Reviewerly/exHarmony huggingface dataset:
Efektivitas Penggunaan Media Pembelajaran Putaran Peluang Terhadap Hasil Belajar Peserta Didik Kelas VIII

This research is motivated by the monotonous learning at SMP Unggulan Al Madinah Diwek Jombang which causes student tend to be less interested in the learning process. It implies that the learning process cannot be effective. The purpose of this study is to describe the effectiveness of â€œPutaran Peluangâ€ learning media reviewed from the students responses and learning outcomes. The type of research method is Pre Experimental research with One-Shot Case Study design, while the research method uses descriptive analysis with quantitative approach. The population is all students of class VIII at SMP Unggulan Al Madinah Diwek Jombang and the sample is 21 students of class VIII B. Data are collected by test using posttest sheet and questionnaire using students respons qustionnair sheet. The results of this study were: (1) Percentage of classical student learning outcomes was 76.1% and (2) Percentage of students positive responses for the use of â€œPutaran Peluangâ€ learning media was 86.1%. It can be concluded that the use of â€œPutaran Peluangâ€ learning media is effective reviewed from studentsâ€™ learning outcomes.
Analisis Wacana Kritis pada Novel Tempurung Karya Oka Rusmini dan Implikasinya dalam Pembelajaran

This research aims to find, describe, interpret, and explain the ideology of women against the domination of patriarchy, myths and customs in Oka Rusmini's Tempurung novel as teaching materials for senior high school students. The approach used in this research is descriptive qualitative. This study uses critical discourse analysis as a blade of analysis. The data source in this study is the Tempurung novel by Oka Rusmini, published by PT. Gramedia Widiasarana Indonesia in 2017. The steps taken in data collection are as follows: read over and over the whole novel to understand its contents completely, record important words, sentences, or data related to the problem under study, record and collect relevant theories related to research, conduct research based on existing theories, and draw conclusions. The steps in data processing are as follows: data description, data classification, data analysis, data interpretation, and evaluation. The data analysis techniques used were: processing and presenting data, reading the entire data, coding the data and descriptions, interpreting and explaining the data. The findings in this study are that the Tempurung novel is a reflection of the ideology of women's resistance to the patriarchal system, the ideology of fighting against myths created by patriarchy, and the ideology of resistance to tradition. The resistence is done to obtain equality in their rights and obligations, so that women can get freedom like men. The female characters in the Tempurung novel fight against the domination of patriarchy, myths, and their own cultural customs in order to realize equal rights between men and women. These findings can be used in the creating of text-based teaching materials as learning materials in the senior high school. The application of the concept of critical discourse analysis in learning will contribute to studentsâ€™ critical thinking in solving various problems in life.
Mapping Of Potential Land Disasters Using GIS (Geographic Information System) In Tanggamus District

Tanggamus Regency is one area that has the potential for landslides because it’s passed over by mountains and hills. This study aims to determine the level of potential landslides and to determine the distribution of areas with potential landslides in Tanggamus Regency. This study aims to determine the level of potential landslides and to determine the distribution of areas with potential landslides in Tanggamus Regency. This study uses descriptive research methods conducted in Tanggamus Regency. The population of this research is all land units in Tanggamus Regency. Determination of the sample in this study using stratified random sampling technique. Data collection tehniques such as observation and documentation. Spatial analysis was performend with geographic information system (GIS) using Arcmap 10.3 software with scoring and overlay methods. The results of the research that have been obtained sahow that the level of potential landslides in Tanggamus Regency consists of four classes, namely non potential (492,31 ha or 0,17%), low potential (161.269,73 ha or 54,48%), moderate potential (124,555,24 ha or 42,84%), and high potential (4.403,53 ha or 1,51%). The distribution of landslide potential areas in the non potential class are spread in Air Naningan Suddistric, Kota Agung Suddistric, Pematang Sawa Suddistric, Semaka Suddistric, Ulu Belu Suddistric, and Wonosobo Suddistric. Potential landslides in the low class are scattered hroughout Tanggamus Regency. The dominant area is scattered in the areas of Ulu Belu Subdistrict. Potential landslides in the moderate class are scattered hroughout Tanggamus Regency. The dominant area is scattered in the areas of Ulu Belu Subdistrict. The high potential class landslides are spread in Kota Agung Suddistrict, Kota Agung Timur Suddistric, Kota Agung Barat Suddistric, Pematang Sawa Suddistric, and Ulu Belu Suddistric.
Library in the village: Village in the library: Creators of their own cultural reality during the COVID-19 pandemic

