Reviewerly/exHarmony huggingface dataset:
Regulating the Technology (Placement)

Abstract This chapter examines the placement phase of MLFT. This chapter starts in Sect. 5.2 by examining the definition of virtual wallets under the EU Anti-Money Laundering Directive. Section 5.2 examines the definition of entity under this Directive. It concludes that whilst the term entity is broadly formulated it creates risk by excluding the non-custodian wallets. Section 5.3 therefore discusses whether non-custodian wallets should be supervised or remain anonymous. This section concludes that non-custodian wallets should supervise itself. Section 5.4 then continues by discussing how these non-custodian wallets can supervise themselves through smart assets and AI. Section 5.5 then continues by analysing the legal requirements for such a supervisory approach. This approach includes creating a digital legal personality for the digital wallet. The digital legal personality could be used to create an insurance system for such wallets in case of damages. In such a case the insurer would have the power to represent the wallet in legal proceedings. Section 5.6 then continues by discussing how such a system could aid in the prevention of digital scams and how transactions to third-countries should be incorporated. Section 5.7 continues by discussing incorporating smart contracts into the regulatory system. Particular attention is paid to regulating the criminal smart contracts (csc). Section 5.8 provides a conclusion and recommendations.
Aspect-Based Sentiment Analysis Using Local Context Focus Mechanism with DeBERTa

Text sentiment analysis, often termed as opinion mining, delves into quantifying individuals' opinions, evaluatio ns, attitudes, and emotions conveyed about entities. Sentiment a nalysis of text can be categorized into text-level, sentence-level, and aspect-level analyses. Aspect-Based Sentiment Analysis (A BSA) represents a detailed sub-discipline within sentiment anal ysis, with its primary goal being to ascertain the sentiment pola rity of specific aspects. The research of pre-training neural mod el has significantly improved the performance of many natural language processing tasks. In recent years, pre training model (PTM) has been applied in ABSA. Therefore, there has been a q uestion, which is whether PTMs contain sufficient syntactic info rmation for ABSA. In this paper, we explored the recent DeBE RTa model (Decoding-enhanced BERT with disentangled attention) to solve Aspect-Based Sentiment Analysis problem. DeBE RTa is a kind of neural language model based on transformer, which uses self-supervised learning to pre-train on a large num ber of original text corpora. Based on the Local Context Focus (LCF) mechanism, by integrating DeBERTa model, we purpos e a multi-task learning model for aspect-based sentiment analys is. The experiments result on the most commonly used the lapto p and restaurant datasets of SemEval-2014 and the ACL twitte r dataset show that LCF mechanism with DeBERTa has signifi cant improvement.
Federated Learning Optimization for Energy Communities in Smart Cities

The enhancement of the parameterization of a Federated Learning (FL) framework for energy communities in Smart Cities (SCs) is crucial to its effectiveness. FL is a framework that allows community members to collaborate to develop better prediction models without the need to share personal critical data information and, therefore, ensure data privacy. In the context of SCs, energy communities are more susceptible to exposing one of the disadvantages of FL: local prediction models lose the capacity of adaptation for each member. This happens because the hyperparameters of such models are equal for all. Since the net demand profiles are strongly influenced by the loads in each building, each prediction model should be parameterized building by building. This work proposes a methodology with two stages using a metaheuristic approach adapted to the context of energy communities in SCs to enhance the FL prediction system. The first stage is the computation of the local hyperparameters, over the local prediction models. Then, the second stage ensures a trade-off between the adaptivity and local accuracy of the prediction models. This methodology was tested using a FL framework customized to the main features of energy communities. A community of 200 buildings, with an integrated system of local renewable generation, was used to test the proposed approach, showing an improvement in the reliability of the day-ahead prediction of the net demand.
SecFob: A Remote Keyless Entry Security Solution

All major vendors currently offer Remote Keyless Entry (RKE) systems for passenger vehicles, which have become a standard feature of modern cars. These systems enable wireless communication between drivers and vehicles, facilitating convenient functions such as remote start and passive keyless entry. Unfortunately, the existing security measures implemented in these systems are often inadequate, leaving them vulnerable to intrusions and a wide array of attacks. To address these vulnerabilities, we present SecFob, a solution that combines quantum random number generation (QRNG), machine learning, and encryption techniques. SecFob is designed to defend against common attack techniques by leveraging the perfect entropy offered by QRNG and by incorporating machine learning techniques, specifically utilizing the Siamese Neural Network. These solutions are cost-effective, portable, and with the use of ML technique, are able to detect amplified signals.
IoT-Paradigm: Evolution Challenges and Proposed Solutions

