Reviewerly/exHarmony huggingface dataset:
Improving Test Quality in E-Learning Systems

Electronic learning systems play an important role in the educational process. They typically provide access to lecture material and practical tasks and are used for testing. A significant advantage of e-learning systems is that they provide instant verification of test results and tools to analyze them. This article presents a number of indicators that allow assessing the ability of test questions to grade students by their levels of knowledge. Test questions not meeting the threshold requirements are proposed to be excluded from the test, thus improving the quality of the test. #CSOC1120.
Gamification of the Graph Theory Course. Finding the Shortest Path by a Greedy Algorithm

Information technology has had a significant impact on modern education. E-learning has become an integral part of the modern educational environment. One of the elements of modern education is gamification, which can essentially increase motivation and efficiency of the learner. The article describes the game of finding the shortest path in the graph developed by the authors. It can be used for gamification of courses related to discrete mathematics and information technology. A computer program has been developed to implement the proposed game, which makes it possible to use it in e-learning. #CSOC1120.
Tevatron: An Efficient and Flexible Toolkit for Neural Retrieval

Recent rapid advances in deep pre-trained language models and the introduction of large datasets have powered research in embedding-based neural retrieval. While many excellent research papers have emerged, most of them come with their own implementations, which are typically optimized for some particular research goals instead of efficiency or code organization. In this paper, we introduce Tevatron, a neural retrieval toolkit that is optimized for efficiency, flexibility, and code simplicity. Tevatron enables model training and evaluation for a variety of ranking components such as dense retrievers, sparse retrievers, and rerankers. It also provides a standardized pipeline that includes text processing, model training, corpus/query encoding, and search. In addition, Tevatron incorporates well-studied methods for improving retriever effectiveness such as hard negative mining and knowledge distillation. We provide an overview of Tevatron in this paper, demonstrating its effectiveness and efficiency on multiple IR and QA datasets. We highlight Tevatron's flexible design, which enables easy generalization across datasets, model architectures, and accelerator platforms (GPUs and TPUs). Overall, we believe that Tevatron can serve as a solid software foundation for research on neural retrieval systems, including their design, modeling, and optimization.
Beyond the Overlapping Users: Cross-Domain Recommendation via Adaptive Anchor Link Learning

Cross-Domain Recommendation (CDR) is capable of incorporating auxiliary information from multiple domains to advance recommendation performance. Conventional CDR methods primarily rely on overlapping users, whereby knowledge is conveyed between the source and target identities belonging to the same natural person. However, such a heuristic assumption is not universally applicable due to an individual may exhibit distinct or even conflicting preferences in different domains, leading to potential noises. In this paper, we view the anchor links between users of various domains as the learnable parameters to learn the task-relevant cross-domain correlations. A novel optimal transport based model ALCDR is further proposed to precisely infer the anchor links and deeply aggregate collaborative signals from the perspectives of intra-domain and inter-domain. Our proposal is extensively evaluated over real-world datasets, and experimental results demonstrate its superiority.
Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning

Reinforcement learning-based recommender systems have recently gained popularity. However, the design of the reward function, on which the agent relies to optimize its recommendation policy, is often not straightforward. Exploring the causality underlying users' behavior can take the place of the reward function in guiding the agent to capture the dynamic interests of users. Moreover, due to the typical limitations of simulation environments (e.g., data ineffi- ciency), most of the work cannot be broadly applied in large-scale situations. Although some works attempt to convert the offline dataset into a simulator, data inefficiency makes the learning pro- cess even slower. Because of the nature of reinforcement learning (i.e., learning by interaction), it cannot collect enough data to train during a single interaction. Furthermore, traditional reinforcement learning algorithms do not have a solid capability like supervised learning methods to learn from offline datasets directly. In this paper, we propose a new model named the causal decision transformer for recommender systems (CDT4Rec). CDT4Rec is an offline reinforce- ment learning system that can learn from a dataset rather than from online interaction. Moreover, CDT4Rec employs the transformer architecture, which is capable of processing large offline datasets and capturing both short-term and long-term dependencies within the data to estimate the causal relationship between action, state, and reward. To demonstrate the feasibility and superiority of our model, we have conducted experiments on six real-world offline datasets and one online simulator.
Comparing natural language processing (NLP) applications in construction and computer science using preferred reporting items for systematic reviews (PRISMA)

