Reviewerly/exHarmony huggingface dataset:
Understanding ProbLog as Probabilistic Argumentation

ProbLog is a popular probabilistic logic programming language/tool, widely used for applications requiring to deal with inherent uncertainties in structured domains. In this paper we study connections between ProbLog and a variant of another well-known formalism combining symbolic reasoning and reasoning under uncertainty, i.e. probabilistic argumentation. Specifically, we show that ProbLog is an instance of a form of Probabilistic Abstract Argumentation (PAA) that builds upon Assumption-Based Argumentation (ABA). The connections pave the way towards equipping ProbLog with alternative semantics, inherited from PAA/PABA, as well as obtaining novel argumentation semantics for PAA/PABA, leveraging on prior connections between ProbLog and argumentation. Further, the connections pave the way towards novel forms of argumentative explanations for ProbLog's outputs.
Stabilizing the Convolution Operations for Neural Network-Based Image and Video Codecs for Machines

Deep convolutional neural networks are generally trained in the floating-point number format. However, the convolution operation in the floating-point domain suffers from numerically unstable behavior due to the limitation of the precision and range of the number format. For deep convolutional neural network-based image/video codec, the instability may cause corrupted reconstructions when the decoder works in a different computing environment. This paper proposes a post-training quantization technique where the convolution operations are performed in the integer domain while other operations are in the floating-point domain. We derived the optimal scaling factors and bits allocation strategy for the input tensor and kernel weights. With the derived scaling factors, the codec can use the significant bits of the single-precision floating-point number for the convolution operations, which does not require the system to support integer operations. Experiments on a learned image codec on machine consumption show that the proposed method achieves the similar performance as the floating-point version while achieving stable behavior on different platforms.
Traffic Matrix Estimation based on Denoising Diffusion Probabilistic Model

The traffic matrix estimation (TME) problem has been widely researched for decades of years. Recent progresses in deep generative models offer new opportunities to tackle TME problems in a more advanced way. In this paper, we leverage the powerful ability of denoising diffusion probabilistic models (DDPMs) on distribution learning, and for the first time adopt DDPM to address the TME problem. To ensure a good performance of DDPM on learning the distributions of TMs, we design a preprocessing module to reduce the dimensions of TMs while keeping the data variety of each OD flow. To improve the estimation accuracy, we parameterize the noise factors in DDPM and transform the TME problem into a gradient-descent optimization problem. Finally, we compared our method with the state-of-the-art TME methods using two real-world TM datasets, the experimental results strongly demonstrate the superiority of our method on both TM synthesis and TM estimation.
Communication Interference Recognition Based on Improved Deep Residual Shrinkage Network

In complex battlefield environments, Flying Ad-hoc NETwork (FANET) faces challenges of manually extracting communication interference signals features, low recognition rate in strong noise environment, and inability to recognize unknown interference types. To solve these problems, one Simple Non-local Correction Shrinkage (SNCS) module is constructed, which modifies the soft threshold function in the traditional denoising method and embeds it into the neural network, so that the threshold can be adjusted adaptively. Local Importance-based Pooling (LIP) is introduced to enhance the useful features of interference signals to reduce noise in the downsampling process, and the joint loss function is constructed by combining cross-entropy loss and center loss to jointly train the model. To distinguish unknown class interference signals, the acceptance factor is proposed, and the One Class Support Vector Machine Simplified Non-local Residual Shrinkage Network (OCSVM-SNRSN) model with the ability of both known class recognition and new class rejection is constructed by combining OCSVM and SNRSN. Experimental results show that the recognition accuracy of the OCSVM-SNRSN model is the highest in the scenario of low Jamming Noise Ratio (JNR). The accuracy is increased by about 4%-9% compared with other methods on the known class interference signal dataset, and the recognition accuracy reaches 99% when the JNR is -6dB. At the same time, compared with other methods, the False Positive Rate (FPR) for recognizing unknown class interference signals drops to 9%.
A Health Management System for Forest Musk Deer Based on YOLOv5

Forest musk deer is a national key protected species, and its population resources are mainly in captivity. However, parasitic diseases have become a major challenge in the captive breeding of these musk deer and are one of the main causes of mortality in these musk deer. To address the difficulties associated with screening and diagnosis, a parasite egg recognition model based on YOLOv5 is proposed, which use the existing computer vision techniques to detect parasites. This model aims to achieve accurate recognition of parasite eggs in forest musk deer. Specifically, the data source-based network architecture is described. The experimental results show that the mean average accuracy of the model is 0.949 in the test set. To enable practical deployment of the model, a forest musk deer health management system based on the WeChat applet is presented. The system includes several key functions such as parasite egg identification, archive management of diseased forest musk deer, and breeding knowledge base. the potential effects of model and health management system on the conservation of forest musk deer resources were discussed, and their application prospects were emphasized. In summary, this work offers a novel and effective approach to the health management of captive forest musk deer, which is critical to ensuring their survival and protecting this important resource.
HiNoVa: A Novel Open-Set Detection Method for Automating RF Device Authentication

