Reviewerly/exHarmony huggingface dataset:
A Comparison of Cyber Security Reports for 2020 of Central European Countries

The aim of the article is to analyze the annual reports on cyber security of Central European countries, i.e. the Czech Republic, Slovakia, Poland, Germany, and Austria.The article focuses on the development of the state of cyber security, actors of threats in cyberspace, cyber threats, and the most common types of attacks.The article evaluates the objectives of cyber-attacks from the point of view of state institutions, organizations, and state and private companies, and they have listed the follow-up measures here.The method used is a critical verbal evaluation with comments and comparative analysis to find the strengths and weaknesses of the evaluated cyber security strategies and learn from them.The experiment of the cyber defense against phishing attacks is mentioned as an example of the cyber defense of individuals.The rules in Microsoft Outlook were used by filtering incoming email messages.The result is promising by stopping 88 % of phishing emails.The discussion and conclusion state that COVID-19 played a big role in the cyber security situation in countries to the analyzed documents.
Deep Stereo Image Compression via Bi-directional Coding

Existing learning-based stereo compression methods usually adopt a unidirectional approach to encoding one image independently and the other image conditioned upon the first. This paper proposes a novel bidirectional coding-based end-to-end stereo image compression network (BCSIC-Net). BCSIC-Net consists of a novel bidirectional contextual transform module which performs nonlinear transform conditioned upon the inter-view context in a latent space to reduce inter-view redundancy, and a bidirectional conditional entropy model that employs interview correspondence as a conditional prior to improve coding efficiency. Experimental results on the InStereo2K and KITTI datasets demonstrate that the proposed BCSIC-Net can effectively reduce the inter-view redundancy and out-performs state-of-the-art methods.
Artificial Intelligence in Breast Imaging

The development and implementation of artificial intelligence (AI) for breast imaging have been ongoing for several decades and have played an important role in clinical practice. With the emergence and maturity of deep learning (DL) algorithms, the application of AI technology in medical imaging has gradually moved to a higher level and broader range. It may break the performance bottleneck of traditional computer-aided detection/diagnosis (CAD) systems. This chapter reviews the three domains of clinical use cases for AI techniques in breast imaging, including risk assessment for screening, breast cancer detection and classification for diagnosis, and therapy selection and outcome prediction for interventions. As for future directions, it is necessary to improve the AI-based system’s interpretability and performance in a clinical application and maximize its clinical impact.
Sentiment Analysis for E-Commerce Reviews Based on Deep Learning Hybrid Model

Along with the emergence of Internet, online shopping has become a fundamental way for consumers to purchase and consume. Sentiment analysis of huge reviews on related e-commerce platforms can effectively improve user satisfaction. In this paper, a two-channel hybrid model sentiment classification algorithm MWVH is proposed, which combines the features of multiple word vectors. For words, word vectors are extracted by combining Word2vec and character-level N-gram embedding methods on different parallel channels. CNN and RNN combined with attention mechanism are each on a channel. Two-channel model structure are adopted to better obtain sentence vector features, which makes up for the deficiency of single network model. Using the data set of 8 cross-border e-commerce APP reviews from APP Store, comparative experiments were conducted on the 2 models (MWVH1.0 and MWVH2.0) and 4 benchmark models proposed. Experimental results show that the proposed two-channel hybrid model (MWVH1.0 and MWVH 2.0) based on the embedding features of multiple words can improve the classification performance. When the amount of data is small, MWVH1.0 model is better, with an average accuracy of 94.62%. However, MWVH2.0 performs better on larger data sets, with an average accuracy of 95.14%.
NEXT-GEN E-COMMERCE in the METAVERS

The Metaverse's goal is to create a virtual world that incorporates augmented reality, virtual reality, 3D holographic avatars, and other forms of communication. In this paper, a new e-commerce experience was developed to contain the new technology in user friendly applications that can be used in the Metaverse as a facility to combine online trading and the new technology of the virtual reality.
Dynamics-Aware Spatiotemporal Occupancy Prediction in Urban Environments

Detection and segmentation of moving obstacles, along with prediction of the future occupancy states of the local environment, are essential for autonomous vehicles to proactively make safe and informed decisions. In this paper, we propose a framework that integrates the two capabilities together using deep neural network architectures. Our method first detects and segments moving objects in the scene, and uses this information to predict the spatiotemporal evolution of the environment around autonomous vehicles. to address the problem of direct integration of both static-dynamic object segmentation and environment prediction models, we propose using occupancy-based environment representations across the whole framework. Our method is validated on the real-world Waymo Open Dataset and demonstrates higher prediction accuracy than baseline methods.
Object Goal Navigation using Data Regularized Q-Learning

