Reviewerly/exHarmony huggingface dataset:
EXPERT SYSTEM TO DETECT ONLINE GAME ADDICTION FOR UNIVERSITY STUDENTS USING THE BACKWARD CHAINING AND CERTAINTY FACTOR APPROACHES

Online gaming addiction harms students' physical health, mental well-being and academic performance. The addiction to playing online games has three categories, namely high, moderate and low, which are rarely known by the general public. The significant of knowledge acquisition on the addiction symptom and preventive activities forces the emergence of new idea on expert system identification platform. Therefore, this research aims to develop an expert system using the Backward Chaining (BC) and Certainty Factor (CF) approaches to detect the initial addiction level of online games for university students. Herein, the BC is used to identify the levelling of online game addiction based on the symptoms experienced by the user. There are thirty-three symptoms (G01-G33) provided through the thorough literature reviews and interviews with psychiatrics. Meanwhile, the CF is applied to calculate the level of certainty in determining the possibility of addiction describing in six scale level interpretation. As a result, the application of these two methods has effectively succeeded and reached proper accuracy in identifying the level of addiction of students towards their behavior on playing online games. The comparison of CF testing values between the system calculation and expert judgement shows the sophisticated result. Thus, this research can be utilized by the medical and psychiatric authorities, parents, and students in assessing their symptoms of addiction as an early warning in facing the possible risks arising from online game addiction.
EXPERT SYSTEM FOR DIAGNOSING CAT DISEASES USING FORWARD CHAINING METHOD ON TELEGRAM BOT

Cats are one of the popular pets kept in Indonesia. On the other hand, there is a potential risk of disease transmission from cats to humans. This research builds an expert system for diagnosing cat diseases based on the symptoms experienced using the forward chaining method. The expert system consists of two main components: the development environment and the consultation environment. In this study, these two components of the expert system are separated into two different platforms. The development environment runs on a website platform, while the consultation environment operates on a Telegram bot platform. System development uses the Expert System Development Life Cycle (ESDLC) development model. This model has six stages, namely, the assessment stage, the knowledge acquisition stage, the design stage, the testing stage, the documentation stage and the maintenance stage. As a result, it assists the general public in performing initial diagnoses of cat diseases.
Evaluasi Program PIPOCITA di UPT SD Negeri Tamalanrea Makassar dengan Model Evaluasi CIPP

This study evaluates one of the educational programs at UPT SD Negeri Tamalanrea, namely the PIPOCITA (Our Little Police Picket) program. This program was evaluated using CIPP Evaluation Model which analysed Context, Input, Process and Product of the program. Data were collected using interviews, observation and documentation. The study involved teachers, students and parents. The results showed that the evaluation of the context of this program met the evaluation criteria for the context of a program, namely the existence of goals to be achieved. The input evaluation contained various resources in carrying out the program, namely the presence of picket schedules, resources for carrying out pickets, and the presence of students and teachers who will do pickets. In the evaluation process, it was found that this program was carried out in groups based on the picket schedule and the person in charge to do the picket are still carrying out supervision and evaluation in each program implementation. The evaluation of the Product found that the results of this program made students more independent and had emotional relationships with teachers, students and parents. Therefore, the PIPOCITA program still needs to be adjusted and improved from various aspects according to the characteristics of students and parents and the community in the school area.
Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features

Predicting the popularity of online news is essential for news providers and recommendation systems. Time series, content and meta-feature are important features in news popularity prediction. However, there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance. This work proposes a novel deep learning model named Multiple Features Dynamic Fusion (MFDF) for news popularity prediction. For modeling time series, long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity. The typical convolution neural network gets headline semantic representation for modeling news headlines. In addition, a hierarchical attention network is exploited to extract news content semantic representation while using the latent Dirichlet allocation model to get the subject distribution of news as a semantic supplement. A factorization machine is employed to model the interaction relationship between meta-features. Considering the role of these features at different stages, the proposed model exploits a time-based attention fusion layer to fuse multiple features dynamically. During the training phase, this work designs a loss function based on Newton’s cooling law to train the model better. Extensive experiments on the real-world dataset from Toutiao confirm the effectiveness of the dynamic fusion of multiple features and demonstrate significant performance improvements over state-of-the-art news prediction techniques.
Underwater Waste Recognition and Localization Based on Improved YOLOv5

