Reviewerly/exHarmony huggingface dataset:
Design of Virtual Reality-Based Hippotherapy Simulator Exergaming Software and Its Controller for Rehabilitation of Children with Cerebral Palsy in Indonesia: An Engineering Concept

Horse riding therapy, also known as hippotherapy, is a popular treatment for children with cerebral palsy (CP). However, the need for trained therapists, massive land use, and expensive maintenance of the horse ranch makes hippotherapy not affordable or even available for most patients in Indonesia. This problem motivates us to consider mechanical horse riding simulator machines to replace actual horse hippotherapy. However, most patients are children and are easily bored when asked to do monotonous activities for an extended period. The room setting also does not give the patient visual inputs that usually help motivates the children in real-horse hippotherapy activities. To solve this problem, we designed an exercise game (exergaming) software which we named \textit{Sirkus Apel}, providing the patients with fun activities while doing the therapy. We also design an inertial sensor-based controller that lets the patients control the in-game horse by their back movements, which also benefits CP patients. To make the visual input enjoyable to the user while also considering the user's safety, we built a convex mirror-based dome virtual reality to provide an immersive 3-D experience. We then project the game content to the dome to provide an immersive experience to the patients making it as if they are riding a real horse inside the game.
Implementasi Algoritma K-Nearest Neighbor dalam Menentukan Buku Berdasarkan Peminatan

The use of computer technology helps a lot in human performance in various data and information management. Therefore, researchers use computers to make analyzes that can predict favorite books based on data from book borrowing records in the library. Researchers use is data mining. Data mining is a term used to describe the discovery of knowledge in databases, using statistical techniques, mathematics, artificial intelligence, and machine learning to extract and identify information that is useful for science. The use of data mining requires a method that can manage book borrowing data so that it gets the predictions of favorite books. The method used is K-Nearest Neighbor (KNN). The results of the accuracy in this study are 98.75%, Prediction of Disinterest with true Not Interest is 28 data, Prediction of Disinterest with true Interest is 1 data, Prediction of Interest with true No Interest is 0 data, Interest Prediction with true Interest is 54 data.
Pengembangan Sistem Presensi Dosen dan Mahasiswa Menggunakan QR Code dengan Framework Codeigniter

Many technologies are constantly evolving to facilitate teaching and learning. The presence system became an obstacle in practicality and recording to a central server in STMIK Mardira Indonesia that has hundreds of students, especially if the presentation is done manually, requiring considerable time and providing opportunities for human error. System Presence Development Lecturer and Student using CodeIgniter Framework-based QR Code using the programming language PHP, JavaScript, jQuery. The method used, namely, observation, interview, literature, and systems development, is used using the model of requirements, analysis, design, implementation, and testing. It can be concluded that the system has been perfected following the schedule of lectures and the system can process data with both faculty and students.
Deteksi Jenis Penyakit melalui Perubahan Warna Kuku dengan Teknik Image Processing

Health is an important thing that is needed in order to maintain endurance, in the modern era types of diseases continue to grow very rapidly, these changes can be caused by several factors, these factors can be from food and lifestyle. , we can't deny the lifestyle in the modern era causes a person to forget to take care of their health, this is because the activities and activities are very dense, sometimes without realizing it makes the body's resistance to a disease less so that our body is susceptible to disease. viruses and diseases, this type of disease can attack and damage internal and external organs. Naturally, the body will respond to changes, these changes can show symptoms of an attack, diseases can be categorized from mild to high levels, for example, people who experience hair loss who continue to show cancer, either early or advanced cancer, another example is nail discoloration, for example. one yellow nail indicates the person has jaundice, another indication the nail has a long black line, this indication can be recognized. as a digestive problem in our body. For those who are experts, they will have experience in recognizing diseases and their indications, solutions will soon be found by buying preventive medicine, or other methods by directly consulting a doctor. The problem faced today is that a person is sometimes unconscious and does not observe changes in the body and even considers these changes to be normal, even though the body is trying to provide a natural warning system for the appearance of a disease symptom. image-processing based application, this application is used to identify changes in nail color and texture, these changes will be identified and processed using an image processing algorithm, this algorithm has a high accuracy value, the application will read nail color changes and identify and diagnose symptoms of disease in our body, the identification results have an accuracy of up to 85%.
Employing the Social Media Platforms, Facebook, in Managing Jordanian Teachers Syndicate Crisis During the period( 2019 – 2020) ( A comparative exploratory study from the viewpoint of teachers and administrators)