Object Goal Navigation requires a robot to find and navigate to an instance of a target object class in a previously unseen environment. Our framework incrementally builds a semantic map of the environment over time, and then repeatedly selects a long-term goal (’where to go’) based on the semantic map to locate the target object instance. Long-term goal selection is formulated as a vision-based deep reinforcement learning problem. Specifically, an Encoder Network is trained to extract high-level features from a semantic map and select a long-term goal. In addition, we incorporate data augmentation and Q-function regularization to make the long-term goal selection more effective. We report experimental results using the photo-realistic Gibson benchmark dataset in the AI Habitat 3D simulation environment to demonstrate substantial performance improvement on standard measures in comparison with a state of the art data-driven baseline.
AdaptiveON: Adaptive Outdoor Local Navigation Method for Stable and Reliable Actions

We present a novel outdoor navigation algorithm to generate stable and efficient actions to navigate a robot to reach a goal. We use a multi-stage training pipeline and show that our approach produces policies that result in stable and reliable robot navigation on complex terrains. Based on the Proximal Policy Optimization (PPO) algorithm, we developed a novel method to achieve multiple capabilities for outdoor local navigation tasks, namely alleviating the robot's drifting, keeping the robot stable on bumpy terrains, avoiding climbing on hills with steep elevation changes, and avoiding collisions. Our training process mitigates the reality (sim-to-real) gap by introducing generalized environmental and robotic parameters and training with rich features captured from light detection and ranging (Lidar) sensor in a high-fidelity Unity simulator. We evaluate our method in both simulation and real-world environments using Clearpath Husky and Jackal robots. Further, we compare our method against the state-of-the-art approaches and observe that, in the real world, our method improves stability by at least 30.7% on uneven terrains, reduces drifting by 8.08%, and decreases the elevation changes by 14.75%.
Research on Garbage Image Classification and Recognition Method Based on Improved ResNet Network Model

Garbage classification recognition has great application value in daily life, while traditional image classification methods have low accuracy and generalization ability. This paper solves the above problems by improving the ResNet model. The specific strategy is to reduce its residual block and add it to the batch normalization layer (batch normalization), and then replace the convolution kernel in the first convolutional layer of the model with a convolution kernel. The ResNet34 (ResNet26) network model and the se attention mechanism module are added after each residual block, and finally an improved residual network model (AResNet26) is formed. On the dataset side, dataset augmentation is performed with the alumentations library. The results show that the AResNet26 model has higher recognition accuracy than ResNet26, ResNet18, ResNet34, VGG-16, AlexNet, and GoogleNet on the garbage image dataset. The recognition accuracy of the improved AResNet26 network model reaches 91.81%, and the training time is also greatly shortened. The research model realizes fast and accurate classification and identification of different types of garbage, and provides reference and technical support for garbage classification and identification methods.
GPT-3 Powered System for Content Generation and Transformation

Enabling computer systems to understand and generate natural language has been an up-and-coming field of research. Latest advancements in Natural Language Processing (NLP) have made headway progress in facilitating this, like the GPT-3 language prediction model created by OpenAI. Given the capacity of the GPT-3 model, this study capitalizes on how the model can be used to generate and transform content without manual help from humans – how well plausibly GPT3-authored text most nearly passes as human-like prompts for content generation and manipulation purposes. This attempt is presented in the context of automated story writing. It also sheds light on the potential abuses of the tool and its raw capabilities as its limitations.
Context-Based Learning

Students who use the Context-Based Learning approach can better understand the material by applying what they have learned to real-world situations and then comparing that information and what they already know. The background is limited to setting space and time in a classroom or lecture theatre. Learning can come from an enhanced environment outside the classroom, as a visit to a heritage site, a museum, or a good book. In this, learners will produce meaning through experiences with our environments, conversations, notes, and adjustments to nearby objects by witnessing the environment around us, assisting with the guides, and measuring the instruments, which will help us understand the meaning. This chapter aims to introduce Context-based Learning, its impact on students’ learning, and later on, creating Context-based learning environments. Later this chapter discusses how the assessment will be done in Context-based learning, along with case studies. Finally, this chapter throws a lite-on Contextual Teaching and Learning.
ViCollAR: A Novel System for 3D Data Visualization using Collaborative Augmented Reality

