Reviewerly/exHarmony huggingface dataset:
Supervised Reinforcement Session Recommendation Model Based on Dual-Graph Convolution

In the field of session-based recommendation by anonymous sessions, the commonly used supervised learning modeling method has the problem of sub-optimal recommendation. The supervised reinforcement learning (SRL) recommendation framework can be used to solve this problem, but there is currently a lack of research on graph state representations of anonymous sessions in this field. In this regard, we propose a supervised reinforcement session recommendation model, HG-SRL, based on global hypergraphs and local session graphs. In this model, we propose, for the first time, a state representation method based on hypergraph neural networks and graph neural networks to bridge the gap in SRL in graph state construction methods. To fully utilize graph information at both global and local levels, we propose a self-matching attention fusion mechanism, which fuses the different levels of information contained in the two graphs through cross-calculation and then embeds them in the final graph state representation. To make the state more comprehensive, we also mine the neighboring sessions of each anonymous session on the global session hypergraph to supplement the neighboring session information of the anonymous session. Experimental tests were conducted on three real-world datasets, which have shown that HG-SRL can effectively improve the accuracy of session recommendations.
<i>MHRR</i>: MOOCs Recommender Service With Meta Hierarchical Reinforced Ranking

The exponential growth of Massive Open Online Courses (MOOCs) surges the needs of advanced models for personalized Online Education Services (OES). Existing solutions successfully recommend MOOCs courses via deep learning models, they however generate weak “course embeddings” with original profiles, which contain noisy and few enrolled courses. On the other hand, existing algorithms provide recommendation orders according to the score of each course while ignoring personalized demands of users. To tackle the above challenges, we propose a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><u>M</u>eta <u>H</u>ierarchical <u>R</u>einforced <u>R</u>anking</i> approach <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MHRR</i> , which consists of a meta hierarchical reinforcement learning pre-trained mechanism and an over-parameterized ranking regressor to enhance the representation learning of courses and learners while refining the ranking result of recommended courses. Specifically, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MHRR</i> combines a user profile reviser and a meta embedding generator to provide course embedding representation enhancement for recommender services. Furthermore, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MHRR</i> transforms learned representations generated from recommender services with Gaussian kernel approximation to over-parameterize the downstream learning to rank (LTR) models with representations in ultra-high dimensionality. We deploy <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MHRR</i> on a real-world MOOCs platform and evaluate it with a large number of baseline models. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MHRR</i> outperforms baseline algorithms on two major metrics, including Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG). Also, we conduct a 7-day online evaluation using the realistic traffic of a large-scale real-world MOOCs platform, where we can still observe significant improvement in real-world applications. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MHRR</i> performs consistently both in the online and offline evaluation.
Semantic Knowledge Base-enabled Zero-shot Multi-level Feature Transmission Optimization

Remote zero-shot object recognition, which involves offloading the zero-shot recognition task from one mobile device to a remote mobile edge computing (MEC) server or another mobile device, is crucial for 6G. To address this challenge, this paper presents a lightweight semantic knowledge base (SKB)-enabled multi-level feature extractor that projects the image into visual, semantic, and intermediate feature spaces. Then, this paper proposes a novel SKB-enabled multi-level feature transmission framework, which utilizes SKB and multi-level feature extractor at both transmitter and receiver. The semantic loss and required transmission latency at each level are characterized, and a multi-level feature transmission optimization problem is formulated to minimize the semantic loss under transmission latency constraint. However, this optimization problem is a multi-choice knapsack problem, which is challenging to solve optimally. To overcome this issue, an enhanced convex concave procedure is proposed to obtain an efficient solution. Furthermore, this paper theoretically analyzes the effects of SKBs on the communication performance when the feature extractors at both ends are the same. Numerical results demonstrate that the proposed design outperforms the benchmarks and provide insights into the impact of SKBs at both ends on performance as well as the tradeoff between transmission latency and zero-shot classification accuracy.
Safety and Security Online

The proliferation of internet penetration presents safety and security challenges in the world. Incidences of online safety, security, women’s rights online and online gender-based violence are on the rise.
NetDistiller: Empowering Tiny Deep Learning via In Situ Distillation

