Reviewerly/exHarmony huggingface dataset:
Deep Knowledge Tracking Method Based on DKVTMN-DTCN Model

With the rapid development of deep neural networks, deep knowledge tracking models have become one of the most important research areas in educational data mining. While most knowledge tracking models assume that the forgetting level of all students is constant, in reality, forgetting levels are affected by time intervals and different learning abilities. The learning ability of the same student changes over time, and there is variability in the learning ability of different students, so students will forget their knowledge to different degrees within the same time interval of answering questions. The DKVMN-DT uses CART to preprocess student behavioral characteristics. However, the time series analysis capability of the decision tree model is very limited, and the prediction results it obtains are basically within a certain training range, so for features with the significant trend, the decision tree cannot directly predict the time series changes in student behavior. In summary, a DKVTMN-DTCN knowledge tracking model that combines a temporal forgetting mechanism based on a priori student ability and automatically extracts temporal features using temporal convolutional networks is proposed to capture students' long-term behavioral characteristics and personalize knowledge forgetting. Experimental results show that the prediction performance of the DKVTMN-DTCN is significantly improved compared with the classical model on both datasets.
An Overview of Smart Contract in Blockchian Technology: Security and Platforms

With the continuous innovation of blockchain technology, smart contracts, as an important part of blockchain, are also receiving more and more attention. In this paper, we introduce smart contracts from three directions: basic technology, security, and development platform. The security of smart contracts is discussed from the perspective of the external environment and internal vulnerabilities of smart contracts, and a comparison of different development platforms for smart contracts is made. Finally, the problems faced by smart contracts and future application directions are summarized.
Introducing Inductive Construction in B with the Theory Plugin

Proving theorems and properties on B models, recursively-defined functions is a convenient tool which is missing in B proofs. The main contribution of this paper is the definition of a new theory without new concrete types and without axioms to enable the use of constructions by induction; This theory has been specified and proved within the Theory Plugin in Rodin. This induction theory clearly improves the existing B prover. This is illustrated in this paper by the implementation of ZFC in the Theory Plugin.
Deep Reinforcement Learning for Autonomous Mobile Robot Navigation

Numerous fields, such as the military, agriculture, energy, welding, and automation of surveillance, have benefited greatly from autonomous robots’ contributions. Since mobile robots need to be able to navigate safely and effectively, there was a strong demand for cutting-edge algorithms. The four requirements for mobile robot navigation are as follows: perception, localization, planning a path and controlling movement. Numerous algorithms for autonomous robots have been developed over the past two decades. The number of algorithms that can navigate and control robots in dynamic environments is limited, even though the majority of autonomous robot applications take place in dynamic environments. A qualitative comparison of the most recent Autonomous Mobile Robot Navigation techniques for controlling autonomous robots in dynamic environments with safety and uncertainty considerations is presented in this paper. The work incorporates different angles like the essential technique, benchmarking, and showing parts of the improvement interaction. The structure, pseudocode, tools, and practical, in-depth applications of the particular Deep Reinforcement Learning algorithms for autonomous mobile robot navigation are also included in the research. This study provides an overview of the development of suitable Deep Reinforcement Learning techniques for various applications.
Supervision Meets Self-supervision: A Deep Multitask Network for Colorectal Cancer Histopathological Analysis

Colorectal cancer is one of the most common types of cancer worldwide and the leading cause of death due to cancer. As such, an early detection and diagnosis is of paramount importance, which is, however, limited due to insufficient medical practitioners available for large-scale histopathological screening. This demands a reliable computer-aided framework that can automatically analyse histopathological slide images and assist pathologists in quick decision-making. To this end, we propose a novel deep learning framework that combines supervised learning with self-supervision for robust learning of histopathological features from colorectal tissue images. Specifically, our framework comprises a multitask training pipeline using deep metric learning that learns the embedding space using triplet loss, which is augmented using a self-supervised image reconstruction module that enhances learning of pixel-level texture features. The downstream classification is done by extracting features using the pre-trained encoder and feeding them into a support vector machine classifier. We perform qualitative and quantitative analysis on a publicly available colorectal cancer histopathology dataset, as well as compare the proposed framework against some state-of-the-art works, where the model is found to outperform several existing works in literature. The source codes of the proposed method can be found at https://github.com/soumitri2001/DMTL-CRCH .
ADAPT- Automated Defence TrAining PlaTform in a Cyber Range

