Reviewerly/exHarmony huggingface dataset:
Analysis of Data Prediction Generate by Admission Student Application using Least Square Method

The number of applicants entering each department at STMIK Sumedang has increased and decreased from year to year. The data can be used to gain knowledge and learning by searching for stored patterns. One of them is to predict the number of registrants in the following year. STMIK Sumedang doesn't have that system yet. Objectives This study aims to apply and analyze the accuracy of the least square method to predict the number of prospective students. This method is very suitable to be used to predict the magnitude of variables in time series. The number of applicants entering each department is entered based on a period, namely Information Engineering, Information Systems, and Information Management. Existing data amounted to five time periods, consisting of five years of new student admission data. To calculate the error rate, mean absolute deviation, mean square error, and mean absolute percentage error is used. With prediction results for 2020 obtained, are 225 people for Informatics Engineering, 82 Information Systems, and 11 Informatics Management. Besides, the results of the measurement of forecasting suitability with these methods include Informatics Engineering as much as 0.79 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> , Information Systems 0.8%, and Information Management as much as 3.4%. The results of this study prove that to predict the number of registrants on the admission of new students using the least square method.
Cultural Discourse in Human-Computer Interactions: Linguistic Aspects of Digital Communication

Human-computer interaction (HCI) has drawn a lot of attention in the past few years from the technological viewpoint, however, there are several aspects that must be considered when dealing with optimizing HCI. Information technology is ubiquitous and so is digital communication which is conducted globally through the network, both the Internet and intranets of companies. Digital communication bears a significant aspect, similarly to any kind of communication, and it is the fact that it is based on cultural discourse. Due to the fact that any digital communication and human-computer interaction are culturally conditioned and it takes place in the global world of interconnectedness, it is always intercultural. The cultural discourse, manifested by linguistic means, of this communication, is one of the most important parameters which must be taken into account if we want to optimize information transfers and attempt to reach relatively lossless information transfer. The paper summarizes the results of the research which dealt with the issue of cultural narratives present in websites. The findings clearly show that the ICT students do not realize the importance of this aspect despite the fact that in business and other related areas the cultural narratives have already been researched sufficiently. The paper will be useful for ICT departments and managers as it draws our attention to this crucial aspect of HCI necessary for future technology enhancements.
Using a Shared SGX Enclave in the UNIX PAM Authentication Service

Confidentiality in the storage and handling of sensitive data is a central concern in computing security; one of the most sensitive data in computer systems is users' credentials. To ensure the confidentiality and integrity of sensitive data, developers can use a Trusted Execution Environment (TEE). One of such TEE is Intel Software Guard Extensions (SGX), which reduces the trusted computing base to a hardware/software concept called enclave. However, using SGX enclaves usually incurs in a performance impact in the application execution. In this paper we propose an enclave sharing approach to reduce the performance overhead in scenarios where multiple enclaves handle the same data. To evaluate this approach, we implemented a SGX-secured OS authentication service. Three prototypes were built, considering distinct concerns about security and performance. Results show that this approach can be used in high demand environments, presenting a small overhead.
Trend Moment Method on Identification of Food Product Sales

Shakila Mart is a retail company that has been established since 1989. The system that runs Shakila Mart is still semi-computerized using Microsoft Excel in the process of recording and determining the prediction of the production of all products and product sales in the following year. The production department has problems in making reports of production needs every month, so in the submission of reports to the leadership requires a long time, so that resulting in late ordering of goods that result in a vacancy of stock. The method used in this study is the Trend Moment which is a method that is widely used in terms of prediction/forecasting. In this study, we are using Shakila Mart sales data as many as 76 items of goods, where the data taken is the total sales of items per month in 2018. The results of this study resulted in a Forecast Sales Forecasting application and had been tested with 76 existing items of data, then the prediction results compared with sales in January 2019, obtained an accuracy of 98.23%.
Feature Selection Based on a Sparse Neural-Network Layer With Normalizing Constraints

Feature selection is important step in machine learning since it has shown to improve prediction accuracy while depressing the curse of dimensionality of high dimensional data. The neural networks have experienced tremendous success in solving many nonlinear learning problems. Here, we propose new neural-network based feature selection approach that introduces two constrains, the satisfying of which leads to sparse FS layer. We have performed extensive experiments on synthetic and real world data to evaluate performance of the proposed FS. In experiments we focus on the high dimension, low sample size data since those represent the main challenge for feature selection. The results confirm that proposed Feature Selection Based on Sparse Neural Network Layer with Normalizing Constraints (SNEL-FS) is able to select the important features and yields superior performance compared to other conventional FS methods.
RETRACTED: High resolution image detection and ultrasonic evaluation of hyperthyroidism based on hospital IoT system

