Reviewerly/exHarmony huggingface dataset:
Design and Analysis of Digital Certificate Verification and Validation using Blockchain-based Technology

The educational institution's digital certification In the current scenario of digital certification with a digital signature, a blockchain technology server is utilized. There is a chance of updating or creating phony certificates in order to prevent the organization's use of digital certification in the future. By enabling tamper-proof data storage, this blockchain technology will prevent server hacking and data modification. In order to prevent counterfeiting in blockchain technology offers the electronic data file that is formed in the ledger using the hash function it tampers with. A secured, unchangeable, authenticated certificate is produced by this method. In the transparent network, a document that has been digitally signed and approved by peers is exchanged. They are granted these certifications. The blockchain is attracting intense media and scientific interest while igniting interest in its possible applications and function in promoting social decentralization [9] and independence from centralized authorities. The impact that the widespread use of this technology will have on our societies, both positively and negatively, has received a lot of attention. Despite all of this thought, there isn't much literature that addresses the issues it might present, except for technological ones. The introduction of blockchain can be likened to the development of the Internet, demonstrating the potential for profound changes in a variety of industries. Although, in accordance with presented a first analogous example of this technology.Finally, the system will use smart contracts to automate processes and ensure data accuracy. Smart contracts are self-executing programs that are stored on a blockchain and can be used to facilitate transactions and agreementsbetween two parties, without the need for a third party. Smart contracts will ensure that the data is accurate and up to date, as well as provide an additional layer of security. This will ensure that the transactions are secure and can be verified by anyone on the network.
MixGAN-TTS: Efficient and Stable Speech Synthesis Based on Diffusion Model

This paper describes MixGAN-TTS, an efficient and stable non-autoregressive speech synthesis based on diffusion model. The MixGAN-TTS uses a linguistic encoder based on soft phoneme-level alignment and hard word-level alignment approach which explicitly extracts word-level semantic information, and introduces pitch and energy predictors to optimally predict the rhythmic information of the audio. Specifically, we use the GAN to replace the Gaussian function to model the denoising distribution, aiming to enlarge the denoising steps size and reduce the number of denoising steps to accelerate the sampling speed of diffusion model. Diffusion model using GAN can significantly reduce the denoising steps, and to some extent solve the problem of not being able to apply in real-time. The mel-spectrogram is converted into the final audio by the HiFi-GAN vocoder. Experimental results show that the MixGAN-TTS outperforms the other models compared in terms of audio quality and mel-spectrogram modeling capability for 4 denoising steps. The ablation studies demonstrate that the structure of MixGAN-TTS is effective.
A Communication-Efficient Local Differentially Private Algorithm in Federated Optimization

Federated optimization, wherein several agents in a network collaborate with a central server to achieve optimal social cost over the network with no requirement for exchanging information among agents, has attracted significant interest from the research community. In this context, agents demand resources based on their local computation. Due to the exchange of optimization parameters such as states, constraints, or objective functions with a central server, an adversary may infer sensitive information of agents. We develop a differentially-private additive-increase and multiplicative-decrease algorithm to allocate multiple divisible shared heterogeneous resources to agents in a network. The developed algorithm provides a differential privacy guarantee to each agent in the network. The algorithm does not require inter-agent communication, and the agents do not need to share their cost function or their derivatives with other agents or a central server; however, they share their allocation states with a central server that keeps track of the aggregate consumption of resources. The algorithm incurs very little communication overhead; for m heterogeneous resources in the system, the asymptotic upper bound on the communication complexity is O(m) bits at a time step. Furthermore, if the algorithm converges in K time steps, then the upper bound communication complexity will be O(mK) bits. The algorithm can find applications in several areas, including smart cities, smart energy systems, resource management in the sixth generation (6G) wireless networks with privacy guarantees, etc. We present experimental results to check the efficacy of the algorithm. Furthermore, we present empirical analyses for the trade-off between privacy and algorithm efficiency.
Semantic feature learning for software defect prediction from source code and external knowledge

Software defects not only reduce operational reliability but also significantly increase overall maintenance costs. Consequently, it is necessary to predict software defects at an early stage. Existing software defect prediction studies work with artificially designed metrics or features extracted from source code by machine learning-based approaches to perform classification. However, these methods fail to make full use of the defect-related information other than code, such as comments in codes and commit messages. Therefore, in this paper, additional information extracted from natural language text is combined with the programming language codes to enrich the semantic features. A novel model based on Transformer architecture and multi-channel CNN, PM2-CNN, is proposed for software defect prediction. Pretrained language model and CNN-based classifier are utilized in the model to obtain context-sensitive representations and capture the local correlation of sequences. A large and widely used dataset is utilized to verify the effectiveness of the proposed method. The results show that the proposed method has improvements in generic evaluation metrics compared with the optimal baseline method. Accordingly, external information can have a positive impact on software defect prediction, and our model effectively incorporates such information to improve detection performance.
Efficient e-mail spam filtering approach combining Logistic Regression model and Orthogonal Atomic Orbital Search algorithm

