Reviewerly/exHarmony huggingface dataset:
Development of Android-Based Learning Media Applications to Determine Validity and Feasibility for Grade VII Middle School Students on Ecosystem Material

The world community has been hit hard by the covid-19 outbreak since 2 years ago until now there are still confirmed cases with a new variant. This also has an impact on the world of education, so that all learning implementers compete to create innovative learning media, easy to apply using mobile phones both online and offline in supporting the process and understanding of student material. The research objective is to develop an android-based learning media application. The development method is using the waterfall process model which is divided into six stages, namely: communication, planning, modeling, construction, and deployment. The assessment aspect observed in the development of learning media is how the media is developed and the teaching materials contained in it. The results of the assessment through filling out instruments on learning media were developed using a scale of 4 from various aspects of observation by experts in their fields, namely: 1) Media expert validators, namely: (a) software engineering aspects 3.75; (b) aspects of learning design 3.71; and (c) visual communication aspect 3.77; 2) Validators of material experts and teachers, namely: (a) aspects of learning objectives 4; (b) aspects of learning materials 3.95; (c) aspects of learning methods 3.5; (d) aspects of learning resources 3.5; and (e) aspects of learning activities 3.75. From this assessment, it can be concluded that the developed learning media has a good predicate and is suitable for use in the learning process
Improving convolutional neural network based on hyperparameter optimization using variable length genetic algorithm for english digit handwritten recognition

Convolutional Neural Networks (CNNs) perform well compared to other deep learning models in image recognition, especially in handwritten alphabetic numeral datasets. CNN's challenging task is to find an architecture with the right hyperparameters. Usually, this activity is done by trial and error. A genetic algorithm (GA) has been widely used for automatic hyperparameter optimization. However, the original GA with fixed chromosome length allows for suboptimal solution results because CNN has a variable number of hyperparameters depending on the depth of the model. Previous work proposed variable chromosome lengths to overcome the drawbacks of native GA. This paper proposes a variable length GA by adding global hyperparameters, namely optimizer and learning speed, to systematically and automatically tune CNN hyperparameters to improve performance. We optimize seven hyperparameters, such as the learning rate. Optimizer, kernel, filter, activation function, number of layers and pooling. The experimental results show that a population of 25 produces the best fitness value and average fitness. In addition, the comparison results show that the proposed model is superior to the basic model based on accuracy. The experimental results show that the proposed model is about 99.18% higher than the baseline model.
Lightweight pyramid residual features with attention for person re-identification

Person re-identification is one of the problems in the computer vision field that aims to retrieve similar human images in some image collections (or galleries). It is very useful for people searching or tracking in a closed environment (like a mall or building). One of the highlighted things on person re-identification problems is that the model is usually designed only for performance instead of performance and computing power consideration, which is applicable for devices with limited computing power. In this paper, we proposed a lightweight residual network with pyramid attention for person re-identification problems. The lightweight residual network adopted from the residual network (ResNet) model used for CIFAR dataset experiments consists of not more than two million parameters. An additional pyramid features extraction network and attention module are added to the network to improve the classifier's performance. We use CPFE (Context-aware Pyramid Features Extraction) network that utilizes atrous convolution with different dilation rates to extract the pyramid features. In addition, two different attention networks are used for the classifier: channel-wise and spatial-based attention networks. The proposed classifier is tested using widely use Market-1501 and DukeMTMC-reID person re-identification datasets. Experiments on Market-1501 and DukeMTMC-reID datasets show that our proposed classifier can perform well and outperform the classifier without CPFE and attention networks. Further investigation and ablation study shows that our proposed classifier has higher information density compared with other person re-identification methods.
Analysis of the potential efficiency of post-filtering noisy images after lossy compression

An increase in the number of images and their average size is the general trend nowadays. This increase leads to certain problems with data storage and transfer via communication lines. A common way to solve this problem is to apply lossy compression that provides sufficiently larger compression ratios compared to lossless compression approaches. However, lossy compression has several peculiarities, especially if a compressed image is corrupted by quite intensive noise. First, a specific noise-filtering effect is observed. Second, an optimal operational point (OOP) might exist where the quality of a compressed image is closer to the corresponding noise-free image than the quality of the original image according to a chosen quality metric. In this case, it is worth compressing this image in the OOP or its closest neighborhood. These peculiarities have been earlier studied and their positive impact on image quality improvement has been demonstrated. Filtering of noisy images due to lossy compression is not perfect. Because of this, it is worth checking can additional quality improvement be reached using such an approach as post-filtering. In this study, we attempt to answer the questions: “is it worth to post-filter an image after lossy compression, especially in OOP’s neighborhood? And what benefit can it bring in the sense of image quality?”. The study is carried out for better portable graphics (BPG) coder and the DCT-based filter focusing mainly on one-component (grayscale) images. The quality of images is characterized by several metrics such as PSNR, PSNR-HVS-M, and FSIM. Possible image quality increasing via post-filtering is demonstrated and the recommendations for filter parameter setting are given.
The Modeling Of Laboratory Information Systems In Higher Education Based On Enterprise Architecture Planning (EAP) For Optimizing Monitoring And Equipment Maintenance

