Reviewerly/exHarmony huggingface dataset:
Data Poisoning Attack Aiming the Vulnerability of Continual Learning

Generally, regularization-based continual learning models limit access to the previous task data to imitate the real-world constraints related to memory and privacy. However, this introduces a problem in these models by not being able to track the performance on each task. In essence, current continual learning methods are susceptible to attacks on previous tasks. We demonstrate the vulnerability of regularization-based continual learning methods by presenting a simple task-specific data poisoning attack that can be used in the learning process of a new task. Training data generated by the proposed attack causes performance degradation on a specific task targeted by the attacker. We experiment with the attack on the two representative regularization-based continual learning methods, Elastic Weight Consolidation (EWC) and Synaptic Intelligence (SI), trained with variants of MNIST dataset. The experiment results justify the vulnerability proposed in this paper and demonstrate the importance of developing continual learning models that are robust to adversarial attacks.
Parts Based Attention for Highly Occluded Pedestrian Detection with Transformers

Despite the significant progress made in pedestrian detection in last decade, detecting pedestrians under heavy occlusion still remains a challenging problem. In state of the art (SOTA), convolutional neural network (CNN) based models, the reason is attributed to non-maximal-suppression (NMS), which often erroneously deletes true positives when one pedestrian is occluding other. SOTA transformer based models do not have such NMS step, yet fail to detect highly occluded pedestrians. In this paper, we study the reasons for such failures. We observe that such models first predict key-points, and then compute the attention at the specific key-points. Our analysis reveals that the key-points do not have any preference towards semantically important body parts. Under heavy occlusion, such key-points end up attending to non-discriminative regions or background, leading to false negatives. We take inspiration from the conventional wisdom of detecting objects using their parts, and bias the attention of proposed transformer architecture towards semantically important, and highly discriminative human body parts. The intervention leads to SOTA results on benchmark Citypersons and Caltech datasets, achieving 30.75%, and 32.96% miss-rate (lower is better) respectively, against 32.6%, and 38.2% by the current SOTA. Code is available at https://ajayshastry08.github.io/pa_dino
Evaluation Of The Level Of Usefulness Of The "Jeknyong"�Application Using The Computer System Usability Questionnare (CSUQ) Method

The Jeknyong application (Ojeke Inyong) is a household garbage collection service application with a transaction value. However, the use of the Jeknyong application has not been maximized because there are obstacles, such as several features still in development. This study discusses the Evaluation of the Level of Usability of the "Jeknyong" Application using the Computer System Usability Questionnaire (CSUQ) method, where Usability is a technique to find out how easily users can use the system, how efficiently and effectively the system can help users achieve goals. Data collection used a questionnaire with Banyumas community subjects who knew the Jeknyong application. The results are in the form of an indexed percentage of 85% in the Agree Interval category. The first variable System Usability (SYSUSE) evaluation results were 5135 or in the agreed interval. The second variable Information Quality (INFOQUAL) was 4588 or in the Fairly Disagree category. The third variable, Interface Quality (INTERQUAL), was 2039 or in the Agree category; the third variable, Overall Usability (OVERALL), gets a result of 709 or is in the Strongly Agree interval.
Improving Listening Skill By using Audio-Lingual (ALM) For Students in The Grade 7th at SMP Dharma Patra Pangkalan susu

This research investigates the implementation of the Audio-lingual Method (ALM) to enhance listening skills among Junior High School students. The study used a classroom action research (CAR) approach, involving three cycles with observations, pre-tests, post-tests, and a questionnaire. The findings revealed that students initially struggle with listening in English due to limited vocabulary, grammatical difficulties, pronunciation issues, and a lack of confidence. The ALM significantly improved students' listening comprehension skills through audio recordings, dialogues, interactive activities, and authentic materials. Around 75% of students felt significantly assisted and found it easier to comprehend English vocabulary after the ALM implementation. However, 15% of students expressed disagreement, citing continued struggles with limited vocabulary. The ALM positively influences students' listening skills by combining listening practice with interactive activities and authentic materials. To further enhance students' listening abilities, teachers should address vocabulary limitations and pronunciation challenges, and employ additional strategies to foster a supportive and enjoyable learning environment. Future research should explore supplementary approaches and resources that complement the ALM to further enhance students' listening skills. Evaluating the long-term impact of the ALM on students' listening abilities would provide valuable insights into its effectiveness over an extended period. By implementing these findings, educators can effectively enhance students' listening skills and promote their overall English language proficiency.&#x0D; &#x0D; Keywords: Listening skills, Audio Lingual Method (ALM), effectiveness, classroom action research (CAR), observations.
Calibrated Explanations for Regression