With the passage of time, many cyber security training programs are being developed. These programs teach skills ranging from ethical hacking to different cyber defence operations. Teaching or training such skills is a complex undertaking and requires complex platforms and tools, like cyber ranges. This is especially true for training and teaching defenders. For example, teaching realistic cyber defence requires building a vulnerable infrastructure instrumented and monitored with complex and sophisticated software. Due to ever-increasing cyber attacks, teaching such cyber defence operations are in high demand. Most of the current research activities within cyber ranges and cyber security training focus on (1) the generation of a general purpose vulnerable infrastructure and (2) the automatic assessment of skill and the generation of appropriate feedback in cyber security exercises. While providing training platforms for general purpose blue-team training is important, it is not enough. There is a need to adapt the training platforms to the evolving skills and competencies required to address the new challenges posed by the evolving cyber threat landscape. On the other hand, there is no specific focus in the current research on SoC (Security Operations Center) training. To tackle the aforementioned challenges, we developed an open source training platform focusing on SoC training, which is adaptable to cope with the new and evolving skills and knowledge requirements. We used our platform in a case study in a university setting.
Blockchain: Legal and Regulatory Issues

Blockchain technology has spurred unprecedented advances in the energy sector, paving the way toward a low-carbon economy and driving socio-economic and environmental transitions. This chapter explores the legal and regulatory challenges of blockchain applications predicated on three key areas: private law, criminal law, and public and administrative law. As these challenges shape global debates, this chapter provides refined and systemic discussions, contributing to new insights and nuances of legal and regulatory aspects. In light of blockchain proliferation, we discuss emerging regulatory trends and dynamics in several countries, which precipitate economic and administrative shifts through a constellation of regulatory enactments and facilitate the rapid roll-out of blockchain technology. In addition, we provide an all-encompassing discussion on the limits, barriers, and technological and legal challenges of blockchain-enabled smart contracts, which is a particularly promising avenue in the carbon economy.
Analyzing Deep Neural Network Algorithms for Recognition of Emotions Using Textual Data

Evaluation of emotion and recognition from textual data is a new and important study in the Natural Language Processing (NLP) field that could provide useful information for various applications. Nowadays people prefer sharing their thoughts, feelings, views, emotions, perceptions, etc. on social media in the form of short articles, e-commerce reviews, posts, and comments. It's important to analyze these textual facts in order to comprehend the nature of people’s thoughts or emotions prevailing on social media. Analyzing emotions from textual data is most challenging for researchers due to text data diversity, and restricted word length. This research article experimented popular deep learning algorithms (LSTM, BiLSTM, GRU, BiGRU) for emotion prediction from textual data extracted from social media. Authors found that performance of experimented models is better than traditional machine learning techniques. Authors adopted Ekman's six primary emotions. The performance of these algorithms is measured on accuracy, recall, precision, and F1-score parameters. The results demonstrate that BiGRU outperforms the other approaches in terms of emotion prediction.
Autonomous Vehicles Forensics-The next step of the Digital Vehicles Forensics