With the continuous development of the economy and society, plastic pollution in rivers, lakes, oceans, and other bodies of water is increasingly severe, posing a serious challenge to underwater ecosystems. Effective cleaning up of underwater litter by robots relies on accurately identifying and locating the plastic waste. However, it often causes significant challenges such as noise interference, low contrast, and blurred textures in underwater optical images. A weighted fusion-based algorithm for enhancing the quality of underwater images is proposed, which combines weighted logarithmic transformations, adaptive gamma correction, improved multi-scale Retinex (MSR) algorithm, and the contrast limited adaptive histogram equalization (CLAHE) algorithm. The proposed algorithm improves brightness, contrast, and color recovery and enhances detail features resulting in better overall image quality. A network framework is proposed in this article based on the YOLOv5 model. MobileViT is used as the backbone of the network framework, detection layer is added to improve the detection capability for small targets, self-attention and mixed-attention modules are introduced to enhance the recognition capability of important features. The cross stage partial (CSP) structure is employed in the spatial pyramid pooling (SPP) section to enrich feature information, and the complete intersection over union (CIOU) loss is replaced with the focal efficient intersection over union (EIOU) loss to accelerate convergence while improving regression accuracy. Experimental results proved that the target recognition algorithm achieved a recognition accuracy of 0.913 and ensured a recognition speed of 45.56 fps/s. Subsequently, Using red, green, blue and depth (RGB-D) camera to construct a system for identifying and locating underwater plastic waste. Experiments were conducted underwater for recognition, localization, and error analysis. The experimental results demonstrate the effectiveness of the proposed method for identifying and locating underwater plastic waste, and it has good localization accuracy.
A New Privacy-Preserving Data Publishing Algorithm Utilizing Connectivity-Based Outlier Factor and Mondrian Techniques

Developing a privacy-preserving data publishing algorithm that stops individuals from disclosing their identities while not ignoring data utility remains an important goal to achieve. Because finding the trade-off between data privacy and data utility is an NP-hard problem and also a current research area. When existing approaches are investigated, one of the most significant difficulties discovered is the presence of outlier data in the datasets. Outlier data has a negative impact on data utility. Furthermore, <i>k</i>-anonymity algorithms, which are commonly used in the literature, do not provide adequate protection against outlier data. In this study, a new data anonymization algorithm is devised and tested for boosting data utility by incorporating an outlier data detection mechanism into the Mondrian algorithm. The connectivity-based outlier factor (COF) algorithm is used to detect outliers. Mondrian is selected because of its capacity to anonymize multidimensional data while meeting the needs of real-world data. COF, on the other hand, is used to discover outliers in high-dimensional datasets with complicated structures. The proposed algorithm generates more equivalence classes than the Mondrian algorithm and provides greater data utility than previous algorithms based on <i>k</i>-anonymization. In addition, it outperforms other algorithms in the discernibility metric (DM), normalized average equivalence class size (Cavg), global certainty penalty (GCP), query error rate, classification accuracy (CA), and F-measure metrics. Moreover, the increase in the values of the GCP and error rate metrics demonstrates that the proposed algorithm facilitates obtaining higher data utility by grouping closer data points when compared to other algorithms.
Blockchain-Based Secure and Fair IoT Data Trading System with Bilateral Authorization

These days, data is regarded as a valuable asset in the era of the data economy, which demands a trading platform for buying and selling data. However, online data trading poses challenges in terms of security and fairness because the seller and the buyer may not fully trust each other. Therefore, in this paper, a blockchain-based secure and fair data trading system is proposed by taking advantage of the smart contract and matchmaking encryption. The proposed system enables bilateral authorization, where data trading between a seller and a buyer is accomplished only if their policies, required by each other, are satisfied simultaneously. This can be achieved by exploiting the security features of the matchmaking encryption. To guarantee non-repudiation and fairness between trading parties, the proposed system leverages a smart contract to ensure that the parties honestly carry out the data trading protocol. However, the smart contract in the proposed system does not include complex cryptographic operations for the efficiency of on-chain processes. Instead, these operations are carried out by off-chain parties and their results are used as input for the on-chain procedure. The system also uses an arbitration protocol to resolve disputes based on the trading proof recorded on the blockchain. The performance of the protocol is evaluated in terms of off-chain computation overhead and on-chain gas consumption. The results of the experiments demonstrate that the proposed protocols can enable the implementation of a cost-effective data trading system.
Media Lagu dalam Pembelajaran IPS untuk Meningkatkan Motivasi Belajar Siswa Kelas V SDN Kertajaya IV/210 Surabaya

