Reviewerly/exHarmony huggingface dataset:
A Decade of Development of Mental Models in Cybersecurity and Lessons for the Future

Mental models are essential in learning how to adapt to new and evolving circumstances. The landscape of best practices in cybersecurity is a constantly changing area, as the list of best practices evolves in response to the increasing complexity and scope of threats. In response, users have adapted to the threats and corresponding countermeasures with mental models that simplify the complex networked environments that they inhabit. This paper presents an overview that spans over a decade of research in mental models of users when dealing with cybersecurity threats and corresponding security measures in different kinds of environments. The lessons from over a decade of research in mental models for cybersecurity offer valuable insights about how users learn and adapt, and how their backgrounds and situational awareness play a critical role in shaping their mental models about cybersecurity.
Gaussian Mixture Model in Dynamic Background of Video Sequences for Human Detection

Background subtraction is the initial key process of human detection. One of the problems faced by background subtraction is various changes or dynamic backgrounds. It can be caused by the illumination or color so the colors of the background and the foreground look similar. To adjust the background changes, a different Gaussian Mixture Model was proposed in this paper. Performance evaluation was conducted qualitatively and quantitatively by comparing GMM -based background subtraction against standard background subtraction. Measurement is carried out using PSNR (Peak Signal Noise Ratio) and MSE (Mean Square Error). The Average MSE of GMM-based was 3,40 and the average PSNR was 11,80. The Average MSE of standard Background Subtraction was 3,98 and the average PSNR was 8,54. The MSE value of GMM-based background subtraction based is lower than the MSE of standard Background Subtraction and the PSNR value is higher. The experiment result proved that GMM-based background subtraction is promising to segment and extract the foreground in detection.
Natural Question Generation using Transformers and Reinforcement Learning

Natural Question Generation (NQG) is among the most popular open research problems in Natural Language Processing (NLP) alongside Neural Machine Translation, Open Domain Chatbots, etc. Among the many approaches taken up to solve this problem, neural networks have been deemed the benchmark in this particular research area. This paper aims at adopting a generator - evaluator framework in a neural network architecture to allow additional focus on the context of the content used for framing a question. The generator uses NLP architectures like transformers (T5) to generate a question given a context while the evaluator uses Reinforcement Learning (RL) to check the correctness of the generated question. The involvement of RL has improved the results (as shown in Table 2), and there is increased computational efficiency as the training is coupled with the policy of RL. This turns the problem into a reinforcement learning task and allows for the generation of a wide range of questions for the same context-answer pair. The given algorithm is tested on the benchmark dataset - SQuAD with BLEU score as the evaluation metric
Deep Learning Approach based Classification of Alzheimer's Disease Using Brain MRI

Alzheimer's disease is a well-known brain disorder which currently still does not have a cure. However, early diagnosis of the disease can help the patient to get proper treatment and slow down the progressiveness of the disease. In medical imaging, deep learning methods have been widely used to assist the medical professionals in Alzheimer's disease diagnosis to classify the normal brain and the stages of Alzheimer's disease. This paper compares the classification performance of deep learning architecture such as MobileNetV2, ResNet-101, DenseNet-121, and the proposed modified convolutional neural network (CNN) model inspired by the VGG16 network using a brain magnetic resonance imaging (MRI) dataset from Kaggle. We evaluate the model performance based on their accuracy, precision, recall, and F1-score and the obtained results show that the classification of Alzheimer's disease using our proposed model is more accurate than the other model with 97.625% accuracy, 98% recall, 98% precision and 98% F1-score. Our study successfully proved that implementation of dropouts in the CNN model can improve the model accuracy and reduce the model training duration, and made valuable insights for medical image diagnosis and future research.
A SYSTEMATIC LITERATURE SURVEY ON DATA SECURITY TECHNIQUES IN A CLOUD ENVIRONMENT

Cloud security is a branch of cyber security that focuses on securing cloud computing systems. It is a method for enterprises to use the internet to access storage and virtual services while saving money on infrastructure, considered as the next-generation architecture since it combines application software and databases into big data centers. However, difficulties with confidentiality, protection, integrity, and compliance may surface when the information is not kept, examined, or computed locally. A wide range of data security-related topics are covered by cloud-based data security. For securing an organization's data across the network, data encryption is a common and effective security strategy that is a great alternative. The most vulnerable data is financial and payment systems, and medical data, which can expose customers or clients critical information. Several academics have also offered a range of approaches to safeguards to maintain data privacy and security while it is being transmitted, but there are still a lot of obstacles to the safety of data that is being stored in the cloud. In this paper, several research papers that have been authored and published in this topic are thoroughly examined and analysed. This study gives a review of the literature on different methods for establishing data security in cloud environment.
PharmaChain 3.0: Blockchain Integrated Efficient QR Code Mechanism for Pharmaceutical Supply Chain