Data visualization refers to the presentation of data in a pictorial or graphical format and it can significantly help users, e.g., decision makers, to see the analytics presented visually, such that they can grasp key concepts or identify new patterns rapidly. Various tools and systems have been developed towards data visualization, but they all suffer from different limitations towards immersive and interactive data visualization. In this paper, we presented our developed system, ViCollAR, which aims to achieve immersive, interactive and collaborative 3D data visualization leveraging Augmented Reality (AR) technologies. ViCollAR allows multiple users to simultaneously and collaboratively view and interact with the visualized data using their bare hands in 3D space, and it allows the users to freely navigate through the real spatial environment during interactions. A prototype has been successfully developed for the targeted system and tested by users.
Predicting Phishing URL Using Filter based Univariate Feature Selection Technique

Phishing URL acts as a trustworthy website and cheats the user by taking all the essential data or the personal information. Hence detecting phishing attack is a very tedious task.Though the organizations are working hard to mitigate this threat still it is the prime concern to alert end users awareness in preventing such threat because phishers often exploit users by visual similarity of the phishing website over the legitimate website.Therefore, our objective is to design a detection framework using feature selection methods of machine learning technique. Univariate feature selection methodology is one of the best pre-processing technique for choosing the most relevant and important feature because it adopts the statistical test which yields the statistical significance between the features for determining the predictive power. It also enhance the learning power of the model in less time. In our experiment, we have used the three univariate feature ranking algorithms such as Information Gain (IG), Chi-square and ANOVA to select the best features that evaluates the supervised machine learning algorithms kNN (k-nearest neighbour), DT (Decision Tree), LR (Logistic Regression) and NB (Naive Bayse). Our experiment demonstrates that, kNN outperforms all and the accuracy is of 99.8%.
Identifying and Addressing Risks in the Early Design of a Sociotechnical System through Premortem

Anticipating risks in software development is always challenging, but particularly so when the software application is part of a novel sociotechnical system with various human and physical components. Our interdisciplinary team of software engineering and human factors researchers is designing such a system. In order to identify and mitigate the risks latent in this previously unexplored space, we have used the premortem method at an early stage in system design. In the premortem, the team ideated failure scenarios across the range of system use, then collaborated on ways to eliminate, mitigate, or monitor the risks of these failures. We have found the premortem method valuable in recognizing and mitigating previously unanticipated risks and in enriching team communication.
Deep Reinforcement Learning based Robot Navigation in Dynamic Environments using Occupancy Values of Motion Primitives

This paper presents a Deep Reinforcement Learning based navigation approach in which we define the occu-pancy observations as heuristic evaluations of motion primitives, rather than using raw sensor data. Our method enables fast mapping of the occupancy data, generated by multi-sensor fusion, into trajectory values in 3D workspace. The computationally efficient trajectory evaluation allows dense sampling of the action space. We utilize our occupancy observations in different data structures to analyze their effects on both training process and navigation performance. We train and test our methodology on two different robots within challenging physics-based simulation environments including static and dy-namic obstacles. We benchmark our occupancy representations with other conventional data structures from state-of-the-art methods. The trained navigation policies are also validated successfully with physical robots in dynamic environments. The results show that our method not only decreases the required training time but also improves the navigation performance as compared to other occupancy representations. The open-source implementation of our work and all related info are available at https://github.com/RIVeR-Lab/tentabot.
Improved YOLOv5 Algorithm Based on CBAM Attention Mechanism

Due to the presence of tiny targets that have a high incidence of missed detection and false detection as well as the occlusion of cars and people, object recognition in road scenes is difficult. We put up a better YOLOv5 object identification model to address this problem. First, to improve the extraction of significant characteristics from cars and pedestrians while suppressing the detection of generic features, we added the CBAM attention module to the YOLOv5 backbone network. Second, we integrate two hyperparameters into the focal loss function to regulate the weight ratio of positive and negative samples and difficult and easy samples, respectively, in order to maximize the positive and negative samples in the data set and solve the issue of imbalance between difficult and easy samples. The experiment is run on the KITTI public dataset, and the mAP value is utilized as the evaluation metric. The experimental findings demonstrate that, when compared to the previous YOLOv5 model, the suggested model enhanced mAP by 1.1%, demonstrating the new model's efficacy.
SoK: Decentralized Randomness Beacon Protocols

The scientific interest in the area of Decentralized Randomness Beacon (DRB) protocols has been thriving recently. Partially that interest is due to the success of the disruptive technologies introduced by modern cryptography, such as cryptocurrencies, blockchain technologies, and decentralized finances, where there is an enormous need for a public, reliable, trusted, verifiable, and distributed source of randomness. On the other hand, recent advancements in the development of new cryptographic primitives brought a huge interest in constructing a plethora of DRB protocols differing in design and underlying primitives. To the best of our knowledge, no systematic and comprehensive work systematizes and analyzes the existing DRB protocols. Therefore, we present a Systematization of Knowledge (SoK) intending to structure the multi-faced body of research on DRB protocols. In this SoK, we delineate the DRB protocols along the following axes: their underlying primitive, properties, and security. This SoK tries to fill that gap by providing basic standard definitions and requirements for DRB protocols, such as Unpredictability, Bias-resistance, Availability (or Liveness), and Public Verifiability. We classify DRB protocols according to the nature of interactivity among protocol participants. We also highlight the most significant features of DRB protocols such as scalability, complexity, and performance along with a brief discussion on its improvement. We present future research directions along with a few interesting research problems.
Multifunctional Radar Cognitive Jamming Decision Based on Dueling Double Deep Q-Network