This study analyses and evaluates the strategies applied in managing the crisis of teachers’ syndicate from the perspectives of teachers and administrative staff. To achieve the objective of the study, a simple random sample of (402) individuals was selected. A questionnaire that has high validity and reliability was used. The research was designed according to a descriptive analytical approach and the hypotheses thereof were tested by using Pearson Correlation Coefficient. The findings and their interpretation proved that there is intensive use for different strategies in the field of Facebook platform. It was clear that there were slight differences between the teachers and administrative staff. The recommendations based on the findings showed that teachers’ syndicate need to improve their skills to manage crises in a scientific manner. They should monitor changes and predict trends of influential actors, in particular, to sustain the desired effect. They should use a mature language away from situation exacerbation.
Perceptual Video Coding based on Adaptive Region-Level Intra-Period

According to the human visual system (HVS), video regions have various visual masking effects and saliency features. However, in the existing perceptual video coding schemes, different regions in a picture basically keep the same coding mode, which does not make full use of the perceptual properties. Therefore, this paper proposes a perceptual video coding method based on adaptive region-level intra-period for conversational video service. Firstly, by considering the perceptual properties of video content, a picture is divided into two groups of regions, namely regions of interest (ROIs) and regions of non-interest (nonROIs). Then, a just noticeable difference (JND)-based distortion propagation estimation model is proposed to determine an appropriate region-level intra-period value for regions according to their content characteristics and perceptual importance. Further, a region-level bit allocation method is used to update the coding resource allocation process. The experimental results demonstrate that the proposed method performs much better than the existing schemes in terms of bits saving and the perceived visual quality.
A Comprehensive Exploration of Pre-training Language Models

&lt;p&gt;Recently, the development of pre-trained language models has brought natural language processing (NLP) tasks to the new state-of-the-art. In this paper we explore the efficiency of various pre-trained language models. We pre-train a list of transformer-based models with the same amount of text and the same training steps. The experimental results shows that the most improvement upon the origin BERT is adding the RNN-layer to capture more contextual information for short text understanding. But there are no remarkable improvement for short text understanding for similar BERT structures.&lt;/p&gt;
Real‐time out‐of‐distribution detection in cyber‐physical systems with learning‐enabled components

Learning-enabled components (LECs) such as deep neural networks are used increasingly in cyber-physical systems (CPS) since they can handle the uncertainty and variability of the environment and increase the level of autonomy. LECs, however, may compromise system safety since their predictions may have large errors, for example, when the data available at runtime are different than the data used for training. This study considers the problem of efficient and robust out-of-distribution detection for learning-enabled CPS. Out-of-distribution detection using a single input example is typically not robust and may result in a large number of false alarms. The proposed approach utilises neural network architectures that are used to compute efficiently the nonconformity of new inputs relative to the training data. Specifically, variational autoencoder and deep support vector data description networks are used to learn models for the real-time detection of out-of-distribution high-dimensional inputs. Robustness can be improved by incorporating saliency maps that identify parts of the input contributing most to the LEC predictions. We demonstrate the approach using simulation case studies of an advanced emergency braking system and a self-driving end-to-end controller, as well as a real-world data set for autonomous driving. The experimental results show a small detection delay with a very small number of false alarms while the execution time is comparable to the execution time of the original LECs.
Progressive Multi-scale Fusion Network for RGB-D Salient Object Detection

Salient object detection(SOD) aims at locating the most significant object within a given image. In recent years, great progress has been made in applying SOD on many vision tasks. The depth map could provide additional spatial prior and boundary cues to boost the performance. Combining the depth information with image data obtained from standard visual cameras has been widely used in recent SOD works, however, introducing depth information in a suboptimal fusion strategy may have negative influence in the performance of SOD. In this paper, we discuss about the advantages of the so-called progressive multi-scale fusion method and propose a mask-guided feature aggregation module(MGFA). The proposed framework can effectively combine the two features of different modalities and, furthermore, alleviate the impact of erroneous depth features, which are inevitably caused by the variation of depth quality. We further introduce a mask-guided refinement module(MGRM) to complement the high-level semantic features and reduce the irrelevant features from multi-scale fusion, leading to an overall refinement of detection. Experiments on five challenging benchmarks demonstrate that the proposed method outperforms 11 state-of-the-art methods under different evaluation metrics.
CAISE: Conversational Agent for Image Search and Editing

