Reviewerly/exHarmony huggingface dataset:
Bi-Selection of Instances and Features Based on Neighborhood Importance Degree

As one of the most important concepts for classification learning, neighborhood granules obtained by dividing adjacent objects or instances can be regarded as the minimal elements to simulate human cognition. At present, neighborhood granules have been successfully applied to knowledge acquisition. Nevertheless, little work has been devoted to the simultaneous selection of features and instances by the use of neighborhood granules. To fill this gap, we investigate in this paper the issue of bi-selection of instances and features based on neighborhood importance degree (NID). Firstly, the conditional neighborhood entropy is defined to measure decision uncertainty of a neighborhood granule. Considering both decision uncertainty and coverage ability of a neighborhood granule, we propose the concept of NID. Then, an instance selection algorithm is formulated to select representative instances based on NID. Furthermore, an NID-based feature selection algorithm is provided for a neighborhood decision system. By integrating the instance selection and feature selection methods, a bi-selection approach based on NID (BSNID) is finally proposed to select instances and features. Lastly, some numerical experiments are conducted to evaluate the performance of BSNID. The results demonstrate that BSNID can take account of both reduction ratio and classification accuracy and, therefore, performs satisfactorily in effectiveness.
Innovative Cybersecurity for Enhanced Data Protection: An Extended Bit-Plane Extraction and Chaotic Permutation-Diffusion Approach in Information Security

In the era of big data, protecting digital images from cyberattacks during network transmission is of utmost importance. While various image encryption algorithms have been developed, some remain vulnerable to specific cyber threats. This paper presents an enhanced version of the image encryption algorithm based on bit-plane extraction (BPCPD) to address its vulnerability to chosen-plaintext attacks. The proposed cryptographic system encompasses three primary phases. The initial phase involves bit-plane extraction from the plaintext image and the generation of random sequences and a random image using multiple chaotic maps, such as the chaotic Arnold map and the chaotic CAT map. The second phase is dedicated to permutation operations, which comprise three sub-phases: multi-layer permutation, multi-round permutation, and recursive permutation. In the third phase, diffusion is introduced to the permuted image through pixel substitution, coupled with XOR operations performed on the respective bit-planes of the random image. To gauge the efficiency of the proposed encryption scheme, a range of experimental analyses are conducted, including histogram analysis, contrast assessment, entropy measurement, correlation analysis, encryption quality assessment, and investigations into noise attacks and occlusion attacks. The results of these experimental analyses, in comparison to an existing encryption scheme, demonstrate that the proposed framework surpasses both BPCPD and other existing encryption schemes in various aspects of performance.
Zero-Shot Learners for Natural Language Understanding via a Unified Multiple-Choice Perspective

Zero-shot learning is an approach where models generalize to unseen tasks without direct training on them. We introduce the Unified Multiple-Choice (UniMC) framework, which is format-independent, compatible with various formats, and applicable to tasks like text classification and sentiment analysis. Furthermore, we design a two-stage tuning method, initially training on multiple-choice formats to develop format-agnostic capabilities, and subsequently enabling direct predictions on unseen tasks for zero-shot learning. Our methodology avoids issues in large-scale models like FLAN, enhancing generalization and reducing parameters. In experiments, UniMC shows State-of-the-Art (SOTA) performance across out-of-domain and in-domain benchmarks, with only 235M parameters, far fewer than previous methods. Moreover, the UniMC-Chinese model excels beyond human performance on benchmarks like EPRSTMT and CHID-FC, underscoring its generalization capacity across languages. Additionally, ablation experiments demonstrate the effectiveness of our design.
Entropy Enhanced Multi-Agent Coordination Based on Hierarchical Graph Learning for Continuous Action Space

In most existing studies on large-scale multi-agent coordination, the control methods aim to learn discrete policies for agents with finite choices. They rarely consider selecting actions directly from continuous action spaces to provide more accurate control; therefore, they are normally unsuitable for more complex tasks. To solve the control issue of large-scale multi-agent systems with continuous action spaces, we propose a novel MARL coordination control method named EHCAMA to derive stable continuous policies, by constructing a new network architecture in an actor–critic framework. By optimizing policies with maximum entropy learning, agents improve their exploration ability in training and acquire an excellent performance in execution. Further, we employ hierarchical graph attention networks (HGATs) and gated recurrent units (GRUs) to improve the scalability and transferability of our method. We simulate the performance of EHCAMA for cooperative tasks with both homogeneous and heterogeneous agents, and compare it with SAC-HGRN, HAMA, AHAC and AGGAT-Comm. The experimental results show that our method consistently outperforms the baselines in large-scale multi-agent scenarios.
Concept and design for a swarm robot and implementation of the collaboration algorithm

