Reviewerly/exHarmony huggingface dataset:
Pengelompokkan Kabupaten/Kota di Provinsi Nusa Tenggara Timur Berdasarkan Indikator Indeks Pembangunan Manusia Menggunakan K-Medoids Clustering

The Human Development Index (HDI) is an indicator to measure success in improving the quality of human life. The utilization of HDI indicators can be used to classify observations into clusters based on several aspects, such as health, education, and economy. The results of the clusters can be used as a reference for evaluating government policies. This study uses HDI indicator data in the East Nusa Tenggara (NTT) Province, which consists: Life Expectancy, Average Years of Schooling, Expected Years of Schooling, and Per Capita Expenditure. The outliers were detected in each variable. Therefore the k-medoids clustering method was used in this study because of their robustness on outliers. The clustering results show that there are 4 clusters formed, with each cluster describing a unique character. Cluster 1 describes the condition of the districts in general. Cluster 2 describes the condition of the districts that are one step ahead of others, which are expected to transform into cities in the future. Cluster 3 describes the condition of the district that needs more attention from the government in all sectors because all the HDI indicators are on the lowest value comparing to the other clusters. On the other hand, cluster 4 describes urban areas which have more developed than other areas, in terms of health, education, and economy. The clustering results can be used as a suggestion for evaluating government policies that have been implemented, in order to create structured equitable development based on data and regional characteristics as well.
Guidance of Unmanned Surface Vehicle Fleet Using Genetic Algorithm-Based Approach

In this contribution, we provide an approach to guidance of a fleet of unmanned surface vehicles. While the vehicles are themselves completely autonomous in their operational locomotion and obstacle avoidance, the task assignment for each vehicle is provided by a global planning process, based on the genetic algorithm. Various possibilities to the genetic algorithm are proposed and tested using a fleet of six vehicles dealing with a complex task.
Augmented Reality Trasnportasi Darat Menggunakan FAST Corner Detection dan Lucas Kanade

The digital age makes learning media more diverse, if previously using print media, then now using all electronics, such as e-books and learning applications. One of the technologies that can be used as a learning medium with the form of objects in real-time known as Augmented Reality (AR). The design of this AR application aims to introduce land transportation to early childhood. The study was developed using the Single Marker method as well as the FAST Corner Detection and Lucas Kanade algorithms. Land transportation that became the case in this study is bicycles, motorcycles, rickshaws, rickshaws, cars, buses, trucks, and trains. Information about land transportation in this AR application can attract children to know more about the various vehicles that can be used. The results of the Realme XT device research showed the accuracy level in AR can best be detected with maximum distance range with the best distance, the best angle to read on, and Realme XT light intensity faster in capturing the response
Mobile Phones and the Uses of Learning in a Training Intervention for Kenyan Community Health Workers

Mobile phones help move training programmes out of the classroom and into community settings where CHWs live and work. This chapter examines what happens to ‘learning’ when training becomes ‘mobile’. To explore this, an analysis of a three-year intervention to train 90 Kenyan CHWs is drawn from science and technology studies (STS). It is argued that when a mobile learning application is deployed in an informal urban settlement and an isolated rural village, its movement makes it available for many more uses than is originally envisioned. These varied uses subject the CHWs to multiple definitions of what it means to learn and conflicting visions of how learning leads to social change. The chapter ends with a discussion of how power circulates through global health policies, mobile devices, CHWs, and the material conditions of extreme poverty to generate controversies over what knowledge matters most for health worker training programmes, and for the broader aims of international development.
Hierarchical Generation of Human Pose With Part-Based Layer Representation

Human pose transfer has been becoming one of the emerging research topics in recent years. However, state-of-the-art results are still far from satisfactory. One main reason is that these end-to-end methods are often blindly trained without the semantic understanding of its content. In this paper, we propose a novel method for human pose transfer with consideration of the semantic part-based representation of a human. In particular, we propose to segment the human body into multiple parts, and each of them represents a semantic region of a human. With the proposed part-based layer generators, a high-quality result is guaranteed for each local semantic region. We design a three-stage hierarchical framework to fuse local representations into the final result in a coarse-to-fine manner, which provides adaptive attention for global consistency and local details, respectively. Via exploiting spatial guidance from 3D human model through the framework, our method can naturally handle the ambiguity of self-occlusions which always causes artifacts in previous methods. With semantic-aware and spatial-aware representations, our method outperforms previous approaches quantitatively and qualitatively in better handling self-occlusions, fine detail preservation/synthesis and a higher resolution result.
Learning to Balance Local Losses via Meta-Learning