The laboratory is a place to conduct scientific research, experiments, measurements, or scientific training. FMIPA UNNES has several laboratories distributed in each department to support student lectures. Through the implementation of practicum in the laboratory, students are expected to be able to find a concept, foster scientific attitudes, and critical thinking skills. Good laboratory management is expected to be able to utilize laboratory resources effectively and efficiently. Laboratory equipment must be ensured to function properly and be ready to be used for practicum. To support this, it is necessary to monitor the condition of the equipment and immediately repair the equipment if any damage is found. The current obstacle is monitoring tool repairs manually, so there are shortcomings such as poor documentation, and equipment conditions that cannot be monitored online. In this study, an information system for monitoring the maintenance of laboratory equipment in the departments in the FMIPA UNNES environment will be built. The research method begins with a literature study, initial data collection and observation, EAP-based system design, system testing, system analysis, and system evaluation. This study uses the SDLC (System Development Life Cycle) approach which is used to develop a product for the Monitoring Information System for the Maintenance of Laboratory Equipment. Testing is done using black box testing. From the results of development and testing, it can be concluded that the system can be used to simplify the process of managing laboratory equipment with a UAT value of 88% suitable for use.
Neural Network Entropy (NNetEn): Entropy-Based EEG Signal and Chaotic Time Series Classification, Python Package for NNetEn Calculation

Entropy measures are effective features for time series classification problems. Traditional entropy measures, such as Shannon entropy, use probability distribution function. However, for the effective separation of time series, new entropy estimation methods are required to characterize the chaotic dynamic of the system. Our concept of Neural Network Entropy (NNetEn) is based on the classification of special datasets in relation to the entropy of the time series recorded in the reservoir of the neural network. NNetEn estimates the chaotic dynamics of time series in an original way and does not take into account probability distribution functions. We propose two new classification metrics: R2 Efficiency and Pearson Efficiency. The efficiency of NNetEn is verified on separation of two chaotic time series of sine mapping using dispersion analysis. For two close dynamic time series (r = 1.1918 and r = 1.2243), the F-ratio has reached the value of 124 and reflects high efficiency of the introduced method in classification problems. The electroenceph-alography signal classification for healthy persons and patients with Alzheimer disease illustrates the practical application of the NNetEn features. Our computations demonstrate the synergistic effect of increasing classification accuracy when applying traditional entropy measures and the NNetEn concept conjointly. An implementation of the algorithms in Python is presented.
MARTSIA: Enabling Data Confidentiality for Blockchain-based Process Execution

Multi-party business processes rely on the collaboration of various players in a decentralized setting. Blockchain technology can facilitate the automation of these processes, even in cases where trust among participants is limited. Transactions are stored in a ledger, a replica of which is retained by every node of the blockchain network. The operations saved thereby are thus publicly accessible. While this enhances transparency, reliability, and persistence, it hinders the utilization of public blockchains for process automation as it violates typical confidentiality requirements in corporate settings. In this paper, we propose MARTSIA: A Multi-Authority Approach to Transaction Systems for Interoperating Applications. MARTSIA enables precise control over process data at the level of message parts. Based on Multi-Authority Attribute-Based Encryption (MA-ABE), MARTSIA realizes a number of desirable properties, including confidentiality, transparency, and auditability. We implemented our approach in proof-of-concept prototypes, with which we conduct a case study in the area of supply chain management. Also, we show the integration of MARTSIA with a state-of-the-art blockchain-based process execution engine to secure the data flow.
Benchmarking FedAvg and FedCurv for Image Classification Tasks

