Reviewerly/exHarmony huggingface dataset:
Machine Learning for Real-Time Anomaly Detection in Optical Networks

This work proposes a real-time anomaly detection scheme that leverages the multi-step ahead prediction capabilities of encoder-decoder (ED) deep learning models with recurrent units. Specifically, an encoder-decoder is used to model soft-failure evolution over a long future horizon (i.e., for several days ahead) by analyzing past quality-of-transmission (QoT) observations. This information is subsequently used for real-time anomaly detection (e.g., of attack incidents), as the knowledge of how the QoT is expected to evolve allows capturing unexpected network behavior. Specifically, for anomaly detection, a statistical hypothesis testing scheme is used, alleviating the limitations of supervised (SL) and unsupervised learning (UL) schemes, usually applied for this purpose. Indicatively, the proposed scheme eliminates the need for labeled anomalies, required when SL is applied, and the need for on-line analyzing entire datasets to identify abnormal instances (i.e., UL). Overall, it is shown that by utilizing QoT evolution information, the proposed approach can effectively detect abnormal deviations in real-time. Importantly, it is shown that the information concerning soft-failure evolution (i.e., QoT predictions) is essential to accurately detect anomalies.
3D Content Encryption Using Multi-Level Chaotic Maps

Nowadays, 3D content is becoming an essential part of multimedia applications, when the 3D content is not protected, hackers may attack and steal it. This paper introduces a proposed scheme that provides high protection for 3D content by implementing multiple levels of security with preserving the original size using weight factor (w). First level of security is implemented by encrypting the texture map based on a 2D Logistic chaotic map. Second level is implemented by shuffling vertices (confusion) based on a 1D Tent chaotic map. Third level is implemented by modifying the vertices values (diffusion) based on a 3D Lorenz chaotic map. Results illustrate that the proposed scheme is completely deform the entire 3D content according to Hausdorff Distance (HD) approximately around 100 after the encryption process. It provides a high security against brute force attack because it has large key space equal to 10165 and secret key sensitivity using NPCR near 99:6% and UACI near 33:4%. The histogram and HD indicate the decrypted 3D content is identical to the origin where HD values approximate zero.
Blockchain Based Certificate Verification System Management

One of the key responsibilities of the Public Key Infrastructure is revocation management. Additionally, the security of any Public Key Infrastructure (PKI) depends on it. Today's revocation methods are susceptible to a single point of failure when network traffic rises due to the growth in the quantity and size of networks as well as the adoption of new paradigms like the Internet of Things and the use of the web. The author uses the strength and resiliency of blockchain to overcome these issues and present a productive decentralized certificate revocation management and status verification system. The author adds a field that specifies which distribution point the certificate will belong to in the event that it is revoked using the certificate structure extension field. Then, the author carries out a thorough assessment of our plan using performance indicators like execution time and data consumption to show that it can fulfill the demands with high effectiveness and little expense. Additionally, the author contrasts the effectiveness of our strategy with two of the most widely-used revocation approaches, namely the Certificate Revocation List and the Online Certificate Status Protocol. The data collected demonstrate that our suggested strategy works better than the existing scheme.
Tracer technology in plastic identification—Past, present and future

The evolution of concepts for the automation of post-consumer waste plastic sorting is chronicled, homing in on fluorescent tracer-based identification in binary combinations of dyes in different plastics.Aspects of the applied research needed for the development of a prototype identification and sorting system in the author's laboratory are also elaborated.This is followed by details of an industrial trial of the system in a commercial enterprise, replacing manual operation by the newly developed automated system, in conjunction with the use of fluorescent tracer-doped plastic bottles of different types.The procedures adopted for the selection of dyes with suitable characteristics for tagging different types of plastic with acceptable compatibility in binary combinations are also elaborated.The preliminary trial showed promise for commercialization.However, for cost-effective sorting of a wide variety of post-consumer plastic with acceptable end purity and commercial viability, there is an evident need for further developments in some areas, notably: better choice of dyes compatible with the constituents of all the polymer types selected for sorting; faster electronic data processing; and mechanical handling of individual items on a conveyer belt at much higher speeds (including enhancing the efficiency of the ejection mechanism).
ChatGPT is not Enough: Enhancing Large Language Models with Knowledge Graphs for Fact-aware Language Modeling

Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities. Some researchers suggest that LLMs could potentially replace structured knowledge bases like knowledge graphs (KGs) and function as parameterized knowledge bases. However, while LLMs are proficient at learning probabilistic language patterns based on large corpus and engaging in conversations with humans, they, like previous smaller pre-trained language models (PLMs), still have difficulty in recalling facts while generating knowledge-grounded contents. To overcome these limitations, researchers have proposed enhancing data-driven PLMs with knowledge-based KGs to incorporate explicit factual knowledge into PLMs, thus improving their performance to generate texts requiring factual knowledge and providing more informed responses to user queries. This paper reviews the studies on enhancing PLMs with KGs, detailing existing knowledge graph enhanced pre-trained language models (KGPLMs) as well as their applications. Inspired by existing studies on KGPLM, this paper proposes to enhance LLMs with KGs by developing knowledge graph-enhanced large language models (KGLLMs). KGLLM provides a solution to enhance LLMs' factual reasoning ability, opening up new avenues for LLM research.
A Shift In Artistic Practices through Artificial Intelligence

The explosion of content generated by Artificial Intelligence models has initiated a cultural shift in arts, music, and media, where roles are changing, values are shifting, and conventions are challenged. The readily available, vast dataset of the internet has created an environment for AI models to be trained on any content on the web. With AI models shared openly, and used by many, globally, how does this new paradigm shift challenge the status quo in artistic practices? What kind of changes will AI technology bring into music, arts, and new media?
Visibility, Promoting, and Marketing of Electronic Reference Books in Academic Libraries

Are electronic reference books visible in academic libraries? A survey administered to librarians at colleges and universities in the southern United States is used to ascertain the presence and promotion of electronic reference books in academic libraries. Findings reveal that electronic reference books are not as visible in academic libraries (both online and in library buildings) as traditional print reference is. Librarians surveyed indicate they promote electronic reference books more than print reference books. To gain an understanding of what needs more marketing, librarians should examine usage statistics. Librarians should employ a variety of ways to market and promote electronic reference books, and assess which methods work best to reach patrons. In addition, librarians and classroom faculty should work together in collection development and in endorsing the usage of electronic reference books.
A Framework for Securing Health Information using Blockchain in Cloud Hosted Cyber Physical Systems

Electronic Health Records (EHRs) have undergone numerous technical improvements in recent years, including the incorporation of mobile devices with the cloud computing technologies to facilitate medical data exchanges between patients and the healthcare professionals.This cutting-edge architecture enables cyber physical systems housed in the cloud to provide healthcare services with minimal operational costs, high flexibility, security, and EHR accessibility.If patient health information is stored in the hospital database, there will always be a risk of intrusion, i.e., unauthorized file access and information modification by attackers.To address this concern, a decentralized EHR system based on Blockchain technology is proposed.To facilitate secure EHR exchange across various patients and medical providers, a reliable access control method based on smart contracts is developed.The cryptocurrency, specifically Ethereum, is incorporated in the suggested system to protect sensitive health information from potential attackers.In the suggested approach, both physicians and patients are required to be authenticated.Patients can register, and a block with a unique hash value will be generated.Once the patient discusses the disease with the physician, the physician can check the patient's condition and offer drugs.For experimental findings, the public Block chain Ganache and solidity remixbased smart contracts to protect privacy is applied and the Ethers are used as the crypto currencies.
A Mechanism on Video Tracking and Recognition with Fast Transmission in Wireless Communications

In recent years, video-based object tracking and recognition have become critical research areas in computer vision and network communication systems.This paper proposes a novel method for video-based object tracking and recognition over fast network transmission using newly developed improved CAMSHIFT and Meanshift algorithms.The new mechanism is not only applies to single object tracking and recognition, but also to multiple objects and objects with motion.The proposed system also utilizes a wireless network to transmit video streams from remote devices to a central server where the tracking and recognition processes take place.The developed approach has several advantages, including the ability to track objects in real-time over wireless networks with low latency and high reliability.The proposed system can be applied in various applications, such as security surveillance, traffic monitoring, and human-computer interaction.In addition, an efficient and effective implementation is designed utilizing a wireless network to transmit video streams where the tracking and recognition processes take place.
Development of Plant-Puzzle Games (PPG) based on the Learning Styles of Junior High School Students to Increase Learning Motivation in Plant Classification Materials

Plant classification material is one of the materials listed in the basic competence of the curriculum applied in Indonesia and is less attractive to students because the characteristics of plants are quite difficult to identify so that the classification is complicated. It is known that most middle-level students really like games which are integrated in learning activities. Therefore, this study aims to develop a media called Plant-Puzzle Games (PPG) which is expected to have a positive effect in the form of increasing the learning motivation of junior high school students in studying plant classification material. The research method used in this study is a research and development method with the ASSURE model as the research design. Based on the results of the media validation test by experts, an average value of 3.25 was obtained, which indicates that the media was included in the valid category. Based on the results of the effectiveness test with the calculation of N-Gain, the average value is 0.49. This shows that, after using PPG media, the learning motivation of students has increased which is in the sufficient category.
The Plausibility of Sampling as an Algorithmic Theory of Sentence Processing