Abstract Artificial Intelligence (AI) is often an integral part of modern decision support systems (DSS). The best-performing predictive models used in AI-based DSS lack transparency. Explainable Artificial Intelligence (XAI) aims to create AI systems that can explain their rationale to human users. Local explanations in XAI can provide information about the causes of individual predictions in terms of feature importance. However, a critical drawback of existing local explanation methods is their inability to quantify the uncertainty associated with a feature's importance. This paper introduces an extension of a feature importance explanation method, Calibrated Explanations (CE), previously only supporting classification, with support for standard regression and probabilistic regression, i.e., the probability that the target is above an arbitrary threshold. The extension for regression keeps all the benefits of CE, such as calibration of the prediction from the underlying model with confidence intervals, uncertainty quantification of feature importance, and allows both factual and counterfactual explanations. CE for standard regression provides fast, reliable, stable, and robust explanations. CE for probabilistic regression provides an entirely new way of creating probabilistic explanations from any ordinary regression model, allowing dynamic selection of thresholds. The performance of CE for probabilistic regression regarding stability and speed is comparable to LIME. The method is model agnostic with easily understood conditional rules. An implementation in Python is freely available on GitHub and for installation using both pip and conda, making the results in this paper easily replicable.
SISTEM PAKAR UNTUK MEDIAGNOSA KODE KERUSAKAN PADA SEPEDA MOTOR INJEKSI YAMAHA DENGAN METODE FORWARD CHAINING BERBASIS WEB

There are many fault codes on the motor. The general public dose not yet know about these damage codes. Many people ignore the importance of maintenance for motorcylcle. Consumers often use motorbikes unnaturally. This will cause damage codes in the motorbike. Here a expert system is important for diagnosis damage codes for motorcycyes. So that people can learn and know the damage codes. Communities to know the solutions and symptomps that occur.
Improving Students’ Speaking Skill in Descriptive Text Using Mind Mapping Method at Seventh Grade Junior High School

Speaking is one of the English language skills that must be mastered by students where there are students who can express their opinions, ideas and understanding in capturing the material presented by the teacher. There are several methods used to improve students' English speaking skills, this time the researcher used the mind map method. The focus of this research is to improve students' speaking ability in English descriptive text. This method is implemented to the research subjects of class VII students of MTs Raudlatuth Thalabah Kediri. The research method uses classroom action research and the data collection starts from student observations, pre-test, and post-test. Based on the results of the findings, the researchers found that the development of students' speaking skills from each test result that had been presented with the explanation of the last post-test data as follows, the lowest score of students in the second cycle post-test was 60.00 and the highest score was 90.00 then the class average value in the second cycle post-test was 78.1481 so that this study resulted in that this mind map method succeeded in improving students' English speaking skills even though some researchers found obstacles in the research process.
Manifold-based Verbalizer Space Re-embedding for Tuning-free Prompt-based Classification

Prompt-based classification adapts tasks to a cloze question format utilizing the [MASK] token and the filled tokens are then mapped to labels through pre-defined verbalizers. Recent studies have explored the use of verbalizer embeddings to reduce labor in this process. However, all existing studies require a tuning process for either the pre-trained models or additional trainable embeddings. Meanwhile, the distance between high-dimensional verbalizer embeddings should not be measured by Euclidean distance due to the potential for non-linear manifolds in the representation space. In this study, we propose a tuning-free manifold-based space re-embedding method called Locally Linear Embedding with Intra-class Neighborhood Constraint (LLE-INC) for verbalizer embeddings, which preserves local properties within the same class as guidance for classification. Experimental results indicate that even without tuning any parameters, our LLE-INC is on par with automated verbalizers with parameter tuning. And with the parameter updating, our approach further enhances prompt-based tuning by up to 3.2%. Furthermore, experiments with the LLaMA-7B&13B indicate that LLE-INC is an efficient tuning-free classification approach for the hyper-scale language models.
Data-efficient GNN models of communication networks using beta-distribution-based sample ranking

Machine learning models for tasks in communication networks often require large datasets to be trained. This training is cost intensive, and solutions to reduce these costs are required. It is not clear what the best approach to solve this problem is. Here we show an approach that is able to create a minimally-sized training dataset while maintaining high predictive power of the model. We apply our approach to a state-of-the-art graph neural network model for performance prediction in communication networks. Our approach is limited to a dataset of 100 samples with reduced sizes and achieves an MAPE of 9.79% on a test dataset containing significantly larger problem sizes, compared to a baseline approach which achieved an MAPE of 37.82%. We think this approach can be useful to create high-quality datasets of communication networks and decrease the time needed to train graph neural network models on performance prediction tasks.
Prediction of the Sea Level from the PUMMA System Using SARIMA

