Reviewerly/exHarmony huggingface dataset:
An Enhanced Forensic Analysis and Security Surveillance Using Deep Reinforcement Learning

Crime prevention relies on forensic analysis and security surveillance. Manual inspection and human skill are used in crime scene object detection and identification, which is time-consuming and subjective. This study proposes deep reinforcement learning for crime scene object detection to improve forensic analysis and security monitoring. Deep reinforcement learning, specifically Deep Q-Networks (DQNs), automates crime scene object detection and identification. We enable an agent to discover and classify things of interest by teaching it to learn from its environment. We use real crime scene photos and computer vision-generated data to train the DQN-based agent. Synthetic data augmentation strengthens and generalises the training model. A incentive mechanism encourages the agent to focus on important things and ignore background elements. We use a large crime scene image dataset to test our approach. Our deep reinforcement learning-based object identification approach is compared against Faster R-CNN and YOLO. Precision, recall, and F1-score assess object detecting accuracy and efficacy. Our experimental results show that our proposed approach outperforms standard object detection algorithms. The deep reinforcement learning-based agent can effectively identify crime scene objects of interest with higher precision, recall, and F1-score. This accuracy enhancement could boost forensic analysis and security surveillance, aiding law enforcement in crime investigation and prevention. Our technique handles complex and diverse crime scene scenarios. It accurately detects firearms, suspicious shipments, and other evidence. This research advances object detection approaches in forensic analysis and security monitoring, giving promising future developments.
Query Expansion Using Word Embedding, Ontology and Natural Language Processing

Query Expansion (QE) is the art of reconstructing specific queries to expand validation presentation, especially in the data mining process in a requirement understanding environment. Expanding requirements is one of the techniques involved in finding information. In the search engine environment, the query extension includes the evaluation of the value of the construction and the extension of search queries to match new documents. In natural language processing (NLP), word embedding is a term used in textbook parsing, usually as a real-valued vector that encodes the meaning of adjacent words in the vector. It is assumed that the space will be analogous in meaning. Word embedding can be achieved using a set of language models and point literacy methods where vocabulary words or expressions are mapped to vectors of real numbers. For query expansion, one method used is natural language processing through word embedding. Other approaches are ontology, machine learning, and deep learning for automatic query expansion. This paper proposes a hybrid approach for query expansion by combining NLP and ontology through word embedding.
Generating and Solving Mazes Using Parallel Minimum Spanning Tree Algorithms

This paper proposes a method for generating and solving mazes using minimum spanning tree algorithms and parallelizing them using OpenMP. The approach involves generating a random maze using Prim's and Kruskal's algorithms, which constructs a minimum-spanning tree from a set of edges. The maze is then solved using Dijkstra's algorithm, which finds the minimum spanning tree from a given starting point. To optimize the algorithms' performance, the parallel processing capabilities of OpenMP are utilized to distribute the workload across multiple processors. The results of the experiments demonstrate the effectiveness of the proposed method in generating and solving mazes efficiently, with significant reductions in computation time achieved through parallelization.
Deep fake Video Detection Using Unsupervised Learning Models: Review

The recent decade has been the most emerging for technical advancements in computer and communication fields. These advancements have made the task of automation much cheaper and available to every other individual at minimum or free of cost. Deep Fakes are one such advancement in the field of image processing. Deep fakes are images or videos developed using deep learning models to create fake images or videos. Although there are advantages of these deep fakes like visual training, powerful simulation, etc... There are also demerits or disadvantages highly possible with these technological advancements such as creating content that never existed, making a person speak something which one has not spoken at all. These mentioned possibilities lead to person identification theft, spreading of false or misinformation, etc... This paper is an attempt to investigate the proposed detection techniques for deep fakes. It focuses on unsupervised deep fake detection techniques and the role of ensemble models for detection.
Emerging Cyber Security and Brute Force Attacks in Hospital Management Information Systems

