Reviewerly/exHarmony huggingface dataset:
A Vulnerability to Storage Security for Cloud Computing

Utilizing technology and new technologies in the digital world requires a safe and reliable environment as well as a consideration for all the challenges they present, and dealing with them. There is no exception to the rule of cloud computing being one of the new technologies in the IT world. Various studies reveal that one of the major challenges of this technology is the security and safety required for consumers to transfer their data into the cloud. In this paper by reviewing and highlighting security challenges in a cloud computing environment, we aim to highlight the security challenges that cloud computing poses. The article also offers some suggestions on how to enhance the security of data storage in cloud computing systems that using these suggestions can somewhat override the problems.
Enhancing the interpretability of anomaly detection methods for the prediction of foreign object contamination

Filters are often employed in fluid transport lines that produce beverages, serving as a countermeasure against foreign objects. However, when these filters become clogged during removal, this can lead to an enlargement of the openings or damage to the mesh, leading to secondary generation of foreign objects. Thus, a system that continuously monitors to prevent accidents before they occur is necessary. However, it can be difficult to learn due to the absence of data from actual abnormal situations, and various elements intertwine on the production floor. Therefore, it is necessary to verify which methods are optimal for responding to these situations, and analyze elements for problem identification and improvement. In this study, we leverage the pressure loss caused by filter clogs in the fluid conveyance lines that produce beverages to validate various anomaly detection methods through real simulations. We test the appropriateness of each method based on the nature of the problem and the characteristics of the data, thereby enhancing the interpretability of the model. We compare the detection capabilities of different anomaly detection methods and perform an analysis of feature importance to clarify the importance of each element. Since the results of anomaly detection methods can vary based on the nature of the problem and the characteristics of the data, making interpretability clear can make the results of the anomaly detection methods easier to understand and improve prediction accuracy. By understanding the causes of abnormalities and the contribution of characteristics, it is possible to suppress the occurrence of foreign objects.
Automate Mobile Robot Actions Strategy Using Grammatical Evolution

Creating a control program for robots needs explicit definition of each action to handle inputs from sensors and produce outputs to actuator. The aim of this project is to use Grammatical Evolution (GE) to automate the process of producing multiple actions as strategy. This algorithm can separate searching space and generated program, thus eliminating human criteria bias when creating a robot controller while able to find more optimized and complex actions strategy. The evaluation is done through partially observed maze problem which objective to find goal without defining starting orientation and discoverable path along the way.
Enhancing the gravitational search algorithm through adaptive parameter control using linear mapping of fitness:

This paper presents a novel multimodal optimization algorithm by modifying our previously proposed gravitational particle swarm algorithm (GPSA). The conventional GPSA has demonstrated its ability to generate multiple design patterns for mechanical engineering problems, surpassing traditional methods. However, an important challenge in the conventional GPSA pertains to determining its control parameter. To address this challenge, the study proposes a modified version of GPSA called αGPSA, where the value of control parameter is adjusted based on a linear mapping of particle fitness values. Experimental evaluations were conducted using a simple multimodal optimization problem. The experimental results demonstrate that the proposed αGPSA achieves better performance in terms of peak ratio and success rate. Furthermore, the proposed αGPSA reduced sensitivity to the control parameter, streamlining the process of parameter selection for users compared to the conventional GPSA.
ROLE OF DIGITAL LITERACY IN ACADEMIC PERFORMANCE OF STUDENTS AT SECONDARY LEVEL

This study was conducted to find out the role of digital literacy in academic performance of students at secondary level. Major objectives of the study were: i) To assess the level of digital literacy among secondary school students, ii) To investigate the role of digital literacy in academic performance of students at secondary school level, iii) To analyze the relationship between students’ digital literacy and academic performance, iv) To ascertain the difference between students’ opinion on the base of demographics i.e. gender, locality, and age. This study used descriptive survey design. All the male and female students of secondary public school of 10th class 4725 (2507 males and 2218 females) of District Okara. As the population of the students of 10h class at secondary level was large. Therefore, the researcher used stratified random sampling technique to select the sample. Finally, students 1375 (716 male and 659 female) were selected. A 35-item questionnaire, self-structured was used to investigate the role of digital literacy in academic performance of students at secondary level. Results revealed that digital literacy has significant role in academic performance of students at secondary level. The result revealed after analyzing data and by seeing the views of students that digital literacy enhanced their intellectual abilities, learning skills and knowledge. Digital literacy has positive role in the academic performance of students at secondary level.
RESEARCH FOR IN-SITU INSPECTION SYSTEM DEVELOPMENT USING ADVANCED SUPER DRAGON ARTICULATED ROBOT ARM