With the continuous advancement of technology, collaborative robots in artificial intelligence have seen a very important and rapid development due to their use in all domains, especially in hostile, dangerous, and hard-to-reach environments where many robots are designed to perform jobs that are dangerous to humans. Several studies have made progress in this domain using various artificial intelligence algorithms. In this paper, we highlight two important algorithms and apply them to a group of robots in different challenging environments based on the Gazebo simulator. The first algorithm is the Search-Based Planning Library (SBPL) algorithm, and the second is the A-star algorithm, and we compare them. The experimental results showed that both algorithms are suitable, but the A-star algorithm is best suited for robots to cooperate with each other to reach the targets without hitting obstacles in the shortest time compared to the SBPL algorithm because making the right decision will take a lot of time.
Impact of organizational positive and negative culture on employee performance

Purpose The purpose of this study is to evaluate the effect of both positive and negative corporate cultures on employee performance in Saudi Arabia, in the presence of performance reviews and factors leading to positive work culture, in an environment that underwent serious modification with COVID-19. The changing work methods (online, remote, etc.) have left their mark necessitating revisiting the needs and capabilities of employees in the work environment. Design/methodology/approach This quantitative study uses primary data from small and medium-sized enterprises (SMEs), non-profit organizations, and transnational organizations, in their transient role and influence on organizational culture change. The data set includes 311 usable responses from 50 randomly selected organizations and is analysed using structural equation modelling to test the proposed model. Findings A healthy corporate culture serves as the basis for increased employee performance in the workplace. The three independent variables, availability of negative culture, availability of positive culture and employee perceived performance, have a positive impact on the dependent variable employee perceived effectiveness of performance reviews; however, factors leading to the development of a positive organizational has a negative influence. Research limitations/implications This study faced a limitation with the potential similarity of responses due to the large number of same-background respondents (engineers). However, the results are indicative of a trend. Moreover, the responses did not allow for cross comparison between responding organizational types (SMEs, non-governmental organizations and multinational corporations) as was planned. Practical implications Managers should motivate their respective employees, through ensuring the diffusion and sustainability of the right culture work environment. This should allow their teams to complete tasks with little or no supervision. Moreover, as the Saudi economy is gearing up for global competitiveness, this performance culture becomes a key for the success of the strategic plans, thus the high importance of the positive culture at work today. Social implications Understanding the importance of positive and negative culture at the managerial level would affect the relationship with employees and improve work environment and job satisfaction. Originality/value This study pinpoints the need to revisit a dimming topic, proving that with the end of the COVID-19 pandemic, managers need to go back to square one. The introduction of the many novel work systems, online, remote work, etc. have changed the work setting and environment. This is requiring a new look at the employees’ perceptions on factors influencing corporate culture and performance.
Pembelajaran Membaca dan Menulis Melalui Penggunaan Bahasa Ibu di SDN 153 Rejang Lebong

Student learning at this level is very important for reading and writing skills which are the driving force for research. Need for learning to read and write to students. are two abilities that must be taught. Lower grade students who learn in their native language usually show better learning outcomes. Purpose of this study was to identify barriers and challenges associated with reading and writing learning as well as the process of learning to read and write through mother tongue. Qualitative method descriptive instruments used interviews, observation, documentation. The results of the study, the first step in learning to read the mother tongue is to introduce letters of the alphabet, string words, read aloud, read sentences, read self-identity, and distinguish letters of the alphabet. Write alphabets, words, sentences, write self-identity, write the difference between capital and lowercase letters. Through asking and answering and interacting with friends, students understand what the teacher teaches, difficulty understanding letters, intellectual and environmental factors.
The Ability of Second Grade Senior High School Students in Speaking Transactional and Interpersonal Conversation at SMA Negeri 7 Padang

Speaking is often tested to the students beside listening, reading and writing. Moreover, speaking is an important skill. Students can express their feeling, ideas or opinion through speaking. In addition, students should be able to retell meaning of the monologue and functional texts or produce a transactional/interpersonal dialogue. The object of the data is the second grade students of SMAN 7 Padang. Speaking test was used to get information about the students’ ability in transactional and interpersonal conversation. The indicators are grammar, vocabulary, comprehension, and fluency. Based on the findings and discussion above there are four conclusions that can be drawn in this research. First, the ability of the students in speaking transactional and interpersonal conversation viewed from grammar aspect is fair. Second, the ability of the students in speaking transactional and interpersonal conversation viewed from vocabulary aspect is fair. Third, the ability of the students in speaking transactional and interpersonal conversation viewed from comprehension aspect was fair. Forth, the ability of the students in speaking transactional and interpersonal conversation viewed from fluency aspect is fair.
LONG SHORT-TERM MEMORY FOR PREDICTION OF WAVE HEIGHT AND WIND SPEED USING PROPHET FOR OUTLIERS