The tremendous increase of using intelligent devices brought by the Internet of Things technology was a success to change the shape and nature of provided services in the digital world. That paradigm is based on sensors, actuators, and other smart devices that collect process, and store data. The variety of smart devices, platforms, applications, and protocols used in different IoT systems led to the emergence of many challenges. In this paper, the authors highlighted the most critical four challenges: security, interoperability, heterogeneity, and scalability. A literature review of the latest proposed solution is conducted and an analysis of the effectiveness of these approaches is provided. As a result of the analysis, no one proposed solution can satisfy the requirements for solving obstacles, since each approach deals with a specific component of the IoT system. The authors concluded that the solution should start with developers to produce more capable, adaptable, and compatible devices that meet the required and emergency requirements besides the need for unified standards to govern the deployment of IoT systems.
Hardness of Learning AES with Gradient-Based Methods

We show the approximate pairwise orthogonality of a class of functions formed by a single AES output bit under the assumption that all of its round keys except the initial one are independent. This result implies the hardness of learning AES encryption (and decryption) with gradient-based methods. The proof relies on the Boas-Bellman type of inequality in inner-product spaces.
A Robust Remote Client Password Authentication Technique for Smart Cities E-Governance Applications

Smart cities are fast proliferating the global space in recent times. In order to achieve the envisioned next-generation cities, the smart city paradigm aspires to develop a completely integrated network-based infrastructure. With the development of innovative smart cities, ICT-based e-governance has made it possible for services to be delivered intelligently and interactively via online platforms. However, the communication between government agencies and other stakeholders in the smart city ecosystem is through open channels, which poses serious security concerns. In order to address this problem, this work proposes a robust remote client password authentication technique for e-governance applications in smart cities. The proposed authentication technique is lightweight, resistant to several attacks, and achieves low computational overhead.
Fall Detection Using Intelligent Walking-Aids and Machine Learning Methods

Walking aids are commonly given to older adults to prevent falls, but paradoxically, their use has been identified as a risk factor for falling, which is a prevalent issue among this population, causing serious injuries, disabilities, and even death. This has resulted in a significant increase in public health care and the development of remote health monitoring technology to enhance home care devices. One of the key issues being addressed is the identification of falling incidents, which can aid in the rapid arrival of assistance and prevent additional harm. This paper is to develop intelligent walking aids using machine learning methods and an M5Stack Core2 microcontroller. These aids analyze information from various sensors such as accelerometers and gyroscopes to identify falls. Touch and location sensors are also utilized to determine the device's usage and location in case of an emergency. The collected data is sent to a web server in JSON format via the M5Core2's WiFi module, allowing for a quick response if necessary. The fall detection system has been extensively tested, resulting in 99.62% accurate identification of falls.
Prodigy: Towards Unsupervised Anomaly Detection in Production HPC Systems

Performance variations caused by anomalies in modern High Performance Computing (HPC) systems lead to decreased efficiency, impaired application performance, and increased operational costs. While machine learning (ML)-based frameworks for automated anomaly detection (often based on time series telemetry data) are gaining popularity in the literature, practical deployment challenges are often overlooked. Some ML-based frameworks require extensive customization, while others need a rich set of labeled samples, none of which are feasible for a production HPC system.
Improving Privacy of Anonymous Proof-of-Stake Protocols

The proof of stake (PoS) mechanism, which allows stakeholders to issue a block with a probability proportional to their wealth instead of computational power, is believed to be an energy-efficient alternative to the proof of work (PoW). The privacy concern of PoS, however, is more subtle than that of PoW. Recent research has shown that current anonymous PoS (APoS) protocols do not suffice to protect the stakeholder’s identity and stake, and the loss of privacy is theoretically inherent for any (deterministic) PoS protocol that provides liveness guarantees. In this paper, we consider the concrete stake privacy of PoS when considering the limitations of attacks in practice. To quantify the concrete stake privacy of PoS, we introduce the notion of $$(T, \delta , \epsilon )$$ -privacy. Our analysis of $$(T, \delta , \epsilon )$$ -privacy on Cardano shows to what extent the stake privacy can be broken in practice, which also implies possible parameters setting of rational $$(T, \delta , \epsilon )$$ -privacy for PoS in the real world. The data analysis of Cardano demonstrates that the $$(T, \delta , \epsilon )$$ -privacy of current APoS is not satisfactory, mainly due to the deterministic leader election predicate in current PoS constructions. Inspired by the differential privacy technique, we propose an efficient non-deterministic leader election predicate, which can be used as a plugin to APoS protocols to protect stakes against frequency analysis. Based on our leader election predicate, we construct anonymous PoS with noise (APoS-N), which can offer better $$(T, \delta , \epsilon )$$ -privacy than state-of-the-art works. Furthermore, we propose a method of proving the basic security properties of PoS in the noise setting, which can minimize the impact of the noise on the security threshold. This method can also be applied to the setting of PoS with variable stakes, which is of independent interest.
Research on Reactive Power Optimization of Distribution Network with Distributed Generation Based on Improved Genetic Algorithm