The aim of this paper is to point out the importance of cultural revitalization of rural areas. The research subject of the paper is the need for new solutions which would support the development of rural libraries in the future. The key hypothesis which is the fundamental is that rural libraries have a powerful impact when facing many socio-economic, demographic, environmental, innovative and technological challenges. Running workshops in the library in the village of Rataje, a branch of the Public Library "Bora Stanković" in Vranje, during the COVID-19 crisis, benefitted both the children and young people who were given the opportunity to spend quality time during the pandemic. The results achieved were evaluated according to the survey among the project's participants. The survey has shown that the effects of the project for the membership are significant because, despite very difficult conditions, the library managed to fulfill its important function in the society and remain the place of communication. In addition, it has influenced the growth of the cultural level of the local population, contributing to the development and implementation of the intellectual potential of its members and, attracted the localcreative intellectuals.
Character Development of Bryce Loski &amp; Juli Baker in â€˜Flippedâ€™ Movie

In literary works, specifically movies, a character is one of the important elements. The development of this character is also important in supporting the storyline in a movie. Character development can be influenced by many factors. Therefore, this paper aims to find out what factors influence the character development and how these factors influence the character development of the main character in the movie. This study uses a qualitative method with sources from the original Flipped video and uses data collection techniques from online sources using the theory of family pattern of interactions by Guralnick. The results of this study indicate that: (1) 12 data indicate the factor that influences the character development of Bryce Loski and Juli Baker, the main character of the Flipped movie, is the family interaction factor. (2) Character development of Bryce Loski and Juli Baker is influenced through Parent-Child Transactions, Family-Orchestrated Child Experiences, and Health and Safety Provided by Family
Comparing Methods to Study Social Acceptance of Smart Glasses

Conducting user studies on social perception of wearable technology requires a framework with sufficient sensitivity to emulate perception in real-life. Using videos can efficiently reach a large number of participants, but what level of video fidelity is necessary to achieve results similar to live interaction? In a study of 48 participants, we examine four different viewing conditions: phone, laptop, big screen, and live demonstration, while investigating the social weight of wearing different glasses (Vuzix Blade, Tooz Devkit, North Focals, or prescription glasses). Contrary to our hypothesis, the live condition was not the most sensitive.
Unsupervised Diffusion Model for Sensor-based Human Activity Recognition

Recognizing human activities from sensor data is a vital task in various domains, but obtaining diverse and labeled sensor data remains challenging and costly. In this paper, we propose an unsupervised statistical feature-guided diffusion model for sensor-based human activity recognition. The proposed method aims to generate synthetic time-series sensor data without relying on labeled data, addressing the scarcity and annotation difficulties associated with real-world sensor data. By conditioning the diffusion model on statistical information such as mean, standard deviation, Z-score, and skewness, we generate diverse and representative synthetic sensor data. We conducted experiments on public human activity recognition datasets and compared the proposed method to conventional oversampling methods and state-of-the-art generative adversarial network methods. The experimental results demonstrate that the proposed method can improve the performance of human activity recognition and outperform existing techniques.
A Robot Just for You: Multimodal Personalized Human-Robot Interaction and the Future of Work and Care

As AI becomes ubiquitous, its physical embodiment—robots–will also gradually enter our lives. As they do, we will demand that they understand us, predict our needs and wants, and adapt to us as we change our moods and minds, learn, grow, and age. The nexus created by recent major advances in machine learning for machine perception, navigation, and natural language processing has enabled human-robot interaction in real-world contexts, just as the need for human services continues to grow, from elder care to nursing to education and training. This talk will discuss our research in socially assistive robotics (SAR), which uses embodied social interaction to support user goals in health, wellness, training, and education. SAR brings together machine learning for user modeling, multimodal behavioral signal processing, and affective computing to enable robots to understand, interact, and adapt to users’ specific and ever-changing needs. The talk will cover methods and challenges of using multi-modal interaction data and expressive robot behavior to monitor, coach, motivate, and support a wide variety of user populations and use cases. We will cover insights from work with users across the age span (infants, children, adults, elderly), ability span (typically developing, autism, stroke, Alzheimer’s), contexts (schools, therapy centers, homes), and deployment durations (up to 6 months), as well as commercial implications.
Projecting life onto machines

Public discussions and imaginaries about AI often center around the idea that technologies such as neural networks might one day lead to the emergence of machines that think or even feel like humans. Drawing on histories of how people project lives onto talking things, from spiritualist seances in the Victorian era to contemporary advances in robotics, this talk argues that the “lives” of AI have more to do with how humans perceive and relate to machines exhibiting communicative behavior, than with the functioning of computing technologies in itself. Taking up this point of view helps acknowledge and further interrogate how perceptions and cultural representations inform the outcome of technologies that are programmed to interact and communicate with human users.
SonarAuth: Using Around Device Sensing to Improve Smartwatch Behavioral Biometrics

