Reviewerly/exHarmony huggingface dataset:
Side-Channel Vulnerabilities in Discrete Ziggurat Sampler in Post-Quantum Cryptography

Discrete Ziggurat (DZ) method is known for its effectiveness in post-quantum cryptography with lattices. However, the side channel was found to be vulnerable to attackers. Despite various prevention techniques, the effectiveness of information leakage is still very high. In this regard, numerous and appropriate prevention methods were proposed by modifying the existing ziggurat method in a way that the attacker does not get any information from the channel. This paper critically reviews current side-channel attacks in a discrete ziggurat-based lattice cryptosystem.
Review on Arabic Handwritten Recognition Using Deep Learning and Machine Learning

Recognizing Arabic handwriting using deep learning and machine learning is a contemporary technique in digital image processing that has potential applications ranging from recognition and indexing of Arabic texts to conversion into electronic texts, and enhancing the user experience on smart devices and computers. the unique and Arabic's use of cursive writing presents a challenge for recognition systems when it comes to identifying handwritten characters, digits, and words. This paper aims to discuss the challenges of recognizing Arabic handwriting and present various approaches to address them. The research involved a comprehensive survey of 40 research papers related to Arabic handwritten recognition, covering a wide range of topics related to the subject. The team compared the accuracy achieved by different methods used in these papers, proposed future work, and the datasets used. Their findings reveal that the highest accuracy achieved was 99.88% using the k-NN algorithm in combination with filters such as HOG, LBP, and Gabor. This approach offers a thorough overview of the cutting-edge in machine learning and deep learning for Arabic handwritten recognition. This study concludes that applying machine learning and deep learning in recognizing Arabic handwriting can achieve high levels of accuracy and can be used to improve user experience and increase the efficiency of document processing. The main recommendation is to further develop algorithms and techniques that use deep learning and machine learning in recognizing Arabic handwriting. This study's findings can be utilized to develop and enhance prove Arabic handwriting recognition systems, as well as to enhance interest and identify future directions for research and development in this field.
SmartDelta project: Automated quality assurance and optimization across product versions and variants

Software systems are often built in increments with additional features or enhancements on top of existing products. This incremental development may result in the deterioration of certain quality aspects. In other words, the software can be considered an evolving entity emanating different quality characteristics as it gets updated over time with new features or deployed in different operational environments. Approaching software development with this mindset and awareness regarding quality evolution over time can be a key factor for the long-term success of a company in today’s highly competitive market of industrial software-intensive products. Therefore, it is important to be able to accurately analyze and determine the quality implications of each change and increment to a software system. To address this challenge, the multinational SmartDelta project develops automated solutions for the quality assessment of product deltas in a continuous engineering environment. The project provides smart analytics from development artifacts and system executions, offering insights into quality degradation or improvements across different product versions, and providing recommendations for the next builds. This paper presents the challenges in incremental software development tackled in the scope of the SmartDelta project, and the solutions that are produced and planned in the project, along with the industrial impact of the project for software-intensive industrial systems.
Multi-Domain Active Learning for Multi-Agent Reinforcement Learning

Multi-domain active learning (MDAL) is the process of simultaneously learning a number of models, each of which is specialized to execute a task in a certain domain, in order to decrease labeling effort by only utilizing the most useful data. In this paper, we propose a novel multi-domain active learning for multi-agent reinforcement learning (MARL) approach. The primary objective of the proposed approach is to enable learning with substantially less user input and provide users with the freedom to explore multiple possibilities in order to find the optimal strategy to maximize their reward. We employ the StarCraft 2 (SC2) learning environment to evaluate the effectiveness of our algorithm when comparing it with cutting-edge approaches. Our research results show that the recommended approach works better in all scenarios.
Fine Tuning Auto Regressive LLMs for Long Document Abstractive Summarization

Generating a short summary from a long document is a challenging task, for which new language models are still being designed and trained based on the available data. Since deep learning models are used for NLP and NLG applications, it requires high computational power to train these models. Further, fine-tuning the weights based on a given context is an important task that needs additional computation space and time. In this paper, we have used Cerebras’ wafer-scale cluster that aims at providing an efficient software and hardware infrastructure that enhances the capabilities of pre-existing models and empowers them to handle lengthy documents well. In addition to analyzing common models along with their pros and cons, other factors such as the context lengths and model sizes have also been analyzed to accommodate lengthy documents as much as possible.
Classification and Clustering of Internet of Things (IoT) for Integrating IoT Services Platform

