel Temporal Graph Convolutional Network (T-GCN) is designed to learn item embeddings based on the TES-Graph. Moreover, we also introduce a Temporal Interest Attention Network (TIAN) to model the complex transition of items with a common user interest. Extensive experiments have been conducted on four widely used benchmark datasets, i.e., Diginetica, Tmall, Nowplaying, and Retailrocket, and the results show that our proposed approach TE-GNN significantly outperforms previous state-of-the-art baseline methods. The implementation of TE-GNN is available in https://github.com/GuTang1997/TE-GNN .
SoK: An Evaluation of the Secure End User Experience on the Dark Net through Systematic Literature Review

The World Wide Web (www) consists of the surface web, deep web, and Dark Web, depending on the content shared and the access to these network layers. Dark Web consists of the Dark Net overlay of networks that can be accessed through specific software and authorization schema. Dark Net has become a growing community where users focus on keeping their identities, personal information, and locations secret due to the diverse population base and well-known cyber threats. Furthermore, not much is known of Dark Net from the user perspective, where often there is a misunderstanding of the usage strategies. To understand this further, we conducted a systematic analysis of research relating to Dark Net privacy and security on N=200 academic papers, where we also explored the user side. An evaluation of secure end-user experience on the Dark Net establishes the motives of account initialization in overlaid networks such as Tor. This work delves into the evolution of Dark Net intelligence for improved cybercrime strategies across jurisdictions. The evaluation of the developing network infrastructure of the Dark Net raises meaningful questions on how to resolve the issue of increasing criminal activity on the Dark Web. We further examine the security features afforded to users, motives, and anonymity revocation. We also evaluate more closely nine user-study-focused papers revealing the importance of conducting more research in this area. Our detailed systematic review of Dark Net security clearly shows the apparent research gaps, especially in the user-focused studies emphasized in the paper.
A backpropagation learning algorithm with graph regularization for feedforward neural networks

The backpropagation (BP) neural network has been widely used in many fields. However, it is still a great challenge to design the architecture and obtain optimal parameters for BP neural networks. For improving the generalization performance, regularization is the most popular technique to train the BP neural networks. In this paper, we propose a novel BP algorithm with graph regularization (BPGR) to obtain optimal parameters, by imposing the graph regularization term to the error function. The essential idea is to force the latent features of hidden layer to be more concentrated, which enhances the generalization performance. Besides, the proposed modified graph regularization facilitates the calculation of gradient and is more capable to penalize the extreme values of weights. Furthermore, the graph regularization can also be integrated with deep neural networks to improve their generalization performance. In addition, we provide the convergence analysis of our method BPGR under some regularity conditions. By comparison on several datasets with five activation functions, experimental results validate the theoretical analysis and demonstrate outstanding performance of BPGR.
Siamese block attention network for online update object tracking

Abstract Visual tracking is a crucial task in computer vision that concentrates on assessing the state of the target in each frame. Siamese based trackers possess an excellent tracking mechanism and balance the accuracy and efficiency well through continuous optimization of network structure. However, the rapid appearance variation and occlusion are still huge challenges to the accuracy and success rate of tracking task. Most Siamese based trackers rely on a fixed object template to match the target in search area and also neglect the importance of feature representation to tracking tasks. Based on the core idea of how to promote the recognition ability of trackers for the dynamic object and keep stable tracking during occlusion process, we present Siamese block attention network for online update object tracking referred to as SBAN. The proposed Siamese block attention module adopts a Siamese network structure to integrate two complementary global descriptors and establish the interdependence among channels to generate channel weights which can enhance the crucial features and restrain inessential ones. We also design an online update target module that can effectively utilize the history tracking information. The final updated target module is the integration of the given template, the process template, and the last tracking results. Experiments on four benchmarks, OTB2015, VOT2018, UAV123, LaSOT, illustrate that our tracker obtains outstanding tracking performance in accuracy and robustness while running at over 53 FPS.
If Not Here, There. Explaining Machine Learning Models for Fault Localization in Optical Networks

Machine Learning (ML) is being widely investigated to automate safety-critical tasks in optical-network management. However, in some cases, decisions taken by ML models are hard to interpret, motivate and trust, and this lack of explainability complicates ML adoption in network management. The rising field of Explainable Artificial Intelligence (XAI) tries to uncover the reasoning behind the decision-making of complex ML models, offering end-users a stronger sense of trust towards ML-automated decisions. In this paper we showcase an application of XAI, focusing on fault localization, and analyze the reasoning of the ML model, trained on real Optical Signal-to-Noise Ratio measurements, in two scenarios. In the first scenario we use measurements from a single monitor at the receiver, while in the second we also use measurements from multiple monitors along the path. With XAI, we show that additional monitors allow network operators to better understand model’s behavior, making ML model more trustable and, hence, more practically adoptable.
LMA-Net: A lesion morphology aware network for medical image segmentation towards breast tumors