The standard training for deep neural networks relies on a global and fixed loss function. For more effective training, dynamic loss functions have been recently proposed. However, the dynamic global loss function is not flexible to differentially train layers in complex deep neural networks. In this paper, we propose a general framework that learns to adaptively train each layer of deep neural networks via meta-learning. Our framework leverages the local error signals from layers and identifies which layer needs to be trained more at every iteration. Also, the proposed method improves the local loss function with our minibatch-wise dropout and cross-validation loop to alleviate meta-overfitting. The experiments show that our method achieved competitive performance compared to state-of-the-art methods on popular benchmark datasets for image classification: CIFAR-10 and CIFAR-100. Surprisingly, our method enables training deep neural networks without skip-connections using dynamically weighted local loss functions.
Detecting Cancerous Tissue in Mammograms Using Deep Neural Networks

Breast cancer cases are steadily increasing over the years. To improve the chances of a successful treatment, it is essential to diagnose and treat lesions in the initial stages. Screening methods, such as mammography, are one of the most effective strategies to achieve this goal. However, identifying some types of lesions can be challenging due to their morphological structure. For instance, calcifications typically have small diameters that range from 0.1mm to 1mm. Detecting calcifications require a careful and detailed analysis of the exams by radiologists. This work proposes a system for detecting malignant calcifications in mammograms using deep convolutional neural networks to assist in this task. The mammography image is split into patches that are classified by the deep convolutional neural network as containing malignant lesions (cancerous tissue) or not (healthy tissue). The patch-wise probabilities of cancer are then summarized so that a whole mammogram decision can be obtained. Moreover, a heatmap is built highlighting regions with a high probability of containing cancerous tissue. This heatmap can be used to draw the attention of radiologists to specific areas. Experiments performed with the CBIS-DDSM dataset indicate that the best model evaluated achieved the F-Measure of 0.99 in the classification task. These results indicate that applying the system in a real environment could improve the detection of cancer cases.
Testing of Image Compression Methods

Seven console codecs BMF 2.01 (BMF.exe), WebP (cwebp.exe, dwebp.exe), PAQ8L (paq-8l_intel.exe), PAQ8P (paq8p_sse2.exe), JPEG-LS (jpeg.exe), JPEG2000 (convert.exe), PNG (opting.exe) are tested on a set of 19 grayscale images and 10 color images (widespread images used in testing compression methods). An image compression program tester was developed. The tester receives images and executable files of image compression programs, and programs are started for each input image to compress and restore the image. The program operation results are contained in an HTML/CSS file, which includes, among other information, the bitrates achieved by the compression programs and the results of checking how successfully the compressed files were restored. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods have been made to compare the effectiveness of the multipredictors that lie at the heart of the Blend-A13+, Blend-16, Blend-20 methods. Partial clones of the Blend-A13+, Blend-16, Blend-20 compression methods consist of multipredictors used in the Blend-A13+, Blend-16, Blend-20, methods, and 13 and 16 elementary predictors used in the Blend-A13+ and Blend-16 methods, respectively. The predictor GAP+ is replaced by the predictor GAP; the modeling like JPEG-LS is replaced by contextual modeling with quantization of a context from differences of pixels from the vicinity of the coded pixel, the arithmetic coder, and the reversible intercolor RGB-YUV transformation from JPEG2000. For many images, the obtained partial clones outperformed the results yielded by the JPEG-LS, JPEG2000, PNG methods and gave results at the level provided by the PAQ8L and WebP compression methods. In general, the BMF2.01 method demonstrated the best results on the test set of images. On the test set of images, the multipredictors from Blend-16 and Blend-20 unexpectedly provided color image compression results poorer than the multipredictor from the Blend-A13+ method. In compressing grayscale images, the multipredictor from Blend-20 yielded better results than the multipredictors from Blend-16, Blend-A13+.
Linear discriminant analysis (LDA)

Discriminant analysis (DA) is a descriptive multivariate technique for analyzing grouped data, i.e. the rows of the data matrix are divided into a number of groups that usually represent samples from different populations (Krzanowski, 2003; McLachlan, 2004; Seber, 2004). Recently DA has also been viewed as a promising dimensionality reduction technique (Dhillon et al., 2002; Hastie et al., 2009). Indeed, the presence of group structure in the data additionally facilitates the dimensionality reduction.
A Least-Squares Derivation of Output Error Feedback for Direct State Estimate Correction in Recurrent Neural Networks

