Reviewerly/exHarmony huggingface dataset:
Implementasi Voice Recognition Dan Sensor Ultrasonik Pada Televisi

Voice or speaker recognition is the ability of a machine or program to receive and interpret dictation or to understand and carry out spoken commands. Library is used by Voice recognition in this system is keras and tensorflow. Keras is one library often use for machine learning. Watching TV is a daily routine that is carried out by almost everyone from all walks of life.Most of them don't pay attention to the process and distance in watching TV.Sensor ultrasonic is used for detect object from TV in ideal distance.
Sistem Pembayaran Hasil Cetak Printer Berbasis Mikrokontroler dan Near Field Communication (NFC)

The print-out price for printer services varies according to the density of the paper and the amount of ink used, and the payment is made manually by calculating the total print output. Therefore a system is created that can display rates and total invoices automatically based on time printed, as well as automatic payments that make it easier for service providers and service users. The design of this system consists of Arduino mega, ESP8266, relay, Near Field Communication (NFC), and printer. This system can distinguish the print price rates according to the time of printing, the time span of printing and the tariff is Rp. 300.00 with a printing time of 1-6 seconds, Rp. 500.00 with a time of 7-13 seconds, and finally Rp. 700, 00 with a time span above 14 seconds. The success rate of this system in classifying print tariffs is 95%.
Rancang Bangun Sistem Pendeteksi Kecelakaan Dan Tracking Lokasi Mobil Berbasis Android

This system is designed to know a car accident has happened and also to track a car location,when the accident happened, the system will send accident’s location data to user/familyAndroid application. Thus, user/family knows the accident and they can call the authorities orambulance as soon as possible to help accident’s victim. This system uses some componentssuch as Arduino Uno as a microcontroller, accelerometer sensor MMA7361 as an accelerationdetector, SIM808 Module as a GSM/GPRS network provider and also as GPS sensor. Thissystem tracks the car’s location continuously then the location will be displayed on maps ofAndroid application in the form of a blue marker. Based on this research the accelerationvalues of x-axis and y-axis of the accelerometer which are included in the accident category,which is ≥9g, when an accident occurs the system will send an accident notification toAndroid application and displayed on maps of Android application in the form of a redmarker, to inticipate of no internet connection an SMS with Google maps’s link of accidentlocation is also sent to the user/family.
RezFind

A typical job posting on any job-hunting portal like Linkedin, Naukri, Indeed, etc., will receive many resumes. Screening a resume manually is a tedious process involving huge costs. Screening resumes also consumes a lot of time for the hiring managers. Sometimes, because of the massive numbers, a few qualified resumes don't get noticed, leading to considerable loss to both the company and a loss of opportunity for the applicant. This study uses advanced natural language processing to automate the resume screening process. It also describes a data mining method to extract relevant information like the eligible applicant's name, contact, and email. RezFind provides a unique scoring system that gives a similarity score between the job description and the resume to keep it very specific to the job posting instead of a generic screening. This process allows keeping the uniqueness of each job role, and the screening quality increases by having a specific job description.
Zero-sample mural superresolution reconstruction for enhanced perceptual quality

Abstract Aiming at the problem of texture loss and poor perceptual quality in low-resolution mural images, this paper proposes a zero-sample mural superresolution reconstruction method called EPZSSR to enhance perceptual quality, and the specific model is obtained by training the image. The algorithm takes the zero-shot superresolution method as the framework, randomly cuts the original image into a 128 * 128 size, performs Gaussian blurring on the image, uses Lanczos interpolation to downsample the smooth image to reduce artifacts, and uses convolutional attention module and skip connection to optimize the network structure. SmoothL1Loss is used to enhance the robustness of the model, and the PI value is introduced as the perceptual quality evaluation index. The experimental results show that compared with other superresolution reconstruction algorithms, the peak signal-to-noise ratio of the algorithm in this paper is increased by 0.98–3.23 dB on average. The mural texture reconstruction effect is better, the PI value is reduced by 0.56 on average, the mural perception quality is better, and the running time is reduced by 89.68 s on average. It has a certain value for mural superresolution reconstruction.
Special issue on knowledge structures: Theoretical developments and applications

Contextuality (or lack thereof) is a property of systems of random variables. Among the measures of the degree of contextuality, two have played important roles. One of them, Contextual Fraction (CNTF) was proposed within the framework of the sheaf-theoretic approach to contextuality, and extended to arbitrary systems in the Contextuality-by-Default approach. The other, denoted CNT2, was proposed as one of the measures within the Contextuality-by-Default approach. In this note, I prove that CNTF=2CNT2 within a class of systems, called cyclic, that have played a prominent role in contextuality research.
Research progress on solar flare forecast methods based on data-driven models