Demand for image editing has been increasing as users' desire for expression is also increasing. However, for most users, image editing tools are not easy to use since the tools require certain expertise in photo effects and have complex interfaces. Hence, users might need someone to help edit their images, but having a personal dedicated human assistant for every user is impossible to scale. For that reason, an automated assistant system for image editing is desirable. Additionally, users want more image sources for diverse image editing works, and integrating an image search functionality into the editing tool is a potential remedy for this demand. Thus, we propose a dataset of an automated Conversational Agent for Image Search and Editing (CAISE). To our knowledge, this is the first dataset that provides conversational image search and editing annotations, where the agent holds a grounded conversation with users and helps them to search and edit images according to their requests. To build such a system, we first collect image search and editing conversations between pairs of annotators. The assistant-annotators are equipped with a customized image search and editing tool to address the requests from the user-annotators. The functions that the assistant-annotators conduct with the tool are recorded as executable commands, allowing the trained system to be useful for real-world application execution. We also introduce a generator-extractor baseline model for this task, which can adaptively select the source of the next token (i.e., from the vocabulary or from textual/visual contexts) for the executable command. This serves as a strong starting point while still leaving a large human-machine performance gap for useful future work. Our code and dataset are publicly available at: https://github.com/hyounghk/CAISE
Assessment of Network Configuration Management (CM) and Data Stewardship in the Indonesian Boarding School

In recent years, an increasing understanding of software development as a set of interconnected processes has affected configuration management (CM). The purpose of CM is to understand network behavior by taking an inventory of users and network devices, bandwidth usage and analyzing this data to provide information on current usage patterns. Usage quotas can be set for individual users or groups, whose optimal access points can be reached after several iterations and some fixes, which data stewardship (DS) comes into the picture to maintain high-quality data in a consistent and accessible way. When achieved, the continuous measurement produces information related to the billing process and assessment of the fair and optimal use of resources within the network system should be conducted. Hence, this study wants to explore the issue that has been challenged primarily by the high school in maintaining the configuration process of the network in the laboratory for teaching and learning by assessing the implementation with set of CM and DS criterion.
Real Time Video Recognition of Signs for Deaf and Dump Using Deep Learning

Abstract: To pass the message is one of the essential prerequisites for endurance in the general public. Gesture based communication is a typical specialized technique for hard of hearing stupid local area. It makes out of an assortment scope of motions, activities and, surprisingly, facial feelings.Gesture based communication is utilized by 70 million individuals all over the planet. Understanding communication via gestures is one of the essential empowering influences in assisting clients of gesture-based communication with speaking with the remainder of the general public. The hard of hearing and dump local area moves back with regards to the intelligent part with ordinary individuals. This makes a tremendous hole among hard of hearing and dump individuals and ordinary individuals. Since our local area have no clue about communication through signing. In this project an application is created which will fill in as a learning instrument first of all in communication via gestures that includes hand recognition. Application is made to change gesture-based communication over to message. An application which makes an interpretation of Sign language to message, which utilizes the portable camera to catch the picture of the hand motion. Then, at that point, the caught picture goes through the series of activity. The CNN model is utilized to extricate the elements of the caught picture and makes an interpretation of it into text.
Toric and non-toric Bayesian networks

In this paper we study Bayesian networks from a commutative algebra perspective. We characterize a class of toric Bayesian nets, and provide the first example of a Bayesian net which is proved non-toric under any linear change of variables. Concerning the class of toric Bayesian nets, we study their quadratic relations and prove a conjecture by Garcia, Stillman, and Sturmfels for this class. In addition, we give a necessary condition on the underlying directed acyclic graph for when all relations are quadratic.
Signatures of cooperative emission in photon coincidence: Superradiance versus measurement-induced cooperativity

Indistinguishable quantum emitters confined to length scales smaller than the wavelength of the light become superradiant. Compared to uncorrelated and distinguishable emitters, superradiance results in qualitative modifications of optical signals such as photon coincidences. However, recent experiments revealed that similar signatures can also be obtained in situations where emitters are too far separated to be superradiant if correlations between emitters are induced by the wave function collapse during an emission-angle-selective photon detection event. Here, we compare two sources for cooperative emission, superradiance and measurement-induced cooperativity, and analyze their impact on time-dependent optical signals. We find that an anti-dip in photon coincidences at zero time delay is a signature of inter-emitter correlations in general but does not unambiguously prove the presence of superradiance. This suggests that photon coincidences at zero time delay alone are not sufficient and time-dependent data is necessary to clearly demonstrate a superradiant enhancement of the spontaneous radiative decay rate.
Contrastive Learning for OOD in Object detection