The application of autonomous vehicles is wide: they are used in industry, logistics, transport, user, etc. The use of self-driving vehicles will have a direct impact on road accidents and violations of rules, therefore it is inevitable to determine liability in case of an accident, and it automatically requires available data on vehicles and infrastructure elements, data extraction tools and methodology. The purpose of vehicle-related forensic investigations is to determine what, when, where, under what circumstances occurred and who was involved, whether liability related to the incident can be determined. These standard practices cannot be applied in case of the autonomous vehicles, due to their specific operation. In order to be able to conduct the investigations and continuously develop the system, a timely collection of evidences may be essential. As technology advances and the range of tools grows, the efficiency of existing forensics solutions needs to be examined. Forensics test methods related to autonomous and cooperative transport systems can be derived from the forensic test methodology and its “digital forensic” branch. On the other hand, they can include completely new elements due to the specialties of the field requiring different preparedness and tools (e.g., engine control electronics vulnerabilities, car manufacturer-specific data storage solutions, custom operating systems). The research in this study summarizes the past and current forensics solutions, examining their applicability to autonomous vehicles.
New Perspectives on Recommender Systems for Industries

Nowadays, recommender systems are increasingly being exploited in many industrial applications, including virtual museums and movie streaming platforms. In the last few years, some new perspectives provided by research paradigms such as deep learning or quantum computing, have arisen. As a result, this paper identifies four new perspectives on recommender systems: e-health, tourism, deep-learning-based, and recommender systems exploiting quantum computing. After discussing them, the paper provides the current state of the art and highlights the possible future directions for industries.
Enhancing Zero-Shot Many to Many Voice Conversion via Self-Attention VAE with Structurally Regularized Layers

Variational auto-encoder (VAE) is an effective neural network architecture to disentangle a speech utterance into speaker identity and linguistic content latent embeddings, then generate an utterance for a target speaker from that of a source speaker. This is possible by concatenating the identity embedding of the target speaker and the content embedding of the source speaker uttering a desired sentence. In this work, we propose to improve VAE models with self-attention and structural regularization (RGSM). Specifically, we found a suitable location of VAE’s decoder to add a self-attention layer for incorporating non-local information in generating a converted utterance and hiding the source speaker’s identity. We applied relaxed groupwise splitting method (RGSM) to regularize network weights and remarkably enhance generalization performance. In experiments of zero-shot many-to-many voice conversion task on VCTK data set, with the self-attention layer and relaxed group-wise splitting method, our model achieves a gain of speaker classification accuracy on unseen speakers by 28.3% while slightly improved conversion voice quality in terms of MOSNet scores. Our encouraging findings point to future research on integrating more variety of attention structures in VAE framework while controlling model size and overfitting for advancing zero-shot many-to-many voice conversions <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> . <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> The work was partially supported by NSF grants DMS-1854434 and DMS-1952644 at UC Irvine.
Flying objects Classification Using Trajectory Images and Convolutional Neural Network

In this paper, we present a new method for flying object classification and recognition based on trajectory images and deep learning approach. First, the video sequence is passed through a Gaussian mixture model (GMM) for flying object detection and tracking. Then the trajectories are determined by the different positions of the objects at successive frames. After that, we collect a large database of trajectories of two classes of objects: drones and birds. Then, the convolutional neural network (CNN) is applied to classify these objects into two classes. The LeNet5 is one of the best choice used in image classification. The CNN is trained using 80% of the database and the rest is used for testing. Experimental results are conducted to demonstrate the performance of the proposed method for drones and birds objects classification. In addition, the automatic approach helps and facilitate to military services to recognize drone from others objects.
Implementation of a New-Optimized ROS-Based SLAM for Mobile Robot

The Simultaneous Location and Mapping (SLAM) problem addresses two critical issues in mobile robotics. The first question is: Where am I? The second question concerns the characteristics of the robot's environment: What does the environment in which I find myself look like? In a SLAM system, a robotic vehicle placed in an unknown environment and position, must build a map of the environment while trying to locate itself on this map. The robot has several sensors that help it retrieve the information it needs. In order to increase TurtleBot3 Burger's localization accuracy and prevent navigation failure, this study describes the creation of an optimized localization and navigation algorithm. SLAM (Simultaneous Localization And Mapping) consists of creating a precise map of a location and locating itself within this map. It aims to make the robot completely autonomous.
Deep learning approach for sign language's handshapes recognition from EMG signals