Because of globalization, many different entities distributed across the locations were able to work together and achieve the availability of services even at remote locations. Supply Chains helped in leveraging such businesses globally with reduced costs and increased efficiency. Pharmaceutical Supply Chain (PSC) is one in which the prescription drugs are moved from the manufacturer to the patient. Providing the right medicine at the right time to the right patient in the right doses coming from the right route is called the five rights of medication. Due to the increased number of participating entities, and interactions between entities and adversaries trying to profit by introducing counterfeit drugs into the supply chain, efficient tracking and tracing mechanism is very much needed in PSC. The current paper proposes an architecture that is integrated with Blockchain, Inter Planetary File System (IPFS) along with QR code technologies to provide a secure QR code mechanism for addressing such tracking and tracing issues in PSC. The proposed model is evaluated for security and efficiency using different metrics.
Fractal Image Compression based on Discrete Wavelet Transform

Fractal Image compression is the type of data compression applied to the digital image to reduce their cost for storage and transmission. In this paper Fractal image compression algorithm using a discrete wavelet transform form has been projected. The concert of the (DWT) projected algorithm is estimated using compression ratio (CR). The experimental result obtained from the projected algorithm gives an improvement in terms of the CR. The Paper concludes with a comparison with the existing algorithm and shows that the projected algorithm performs quite efficiently.
Transformer Based Technique for High Resolution Image Restoration

Due to the difficulty of the problem, there have been many different approaches and algorithms developed to date. Defocus deblurring, single image motion deblurring, image deraining, and image denoising are some of the further branches of picture restoration. There are numerous algorithms that are appropriate in a variety of circumstances. For instance, gaussian noise like pepper and salt noise responds well to the median filter. The adaptive filter, on the other hand, performs particularly well in DSP and ANC applications. It has been discovered that methods based on deep neural networks, such as the restormer, can occasionally outperform earlier algorithms. We look for the best algorithm for image denoising, defocus deblurring, single image motion deblurring, and picture deraining in this work. Although restormer is shown to perform better than the others in the majority of circumstances, hybrid median filter is sometimes found to perform better than all of them combined.
Clustered Graph Federated Personalized Learning

This paper proposes a graph federated learning approach wherein multiple servers collaborate to enhance personalized learning over clustered clients, essentially performing correlated learning tasks. In contrast to earlier approaches, relying on a cluster-dedicated server topology, the proposed graph federated multitask learning (GFedMt) framework adopts a more general setting wherein clients of the same cluster are distributed across servers. In order to address problems with unbalanced client distributions among servers and clusters as well as data shortage of isolated clients, servers perform intra-cluster and inter-cluster learning collaboratively through local interaction with neighboring servers. Clients use the alternating direction method of multipliers (ADMM) to learn their local models. Numerical simulations demonstrate the ability of the proposed method to ensure fast and accurate convergence when data is scarce.
Disentangling Learnable and Memorizable Data via Contrastive Learning for Semantic Communications

Achieving artificially intelligent-native wireless networks is necessary for the operation of future 6G applications such as the metaverse. Nonetheless, current communication schemes are, at heart, a mere reconstruction process that lacks reasoning. One key solution that enables evolving wireless communication to a human-like conversation is semantic communications. In this paper, a novel machine reasoning framework is proposed to pre-process and disentangle source data so as to make it semantic-ready. In particular, a novel contrastive learning framework is proposed, whereby instance and cluster discrimination are performed on the data. These two tasks enable increasing the cohesiveness between data points mapping to semantically similar content elements and disentangling data points of semantically different content elements. Subsequently, the semantic deep clusters formed are ranked according to their level of confidence. Deep semantic clusters of highest confidence are considered learnable, semantic-rich data, i.e., data that can be used to build a language in a semantic communications system. The least confident ones are considered, random, semantic-poor, and memorizable data that must be transmitted classically. Our simulation results showcase the superiority of our contrastive learning approach in terms of semantic impact and minimalism. In fact, the length of the semantic representation achieved is minimized by 57.22% compared to vanilla semantic communication systems, thus achieving minimalist semantic representations.
Online Filtering over Expanding Graphs