Smartwatches are used by millions of people for applications in health, finance, and communication. As the computational power and range of applications supported by these devices expand, it is becoming more and more important to secure access to them. While various user authentication technologies have been extensively explored in smartphone use scenarios (e.g., FaceID, fingerprint, PIN, or pattern) the applicability of these approaches to smartwatches is typically limited due to the small watch form factor. To improve authentication on smartwatches, we propose SonarAuth, a novel user authentication system for unmodified commercial smartwatches using behavioral biometrics derived from motion, touch, and around-device motions. To capture in-air hand motions, we adapted an existing sonar system for smartwatches. We collected data from 24 participants from single touch to the watch screen with the thumb, index, and middle fingers. Using a multimodal deep learning classifier, we achieved a promising mean Equal Error Rate(EER) of 6.41% for user authentication based on a single thumb tap. We note that our system is usable and has good potential to be combined with other authentication modalities.
Tangible E-Textile Interactive Interface for Digital Patternmaking

A common method of generating a garment pattern is by draping fabric on a 3D form. Many apparel designers prefer this tangible process rather than the more abstract process of drafting patterns in 2D using CAD software, but the visualization benefits of 3D rendering remain important to product development. Currently, draped patterns must be manually digitized to transfer the shape to the CAD environment, a cumbersome process. Here, we explore augmenting the fabric used to drape patterns with e-textile components, such that pinning the fabric to the form directly digitizes the pattern shape. The e-textile interface uses a keypad matrix approach with rows and columns on opposite sides of the fabric. Passing a metallic pin through a row/column intersection creates a circuit connection that a microcontroller reads and maps to a 2D spatial layout on the screen. The proof-of-concept device developed here is a first step toward an automatic digitizing system that allows designers to preserve manual skills and processes while integrating more efficiently with digital systems.
Social Prosthesis: Social Interaction Through 3D Dynamic Makeup

Prosthetic makeup is the use of prosthetic materials for cosmetic or makeup effects to extend the skin and features. Commonly used to simulate wounds or exaggerate physical characteristics, prosthetic makeup is usually created for film or theatrical purposes, rather than for everyday fashion or social wearability. Social Prosthesis is a design project which aims to introduce interactivity, movement, and aesthetic within silicone prosthetics by providing design considerations and fabrication techniques unique to on-face wearables. Through opening up opportunities for cosmetic expression and storytelling through dynamic makeup, Social Prosthesis invokes the sociality of beauty—the change and movement that happens when we alter our appearances in contact with others.
NAVIGATING THE FUTURE: ARTIFICIAL INTELLIGENCE'S GROWING INFLUENCE ON DECISION MAKING

Purpose: The purpose of this study article is to investigate the impact of AI (system capability, user behavior, training and development, and expert availability) on decision making in Bahrain telecommunications company. Theoretical framework: The deployment of AI systems has expanded tremendously in recent years, and decision making is no exception to its disruptive potential (Jones et al., 2022). AI algorithms and technologies are being used by organizations and individuals to assist and improve decision-making processes. To fully reap the benefits of AI, however, a thorough knowledge of the intricate interplay between AI and decision making is required. Design/methodology/approach: In this study, a quantitative research design is utilized since it permits statistical tools to be used to assess the association between variables. This quantitative study's research approach will be cross-sectional, with the goal of examining the role of artificial intelligence (AI) in decision making. Data were collected by questionnaire from a sample of 195 employees from a Bahraini telecommunications company. Findings: The results show that there was an association between all the artificial intelligence components and decision making. This finding is consistent with the previous studies, which claims that artificial intelligence lead to decision making (Prasanth, et al., 2023; Ferreira &amp; Monteiro, 2021; Jarrahi, 2018). Research, Practical &amp; Social implications: The AI has .the potential to totally revolutionize the way organizations make decisions. However, in order to preserve customer confidence and prevent unforeseen repercussions, enterprises must use AI responsibly and transparently. The use of AI for decision-making, both for businesses and consumers, is undeniably the way of the future. Originality/value: The value of the study understanding the impact of AI on decision making is becoming increasingly crucial as the technology advances. AI system integration can improve decision-making abilities, increase efficiency, reduce risks, offer access to expert information, and raise ethical concerns and obstacles in organizations.
The Development and Challenges of Face Alignment: A survey