We have proposed to apply a long-reach articulated robot arm for the internal investigation of the Fukushima Daiichi Nuclear Power Plants. This report describes the development of the articulated robot arm. To search at the bottom of the Reactor Pressure Vessel (RPV), we need to deliver measurement devices five meters above the X-6 penetration in the case of the No.2 unit. Thus, we develop a lightweight telescopic boom that can be mounted at the tip of the 10-m-long articulated robot arm “Super Dragon.” We increased the joint stiffness at the distal joint by introducing a torsional spring mechanism to achieve posture change of the telescopic boom from horizontal to vertical. We successfully reconstructed the surrounding environment using Structure from Motion by camera images obtained by the experiment.
DECOMMISSIONING WITH OVERHEAD CRANES OVERHANGING THE REACTOR BUILDING FROM THE FORMER TURBINE BUILDING

It is necessary to accurately grasp the circumstances for the decommissioning plan of Tokyo Electric Power Co. Inc.'s Fukushima Daiichi Power Station. The decommissioning of Unit 1 will be carried out first. Since March of 2022, TEPCO has been investigating the inside of Unit 1's containment vessel using underwater robots. At the entrance of the pedestal that supports the reactor, it was confirmed that the reinforcing bars and the concrete around the inner skirt had become exposed. These are extremely important for earthquake resistance. If a seismic event occurs, the seismic force on the reactor will not be transferred to the bedrock supporting the power plant. According to our trial calculation, if an earthquake of 440 gal or more occurs, it is predicted that the pedestal base would fail and the reactor would collapse. First of all, it is necessary to urgently take countermeasures against the reactor falling over. This document proposes an emergency countermeasure plan. After that, we propose a decommissioning plan for the entire power plant that can be implemented with current technology.
The electric induction like a behavior of electrons is proof of the new theory of new axioms and laws

The Theory of new Axioms and Laws contains 2 new Axioms and 8 Laws and it is invented by the same author.The classical axiom (Maxuell 1864) states that the uniform vortex is closed and verifies the Classical Field Theory. In contrast, the first new axiom (Axiom1) asserts that the ununiform vortex is always open and on this basis develops a new Theory of Open vortices.The second new axiom (Axiom2) shows that open vortices in nature (not in technic) are mutually orthogonal. On this basis, the electron is defined as orthogonal to the proton. The Law1, applied to the electron, affirms that a decelerating transverse open vortex generates an accelerating longitudinal vortex in its center. As one of result of the decelerating transverse vortex, the electron becomes a strong eccentric. The center of body moves from Geometric center to a new center, which is the center of Gravity. This means that during its pulsation, this eccentric body emits a transverse electric wave with a greater amplitude from the convex side of eccentric spiral than from its flattened side. As other of result is that the eccentric electron has an open active end of its decelerating transverse vortex. This means that for flowing of Electric wave only 1 axis (coordinate)phasing and ordering is required.Therefore, the electrons arrangement their open ends (tails) towards positive end of a conductor and the movement of theis own Electric wave is also to positive end of the Conductor. But the phenomenon induction of Electric current is connected with phasing of the electrons along the 3 axes. In case of outer hitting of Magnetic lines, electrons arrange themselfs that their own axes are parallel to outer Magnetic lines and to be at minimal distance to them. What's more- the existence of phenomena induction is direct evidence of exactly this structure of the electron. The inner structure of the electron as open decelerating very eccentric vortex is what causes it to react of the external impact from lines of force of external Magnetic field, as a particle possessing some internal intelligence
Personalized Fair Split Learning for Resource-Constrained Internet of Things

With the flourishing development of the Internet of Things (IoT), federated learning has garnered significant attention as a distributed learning method aimed at preserving the privacy of participant data. However, certain IoT devices, such as sensors, face challenges in effectively employing conventional federated learning approaches due to limited computational and storage resources, which hinder their ability to train complex local models. Additionally, in IoT environments, devices often face problems of data heterogeneity and uneven benefit distribution between them. To address these challenges, a personalized and fair split learning framework is proposed for resource-constrained clients. This framework first adopts a U-shaped structure, dividing the model to enable resource-constrained clients to offload subsets of the foundational model to a central server while retaining personalized model subsets locally to meet the specific personalized requirements of different clients. Furthermore, to ensure fair benefit distribution, a model-aggregation method with optimized aggregation weights is used. This method reasonably allocates model-aggregation weights based on the contributions of clients, thereby achieving collaborative fairness. Experimental results demonstrate that, in three distinct data heterogeneity scenarios, employing personalized training through this framework exhibits higher accuracy compared to existing baseline methods. Simultaneously, the framework ensures collaborative fairness, fostering a more balanced and sustainable cooperation among IoT devices.
TRENDS AND MAIN PROBLEMS OF THE DEVELOPMENT OF ADDITIONAL PROFESSIONAL EDUCATION ON THE BASIS OF UNIVERSITIES