The rising sea levels can threaten millions of people residing along the coast or lowlands. The risk can be mitigated by the sea-level prediction done by collecting information on the likelihood of rising sea levels. The Ministry of Marine Affairs and Fisheries of Indonesia has developed Perangkat Ukur Murah untuk Muka Air Laut (Inexpensive Device for Sea Level Measurement, PUMMA) to measure sea levels. PUMMA is located in remote monitoring stations based on Indonesian maritime area. The PUMMA system currently lacks a prediction feature. This objective of this study is to model the sea-level prediction using the dataset for one year, from July 2021 until July 2022. The seasonal autoregressive integrated moving average (SARIMA) method was used because SARIMA proved to be a flexible and versatile method for a dataset having noncomplex nature and seasonal patterns. This study has developed several models of the SARIMA. The model performance was evaluated using the mean absolute percentage error (MAPE), R-squared, mean square error (MSE), and root mean square error (RMSE) metrics. The SARIMA(1, 1, 0)(1, 1, 1)12 model achieved the lowest prediction error with an R-squared of 0.508, MSE of 0.0479, and RMSE of 0.069. Based on the performance, SARIMA(1, 1, 0)(1, 1, 1)12 model is feasible for predicting sea levels using the PUMMA dataset.
Gastric Disease Diagnostic Expert System Application Using the Fuzzy Mamdani Method

The level of awareness among Indonesian society regarding the importance of maintaining gastric health is still very low, even though gastric diseases can significantly disrupt daily activities. In a medical examination, a doctor detects diseases in a patient's body based on their symptoms or complaints. The patient's action is to meet the doctor in person, and the doctor asks about the symptoms experienced by the patient. In the manual system, there is a drawback where patients have to visit the doctor for consultation or to have their diseases examined, and they also need to prepare the necessary fees for the examination. Such a manual system can be simplified with an information system where patients don't need to visit the doctor to diagnose their diseases. Therefore, the researcher will develop a gastric disease diagnostic expert system application using the fuzzy Mamdani method. The aim is to make it easier for patients/the public to identify the type of disease based on the symptoms experienced, as well as to provide information on solutions, actions, and medication for the disease. The methodology used in developing the gastric disease diagnostic expert system application involves four stages: fuzzification process, implication function, inference process (rules), and defuzzification. The research flow includes data collection through interviews and data sampling, data analysis, calculation using the fuzzy Mamdani method, implementation, and testing using a black box. The result of this research is a gastric disease diagnostic expert system application using the fuzzy Mamdani method with an accuracy of 65%. This application can help individuals to identify the type of disease based on the symptoms experienced without having to immediately consult a doctor, thus avoiding potential issues
Embedovanje u zaštiti autorskih prava i naknada štete

The paper analyzes whether through embedding - inserting a part of a certain document into a computer document on the Internet - e-mail violates copyright, considering that in such a way it is hidden from the original document or it only satisfies the interest of the public for better information. Copyright provides the author with a legal monopoly and encourages creative innovation. However, excessive protection does not satisfy the general social interest. Therefore, establishing a balance between these two interests is necessary.
ANALISIS DAN PERANCANGAN APLIKASI E-LIBRARY SEKOLAH BERBASIS WEB MENGGUNAKAN METODE SCRUM

With advances in information technology, searching and accessing data can be done quickly, efficiently and accurately, one of which is in the library. With current technological developments, it is necessary to have an information system that handles library administration. The information system is related to book data management, member data management, book lending circulation data management. Likewise with cases of problems experienced with the Batam 19 State High School library system. However, so far the library at SMA N 19 Batam still uses a manual management system with all book borrowing transaction processes written in an archive. The lack of use of technology to facilitate access and management of the library at the school can result in too much data being entered using paper and making it difficult for librarians to manage it. Thus causing the level of accuracy of search and storage to be less attention. Therefore researchers are interested in conducting research entitled "Analysis and Design of Web-Based School E-library Applications Using the Scrum Method", to improve the quality and efficiency of designing e-library applications in meeting the needs of the SMA N 19 Batam library.&#x0D; &#x0D; Keywords: scrum methods, e-library, web based.
Analysis of Brute Force Attacks Using National Institute Of Standards And Technology (NIST) Methods on Routers

Abstract&#x0D; The rapid development of technology, including the internet, can trigger data security issues that harm individuals, organizations, or government agencies. One type of attack often used is a Brute Force attack, which falls under the category of cybercrime. The National Institute of Standards and Technology (NIST) often analyzes digital evidence in these cases. This study analyzes Brute Force attacks using NIST methods on a router that is an additional router to the main router. On the network at the Faculty of Engineering, Universitas Muhammadiyah Magelang (UNIMMA), information about the attack and patterns used by the attacker were successfully obtained, including the IP address and time of the attack. It is concluded that the additional router is vulnerable to Brute Force attacks, and firewall settings are necessary to secure it.
RANCANG BANGUN SISTEM INFORMASI POINT OF SALE BERBASIS WEB