To solve the inefficient and imprecise problem using the Deep Q-network (DQN) algorithm for the radar jamming decision, this paper proposes a multifunctional radar jamming decision optimization method based on the Dueling Double Deep Q-network (D3QN). First, we use a value function reflecting the radar state’s change, and an advantage function related to radar state S and jamming action A to improve the cognitive jamming level for unknown radar modes. Then, using the dueling networks for jamming strategy selection and effectiveness evaluation can further improve decision accuracy. Finally, we propose a prioritized experience replay mechanism during network training to shorten the decision-making time. The experimental results show that our proposed method completes decision tasks 2.1 times more efficiently than the DQN and improves decision accuracy by approximately 10% over DQN.
Edge Computing With Complementary Capsule Networks for Mental State Detection in Underground Mining Industry

Most safety accidents are caused by human factor in underground resource mining industry. This is because the nonuniform lighted and noisy and dangerous environment easily evokes the negative mental state and causes the nonstandard production operation. Aiming at the difficult problem to be solved urgently, this article proposes an edge computing mental state framework of the Internet of Things in the underground mining industry. Moreover, a filtering algorithm using a defined threshold function is developed. Furthermore, an complemented capsule network model is constructed by using two residual modules. Specially, a two-stage mental state fusion algorithm is proposed with electrocardiogram signals and facial expression. Finally, the mental state variation characteristics are explored with the underground illuminating and coloring. Experiments show that the mental state detection accuracy is increased by 2.6%. The higher mental arousal is at the illumination between 320 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lx</i> and 330 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Lx</i> .
Alleviating Sparsity Problem of Recommender System with No Extra Input Data

Recommender systems is one of the major technical research tracks in the internet and big data era. However, in many small or medium-sized corporations that lack big data, sparsity is a frequently encountered problem in real world context settings. The sparsity problem also poses problems to recommender system designers during the initial stage after the system is put online. In spite of the criticality of the sparsity problem, the research and investigation on sparsity problem has been slim in the research community. In this paper, we propose to use ZeroMat (invented in 2021) as the preprocessing step before recommender system execution to solve the sparsity problem. We prove by experiments our hybrid method outperforms single models such as ZeroMat and the classic version of Matrix Factorization.
Valuable Features of Hybrid Teaching in a Higher Education Context

Even if there is no precise definition of hybrid teaching, it is generally referred to as having both face-to-face and remote audiences for the same class of students. Hybrid teaching has peculiar features in the way it integrates technology into educational spaces. This paper aims at understanding how first-year university students approach and take advantage of the features of hybrid teaching. The research has been carried out through a quantitative and qualitative study within a first-year module in Mathematics and Biostatistics at the Bachelor's degree in Biotechnology at the University of Turin. In this module, students could not only choose whether to attend classes face-to-face or remotely, but they were also supported by an online course with all the useful contents: recording of lectures, presentation of course contents, interactive resources, in-depth materials, and assessment with feedback. Results show a high appreciation of hybrid teaching and its benefits, usefulness, simplicity, high flexibility, facilitation of students' time management, and fulfillment of learning needs, giving additional value to face-to-face attendance.
Network-Based Machine Learning Detection of Covert Channel Attacks on Cyber-Physical Systems

Most of the recent high-profile attacks targeting cyber-physical systems (CPS) started with lengthy reconnaissance periods that enabled attackers to gain in-depth understanding of the victim’s environment. To simulate these stealthy attacks, several covert channel tools have been published and proven effective in their ability to blend into existing CPS communication streams and have the capability for data exfiltration and command injection.In this paper, we report a novel machine learning feature engineering and data processing pipeline for the detection of covert channel attacks on CPS systems with real-time detection throughput. The system also operates at the network layer without requiring physical system domain-specific state modeling, such as voltage levels in a power generation system. We not only demonstrate the effectiveness of using TCP payload entropy as engineered features and the technique of grouping information into network flows, but also pitch the proposed detector against scenarios employing advanced evasion tactics, and still achieve above 99% detection performance.