Classic Machine Learning techniques require training on data available in a single data lake. However, aggregating data from different owners is not always convenient for different reasons, including security, privacy and secrecy. Data carry a value that might vanish when shared with others; the ability to avoid sharing the data enables industrial applications where security and privacy are of paramount importance, making it possible to train global models by implementing only local policies which can be run independently and even on air-gapped data centres. Federated Learning (FL) is a distributed machine learning approach which has emerged as an effective way to address privacy concerns by only sharing local AI models while keeping the data decentralized. Two critical challenges of Federated Learning are managing the heterogeneous systems in the same federated network and dealing with real data, which are often not independently and identically distributed (non-IID) among the clients. In this paper, we focus on the second problem, i.e., the problem of statistical heterogeneity of the data in the same federated network. In this setting, local models might be strayed far from the local optimum of the complete dataset, thus possibly hindering the convergence of the federated model. Several Federated Learning algorithms, such as FedAvg, FedProx and Federated Curvature (FedCurv), aiming at tackling the non-IID setting, have already been proposed. This work provides an empirical assessment of the behaviour of FedAvg and FedCurv in common non-IID scenarios. Results show that the number of epochs per round is an important hyper-parameter that, when tuned appropriately, can lead to significant performance gains while reducing the communication cost. As a side product of this work, we release the non-IID version of the datasets we used so to facilitate further comparisons from the FL community.
BERTino: an Italian DistilBERT model

The recent introduction of Transformers language representation models allowed great improvements in many natural language processing (NLP) tasks. However, if on one hand the performances achieved by this kind of architectures are surprising, on the other their usability is limited by the high number of parameters which constitute their network, resulting in high computational and memory demands. In this work we present BERTino, a DistilBERT model which proposes to be the first lightweight alternative to the BERT architecture specific for the Italian language. We evaluated BERTino on the Italian ISDT, Italian ParTUT, Italian WikiNER and multiclass classification tasks, obtaining F1 scores comparable to those obtained by a BERTBASE with a remarkable improvement in training and inference speed.
Blockchain-based Immutable Evidence and Decentralized Loss Adjustment for Autonomous Vehicle Accidents in Insurance

In case of an accident between two autonomous vehicles equipped with emerging technologies, how do we apportion liability among the various players? A special liability regime has not even yet been established for damages that may arise due to the accidents of autonomous vehicles. Would the immutable, time-stamped sensor records of vehicles on distributed ledger help define the intertwined relations of liability subjects right through the accident? What if the synthetic media created through deepfake gets involved in the insurance claims? While integrating AI-powered anomaly or deepfake detection into automated insurance claims processing helps to prevent insurance fraud, it is only a matter of time before deepfake becomes nearly undetectable even to elaborate forensic tools. This paper proposes a blockchain-based insurtech decentralized application to check the authenticity and provenance of the accident footage and also to decentralize the loss-adjusting process through a hybrid of decentralized and centralized databases using smart contracts.
Multi-view GCN for Loan Default Risk Prediction

Abstract Loan default risk prediction is a major application of machine learning for financial institutions to evaluate the client's default probability. Existing deep learning models rarely consider the connection among application records for loan default detection. We believe similar records, as auxiliary information, are also significant for loan default prediction, particularly for those records with many missing data. Additionally, in practical scenarios, the data distribution is imbalanced since the default records are small samples, which may also lead the model to achieve sub-optimal results. To this end, we propose multi-view loan application graphs, dubbed MLAGs, for small sample augmentation. Additionally, based on the graph convolution, similar records can also be aggregated to alleviate the issue of missing values. Moreover, a multi-view graph convolution network, named MGCN, is applied for loan default risk prediction. We conduct experiments on three public datasets from real-world home credit and P2P lending platforms, which show that MGCN outperforms both conventional and deep learning models.
The Impact of Organizational Justice on Job Commitment among the Employees in Jordanian Commercial Banks: Case of Arab Bank

The study aimed to identify the effect of organizational justice on the job commitment among the employees in Jordanian commercial banks – the case of Arab Bank. The correlation descriptive approach was used for its suitability for the purposes of the study, and the study sample was selected from the employees working in the General Administration building of the Arab Bank according to the sample selection table from The study population was randomly assigned, whose number is (186) employees.The results of the study showed that the management in the Arab Bank applying the four organizational justice dimensions (distributive justice, procedural justice, evaluative justice and interactive justice) were moderate, and the results showed that the job commitment of Arab Bank employees was also moderate.When testing the hypotheses of the study, it was found that there is a statistically significant differences at the level (α = 0.05) of organizational justice in its dimensions (distributive justice, procedural justice, evaluative justice and interactive justice) on the job commitment among employees at the Arab Bank.The study recommended the necessity of working to raise the level of fairness of the administrative procedures applied in the Arab Bank, and work to raise the level of interactive justice by holding periodic meetings within the bank to discuss the needs and requirements of employees. Keywords : Organizational Justice, Job Commitment, Jordanian Commercial Banks, Arab Bank DOI: 10.7176/JLPG/130-07 Publication date: March 31 st 2023
Investing in Higher Education (Human Capital) Key Factors of Economic Development in Global Flows