Information technology nowadays focuses on service environments provided by the “Internet of Things or IoT” due to this rapid growth of advanced technologies such as wireless connectivity, sensors, and cloud computing. In other words, building management, smart homes, surveillance services, as well as smart farms are all examples of where IoT technology is now widely deployed. IoT is the next step in computing, connectivity, and communication, with actual items all around the earth connecting to the network and exchange information. This is an information-driven strategy. As a result, it can be considered as a rapidly developing physical network of developing physical things. Using different protocols, the devices reliably and safely transmit information obtained from their environs through the medium of communication. To solve difficulties of compatibility and interoperability, the protocols implicated are being standardised. In the system, each linked object is uniquely identified and managed. The aim of this paper is to develop classification as well as clustering system of IoT service. Primary quantitative or “survey and secondary qualitative methods” are used in this research to collect statistical and theory-based information.
Deep Learning for Automated Lesion Detection in Mammography

Deep learning for automated lesion detection in mammography has gained widespread attention due to its potential to reduce the time needed for radiologists to detect lesions accurately. Deep learning models are now being widely used in automated lesion detection in digital mammography. These models are able to classify suspicious regions that refer to symptoms of breast cancer better than classical image processing techniques. However, traditional models are limited by the lack of large public datasets that are representative of the variety of lesions that can exist in mammograms. Recent works have proposed deep learning models trained on large public datasets such as Digital Database for Screening Mammography (DDSM) and Digital Mammographic Image Archive (MI-AMI) that have achieved promising performance. These deep learning models use imaging and anatomical information for lesion detection, which results in better performance than classical approaches. Future research could extend the use of deep learning by exploring new vector-based approaches, integrating more data from mammography and clinic specialties, and performing holistic analyses to gain better insights..
A Transparent and Privacy-Preserving Job Search Platform Built on the Ethereum Blockchain Framework

As they can change and alter data governance, blockchain technologies are advancing at a faster rate and are regarded as one of the greatest technologies. These technologies are utilized in a variety of sectors, including the public sector, the supply chain, and e-government. Blockchain offers a reliable and secure method. The technology offers a framework for considerably better re-deployment of the current systems. Several nations have already embraced the use of blockchain in a variety of government operations. This research paper introduces the dApp of a blockchain-based job recruitment platform in light of the significance and influence of blockchain technology. The blockchain-based framework presented in this research study will speed up the current time-consuming hiring procedure.
Design and Analysis of Multifunctional Convertible Electric Wheelchair using a Joystick

There are millions of people who are unable to walk independently due to variety of reasons including accidents, disabilities, aging etc. Therefore, designing an autonomous wheelchair that addresses the diverse needs of individuals with mobility challenges. This paper presents an electric wheelchair which is controlled by a joystick controller in Proteus software and designed in SolidWorks. The proposed electric wheelchair can be transformed into a stretcher, providing enhanced comfort for individuals during leisure activities and at night. The user experience is further improved by the hardware implementation of a prototype electric wheelchair with capabilities for adjusting the backrest and leg rests. The multifunctionality of the wheelchair not only enhances the quality of life for individuals but also facilitates better medical treatment. The integration of multifunctional features, along with user-friendly controls and cost-effectiveness, makes this electric wheelchair an efficient solution for enhancing mobility and improving the overall wellbeing of individuals with physical disabilities.
Deep Learning Based Sentiment Analysis on Drug Reviews

Sentiment analysis has emerged as a crucial tool. Analyzing drug reviews offers useful insights on user perceptions and experiences with treatments in the field of pharmaceuticals. In order to understand the underlying feelings and emotions of consumers in the healthcare industry and conduct sentiment analysis on medicine reviews, this study applies sophisticated natural language processing (NLP) methods. The study uses an enormous database composed of medication reviews that were obtained from a number of widely used websites. To ensure reliable sentiment analysis results, the textual input is cleaned and tokenized using NLP preparation techniques. Deep learning and machine learning techniques were employed to classify evaluations as favorable, negative, or neutral. To examine how people feel about medicine reviews, we are using LSTM and BiLSTM methods with an accuracy of 85.12% and 89.3%, respectively.
A Transparent CAPTCHAS Verification System for Cloud-Based Smart and Secure Applications