The information technology revolution has transformed the healthcare industry, creating a truly "information age" among hospitals and health care organizations. With the introduction of electronic medical records (EMR) and medical imaging systems, hospitals now have access to vast amounts of patient data. However, this data is not only necessary and useful, but it also creates new security concerns due to the potential sensitivity of the information it stores. As such, cyber security is of paramount importance in hospital management information systems, particularly when considering the effects of brute force attacks. Brute force attacks are a type of cyber attack that continuously guesses username and password combinations until the attacker gains access to the system. Given the variety of information stored in hospital management information systems, these attacks can result in serious data breaches and financial losses for health care organizations. In order to protect against brute force attacks, hospitals must implement robust security measures such as strong passwords, user authentication, and multi-factor authentication. Additionally, they should ensure that all patient data is kept secure and encrypted, and that the proper tools and technologies are in place to detect and respond to potential threats. By taking the necessary steps to secure hospital management information systems, health care organizations can provide quality care to their patients while minimizing the risks related to cyber security. Furthermore, through comprehensive training and continuous monitoring of systems and processes, proper security protocols can be sustained and updated as necessary. This will not only reduce threats against data security, but also help hospitals protect their valuable patient data and maintain strong trust relationships with their clients.
Constraint Inference in Control Tasks from Expert Demonstrations via Inverse Optimization

Inferring unknown constraints is a challenging and crucial problem in many robotics applications. When only expert demonstrations are available, it becomes essential to infer the unknown domain constraints to deploy additional agents effectively. In this work, we propose an approach to infer affine constraints in control tasks after observing expert demonstrations. We formulate the constraint inference problem as an inverse optimization problem, and we propose an alternating optimization scheme that infers the unknown constraints by minimizing a KKT residual objective. We demonstrate the effectiveness of our method in a number of simulations, and show that our method can infer less conservative constraints than a recent baseline method, while maintaining comparable safety guarantees.
Nonlinear Effect Model of Fiscal Expenditure on Economic Growth Based on Constrained Clustering Algorithm

In the current market, the adjustment of the market itself alone is far from being able to cope with the increasingly complex economic situation. Excessive increase in fiscal expenditure will have a crowding-out effect on private capital, weaken the private sector’s investment motivation, cause private investment to decrease, and restrict the growth of the real economy to some extent. In view of this, this paper explores the nonlinear effect of fiscal expenditure on economic growth based on constrained clustering algorithm, and builds a model in this paper. In order to verify the effectiveness of the model, further analysis is carried out through experiments. The experimental results show that when the ratio of government economic expenditure to non-economic expenditure is less than the threshold value of 70%, the estimated coefficient is 1.934, indicating that increasing fiscal expenditure has crowding-in effect on private investment. Estimating the optimal scale of fiscal expenditure and the optimal proportion of fiscal expenditure structure in India based on constrained clustering algorithm can undoubtedly provide important theoretical and empirical basis for reasonably maintaining the government’s efforts to regulate resources, scientifically and reasonably determining the scale of fiscal expenditure and judging the adjustment direction of fiscal expenditure structure in the future.
Effects on Presence, Agency, and Walking in Immersive Virtual Reality of Self-Induced and Estimated Footstep-Sounds

This paper investigates the influence of footstep sounds on the sense of presence, agency, and sensation of walking in immersive virtual reality (VR). Three scenarios were compared: (1) precisely timed footstep sounds triggered by physical steps using in-shoe sensors, (2) footstep sounds estimated based on body-tracked motion, and (3) a baseline scenario without footstep sounds. A within-group design with randomized order was used to compare these scenarios. Results show that accurate footstep sounds significantly enhance presence and agency in immersive VR. However, the scenario with estimated footsteps shows mixed results, with a slight decrease in presence and non-significant effects on agency. The sensation of walking was significantly improved regardless of the sound triggering method. Some users reported a sense of external sources for the steps in the estimated scenario. Participants also reported a shift in spatial focus, with more attention paid to the immediate environment when footsteps were present. These findings underscore the importance of realistic auditory cues to enhance immersion in VR environments.
Study of Pseudo-Random Number Generators with Feedback Shift Register

The object of this article is the development and study of properties of pseudo-random number generators with linear and nonlinear feedback shift registers. Assumptions are made about the retention of periodic properties when switching from linear to nonlinear feedbacks. This article discusses the use of stochastic adders and dynamically evolving substitution blocks in feedback circuit are considered. The statistical properties of generators of different architectures are analyzed. Solutions to improve statistical properties of pseudo-random number generators are proposed.
Myanmar Sports Sub-News Categorization Using Chi-Square, Bag of Words, and Cosine Similarity Combining with Smooth TF-IDF