Words that are more surprising given context take longer to process. However, no incremental parsing algorithm has been shown to directly predict this phenomenon. In this work, we focus on a class of algorithms whose runtime does naturally scale in surprisal-those that involve repeatedly sampling from the prior. Our first contribution is to show that simple examples of such algorithms predict runtime to increase superlinearly with surprisal, and also predict variance in runtime to increase. These two predictions stand in contrast with literature on surprisal theory (Hale, 2001; Levy, 2008a) which assumes that the expected processing cost increases linearly with surprisal, and makes no prediction about variance. In the second part of this paper, we conduct an empirical study of the relationship between surprisal and reading time, using a collection of modern language models to estimate surprisal. We find that with better language models, reading time increases superlinearly in surprisal, and also that variance increases. These results are consistent with the predictions of sampling-based algorithms.
Implementasi Algoritma Naïve Bayes Menggunakan Feature Forward Selection dan SMOTE Untuk Memprediksi Ketepatan Masa Studi Mahasiswa Sarjana

The punctuality of students in completing their studies is an important aspect of the study program. Because there are still students who have not been able to complete their studies on time. The purpose of this study is to determine the factors that influence students in completing their studies by extracting student academic data to obtain a classification model that can be used to predict the accuracy of the study period. The classification method for predicting the accuracy of the student's study period uses the Naive Bayes algorithm using the Feature Forward Selection and SMOTE. The method for data processing in this study uses CRISP-DM. The results of this study are in the form of a classification model to predict the accuracy of the study period of students who obtain a fairly high accuracy value of 87.13%, a recall value of 83.82%, and a precision value of 89.76%, and an AUC value of 0.92. included in the category of Excellent Classification. The use of SMOTE has succeeded in handling Imbalanced Class on the data, and the application of Feature Forward Selection resulted in 5 factors that most influence the accuracy of the student's study period, namely the attributes of Gender, School Category, Year of Entry, Study Program and Grade Point Average for the third semester. The prediction model generated using the Naïve Bayes algorithm, Feature Forward Selection, and SMOTE is expected to help study programs to find out earlier the possibility of students completing their studies on time or not on time.
Identifying Significant Factors Affecting the Human Development Index in East Java Using Ordinal Logistic Regression Model

The achievement of human development can be reviewed from the Human Development Index (HDI), which is sourced from indicators of health, education, and state income. East Java Province is the second-most populous province in Indonesia which has a high work intensity in economic aspects and abundant natural and human resources. However, judging from the level of the human development index, East Java Province occupies the lowest position compared to other provinces on the island of Java. This study aims to identify significant factors affecting East Java's HDI using ordinal logistic regression. The data from Indonesia Statistics (Indonesian: Badan Pusat Statistik, BPS) of East Java province in 2020 includes seven variables, namely Gross Regional Domestic Product (GRDP), high school participation rate, infant mortality rate, health facilities, population density, labor force participation rate, and open unemployment rate. This study produced two ordinal logistic regression models in medium category HDI and high category HDI with a classification accuracy value of 97.37%. From this model obtained, a significant factor affecting the HDI of East Java is the participation rate of high school and health facilities.
Visualizing Deep Neural Networks with Topographic Activation Maps

Machine Learning with Deep Neural Networks (DNNs) has become a successful tool in solving tasks across various fields of application. However, the complexity of DNNs makes it difficult to understand how they solve their learned task. To improve the explainability of DNNs, we adapt methods from neuroscience that analyze complex and opaque systems. Here, we draw inspiration from how neuroscience uses topographic maps to visualize brain activity. To also visualize activations of neurons in DNNs as topographic maps, we research techniques to layout the neurons in a two-dimensional space such that neurons of similar activity are in the vicinity of each other. In this work, we introduce and compare methods to obtain a topographic layout of neurons in a DNN layer. Moreover, we demonstrate how to use topographic activation maps to identify errors or encoded biases and to visualize training processes. Our novel visualization technique improves the transparency of DNN-based decision-making systems and is interpretable without expert knowledge in Machine Learning.
An ensemble model to detect packet length covert channels