Data processing tasks over graphs couple the data residing over the nodes with the topology through graph signal processing tools. Graph filters are one such prominent tool, having been used in applications such as denoising, interpolation, and classification. However, they are mainly used on fixed graphs although many networks grow in practice, with nodes continually attaching to the topology. Re-training the filter every time a new node attaches is computationally demanding; hence an online learning solution that adapts to the evolving graph is needed. We propose an online update of the filter, based on the principles of online machine learning. To update the filter, we perform online gradient descent, which has a provable regret bound with respect to the filter computed offline. We show the performance of our method for signal interpolation at the incoming nodes. Numerical results on synthetic and graph-based recommender systems show that the proposed approach compares well to the offline baseline filter while outperforming competitive approaches. These findings lay the foundation for efficient filtering over expanding graphs.
Research on Automatic Generation of Text Summaries in News Key Information Extraction

This paper presents a step-by-step improvement method for extracting key information from news categories to compose text summaries. First, combine the traditional extractive automatic text summarization, perform word segmentation, term frequency statistics and other operations on the sentences, calculate the impact factor of the word segmentation and the weight and proportion of each sentence, and select several sentences with higher scores as the result of first screening. Then, the sentences screened in the first pass are input as candidate abstracts into the improved BERT model to generate word segmentation vector representations, and the abstract rate of each sentence in the candidate abstracts is obtained through a series of calculations in the linear layer and the softmax layer. Finally, the score of the abstract rate is combined with the weights of various influencing factors obtained in the first screening process, and all the obtained word segmentations are recombined to obtain an abstract that can summarize the main theme of the full text. The summary evaluation results show that this method improves the ROUGE score to a certain extent, making the filtered content closer to the required information.
Hack The Problem

Cybersecurity education domain comprises of different curriculum design and teaching methods such as traditional lecture-based instruction, lectures coupled with lab exercises, peer instruction teaching, and concept maps. Traditional lectures combined with hands-on exercises is often the instructional de facto in this discipline. However, these narrowly-focused hands-on exercises such as NDG Netlabs, are typically designed in a hand holding fashion where students are instructed to follow a step-by-step approach to complete a series of tasks associated to the topic. A key missing piece in this pedagogy is the absence of problem solving & critical thinking skills. Hence, the approach fails short in providing students with an opportunity to develop a deeper understanding of often complex and invariably intertwined cybersecurity concepts - a norm in the real world cybersecurity incidents. The authors believe that problem-based learning (PBL) pedagogy holds substantial promise in addressing these shortcomings and improving student learning in cybersecurity education. The project proposes a PBL approach for a ethical hacking and network defense curriculum.
Nonmyopic Distilled Data Association Belief Space Planning Under Budget Constraints

Autonomous agents operating in perceptually aliased environments should ideally be able to solve the data association problem. Yet, planning for future actions while considering this problem is not trivial. State of the art approaches therefore use multi-modal hypotheses to represent the states of the agent and of the environment. However, explicitly considering all possible data associations, the number of hypotheses grows exponentially with the planning horizon. As such, the corresponding Belief Space Planning problem quickly becomes unsolvable. Moreover, under hard computational budget constraints, some non-negligible hypotheses must eventually be pruned in both planning and inference. Nevertheless, the two processes are generally treated separately and the effect of budget constraints in one process over the other was barely studied. We present a computationally efficient method to solve the nonmyopic Belief Space Planning problem while reasoning about data association. Moreover, we rigorously analyze the effects of budget constraints in both inference and planning.
The Use of a Genetic Algorithm to Alleviate the Limited Content Issue in a Content-Based Recommendation System

Recommendation systems today present a technology designed to make things easier for consumers. They can be used to suggest ideas, solutions, products, and even people in various fields, including medicine, education, tourism, e-commerce and industry. The most present study we identified is in the e-commerce industry, supporting people in finding what they desire. A recommender system can, however, present consumers with helpful information on topics that may be important to them. The user may become dissatisfied receiving suggestions that are identical to their preferences, resulting in the problem of over-specialization. Limited content data leads to over-specialization since content-based techniques promote products closely connected to the client profile instead of novelties. In this research, we are concerned explicitly with suggesting novel, unpredictable, and startling objects that may be loved by consumers and may help make up for the lack of content. Genetic algorithms may be used to incorporate innovations and a sense of unpredictability, which we need to accomplish in order to propose both unfamiliar and unforeseen products alongside more familiar ones. This article discusses an innovative method to overcome the limited content problem and enhance the results of a content-based recommendation system.
An Intelligent Monitoring Approach Based on WiFi Sensing for Smart Hospital