In recent years, text news, unstructured data, are rapidly growing over the Internet. To automatically categorize and organize text news, text classification is used. It is applied in topic labeling, spam detection, and sentiment analysis. This paper proposes a hybrid method for automatic Myanmar sports sub-news categorization. Myanmar sports news has been categorized into 19 sports sub-news categories by combining Chi-Square and bag of words (BOW) based system and the term frequency-inverse document frequency (TF-IDF) based system. The classification method used for the systems is cosine similarity. The experimental results showed that the hybrid approach achieved better accuracy (98.85%) than the separated approaches (Chi-Square and BOW based system 96.18% and TF-IDF based system 93.13%) without hybridization.
Efficient Indian Sign Language Interpreter For Hearing Impaired

Indian Sign Language (ISL) is the principal mode of communication for most dumb and deaf citizens nationwide. ISL uses signs of movements of the hands, faces, or body postures to transmit the needed message and emotion. These signs may be read by those that are unaware of ISL. It is a fully developed natural language that has its lexicon as well as its syntax. Translation systems are essential to eliminate the communication barrier between the community of deaf and hard-of-hearing people. To facilitate effective two-way communication between the hearing-impaired and the general population of society, the proposed system is an end-to-end human interface architecture capable of recognizing and understanding spoken language and performing the necessary decoding of ISL signals. Results show that the proposed system performs well in recognizing Indian sign language.
Development of a Unified Map of the Region Using AI Technology

This paper presents, the algorithms of implementation of a unified regional map, which includes the function of independent addition of regional sites of interest through artificial intelligence technology, as well as the function of automatic generation of recreation routes based on user preferences.
Intelligent Docker Container Orchestration for Low Scheduling Latency and Fast Migration in Paas

Most modern cloud Platform as a Service (PaaS) systems are built on the basis of application containerization technology. The containerization schedulers currently in use do not have intelligent algorithms for placing containers across nodes. They work on the principle of placing in the “first suitable slot”. This article discusses two issues related to intelligent decision-making about moving containers between servers: the lethality of obtaining metrics for monitoring the current state of the platform and taking into account the influence of container deployment parameters on the speed of transformations. The article proposes a way to determine the necessary and sufficient number of monitoring services for latency management. The paper also considers the influence of microservices launch parameters, the size of the container image on the time of transferring the container between nodes, and proposes a model for assessing the complexity of transferring containers between nodes.
Advanced Fog Neural Network Design Method

The problem of implementing neural networks on low-performance devices has been considered in many works by various authors around the world. These papers present methods and algorithms for the implementation of neural networks on microcontrollers, FPGAs and other devices. In previous works, method for dividing artificial neural network into block neural network was described, as well as method for synthesizing neural network devices for fog computing. The purpose of this work is to improve the method of synthesizing artificial neural network devices in the direction of increasing the fault tolerance of these devices. The research methodology is based on mathematical modeling of a neural network, circuit modeling of a cascade of neural network devices, analysis of the experimental results for fault tolerance. As a result of the research, we improved neural network devices synthesis method in terms of fault tolerance.
A Trajectory Based Optimization Approach for Hybrid Observer Design

This paper presents a study on developing a hybrid 3D position observer for a rover with acceleration and relative distance measurements. The observer design utilizes two different methodologies; a Trajectory Based Optimization Design (TBOD) and a Linear Matrix Inequality (LMI) method. We prove that, under the proposed solutions, the boundedness of the estimation error is guaranteed. The performance of the observer is evaluated and compared to a standard EKF using comprehensive Monte Carlo simulations.
Sigmoid-based Neuron Pruning Technique for MLPs on IoT Edge Devices

Efficient and accurate deployment of Multilayer Perceptron (MLP) models on Internet of Things (IoT) edge devices is a critical challenge due to resource limitations and power constraints. In this paper, we propose a novel sigmoid-based neuron pruning technique specifically designed for MLPs on IoT edge devices. The technique selectively prunes redundant neurons that contribute minimally to the network’s performance, resulting in reduced computational complexity, memory usage, and power consumption, while maintaining comparable accuracy levels to the original model. Our approach involves training a simple MLP network with an input layer, hidden layer, and output layer on the cloud. Model weights and parameters are quantized to 8-bit fixed-point representation to reduce memory requirements. During the forward pass of the MLP, mean values of the hidden layer’s output are calculated, and neurons with mean values falling within a predefined range are identified as candidates for pruning. The pruning process is implemented using the sigmoid activation function, modifying the output values of the identified neurons accordingly. Neurons with mean values within a narrow range of around 0.5 are considered for pruning. To minimize accuracy loss while reducing computational complexity, the output values of these neurons are set to an optimal approximation value, typically chosen close to the mean value of the neuron’s output. The optimal approximation value is determined iteratively to achieve the best accuracy. Our proposed sigmoid-based neuron pruning technique offers several novel contributions. We observe a savings of 37.14% of multipliers and adders with a 1.5% loss in model accuracy on average for three benchmark datasets of Iris flower classification, Breast Cancer classification, and MNIST handwritten dataset classification.
Class-Balanced by SMOTE &amp; Filtering Mechanism Combined with XGBoost Algorithm for Classifying Imbalanced Data

