Reviewerly/exHarmony huggingface dataset:
The Development and Status of Speech Recognition: An Overview

Speech recognition has made remarkable progress in the last two decades, gradually moving from the laboratory to many different application scenarios. The core of speech recognition includes two processes: encoding and decoding, among which the decoding process can be divided into two parts: acoustic model and language model. After more than seventy years of development, speech recognition can be broadly divided into three phases in terms of technical direction: GMM-HMM era, DNN-HMM era, and end-to-end era. Finally, this paper will summarize the problems and future development direction of speech recognition through comparative study.
Prediction of Vistula water surface level by applying the new group method of data handling

In this study, novel data-driven models were derived and applied to predict water surface level for two stations of the Vistula river. The applied data-driven models comprise of quadratic polynomial group method of data handling (QP-GMDH), nonlinear group method of data handling (N-GMDH), linear regression equation (LRE), and nonlinear regression equation (NRE). These models were trained by particle swarm optimisation (PSO) and teaching-learning-based optimisation (TLBO) meta-heuristic algorithms. The results confirmed the superiority of GMDHs with respect to the regression equations for big and small datasets. According to the results, the N-GMDH derived in the study is about 4.68% more accurate than the standard GMDH. The TLBO algorithm is about 2.06% more accurate than the popular and widely applied PSO approach. The TLBO method is also more stable in reaching global solutions in comparison with the PSO method.
ESCA: Embedded System Configuration Assistant

The core center of present-day control and processing systems is dependent on embedded systems. Every day, we come across new devices and hardware equipped with embedded software, gradually increasing the number of embedded systems. The size of embedded software is growing, and the requirements are growing even faster. As day advances, systems are becoming more complex, and we face difficulty in figuring out the best suitable system for a given purpose and specification. In this paper, we propose Embedded System Configuration Assistant (ESCA), a combination of two topics: Embedded Systems and Product Line Engineering that provide a scalable solution to the problem. ESCA selects the best configuration of hardware and devices required to build embedded systems for a particular purpose by identifying the system through a set of requirements and constraints provided by the user. ESCA adopts product line engineering approach which allows the development of diversified quality software in less amount of time, reduced development cost, and higher quantity. We use variability modeling which enables ESCA to have customization, extension, and configuration ability for large input size. Therefore, ESCA is scalable and fast in finding possible configurations of an embedded system. A prototype tool is developed to demonstrate the feasibility of the proposed ESCA.
Eliminating Redundant Actions from Plans Using Classical Planning

Even though automated planning is PSPACE-complete in general, satisficing planners are able to solve large planning tasks quickly. However, the found plans are often far from optimal and may even contain actions that can be removed while maintaining a valid plan. The problem of finding and eliminating the most expensive set of such redundant actions in a plan is NP-complete and there is a compilation to MaxSAT that solves it. Here, we introduce a simple and natural formulation of the problem as a planning task. Solving it with an optimal planner guarantees finding a minimal reduction. Our experiments show that this is competitive with the previous state of the art for optimal action elimination.
The Impact of Structure in Answer Set Counting: Fighting Cycles and its Limits

Answer Set Programming is a widely used paradigm in knowledge representation and reasoning, which strongly relates to the satisfiability (SAT) of propositional formulas. While in the area of SAT the last couple of years brought significant advances and different techniques for solving hard counting-based problems (e.g., #SAT, weighted counting, projected counting) that require more effort than deciding satisfiability, ASP still falls short. Intuitively, one explanation for this lies in the structure of a program, that – compared to SAT – was shown to yield strong evidence for being slightly less useful during solving. Indeed, for the well-known structural measure treewidth that plays an important role in counting-based variants of SAT, ASP is expected to be at least slightly harder than SAT. The underlying source of this hardness increase lies in cyclic dependencies in the positive dependency graph. In this work, we consider which strategies are appropriate to tackle counting-based problems for ASP depending on cycle lengths. To this end, we present different encodings to counting-based variants of SAT that thereby directly utilize recent advances. For small cycle lengths, we demonstrate a novel strategy based on feedback vertex sets. While medium cycle lengths still leave room for future improvements, surprisingly, in case of cycles that are significantly larger than the structural dependencies (treewidth), we can even obtain a polynomial algorithm.
Grounding LTLf Specifications in Image Sequences