Traditional sensing techniques for smart hospitals are recognized to be labor- and technically-intensive for practical adoption. As a result, researchers are employing alternate sensing techniques. One of the major technologies enabling connection for smart hospital services is WiFi. With its sensitivity to environmental dynamics, WiFi signal is now widely used for a variety of sensing applications, such as gesture recognition and fall detection, in addition to its primary usage for communication. WiFi-based sensing offers non-contact sensing in a private mode, simultaneous sensing and data transmission without additional connection infrastructure, and remote sensing without handheld sensors. This paper aims to investigate existing systems and methods that use WiFi sensing, conduct experiments to analyze how different environments as well as system parameters affect data and the ability of monitoring human activity without the need for sensors on their bodies.
An Image Encryption Algorithm Based on Substitution and Diffusion Chaotic Boxes

In this article, a new method for constructing a substitution table will be proposed for implementation in an image encryption algorithm controlled by chaotic maps. The substitution and diffusion operations are based on new functions. After loading the original image, this image is transformed into three vectors. The encryption of an image goes through two phases. In the first phase, a change of the value of a pixel is established using S-box. In the next phase, a CBC diffusion operating mode is used by integrating a new diffusion function and a second substitution table denoted D-box. This last operation protects our system from any differential attack.
Yolov2 Implementation and Optimization for Moroccan Traffic Sign Detection

Image and video processing is a major contributor of object's detection, since only video sensor can analyze a lot of visual information specific of road scene. Notably road signs identification in images. Which is an important issue for Advanced Driver Assistance Systems (ADAS system), seeing that the result of detection is given to another ADAS modules i.e., planning (behavior planning and prediction), to make decision, thus the accurate and real time detection is required for vehicle safety, limitation of accidents and road management applications. The purpose of this study is to implement an algorithm for Moroccan traffic sign detection, what for solving the problems that appear in the road scene such as an occlusion, changes in luminosity and response on real time. We have proposed and optimized an implementation of the Yolov2 algorithm based on convolutional neural network (CNN) for Moroccan traffic sign. The obtained results are a precision of 100% for stop and mandatory signs, and more than 65% for the other signs, with a frame per second FPS of 10.
Nostalgia enhances route learning in a virtual environment

Salient landmarks enhance route learning. We hypothesised that semantically salient nostalgic landmarks would improve route learning compared to non-nostalgic landmarks. In two experiments, participants learned a route through a computer-generated maze using directional arrows and wall-mounted pictures. On the test trial, the arrows were removed, and participants completed the maze using only the pictures. In the nostalgia condition, pictures were of popular music artists and TV characters from 5 to 10 years ago. In the control condition, they were recent pictures of these same artists and characters. In Experiment 1, in the test trial, participants in the nostalgia condition completed the maze faster than controls. Experiment 2 conceptually replicated these findings and extended them by exploring boundary conditions. Participants had to learn two mazes sequentially. In Maze 1, we placed nostalgic/control landmarks only at non-decision points (whereas we placed them at decision points in Experiment 1). In Maze 2, we placed nostalgic/control landmarks at decision points during acquisition but removed them in the test trial (whereas they were present in the test trial in Experiment 1). In both mazes, participants in the nostalgia (compared to control) condition completed the test trial faster.
Holobot

Nowadays, telepresence systems based on the Extended Reality (XR) system are actively developed and used for remote collaboration due to COVID-19. Still, several issues, such as limited traversable space in Virtual Reality (VR) and require all participants to wear head-mounted display (HMD), stop these systems from being used in our daily life. On the other hand, telepresence robots have been used in various fields before the pandemic. However, these robots also have a limitation in that the current form is incapable of delivering non-verbal expressions, which is essential for social interaction. Therefore, we present a Holobot, a telepresence robot based on the XR system. A remote user connects to the Holobot through VR HMDs, and the Holobot augments a virtual avatar that projects users' facial and gesture expressions. We developed a prototype and conducted a simple field test in the exhibition to receive feedback. VR participants enjoyed exploring remote spaces and interacting with each other through Holobot. Furthermore, remote space participants mentioned that a 1:1 scale avatar helped to build co-presence with the VR user. Based on these insights, we think Holobot could provide design guideline for future telepresence robot. For further approach, we plan to improve our prototype and conduct user test for structured evaluation of our system.