Learning media is often used in the implementation of effective learning so that students can be more involved in the classroom. However, many teachers continue to use traditional teaching methods, causing students to be less engaged in learning. Teachers can use song media as one of the learning media. The purpose of this study was to (1) analyze the research planning stages of using song media in social studies learning in class V SDN Kertajaya IV/210 Surabaya. (2) Describe the results of the use of music media in social studies learning on the learning motivation of fifth grade students at SDN Kertajaya IV/210 Surabaya. This study used qualitative research methods. The subjects of this study were 20 fifth grade students. This study uses primary data sources. Data collection techniques include observation, interviews, tests, questionnaires, and documentation. Test the validity of the data using technical triangulation. Data reduction, data presentation, and drawing conclusions are the data analysis techniques used. The results of the study revealed that social studies learning with the media of European arrival historical songs included three stages, namely planning, implementation, and evaluation. Learning planning consists of 2 stages: observation and study of literature. The stages of learning implementation are divided into three parts, namely preparatory activities, core activities, and closing activities. Learning is carried out at the assessment stage by examining achievements and deficiencies that occur during the learning process. The use of music media in social studies learning shows that the learning process runs smoothly and has the potential to increase student learning motivation.
GRATDet: Smart Contract Vulnerability Detector Based on Graph Representation and Transformer

Smart contracts have led to more efficient development in finance and healthcare, but vulnerabilities in contracts pose high risks to their future applications. The current vulnerability detection methods for contracts are either based on fixed expert rules, which are inefficient, or rely on simplistic deep learning techniques that do not fully leverage contract semantic information. Therefore, there is ample room for improvement in terms of detection precision. To solve these problems, this paper proposes a vulnerability detector based on deep learning techniques, graph representation, and Transformer, called <i>GRATDet</i>. The method first performs swapping, insertion, and symbolization operations for contract functions, increasing the amount of small sample data. Each line of code is then treated as a basic semantic element, and information such as control and data relationships is extracted to construct a new representation in the form of a Line Graph (LG), which shows more structural features that differ from the serialized presentation of the contract. Finally, the node information and edge information of the graph are jointly learned using an improved Transformer–GP model to extract information globally and locally, and the fused features are used for vulnerability detection. The effectiveness of the method in reentrancy vulnerability detection is verified in experiments, where the F1 score reaches 95.16%, exceeding state-of-the-art methods.
NETWORK PROGRAMMABILITY FOR NETWORK ISSUE USING PARAMIKO LIBRARY

In a company, information technology is needed, especially computer networks, to facilitate data communication. The management of a computer network, of course, requires good administration. The criteria for whether or not a network is good can be seen from the performance, reliability, and security indicators so that it will not cause network issues. Events such as server downs, data loss, lost connections, and undetected computers cause the organization's business performance to be disrupted. This study's purpose is to detect network issues with network programmability technology automatically. Paramiko library supports network automation systems and implements OSPF routing protocol in finding the shortest path to send network packets. This study uses the PPDIOO flow, namely prepare, plan, design, implement, operate, and optimize, because it is considered by the flow of making network detection tools. The results showed that the design and implementation of a small-scale network were successfully built by utilizing network programmability technology and the paramiko library, which helps detect network conditions at any time. This design has a dashboard, provisioning, assurance, and policy features that allow administrators to manage and monitor information on each network device. The network design is fitted with REST-API technology and security through a secure shell (ssh) from the Network Controller that can detect the device's connection conditions and the device's health and update the DNS configuration used. Network Issues that have been seen are devices being down, and the connection being lost. Future research can improve features for network troubleshooting when the connection is lost.
APLIKASI LAPORAN KEBERANGKATAN DAN KEDATANGAN ALAT ANGKUT KANTOR IMIGRASI JAMBI

Information System Application Reporting Departure and Arrival of Transport Equipment of the Jambi Immigration Office is a system of convenience in making reports on recording ship departures and ship arrivals at ports from things such as data recap, report entry and also ease of inspection on ships dating and departing. From where in building the system in this system development tools are used, namely Data Flow Diagrams (DFD) and Context Diagrams, as well as using the PHP and HTML programming languages ​​and MySQL as databases.
Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy

In this work we describe the High-Dimensional Matrix Mechanism (HDMM),a differentially private algorithm for answering a workload of predicate counting queries. HDMM represents query workloads using a compact implicit matrix representation and exploits this representation to efficiently optimize over (a subset of) the space of differentially private algorithms for one that is unbiased and answers the input query workload with low expected error. HDMM can be deployed for both ϵ-differential privacy (with Laplace noise) and (ϵ, δ)-differential privacy (with Gaussian noise), although the core techniques are slightly different for each. We demonstrate empirically that HDMM can efficiently answer queries with lower expected error than state-of-the-art techniques, and in some cases, it nearly matches existing lower bounds for the particular class of mechanisms we consider.
Fuzzy Logic-based Integrated Cooling System to Improve PV Efficiency