The development of additional professional education entails a number of transformation processes affecting all infrastructure of this area of educational activity. In addition to global trends, such as online learning, additional vocational education has its own trends, such as the use of the “flipped classroom” method, the integration of additional vocational education courses into the structure of basic educational programs. At the same time, there are problems without solutions to which transformation would not be effective. For example, a decrease in the purchasing power of the audience, the lack of clear channels for promoting additional professional programs based in higher education institutions, and the low degree of digital literacy of part of the audience. An analysis of the above positions was carried out and options were identified, the provision of which in the organization is a condition for compliance with the modern agenda.
Effects of digital transformation on the learning quality of Human Resource Management (HRM) students at UEF

Nowadays, the application of digital transformation has been changing the nature and management style of many industries. The field of higher education is not out of general trend. Understanding the importance of applying digital transformation in learning and teaching activities at universities, the author conducted a study to explore the components of digital transformation that affect the learning quality of Human Resource Management (HRM) students. The scale is built on the basis of studying literature review, qualitative and quantitative research. Based on a survey sample with 210 respondents including students and alumni of the Human Resources Management major at UEF, the result indicates that there are three components affecting to quality of learning of HRM students: (1) Quality of teaching; (2) IT infrastructure; Ability to use technology with 24 observed variables which have been confirmed to be valid and reliable.
Time Series Forecasting during Software Project State Analysis

Repositories of source code and their hosting platforms are important data sources for software project development and management processes. These sources allow for the extraction of historical data points for the product development process evaluation. Extracted data points reflect the previous development experience and allow future planning and active development tracking. The aim of this research is to create a predictive approach to control software development based on a time series extracted from repositories and hosting platforms. This article describes the method of extracting parameters from repositories, the approach to creating time series models and forecasting their behavior. Also, the article represents the proposed approach for software project analyses based on fuzzy logic principles. The novelty of this approach is the ability to perform an expert evaluation of different stages of software product development based on the forecasted values of interested parameters and a fuzzy rule base.
Probability-Based Strategy for a Football Multi-Agent Autonomous Robot System

The strategies of multi-autonomous cooperative robots in a football game can be solved in multiple ways. Still, the most common is the “Skills, Tactics and Plays (STP)” architecture, developed so that robots could easily cooperate based on a group of predefined plays, called the playbook. The development of the new strategy algorithm presented in this paper, used by the RoboCup Middle Size League LAR@MSL team, had a completely different approach from most other teams for multiple reasons. Contrary to the typical STP architecture, this strategy, called the Probability-Based Strategy (PBS), uses only skills and decides the outcome of the tactics and plays in real-time based on the probability of arbitrary values given to the possible actions in each situation. The action probability values also affect the robot’s positioning in a way that optimizes the overall probability of scoring a goal. It uses a centralized decision-making strategy rather than the robot’s self-control. The robot is still fully autonomous in the skills assigned to it and uses a communication system with the main computer to synchronize all robots. Also, calibration or any strategy improvements are independent of the robots themselves. The robots’ performance affects the results but does not interfere with the strategy outcome. Moreover, the strategy outcome depends primarily on the opponent team and the probability calibration for each action. The strategy presented has been fully implemented on the team and tested in multiple scenarios, such as simulators, a controlled environment, against humans in a simulator, and in the RoboCup competition.
ACCOUNTING’S STUDENTS SOFT SKILLS DEVELOPMENT IN INDUSTRIAL REVOLUTION 4.0 (CASE STUDY AT UNIVERSITIES IN BENGKULU)

Job competition in the era of the industrial revolution 4.0 requires accountants to have more skills in addition to the knowledge gained in college; these skills are called soft skills. However, the reality is that many accountants still lack master soft skills. This problem cannot be separated from the role of universities as providers of accounting graduates. This study aims to describe the role of universities, especially accounting study programs, in developing the soft skills of accounting students in the era of the industrial revolution 4.0. This research is qualitative research with a case study approach. The informants in this study were four managers of accounting departments, four managers of accounting student organizations, and one manager of MBKM centers working in 4 universities in Bengkulu City. The data used in this study was obtained through direct interviews with data analysis techniques in data collection, data reduction, data presentation, and conclusions. The results of this study show that Bengkulu City College has developed the soft skills of accounting students through soft skills courses, integrating soft skills development into learning, encouraging accounting students to participate in MBKM programs, organizational activities, seminars, FMD, training, and soft skills competency tests. This research helps the manager of the Bengkulu City accounting study program understand and re-evaluate the current state of soft skills development of accounting students so that it can be better in the future.
Pose-aware Disentangled Multiscale Transformer for Pose Guided Person Image Generation