Contrastive learning is commonly applied to self-supervised learning, and has been shown to outperform traditional approaches such as the triplet loss and N-pair loss. However, the requirement of large batch sizes and memory banks has made it difficult and slow to train. Recently, Supervised Contrasative approaches have been developed to overcome these problems. They focus more on learning a good representation for each class individually, or between a cluster of classes. In this work we attempt to rank classes based on similarity using a user-defined ranking, to learn an efficient representation between all classes. We observe how incorporating human bias into the learning process could improve learning representations in the parameter space. We show that our results are comparable to Supervised Contrastive Learning for image classification and object detection, and discuss it's shortcomings in OOD Detection
What Features Influence Impact Feel? A Study of Impact Feedback in Action Games

Making the hit effect satisfy players is a long-standing problem faced by action game designers. However, no research systematically analyzed which game design elements affect such game feel. There is not even a term to describe it. So, we propose to use impact feel to describe the player's feeling when receiving juicy impact feedback. After collecting player's comments on action games from Steam's top seller list, we trained a natural language processing (NLP) model to rank action games with their performance on impact feel. We presented a 19-feature framework of impact feedback design and examined it in the top eight and last eight games. We listed an inventory of the usage of features and found that hit stop, sound coherence, and camera control may strongly influence players' impact feel. A lack of dedicated design on one of these three features may ruin players' impact feel. Our findings may become an evaluation metric for future studies.
Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge

How can we accurately recommend actions for users to control their devices at home? Action recommendation for smart home has attracted increasing attention due to its potential impact on the markets of virtual assistants and Internet of Things (IoT). However, designing an effective action recommender system for smart home is challenging because it requires handling context correlations, considering both queried contexts and previous histories of users, and dealing with capricious intentions in history. In this work, we propose SmartSense, an accurate action recommendation method for smart home. For individual action, SmartSense summarizes its device control and its temporal contexts in a self-attentive manner, to reflect the importance of the correlation between them. SmartSense then summarizes sequences of users considering queried contexts in a query-attentive manner to extract the query-related patterns from the sequential actions. SmartSense also transfers the commonsense knowledge from routine data to better handle intentions in action sequences. As a result, SmartSense addresses all three main challenges of action recommendation for smart home, and achieves the state-of-the-art performance giving up to 9.8% higher mAP@1 than the best competitor.
Mixed-Precision Neural Networks: A Survey

Mixed-precision Deep Neural Networks achieve the energy efficiency and throughput needed for hardware deployment, particularly when the resources are limited, without sacrificing accuracy. However, the optimal per-layer bit precision that preserves accuracy is not easily found, especially with the abundance of models, datasets, and quantization techniques that creates an enormous search space. In order to tackle this difficulty, a body of literature has emerged recently, and several frameworks that achieved promising accuracy results have been proposed. In this paper, we start by summarizing the quantization techniques used generally in literature. Then, we present a thorough survey of the mixed-precision frameworks, categorized according to their optimization techniques such as reinforcement learning and quantization techniques like deterministic rounding. Furthermore, the advantages and shortcomings of each framework are discussed, where we present a juxtaposition. We finally give guidelines for future mixed-precision frameworks.
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment

The size of deep learning models in artificial intelligence (AI) software is increasing rapidly, which hinders the large-scale deployment on resource-restricted devices (e.g., smartphones). To mitigate this issue, AI software compression plays a crucial role, which aims to compress model size while keeping high performance. However, the intrinsic defects in the big model may be inherited by the compressed one. Such defects may be easily leveraged by attackers, since the compressed models are usually deployed in a large number of devices without adequate protection. In this paper, we try to address the safe model compression problem from a safety-performance co-optimization perspective. Specifically, inspired by the test-driven development (TDD) paradigm in software engineering, we propose a test-driven sparse training framework called SafeCompress. By simulating the attack mechanism as the safety test, SafeCompress can automatically compress a big model to a small one following the dynamic sparse training paradigm. Further, considering a representative attack, i.e., membership inference attack (MIA), we develop a concrete safe model compression mechanism, called MIA-SafeCompress. Extensive experiments are conducted to evaluate MIA-SafeCompress on five datasets for both computer vision and natural language processing tasks. The results verify the effectiveness and generalization of our method. We also discuss how to adapt SafeCompress to other attacks besides MIA, demonstrating the flexibility of SafeCompress.