Hyperthyroidism is a disorder of your thyroid gland, an organ located in front of your neck, which regulates your body's use of hormonal energy for release. The thyroid gland causes hyperthyroidism, hyperthyroidism and has more thyroid hormones than your body needs. The most advanced problems are characteristic of deep features or both based on, but its performance is still limited and detected. To overcome these problems, The Proposed Logistical Support Vector (LSV) algorithm based on ultrasonic hyperactivity using the Internet of Things detect hyperthyroidism. The proposed LSV algorithm is based on an ultrasonic sensor on the Internet of Things to improve the detection of hyperthyroid segmentation automation accuracy. The volume of the thyroid gland is done by is calculated using the results of the segmentation and compares ground truth and segmentation results of performance analysis. The proposed Logistical Support Vector (LSV) algorithm reduces the target class detection imbalance of training samples for training and binary-weighted cross-entropy loss function. By popular open data sets, experiment with the thyroid digital image database, confirmed the most advanced methods, advantages of this method
Spatially Localized Perturbation GAN (SLP-GAN) for Generating Invisible Adversarial Patches

Deep Neural Networks (DNNs) are very vulnerable to adversarial attacks because of the instability and unreliability under the training process. Recently, many studies about adversarial patches have been conducted that aims to misclassify the image classifier model by attaching patches to images. However, most of the previous research employs adversarial patches that are visible to human vision, making them easy to be identified and responded to. In this paper, we propose a new method entitled Spatially Localized Perturbation GAN (SLP-GAN) that can generate visually natural patches while maintaining a high attack success rate. SLP-GAN utilizes a spatially localized perturbation taken from the most representative area of target images (i.e., attention map) as the adversarial patches. The patch region is extracted using the Grad-CAM algorithm to improve the attacking ability against the target model. Our experiment, tested on GTSRB and CIFAR-10 datasets, shows that SLP-GAN outperforms the state-of-the-art adversarial patch attack methods in terms of visual fidelity.
Interrelation of Ideas About Self-Efficacy with Motivation to Succeed in Young People Taking University Psychological and Legal Courses

Due to the growing interest in the phenomenon of self-efficacy, the psychological science examines the directions of studying self-efficacy in domestic and foreign exploratory development. The attention is focused on studies that reveal the interrelation of the self-efficacy phenomenon with other psychological phenomena, such as motivation to succeed and avoidance of failure, as well as studies devoted to the features of self-efficacy ideas in male and female students. There are publications on the data of the empiric investigation of interrelation of self-efficacy and success motivation in young people at various university training stages with presentations of analysis of differences in correlation features in male and female students. A conclusion is made that there are differences existing between the investigated indicators of self-efficacy in substantive work, self-efficacy in interpersonal communication and success motivation in the first-year and the second-year students, as well as the differences existing in the outlined indicators in the young people taking university psychological and legal courses.
High Fidelity Interactive Video Segmentation Using Tensor Decomposition, Boundary Loss, Convolutional Tessellations, and Context-Aware Skip Connections

We provide a high fidelity deep learning algorithm (HyperSeg) for interactive video segmentation tasks using a convolutional network with context-aware skip connections, and compressed, ”hypercolumn” image features combined with a convolutional tessellation procedure. In order to maintain high output fidelity, our model crucially processes and renders all image features in high resolution, without utilizing downsampling or pooling procedures. We maintain this consistent, high grade fidelity efficiently in our model chiefly through two means: (1) We use a statistically-principled, tensor decomposition procedure to modulate the number of hypercolumn features and (2) We render these features in their native resolution using a convolutional tessellation technique. For improved pixel-level segmentation results, we introduce a boundary loss function; for improved temporal coherence in video data, we include temporal image information in our model. Through experiments, we demonstrate the improved accuracy of our model against baseline models for interactive segmentation tasks using high resolution video data. We also introduce a benchmark video segmentation dataset, the VFX Segmentation Dataset, which contains over 27,046 high resolution video frames, including green screen and various composited scenes with corresponding, hand-crafted, pixel-level segmentations. Our work presents an extension to improvement to state of the art segmentation fidelity with high resolution data and can be used across a broad range of application domains, including VFX pipelines and medical imaging disciplines.
Understanding Motion in Sign Language: A New Structured Translation Dataset

