Reviewerly/exHarmony huggingface dataset:
Unveiling the potential of an evolutionary approach for accurate compressive strength prediction of engineered cementitious composites

The different human activities in numerous fields of civil engineering have become possible due to recent development in soft computing. As many researchers have widely extended the use of evolutionary numerical methods to predict the mechanical properties of construction materials, it has become necessary to investigate the performance, accuracy, and robustness of these approaches. Gene Expression Programming (GEP) is a method that stands out among these methods as it can generate highly accurate formulas. In this study, two models of GEP are used to anticipate the compressive strength of engineered cementitious composite (ECC) containing fly ash (FA) and polyvinyl alcohol (PVA) fiber at 28 days. The experimental results for 76 specimens, which are made with ten different mixture properties, are taken from the literature to build the models. Considering the experimental results, four different input variables in the GEP approach are used to arrange the models in two modes: sorted data distribution (SDD) and random data distribution (RDD). Prognosticating the compressive strength values based on the mechanical properties of ECC containing FA and PVA will be possible for the models of the GEP method by using these input variables. The comparison between the experimental results and the results of training, testing, and validation sets of two models (GEP-I and GEP-II), each of which has two distinct distribution modes, is done. It is observed that both modes of RDD and SDD lead to responses with the same accuracy (R-square more than 0.9). Nevertheless, the GEP-I (SDD) model was chosen as the best model in this study based on its performance with the validation data set.
FUZZY MODEL FOR TIME SERIES FORECASTING

Abstract: In 2007, in Kazakhstan, there was a transition of TDM (Time Division Multiplexing) circuit-switched technologies to IP (Internet Protocol) packet technology, which created a modern infrastructure for the ICT (information communication technologies) sphere. The advent of the IoT (Internet of Things) concept has led to the growth of a functioning network at a faster rate. It is currently developing in the direction of a cognitive infocommunication network. Its evolutionary development is characterized by a change in the volume of transmitted information, types of its presentation, methods of transmission and storage, the number of sources and consumers, distribution among users, requirements for timeliness and reliability (quality) [1]. Types of traffic and their structure are changing, therefore data processing becomes more complicated. For this reason, the tasks of analyzing and predicting network traffic remain relevant.&#x0D; In this work, the prediction of the measured traffic on a real network is performed. The series under study shows the totality of packets transmitted over the backbone network for each second. Forecasting of a one-dimensional time series is carried out on the basis of fuzzy logic methods. This class of models is well suited for modeling nonlinear systems and time series forecasting. The use of fuzzy sets is based on the ability of fuzzy models to approximate functions, as well as on the readability of rules using linguistic variables. The results of the software algorithm of fuzzy inference models were obtained using the Python environment. Membership functions, predictive graphs were built and their evaluation was carried out. The numerical values of the root mean square error (MSE) are calculated. As a result, it was found that the Cheng fuzzy prediction model has higher forecast accuracy than the Chen forecasting method.
METHOD OF COORDINATION OF MOTION OF SWARM ROBOTIC SYSTEMS

Maintaining a specific geometric pattern is essential in various applications where groups of autonomous robots must follow a given path. Proper organization of the geometric pattern can lead to several benefits such as cost reduction, increased system reliability, and efficiency while providing a reconfigurable and flexible structure of the system. Military missions and traffic systems are examples where maintaining certain geometric patterns are widely used. However, little is known about how to develop an effective algorithm that guarantees collision avoidance and obstacle avoidance while maintaining the geometric pattern. This paper presents an algorithm for movement with a certain geometric structure of a group of autonomous mobile robots that maintains the required geometric pattern and ensures the avoidance of collisions and obstacles. The proposed algorithm is behavior-based and utilizes a set of rules that allow the robots to navigate around obstacles and avoid collisions. The algorithm's performance is demonstrated through simulations in a variety of scenarios with different numbers of robots and geometric patterns. The algorithm proposed in this paper provides an effective solution for controlling a group of autonomous mobile robots to maintain a certain geometric pattern. The proposed algorithm has the potential to be utilized in numerous applications where multiple robots must work together to achieve a common goal while maintaining a specific formation. The use of behavior-based approach and obstacle avoidance rules ensures that the robots avoid collisions and obstacles while maintaining the required pattern
Analysis of self-esteem and self-image based on beauty standards