The reason fishermen lose control is wave height and wind speed. The impact is also felt by all users of the marine sector. This research uses the Long Short Term Memory (LSTM) method because this method has accurate values in the forecasting process with a lot of historical data and uses the Prophet method to detect outliers with Newton interpolation to replace the detected outlier data. The total number of data was 2074 obtained from BMKG Perak Surabaya from January 2020 to November 2022 at four research points, namely north, northeast, east and south points. The test results provide varying error values with MAPE as the model evaluation value. The error value for sea wave height at the north, northeast, east and south points is 13.32 respectively; 13.32; 9.32 and 8.85 with data without interpolation. Meanwhile, the error value in the wind speed data is 14.74; 14.85; 15.14 and 14.52 with a 3rd order Newton interpolation process at the northeast and east points. MAPE values below 20% prove that the LSTM model is good for predicting wave height and wind speed data at four points in Sumenep Regency. The system implementation is made into a web-based application.
DEEP LEARNING ARCHITECTURE BASED ON CONVOLUTIONAL NEURAL NETWORK (CNN) IN IMAGE CLASSIFICATION

In current technological developments, Deep Learning is one of the most popular studies today, especially in the fields of machine learning and computer vision, GPU Acceration Technology is one of the reasons for the development of Deep Learning. Deep Learning has a very good ability to solve classic problems in the field of computer vision, one of which is in the case of object classification in images. one of the deep learning methods that is often used in image processing is the Convolution Neural Network (CNN) which is a development of the Multi Layer Perceptron method. This study uses the CNN architecture which consists of a convolution layer, as well as a fully connected layer, and will also determine the appropriate Optimizer and Loss function for CNN. The implementation of this method uses Google Colab (Tensorflow and Keras) with the Python programming language. In the training process using CNN, setting the number of epochs is done to improve accuracy in image classification, in the first scenario using epoch 20 produces an average accuracy of 99.45 with a loss value of 1.66. In the second scenario using epoch 15 produces an average accuracy value of 99.00 with a loss value of 2.92. then in the third scenario with a number of epochs 10 it produces an average accuracy value of 95.55 with a loss value of 95.55, while in the last scenario with a number of epochs 5 it produces an average accuracy value of 73.6 with a loss value of 51.92. From the 4 trial scenarios using the CNN method gives effective results and produces a fairly good accuracy value with an average accuracy and loss value of 99.99%. As well as the results of an average loss of 4.
TEACHERS’ STRATEGIES TO IMPROVE STUDENTS’ VOCABULARY IN EFL CLASSROOM (CASE STUDY AT SMP 27 DEPOK)

Vocabulary is one of important aspects in English skill. Without proper vocabulary, the English skill of students would be getting difficult or blunt. This study aims to find out the problem faced by students and teacher's strategies to improve students' English vocabulary at SMP Negeri 27 Depok. The sample of this research is 34 students; divided into two classes, A and B class. This research is categorized as the case-study research by conducting a descriptive qualitative method. The steps that writer collects the data by observing and interviewing both students and teacher as well. As a result of this study, there are some problems faced by students in improving vocabulary, as follows: (1) Students of SMP Negeri 27 Depok have difficulty understanding new vocabulary, (2) Students do not memorize vocabulary, and finally, (3) Students are still lacking in terms of writing structure. The strategies that are implemented: (1) Encouraging students to use e-dictionary, (2) Encouraging students to memorize more vocabulary, (3) Teachers teach through some classical games, and lastly, (4) The teacher gives students a task to complete vocabulary exercises or writing sentences using new words to practice their writing skills. The strategies are able to sharpen the students’ abilities in writing skills as they follow English learning effectively.
Analisis Lalu Lintas Data Internet Gim Multipemain Battle Royale Berbasis Konsol di Jakarta

Playing games on portable or non-portable console platforms is an activity that can be done anywhere when having free time. However, playing online-based games requires reliable Internet speed in Jakarta, both wired and wireless, in several areas, specifically in multiplayer online games like Fortnite, which are busy playing multi-platforms. This study aims to determine Internet data traffic when playing in 1 match. This research activity was carried out using quantitative methods, measured descriptively, using non-probabilistic samples, and using primary data that was recorded and worked on independently. Obtained in 1 time playing the game Fortnite has an average downstream of 10.52 ± 4.41 KBps, upstream of 3.08 ± 1.12 Kbps, ping of 103.44 ± 10.74 milliseconds, and 0% packet loss. Data traffic, which is only 0.12% downstream and 0.04% upstream, shows that this game is friendly to Internet data traffic to be played on game consoles using a postpaid cable network in Jakarta.
AN ENSEMBLE LEARNING-BASED ALERT TRIGGER SYSTEM FOR PREDICTIVE MAINTENANCE OF ASSETS WITH IN-SITU SENSORS