A critical challenge in neuro-symbolic (NeSy) approaches is to handle the symbol grounding problem without direct supervision. That is mapping high-dimensional raw data into an interpretation over a finite set of abstract concepts with a known meaning, without using labels. In this work, we ground symbols into sequences of images by exploiting symbolic logical knowledge in the form of Linear Temporal Logic over finite traces (LTLf) formulas, and sequence-level labels expressing if a sequence of images is compliant or not with the given formula. Our approach is based on translating the LTLf formula into an equivalent deterministic finite automaton (DFA) and interpreting the latter in fuzzy logic. Experiments show that our system outperforms recurrent neural networks in sequence classification and can reach high image classification accuracy without being trained with any single-image label.
Impact of Community-Police Partnership on Community Policing in Lurambi Sub-County, Kenya

The primary mission of police departments as seen by both the public and police professionals is crime management.Collection of crime intelligence is achieved when there is good relationship between police and community members.This study sought to investigate the impact of the community-police partnership on community policing in Lurambi Sub-County, Kenya.The study was underpinned by the social capital theory, cognitive dissonance theory, and routine activity theory.Descriptive research design was adopted.The target population was community members in Lurambi Sub-County.The study targeted a sample of 384 community members as primary respondents.Besides, Police Officers and County law enforcement officers involved in community policing participated in the study as key informants.Stratified random sampling was used to select community members, while purposive sampling was used to select informants for the study.Primary data from community members was collected using questionnaires, while interviews and focus group discussions were used to collect secondary data.The study adopted a mixed-methods approach where quantitative and qualitative data were collected and analyzed.Study data were analyzed using the statistical package for the Social Sciences version 27 for windows.Descriptive and inferential statistics were calculated for the quantitative data, while thematic analysis was used to analyze qualitative data.Results of the study revealed a statistically significant and positive relationship between community-police partnership and community policing in Lurambi Sub-County (r= 0.617; P<0.05).Regression analysis was conducted to determine the variation in community policing outcomes as predicted by the effect of community police partnership in Lurambi Sub-County.The coefficient of determination (R2) was found to be 0.312, implying that the impact of community-police partnership accounted for 31.2% of the variance on community policing outcomes in Lurambi Sub-County.The study concluded that community-police partnership has a significant influence on community policing in Lurambi Sub-County.Consequently, the study recommended that community policing actors and stakeholders develop policies that promote trust between the police and community members by cultivating teamwork and shared crime prevention goals since establishing and maintaining mutual trust is the central goal of community policing.
Multi-population particle swarm optimization concerning the worst information

Currently, the majority of multi-population algorithms ignore the worst particles and instead learn from the gbest or lbest. Hence, it is simple for these strategies to result in inadequate population variety, which causes early convergence. A multi-population particle swarm optimization considering the worst particles into account (CWMPSO) is suggested to demonstrate the importance of the worst particle information. In addition to considering the best particle, the CWMPSO algorithm concentrates on the worst particle globally. The algorithm divides the population into three subgroups, which learns from the gbest, gworst, and gbest + gworst, respectively. The development of the global worst particle information can be achieved by group learning, allowing for a more thorough exploration of potential optimum solutions and an avoidance of local functional ones. 30 complex CEC2017 benchmark functions were used in this work to test a range of algorithms including CWMPSO with various dimensions. The outcomes demonstrate that, in every dimension, the CWMPSO algorithm is better than other advanced PSOs.
Learning Market Equilibria Using Performative Prediction: Balancing Efficiency and Privacy

We consider a peer-to-peer electricity market modeled as a network game, where End Users (EUs) minimize their cost by computing their demand and generation while satisfying a set of local and coupling constraints. Their nominal demand constitutes sensitive information, that they might want to keep private. We prove that the network game admits a unique Variational Equilibrium, which depends on the private information of all the EUs. A data aggregator is introduced, which aims to learn the EUs’s private information. The EUs might have incentives to report biased and noisy readings to preserve their privacy, which creates shifts in their strategies. Relying on performative prediction, we define a decision-dependent game $\mathcal{G}^{\mathrm{stoch}}$ to couple the network game with a data market. Two variants of the Repeated Stochastic Gradient Method (RSGM) are proposed to compute the Performatively Stable Equilibrium solution of $\mathcal{G}^{\mathrm{stoch}}$, that outperform RSGM with respect to efficiency gap minimization, privacy preservation, and convergence rates in numerical simulations.
Decentralized Banking Services using Blockchain Technology

Blockchain based crypto based currencies have made it possible to implement a fund transfer system in a more efficient, hassle-free and secure way. The present banking system contains human intervention at many stages wherein system users can modify bank details, bank balance in a fraudulent way. In the classical banking system, users have to wait for a longer time to deposit and withdraw money. A decentralized banking system allows a user to carry out various transactions and manage one’s own data securely. Thus, a decentralized banking system has no room for fraudulent activities and causes no capital loss. The proposed system has three functionalities: User Wallet, a lending platform and a trading platform. The lending platform consists of market funds of all the tokens from which every user can buy a specific amount of tokens. User wallet contains transaction history along with the amount of tokens sent. The trading platform consists of a live price chart of ZB/Eth Thus, users can borrow or lend money from their home or workplace within moments.
Efficient Artistic Image Style Transfer with Large Language Model (LLM): A New Perspective

With the development of intelligent information systems, image style transfer technology has been widely known. However, many image style transfer methods can only target one style, which is inefficient in application. In this study, the novel efficient artistic image style transfer with LLM is proposed. To be gin with the study, the related single style transfer models are reviewed to serve as the background. Then, the novel image demosaicing is designed to serve as the pre-processing for the complex images. As the core of the model, novel style transfer algorithm is proposed with the LLM. The novel neural network organization is designed and the core functions are optimized. Furthermore, to validate the performance, the visualzied style transfer test is conducted and the numerical simulation results on the efficiency is tested.
Efficient Federated Learning Via Local Adaptive Amended Optimizer With Linear Speedup

Adaptive optimization has achieved notable success for distributed learning while extending adaptive optimizer to federated Learning (FL) suffers from severe inefficiency, including (i) rugged convergence due to inaccurate gradient estimation in global adaptive optimizer; (ii) client drifts exacerbated by local over-fitting with the local adaptive optimizer. In this work, we propose a novel momentum-based algorithm via utilizing the global gradient descent and locally adaptive amended optimizer to tackle these difficulties. Specifically, we incorporate a locally amended technique to the adaptive optimizer, named Federated Local ADaptive Amended optimizer ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedLADA</i> ), which estimates the global average offset in the previous communication round and corrects the local offset through a momentum-like term to further improve the empirical training speed and mitigate the heterogeneous over-fitting. Theoretically, we establish the convergence rate of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedLADA</i> with a linear speedup property on the non-convex case under the partial participation settings. Moreover, we conduct extensive experiments on the real-world dataset to demonstrate the efficacy of our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FedLADA</i> , which could greatly reduce the communication rounds and achieves higher accuracy than several baselines.
Evolutionary Multitasking Optimization Enhanced by Geodesic Flow Kernel

In an era of parallel computing, evolutionary multitasking optimization (EMT) has become a popular optimization paradigm due to its ability to optimize several tasks simultaneously. The common knowledge can improve the solving quality and efficiency for each component optimization task when transferred among tasks. Therefore, the performances of traditional EMT algorithms mostly rely on the correlation between tasks. In the field of EMT, a key issue needing to be solved urgently is the impact of negative transfer when tackling optimization tasks with low correlation. In order to overcome the short board of this situation, this paper proposes a multiobjective EMT algorithm EMT-GFK. In the proposed algorithm, a union subspace of the optimization tasks is designed to extract the compact information. Furthermore, the geodesic flow kernel based domain adaptation is applied to learn a nonlinear mapping matrix, which can increase the correlation between tasks. The numerical experiments and results analysis on the MO-MTO test suits demonstrate the effectiveness of proposed EMT-GFK.
Optimal Allocation Strategy of Cloud Resources With Uncertain Supply and Demand for SaaS Providers

How to make full use of IaaS resources while guaranteeing the quality of service conditions is one of the main issues facing IaaS resource allocation.However, the current IaaS resource allocation schemes at the IaaS and PaaS levels do not consider the uncertainty of SaaS user access (i.e., resource demand), resource availability, and SaaS provider revenue.For the existing cloud resource configuration schemes at the IaaS and PaaS levels, the randomness of user access (resource demand) and resource supply of applications, as well as the revenue of application providers, are not considered enough, the optimal allocation strategy of cloud resources with uncertain supply and demand for SaaS providers is proposed.This strategy is based on the SaaS level, which ensures that the SaaS provider's revenue is maximized without violating QoS constraints and also facilitates the IaaS provider to allocate and fully utilize IaaS resources accurately.The strategy proposes not only optimal allocation strategies for IaaS resources under three scenarios: uncertain demand and certain supply, certain demand and uncertain supply, uncertain demand and uncertain supply, but also establishes quantitative models of resources and demand.The effectiveness and efficiency of the three algorithms are verified through experiments, and the realization shows that the revenue and IaaS resource utilization of the SaaS provider is effectively improved without violating the QoS constraint under the condition of uncertain demand and supply at the same time.
Minimax Concave Penalty Regression for Superresolution Image Reconstruction

Fast and robust superresolution image reconstruction techniques can be beneficial in improving the safety and reliability of various consumer electronics applications. The least absolute shrinkage and selection operator (LASSO) penalty is widely used in sparse coding-based superresolution image reconstruction (SCSR) tasks. However, the performance of the previously developed models is constrained by bias generated by the LASSO penalty. Meanwhile, no efficient and fast computing algorithms are available for unbiased l0 regression, and this situation restricts the practical application of l0-based SCSR methods. To address bias and efficiency problems, we propose a model called minimax concave penalty-based superresolution (MCPSR). First, we introduce a minimax concave penalty (MCP) into the SCSR task to eliminate bias. Second, we design a convergent, efficient algorithm for solving the MCPSR model and present a strict convergence analysis. Numerical experiments show that this model and the designed supporting algorithm can produce reconstructed images with richer textures at a fast computing speed. Moreover, MCPSR even shows robustness in the superresolution reconstruction of noisy images compared with other SCSR methods and has two flexible parameters to control the smoothness of the final reconstruction results.
TI-BIoV: Traffic Information Interaction for Blockchain-Based IoV With Trust and Incentive

Recent Blockchain-based Internet of Vehicles (BIoV) solutions are proposed to provide the capabilities of trust management and incentive distribution for traffic information interaction in decentralized trustless Internet of Vehicles (IoV). However, existing trust management methods in BIoV are designed based on subjective user feedback, which is vulnerable to bad-mouthing and collusion attacks. Besides, these incentive strategies achieve accurate information interaction based on the game theory, yet it is challenging for the practical IoV scenario without completely explicit parameters. To address these issues, we propose TI-BIoV, a traffic information interaction system based on three blockchains for IoV with the nonsubjective trust evaluation and optimal incentive with partial inexplicit parameters. Specifically, a nonsubjective trust mechanism is designed based on the traffic information offset calculated by other related traffic information, which ensures the change of vehicle trust value without any subjective factors. On this basis, a trust-based consensus protocol, which selects entities with high trust values as participants, is given to realize the reliable public audit of transactions. According to traffic information accuracy measurements, we develop a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula> -learning-based algorithm to encourage vehicles continuously submit accurate traffic information and optimally schedule the incentive for both platform and vehicle via training with incompletely explicit parameters of TI-BIoV. Finally, we analyze the security properties and common attacks of TI-BIoV and implement a prototype. The experimental results show that TI-BIoV achieves reliable consensus with nonsubjective trust evaluation and runs stably for a long time with two-sided incentive strategies.
Privacy Preserving Techniques and Their Applications in Elearning

The paper summarizes contemporary methods and techniques for privacy preservation as some challenging issues are analyzed and presented. A bibliometric approach is utilized in order for the "big picture" to be outlined, showing current research status and trending topics. The bibliographic data are taken from scientific database Scopus and processed through specialized software. In addition, a detailed review is also performed to classify problems and solutions in the area of privacy preservation. Special attention is given to possibilities for data privacy protection in intelligent eLearning environments. The role of machine learning for creating more secure data models is pointed out. A conceptual model, summarizing the findings, is proposed.
Optimum Tilt Angle and Orientation of Photovoltaic Thermal System for Application in Greater Toronto Area, Canada

&lt;p&gt;We present a study conducted to obtain optimum tilt angle and orientation of a solar panel for the collection of maximum solar irradiation. The optimum tilt angle and orientation were determined using isotropic and anisotropic diffuse sky radiation models (isotropic and anisotropic models). The four isotropic models giving varying optimum tilt angles in the range of 37 to 44°. On the other hand, results of the four anisotropic models were more consistent, with optimum tilt angles ranging between 46–47°. Both types of models indicated that the collector tilt should be changed four times a year to receive more solar radiation. The results also indicate that the solar panel should be installed with orientation west or east of due south with a flatter tilt angle. A 15° change in orientation west or east of due south results in less than 1% reduction of the total solar radiation received. For a given optimum tilt angle, the effect of photovoltaic/thermal (PV/T) orientation west or east of due south on the outlet temperature was determined using a one-dimensional steady state heat transfer model. It was found that there is less than 1.5% decrease in outlet temperature for a PV/T panel oriented up to 15° east or west of due south from March to December. This result indicates that existing roofs with orientations angles up to 15° east or west of due south can be retrofitted with a PV/T system without changing the roof shape.&lt;/p&gt;