Abstract Eruption of solar flares is a complex nonlinear process, and the rays and high-energy particles generated by such an eruption are detrimental to the reliability of space-based or ground-based systems. So far, there are not reliable physical models to accurately account for the flare outburst mechanism, but a lot of data-driven models have been built to study a solar flare and forecast it. In the paper, the status of solar-flare forecasting is reviewed, with emphasis on the machine learning methods and data-processing techniques used in the models. At first, the essential forecast factors strongly relevant to solar flare outbursts, such as classification information of the sunspots and evolution pattern of the magnetic field, are reviewed and analyzed. Subsequently, methods of resampling for data preprocessing are introduced to solve the problems of class imbalance in the solar flare samples. Afterwards, typical model structures adopted for flare forecasting are reviewed from the aspects of the single and fusion models, and the forecast performances of the different models are analyzed. Finally, we herein summarize the current research on solar flare forecasting and outline its development trends.
Which part of a picture is worth a thousand words: A joint framework for finding and visualizing critical linear features from images

Images are one of the most dominant data sources in information systems, and the convolution neural network (CNN) is the most popular model to process them in recent decades. Nevertheless, visual features extracted by CNN are of high dimension and redundancy, which reluctantly result in dilemmas in both semantic interpretation and downstream prediction. In this study, an integrated framework is introduced that selects critical features from a trained CNN model and visualizes them with the explainable AI method. To build this framework, an approximately linear relationship between the visual features and the dependent variable is analytically proved, in terms of considering products of activations and gradients in CNN. Four datasets with both classification and regression tasks serve as case studies to evaluate the framework. Results of all cases consistently show that CNN extracted features are linearly correlated with the independent in arbitrary layers, and the linear feature selection methods are successful in targeting the critical ones. In particular, the highest 0.61Adj.R2 is achieved in the SCUT-FBP dataset for regression with only 15 selected features, which is competent to the performance of all 2048 features. These critical features can be further mapped to raw images with Grad-CAM to illustrate their semantics, which is proved to be closer to human perception than that of others. In addition, these critical features also help improve the prediction performance of downstream tasks. Both theoretical and practical implications of the proposed framework are discussed.
Identification of Ethnogenesis in the Communication and Adaptation Process of the Alor Community in the Sunda Culture Area

Facing a new environment, the differences in values, culture, norms, language styles, and social behavior between the Alor and Sundanese have created a space for ethnogenesis that is faced with being able to adapt to each other in interacting and coexisting. Through the formation of communication and the process of adaptation, it is the starting point for the Alor people to improve the quality of life in terms of education, economy, social, politics and culture. This article aims to reflect on the process of interaction, communication interaction patterns and the process of adaptation of the Alor people to the Sundanese based on the Anxiety/Uncertainty Management Theory approach. Descriptive qualitative method by prioritizing the post-positivist paradigm was used on six purposive sample informants which were taken based on the Snowball sampling technique. This study shows that interaction occurs as an interest in realizing a harmonious life through verbal and non-verbal communication interaction patterns according to Sundanese cultural characteristics and the reflection of the adaptation process carried out is seen from three dimensions, namely cognitive, affective, and behavioral. Alor people learn language styles, values, manners, social behavior, prioritize indigenous culture, put aside personal culture, and promote good socialization by the Alor people towards the Sundanese.
Elliptic Curves of Type y2=x3−3pqx Having Ranks Zero and One

The group of rational points on an elliptic curve over Q is always a finitely generated Abelian group, hence isomorphic to Zr×G with G a finite Abelian group. Here, r is the rank of the elliptic curve. In this paper, we determine sufficient conditions that need to be set on the prime numbers p and q so that the elliptic curve E:y2=x3−3pqx over Q would possess a rank zero or one. Specifically, we verify that if distinct primes p and q satisfy the congruence p≡q≡5(mod24), then E has rank zero. Furthermore, if p≡5(mod12) is considered instead of a modulus of 24, then E has rank zero or one. Lastly, for primes of the form p=24k+17 and q=24ℓ+5, where 9k+3ℓ+7 is a perfect square, we show that E has rank one.
Effectual pre-processing with quantization error elimination in pose detector with the aid of image-guided progressive graph convolution network (IGP-GCN) for multi-person pose estimation