In the modern era, the classification dilemma of Imbalanced Datasets (IDs) has received a sustained interest, both in practical and theoretical aspects. One widely used Oversampling (OS) approach to handle Imbalanced Classification (IC) is the "Synthetic Minority Over-sampling Technique (SMOTE)". Over the past decade, researchers have proposed various SMOTE-based approaches, such as Borderline SMOTE (B-SMOTE), GSMOTE, SVM SMOTE, and among others. In this paper, we propose the Class-Balanced by SMOTE & Filtering combined with XGBoost (BSF-XGBoost) approach to address highly imbalanced datasets. This prototype simultaneously tackles feature selection with different filtering mechanisms and the class imbalance dilemma. The XGBoost approach offers several hyperparameters that focus more on the Minority Class (Minc) misclassification through the training stage. To evaluate the performance of the BSF-XGBoost algorithm, we conducted experiments on 10 highly imbalanced binary-class datasets, employing existing classification models, and comparing the results with various OS approaches. Finally, the findings indicate that our proposed algorithm exhibits slightly better performance compared to other existing classification approaches and variants of SMOTE-based methods.
QR Code Authentication System as an Ultimate Tool for Personal Cybersecurity

According to various research, the human factor plays a drastic part in cybersecurity concerns as people tend to be negligent towards their personal data and credentials. The article analyzes some of the existing authentication methods and proposes one of the possible solutions to this problem – a universal proxy service that handles credentials and securely delivers these on-demand via QR (Quick Response) code system. This software splits into three parts: mobile application (password manager), web application, and developer tool for third-party integration. This article is an extended version of a previous one “Development of QR-code Authentication Software as Internet-Service”
Asymptotically Optimal Finite-Dimensional Approximations for Linear Filtering with Infinite-Dimensional Measurements

This work proposes a novel approach to approximate optimal linear filters for discrete-time linear Gaussian systems with infinite-dimensional measurements and finite- dimensional states. Assuming scalar-valued states for simplicity, we formulate the problem in terms of optimally selecting <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> points at which to sample the infinite-dimensional measurement, in order to minimize the mean-squared filtering error. We show that for large N, this problem can be expressed using the notion of an asymptotic point density function from the field of high-resolution quantization theory. To the best of the authors' knowledge, this method has not been considered in infinite- dimensional filtering previously. This leads to a characterization in terms of an Urysohn integral equation, which can be solved numerically to yield an asymptotically optimal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$N$</tex> -point filter. The mean-squared approximation error is proportional to N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , which is faster than the typical N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> decay of high-resolution quantization and suggests that this approximation method will be useful even for moderate or small N. These properties are verified by simulations based on a linearized pinhole camera measurement model.
Semantic Text Summarization for Myanmar Language using Word Embedding and Graph Based Approach

Text summarization is shortening a set of data computationally, that represents the most important or informative information within the original content. With the increment of information on the internet daily, people are expecting to read documents and texts without having to encounter unnecessary information. Text summarization of other languages have improved, but there are still many challenges for the Myanmar text summarization systems are mainly based on frequencies of words, and these systems are not equivalent in a semantic way. They cannot solve the ambiguity of the Myanmar language. The text has many meanings further the literal meaning of the words. It is very hard for computers to interpret the meaning of the words. Semantic assists in extracting important information from achieving human level understanding from the computers. This paper proposed a semantic Myanmar text summarization system that is mainly based on the integration of the word embedding model and TextRank algorithm for inter-sentence relation. Graphs are everywhere in Natural Language Processing (NLP) and a robust representation of formalism. A graph-based approach for text summarization is an unsupervised technique, is much more comfortable to visualize and is more popular than another summarization. The proposed model accuracy reached an F1 score of 0.6 on average.