Failure of assets, such as machines, engines, or equipment, can cause significant loss to an enterprise. Real-time monitoring of the asset operation and early detection of failure could guide mechanics or engineers to check the assets in time and reduce the chance of a breakdown. With the availability of Internet of Things (IoT) technologies and the development of machine learning, predictive maintenance has become an effective way to monitor asset performance and detect anomalies using sensor data and historical information. However, many use cases need more information to evaluate the results. Lack of validation can undermine confidence in predictive results. To solve this problem, a novel alert trigger schema that integrates sensor fusion, feature extraction, machine learning, ensemble strategy, and an alert format is proposed. The method offers a comprehensive and reliable approach to detecting anomalies based on unlabeled data, utilizing sensor and decision-level fusion techniques. Meanwhile, eight anomaly detection techniques were investigated, including K-Means Clustering, Gaussian Mixture Model, Autoencoder, and Isolation Forest. Various algorithms generate results with differing degrees of confidence. These results are consolidated into a single indicator representing the alert level. This amalgamation of data ensures the provision of robust and reliable predictions. Instead of simply combing the alert information, confidence in different algorithms is reflected in adding different weights in the ensemble process. In addition, while other existing frameworks focus on evaluating the algorithm’s accuracy, more effort was put into demonstrating a levelbased alert system, showing diagnosis changing with time. In that way, mechanics or engineers can get information to check the asset’s status. The proposed framework was validated using a synthetic dataset based on recordings from a rotating fault simulator that generates multi-modal data, including accelerometer, acoustic, and tachometer data, representing the run state of the rotating components. The alert system showed different levels of warning for predictive maintenance. The framework is designed with high flexibility and scalability. Therefore, this framework can be generalized to other in situsensor data from various assets.
Design Of A Web-Based Membership Data Processing System At Vizta Gym Using A Prototype Method

Vizta Gym is a fitness center with a modern, clean, comfortable concept and complete facilities. Vizta Gym, which is present in several places in busy centers or malls in Medan City, also makes it easier for its members to carry out physical fitness sports activities wherever the Vizta Gym branch is located. However, the member verification process is still an obstacle. Someone who activates a member at one Vizta Gym branch and wants to do sports activities at another Vizta Gym branch must be verified manually. Apart from that, members often forget the sports schedule that has been prepared or planned in advance and the member's active deadline. The aim of this research is to build a Vizta Gym membership data processing system using the prototype method and unified modeling language. The system that has been designed and built has succeeded in running well, making it easy for system administrators to process fitness training data and making it easy for members to find out information on training schedules and transactions.
MODEL-BASED MULTI-UAV PATH PLANNING FOR HIGH-QUALITY 3D RECONSTRUCTION OF BUILDINGS

Abstract. Unmanned aerial vehicle (UAV) photogrammetry is widely used for acquiring high-quality 3D models of urban areas. However, the completeness and quality of the reconstructed model can be affected by complex or concave structures when using common flight paths. Furthermore, flight paths with multi-altitude and multi-attitude waypoints can be time-consuming and may not be efficiently implemented by a single drone. To address these challenges, we propose a model-based multi-UAV path planning method for capturing images that enable complete and precise 3D reconstruction of buildings. Our method analyzes the geometry of the input coarse model and plans viewpoints based on the reconstructability related to completeness and precision. By solving a multiple travelling salesmen problem (mTSP), individual flight paths for each UAV are generated. We conducted a real-world experiment comparing the performance and efficiency of the proposed method with two existing solutions. Results the proposed method produces a more complete 3D reconstruction with fewer images compared to the existing methods. It also shows that using two UAVs with the proposed method can significantly reduce the overall time required for 3D reconstruction.
Coherence in multipath interference via quantum Fisher information

Wave-particle duality, as complementarity displayed in interferometry, has been investigated extensively from both theoretical and experimental perspectives. In this work, we quantify coherence in multipath interference via quantum Fisher information and illuminate its basic properties. We prove that any rational quantifier of coherence satisfying monotonicity under incoherent operations is nonincreasing in the presence of path detectors. This quantitatively implies that increase on the path information will lead to the loss of coherence or interference (wave feature), which is consistent with wave-particle duality. Furthermore, we provide an operational illustration of wave feature as quantum Fisher information of phase shift parameters encoded in each interference path. By dividing the variance of a quantum state relative to a von Neumann measurement into quantum uncertainty and classical uncertainty, we establish a coherence-predictability-correlations triality relation, with coherence (wave feature) quantified by quantum Fisher information, predictability quantified by purity of a classical probability distribution, and correlations quantified by classical uncertainty relative to the von Neumann measurement determined by the interference paths. Finally, we make a comparison between this triality relation and the wave-particle-mixedness triality relation in the literature.