In times of technological boom, web-based or internet applications use increases daily. Therefore, the need for security is increasing daily due to technological advancements. This paper presents a novel and Transparent CAPTCHA Verification System explicitly designed for cloud-based secure applications. The system generated text from image captchas using advanced deep-learning techniques. Another contribution to this system is that we utilized explainable AI (XAI) to ensure transparency throughout the working process. The generated explanations provide valuable insights into the conversion from image to text, fostering user trust and comprehension. The novel CAPTCHA Verification System has the capacity to significantly enhance the lives of individuals with disabilities (PwD) by providing an inclusive and user-centric approach for verifying their identity across diverse online platforms. By ensuring a transparent and precise transformation of image captchas into text, this system empowers PwD to effortlessly participate in cloud-based applications, enriching their digital interactions and overall well-being. This system has promising applications in various domains, especially in captcha-based verification, where it can significantly improve security and user experience.
Extreme Supervised Algorithm for Day Ahead Market Price Forecasting

Deregulation of electricity markets has ushered in a new era of heightened competition, allowing for the inclusion of fresh market entrants. However, market participation bears challenges related the extremely high volatility of prices that are affected by too many interrelated factors, such as weather conditions, production, consumption, renewable production, fuel prices, unexpected socio-political and health events, etc. Therefore, the electricity market shows unexpected changes that can lead to very high (extremely high) or very low levels (negative) prices, exposing the participants to high financial risks. Given the stochastic nature of electricity energy prices, this work employs the Extreme Learning Machine in conjunction with the Bootstrap method for forecasting electricity prices for the next day. Two distinct cases are examined. In the first case, the prediction model is trained only with historical market price data, while in the second one with forecast data. The reason this is done is to see which data gives better forecasts. The methodology was applied to German and Finnish market data, 2019–2022.
Smart Students for Smart Cities

This paper presents some criteria for having, at a given time, enough smart students to build a smart university, taking into account the university variability of the total number of students. Smart cities have smart universities, smart universities must have smart teachers and students. A holistic approach is suggested in order to implement a smart index that can be integrated for each university in smart cities, based on Lefebvre's idea “the city must be understood as a work of art”.
Vertical Federated Learning in Malware Detection for Smart Cities

Malware detection is fundamental to smart city cyberphysical systems, considering their requirements for safety which is dependent on their security. Malware detection systems extract features from software samples through static and/or dynamic analysis and classify them as malware or benign, based on the features. Modern malware detection systems employ Deep Neural Networks (DNNs) whose accuracy increases as more data are analyzed and exploited. However, data sharing among organizations, even for malware, is limited due to privacy and intellectual property constraints. In this paper we investigate the effectiveness of vertical federated learning (VFL) for developing aggregated DNNs among autonomous interconnected organizations. We analyze a solution where multiple organizations use independent malware analysis platforms as part of their Security Operations Centers (SOCs) and train their own local DNN model on their own private data. Employing a DNN, we investigate VFL scalability in terms of the number of participants, considering accuracy and training execution time as the determining factors. We evaluate the approach using the EMBER benchmark dataset and demonstrate that the accuracy of a centralized model is achieved by VLF as well, independently of the number of clients, while training execution time scales linearly for the evaluated federation sizes and remains within acceptable bounds for adoption in practice. Furthermore, we evaluate the achieved accuracy per client (federation member) demonstrating that individual accuracy is significantly smaller per client. The significant improvement in accuracy that clients achieve by participating in the federation provides strong motivation for clients to participate in the federation, while FL provides them with the necessary privacy that is achieved by sharing features rather than samples.
Architecture for Privacy-Preserving Greenhouse Gas Emissions Calculation Among Multiple Organizations Using Homomorphic Encryption

To curb global warming, the calculation of the amount of greenhouse gas (GHG) emissions must be discussed. The GHG protocol, an international GHG accounting standard, requires the calculation of indirect GHG emissions, called Scope 3 GHG emissions, throughout the supply chain. Accurate calculation of GHG emissions requires primary data, such as sensor data. However, because Scope 3 GHG emissions cross multiple organizations, the calculation of indirect emissions has some problems from the perspectives of trade secrecy, cost of aggregation, and data integrity. In this study, we propose an architecture to solve those problems using homomorphic encryption. Trade secrets are protected because GHG emissions are calculated in encrypted form. Aggregation is simplified because the calculated data are stored in the database organized for information extraction. If there is a problem with the data, it can be traced back. To evaluate the validity of the proposed architecture, we performed a case study focusing on GHG emissions from vehicle travel, which is part of a real supply chain. We experimentally evaluated the computation time at the edge and the data capacity to be communicated on the local 5G network. Consequently, our proposed architecture worked well and could be used to calculate Scope 3 GHG emissions.
Understanding Citizen Feedback of Jakarta Government Super App: Leveraging Deep Learning Models