Photovoltaic (PV) temperature is an important parameter that can influence PV performance. The increase in PV temperature can cause a drop in PV output voltage, which indicates that the PV does not operate optimally. In this paper, a design of an integrated cooling system based on fuzzy logic to control the PV temperature is presented. This, in effect, will potentially ensure that the PV performance is at its maximum condition. In this work, the temperature of the PV is kept constant by attaching a cooling system that carries water inside it. The constant temperature of the cooling system is carried out through a radiator and thermoelectric with electronic control. The experiments are carried out using two 320-watt PV systems; one with the cooling mechanism and the latter without the cooling system as a control. Several important data such as temperature, voltage, current, and radiation measurement in each PV were collected for three days from 10:00 to 17:00 with an interval of 10 minutes for each measurement. The results of the integrated cooling system measurement showed an increase in PV power generation and efficiency of 1.32% compared to the control PV unit. From the results of the study, it can be concluded that the fuzzy logic-controlled cooling system contributed to the increase in PV performance.
Social Reality and Modern Science

As science developed many of the established facts tended to appear in a new light and were seen from an aspect that had earlier been ignored and as a rule new scientific theory originated from the clash of old theories and new facts. Not only that, science has reached at the highest peak of its development. Nevertheless, in this era of science and technology, it has not been fully harnessed to the welfare of humanity. The world today is in a serious crisis and humanity is being constantly threatened or misuse of science. Science ‘therefore’ is not only a blessing but also presents an unprecedented threat to the very existence of the human race. This unhappy consequence is not because of science itself but because of the antagonistic nature of social relationships. It is very clear that, social reality is distinct from biological relating or individual cognitive reality representing as it does a phenomenological level created through social interaction and transcending thereby individual motives and actions. We cannot hope to understand the role of a social scientist properly unless we have some idea of the reality with which he is supposed to be involved either passionately or dispassionately. Philosophy and Progress, Vol#71-72; No#1-2; Jan-Dec 2022 P 29-43
PERANCANGAN SISTEM INFORMASI TAMBAT LABUH KAPAL DI PELABUHAN PERIKANAN NUSANTARA KARANGANTU

The purpose of this study is to make it easier to access data and make reports needed by those who need it. The method used in this study is the waterfall model, which begins with research on the current system, design, coding, and maintenance of archive management systems so that archive management information and archive management reports can run quickly, precisely and accurately. The results obtained from this study are regarding the design of a mooring information system that is integrated with a database to support the process of managing ship arrivals and departures easily, quickly, and accurately. In making a ship's arrival and departure certificate, it can be documented into a database, so that it can make it easier if you want to report ship arrival and departure data. The conclusion that can be drawn from this information system is that archive management can be well documented because it is equipped with a database, storage has become a single unit, can reduce redundancy or duplicate data, can display required mooring management information so that it can facilitate officers in making arrival management reports. and ship departure.
ANALISIS SENTIMEN DAN PEMODELAN TOPIK PADA TWEET TERKAIT DATA BADAN PUSAT STATISTIK

Annually, the Central Bureau of Statistics, known in Indonesia as BPS or Badan Pusat Statistik, conducts a routine Data Needs Survey (Survei Kebutuhan Data or SKD) to identify data requirements and the level of consumer satisfaction with the quality of data produced by BPS. However, SKD respondents are limited to consumers who have received services from the Integrated Statistics Services (Pelayanan Statistik Terpadu or PST) unit at BPS within a specific year. To gather opinions from the wider public accessing BPS data through channels other than the PST unit, an alternative approach is necessary – particularly through social media, specifically Twitter.&#x0D; This study employs Twitter data to analyze public sentiment regarding BPS data. To understand the distribution of topics discussed within the community about BPS data indicators, topic modeling has been employed. The sentiment analysis process utilizes IndoBERT, an Indonesian language Bidirectional Encoder Representations from Transformers (BERT) model. For topic modeling, the Latent Dirichlet Allocation (LDA) method is utilized.&#x0D; The results of sentiment analysis during the period 2020 - 2022 reveal that tweets related to BPS data generally convey a neutral sentiment. Meanwhile, the topic modeling process generates a range of topics, with variations observed in each year. Throughout 2020 - 2022, the most frequently discussed topics align with the statistical data from the 2020 - 2022 Data Needs Survey's data requirements section, reflecting the diversity of data needs.