In order to effectively reduce the active power loss of the distribution network and ensure the safety and economy of the distribution network operation. The traditional genetic algorithm is easy to fall into ‘precocity’, weak local search ability, many iterations and long time consuming. An improved genetic algorithm is proposed, and an improved genetic operator is designed, which makes the algorithm greatly improved in convergence and iteration times, and gives full play to the advantages of genetic algorithm in global fast search. Based on the improved genetic algorithm, this paper studies the distribution network with distributed power supply, and verifies the IEEE33 node. The distribution network’s network loss and node voltage are effectively improved by the improved genetic algorithm. The outcomes demonstrate the algorithm’s excellence and applicability.
An Adaptive Method for Dynamic Background Compensation Based on SIFT

To address the issue of poor object detection and tracking performance in dynamic scenes, a low-complexity adaptive dynamic background compensation algorithm is proposed to improve the real-time and accuracy of object detection and tracking. The algorithm uses the SIFT feature point matching algorithm to assist in calculating the background motion parameters and introduces an adaptive coefficient with feedback mechanism to reduce the matching calculation range of background motion parameters, thereby achieving dynamic background compensation. While improving the accuracy of background compensation, the algorithm also speeds up the computation to some extent. Experimental results show that this algorithm can improve the accuracy of detecting and tracking moving objects in video sequences with complex dynamic backgrounds while occupying only a small amount of system resources.
Three Distributions in the Extended Occupancy Problem

Abstract The classical and extended occupancy distributions are useful for examining the number of occupied bins in problems involving random allocation of balls to bins. We examine the extended occupancy problem by framing it as a Markov chain and deriving the spectral decomposition of the transition probability matrix. We look at three distributions of interest that arise from the problem, all involving the noncentral Stirling numbers of the second kind. These distributions give a useful generalisation to the binomial and negative-binomial distributions. We examine how these distributions relate to one another, and we derive recursive properties and mixture properties that characterise the distributions.
Full Cycle Data Protection Scheme of Power Parameters in Smart Grid Based on Integer Fully Homomorphic Encryption

Power parameter data protection is an important guarantee for the smooth operation of smart grid. In order to realize the full life cycle data protection of power parameters based on lattice cipher and DALCM model, this paper proposes a full life cycle data protection scheme for power parameters generated by smart terminals in power grids. The scheme in electricity parameter data phase, the electricity parameter for data encryption, the encrypted cryptograph no longer decrypt data in data processing stage, ciphertext survival to data disposal, scheme proposed can provide data security support for the operation of the smart grid, from the perspective of data security to ensure the smooth operation of the smart grid.
Modulated Audio Replay Attack and Dual-Domain Defense

Automatic speech recognition (ASR) systems have been widely deployed in modern smart devices to provide convenient and diverse voice-controlled services. Since ASR systems are vulnerable to audio replay attacks that can spoof and mislead ASR systems, a number of defense systems have been proposed to identify replayed audio signals based on the speakers’ unique acoustic features in the frequency domain. In this chapter, we uncover a new type of replay attack called modulated replay attack, which can bypass the existing frequency-domain-based defense systems. The basic idea is to compensate for the frequency distortion of a given electronic speaker using an inverse filter that is customized to the speaker’s transform characteristics. Our experiments on real smart devices confirm the modulated replay attacks can successfully escape the existing detection mechanisms that rely on identifying suspicious features in the frequency domain. To defeat modulated replay attacks, we design and implement a countermeasure named DualGuard. We discover and formally prove that no matter how the replay audio signals could be modulated, the replay attacks will either leave ringing artifacts in the time domain or cause spectrum distortion in the frequency domain. Therefore, by jointly checking suspicious features in both frequency and time domains, DualGuard can successfully detect various replay attacks including the modulated replay attacks. We implement a prototype of DualGuard on a popular voice interactive platform, ReSpeaker Core v2. The experimental results show DualGuard can achieve 98% accuracy on detecting modulated replay attacks.
Procedural Strategies