Breast tumor segmentation plays a critical role in the diagnosis and treatment of breast diseases. Current breast tumor segmentation methods are mainly deep learning (DL) based methods, which exacted the contrast information between tumors and backgrounds, and produced tumor candidates. However, all these methods were developed based on traditional standard convolutions, which may not be able to model various tumor shapes and extract pure information of tumors (the extracted information usually contain non-tumor information). Besides, the loss functions used in these methods mainly aimed to minimize the intra-class distances, while ignoring the influence of inter-class distances upon segmentation. In this paper, we propose a novel lesion morphology aware network to segment breast tumors in 2D magnetic resonance images (MRI). The proposed network employs a hierarchical structure that contains two stages: breast segmentation stage and tumor segmentation stage. In the tumor segmentation stage, we devise a tumor morphology aware network to incorporate pure tumor characteristics, which facilitates contrastive information extraction. Further, we propose a hybrid intra- and inter-class distance optimization loss to supervise the network, which can minimize intra-class distances meanwhile maximizing inter-class distances, hence reducing the potential false positive/negative pixels in segmentation results. Verified on a clinical 2D MRI breast tumor dataset, our proposed method achieves eminent segmentation results and outperforms state-of-the-art methods, implying that the proposed method has a good prospect for clinical use.
AI-based production and application of English multimode online reading using multi-criteria decision support system

Reading and writing English have greater significance in learning oral English and comprehensive skills. Artificial Intelligence (AI) is important in many aspects of our lives, including education, healthcare, business, and so on. AI has allowed for significant advancements in the educational system. It has quickly risen to the top of the list of the most rapidly expanding educational technology disciplines. Through its creation, AI has contributed to the creation of new educational and knowledge techniques that are currently being researched across a wide range of fields. Chatbots, Robots' Assistant, Vidreader, Seeing AI, Classcraft, 3D holograms, and other AI-based programmes were developed to assist both teaching staff and students in using and improving the educational system. In the sphere of education, AI is focusing on sentimentalized artificial learning aids and smart instruction systems. The primary goal and objective of the education business is to construct an intelligent education system, which is now possible thanks to the development of teaching assistant robots, smart classrooms based on AI, and English teaching assistance, among other things. Artificial Intelligence techniques may now be employed at all stages of learning to improve the educational system. During the COVID-19 illness, students and teachers took their education and instruction online in a variety of ways. Learning can be done digitally so that folks do not fall behind in their education. The proposed study has considered multi-criteria decision support systems (MCDM) for AI-enabled production and application of English multimode online reading. This study has offered the application of the super decision tool to facilitate the experimental work. As a result of this, researchers will be able to find and design new solutions to the subject.
A Systematic Review and Analysis of Intelligence-Based Pathfinding Algorithms in the Field of Video Games

This paper provides a performance comparison of different pathfinding Algorithms used in video games. The Algorithms have been classified into three categories: informed, uninformed, and metaheuristic. Both a practical and a theoretical approach have been adopted in this paper. The practical approach involved the implementation of specific Algorithms such as Dijkstra’s, A-star, Breadth First Search, and Greedy Best First. The comparison of these Algorithms is based on different criteria including execution time, total number of iterations, shortest path length, and grid size. For the theoretical approach, information was collected from various papers to compare other Algorithms with the implemented ones. The Unity game engine was used in implementing the Algorithms. The environment used was a two-dimensional grid system.
A Deep Learning Based Approach for Context-Aware Multi-Criteria Recommender Systems

Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users' demands based on their preference profiles.Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems.CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations.However, the majority of CARSs use ratings as a unique criterion for building communities.Meanwhile, MCRSs utilize user preferences in multiple criteria to better generate recommendations.Up to now, how to exploit context in MCRSs is still an open issue.This paper proposes a novel approach, which relies on deep learning for context-aware multi-criteria recommender systems.We apply deep neural network (DNN) models to predict the context-aware multi-criteria ratings and learn the aggregation function.We conduct experiments to evaluate the effect of this approach on the real-world dataset.A significant result is that our method outperforms other state-of-the-art methods for recommendation effectiveness.
Applicability of Quantum Computing on Database Query Optimization

We evaluate the applicability of quantum computing on two fundamental query optimization problems, join order optimization and multi query optimization (MQO). We analyze the problem dimensions that can be solved on current gate-based quantum systems and quantum annealers, the two currently commercially available architectures.
Using NLP to Quantify Program Decomposition in CS1

Decomposition is a problem-solving technique that is essential to software development. Nonetheless, it is perceived as the most challenging programming skill for learners to master. Researchers have studied decomposition in introductory programming courses through guided experiments, case studies, and surveys. We believe that the rapid advancements in scientific fields such as machine learning and natural language processing (NLP) opened up opportunities for more scalable approaches.