Iterative least-mean square learning algorithms for recurrent neural networks, such as dynamic backpropagation, have been derived to adapt network weights, but do not directly correct the state estimate. The network estimate of the unknown system’s state is propagated without direct compensation. This paper proposes a more general least-mean square problem that produces both dynamic backpropagation weight adjustment and linear output error feedback state estimate correction. The resulting topology is that of an extended Kalman filter with a feedforward network generating the state predictions. The output error feedback eliminates the need to perform state estimate regulation indirectly through weight adjustments.
EMM-CLODS: An Effective Microcluster and Minimal Pruning CLustering-Based Technique for Detecting Outliers in Data Streams

Detecting outliers in data streams is a challenging problem since, in a data stream scenario, scanning the data multiple times is unfeasible, and the incoming streaming data keep evolving. Over the years, a common approach to outlier detection is using clustering-based methods, but these methods have inherent challenges and drawbacks. These include to effectively cluster sparse data points which has to do with the quality of clustering methods, dealing with continuous fast-incoming data streams, high memory and time consumption, and lack of high outlier detection accuracy. This paper aims at proposing an effective clustering-based approach to detect outliers in evolving data streams. We propose a new method called Effective Microcluster and Minimal pruning CLustering-based method for Outlier detection in Data Streams (EMM-CLODS). It is a clustering-based outlier detection approach that detects outliers in evolving data streams by first applying microclustering technique to cluster dense data points and effectively handle objects within a sliding window according to the relevance of their status to their respective neighbors or position. The analysis from our experimental studies on both synthetic and real-world datasets shows that the technique performs well with minimal memory and time consumption when compared to the other baseline algorithms, making it a very promising technique in dealing with outlier detection problems in data streams.
Digital Literacy Curriculum in Elementary School

Advances in technology and information have an impact on the ease and abundance of various information resources that are obtained digitally and are not limited so that all levels of education need to adapt to technological advances including elementary schools in managing curriculum and learning. The purpose of this research is to find out how digital literacy is in elementary schools. The research method uses a systemic literature review by analyzing 15 relevant articles on digital literacy in elementary schools and 10 article about curriculum digital literacy. Research findings show that the digital literacy curriculum is carried out by integrating digital literacy in all areas of the school. In the implementation of learning, elementary school students still consider the teacher as the only source of information. Therefore, it is necessary to have a collaborative role between teachers, principals and parents in integrating digital literacy in learning and overcoming something unwanted. Digital literacy in elementary schools places more emphasis on media literacy in learning which has an impact on interactive and collaborative learning, one of which is video, stop-motion and social media. Indirectly digital literacy can improve student learning outcomes.
Cryptanalysis of the SoDark Cipher for HF Radio Automatic Link Establishment

The SoDark cipher is used to protect transmitted frames in the second and third generation automatic link establishment (ALE) standards for high frequency (HF) radios. The cipher is primarily meant to prevent unauthorized linking and attacks on the availability of HF radio networks. This paper represents the first known security analysis of the cipher used by the second generation ALE protocol—the de facto world standard—and presents a related-tweak attack on the full eight round version of the algorithm. Under certain conditions, collisions of intermediate states several rounds into the cipher can be detected from the ciphertext with high probability. This enables testing against the intermediate states using only parts of the key. The best attack is a chosen-ciphertext attack which can recover the secret key in about an hour with 100% probability, using 29 chosen ciphertexts.
Students’ Perception towards E-Learning: A Descriptive Analysis at Uitm Kampus Bandaraya Melaka

Covid-19 pandemic has changed the operating system in Higher Education Institutions in Malaysia. New norms had been implemented to face the situation of the pandemic. The academic system must go on so that the students will not be left behind. Open and Distance Learning (ODL) which is generally known as online education or E-Learning plays a vital role in the country's education system. The aim of this study is to examine the factors that influence the e-learning acceptance of students in UiTM City Campus Melaka as perceived by the students. This study utilized a quantitative research approach and the data has been collected through questionnaires. Students undertaking Strategic Management subject were chosen as a population and the sample size was 117. To answer the objective of this study, Statistical Package for the Social Science (SPSS) was used. The finding of this study shows respondents agree that the use of e-learning on the Strategic Management course gives ease for them during this ODL. Students perceived that the most important factor influencing online learning is the instructor. This is followed by accessibility and university support. This finding is important to the university to strategize and make plans in enhancing the implementation of e-learning for their students. Since this study focuses on the students from UiTM City Campus Melaka, it is suggested that future research to extend this study to a bigger scope that include other UiTM branches, public universities or private universities.
Cyber Security Concerns and Competitiveness for Selected Medium Scale Manufacturing Enterprises in the Context of Covid-19 Pandemic in Kenya