New capabilities in wireless network security have been enabled by deep learning, which leverages patterns in radio frequency (RF) data to identify and authenticate devices. Open-set detection is an area of deep learning that identifies samples captured from new devices during deployment that were not part of the training set. Past work in open-set detection has mostly been applied to independent and identically distributed data such as images. In contrast, RF signal data present a unique set of challenges as the data forms a time series with non-linear time dependencies among the samples. We introduce a novel open-set detection approach based on the patterns of the hidden state values within a Convolutional Neural Network Long Short-Term Memory model. Our approach greatly improves the Area Under the Precision-Recall Curve on LoRa, Wireless-WiFi, and Wired-WiFi datasets, and hence, can be used successfully to monitor and control unauthorized network access of wireless devices.
Assessing Cybersecurity Hygiene and Cyber Threats Awareness in the Campus - A Case Study of Higher Education Institutions in Portugal and Poland

Cybersecurity skills are of utmost importance to prevent or mitigate the impact of cyberattacks. In higher education, there are graduations related to Information Technology (IT), where students are expected to develop technical skills, including cybersecurity. Thus, it is relevant to assess students' cybersecurity awareness regarding cybersecurity hygiene and cyber threats when they start their academic studies and to verify whether there are context-dependent differences. This paper presents the results of an assessment regarding the cybersecurity awareness level of 110 first-year students from computer science graduations from two different countries, Poland and Portugal. The assessment was designed as a survey divided into the following two main groups of questions: (1) awareness regarding cybersecurity hygiene and (2) awareness regarding major cyber threats considered in the European Union Agency for Cybersecurity (ENISA) 2021 cyber threat report. The survey results show that Polish and Portuguese students present different self-perceptions and knowledge regarding cybersecurity hygiene and knowledge of cybersecurity. In these areas, Polish students are generally more confident than Portuguese students. Also, Polish students presented better scores around 70%, against the ones obtained by the Portuguese students, scoring around 58%.
Keep Spending: Beyond Optimal Cyber-Security Investment

We introduce an efficient solution for Stackelberg games in the context of a class of Security games and bounded rational attackers. These games model a threat scenario where an attacker can launch multi-stage attacks against a defender who can deploy defensive controls subject to some budget constraints. Because the optimal solution in these games may leave some unspent budget, the question of what to do in this situation arises. In this work, we suggest investing it iteratively in the closest sub-optimal solutions until possible. Here we develop the needed theory and framework, starting from defining sub-optimality and solving the corresponding optimisations. By using total unimodularity and precise linear programming (LP) relaxation, we provide an efficient computational solution to these games. The security improvement of the proposed approach is illustrated with an AI threat scenario.
Neural Network Framework for Modulation and Signal Classification

The advancements in wireless communications have catapulted the usage of wireless technologies for plethora of use cases. This has led to large scale usage of different wireless signals for different tasks. The need to identify any particular signal is essential for further processing and information retrieval. Therefore, detecting the type of signal and modulation is crucial before we start processing it and working with it. In this paper, we propose a novel neural network that performs this task for us. We have proposed a model with low complexity, which produces exceptional accuracy on the dataset. Our model offers 99% prediction accuracy for modulation and signal classification for data having SNR. Besides, the number of parameters involved is also substantially low. Proposed model outperforms previous state of the art models in terms of accuracy and computational complexity.
Environmental Impact of Extra High Voltage Systems in Switch Yard Transmission Lines

This study article examines the impact of electric and magnetic fields generated by EHV (Extra High Voltage) transmission lines on the environment and human health. It covers the characteristics, size, and measurement techniques of magnetic and electric fields. The study investigates the direct effects of electric and magnetic fields on human health, including sensory perception, behavioral and physiological consequences, and potential risks. Additionally, it examines the effects of magnetic fields on human tissues and the findings of epidemiological studies on the relationship between magnetic field exposure and a variety of negative health impacts. The research also discusses the relationship between audible noise produced by corona and corona loss along transmission lines. This study explains the corona noise signal spectrum and how it affects our perception of sound. The article also emphasizes the impact of EHV transmission line electromagnetic noise on radio and television reception.
Service-oriented Intelligent OODA Loop