In this chapter, we first discuss analysis methods for understanding processes of relative importance and compositionality in the model using attribution signals. In the second part, we introduce methods to link these interpretability metrics to phenomena that we observe in cognitive signals. We then discuss how we can adjust the target objective and the inductive bias of the model towards cognitively more plausible procedural patterns.
Improved Network Anomaly Detection Method

With the development and updating of the Internet, network security issues have become diverse, and network attacks have greatly affected our lives. Network anomaly detection plays a pivotal role. Based on the existing network anomaly detection method of China Telecom network management platform, this paper proposes an improved network anomaly detection model, and improves the accuracy of network anomaly detection through the analysis of data traffic. Finally, combined with the pilot situation of a branch of China Telecom, a new generation of intelligent analysis model was trained, which verified the effectiveness of the system and provided new insights for optimizing other application systems.
Integrating Mid-Air Gestures into an Extended Reality Application: A Methodological Approach

Virtual reality (VR) is increasingly being incorporated into people's everyday lives as well as professional applications, science and research. Therefore, the question arises whether there may be interaction modes that are more suitable than the standard controllers with which these devices are typically equipped. To address this question, it is necessary to conduct human-centered studies, which are typically applied in the field of human factors research. To facilitate similar research approaches we present a framework to integrating mid-air gesture interaction into VR so that users can perform gestures without touching a button, a surface or a device into an HTC Vive VR Headset. For gesture recognition, we use a Leap Motion device, which is mounted at the front of the HTC Vive. We built a basic application using virtual 3D blocks that users can rotate and move. We used Unity to implement this application. This paper describes our methodological approach to building the setup.
An Evaluation of the Effectiveness of OpenAI's ChatGPT for Automated Python Program Bug Fixing using QuixBugs

In recent years, the use of Artificial Intelligence (AI) has become increasingly common in various fields, including in software development. One such field is where AI can automatically detect and fix bugs in code. GPT-3.5 is a state-of-the-art language model developed by OpenAI that has been trained on a massive amount of text data to generate natural language responses to a wide range of prompts. One of the main challenges in software development is bug fixing, which can be a time-consuming and complicated process. QuixBugs is a framework for evaluating automatic program repair techniques, which can be used to test the effectiveness of GPT-3.5 and similar bug-fixing tools. This paper evaluates the effectiveness of GPT-3.5 in automatically fixing bugs in Python code using QuixBugs. Through testing with 40 different Python bugs, We discovered that GPT-3.5 was able to accurately fix 30 out of 40 bugs cases from QuixBugs benchmark. Compared with other tools like standard program repair and Codex, ChatGPT outperformed them significantly. These findings highlight the potential of ChatGPT as a powerful tool for enhancing code quality and reducing the burden of manual bug fixing.
Comparative Study of Compression Functions in Modern Web Programming Languages

With the increasing volume of data being generated and transferred every day, storage space has become a valuable resource. The huge size of these files, such as multimedia or software ones, have also a negative impact when it comes to the bandwidth usage they require while sharing them. On the other hand, it becomes even harder for this data to coexist in different operating systems and to offer the needed security since we face different types and formats of them. The file compression process comes as a solution to all of the above problems. It helps to optimize storage space, improve transfer speeds, enhance security, reduce bandwidth usage, and ensure compatibility across different platforms. Recently, the explosion of data generated by the Internet of Things (IoT), social media, and other sources has put even greater pressure on file compression technologies since they needed to apply to programming languages that run on web servers. As more and more data are generated, the need for efficient, effective compression algorithms becomes increasingly important. Studying the way these algorithms behave in a specific web programming language is also important. Based on historical usage and current trends in the web development industry regarding programming languages, Python, JavaScript, and PHP continue to be widely used and popular among developers. Some key factors contributing to their popularity include their ease of learning, extensive community support, versatility, web-focused features, integration capabilities, availability of frameworks, and improvements in performance and scalability. In this article, we will explore different optimization methods used for image and file compression and how they are integrated into modern web programming languages.