The individual's perception of body self-image influences their well-being, self-esteem, physical and mental health and quality of life. This study aimed to analyze self-esteem and self-image based on beauty standards, carried out through field research, with a descriptive nature and a quantitative-qualitative approach, with a sample of 128 participants, with four instruments used to collect data. data: sociodemographic questionnaire; Rosenberg's Self-Esteem Scale (1965) based on Hutz's (2000) adaptation for Brazil; a questionnaire built with eight questions about beauty standards and the silhouette scale Stunkard, Sorensen and Schlusinger (1983). Through the results obtained, it was found that most people follow pages on social networks about standards of beauty, aesthetics, body, considering body appearance as an indicator of beauty and having self-demand to comply with standards. Psychology can contribute to the knowledge and better relationship of this topic to mental health.
GRİ KURT OPTİMİZASYONUNDA KURT LİDERİNİN GELİŞTİRİLMESİ

The development of optimization algorithms attracts the attention of many analysts as it has advantages such as increasing performance, revenue, and efficiency in various fields, and reducing cost. Swarm-based optimization algorithms, which are among the meta-heuristic methods, are more commonly preferred because they are generally successful. In this study, the alpha wolf class, also called the wolf leader class, in the Grey Wolf Optimization (GWO), has been improved with the Whale Optimization Algorithm (WOA). This improved method is called ILGWO. To evaluate the ILGWO, 23 benchmark test functions, and 10 CEC2019 test functions were used. After running 30 iterations of the suggested algorithm, average fitness and standard deviation values have been acquired; these findings have been compared to the literature. Based on the literature's comparisons of the algorithms, the ILGWO algorithm has achieved the most optimal result in 5 of 7 functions for unimodal benchmark functions, 3 of 6 functions for multimodal benchmark functions, 9 of 10 functions for fixed-dimension multimodal benchmark functions, and 8 of 10 functions for CEC2019 test functions. So the proposed algorithm is generally better than the literature results. It has been found that the suggested ILGWO is encouraging and may be used in a variety of implementations.
Kegiatan preservasi koleksi di Perpustakaan Institut Teknologi Nasional

The Library of the National Institute of Technology (Itenas) has many collection of organic materials. The collection in this library is vulnerable to collection mild damage to severe levels so that preservation activities are one way to overcome them. The purpose of this research to find out preservation activity collection at The Library of the National Institute of Technology through preventive, curative, and restorative. The research method used qualitative descriptive. The results showed that biota like termite, and human factors like vandalism were the causes of the damage to the library collections. Librarian have been done preservation activity to minimize collection damage through preventive, curative, and restorative. Preventive preservation activity carried out through educational programs for library staff, provision and arrangement of collection storage areas, environmental control programs and implementation of security systems using CCTV. Curative preservation activity carried out through library cleaning as a periodically, it’s name Jumat Bersih (Clean Friday) Program, and fumigation activities. Restorative preservation activity carried out through binding and mending of damaged library collections. The preservation activities at The Library of National Institute of Technology has some constrains including lack of budget, facilities, and the small number of librarian who worked in preservation activities. Conclusion this research is librarian at The Library of National Institute of Technology has conducted preservation activities with proper procedure consist of preventive, curative, and restorative activities.
A scene understanding network based on driving scene

Accurate prediction of the surrounding traffic environment is crucial for the safety of autonomous vehicles. However, the limitation of onboard system resources and the complexity and diversity of driving scenes hinder the deployment of scene understanding in the auto-drive system. This paper optimizes the backbone network, uses deep separable convolution to reduce the complexity of network operations and uses multiple attention mechanisms in the decoding stage. On this basis, this paper adopts the shared strategy for the feature extraction module and jointly trains the semantic segmentation and Object detection, which can reduce the network parameters, improve the reasoning speed, and improve the accuracy. We have evaluated the proposed method on the public data set. The results show that our method achieves the most advanced performance and can balance speed and accuracy.
ANALISIS ‬BIBLIOMETRIK TREND PENELITIAN JURNAL IQRA (JURNAL PERPUSTAKAAN DAN INFORMASI) PERIODE 2017-2021 MENGGUNAKAN ‬VOS VIEWER‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

