Reviewerly/exHarmony huggingface dataset:
Honeypot Detection and Classification Using Xgboost Algorithm for Hyper Tuning System Performance

The purpose of this research paper is to detect and classify the hidden honeypots in Ethereum smart contracts. The novelty of the work is in hypertuning of parameters, which is the unique addition along with classification. Nowadays, blockchain technologies are the grooming technologies. In the current trend, the attackers are implementing a new strategy that is much more proactive. The attackers attempt to dupe the victims by sending seemingly vulnerable contracts containing hidden traps. Such a seemingly vulnerable contract is called a honeypot. This work aims to detect such deployed honeypots. A tool named Honeybadger has been presented. It is a tool that uses symbolic execution to detect honeypots by analyzing contract bytecode. In this system, we consider different cases such as fund movement between the contractor and contract, the transaction between sender and participant, and several other contract features in terms of source code length and compilation information. In the methodology used, the features are then trained and classified using a machine learning algorithm (XGBoost and gradient boosting with hyper tuning) into Balance Disorder, Hidden State Update, Hidden Transfer, Inheritance Disorder, Skip Empty String Literal, Straw Man Contract, Type Deduction Overflow, and Uninitialized Struct. Through this algorithm, we developed a machine-learning model that detects and classifies the hidden honeypots in Ethereum smart contracts. Hypertuning of parameters is the unique addition along with classification that separates the rest of the studies done in this area.
CroPAiD: Protection of Information in Agriculture Cyber-Physical Systems Using Distributed Storage and Ledger

The agricultural domain has had a significant role throughout history in human societies across the globe. With the fast growth of communication and information systems, the structure of farming procedures has evolved to new modern standards. Although multiple features helped gain from these advancements, there are many current and rising threats to security in the agricultural domain. The present paper gives novel methods and architectural designs and implements distributed ledger through the Tangle platform. Initially, the article discusses the threats and vulnerabilities faced in the farming sector and presents an extensive literature survey, and later conducts an experiment for distributing data through a tangle distributed ledger system. The authors highlight the limitations of central, cloud, and blockchain and suggest mitigation measures through distributed IOTA systems and distributed storage facilities for data and the possible influence these solutions can bring in the aspects of data security in the agricultural sector.
Fastensor: Optimise the Tensor I/O Path from SSD to GPU for Deep Learning Training

In recent years, benefiting from the increase in model size and complexity, deep learning has achieved tremendous success in computer vision (CV) and (NLP). Training deep learning models using accelerators such as GPUs often requires much iterative data to be transferred from NVMe SSD to GPU memory. Much recent work has focused on data transfer during the pre-processing phase and has introduced techniques such as multiprocessing and GPU Direct Storage (GDS) to accelerate it. However, tensor data during training (such as Checkpoints, logs, and intermediate feature maps), which is also time-consuming, is often transferred using traditional serial, long-I/O-path transfer methods. In this article, based on GDS technology, we built Fastensor, an efficient tool for tensor data transfer between the NVMe SSDs and GPUs. To achieve higher tensor data I/O throughput, we optimized the traditional data I/O process. We also proposed a data and runtime context-aware tensor I/O algorithm. Fastensor can select the most suitable data transfer tool for the current tensor from a candidate set of tools during model training. The optimal tool is derived from a dictionary generated by our adaptive exploration algorithm in the first few training iterations. We used Fastensor’s unified interface to test the read/write bandwidth and energy consumption of different transfer tools for different sizes of tensor blocks. We found that the execution efficiency of different tensor transfer tools is related to both the tensor block size and the runtime context. We then deployed Fastensor in the widely applicable Pytorch deep learning framework. We showed that Fastensor could perform superior in typical scenarios of model parameter saving and intermediate feature map transfer with the same hardware configuration. Fastensor achieves a 5.37x read performance improvement compared to torch.save () when used for model parameter saving. When used for intermediate feature map transfer, Fastensor can increase the supported training batch size by 20x, while the total read and write speed is increased by 2.96x compared to the torch I/O API.
An Efficient and Secure Mechanism for Ubiquitous Sustainable Computing System

Internet of Things (IoT) devices are frequently utilized to collect information around humans’ daily routine, producing a need for them to regularly pair with each other. With the increasing interest in digitizing human’s natural environment and evolution of advanced application scenarios, the wireless communication networks have turned into a key player for IoT devices. As IoT devices are resource-constrained and transmit the perceived information regularly to its corresponding participant, it is mandatory for the devices to adopt a lightweight authentication scheme to overcome their limited energy availability and avoid security and privacy issues in ubiquitous sustainable computing system. Researchers have proposed protocols for IoT devices in wireless communication networks, many of which neglect numerous serious security weaknesses such as loss of identity preservation, vulnerability to replay, Man-in-the-Middle, eavesdropping attacks, and loss of key secrecy. Additionally, various security and efficiency threats in the proximity-based device authentication scheme, such as device cloning and identity loss, have a large signaling overhead. Furthermore, we evaluate the performance of the proposed schemes in terms of computation, communication and storage overhead. The results illustrate the implementation advantages and suitability of the proposed schemes for low-powered devices compared to existing protocols.
Adversarial Attacks on Resource Management in P2P Wireless Communications