&lt;span lang="EN-US"&gt;Covert channel techniques have enriched the way to commit dangerous and unwatched attacks. They exploit ways that are not intended to convey information; therefore, traditional security measures cannot detect them. One class of covert channels that difficult to detect, mitigate, or eliminate is packet length covert channels. This class of covert channels takes advantage of packet length variations to convey covert information. Numerous research articles reflect the useful use of machine learning (ML) classification approaches to discover covert channels. Therefore, this study presented an efficient ensemble classification model to detect such types of attacks. The ensemble model consists of five machine learning algorithms representing the base classifiers. The base classifiers include naive Bayes (NB), decision tree (DT), support vector machine (SVM), k-nearest neighbor (KNN), and random forest (RF). Whereas, the logistic regression (LR) classifier was employed to aggregate the outputs of the base classifiers and thus to generate the ensemble classifier output. The results showed a good performance of our proposed ensemble classifier. It beats all single classification algorithms, with a 99.3% accuracy rate and negligible classification errors.&lt;/span&gt;
TEACHING READING BY COLLABORATIVE STRATEGIC READING: AN ACTION RESEARCH

English language learners experienced difficulties in understanding readings in English. This research aims to improve students’ reading comprehension by implementing Collaborative Strategic Reading (CSR) at second semester students of English Department, STKIP Panca Sakti Bekasi. This research used a Classroom Action Research (CAR) was conducted in two cycles. There are qualitative and quantitative data collected in this research. The former data were gained by analyzing field note, observation sheet and interview while the latter ones were obtained from pre-test, post-test and portfolio of pre-cycle, cycle I and cycle II. The result shows that the mean score of students’ reading performance increases in four dimensions not only on literal, interpretative, applicative but also on critical understanding. The finding indicates that the students’ mean score at pre-cycle (68) increases to 77 and to 87 in the first and second cycles respectively. For interpretative understanding, the average score at pre-cycle was 61, increasing to 69 in the first cycle and to 80 in the second cycle. For applicative understanding, the average score at pre-cycle was 62, increasing to 64 in the first cycle and to 84 in the second cycle. Finally, the critical understanding also increases, from 59 at pre-cycle to 65 in the first cycle and to 85 in the second cycle. Therefore, the implementation of CAR effectively improves the students’ reading performance.
NEEDS ANALYSIS OF VOCATIONAL HIGH SCHOOL TEXTBOOK TO LOCAL CULTURE MATERIALS AND THE 21ST CENTURY COMPETENCIES

The needs to know local cultural materials and skills for the future of students in the 21st century motivated researchers to concentrate more on them. In this recent research, an English textbook developed with Palembang local culture in mind and reviewed by students considers student needs. In SMK Negeri 3 Palembang, the travel agent competency program had 35 graders who completed a questionnaire with 45 questions. Questionnaires and semi-structured interviews were used to collect and quantitatively and qualitatively interpret the details. The findings showed: 1) English must be taught through technology to support students; (2) English is essential to students' future careers; (3) no existing English language education materials are included with local Palembang culture; and (4) two main reasons for integrating local Palembang culture into educational materials were to enhance the knowledge of students. Due to the queries, learning resources such as blogs, Twitter, video calls, and voice notes became important for the promotion of skills in the 21st century.
Upgrade Games Cognitive And Literacy For Android - Based Kindergarten Children

The growth of children in kindergarten is crucial for developing their thinking patterns and creativity. Gadgets can influence children's cognitive and literacy abilities, so a cognitive game is needed to help improve these skills. The development of the game uses VAK and IMSDD methods to improve cognitive abilities and literacy better than conventional learning. The alpha testing showed that more than 90% of respondents agreed that the SITEKA game application met their needs and increased children's concentration and learning effectiveness by over 40%
A Multi-view Knowledge Graph Embedding Model Considering Structure and Semantics

The essence of knowledge representation learning is to embed the knowledge graph into a low-dimensional vector space to make knowledge computable and deductible. Semantic indiscriminate knowledge representation models usually focus more on the scalability on real world knowledge graphs. They assume that the vector representations of entities and relations are consistent in any semantic environment. Semantic discriminate knowledge representation models focus more on precision. They assume that the vector representations should depend on the specific semantic environment. However, both the two kinds only consider knowledge embedding in semantic space, ignoring the rich features of network structure contained between triplet entities. The MulSS model proposed in this paper is a joint embedding learning method across network structure space and semantic space. By synchronizing the Deepwalk network representation learning method into the semantic indiscriminate model TransE, MulSS achieves better performance than TransE and some semantic discriminate knowledge representation models on triplet classification task. This shows that it is of great significance to extend knowledge representation learning from the single semantic space to the network structure and semantic joint space.