Boosting the task accuracy of tiny neural networks (TNNs) has become a fundamental challenge for enabling the deployment of TNNs on edge devices, which are constrained by strict limitations in terms of memory, computation, bandwidth, and power supply. To this end, we propose a framework called NetDistiller to boost the achievable accuracy of TNNs by treating them as subnetworks of a weight-sharing teacher constructed by expanding the number of channels of the TNN. Specifically, the target TNN model is jointly trained with the weight-sharing teacher model via 1) gradient surgery to tackle the gradient conflicts between them and 2) uncertainty-aware distillation to mitigate the overfitting of the teacher model. Extensive experiments across diverse tasks validate NetDistiller’s effectiveness in boosting TNNs’ achievable accuracy over state-of-the-art methods. Our code is available at https://github.com/GATECH-EIC/NetDistiller.
SPA<sup>2</sup>Net: Structure-Preserved Attention Activated Network for Weakly Supervised Object Localization

By exploring the localizable representations in deep CNN, weakly supervised object localization (WSOL) methods could determine the position of the object in each image just trained by the classification task. However, the partial activation problem caused by the discriminant function makes the network unable to locate objects accurately. To alleviate this problem, we propose Structure-Preserved Attention Activated Network (SPA2Net), a simple and effective one-stage WSOL framework to explore the ability of structure preservation of deep features. Different from traditional WSOL approaches, we decouple the object localization task from the classification branch to reduce their mutual influence by involving a localization branch which is online refined by a self-supervised structural-preserved localization mask. Specifically, we employ the high-order self-correlation as structural prior to enhance the perception of spatial interaction within convolutional features. By succinctly combining the structural prior with spatial attention, activations by SPA2Net will spread from part to the whole object during training. To avoid the structure-missing issue caused by the classification network, we furthermore utilize the restricted activation loss (RAL) to distinguish the difference between foreground and background in the channel dimension. In conjunction with the self-supervised localization branch, SPA2Net can directly predict the class-irrelevant localization map while prompting the network to pay more attention to the target region for accurate localization. Extensive experiments on two publicly available benchmarks, including CUB-200-2011 and ILSVRC, show that our SPA2Net achieves substantial and consistent performance gains compared with baseline approaches. The code and models are available at https://github.com/MsterDC/SPA2Net.
Retrieval Transformation: Dynamic Searchable Symmetric Encryption With Strong Security

Extending the single-user searchable symmetric encryption (SSE) immediately to the multiuser scenario is not straightforward. In the multiuser scenario, recipients cannot safely achieve multisearchable computing without using a third party or specified users, which diminishes the application's utility. Moreover, there are two issues related to pattern privacy and file updating: One is that the majority of extant SSEs only emphasize the privacy of a partial pattern (one of the search/access/size patterns), resulting in information leakage. The second issue is that, with the exponential expansion of the number of files, it is unclear how to achieve unrestricted index capacity management while efficiently updating the encrypted index. To overcome the aforementioned issues, we offer a general dynamic named <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$DSSE$</tex-math></inline-formula> , that is blended with the pseudorandom function and the integer vector homomorphic encryption. Combining <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity and delayed write-back techniques, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$DSSE$</tex-math></inline-formula> can realize multisearchable computing without relying on a third party while simultaneously guaranteeing multiple patterns and greater forward/backward privacies. The combination of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -anonymity and delayed write-back solves the unnecessary waste of communication overhead and the problem of pattern leakage in extreme cases. <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G$</tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$DSSE$</tex-math></inline-formula> achieves noninteractive update, even batch update, and nonrestrictive index capacity management in the encrypted index. Meanwhile, it can be expanded flexibly to ensure the precision and integrity of search results.
Human Rights Based-Approach in the Design and Implementation of Digital ID

A human rights-based approach is pivotal in the lifecycle of the design and implementation of digital ID. Despite the importance and value of digital ID, the process of applying for a digital ID to the removal or invalidation of the records exacerbates pre-existing inequalities. This is because Digital ID is not inherently neutral. The design and implementation of digital ID may lead to structural inequalities and discrimination to vulnerable and marginalized groups in the society. Enrollment in digital ID systems should be as close to universal as possible across all parts of the population, including vulnerable groups. Policymakers should strive to eliminate barriers to registration, whether rooted in policy, logistics, or the cost of obtaining a digital ID. While digital ID systems have been claimed to bring efficacy in user identification, verification and authentication, their data justice implications have been questioned. In particular, the mandatory registration based on biometric identification has been found to magnify exclusions, imply informational asymmetries, and reproduce policy structures that negatively affects vulnerable groups.
Vehicle Accident Analysis Using Data Mining Techniques