Despite the increasing use of natural language processing (NLP) in the construction domain, no systematic comparison has been conducted between NLP applications in construction and the latest advancements in NLP within the computer science domain. Therefore, this study compares NLP studies in these two domains. Firstly, a bibliometric analysis was performed on 55 publications in state-of-the-art NLP studies, which identified four main research areas in NLP. Secondly, a systematic review of 202 NLP studies in construction was conducted, presenting representative application areas of NLP and their current technical status. The results reveal a decreasing technology gap between NLP in construction and the state-of-the-art. However, the comparison also highlighted gaps in application areas and methodologies, and eight future research opportunities were proposed. This review serves as a foundation for future studies that aim to apply state-of-the-art NLP technologies in the construction domain.
Deflectometry for specular surfaces: an overview

Deflectometry as a technique to assess reflective surfaces has now existed for some 40 years. Its different aspects and variations have been studied in multiple theses and research articles; reviews are available for certain subtopics. Still a field of active development with many unsolved problems, deflectometry now encompasses a large variety of application domains, hardware setup types, and processing workflows for different purposes, and spans a range from qualitative defect inspection of large vehicles to precision measurements of microscopic optics. Over these years, many exciting developments have accumulated in the underlying theory, in the systems design, and in the implementation specifics. This diversity of topics is difficult to grasp for experts and non-experts alike and may present an obstacle to a wider acceptance of deflectometry as a useful tool for research and industrial applications. This paper presents an attempt to summarize the status of deflectometry and to map relations between its notable branches. Its aim is to provide a communication basis for experienced practitioners and also to offer a convenient entry point for those interested in learning about the method. The list of references introduces some prominent trends and established research groups in order to facilitate further self-directed exploration.
An improved lightweight small object detection framework applied to real-time autonomous driving

Recent deep learning-based object detectors have shown compelling performance for the detection of large objects in autonomous driving applications. However, the detection of small objects like traffic signs and traffic lights is challenging owing to the complex nature of such objects. This article investigates how an existing object detector can be adjusted to address specific tasks and how these modifications can impact the detection of small objects. In particular, we explore and introduce architectural changes to the different components of the popular YOLOv5 model in order to improve its performance in the detection of small objects for autonomous driving. Initially, we propose group depthwise separable convolution as the improved convolution unit to replace standard convolution. We then integrate this unit to create the attention-based dilated CSP block. Lastly, this block is combined with several proposed modules, including the improved SPP, improved PANet, and improved information paths, to form our IS-YOLOv5 model. We also integrate kernel pruning on the network to accelerate the model deployment on vehicle-mounted mobile platform due to limited computing resources and real-time constraints. Specifically, we propose the versatile network pruning (VNP) technique based on Taylor criterion ranking to prune less-essential kernels in the network. We will show that our modifications barely increase the complexity but significantly improve the detection accuracy and speed. Compared to the conventional YOLOv5, the proposed IS-YOLOv5 model increases the mAP by 8.35% on the BDD100K dataset. Besides, our proposed model improves the detection speed in FPS by 3.10% compared to the YOLOv5 model. When using the VNP scheme, FPS is further increased by 52.14%, while the model size and complexity are reduced by 39.29% and 47.81%, with almost no change in mAP. Nevertheless, when compared to state-of-the-art models, IS-YOLOv5+VNP is found to be conducive to the deployment in autonomous driving systems.
Unifying Flow, Stereo and Depth Estimation

We present a unified formulation and model for three motion and 3D perception tasks: optical flow, rectified stereo matching and unrectified stereo depth estimation from posed images. Unlike previous specialized architectures for each specific task, we formulate all three tasks as a unified dense correspondence matching problem, which can be solved with a single model by directly comparing feature similarities. Such a formulation calls for discriminative feature representations, which we achieve using a Transformer, in particular the cross-attention mechanism. We demonstrate that cross-attention enables integration of knowledge from another image via cross-view interactions, which greatly improves the quality of the extracted features. Our unified model naturally enables cross-task transfer since the model architecture and parameters are shared across tasks. We outperform RAFT with our unified model on the challenging Sintel dataset, and our final model that uses a few additional task-specific refinement steps outperforms or compares favorably to recent state-of-the-art methods on 10 popular flow, stereo and depth datasets, while being simpler and more efficient in terms of model design and inference speed.
The Sustainability of Investing in Cryptocurrencies: A Bibliometric Analysis of Research Trends

This paper explores the state of the art in the cryptocurrency literature, with a special emphasis on the links between financial dimensions and ESG features. The study uses bibliometric analysis to illustrate the history of cryptocurrency publication activity, focusing on the most popular subjects and research trends. Between 2014 and 2021, 1442 papers on cryptocurrencies were published in the Web of Science core collection, the most authoritative database, although only a tiny percentage evaluated ESG factors. One of the most common criticisms of cryptocurrencies is the pollution derived from energy consumption in their mining process and their use for illicit purposes due to the absence of effective regulation. The study allows us to suggest future research directions that may be beneficial in illustrating the environmental effect, studying financial behavior, identifying the long-term sustainability of cryptocurrencies, and evaluating their financial success. This study provides an in-depth examination of current research trends in the field of cryptocurrencies, identifying prospective future research directions.
LIDN: A novel light invariant image dehazing network