The OODA (Observe, Orient, Decide, Act) loop is a widely used decision-making model in various domains, including military operations, business strategy and cybersecurity. With the emergence of intelligent technologies, the intelligent OODA loop has gained increasing attention in recent years. However, the implementation of the intelligent OODA loop remains a challenging task as the intelligent algorithms introduce additional complexity in service implementation and service communication. To address this issue, this paper proposes to employ a service-oriented architecture for implementing the intelligent OODA loop. The proposed service-oriented intelligent OODA loop comprises several key components, including task decomposition, service description definition, intelligent service communication design, security and flow management strategies, and integration of necessary intelligent hardware. The proposed service-oriented intelligent OODA loop has the potential to provide significant benefits in improving speed, accuracy, scalability, cost-effectiveness and adaptability.
Interpretable Clinical Trial Search using Pubmed Citation Network

Clinical trials are an essential source of information for practicing Evidence-Based Medicine because they help to determine the efficacy of newly developed treatments and drugs. However, most of the existing trial search systems focus on a specific disease (e.g., cancer) and utilize disease-specific knowledge bases that hinder the adaptation of such methods to new diseases. In this work, we overcome both limitations and propose a graph-based model that explores both clinical trials and the Pubmed databases to alleviate the shortage of relevant clinical trials for a query. We construct a large heterogeneous graph (750K nodes and 1.2 Million edges) made of clinical trials and Pubmed articles linked to clinical trials. As both the graph edges and nodes are labeled, we develop a novel metapath-based similarity search (MPSS) method to retrieve and rank clinical trials across multiple disease classes. We primarily focus on consumers and users that do not have any prior medical knowledge. As there are no multiple disease-wide trial search evaluation datasets, we contribute a high-quality, well-annotated query-relevant trial set comprising around 25 queries and, on average, approximately 95 annotated trials per query. We also perform a detailed evaluation of MPSS on the TREC Precision Medicine Benchmark Dataset, a disease-specific clinical trial search setting. We make all the codes and data publicly available at https://github.com/roysoumya/MPSS-clinical-trial-search.
SleepSynth: Evaluating the use of Synthetic Data in Health Digital Twins

Health Digital Twins (HDTs) are virtual replicas of a patient’s physical/actual data. The major setbacks for applying Machine Learning (ML) in HDTs are the lack of availability of patients’ data due to privacy concerns and Artificial Intelligence (AI) bias. Given these shortcomings, synthetic data has been leveraged to solve privacy issues and increase diversity in datasets. In this paper, we evaluate four synthetic data generation models namely, Gaussian Copula, Conditional Tabular Generative Adversarial Network (CTGAN), CopulaGAN, and Tabular Variational Autoencoder (TVAE) which are used to generate synthetic data for actual sleep data retrieved from a wearable device. Gaussian Copula performed best in capturing the correlation between the variables with the real data with a quality score of approximately 96%. Additionally, we evaluate the efficacy of the synthetic generation models by training five well-known ML models on the generated synthetic data. Our experimental results show that the ML models trained on the synthetic data achieve an MAE (Mean Absolute Error) of less than 10% in the prediction of sleep quality score. The results from this work indicate that synthetic data could be used for ML tasks while preserving the privacy of data subjects.
Real Time Traffic Monitoring using YOLO V5

This research article presents a real time traffic monitoring system for identifying bike riders who are riding without helmets. The proposed approach utilizes You Only Look Once (YOLO) V5 deep learning algorithm for object detection. The objective of the study is to improve road safety by detecting potential hazards for bikers without helmets. By using the YOLO V5 algorithm, real-time object detection can be achieved, enabling timely and effective intervention. A video dataset is collected from the CCTV installed at the entrance of the college. The video contains two wheelers riding with and without helmets. The image frames are manually annotated and trained to detect helmetless bike riders. The effectiveness of the proposed methodology is assessed in terms of the mean Average Precision (mAP) calculated from various test cases.
Gas Fee Reduction by Detecting Loop Fusible Patterns in Ethereum Smart Contract

Smart contracts are full-fledged programs that runs on Blockchains. The largest Blockchain-based platform is Ethereum. It facilitates Smart Contracts, written in a programming language called Solidity, to run on the virtual machine. Ethereum charges developers an execution fee in the form of Gas used to purchase computing resources. In this paper, we discovered that the Solidity compiler could not do loop optimization, Code refactoring, and Dead code elimination. Solidity can not perform other optimization like detecting duplicate writes, inefficient use of data types, Inefficient use of global variables, etc. Furthermore, thus Smart Contracts which are not optimized cost more Gas. Smart contracts contain costly Gas byte codes, contributing to various Gas expensive patterns. In this study, we found that many Smart Contracts contain mergeable loops. Based on symbolic execution, we developed a method called GASaVER to detect merge-able loops that can merge to reduce the bytecode size and the Gas fee charged, achievable because costly Gas patterns have a sequence that can be replaced by ones with the same semantics but requires less Gas.
Generative Adversarial Network as Data Balance and Augmentation Tool in Histopathology of Breast Cancer