This paper explores adversarial attacks on a Graph Neural Network (GNN) based radio resource management in point-to-point (P2P) communications. The trained GNN model, which receives information from transceiver pairs, is targeted during the test phase. The paper introduces a novel adversarial attack that modifies the vertices of the GNN model, taking into account various constraints. The attack’s effectiveness is evaluated based on the number of users and signal-to-noise ratio (SNR). The proposed attack formulates optimization problems aimed at minimizing system communication quality, incorporating specific constraints.
ULEEN: A Novel Architecture for Ultra-low-energy Edge Neural Networks

‘‘Extreme edge” 1 devices, such as smart sensors, are a uniquely challenging environment for the deployment of machine learning. The tiny energy budgets of these devices lie beyond what is feasible for conventional deep neural networks, particularly in high-throughput scenarios, requiring us to rethink how we approach edge inference. In this work, we propose ULEEN, a model and FPGA-based accelerator architecture based on weightless neural networks (WNNs). WNNs eliminate energy-intensive arithmetic operations, instead using table lookups to perform computation, which makes them theoretically well-suited for edge inference. However, WNNs have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by binary neural networks (BNNs) to make significant strides in addressing these issues. We compare ULEEN against BNNs in software and hardware using the four MLPerf Tiny datasets and MNIST. Our FPGA implementations of ULEEN accomplish classification at 4.0–14.3 million inferences per second, improving area-normalized throughput by an average of 3.6× and steady-state energy efficiency by an average of 7.1× compared to the FPGA-based Xilinx FINN BNN inference platform. While ULEEN is not a universally applicable machine learning model, we demonstrate that it can be an excellent choice for certain applications in energy- and latency-critical edge environments.
Wearable Technology Usage in Race Walking

The use of temporal sensor data related to human activity has greatly increased in recent years thanks to smart technology has become widespread and even ubiquitous in our daily lives. Today wearable technology has many roles in our daily life. Wearable technological products allow us to easily obtain a lot of data in the field of sport and health. This data can make it possible to monitor physical condition, athletic performance, training load and can even be used to prevent injury mechanisms or to analyse rehabilitation. Athletics is one of the sports in which performance is tracked metrically and chronometrically. The only exception is race walking which based on subjective observation of the judges in this sport. There is a need for the development and use of wearable technological devices that can support referee judgment in walking and can be used for fair decisions in disqualification and penalty time applications during the competition.
Hierarchical Synergy-Enhanced Multimodal Relational Network for Video Question Answering

Video question answering (VideoQA) is challenging as it requires reasoning about natural language and multimodal interactive relations. Most existing methods apply attention mechanisms to extract interactions between the question and the video or to extract effective spatio-temporal relational representations. However, these methods neglect the implication of relations between intra- and inter-modal interactions for multimodal learning, and they fail to fully exploit the synergistic effect of multiscale semantics in answer reasoning. In this article, we propose a novel hierarchical synergy-enhanced multimodal relational network (HMRNet) to address these issues. Specifically, we devise (i) a compact and unified relation-oriented interaction module that explores the relation between intra- and inter-modal interactions to enable effective multimodal learning; and (ii) a hierarchical synergistic memory unit that leverages a memory-based interaction scheme to complement and fuse multimodal semantics at multiple scales to achieve synergistic enhancement of answer reasoning. With careful design of each component, our HMRNet has fewer parameters and is computationally efficient. Extensive experiments and qualitative analyses demonstrate that the HMRNet is superior to previous state-of-the-art methods on eight benchmark datasets. We also demonstrate the effectiveness of the different components of our method.
Detection of Aircraft in Satellite Images using Multilayer Convolution Neural Network