Jakarta Kini (JAKI) is a mobile application created by Jakarta Government to facilitate the Jakarta residents towards public services. Sentiment analysis of users' reviews should be done to provide an understanding of the essence of the issues which JAKI's users face. In this paper, word cloud analysis and a comparison of several deep learning methods were done to do the sentiment analysis of JAKI's reviews. Word cloud analysis indicates the satisfaction of the users as well as the need for several improvements that should be made by the JAKI developers. Furthermore, several deep learning techniques were employed such as LSTM, BiLSTM, GRU, BiGRU, and IndoBERT since their performance is better than conventional machine learning. Results show that the IndoBERT model outperforms another model. This shows that IndoBERT can effectively be used for this Indonesian sentiment analysis task and can be used as a reference method for analyzing Indonesian reviews in JAKI and other mobile apps in Google Play Store and Apple App Store.
LSTM-Powered Point Process Modelling for Idle State Detection in Cyclic Energy Data

Electrical appliances often consume power when inactive in what is referred to as standby loss, which may be used to drive displays, remote operation response, recharging, etc. Energy efficiency is now another consumer consideration when deciding on new electrical appliances, and is drawing attention from governmental energy departments, which manufactures have reacted to improve upon through the design of more intelligent systems. Our objective is to retrofit a device to older appliances not fitted with this technology, which draw substantially more energy when active than when idle (e.g., laundry machines or televisions). With this metering in place, we aim to deliver forecasts where it would be beneficial to power down the appliance to conserve energy. Deep learning is becoming the mainstream technique to address regression problems and has demonstrated profound success. In this paper, we prototype a novel LSTM neural network, a variant of recurrent neural network renown for its capabilities in sequence modelling, to forecast the interval until the next usage in an online fashion, inspired by temporal point processes. The model is given a strong basis of offline training across a variety of devices and usage patterns for transfer before moving into an online environment, where it performs at a mean absolute error (MAE) loss of 0.157. When taken online, the model engages a novel episodic memory bank to refine its predictions for one particular machine.
Multiobjective Fuzzy Competitive Swarm Optimization for High-Dimensional Feature Selection

Feature selection (FS) is a fundamental technique in machine learning and data mining that aims to choose the most relevant and important features from a high-dimensional feature space. This process can enhance the performance and generalization ability of the classification model. However, classifying high-dimensional datasets presents challenges including high computational cost and stagnation in local optima. Evolutionary algorithms (EAs) have been widely applied in FS to mitigate these issues given their global search capabilities. In this study, we propose a multiobjective fuzzy competitive swarm optimization (MOFCSO) algorithm for FS on high-dimensional data. First, a fuzzy logic-based approach is proposed to classify the competitive particles, enabling better exploration of the search space. Then, a self-learning mechanism for failed particles is introduced to further enhance the global search. To demonstrate the proposed algorithm, we contrast it with multiple progressive algorithms. The experimental outcomes indicate the proposed algorithm is a valid method for choosing features in data with high dimensions.
An Empirical Study on Neural Networks Pruning: Trimming for Reducing Memory or Workload

Most of existing studies on neural network pruning only consider memory-based pruning strategies. However pruning for computational workload is often more important in hardware deployments due to a greater focus on model computation reductions. In addition, most pruning schemes restore model accuracy during pruning at the expense of adding hyperparameters, extending training time and training complexity. This work proposes a statistical-based globally soft iterative pruning scheme. With little extra calculation, an extremely sparse model can be obtained without additional hyperparameters and extended training time. Moreover, this work proposes the concept of computational intensity to balance model memory and computational workload during pruning. Focusing on memory orientated pruning, we can achieve <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{303}\times, \mathbf{100}\times$</tex> and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{25}\times$</tex> parameter compression on LeNet-5 (MNIST), VGG (CIFAR-10) and AlexNet (ImageNet) models, respectively. In particular, combined with cluster quantization, the LeNet-5 model parameters can be compressed to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{3232}\times$</tex> . Focusing on workload orientated pruning, we can reduce the computation by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7}.\mathbf{6}\times$</tex> on the AlexNet model, without accuracy loss, significantly higher than prior work. In addition, in order to verify the versatility of the pruning method, we also migrate the pruning task to the object detection and implement <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{10}\times$</tex> parameter compression and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{2}.\mathbf{8}\times$</tex> computation compression for YOLOv2 with reduced mAP within 1%.