Sign languages are the main mechanism of communication and interaction in the Deaf community. These languages are highly variable in communication with divergences between gloss representation, sign configuration, and multiple variants, among others, due to cultural and regional aspects. Current methods for automatic and continuous sign translation include robust and deep-learning models that encode the visual signs representation. Despite the significant progress, the convergence of such models requires huge amounts of data to exploit sign representation, resulting in very complex models. This fact is associated to the highest variability but also to the shortage exploration of many language components that support communication. For instance, gesture motion and grammatical structure are fundamental components in communication, which can deal with visual and geometrical sign misinterpretations during video analysis. This work introduces a new Colombian sign language translation dataset (CoL-SLTD), that focuses on motion and structural information, and could be a significant resource to determine the contribution of several language components. Additionally, an encoder-decoder deep strategy is herein introduced to support automatic translation, including attention modules that capture short, long, and structural kinematic dependencies and their respective relationships with sign recognition. The evaluation in CoL-SLTD proves the relevance of the motion representation, allowing compact deep architectures to represent the translation. Also, the proposed strategy shows promising results in translation, achieving Bleu-4 scores of 35.81 and 4.65 in signer independent and unseen sentences tasks.
Cloud Classification from NOAA Satellite Image Using Learning Vector Quantization Method

Cloud images from NOAA satellites 18 and 19 are essential for weather forecasting and climate analysis. Imagery from satellites in the cloud's shape can be distinguished based on the cloud (low, middle, and high). This paper uses the multilevel thresholding segmentation method compared with the FCM method (fuzzy c-mean clustering). The segmented data with the two methods are classified using the LVQ method. This study's results obtained the accuracy of the cloud data recognition segmented using multilevel thresholding of 72.22% and cloud data segmented using FCM of 83.33%.
An Ontological Approach to the Text Sample Size Adaptation for the False Pseudonyms Detection

The paper deals with the problem of text feature vector forming under the conditions of heterogeneous sources of training sets. To create a service of victim behavior prevention it is necessary to process huge volumes of information, including the processing of text samples to create the text feature vectors. We propose to use the ontological approach to select the shortest possible text samples and to use the appropriate text processing methods. We examined the wide range of literature presented in this area, and conducted an ontology, which describes the relations between the data source types and text processing approaches. Such ontology makes it possible to select the optimal method of the text processing in terms of time and computational resources consumption.
Massive Semi-structured Data Platform Based on Elasticsearch and MongoDB

Nowadays, the NoSQL databases always are chosen to solve the problem of massive semi-structured data storage, but the built-in analysis module still has a lack of real-time data processing. To solve this problem, a real-time storage analysis solution based on Elasticsearch and MongoDB was proposed in this paper. The proposed solution uses MongoDB cluster for real-time data storage and combines MongoDB cluster and Elasticsearch cluster as analysis module to meet the need of real-time analysis. Analysis requirements can be divided into complex statistical analysis and query requirements. Query operations are executed directly in MongoDB cluster, complex statistical analysis such as group statistical analysis, should be done in both Elasticsearch cluster and MongoDB cluster. Experimental results show that in this platform, 99.96% of the storage requirements can be responded in 100 ms and all the analysis requirements can be responded in seconds. Therefore, this solution can meet the need of real-time storage and analysis on massive semi-structured data.
ST-Xception: A Depthwise Separable Convolution Network for Military Sign Language Recognition

Military sign language is an important form of tactical communication, especially in restrict situations where either distance or a requirement for silence precludes oral means. Unfortunately, when soldiers cannot see each other, the communication mode of tactical gestures is no longer effective, which may hinder military operations. Vision-based approaches have been at the forefront in the field of hand gesture recognition. However, there still lacks of specific datasets and models for the task of military sign language recognition. In this paper, we collected a new first-person dataset named MSL, which contains 16 classes of 3, 840 tactical gesture samples on battle scenario with more than 11, 0000 video frames performed by 10 subjects. Moreover, we present a novel deep network, called ST-Xception architecture, in light of the depthwise separable convolutions to recognize such military sign language. By expanding the convolution filters and pooling kernels into 3D, our network can characterize the inherent spatio-temporal relationship of a certain tactical hand gesture. In particular, we further reduce computational cost and relieve overfitting by replacing the fully connected layers with adaptive average pooling. Experimental results show that our model outperforms existing models both on our in-house MSL dataset and two other benchmark datasets.
Are we ready to use mobile devices in higher education? A case study from a central-eastern European university