The development of the business world encourages companies to always try to improve the quality of products and services to consumers. Goods purchase services can be done electronically and can also be done online. The implementation of these business solutions is a commitment to increasing customer competitive advantage in terms of efficiency, effectiveness, performance, and business development. This study aims to design and develop a point of sale (POS) application to support a purchasing service system that can help CV Berdikari owners in data management. Making this POS application begins with collecting all the data needed using observation, interview, and literature study methods, designing Model Prototyping applications with application design tools in the form of flowcharts and Unified Modeling Language (UML) until the implementation of this POS application. With the implementation of this point of sales (POS) application, it can help users who are directly related to this POS application, especially customers, employees and owners in the process of managing reports and controlling their business activities.
Expert System For Detection of Cataracts Disease Using The Certainty Factor Method

A cataract is an eye disease that causes visual impairment in the eye, the most significant cause of blindness in Indonesia. The rate of blindness in Indonesia caused by cataracts has reached 35% among the elderly 50 years and over. With the development of technology and the shortage of ophthalmologists, an expert system is needed to assist eye health experts by incorporating expert intelligence into the system in the form of fact-based data from the interview results. So that with this expert system, it is hoped that it can help society find cataracts in the eye as a form of early prevention of the chance of suffering from cataracts. The certainty factor method is used in the system to determine the certainty value of the facts that have been entered into the system to obtain a percentage level with a value of 93% so that with the help of this method, system users can find out the type of disease from each symptom
Polarization Over the Unfolding 2023 Judicial Reform in Israel: A Real-Time Snapshot

This work explored a rapidly unfolding polarizing issue – Israel’s Judicial Reform, introduced in January 2023. We documented mass polarization over whether the judicial reform presents a threat to democracy and examined its predictors. We found evidence that different types of polarization are related, but distinct constructs. Specifically, issue-based, affective, and perceived societal polarization are differentially predicted by trust in democratic institutions (i.e., government, judiciary, and media), constructive patriotism, and social identity. Constructive patriotism and trust in institutions predicted issue-based and affective polarization, whereas social identity predicted only issue-based polarization. These effects vary across pro- and anti-reform individuals. Perceived societal polarization was predicted by generalized trust and universalism/benevolence values across the polarized camps. These types of polarization differentially predicted downstream consequences of polarization–conflict management attitudes, acts of, and responses to, civil protests, and outgroup attitudes. We discuss the results in the context of growing anti-liberal-democratic movements and the importance of democratic education.
IoT and Blockchain - A New Paradigm for Supplier Management in Manufacturing Plants

This paper discusses how the combination of Internet of Things (IoT) and Blockchain technology can create a new paradigm for supplier management in manufacturing plants. IoT enables the comprehensive digitalization of an entire manufacturing plant while providing a trove of data about the daily operations and performance of suppliers, machine performances, and other related activities. Blockchain technology provides a distributed ledger technology to store, share, and manage this data with trust and security, ensuring that all stakeholders within the supply chain are accountable and have an immutable record of their activities and performance. The paper discusses the potential opportunities and benefits that this new paradigm promises for manufacturers through improved supplier management, automated processes, reduced costs, and enhanced transparency. It also examines the challenges associated with making this system functional such as legal issues, system integration, and security concerns. Finally, the paper provides an overview of potential uses of this technology and concludes with a discussion of the future prospects of using IoT and Blockchain in supplier management.
Bridge the Gap between Visual Difference Prediction Model and Just Noticeable Difference Subjective Datasets

In video compression applications, the term 75%SUR (Satisfied User Ratio) is used to describe the compression parameter with which only 75% of the users can not notice the difference between one compressed media and its source. 75%SUR is widely used in JND (Just Noticeable Difference) modeling as a common threshold to standardize differences in users' perceptions of JND location. Visible difference detection is an essential step in JND prediction. However, Visible Difference Predictors (VDP), as objective quality metrics, are usually calibrated and applied on media quality datasets, no study has yet trained or applied the VDP on JND datasets. In this work, we will explore the feasibility of using the VDP model in predicting SUR and JND. We focus on Video Wise JND(VW-JND) and propose the model Extend-FvVDP, which maps the continuous quality scores output from the current best-performing VDP model, the FovVideoVDP, to VW-JND ground truth. Finally, Extend-FvVDP got a mean SUR prediction error of 0.0624, a mean JND prediction error of 1.9318. Our results show that VDP still performs on the JND datasets, and the JND prediction using VDP has the potential to exceed that of pure deep learning models.