With the increasing demand for efficient image dehazing solutions in computer vision algorithms, particularly for autonomous systems, a research focus on low-inference time yet high-performance dehazing techniques has emerged. Existing dehazing methods predominantly rely on either daytime or nighttime haze models, limiting their effectiveness in handling haziness under varying lighting conditions. To address this limitation, this research paper introduces the Light Invariant Dehazing Network (LIDN), an end-to-end image dehazing network consisting of four sub-modules: Feature Extractor, Deep Global Atmospheric Light Estimator, Medium Transmission Extractor, and Encoder-Decoder. The proposed model, trained using Quadruplet loss, effectively reduces artifacts and produces sharper dehazed images. Extensive experiments conducted under diverse lighting conditions demonstrate the superior performance of the proposed LIDN model compared to state-of-the-art daytime and nighttime dehazing approaches. Remarkably, the proposed model achieves these exceptional results with a runtime of only 0.24s per image, making it highly efficient than existing dehazing algorithms.
An Enhanced Dwarf Mongoose Optimization Algorithm for Solving Engineering Problems

This paper proposes a new Enhanced Dwarf Mongoose Optimization Algorithm (EDMOA) with an alpha-directed Learning Strategy (LS) for dealing with different mathematical benchmarking functions and engineering challenges. The DMOA’s core concept is inspired by the dwarf mongoose’s foraging behavior. The suggested algorithm employs three DM social categories: the alpha group, babysitters, and scouts. The family forages as a team, with the alpha female initiating foraging and determining the foraging course, distance traversed, and sleeping mounds. An enhanced LS is included in the novel proposed algorithm to improve the searching capabilities, and its updating process is partially guided by the updated alpha. In this paper, the proposed EDMOA and DMOA were tested on seven unimodal and six multimodal benchmarking tasks. Additionally, the proposed EDMOA was compared against the traditional DMOA for the CEC 2017 single-objective optimization benchmarks. Moreover, their application validity was conducted for an important engineering optimization problem regarding optimal dispatch of combined power and heat. For all applications, the proposed EDMOA and DMOA were compared to several recent and well-known algorithms. The simulation results show that the suggested DMOA outperforms not only the regular DMOA but also numerous other recent strategies in terms of effectiveness and efficacy.
Learning consistent region features for lifelong person re-identification

The lifelong person re-identification (LRe-ID) model retrieves a person across multiple cameras in continuous data streams and learns new coming datasets incrementally. However, there are two well-known challenges: catastrophic forgetting and generalization loss, which arise due to parameter updates and domain shifts. While there has been encouraging progress in balancing these challenges, few existing methods have addressed them from a unified feature perspective. Inspired by the Complementary Learning Systems theory, an effective framework is proposed to share consistent features and extract discriminative features for each sample. Specifically, this framework consists of Property Region Features, Feature Adaption and Feature Perspicacity. Property Region Features are the parametric representation of consistent region features, Feature Adaption and Feature Perspicacity are responsible for diversity features generation and discriminative features extraction, respectively. Moreover, a cascade knowledge distillation structure is introduced to guarantee Property Feature consistency, and correspondingly, a weighted distillation loss function is designed to prevent generalization loss on current domains caused by overlapping historical knowledge. Extensive experiments conducted on twelve Re-ID datasets, including both rehearsal and no-rehearsal settings, clearly validate the superiority of our method over state-of-the-art competitors, with significantly improved performance. The source code will be released on https://github.com/whisperH/ConRFL/.
Decision trees: from efficient prediction to responsible AI

This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which the research is situated, and summarizes strengths and weaknesses of decision trees in this context. The main goal of the article is to clarify the broad relevance to machine learning and artificial intelligence, both practical and theoretical, that decision trees still have today.
Adversarial Driving: Attacking End-to-End Autonomous Driving

As research in deep neural networks advances, deep convolutional networks become promising for autonomous driving tasks. In particular, there is an emerging trend of employing end-to-end neural network models for autonomous driving. However, previous research has shown that deep neural network classifiers are vulnerable to adversarial attacks. While for regression tasks, the effect of adversarial attacks is not as well understood. In this research, we devise two white-box targeted attacks against end-to-end autonomous driving models. Our attacks manipulate the behavior of the autonomous driving system by perturbing the input image. In an average of 800 attacks with the same attack strength (epsilon=1), the image-specific and image-agnostic attack deviates the steering angle from the original output by 0.478 and 0.111, respectively, which is much stronger than random noises that only perturbs the steering angle by 0.002 (The steering angle ranges from [-1, 1]). Both attacks can be initiated in real-time on CPUs without employing GPUs. Demo video: https://youtu.be/I0i8uN2oOP0.
Plenoptic Modeling: An Image-Based Rendering System