While international research indicates the positive impact of using tablets in teaching, successful implementation of mobile learning (m-learning) is no matter of accident but depends on users' approach to and acceptance of such technology. In this study, we examine whether students of the private European University are technologically ready for m-learning. We also sought to predict students' intention to use m-learning in future classes. To answer the research questions, a survey was administered to 640 students, and text-based answers were transformed into numbers. We then analyzed the questionnaire data and formulated 6 hypotheses, which were verified using a nonparametric method. The results revealed that participants are mentally and technologically ready for m-learning. They not only have positive attitudes towards m-learning but are also highly motivated to use mobile devices in their future education. Moreover, students already use mobile devices to support their learning despite it not being formally included in the curriculum. In analyzing what factors make students accept or resist m-learning, we found the technological maturity of our students - in this case to be vital. Furthermore, m-learning is a promising pedagogical technology to be implemented in higher education environments.
Feature Extraction and Matching of Slam Image Based on Improved SIFT Algorithm

In order to improve the robustness and accuracy of slam system, the Improved SIFT algorithm is used to extract the image features. Firstly, the characteristics of the image in slam are analyzed and the image preprocessing is carried out to reduce the gray mutation. Secondly, in order to meet the real-time requirements, the feature descriptors of sift are simplified to improve the speed. Using the continuity of slam image, the method of pixel neighborhood matching reduces the time of feature matching and reduces the error matching rate of repeated texture. GPU is used to implement the Improved SIFT feature algorithm. Finally, the simulation results show that the trajectory accuracy is improved by more than 35% and the image processing time is about 12ms. At the same time, the system accuracy is improved.
Interactive Visual Analysis of Mass Spectrometry Imaging Data Using Linear and Non-Linear Embeddings

Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios (m/z-ratios), each linked to an individual molecular ion species. Traditional analysis tools focus on few individual m/z-ratios, which neglects most of the data. Recently, clustering methods of the spectral information have emerged, but faithful detection of all relevant image regions is not always possible. We propose an interactive visual analysis approach that considers all available information in coordinated views of image and spectral space visualizations, where the spectral space is treated as a multi-dimensional space. We use non-linear embeddings of the spectral information to interactively define clusters and respective image regions. Of particular interest is, then, which of the molecular ion species cause the formation of the clusters. We propose to use linear embeddings of the clustered data, as they allow for relating the projected views to the given dimensions. We document the effectiveness of our approach in analyzing matrix-assisted laser desorption/ionization (MALDI-2) imaging data with ground truth obtained from histological images.
An Area-Efficient Recurrent Neural Network Core for Unsupervised Time-Series Anomaly Detection

Since most sensor data depend on each other, time-series anomaly detection is one of practical applications of IoT devices. Such tasks are handled by Recurrent Neural Networks (RNNs) with a feedback structure, such as Long Short Term Memory. However, their learning phase based on Stochastic Gradient Descent (SGD) is computationally expensive for such edge devices. This issue is addressed by executing their learning on high-performance server machines, but it introduces a communication overhead and additional power consumption. On the other hand, Recursive Least-Squares Echo State Network (RLS-ESN) is a simple RNN that can be trained at low cost using the least-squares method rather than SGD. In this paper, we propose its area-efficient hardware implementation for edge devices and adapt it to human activity anomaly detection as an example of interdependent time-series sensor data. The model is implemented in Verilog HDL, synthesized with a 45 nm process technology, and evaluated in terms of the anomaly capability, hardware amount, and performance. The evaluation results demonstrate that the RLS-ESN core with a feedback structure is more robust to hyper parameters than an existing Online Sequential Extreme Learning Machine (OS-ELM) core. It consumes only 1.25 times larger hardware amount and 1.11 times longer latency than the existing OS-ELM core.
Compressive Sensing of Vibration Signals of Power Transformer

Vibrations signals of an operated transformer are mainly originated from winding vibration and core vibration. By monitoring the vibration signals in transformer tank via on-line vibration monitoring system, it is possible to capture the incipient mechanical failures at all time while transformer is working. However, a large amount of vibration signals of transformer would produce in the on-line vibration monitoring system, which is a heavy burden for data storage and transmission in the construction of Ubiquitous Electric Internet of Things (UEIOF). To solve this problem, this paper presents a method to compress the vibration signals of transformer according to the compressive sensing theory when considered the spare features of vibration signals. The sparse random matrix, the dictionary and the reconstruction algorithm are built and trained to compress and restore the vibration signals of power transformer. The calculated results of a 35kV oil-immersed transformer in load experiment have shown that the original vibration signals are compressed to 1/8 with the average reconstruction error of 0.06% by the proposed method.