The societal costs of fatalities and damages caused by various vehicle accidents are significant. This paper describes our study of the severity of injury caused by traffic accidents using Logistic Regression, decision treesand Naive Bayes. Data mining is a technique for analyzing huge datasets and identifying significant patterns in them, involving methods from machine learning, statistics, and database systems. This paper attempts to analyse vehicle accidents using three AI-based methods: Decision Tree, Naive Bayes, and Logistic Regression. To achieve greater accuracy, each of these algorithms have been evaluatedusing distinct parameters with optimization techniques. Each method's accuracy is compared in order to determine the best and most accurate methodology for predicting the damage that would occur in a vehicle accident
Advancing biomedical engineering through a multi-modal sensor fusion system for enhanced physical training

&lt;abstract&gt; &lt;p&gt;In this paper, we introduce a multi-modal sensor fusion system designed for biomedical engineering, specifically geared toward optimizing physical training by collecting detailed body movement data. This system employs inertial measurement units, flex sensors, electromyography sensors, and Microsoft's Kinect V2 to generate an in-depth analysis of an individual's physical performance. We incorporate a gated recurrent unit- recurrent neural network algorithm to achieve highly accurate body and hand motion estimation, thus surpassing the performance of traditional machine learning algorithms in terms of accuracy, precision, recall, and F1 score. The system's integration with the PICO 4 VR environment creates a rich, interactive experience for physical training. Unlike conventional motion capture systems, our sensor fusion system is not limited to a fixed workspace, allowing users to engage in exercise within a flexible, free-form environment.&lt;/p&gt; &lt;/abstract&gt;
Digital Competences and Didactic Technologies Training for Teachers in Service: From DigCompEdu to a National Framework

In the Italian school systems, teachers are not obliged to attend training courses about digital competences, innovative didactic methodologies or didactic technologies. As a matter of fact, there are some previsions about providing to teachers the possibility to train on the topics above mentioned (and many mores); the training, in a longlife learning perspective [1] is also highly recommended, but there are no statutory provisions [2]. This contribution proposes a framework in order to provide a structured training for teachers in services, linking the training to the possibility to develop a career in the Italian school systems, possibility that has always been theorized, but never implemented. The framework should be introduced and structured by the Italian Higher Education Institutions, modeling it on the multilevel organization of the DigCompEdu, starting from the earliest levels (A1 and A2), giving teachers the possibility to become leaders and innovators [3] in the training topic they choose to train in.
Towards Better Tool Support for Code Comprehension

In software development, bug fixing and feature development requires a thorough understanding of all details and consequences of the planned changes. For long-existing large software systems, the code-base has been developed and maintained for decades by fluctuating teams, thus original intentions are lost, the documentation is untrustworthy or missing. Most times, the only reliable information is the code itself. Source code comprehension of such large software is an essential, but usually very challenging task. The comprehension process and approach of existing systems is fundamentally different from writing new software, and the usual development tools provide poor support in this area. Throughout the years, different tools have been developed with various complexity and feature set for code comprehension but none of them fulfilled all specific requirements yet. In this paper we discuss the most accepted models for code comprehension, the required feature set for tools supporting the comprehension process and the various approaches of existing solutions. We present CodeCompass – an open source LLVM/Clang based tool to help understanding large legacy software systems – in detail to analyse the required interface, possible design choices and implementation considerations. Based on the LLVM/Clang compiler infrastructure, CodeCompass gives exact information on complex C/C++ language elements such as inheritance, overloading, variable usage, possible function pointer and virtual function calls etc. These are all features for which various existing tools provide only partial support. Although CodeCompass supports mainly the C and C++ programming languages, it has a restricted support for Java and Python languages as well, therefore our investigation is language-independent.
Code Quality Metrics for Functional Features in Modern Object-Oriented Languages

The evolution of main-stream object-oriented languages such as Java and C# has introduced new code constructs that originate from the functional programming paradigm. We hypothesise that a relationship exists between the usage of these constructs and the error-proneness of code. We define a number of measures specifically focusing on functional programming constructs in the context of object-oriented languages. Based on these measures we define a metric that relates the usage of the functional programming constructs to error-proneness of classes. We validate our metric and confirm our hypothesis using an established methodology for empirical validation of code metrics. Our results presented in this paper grant new insights into the evolution of (increasingly) multi-paradigm programming languages at the cross-roads of the functional and the object-oriented programming paradigms.
The Electrodermal Activity of Player Experience in Virtual Reality Games: An Extended Evaluation of the Phasic Component