The automatic identification of various spatial entities within satellite imagery is a crucial undertaking for interpreting such images. Numerous research papers have explored the segregation, identification, and geolocation of objects of interest, such as airplanes, vehicles, and human elements, within satellite images. The detection of aircraft from satellite imagery is particularly significant for gathering operational intelligence. Detecting aircraft within the environment is achieved through active remote sensing methods, such as RADAR and LASER. Various algorithms have been devised and developed specifically for aircraft detection in satellite imagery. The advent of AI-based techniques has brought about a transformative shift in object detection within remotely sensed images. This paper proposes a methodology employing a convolutional neural network (CNN) for the detection of aircraft within satellite imagery. Initially, an image dataset is generated using QGIS software, which is then partitioned into training and testing datasets. A multi-layered CNN model is employed to train and evaluate the dataset. Subsequently, the trained CNN is applied to remotely sensed images to detect the presence of aircraft within the scene. The aircraft detection accuracy from randomly selected satellite images is reported to be 95%.
Standardization of Extended Reality (XR) over 5G and 5G-Advanced 3GPP New Radio

Extended Reality (XR) is one of the major innovations to be introduced in 5G/5G-Advanced communication systems. A combination of augmented reality, virtual reality, and mixed reality, supplemented by cloud gaming, revisits the way that humans interact with computers, networks, and each other. However, efficient support of XR services imposes new challenges for existing and future wireless networks. This article presents a tutorial on integrating support for the XR into the 3GPP New Radio (NR), summarizing a range of activities handled within various 3GPP Service and Systems Aspects (SA) and Radio Access Networks (RAN) groups. The article also delivers a system level simulation study evaluating the performance of different XR services over NR Release 17. The article concludes with a vision of further enhancements to better support XR in NR Release 18 and future NR releases also outlining open problems in this area.
Validation of the medium-range and sub-seasonal forecast of solar irradiance and wind speed using ECMWF

Medium-range and sub-seasonal forecasts of solar irradiation and 10-m wind speed are commonly assessed with an ensemble forecast over the Indo-China region. In this paper, we forecast data of surface solar irradiance and 10-m wind using ensembles of forecasts from the European Centre for Medium-Range Weather Forecasts operational ensemble (ECMWF; EC-ENS) with a 6-h time-step. We compare our forecasts with observations derived from the China Meteorological Agency (CMA) as gridded observations over the Indo-China region. The assessment is carried out on both mid-range and sub-seasonal forecasts over Indo-China domain wise, using several metrics to assess different attributes of forecast quality. The results show that the errors of the forecasts depends on the choice of the climate variable, the period of the year. The major source of energy on Earth is solar irradiation and wind, and the amount of solar irradiation and wind hitting the Earth's surface is impacted by the atmosphere. Solar irradiation also plays an important role in global temperature, and even minor variations in the sun's energy output create significant changes in the earth's climate. All data cover a period from June 2021 to May 2022 in hourly and six-hourly total scales for CMA and EC-ENS. The validation demonstrates that the mean absolute error, root mean square error, and mean bias error amongst datasets are all different. Furthermore, the spatially continuous hourly predictions accurately capture regional variations and reconstruct the diurnal cycles of solar irradiation and wind on fine scales. Such accurate knowledge is useful for the prediction of site selection for solar and wind power plants and research on regional climate changes.
Block-Privacy: Privacy Preserving Smart Healthcare Framework: Leveraging Blockchain and Functional Encryption

Early adoption of Internet of Medical Things (IoMT) are enhancing the healthcare sector in all directions. Though the advances are adding advantages to the existing systems, the security and privacy of medical data remain a challenge. The increase in IoMT and mobile healthcare devices presence on untrusted networks can make the situation more complicated for healthcare system users. Moreover, they are pushing critical data to centralized locations like cloud, where the patient lacking control on his data. In this regard, a secure IoT framework is desirable which is capable of preserving the integrity and confidentiality of the medical data. Due to this, we proposed a novel architecture which leverages blockchain, IPFS, zero-knowledge protocols, and functional encryption technologies to provide decentralised healthcare system privacy and security. The proposed system helps the healthcare system administrators maintain data confidentiality, availability, integrity, and transparency over an untrusted peer-to-peer network without any human interference. Moreover, the system eliminates the requirement for a centralised server for functional encryption operations using hybrid computing paradigms. Finally, the proposed system suggests a novel mechanism to minimise the latency in data sharing over the network without compromising data security and privacy. To describe the working principle of this architecture a logical analysis is carried out which shows that the system is capable of providing the desired security and privacy.
Optimization of Sparsity-Constrained Neural Networks as a Mixed Integer Linear Program

Abstract The literature has shown how to optimize and analyze the parameters of different types of neural networks using mixed integer linear programs (MILP). Building on these developments, this work presents an approach to do so for a McCulloch/Pitts and Rosenblatt neurons. As the original formulation involves a step-function, it is not differentiable, but it is possible to optimize the parameters of neurons, and their concatenation as a shallow neural network, by using a mixed integer linear program. The main contribution of this paper is to additionally enforce sparsity constraints on the weights and activations as well as on the amount of used neurons. Several experiments demonstrate that such constraints effectively prevent overfitting in neural networks, and ensure resource optimized models.
A Movie Recommender System Based on User Profile and Artificial Bee Colony Optimization