Image-based rendering is a powerful new approach for generating real-time photorealistic computer graphics. It can provide convincing animations without an explicit geometric representation. We use the "plenoptic function" of Adelson and Bergen to provide a concise problem statement for image-based rendering paradigms, such as morphing and view interpolation. The plenoptic function is a parameterized function for describing everything that is visible from a given point in space. We present an image-based rendering system based on sampling, reconstructing, and resampling the plenoptic function. In addition, we introduce a novel visible surface algorithm and a geometric invariant for cylindrical projections that is equivalent to the epipolar constraint defined for planar projections.
LMNS-Net: Lightweight Multiscale Novel Semantic-Net deep learning approach used for automatic pancreas image segmentation in CT scan images

In the study and research of medical images, the sharp and smooth pancreatic segmentation challenge is a critical and challenging one. The most widely utilized and effective technique for pancreatic segmentation with smooth and precise results is a proposed LMNS-net deep learning, bottom-up approach. The proposed LMNS-net is used to automatically segment the pancreas in clinical abdominal computed tomography (CT) images. For the segmentation of the acute pancreas using several angles of CT scans, such as coronal, axial, and sagittal, a proposed LMNS-net model is used. In the LMNS-Net model, 12 layers are used with 4 convolution layers. LMNS-Net model is used for many organ segmentation from CT scans clinical images with high accuracy. The lightweight multiscale block is used in the proposed approach which is aggregating the required feature only so unused information dropout at the convolution layer. The computation time-period is reduced as related to the state-of-art. Validation is 99.78% and loss values vary from 1 to 0 only. The LMNS-Net model achieved a dice similarity index score up to 88.68±57.49%. The LMNS-Net model takes 1 to 3 s for the segmentation of medical CT images in the testing process. The LMNS-Net model takes very less time for testing purposes as compared to other approaches. Top-down approaches are not useful in the detection of pancreatic cancer images from CT scan images. In Bottom-Up approaches, the LMNS-Net is useful to detect accurate sharpness of the pancreas and kidney, the pancreatic cancer-affected area within less time only. In medical image segmentation, the time of training deep learning approach is a key constraint. “VGG-16” and “VGG-19” are taking too much time, so more time is required for disease diagnosis. The training time of the LMNS-Net model is reduced as compared to the State-of-Arts.
Two Variant Modeling Methods for MBPLE at Airbus

Abstract Product Line Engineering (PLE) is a fundamental component of systems engineering. As postulated in INCOSE's Vision 2025, Model‐Based Systems Engineering (MBSE) is becoming the norm for systems engineering. Thus, we need to integrate PLE into MBSE. An important aspect of this integration is the definition of model‐based methods. This paper defines two possible approaches for variant modeling with SysML applied at Airbus and provides insights into their application in practice.
A graph-based interpretability method for deep neural networks

With the development of artificial intelligence, the most representative deep learning has been applied to various fields, which is greatly influencing human society. However, deep neural networks (DNNs) are still a black-box model, and the process how they make decisions internally is still difficult to understand and control. At the same time, DNNs take up more hardware resources, resulting in high energy consumption. Therefore, it is significant to study the characteristics of deep AI models and deeply understand the interactions between parameters within AI models so as to improve the interpretability of DNNs, optimize their structure and increase their computational efficiency. In this paper, we propose a graph-based interpretability method for deep neural networks (GIMDNN). The running parameters of DNNs are modeled as a graph by using a kernel function or the Graph Transformer Networks (GTN), where the nodes of the graph are obtained by dimensional mapping of the parameters of the DNNs, and the edges are calculated by the Gaussian kernel function. The generated graphs are classified by a graph convolutional network (GCN). The association relationship between the adjacent layers and the running mechanism of DNNs are analyzed, and the importance of the parameters of each layer in the DNNs for the final classification result can be obtained. Convolutional neural networks (CNNs) are one of the most representative models in DNNs. The proposed method is experimentally evaluated on the CNNs. The experimental results show that the proposed method can interpret the associations among the weight parameters as well as the correlation between two adjacent layers. Therefore, the DNNs for special tasks, such as portable applications, edge computing, and so on, can be customized, the number of parameters can be reduced. It is valuable to interpret the operation and principle of CNNs.