Abstract Multi-person pose estimation (MPE) remains a significant and intricate issue in computer vision. This is considered the human skeleton joint identification issue and resolved by the joint heat map regression network lately. Learning robust and discriminative feature maps is essential for attaining precise pose estimation. Even though the present methodologies established vital progression via feature map’s interlayer fusion and intralevel fusion, some studies show consideration for the combination of these two methodologies. This study focuses upon three phases of pre-processing stages like occlusion elimination, suppression strategy, and heat map methodology to lessen noise within the database. Subsequent to pre-processing errors will be eliminated by employing the quantization phase by embracing the pose detector. Lastly, Image-Guided Progressive Graph Convolution Network (IGP-GCN) has been built for MPE. This IGP-GCN consistently learns rich fundamental spatial information by merging features inside the layers. In order to enhance high-level semantic information and reuse low-level spatial information for correct keypoint representation, this also provides hierarchical connections across feature maps of the same resolution for interlayer fusion. Furthermore, a missing connection between the output high level information and low-level information was noticed. For resolving the issue, the effectual shuffled attention mechanism has been proffered. This shuffle intends to support the cross-channel data interchange between pyramid feature maps, whereas attention creates a trade-off between the high level and low-level representations of output features. This proffered methodology can be called Occlusion Removed_Image Guided Progressive Graph Convolution Network (OccRem_IGP-GCN), and, thus, this can be correlated with the other advanced methodologies. The experimental outcomes exhibit that the OccRem_IGP-GCN methodology attains 98% of accuracy, 93% of sensitivity, 92% of specificity, 88% of f1-score, 42% of relative absolute error, and 30% of mean absolute error.
Meet‐in‐the‐middle attacks on round‐reduced CRAFT based on automatic search

Abstract CRAFT is a lightweight block cipher designed by Beierle et al. to effectively resist differential fault attacks at fast software encryption 2019. In this article, Demirci‐Selçuk meet‐in‐the‐middle (DS‐MITM) attacks on round‐reduced CRAFT based on automatic search are proposed. A DS‐MITM automatic search model for CRAFT was constructed, and then, the automatic search model was used to detect a 9‐round DS‐MITM distinguisher. The strong relations between the round‐subtweakeys were observed and the key‐dependent sieve technique was adopted to reduce the memory complexity of the attack. Based on the 9‐round distinguisher, a 19‐round DS‐MITM attack can be presented. Due to the strong key relations, the time complexity can be reduced by the key‐bridging technique and the equivalent round‐subtweakey. The time complexity of the 19‐round DS‐MITM attack is 2 114.68 19‐round CRAFT encryption, the data complexity is 2 56 chosen plaintexts, and the memory complexity is 2 109 64‐bit blocks. Adding one round to the end of the 19‐round DS‐MITM attack, a 20‐round DS‐MITM attack can be proposed. The time complexity of the 20‐round attack is 2 126.94 20‐round CRAFT encryption, the data complexity is 2 56 chosen plaintexts, and the memory complexity is 2 109 64‐bit blocks.
Providing Dual Awareness using Multimodal Cues for Collaborative Manipulation in Virtual Environments

Virtual reality (VR) collaborative manipulation requires a proper understanding of the user’s self, other collaborators, and shared objects in the virtual environment. Users must be aware of the movements of themselves and others to coordinate their actions when they hand over an object or move it together. The awareness of collaboration plays an essential role in virtual environments. However, previous studies have focused on providing awareness only for cooperative manipulation. This study proposes a novel framework for dual awareness that comprehensively manages the state of interactions between multiple users and shared virtual objects and provides distinct sensory cues to distinguish themselves from collaborators. The experimental results showed that participants could easily and quickly collaborate on manipulation using our feedback, and they also answered positively in the subjective evaluation.
An Effective and Efficient Approach for 3D Recovery of Human Motion Capture Data

In this work, we propose a novel data-driven approach to recover missing or corrupted motion capture data, either in the form of 3D skeleton joints or 3D marker trajectories. We construct a knowledge-base that contains prior existing knowledge, which helps us to make it possible to infer missing or corrupted information of the motion capture data. We then build a kd-tree in parallel fashion on the GPU for fast search and retrieval of this already available knowledge in the form of nearest neighbors from the knowledge-base efficiently. We exploit the concept of histograms to organize the data and use an off-the-shelf radix sort algorithm to sort the keys within a single processor of GPU. We query the motion missing joints or markers, and as a result, we fetch a fixed number of nearest neighbors for the given input query motion. We employ an objective function with multiple error terms that substantially recover 3D joints or marker trajectories in parallel on the GPU. We perform comprehensive experiments to evaluate our approach quantitatively and qualitatively on publicly available motion capture datasets, namely CMU and HDM05. From the results, it is observed that the recovery of boxing, jumptwist, run, martial arts, salsa, and acrobatic motion sequences works best, while the recovery of motion sequences of kicking and jumping results in slightly larger errors. However, on average, our approach executes outstanding results. Generally, our approach outperforms all the competing state-of-the-art methods in the most test cases with different action sequences and executes reliable results with minimal errors and without any user interaction.
Development of Information System Using Relevant Search