Thanks to its effectiveness, electrodermal activity (EDA) has been previously included as an evaluation metric, within analyses of user experience. In this study, the phasic component of participants’ EDA data is examined in relation to their reported experiences when playing a set of virtual reality games, that featured the HTC Vive and Leap Motion controllers for input. Two models are used in the analysis of the phasic component: a deconvolution model and a convex optimization model. Despite having significant differences in their player experiences, results indicate that there are not many significant differences in the phasic component data. Even if some weak correlations were found, the majority of results show no linear correlations between the phasic component data and the reported experience variables. This shows that the phasic component of EDA data should be further investigated in conjunction with other psychophysiological signals because it has only recently demonstrated a weak link with player experience.
Finding Code Clone Refactoring Techniques by Mapping Clone Context

Reducing clones in source code is one of the techniques to improve the maintainability of a software system. Which refactoring technique to use depends on where a clone is found and what the relation between clone instances in a clone class is. We define three influencing factors on how a clone should be refactored: relation, location, and contents. The relation describes the inheritance relation among the clone instances in a clone class. The location describes where a clone instance is found in the source code. The contents describe what a clone instance spans. Based on experiments on a corpus of open-source Java projects we find that most clones (77%) are in the body of methods or constructors and thus the “Extract Method” refactoring technique applies. What further techniques are required for the refactoring depends on the relation among the clone instances of a clone. We define four relations that require different further refactoring techniques: Common Class, Common Hierarchy, Common Interface, and Unrelated. The closer classes are related, the more favorable refactoring clones by such relations becomes. 37% of clones are in the same class, 24% share an inheritance hierarchy, 24% are unrelated and 15% have a common interface.
On the Number of Monte Carlo Runs for Stochastic Processes

UAV applications in defense industry are increasing in recent years, and most of these applications such as collision avoidance systems, target tracking, navigation, and guidance algorithms are based on stochastic dynamic equations (SDEs) which are suitable to be analyzed by Monte Carlo simulations. The purpose of this paper is to define a strategy to select the number of Monte Carlo runs of each simulation. For this reason, a Monte Carlo simulator for UAV’s trajectory estimation algorithms is used, and the computational load and accuracy of the population parameters are considered to select the number of Monte Carlo runs.
A Quantitative and Qualitative Analysis on a GAN-Based Face Mask Removal on Masked Images and Videos

In 2020 and beyond, there are more and more opportunities to communicate with others while wearing a face mask. Since masks hide the mouth and facial muscles, it becomes more challenging to convey facial expressions to others while wearing a face mask. In this study, we propose using generative adversarial networks (GAN) to complement facial regions hidden by masks on images and videos. We defined the custom loss function that focuses on the error of the feature point coordinates of the face and the pixels in the masked region. As a result, we were able to generate higher-quality images than existing methods. Even when the input was video-based, our approach generated high-quality videos with fewer jittering and pixel errors than existing methods.
Semi-automated Generation of Accurate Ground-Truth for 3D Object Detection

Visual algorithms for traffic surveillance systems typically locate and observe traffic movement by representing all traffic with 2D boxes. These 2D bounding boxes around vehicles are insufficient to generate accurate real-world locations. However, 3D annotation datasets are not available for training and evaluation of detection for traffic surveillance. Therefore, a new dataset for training the 3D detector is required. We propose and validate seven different annotation configurations for automated generation of 3D box annotations using only camera calibration, scene information (static vanishing points) and existing 2D annotations. The proposed novel Simple Box method does not require segmentation of vehicles and provides a more simple 3D box construction, which assumes a fixed predefined vehicle width and height. The existing KM3D CNN-based 3D detection model is adopted for traffic surveillance, which directly estimates 3D boxes around vehicles in the camera image, by training the detector on the newly generated dataset. The KM3D detector trained with the Simple Box configuration provides the best 3D object detection results, resulting in 51.9% AP3D on this data. The 3D object detector can estimate an accurate 3D box up to a distance of 125 m from the camera, with a median middle point mean error of only 0.5–1.0 m.