Electromyographic (EMG) signals are beginning to be increasingly used as alternatives for gesture recognition. Indeed, EMG signals are used to control machines (drones, robots, VR, PowerPoint presentation…). In the field of assistive technologies, EMG signals offer new solutions for people with disabilities. Namely, the control of prostheses for disabled people or the control of wheelchairs or computers for paralyzed people. Through this work we aim to use electromyographic signals for the recognition of sign languages alphabets. The signals are received by 8 sensors mounted on an armband that the deaf person can wear on the forearm. We propose a new approach for the classification of EMG signals using an approach based on new deep learning solutions. We used a sequence of CNN layers followed by two LSTM layers to extract features from Electromyographic data. We implemented and evaluated the proposed approach using 28 handshapes representing Arabic alphabets. We obtained an average accuracy of 98,49%.
STEM-integrated physics digital teaching material to develop conceptual understanding and new literacy of students

Learning should be able to develop the students' literacy skills in the industrial revolution 4.0. However, the literacy of students related to this era was still low. The solution to solve the problem is to develop STEM-integrated physics digital teaching material (PDTM). The research objective was to investigate the effect of using PDTM on students’ conceptual understanding and new literacy. The research method was quasi-experimental with a nonequivalent control group design. Instruments for collecting data consist of written tests and performance assessments. Data analysis used a comparison test of two independent groups: t-test and Mann-Whitney U test. The results of the data analysis state that the use of STEM-integrated PDTM has a positive effect on students’ conceptual understanding and new literacy. The students’ new literacy skills include data, technology, and human literacy. These results indicate that using STEM-integrated PDTM in physics learning effectively develops students’ conceptual understanding and new literacy skills.
A multi-scale video surveillance based information aggregation model for crime prediction

When studying the relationship and potential association between various data, researchers face multiple challenges: first, researchers need to collect data from multiple knowledge systems and channels, which challenges the breadth of expertise of researchers; second, it is challenging to perceive the changes of application scenarios and environment through these data such as image unstructured data that may contain useful information; third, short term behavior prediction with more frequent changes is challenging, especially when the sensitivity of the prediction model is insufficient and there is a lack of support from other feature data. This article designs a multi-scale information aggregation prediction model based on video surveillance for predicting and analyzing short-term time series with less data, such as criminal behavior. Firstly, a multi-scale human traffic perception and quantification model was established based on FairMOT to obtain human traffic time series data, which is an important association to strengthen the main features of subsequent short-term time series. Then, an improved prediction model, the Anti Saturation Gate Control Model (ASGCM), is designed to introduce entry-level techniques and add anti-saturation conversion modules, making ASGCM more sensitive, helping to reduce dependence on long-term features, and suppressing gradient disappearance and exploration problems. Finally, the application of urban crime prediction was used as an example for validation, and two sets of data were tested using a single sequence and a fused sequence, respectively. The results showed that the ASGCM method proposed in this paper can improve prediction accuracy while reducing computational complexity.
Mapping the Literature on Implementation of Blockchain in Agriculture: A Systematic Review

Introduction: As the human population grows, consumer demand for digital services tailored to their specific needs also increases. To improve the financial performance of farms and meet the need for food of a growing population, farmers and agribusinesses have started incorporating distributed ledger technology into agricultural and farm management software. These developments in the agriculture sector may lead to realising sustainable development goals.Purpose: Several researchers have done studies to explore the features and benefits of blockchain technology in the field of agriculture. There is a need to analyse the available literature to identify the use of this technology in agriculture and the scope of further research. This chapter will mainly focus on its publication trend, journal productivity and impact, prolific studies, and coherent themes.Methodology: For a comprehensive review, bibliometric and content analysis of 71 open-access articles collected through a structured database of Mendeley is done. These articles were published during 2017–2021.Findings: The execution of blockchain is continuously increasing in the agriculture sector, which has resulted in automation in supply chain management, land registrations, and crop insurance. The study revolves around supply chain management, digitisation of agriculture, and sustainable economic development. This study’s conclusions can help agriculturalists improve their understanding of blockchain implementation in agriculture. The study also gives directions for future research.
Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion.