Reviewerly/exHarmony huggingface dataset:
Integrating Cognitive Map Learning and Active Inference for Planning in Ambiguous Environments

Living organisms need to acquire both cognitive maps for learning the structure of the world and planning mechanisms able to deal with the challenges of navigating ambiguous environments. Although significant progress has been made in each of these areas independently, the best way to integrate them is an open research question. In this paper, we propose the integration of a statistical model of cognitive map formation within an active inference agent that supports planning under uncertainty. Specifically, we examine the clone-structured cognitive graph (CSCG) model of cognitive map formation and compare a naive clone graph agent with an active inference-driven clone graph agent, in three spatial navigation scenarios. Our findings demonstrate that while both agents are effective in simple scenarios, the active inference agent is more effective when planning in challenging scenarios, in which sensory observations provide ambiguous information about location.
Experimental Investigation and Soft Computing-based Assessment using ANN-MOGWO-A Hybrid Approach for Inconel(825)

In this paper, a strategy has been set for minimizing the corner error in the modification of pulse and non-pulse parameters. Taguchi’s philosophy has been used to design the experiment by varying process parameters (i.e. Spark-on Time (STon), Wire Tension (WT), Servo-Voltage (Sv), Discharge Current (DC) and Wire-speed (Sw)), to explore the machining outcomes. The response characteristics have been measured in terms of cutting speed (Cs), Corner Error (CD) and surface Roughness (RA) using Topas plus X wire of [Formula: see text] 0.25[Formula: see text]mm diameter. The machining performance characteristics were analyzed using main effect plots and analysis of variance (ANOVA). Furthermore, a soft computing-based hybrid optimization technique (Artifical Neural Network (ANN)-based Multi-Objective Grey Wolf Optimizer (MOGWO)) has been utilized to search the multi-optimum parameter setting for superior machining results. The most significant parameter observed is DC for Cs, which is determined to be 34.81%. Moreover, WT found 26.29% and 34.10% for CD and RA, respectively. The confirmation test shows that the maximum absolute percentage errors are observed as 3.89%, 6.3% and 9.7% for Cs, CD and RA, respectively. The proposed hybrid technique can generate superior solutions compared to the existing algorithms. Notably, the outcomes obtained on new instances exhibit potential, purposeful, and efficacy.
Enhancing Peer-to-Peer Energy Trading Efficiency and Fairness Through a Hybrid Auction Mechanism in Decentralized Smart Grids

This paper explores hybrid auction mechanism to incentivize peer-to-peer (P2P) energy trading in decentralized smart grid system to maximize societal welfare. P2P energy trading is a promising approach for leveraging renewable energy sources, allowing users to generate and exchange energy within local networks. In this context, buyers represent consumers, while sellers are prosumers, acting as both energy producers and consumers over time. Buyers are incentivized to procure energy from sellers due to the lower cost and greener source of energy compared to the centralized grid. In addition, the production and dissemination of renewable energy on a smaller scale mitigates the burden on transmission infrastructure, thereby decreasing reliance on the primary power grid. To optimize energy trading, We propose a hybrid auction that takes into account the constraints of grid infrastructure, such as transmission line capacity and related expenses, in order to increase the profitability of energy trading. The mechanism ensures individual rationality, truthfulness, computational efficiency, and fairness in the bidding process. Through mathematical examination and simulation experiments, the hybrid auction mechanism is assessed for its efficacy in enhancing energy trading efficiency and facilitating equitable resource allocation. This research contributes to the advancement of P2P energy trading mechanisms, enabling efficient use of renewable energy resources in decentralized smart grid environment.
An Adaptive Strong Tracking Cubature Kalman Filter with Unknown Measurement Noise Covariance and Its Application

Strong tracking Kalman filter is proposed to address the performance degradation and divergence caused by process model uncertainty. However, strong tracking filters depend on prior information about the measurement noise, which is frequently unknown or time-varying in real-world applications. This paper proposed an adaptive strong tracking cubature Kalman filtering algorithm with unknown measurement noise covariance. First, we introduce a modified pseudo-measurement-based covariance estimation method. It estimates measurement covariance by calculating the second-order mutual difference between the measurement sequence and pseudo measurement sequence. Second, we propose a filter divergence detecting method to help decide when to adjust the prediction error covariance matrix. The accuracy of the measurement noise covariance matrix estimating method and the effectiveness of filter divergence detecting methods have been proved by simulation results, respectively. As a result, the proposed filter outperforms several other algorithms in precision or robustness with inaccurate measurement noise covariance.
Copy-Move Forgery Detection in Homologous Video Sequences Based on Deviation Matrix

With the popularity of digital multimedia equipment and the widespread use of graphics video editing and processing software, digital video tampering becomes easier, and the reliability of multimedia data is questioned. Therefore, detecting whether digital video is tampered is very important for information security. Aiming at the copy-move tampering of homologous video sequences, this paper proposes a method to detect copy-move tampering of video sequences by measuring the correlation between image contents. Firstly, the video frame content is transformed into a series of continuous image frames, the image is divided into blocks, and the eight feature vectors of each frame are extracted. Then the correlation between frames is calculated by using the Euclidean distance, and the dynamic deviation threshold is constructed by adding the deviation matrix to detect the copy-move tampering sequence. The experimental results show that the proposed method can achieve ideal results for copy-move forgery detection of homologous video sequences.
Research on Novel Lightweight Object Detection and Recognition Algorithms

Object detection have broad applications in industrial production, medicine, and people’s lives. It is also an important research direction in the field of computer vision. Exploring the application of target detection technology has important practical significance. This paper focuses on detecting safety helmet-wearing in the construction site scene and manufacturing. Our paper presents novel and lightweight algorithms for detecting helmet-wearing. Our work has three main contributions. Firstly, we adopt MobileNet as a backbone feature extraction network with fewer parameters. Secondly, our algorithm uses DSC to reduce the number of parameters. Moreover, we add an AM to our methods. The methods we proposed have been proven to be effective through experiments.
A Dynamic Chunking Algorithm Approach for Data Deduplication

As a commonly used data reduction technology in storage systems, deduplication technology is used to eliminate redundant data in data and ensure that the deduplicated data can be recovered accurately, which can effectively improve the utilization rate of storage space and bandwidth. As the core of data duplication technology, the data chunking algorithm can directly affect the efficiency of deduplication. In this paper, Dynamic Chunking Algorithm (DCA) is proposed to optimize the fixed long chunking algorithm and CDC algorithm, and experiments show that DCA achieves good results in dedup ratio, chunking speed and metadata suppression compared with the above two algorithms.
A Study on “Security of Cyber-Physical Systems in the Cloud”

The existing security models are built with certain assumptions. The solutions like distributed accountability, provable data possession (PDP), Third Party Auditing (TPA) and so on are secure as long as the assumptions hold true. To ensure fool proof security for cloud storage security little research has been made on quantum key cryptography. Since the quantum key distribution is unconditionally secure, we propose a new scheme known as Cloud QKDP (Quantum Key Distribution Protocol for Cloud Computing) which exploits the benefits of quantum mechanisms to secure cloud storage and data dynamics. We consider a case study in which three parties such as cloud server, data owner and trusted client have provably secure communications with our proposed scheme which uses random oracle model. Our empirical study revealed mixture of success and failure rates with private and public clouds respectively.
Research on Cyber Security Situation Awareness System Based on Knowledge Graph

In recent years, cyber security incidents have occurred frequently, and the field of cyberspace security is facing increasingly severe challenges. Considering that cyber security situational awareness is facing issues such as multiple types of data, discrete distribution, and inconsistent content, it is necessary to integrate the idea of knowledge graph architecture, face massive data collection, and efficiently integrate resources to obtain more useful information, Knowledge graph inject new vitality into cyber security research in terms of overall data analysis and decision support. This article first briefly introduces the research related to cyber security situational awareness and knowledge mapping. Secondly, it constructs a model of cyber security situational awareness system based on knowledge mapping, analyzes the knowledge graph from data acquisition, information fusion, and decision support, and realizes in-depth mining of the intrinsic value of situational awareness data through cyber security attributes and mapping relationships.
Hybrid Learning Method with Class-based Color Augmentation for Clothing Parsing

Clothing parsing has drawn much attention due to its growing application scenarios. However, the main difficulty of clothing parsing lies in its small-scale and extremely unevenly distributed datasets, which makes existing methods based on supervised learning fail to effectively utilize global semantic information and often perform poorly. Also, current methods usually ignore the huge difference in color distribution between different kinds of clothing. In this paper, we propose a clothing parsing method based on weakly supervised learning, which adds a semi-supervised consistency loss and a contrastive loss for unsupervised contrastive learning to jointly train the model. Furthermore, we design a new image color augmentation algorithm for category prior information which is used for data augmentation in the above semi-superivised learning process. The proposed method can be combined with any encoder-decoder architecture model. Experimental results on models including PSPNet and Deeplabv3 declare that our method can effectively improve performance on clothing parsing.
Parallel Optimization of SLIC Algorithm for New-Generation Sunway Processors

This study aims to explore optimization methods for superpixel segmentation algorithms in large-scale image processing. Due to the significant time consumption of existing superpixel segmentation algorithms when dealing with large-scale image data, this study focuses on parallel optimization of the SLIC superpixel segmentation algorithm on the new-generation Sunway manycore processor SW26010-Pro. The research analyzes the characteristics of the SW26010-Pro heterogeneous manycore processor, existing superpixel image segmentation algorithms, and the SLIC superpixel segmentation algorithm. The study primarily analyzes hot-spot functions and improves the algorithm's running speed by 6-7 times through optimizing processes such as RGB-to-CIELab color space conversion, cluster updating, and seed point updating. It effectively presents a parallel execution scheme for the SLIC superpixel algorithm on the SW26010-Pro.
The Research on Dynamic Data Trustworthy Decision-Making Mechanism of Artificial Intelligence

This paper makes a summary for current artificial intelligence development during perceptual intelligence stage to cognitive intelligence stage, pointing out the future development trend: dynamic data trustworthy decision-making based on scene deep fusion. For the scene extension we build the two-dimensional coordinate system with two axes: scene dynamic level and decision level. Four fundamental trustworthy decision-making models are designed and illustrated in this paper. One of the models: the hidden layer decision-making is used to test the feasibility of the trustworthy decision-making theory and mechanism, which gains better outcome than the previous static model without decision factor.
UAV Cluster Task Assignment Algorithm Based on Improved Artificial Gorilla Troops Optimizer

Efficient task execution and optimized combat effectiveness can be achieved when a cluster of Unmanned Aerial Vehicles (UAVs) work collaboratively by assigning various tasks to each UAV reasonably. This paper suggests an algorithm for assigning tasks to a cluster of UAVs using an improved version of the Artificial Gorilla Troops Optimizer (GTO) that incorporates multiple strategies. The proposed algorithm adopts the Halton sequence to generate the initial population to ensure diversity. It uses an information sharing search strategy to enhance communication between the silverback gorilla and the population, effectively jumping out of the local optimal solution. In addition, the problem of inadequate solution accuracy caused by rapid convergence in the middle and later stages of iteration is improved using the golden sine strategy to coordinate GTO’s global searching and local mining capabilities. Based on the experimental results, it has been determined that the proposed algorithm outperforms other swarm intelligence algorithms in terms of convergence value and rate across various test functions. Additionally, it has been found to achieve faster and more stable task assignment results.
The effectiveness of CTL on the students’ news writing skill

This study examined the impact of the Contextual Teaching and Learning (CTL) approach onstudents’ news writing skills. This is a quantitative study that employs experimental methods. Thisstudy’s population consisted of eighth-grade students from SMPN 2 Padangpanjang who were enrolledin the 2020/2021 academic year. VIII B was chosen as the experimental class, and VIII A was chosen asthe control class. The homogeneity test was used to determine the homogeneity of variance in the pop-ulation class. The Barlett test was used to determine the number of samples, which was determined tobe two classes of 64 people. Writing news performance tests was used as a data collection technique.The t-test formula is used to determine the effect as well as test the hypothesis. The results revealedthat the control class students’ average news writing skills were more than adequate, whereas the ex-perimental class students’ news writing skills were perfect. This is because the experimental class hasimplemented CTL, which makes it easier for students to write news using the language’s structure andrules. Based on the study’s findings, it is possible to conclude that the use of CTL has an impact onstudents’ news writing abilities
ACCOUNTING OPTIMISED SKILL SET AND THE FOURTH INDUSTRIAL REVOLUTION - THE VIEW OF PROFESSIONAL ACCOUNTING BODIES

The digital transformation of the accounting field is triggering a reconfiguration of the optimized skill set that is required of practitioners worldwide. Emerging technologies have set foot into accounting daily processes, triggering the need that practitioners upskill their digital competences, while also emphasizing the relevance of soft and technical skills. The optimal accounting profile, adapted to the needs of the Fourth Industrial Revolution is being analyzed in the current paper through means of a thematic literature review of the reports of professional accounting bodies. Professional accounting bodies are key change-agents that enable the optimization of financial activities and help organizations improve activities and reach their goals. In this sense, they provide the most updated information in terms of skills and competencies required of accounting practitioners. The results of the literature review present three distinct skill categories. The three groups consist of technical skills, meaning traditional accounting skills, soft skills, meaning cognitive related and behavioral skills, and lastly the study addresses the topic of digital skills which are a direct result of technological innovations in the field. The digital skill group is being validated as an individual group through the recurrence and the interest that literature of accounting bodies has on these skills.
Technologies for Innovative Potential Development of University Students Majoring in Engineering

The article deals with the didactic aspect of the implementation of the techniques and methods of creativity in the educational process, together with the gamification mechanics. An attempt was made to reveal the essence of the use of techniques and methods of creativity in the educational process, as well as how gamification contributes to the disclosure of the innovative potential of engineering students. The scientific novelty lies in the fact that the technology for the formation of the innovative potential of engineering students by gamification mechanics in the conditions of university education has been proposed and implemented. A set of pedagogical conditions is proposed that contributes to the innovative potential development of university students majoring in engineering by gamification mechanics (raising the general cultural level in order to strengthen the cultural component of professional competence; formation of motivation in project self-realization; stimulating the student to achieve the final result within the time limits established by the regulations; introduction of a bonus system; practice-oriented work with real projects that are in demand on the labor market; the sequence of studying special forms, techniques, methods, techniques, strategies, algorithmic instructions for solving professional problems, etc.).
Exploration and Practice of Regional Intelligent Operation Application

Nanchang has created a new mode of education by focusing on smart enrollment, smart examination and smart homework, and realized a new way of education based on educational data to make people run fewer errands and more data.
An Improved Particle Swarm Optimization Algorithm

Particle Swarm Optimization (PSO) is a sophisticated optimization technique deeply rooted in artificial intelligence. This meta-heuristic algorithm addresses complex, nonlinear, and multidimensional problems, often delivering commendable solutions with minimal parameter tuning. PSO iteratively enhances candidate solutions at its core, progressively converging toward near-optimal outcomes, drawing inspiration from natural phenomena like birds and fish flocking behaviour. The PSO process commences by initializing a pool of random solutions and diligently pursuing the best solution through successive iterations. Since its inception in 1995, PSO has undergone numerous refinements to optimize its performance and outcomes through parameter fine-tuning. This paper advances the conventional PSO framework by introducing an improved PSO variant characterized by a linearly diminishing inertia weight and a novel velocity equation. Subsequently, the proposed PSO variant undergoes rigorous testing against a benchmark function set to assess its performance. It is compared to alternative PSO variants to gauge its competitiveness. Furthermore, its efficacy in practical applications is validated by scrutinizing an engineering design optimization problem and engaging in comparative experiments with other algorithmic iterations.
A Blockchain-Based Credentials for Food Traceability in Agricultural Supply Chain

In recent years the prominence of food product traceability has significantly increased due to growing concerns regarding food safety and ensuring equitable market access for farmers. Presently, supply chain information systems collect, transmit and store records in centralized systems. These conventional digital systems create fragmented data silos vulnerable to a single point of failure. These limitations pose significant challenges in establishing trust factors such as transparency, traceability, integrity, and privacy among the various actors involved in the agri-supply chain. To overcome these challenges, a decentralized technology like blockchain has immense potential to enhance traceability by addressing security and privacy aspects. This study proposes a blockchain-based framework called AgriFoodCredChain for food product traceability using Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs). Initially, the study outlines a secure mechanism for establishing DID communication channels, assuring robust and reliable data exchange. Furthermore, methods for privacy-preserved issuance and verification of credentials for food traceability are developed and presented. The implementation of the framework is conducted utilizing the identity blockchain named Hyperledger Indy and Aries library, considering particular workflow in the food supply chain. Evaluating the framework w.r.t credential issue and verifying time proves that the solution implemented is scalable and satisfies the requirements for real-time adoption.
Using SAT Solvers to Reverse-Engineer FSM Models of Digital Devices

Inferring a functional specification from an existing digital design is a challenge that is suitable with reverse-engineering methods. One of the most widely used functional specification formats is a finite state machine (FSM). This article studies the possibility of blind passive specification mining for a digital device, where the device is treated as a “black box”. The presented approach treats an input and output signal waveform as the transition graph of an incomplete deterministic FSM and learns the FSM through FSM minimization. It employs a Boolean satisfiability problem (SAT) solver to find a minimal FSM that complies with observed object behavior. The known approach to identifying state machines in discrete event systems is adapted to operate with variables in the form of coloring and transition tables. The developed implementation produces a synthesizable specification in hardware description language (HDL) and a state diagram in unified modeling language (UML). The proposed approach for inferring an FSM from a waveform trace can serve as a supplementary tool during reverse engineering to provide developers with meaningful insight regarding the analyzed device. The presented case study defines metrics of successful FSM inference and applies them to a synthetic FSM and a real-world example FSM to demonstrate the applicability of the approach.