The method of developing an information system of scientific activity in universities using relevant search is proposed. The current state of development of information systems is studied. The factors determining the implementation of information systems are analyzed. The concept of relevance is substantiated as a measure of the correspondence of the search results to the task set in the search query, determined by the search system. The analysis of information systems, which are considered as management systems, combining systems of saving and issuing information, and systems that provide information exchange in the management process, has been carried out. The areas of automation of information management activities in organizational structures, scientific institutions, institutions of higher education are considered. The key characteristics of the information system under development are defined. The analysis of the existing information systems of the departments was carried out and their features and shortcomings were determined. Taking into account modern problems, the tasks and criteria for creating an information system were formed. Analysis of modern web technologies, web framework was performed and the most effective of them were selected based on the results of the conducted research.
The Impact of 4English Mobile App of EFL Students' Reading Performance in a Secondary Education Context

The main goal of this study was to examine how the 4English mobile app affected 10th-grade students' reading abilities and how they felt about using it to learn to read. At a high school in the province of An Giang, Vietnam, 90 10th graders are chosen and split equally into two groups. The curriculum, materials, school resources, and classroom instruction are the same for both groups. The experimental group is instructed to use the 4English mobile app to improve their reading skills, while the control group is given traditional reading assignments from the teacher. Four research tools employed in the study were a questionnaire, an interview, a pretest, and a posttest. The results demonstrate that the experimental participants performed better in the posttest and had favorable sentiments toward using the 4English mobile app to teach reading.
Overcoming entropic limitations on asymptotic state transformations through probabilistic protocols

The quantum relative entropy is known to play a key role in determining the asymptotic convertibility of quantum states in general resource-theoretic settings, often constituting the unique monotone that is relevant in the asymptotic regime. We show that this is no longer the case when one allows stochastic protocols that may only succeed with some probability, in which case the quantum relative entropy is insufficient to characterize the rates of asymptotic state transformations, and a new entropic quantity based on a regularization of the Hilbert projective metric comes into play. Such a scenario is motivated by a setting where the cost associated with transformations of quantum states, typically taken to be the number of copies of a given state, is instead identified with the size of the quantum memory needed to realize the protocol. Our approach allows for constructing transformation protocols that achieve strictly higher rates than those imposed by the relative entropy. Focusing on the task of resource distillation, we give broadly applicable strong converse bounds on the asymptotic rates of probabilistic distillation protocols, and show them to be tight in relevant settings such as entanglement distillation with non-entangling operations. This generalizes and extends previously known limitations that only applied to deterministic protocols. Our methods are based on recent results for probabilistic one-shot transformations as well as a new asymptotic equipartition property for the projective relative entropy.
Semi-supervised sentiment clustering on natural language texts

Abstract In this paper, we propose a semi-supervised method to cluster unstructured textual data called semi-supervised sentiment clustering on natural language texts. The aim is to identify clusters homogeneous with respect to the overall sentiment of the texts analyzed. The method combines different techniques and methodologies: Sentiment Analysis, Threshold-based Naïve Bayes classifier, and Network-based Semi-supervised Clustering. It involves different steps. In the first step, the unstructured text is transformed into structured text, and it is categorized into positive or negative classes using a sentiment analysis algorithm. In the second step, the Threshold-based Naïve Bayes classifier is applied to identify the overall sentiment of the texts and to define a specific sentiment value for the topics. In the last step, Network-based Semi-supervised Clustering is applied to partition the instances into disjoint groups. The proposed algorithm is tested on a collection of reviews written by customers on Booking.com . The results have highlighted the capacity of the proposed algorithm to identify clusters that are distinct, non-overlapped, and homogeneous with respect to the overall sentiment. Results are also easily interpretable thanks to the network representation of the instances that helps to understand the relationship between them.
EFL Students' Perceptions and Practices Regarding Online Language Learning

Virtual teaching and learning have become a hot topic in the Covid-19 pandemic. This chapter will present an investigation of learners' perceptions and practices of learning online. The current research was conducted with 161 EFL students at Dong Thap University, Vietnam. Questionnaires and observations were set to collect data. The findings showed that most students indicated benefits of online learning, such as saving time and money, protecting their health from Coronavirus, and being a suitable way of learning during lockdown time. However, in practice, they felt stress in long online classes. They could not fix technological problems as well as understand rules, technology culture, and attitudes when learning online. Furthermore, freshmen met several difficulties in learning English major online rather than seniors. Participants also showed their wishes and suggestions to improve virtual language teaching and learning platforms in the distant future.