This study hence sought to establish data security concerns for selected medium scale manufacturing enterprises in Kenya. Descriptive research design was adopted in obtaining data from 76 senior executives. The study recorded 63.33% response rate with 67% of the enterprises reporting having experienced a cyber related incident within the last one year. Theft of intellectual property was the most cited cyber security threat at 38%, followed by phishing (24%) and malware (22%). Despite this, only about 6% of the executives are conversant with the applicable data security laws in the country, while 50% are not sure whether their enterprises’ cyber risk management program and capabilities are aligned to industry standards. The study concluded that cybercrime is a global problem that exposes businesses to legal suits by customers due to data breach, and may inhibit a firm’s competitiveness due to loss of customer confidence, stolen business strategies and intellectual property. There is need therefore for governments globally to collaborate in facilitating capacity building for entrepreneurs and partner in providing policy solutions to address cyber security. This would enhance contributions by e-commerce compliant businesses towards global sustainable development.
Novel face detection algorithm with a mask on neural network training

Discovering objects and knowing their number has been discussed in many works. Face detection technology is important for the visual scene, Deep learning theory using computer technology to discover the face, which is a wide field in marketing, traffic and security system control systems, in addition to photography. Facial recognition algorithms or face detection Include steps for the facial image to extract features to match them with a database. The face has a biometric feature. The facial feature consists of prominent and easily identifiable information that is responsible for distinguishing the objects that distinguish the face, the distance between the eyes, the shape of the nose, and The mouth for the device to perform a training group and record the data. Matlab program helps to dispense with training because MATLAB provides the instruction (CascadeObjectDetector) for facial recognition and the Viola-Jones algorithm with the result of separating the detection results in the form of subsets. In this work, a new algorithm is created for a group of elements in the unit picture And run a face detection code to highlight the background to store the information of each image in a specified folder and the face detection techniques by proposing a new algorithm to detect a face from among a group of faces, distinguish it and make it a file of its own, all of that using the Matlab program to train the neural network for face recognition.
J-EDA: A workbench for tuning similarity and diversity search parameters in content-based image retrieval

Similarity searches can be modeled by means of distances following the Metric Spaces Theory and constitute a fast and explainable query mechanism behind content-based image retrieval (CBIR) tasks. However, classical distance-based queries, e.g., Range and k-Nearest Neighbors, may be unsuitable for exploring large datasets because the retrieved elements are often similar among themselves. Although similarity searching is enriched with the imposition of rules to foster result diversification, the fine-tuning of the diversity query is still an open issue, which is is usually carried out with and a non-optimal expensive computational inspection. This paper introduces J-EDA, a practical workbench implemented in Java that supports the tuning of similarity and diversity search parameters by enabling the automatic and parallel exploration of multiple search settings regarding a user-posed content-based image retrieval task. J-EDA implements a wide variety of classical and diversity-driven search queries, as well as many CBIR settings such as feature extractors for images, distance functions, and relevance feedback techniques. Accordingly, users can define multiple query settings and inspect their performances for spotting the most suitable parameterization for a content-based image retrieval problem at hand. The workbench reports the experimental performances with several internal and external evaluation metrics such as P × R and Mean Average Precision (mAP), which are calculated towards either incremental or batch procedures performed with or without human interaction.
DVAMN: Dual Visual Attention Matching Network for Zero-Shot Action Recognition

Zero-Shot Action Recognition (ZSAR) aims to transfer knowledge from a source domain to a target domain so that the unlabelled action can be inferred and recognized. However, previous methods often fail to highlight information about the salient factors of the video sequence. In the process of cross-modal search, information redundancy will weaken the association of key information among different modes. In this paper, we propose Dual Visual Attention Matching Network (DVAMN) to distill sparse saliency information from action video. We utilize dual visual attention mechanism and spatiotemporal Gated Recurrent Units (GRU) to establish irredundant and sparse visual space, which can boost the performance of the cross-modal recognition. Relational learning strategy is employed for final classification. Moreover, the whole network is trained in an end-to-end manner. Experiments on both the HMDB51 and the UCF101 datasets show that the proposed architecture achieves state-of-the-art results among methods using only spatial and temporal video features in zero-shot action recognition.