This paper explores Wasserstein Generative Adversarial Network Gradient-Penalty (WGAN-GP) for data balance in the medical domain where data scarcity and imbalance are common. The study applies Transfer Learning with pre-trained models from ImageNet on histopathological breast cancer data, both unbalanced and balanced. WGAN-GP was used to overcome the challenge of generating synthetic images to balance the data and improve the accuracy of the classification task. The highest results were shown by VGG16 with a balanced dataset by WGAN-GP in 300 epochs (95.40% accuracy, 96.56% sensitivity, 94.91% specificity). Results showed an improvement in accuracy from 84.25% to 95.40%.
Robust Zero-watermarking Algorithm for Medical Images Based on ORB and DCT

With the rapid development of the Internet technology field, the use of the Internet to transmit medical images has gradually become an efficient and convenient transmission method. It improves the efficiency of medical services, which allows medical institutions and doctors to share medical images and diagnostic information quickly. However, there are also some potential security risks in the transmission of medical images on the Internet, and data leakage and data tampering occur frequently. This paper proposes a zero-watermarking algorithm for medical images based on Oriented FAST and Rotated BRIEF (ORB) and Discrete Cosine Transform (DCT) to solve the above problems. Firstly, this algorithm is used to extract the feature vector of the medical image, and then the feature sequence of the medical image is obtained by combining the perceptual hashing algorithm, and the logistic map is used to perform chaotic scrambling encryption on the watermark image. Finally, the zero-watermarking technology is used to embed and extract the watermark. The experimental results show that the algorithm can effectively extract and restore the watermark under conventional attacks and geometric attacks and can effectively protect the information security of medical images, and the algorithm has good robustness and invisibility, which has a particular development prospect in the field of medical images.
One Time QR-Code for Fake Product Identification

The current supply chain contains a large number of counterfeit goods. It is essential to have a system in place that allows customers to verify all the information about the product customers are purchasing in order to determine whether or not it is authentic. Counterfeit goods have been a significant factor in the manufacturing of goods in recent years. The company name, revenue, and profit of the companies are impacted by this. The suggested method that scans QR codes to find counterfeit food items on the market. The technology links the physical product to its Firebase object by employing QR codes, which are specific to each food item. Product information like name, description, photos, and other properties can be stored using Firebase. Customers can access the items in firebase using a mobile phone app and scan the QR code to confirm the product's legitimacy and the QR-code is generated by using the QR-code-generator packages and for reading the data and scanning the QR-code using the QR-Reader and QR-Scanner packages.
Exploring the Performance and Efficiency of Transformer Models for NLP on Mobile Devices

Deep learning (DL) is characterised by its dynamic nature, with new deep neural network (DNN) architectures and approaches emerging every few years, driving the field's advancement. At the same time, the ever-increasing use of mobile devices (MDs) has resulted in a surge of DNN-based mobile applications. Although traditional architectures, like CNNs and RNNs, have been successfully integrated into MDs, this is not the case for Transformers, a relatively new model family that has achieved new levels of accuracy across AI tasks, but poses significant computational challenges. In this work, we aim to make steps towards bridging this gap by examining the current state of Transformers' on-device execution. To this end, we construct a benchmark of representative models and thoroughly evaluate their performance across MDs with different computational capabilities. Our experimental results show that Transformers are not accelerator-friendly and indicate the need for software and hardware optimisations to achieve efficient deployment.
Cloud Storage Confidentiality Using Knight Swarm Algorithm

The security of armed forces or defence data is always crucial and it is critical to maintain throughout the centuries. In the recent era, the usage of cloud storage for military applications has grown in a large scope. However, it leads the way to n number of security issues regarding tactful data. In order to address this issue, a multitude of scientific papers and algorithms have been devised to protect information and enhance its confidentiality, thereby preventing unauthorized individuals such as intruders and hackers from gaining access to it. Compared to traditional steganography methods that utilize text or image files, video steganography involves concealing feeble information within video files is one of the finest solutions to this. This method adds an extra degree of security to cloud-based military applications. Nevertheless, video steganography confronts some challenges, such as the secrecy, integrity, and complexity of video files while executing, embedding, and extracting. This study explores the use of video steganography in military applications on cloud networks and provided a methodology for improving the confidentiality and integrity of the information. The proposed Knight Swarm algorithm seeks to present a way for high data storage in AVI video files while preserving the privacy of the embedded message. In addition, this approach maintains security by preventing the secret message from being extracted without knowing the decoding rules. In military applications, video steganography must be implemented with care to ensure that the disguised data does not compromise the system's security and secrecy. It is crucial to balance the advantages of video steganography against the risks, and only authorized persons should have access to sensitive military data.