Phishing emails called spam have created a need for reliable and intelligent spam filters. Machine-learning techniques are effective, but current methods such as Logistic Regression (LR), Support Vector Machine (SVM), Decision Trees (DT), and Naive Bayes (NB) sometimes produce low detection rates and struggle with large amounts of data. Motivated by these concerns, we propose an efficient spam-filtering approach, OAOS-LR, combining an improved Atomic Orbital Search (AOS) algorithm with an LR classification model. To remove the deficiency of low detection rate produced by the standard LR method due to the utilization of the gradient descent technique, we train it with our proposed OAOS approach, which uses AOS and Orthogonal learning to enhance the search capabilities of the conventional algorithm. In the experimental study, we first evaluated the performance of OAOS over the IEEE Congress on Evolutionary Computation (CEC’20) benchmarks against five different metaheuristics to prove its effectiveness in improving its convergence rate and reducing the probability of falling in local optima. After that, the proposed technique LR-OAOS was applied to the spam filtering problem using CSDMC2010 and Enron datasets and tested against the most recent machine learning and metaheuristic approaches using standard statistical measures and plots. OAOS-LR significantly outperformed other methods with an average F1-score success rate of 95.45% and 96.30% on CSDMC2010, and 74.80% and 78.33% on Enron, respectively with the number of feature spaces equal to 500 and 1000.
Innovative Business Research in Finance and Marketing System Based on Ethically Governed Artificial Intelligence

The purpose of this study is to enquire about potential of using ethically governed artificial intelligence (AI) in the finance and marketing systems of businesses. The integration of AI in business processes has been proven to be effective in improving efficiency and productivity. However, The potential negative moral repercussions of AI have been raised as a serious issue, especially in the context of decision-making. Therefore, this study proposes a framework for an innovative business research system that integrates AI and ethical principles to ensure responsible decision-making.The proposed system will be based on a set of ethical principles, including transparency, accountability, and fairness. These principles will guide the development and implementation of the AI system, ensuring that it operates within ethical boundaries. The system will be designed to deal with a lot of information, allowing businesses to make informed decisions about finance and marketing strategies. The A combination of questionnaires, in-person interviews, and online content will be used to compile this study’s findings case studies, gathering information from business leaders and AI experts. The data collected will be used to assess the effectiveness of the proposed system in improving business performance and to identify any ethical concerns that may arise.The expected outcomes of this research are Development of an innovative business research system that integrates AI and ethical principles for finance and marketing decision-making. Identification of the potential benefits and ethical concerns of using AI in business processes. Recommendations for businesses to ensure responsible and ethical use of AI in their operations. Overall, this research will contribute to the understanding of the potential of AI in business decision-making while ensuring that ethical principles are adhered to. The proposed system has the potential to revolutionize the way businesses operate, providing them with a competitive advantage while upholding ethical standards.
Optimal Repairs in Ontology Engineering as Pseudo-Contractions in Belief Change

The question of how a given knowledge base can be modified such that certain unwanted consequences are removed has been investigated in the area of knowledge engineering under the name of repair and in the area of belief change under the name of contraction. Whereas in the former area the emphasis was more on designing and implementing concrete repair algorithms, the latter area concentrated on characterizing classes of contraction operations by certain postulates they satisfy. In the classical setting, repairs and contractions are subsets of the knowledge base that no longer have the unwanted consequence. This makes these approaches syntax-dependent and may result in removal of more consequences than necessary. To alleviate this problem, gentle repairs and pseudo-constractions have been introduced in the respective research areas, and their connections have been investigated in recent work. Optimal repairs preserve a maximal amount of consequences, but they may not always exist. We show that, if they exist, then they can be obtained by certain pseudo-contraction operations, and thus they comply with the postulates that these operations satisfy. Conversely, under certain conditions, pseudo-contractions are guaranteed to produce optimal repairs.
Alleviating High Gas Costs by Secure and Trustless Off-chain Execution of Smart Contracts