Pose Guided Person Image Generation (PGPIG) is a task that involves generating an image of a person in a target pose, given an image in a source pose, source pose information and target pose information. Many of the existing PGPIG techniques need extra pose-related data or tasks, which cause the limitations on their applicability. In addition CNNs are used as the feature extractor which do not have long-range dependency. However, CNNs can only extract features from neighboring pixels and cannot consider image consistency. This paper introduces a PGPIG network that solves these challenges by incorporating modules that use Axial Transformers with wide receptive fields. The proposed approach disentangles the PGPIG task into two subtasks: “rough pose transformation" and “detailed texture generation." In “rough pose transformation," lower-resolution feature maps is processed by Axial Transformer-based blocks. These blocks employ an Encoder-Decoder structure, which allows the network to use the pose information well and improves the stability and performance of the training. The latter subtask employs a CNN network with Adaptive Instance Normalization. Experimental results show the competitive performance of the proposed method compared to existing methods. The proposed method achieves lowest LPIPS in Deep Fashion dataset and FID in Market-1501 dataset. Remarkably, despite the great results obtained, the number of parameters of the proposed network is significantly less in contrast to existing methods.
On the Size and Width of the Decoder of a Boolean Threshold Autoencoder

In this brief paper, we study the size and width of autoencoders consisting of Boolean threshold functions, where an autoencoder is a layered neural network whose structure can be viewed as consisting of an encoder, which compresses an input vector to a lower dimensional vector, and a decoder which transforms the low-dimensional vector back to the original input vector exactly (or approximately). We focus on the decoder part and show that [Formula: see text] and O(√{Dn}) nodes are required to transform n vectors in d -dimensional binary space to D -dimensional binary space. We also show that the width can be reduced if we allow small errors, where the error is defined as the average of the Hamming distance between each vector input to the encoder part and the resulting vector output by the decoder.
TP-PBFT: A Scalable PBFT Based On Threshold Proxy Signature for IoT-Blockchain Applications

Consensus protocol is one of the core technologies of IoT-blockchain applications, which is used to ensure the consistency of data between terminal devices that do not trust each other. Practical Byzantine Fault Tolerance (PBFT) is a typical consensus algorithm. Due to its advantages of low computational power and complexity, PBFT is deemed more suitable for IoT-blockchain applications. PBFT can tolerate 1/3 faulty nodes in a blockchain network, which can be malicious or unresponsive. In this work, if a node does not respond to messages from other nodes, it can be regarded as an offline node. Therefore, when more than a third of the nodes go offline, the blockchain network breaks down. However, in IoT applications, this situation is likely to occur and greatly limits the security and stability of IoT-blockchain networks. In order to solve the above problem, we propose a novel threshold proxy signature-based PBFT (TP-PBFT) consensus for IoT-blockchain applications. We construct a new threshold proxy signature scheme that enables the proxy signers to sign messages on behalf of the offline nodes. In addition, we design a “two-step clustering” method to construct a double-layer architecture that improves the scalability of PBFT. Meanwhile, a reputation mechanism is introduced to evaluate the quality of the nodes. The experimental results show that our TP-PBFT consensus protocol can reach consensus when the number of offline nodes more than 1/3.
Task-Oriented Edge Networks: Decentralized Learning Over Wireless Fronthaul

This paper studies task-oriented edge networks where multiple edge internet-of-things nodes execute machine learning tasks with the help of powerful deep neural networks (DNNs) at a network cloud. Separate edge nodes (ENs) result in a partially observable system where they can only get partitioned features of the global network states. These local observations need to be forwarded to the cloud via resource-constrained wireless fronthual links. Individual ENs compress their local observations into uplink fronthaul messages using task-oriented encoder DNNs. Then, the cloud carries out a remote inference task by leveraging received signals. Such a distributed topology requests a decentralized training and decentralized execution (DTDE) learning framework for designing edge-cloud cooperative inference rules and their decentralized training strategies. First, we develop fronthaul-cooperative DNN architecture along with proper uplink coordination protocols suitable for wireless fronthaul interconnection. Inspired by the nomographic function, an efficient cloud inference model becomes an integration of a number of shallow DNNs. This modulized architecture brings versatile calculations that are independent of the number of ENs. Next, we present a decentralized training algorithm of separate edge-cloud DNNs over downlink wireless fronthaul channels. An appropriate downlink coordination protocol is proposed, which backpropagates gradient vectors wirelessly from the cloud to the ENs. Numerical results demonstrate the viability of the proposed DTDE framework for optimizing task-oriented edge networks.