In this study, a new algorithm for recommending movies to viewers has been proposed. To do this, the suggested method employs data mining techniques. The proposed method includes three steps for generating recommendations: "preprocessing of user profile information," "feature extraction," and "recommendation." In the first step of proposed method, the user information will be examined and transformed into a form that can be handled in the next phases. In the second step of the proposed method, user attributes are then extracted as a collection of their individual qualities, as well as the average rating of each user for various genres. The bee colony optimization algorithm is then used to select the optimal features. Finally, in the third step of the proposed method, the ratings of similar users are utilized to offer movies to the target user, and the similarities between various users are determined using the characteristics calculated for them, as well as the Euclidean distance criteria. The proposed method was evaluated using the MovieLens database, and its output was assessed in terms of precision and recall criteria; these results show that the proposed method will increase the precision by an average of 1.39% and the recall by 0.8% compared to the compared algorithms.
ПОВЫШЕНИЕ УСТОЙЧИВОСТИ ПРОВЕДЕНИЯ НАУЧНЫХ ИССЛЕДОВАНИЙ И СОБЛЮДЕНИЕ ПРИНЦИПОВ АКАДЕМИЧЕСКОЙ ДОБРОЧЕСТНОСТ В СИСТЕМЕ ВЫСШЕГО ОБРАЗОВАНИЯ

Modern institutional transformations in science as a sphere of human cognitive activity, awareness of the tasks and functions of science, criteria for recognizing the results of scientific activity make it necessary to improve and further develop the proc
Implementation and Deployment of Privacy Preservation and Secure Data Storage Techniques in Cloud Computing

Successful cloud computing (CC) lets businesses and people outsource data processing, storage, and access. CC has numerous advantages but new privacy and security risks. Data owners lose control over their data at external providers, making it subject to misuse, distribution, and access. The current data security method involves encryption. Encryption increases computing complexity, especially when data is dispersed throughout several CSP servers. Simple secrecy technologies include encryption and fragmentation. HSSOA-FEW is a hybrid sparrow search optimisation method for cloud security employing fragmentation and encryption. HSSOA-FEW holds data on CSP server with minimal encryption. HSSOA-FEW considers security and data storage. HSSOA-FEW also included sparrow search algorithm (SSA) with particle swarm optimisation. Additionally, HSSOA-FEW uses fused encryption and decryption. HSSOA-FEW controls and secures cloud servers. The suggested HSSOA-FEW system is tested extensively to improve performance. The experiments indicated HSSOA-FEW outperformed others.
Privacy-Preserving Federated Machine Learning Techniques

Machine learning is increasingly used for data analysis, but centralized datasets raise concerns about data privacy and security. Federated learning, a distributed method, enables multiple entities to cooperatively train a machine learning model. Clients use their local datasets to train local models, while a central aggregator aggregates updates and computes a global model. Privacy-preserving federated learning (PPFL) addresses privacy issues in sensitive and decentralized data situations. PPFL integrates federated learning with privacy-preserving approaches to achieve both privacy and model correctness.
A Survey on Privacy Preserving and Trust Building Techniques of Blockchain-Based Systems

Blockchain technology has gained significant attention in recent years due to its decentralized and immutable nature, and has made itself ideal for various applications such as finance, supply chain management, and healthcare. However, privacy and trust issues have emerged as major challenges in blockchain-based systems/applications. This work provides a detailed explanation of the existing privacy-preserving and trust-building techniques in blockchain-based systems. The authors begin by discussing the fundamental concepts of blockchain technology and its features that affect privacy and trust. Further, they discuss the privacy challenges faced by blockchain systems. Various privacy-preserving techniques, such as encryption, zero-knowledge proofs, and mix-zones, are explained in detail (with highlighting their strengths and limitations). In addition to privacy, trust is an important aspect of blockchain-based systems.
Enhancing Credit Card Fraud Detection and Prevention

This chapter explores using auto-encoders and attention mechanisms to enhance privacy-preserving capabilities of federated machine learning for credit card fraud detection. The proposed system uncovers latent features in credit card transactions, leveraging distributed training for user privacy. Empirical evaluation on real-world data shows its proficiency in identifying fraud. The method offers privacy preservation, scalability, and resilience. The model's performance across imbalanced and balanced datasets highlights the role of balanced data in optimizing fraud detection. This approach integrates accuracy, privacy preservation, and security. Considering fraudsters' sophistication, this research introduces a strategy to counter credit card fraud while preserving confidentiality. In summation, this chapter presents a framework for deploying privacy-focused federated machine learning in credit card fraud detection and prevention, fostering privacy-preserving applications across domains.