Finding the face key-points location or which is also called Face alignment has gotten a lot of interest in the last twenty years because of its wide range of applications in the major of automatic face analysis. Nonetheless, this task has proven an extremely challenging task in an unconstrained environment because of multiple confusing factors, such as occlusions, position, illumination, and expression. However, this task is still an open problem, even though researchers still developing a great many techniques to face the challenges of this problem, in this survey, a critical review of the current literature in the face alignment field had already been presented focusing on those methods tackling the total of this topic's challenges and difficulties under uncontrollable circumstances. Especially, the existing face alignment techniques have been categorized existing referring to more than the type of categorizing. Then represent a survey of the recent related works in face alignment and lastly, we discuss the general steps of the face alignment system.
A comparative Study of Image Enhancement Techniques for Natural Images

Enhancement is most interesting parts in image processing field. It uses to enhance the structural appearance for picture without the degradation in the original input image. The enhancement techniques become the important key and simply extraction features by removing noise and another items inside an image. Several enhancement techniques have achieved with different and inaccurate results. The aim of this paper, the nature images quality was improved by the many enhancement techniques like of Histogram Equalization (HE), Local Histogram Equalization (LHE), Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), Brightness Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE) and Recursive sub-image histogram equalization (RSIHE). In evaluation step, the performance of all these techniques are examined by values measurements of SSIM (Structural Similarity Index Matrix), Entropy, Peak Signal-to-Noise Ratio (PSNR) and Signal to Noise Ratio (SNR). The comparisons of the better existing results are given because to explain the best possible technique that can be used as suitable image enhancement. The results of the enhanced of 15 nature images have showed DSIHE technique has a batter a values of SSIM and Entropy with 0.9885 and 59975 respectively. Overall, based on the PSNR and SNR values, the CLAHE technique are recorded values higher than of other six techniques in 21.2952 and 192932 values.
Traffic Load Balancing based on Congestion Avoidance (TLBCA) in high-speed computer networks

Congestion, and how to manage it or prevent its occurrence, is one of the most crucial and cutting-edge subjects in the world of networks since it has a significant influence on the computer network and the quality of service (QoS). Network efficiency is decreased by congestion, which frequently results in service interruptions. In order to preserve the continuity of data flow in the network, it is necessary to design approaches and processes to avoid congestion or to minimize its effects. Congestion are avoided at two levels, knot and link, by using different techniques, including close loop or open loop. This paper proposed a new technique (TLBCA) to distribute packets arriving at a specific node on the links connected to it, which leads to the same destination, to avoid congestion on one of the links and out of service, which then leads to more load on the other links until the network collapses. TLBCA is based on the popular algorithm Round Robin. ECMP and TinyFlow, the two most widely used comparable algorithms in this sector, performed around 20% worse than the approach when it was simulated using OMNET++.
Pixel Based Techniques for Gray Image Compression: A review

Currently, with the huge increase in modern communication and network applications, the speed of transformation and storing data in compact forms are pressing issues. Daily an enormous amount of images are stored and shared among people every moment, especially in the social media realm, but unfortunately, even with these marvelous applications, the limited size of sent data is still the main restriction's, where essentially all these applications utilized the well-known Joint Photographic Experts Group (JPEG) standard techniques, in the same way, the need for construction of universally accepted standard compression systems urgently required to play a key role in the immense revolution.&#x0D; This review is concerned with Differential pulse code modulation (DPCM) and pixel-based techniques, where the spatial domain is exploited to compress images efficiently in terms of compression performance and preserving quality. The new pixel-based method overcomes predictive coding constraints with fewer residues and higher compression ratios.
A Proposed Arabic Text Classification Model using Multi-Label System

Multi-label text classification has grown in popularity in recent years, with each document being assigned numerous categories simultaneously. The Arabic Language has a very complex morphology and a vibrant nature; nonetheless, there needs to be more research on this topic for the Arabic Language. As a result, this study aims to present a method for the multi-label classification of Arabic texts based on binary relevance and the label power set transformation method. Three classification classifiers: namely logistics regression(LR), Random forest (RF), and multinomial naïve Bays (MNB), were experimentally assessed in this thesis. Furthermore, chi-square feature selection was investigated to improve the performance of the proposed model. The experimental results are implemented in Python programming using the "RTANews" multi-label Arabic text classification dataset. The results suggest that binary relevance combined with logistics regression produces the best results. It performed well, with an averaged micro-Recall of 0.8646. At the same time, the best result was produced by label power-set with the same algorithm and metrics of 0.8418 for the suggested multi-label Arabic text classification model.