The phenomenon of globalization began as simple trade exchanges, but it has since developed into a multifaceted phenomenon that affects all aspects of people lives and nations around the world, from the biggest and most developed, which have millions of residents, to those with only a few thousand people and a small area.Starting with the political, economic, social, cultural, technological, and exchange of experiences in all areas, the advantages of this phenomena are widespread and diversified.There is no disputing the fact that everyone agrees that raising the standard and quality of education is one of the top concerns for all nations, whether developed and developing.Although education is first and foremost a fundamental human right, it may also be seen as a tool for advancing economic growth in any nation and as a significant driver of productivity and efficiency in the local, regional, and global economies.It is beyond dispute that resources (human, financial, and natural) are important, but only effective utilization of those resources can guarantee ongoing economic progress and social wellbeing.
PSNet: Position-Shift alignment Network for image caption

Abstract Recently, Transformer-based models have gained increasing popularity in the field of image captioning. The global attention mechanism of the Transformer facilitates the integration of region and grid features, leading to a significant improvement in accuracy. However, combining two features through direct fusion may lead to inevitable semantic noise, which is caused by non-synergistic issue of the region and grid features, meanwhile, the additional detector to extract region features also decrease the efficiency of the model. In this paper, we introduce a novel Position-Shift alignment Network (PSNet) to exploit the advantages of the two features. Concretely, we embed a simple detector DETR into the model and extracted region features based on grid features to improve model efficiency. Moreover, we propose a P Shift Alignment module to address semantic noise caused by non-synergistic issue of the region and grid features. To validate our model, we conduct extensive experiments and visualization on the MS-COCO dataset, and results show that PSNet is qualitatively competitive with existing models under comparable experimental conditions.
THE ROLE OF THE TEACHER TO OVERCOME THE LAZY STUDENTS AT SMA NEGERI 1 ATAMBUA INDONESIA

This qualitative study aimed to (1) knows the factors that cause students to be lazy in learning at SMA Negeri 1 Atambua and (2) know the role of the teacher to overcome those lazy students at SMA Negeri 1 Atambua. The subjects of the study were students, teachers, and vice principals. The data were collected through observation, interviews, and documentation. The data were analyzed through data reduction, data display, and drawing conclusions. The study showed that the factors that cause students to be lazy in learning, such as being full of assignments, learning methods, social environment, cellphone, social media, and poorly organized classrooms. Thus, there were 4 students who only got scores of 63, 66, 64, and 68, while the standard minimum criteria (KKM) was 70. Besides, teachers provided direct personal guidance and visited the students to overcome the lazy students in learning.
DEVELOPMENT OF INTERACTIVE LEARNING MEDIA TO IMPROVE LEARNING LOCAL SCRIPT IN SUMBAWA

The local script is one of the nation's cultural treasures. One of the local characters owned by the Indonesian people is the Satera Jontal script. Satera Jontal is a local script from Sumbawa, West Nusa Tenggara. This script is used to write and read the Sumbawa language which was spoken on the western part of the island of Sumbawa in the past. The purpose of this research is to increase student interest in learning Sumbawa script through interactive learning media. Research Methods This research uses a quantitative descriptive approach. Retrieval of data in this study was collected through a survey method by distributing questionnaires to elementary school (SD) children in grade VI (six). The survey was conducted to determine the interest in learning Sumbawa (Sumatran Jontal) characters through the use of interactive learning media. The results showed that students' interest in Sumbawa script was still very low. Therefore, efforts are made to increase student interest in learning Sumbawa script. These efforts must pay attention to the following: the interactive learning media used must be appropriate so that it makes it easier for students to learn Sumbawa script.
THE CORRELATION OF SOCIAL ENVIRONMENT TOWARD CHILDREN'S LEARNING ACHIEVEMENT AT SMA PGRI KUPANG

This study specifically looked at how the social environment affects student achievement at SMA PGRI Kupang. The objective of this study was to explain the relationship between social environment and learning achievement at SMA PGRI Kupang. An ex post facto quantitative method was used to examine the relationship between social environment and children's learning achievement. The data were collected using a form of questionnaires and documentation through 14 students (XI IPS) as samples. The data was analyzed using some techniques, including; descriptive statistical analysis, mean analysis, average value analysis, and correlation analysis to test the hypothesis. The product-moment correlation test was a statistical analysis technique. This study found a strong link between children's learning achievement and their social environment. One of the factors of success was determined by the social environment. As a result, SMA PGRI Kupang had a bad environment that can affect learning activities and lower children's learning achievement. Based on the data analysis, 0.73 at a 5% significance level, and rtable score was 0.24. This means that the value is greater than the rtable value, with 0.73 greater than 0.24. The first hypothesis (Ho) was accepted, while the alternative hypothesis (Ha) was rejected.