Smart contracts are programs that are executed on the blockchain and can hold, manage and transfer assets in the form of cryptocurrencies. The contract's execution is then performed on-chain and is subject to consensus, i.e. every node on the blockchain network has to run the function calls and keep track of their side-effects including updates to the balances and contract's storage. The notion of gas is introduced in most programmable blockchains, which prevents DoS attacks from malicious parties who might try to slow down the network by performing time-consuming and resource-heavy computations. While the gas idea has largely succeeded in its goal of avoiding DoS attacks, the resulting fees are extremely high. For example, in June-September 2022, on Ethereum alone, there has been an average total gas usage of 2,706.8 ETH ≈ 3,938,749 USD per day. We propose a protocol for alleviating these costs by moving most of the computation off-chain while preserving enough data on-chain to guarantee an implicit consensus about the contract state and ownership of funds in case of dishonest parties. We perform extensive experiments over 3,330 real-world Solidity contracts that were involved in 327,132 transactions in June-September 2022 on Ethereum and show that our approach reduces their gas usage by 40.09 percent, which amounts to a whopping 442,651 USD.
Rotated-DETR: an End-to-End Transformer-based Oriented Object Detector for Aerial Images

Oriented object detection in aerial images is a challenging task due to the highly complex backgrounds and objects with arbitrary oriented and usually densely arranged. Existing oriented object detection methods adopt CNN-based methods, and they can be divided into three types: two-stage, one-stage, and anchor-free methods. All of them require non-maximum suppression (NMS) to eliminate the duplicated predictions. Recently, object detectors based on the transformer remove hand-designed components by directly solving set prediction problems via performing bipartite matching, and achieve state-of-the-art performances in general object detection. Motivated by this research, we propose a transformer-based oriented object detector named Rotated DETR with oriented bounding boxes (OBBs) labeling. We embed the scoring network to reduce the tokens corresponding to the background. In addition, we apply a proposal generator and iterative proposal refinement module in order to provide proposals with angle information to the transformer decoder. Rotated DETR achieves state-of-the-art performance on the single-stage and anchor-free oriented object detectors on DOTA, UCAS-AOD, and DIOR-R datasets with only 10% feature tokens. In the experiment, we show the effectiveness of the scoring network and iterative proposal refinement module.
AI4CITY - An Automated Machine Learning Platform for Smart Cities

Nowadays, the general interest in Machine Learning (ML) based solutions is increasing. However, to develop and deploy a ML solution often requires experience and it involves developing large code scripts. In this paper, we propose AI4CITY, an automated technological platform that aims to reduce the complexity of designing ML solutions, with a particular focus on Smart Cities applications. We compare our solution with popular Automated ML (AutoML) tools (e.g., H2O, AutoGluon) and the results achieved by AI4CITY were quite interesting and competitive.
Formalizing an Efficient Runtime Assertion Checker for an Arithmetic Language with Functions and Predicates

Runtime Assertion Checking (RAC) is a lightweight formal method that verifies formal code annotations, typically assertions, at runtime. The main RAC challenge consists in generating code that is both sound and efficient for checking expressive properties. In particular, checking formal arithmetic properties usually requires to use machine integer arithmetic to be efficient, but needs to rely on an exact yet slower arithmetic library to be sound.
Arabic Aspect Category Detection for Hotel Reviews based on Data Augmentation and Classifier Chains

Recently, the amount of content generated on online hospitality platforms has increased exponentially and has changed people's ways of life. Consumers often refer to online reviews before deciding which hotel to choose. These reviews provide firsthand information, essential to improving hotel services' quality. However, the massive amount of review data and its unstructured nature make it a difficult challenge. Indeed, many researchers were interested in exploring the field of sentiment analysis in the hotel industry. In particular, they have given more attention to aspect-based sentiment analysis, which categorizes opinions by aspect and identifies the sentiment related to each aspect. However, studies examining the Arabic language are limited compared to English. Our paper aims to explore aspect category detection as a sub-task of aspect-based sentiment analysis using Arabic reviews. We relied on the SemEval-2016 Arabic dataset for hotel reviews. As this data suffers from an imbalanced distribution, we propose an approach for multi-label data augmentation of the minority classes in this used dataset. Then, we propose a specific preprocessing for this Arabic reviews dataset. Our aspect category prediction approach is based on the classifier chains technique. In fact, unlike previous works that treat each label separately, we handle the dependencies between the various labels. Our findings show that our proposed approach achieves a good F1 score that outperforms the pioneering related work approaches.
Fuzzy Logic-Based Trajectory Planning for Mobile Robots in an Uncertain and Complex Environment