This study aims to determine the map of the development of research trends in the IQRA Journal for the 2017-2021 period. This study formulates the problem of how the development of article publication trends and literature patterns in IQRA articles (Library and Information Journal) published in 2017-2021. This study used a descriptive research method with a quantitative approach using bibliometric analysis techniques. Mapping is done with co-word based mapping. The research sample is 89 IQRA articles for 2017-2021. In this study, two supporting applications in bibliometrics were used, namely Mendeley and VOSViewer. The results of the study show that the visualization of the results of research data processing using the 2017-2021 IQRA journal metadata is grouped into 4 clusters. The results of the VOSViewer analysis seen from the overlay visualization that the research topic in the journal IQRA 2017-2021 which has been carried out for a long time is about librarians and the latest research on services. The results of the density visualization mean that the keyword groups in the 2017-2021 IQRA journal have a relationship with each other, in which the most frequently researched topic is the library. Judging from its development, the most published articles published by the IQRA Journal were in 2019 and the lowest was in 2021.
The Impact of Distant Education on the Art and Design Students’ Performance in Practical Courses during the Corona Pandemic. The School of Art and Design at the University of Jordan as a Model

Objectives: The study aimed to reveal the extent to which students are affected by the transition to e-learning in light of the Corona pandemic, and how it affects their academic achievement and performance. The study also aimed to identify the reality of e-learning for practical courses in the Faculty of Arts and Design at the University of Jordan in light of the Corona pandemic.&#x0D; Methods: The descriptive survey approach was adopted in this study as it is the most suitable approach for the study's topic. E-learning for practical courses was measured using a questionnaire distributed to 120 students to collect data from the study sample. The collected data was then analyzed using SPSS to obtain results that could help answer the research questions.&#x0D; Results: The study showed that there are differences in students' performance as a result of the transition to e-learning, which has had a negative impact on their academic achievement. It also revealed that practical courses in the Faculty of Arts and Design at the University of Jordan were negatively affected in terms of learning outcomes compared to in-person learning.&#x0D; Conclusion: The study concluded that the e-learning process falls short of achieving the goals of education in practical courses, necessitating the development of e-learning programs and platforms that are aligned with the requirements of these courses.
Scheduling Most Valuable Committees for the Sharded Blockchain

In a sharded blockchain, transactions are processed by a number of parallel committees. Thus, the transaction throughput can be largely boosted. A problem is that some groups of blockchain nodes consume large latency to form committees at the beginning of each epoch. Moreover, the heterogeneous processing capabilities of different committees also result in imbalanced consensus latency. Such imbalanced two-phase latency brings a large cumulative age to the transactions pending in transaction pool. Consequently, the blockchain throughput can be significantly degraded. We believe that a good committee-scheduling strategy can reduce the cumulative age of transactions, and thus benefit the throughput. However, we have not yet found a committee-scheduling mechanism that works for accelerating block formation in the context of blockchain sharding. To this end, this paper studies a fine-balanced tradeoff between the transactions’ throughput and their cumulative age in a large-scale sharded blockchain. We formulate this tradeoff as a utility-maximization problem, which is proved NP-hard. To solve this problem, we propose an online distributed Stochastic-Exploration (SE) algorithm, which guarantees a near-optimal system utility. We then rigorously analyze three theoretical properties of the proposed algorithm, including the theoretical convergence time, the probability of committees’ failure due to Sybil attacks, as well as the performance perturbation brought by committees’ offline events. Finally, we evaluate the proposed algorithm using the dataset of real-world blockchain transactions. The simulation results demonstrate that the proposed SE algorithm outperforms other baselines in terms of system utility, the valuable degree of yielded solutions, latency, and throughput performance.
Contrastive Learning of Person-Independent Representations for Facial Action Unit Detection

Facial action unit (AU) detection, aiming to classify AU present in the facial image, has long suffered from insufficient AU annotations. In this paper, we aim to mitigate this data scarcity issue by learning AU representations from a large number of unlabelled facial videos in a contrastive learning paradigm. We formulate the self-supervised AU representation learning signals in two-fold: 1) AU representation should be frame-wisely discriminative within a short video clip; 2) Facial frames sampled from different identities but show analogous facial AUs should have consistent AU representations. As to achieve these goals, we propose to contrastively learn the AU representation within a video clip and devise a cross-identity reconstruction mechanism to learn the person-independent representations. Specially, we adopt a margin-based temporal contrastive learning paradigm to perceive the temporal AU coherence and evolution characteristics within a clip that consists of consecutive input facial frames. Moreover, the cross-identity reconstruction mechanism facilitates pushing the faces from different identities but show analogous AUs close in the latent embedding space. Experimental results on three public AU datasets demonstrate that the learned AU representation is discriminative for AU detection. Our method outperforms other contrastive learning methods and significantly closes the performance gap between the self-supervised and supervised AU detection approaches.
Fast black box attack of face recognition system based on metaheuristic simulated annealing

Deep neural networks (DNNs), which have high accuracy prediction and stable network performance, have been widely deployed in various fields. However, the adversarial example, a sample of input data which has been modified very slightly in a way, may easily cause a DNN to maximize loss. Instead of white box attack being able to obtain gradient information, most DNN based systems in actual use can only be attacked by multiple queries. In this paper, we regard face recognition (FR) system as target, and propose a new method named SA-Attack to generate adversarial samples which cannot be distinguished by human within very limited queries. Experiments show that SA-Attack can successfully attack advanced face recognition models, including public and commercial solutions, which proves the practicability of our method.
Teaching Resource Sharing System in OBE Mode Based on Data Mining Technology

The association rule method describes the rules of the relationship between data items in the database, and explores the association relationship between variables through the confidence and support degree. Depending on the appearance of some items in a transaction, other items in the same transaction can be derived, that is, relationships hidden in the data or associations between each other. In this paper, an OBE teaching resource sharing system based on data mining technology is designed, and a mathematical model consisting of the definition of distance between rules, extraction of user preference features, similarity calculation and generation of recommendation results is constructed. According to the business process, the system function composed of teaching resource collection, teaching resource processing, teaching resource storage, teaching resource mining, teaching resource recommendation, teaching resource retrieval and teaching resource statistics is constructed. And the specific function is explained.
Machine Learning Based Algorithm for Earthquake Monitoring

In this paper, we are trying to predict the Earthquake before it happens using seismic indicators anywhere around the world. It is an important task, on detecting seismic events from seismic time series using machine learning techniques. When we train a dataset for any one location it should also be applicable to detect at the other locations. Earthquakes are one of the most dangerous and deadly natural disasters. It is very important for the safety of all human beings as well as all the animals living on the planet to predict the earthquake way before it is going to happen. The population of the earth is increasing at a very rapid rate so the human population is also settling in the areas where the region is at very high risk of earthquakes which also causes more people to be affected by the impact of the earthquakes and more people to die. During this research various machine learning techniques were used such as Support Vector Machine, Random Forest, Neural Network, Convolutional Neural Network, Quantum Machine Learning, K-Nearest Neighbor, Machine Learning Risk Prediction Algorithm, Deep learning Model, and Regression.
SECURE CLOUD STORAGE WITH DEDUPLICATION TECHNIQUE

Data de-duplication refers to provide the cloud providers a way to manage the uncontrollable data and the challenges of cloud storage.The success of IOT and social media led us to face big data challenges.Big data is interesting but also becomes a critical challenge for cloud service providers.Data storage and management become also the topic of discussion where big data generate opportunity for business as well as come with big issues for the cloud providers.In this paper, we discussed the issues of redundant data and techniques to prevent the data redundancy on the cloud.Third party auditor checks the user's data for correctness and gives the accuracy of the data that is stored in cloud server.The communication and computation overhead are reduced.Deduplication technique is used to check whether the file that user need to store in cloud storage is already exist at cloud server or not.
Design of Data Forwarding Software for Multi-model Temperature Humidity Controllers Based on Proxy Technology

Aiming to extending compatibility of original temperature/humidity monitor system, making Centralized temperature/ humidity monitor software can access different temperature/humidity controllers made in various manufacturers, and maintaining the integrity of it, in this paper, a novel design of data forwarding architecture is proposed.The design developed in Delphi based on SOCKS5 protocol to support different models of target temperature/humidity controller distributed by LAN network by using UDP or TCP component to encapsulate and decapsulate the original data packet from the Centralized software.The experimental results demonstrated that the designed software has met the requirements, that this proposal is feasible and it achieves system scalability at minimal development costs.
Eye Ball Based Cursor Movement Control

Eye tracking technology has been widely used in the field of human-computer interaction as an assistive technology for individuals with motor disabilities. This study presents a novel approach for controlling the movement of a computer cursor using eye tracking techniques. The proposed method relies on the user's eye movement patterns to accurately and intuitively move the cursor on the screen. The system is designed to be simple, easy to use, and accessible to a wide range of users. Results from a preliminary user study show that the proposed method can achieve high accuracy and efficiency in cursor control. This research has potential applications in various fields, such as gaming, virtual reality, and accessibility.