Mobile robot trajectory planning is a crucial aspect of robot navigation and control. It involves the creation of algorithms and strategies that enable a robot to move from one location to another while avoiding obstacles, following a predetermined path, and maintaining a desired speed and acceleration. However due to the physical constraints of the application theatre, it is really trivial issue to construct a path due to the missing and uncertain data of the environment. Fuzzy logic is a mathematical system that permits the representation and manipulation of imprecise or uncertain data. It is based on fuzzy sets, which permit continuous rather than discrete values, and fuzzy rules, which permit the representation of complex relationships between variables. Therefore, in this paper we aimed to develop a fuzzy logic-based trajectory planning algorithm for mobile robots in a dynamic and uncertain environment. Experimental results showed that the proposed approach reaches applicable trajectory paths for complex environments.
Dynamic FET-based memristor with relaxor antiferroelectric HfO2 gate dielectric for fast reservoir computing

Reservoir computing (RC), as a framework for artificial intelligence (AI) computation, is derived from recurrent neural networks, but with higher efficiency benefits from its much simpler training process. A hardware-based physical RC employs a reservoir that is a fixed non-linear system, and in a simple RC structure, the reservoir is ideally a short-term dynamic field effect transistor-based memristor. In this work, we demonstrate that reservoir computing employing a polarization-modulated transistor as a physical reservoir with a relaxor antiferroelectric back gate significantly reduces parameters, power consumption, and calculation steps of the neural network. Here, relaxor antiferroelectricity is realized in an Al-doped Hf0.5Zr0.5O2 (Al:HZO) thin film, and with the poling process-sensitive modulation of the Al:HZO gate dielectric, a dynamic and polarization field-modulated transistor is demonstrated as a short-term memristor. This short-term memory behavior is further used to encode binary images to form a reservoir for fast image processing, where the reservoir mimics small convolution operations to convert images. This reservoir computing is also applied to image classification, denoising, and similarity judgment, and very reliable results are obtained. This work provides a new approach to hardware-based physical reservoir computing.
Multilayer Grad-CAM: An effective tool towards explainable deep neural networks for intelligent fault diagnosis

As a tool to explain deep neural networks using gradient information, Gradient-weighted Class Activation Map (Grad-CAM) provides a potential way for explainable artificial intelligence. However, for vibration signals in machine fault diagnosis, feature resolution of Grad-CAM decreases with the deepening of network layers, which weakens network explainability. To address this issue, a novel Multilayer Grad-CAM (MLG-CAM) is proposed as an effective tool to explain what networks have learned. Meanwhile, three indicators are defined to quantify explainability of deep neural networks. The MLG-CAM uses gradients flow of multiple convolutional layers to obtain activation maps in various resolutions. A comprehensive activation map is then produced by layer-weighted summation of above activation maps. Experiments indicate MLG-CAM not only highlights cyclo-stationary impulses in time domain but also emphasizes fault characteristic frequency in frequency domain. These results prove MLG-CAM as an effective way to explain deep neural networks and build up trustworthiness of networks.
Worst-case complexity of an SQP method for nonlinear equality constrained stochastic optimization

A worst-case complexity bound is proved for a sequential quadratic optimization (commonly known as SQP) algorithm that has been designed for solving optimization problems involving a stochastic objective function and deterministic nonlinear equality constraints. Barring additional terms that arise due to the adaptivity of the monotonically nonincreasing merit parameter sequence, the proved complexity bound is comparable to that known for the stochastic gradient algorithm for unconstrained nonconvex optimization. The overall complexity bound, which accounts for the adaptivity of the merit parameter sequence, shows that a result comparable to the unconstrained setting (with additional logarithmic factors) holds with high probability.
Fast and accurate flow measurement through dual-camera light field particle image velocimetry and ordered-subset algorithm

Light field particle image velocimetry (LF-PIV) can measure the three-dimensional (3D) flow field via a single perspective and hence is very attractive for applications with limited optical access. However, the flow velocity measurement via single-camera LF-PIV shows poor accuracy in the depth direction due to the particle reconstruction elongation effect. This study proposes a solution based on a dual-camera LF-PIV system along with an ordered-subset simultaneous algebraic reconstruction technique (OS-SART). The proposed system improves the spatial resolution in the depth direction and reduces the reconstruction elongation. The OS-SART also reduces the computational time brought by the dual-camera LF-PIV. Numerical reconstructions of the particle fields and Gaussian ring vortex field are first performed to evaluate the reconstruction accuracy and efficiency of the proposed system. Experiments on a circular jet flow are conducted to further validate the velocity measurement accuracy. Results indicate that the particle reconstruction elongation is reduced more than 10 times compared to the single-camera LF-PIV and the reconstruction efficiency is improved at least twice compared to the conventional SART. The accuracy is improved significantly for the ring vortex and 3D jet flow fields compared to the single-camera system. It is therefore demonstrated that the proposed system is capable of measuring the 3D flow field fast and accurately.