Reviewerly/exHarmony huggingface dataset:
Sign Language Translator Using Deep Learning

People suffering from speech impairment can't communicate using hearing and speech methods, they believe signing for communication. Sign language is employed among everybody who is speech impaired, but they find a tough time in communicating with people which are non-signers (people aren’t proficient in sign language). So, requirement of a symbol language interpreter may be a must for speech impaired people. There has been favourable progress within the field of gesture recognition and motion recognition with current advancements in deep learning. There has been quite a significant development in computer vision which would enable us to easily track the hand gestures. The proposed system tries to try to a true time translation of hand gestures into equivalent English text. This system takes hand gestures as input through video and translates it text which might be understood by a non-signer. There will be use of CNN for classification of hand gestures. By deploying this technique, the communication gap between signers and non-signers will be reduced and they will be easily able to communicate with normal people.
A holistic approach towards detection, tracking, and recognition of face

To detect and track the face in a complex environment is still challenging due to variations in speed, illumination, occlusion, rotation, scaling, pose, and imposters. Therefore, a computationally efficient model for face and facial parts detection/tracking is proposed in this work. A deep convolutional neural network (DCNN) based model is designed to detect the face and track it in the successive frames using a point-tracker. The DCNN is developed by re-designing the AlexNet network and tune the weights corresponding to the new classes. A shallow architecture is designed to recognize the face by utilizing hand-craft features and proposed a new distance feature. This model achieves a relative improvement of ~8% over the baseline models. To evaluate the performance of the proposed model, five image databases are used, i.e., the ORL face database, Yale face database, JAFFE database, CK, and the LNMIIT database. The proposed models achieved significant detection and recognition accuracy at a low computation time.
Mengidentifikasi Gagasan Pokok Paragraf Melalui Strategi Pembelajaran Partisipatif di Kelas IV SDN 1 Pangkalan Satu

As happened in class IV Semester 1 of SDN 1 Pangkalan Satu that student achievement in Indonesian language lessons on identifying the main ideas of paragraphs is still low. The results of classroom action research (CAR) carried out by researchers showed that student learning achievement increased from before the action was implemented. From the first cycle to the second cycle, 26 students who finished studying or 100% of the total number of students had reached the KKM and even exceeded the class KKM target. Therefore, the Indonesian language learning material to identify the main idea of a paragraph with a participatory learning strategy for fourth-grade students is considered successful and the implementation stops in the second cycle.
FedQOGD: Federated Quantized Online Gradient Descent with Distributed Time-Series Data

We investigate an online federated learning (in short, OFL), in which many edge nodes receive their own time-series data and train a sequence of global models under the orchestration of a central server while keeping data localized. In this framework, we propose a communication efficient federated quantized online gradient descent (FedQOGD) by means of a stochastic quantization and partial node participation. We theoretically prove that FedQOGD over T time slots can achieve an optimal sublinear regret bound ${\mathcal{O}}(\sqrt T )$ for any quantization level (e.g., 1-level quantization), even when every node can participate in a learning process sporadically. Our analysis reveals that FedQOGD yields the same asymptotic performance as the centralized counterpart (i.e., all local data are gathered at the central server) while having a low-communication overhead and preserving an edge-node privacy. Finally, we verify the effectiveness of our algorithm via experiments with a real-world MNIST dataset on online classification task.
Development of Japanese Language Proficiency to Upgrade Manpower for Working Abroad

This research aims to 1) Survey the in the interest of Japan and Japanese of manpower who want to go out for work abroad in Phetchabun province and 2) To enhance the Japanese language ability to upgrade the manpower to go to work abroad in Phetchabun Province. The research sample consisted of 15 people in Phetchabun province who were about to go out for working abroad. The research instruments were 1) a test of Japanese language consisting of 30 multiple-choice test items for pre-testing and post-testing 2) a questionnaire on the interest of the manpower in Phetchabun province who were about to go out for working abroad, and 3) six online Japanese language lessons. Data were statistically analyzed using the mean, standard, and t-test. The results of the research showed that 1. Regarding interest information of the about to work aboard manpower in Phetchabun province, there are opinions about the importance of the Japanese language in which the opinions on the roles and importance of the Japanese language to work in foreign countries are at a high level ( = 3.81, S.D. = 0.89) 2. Japanese language ability of the sample group before studying the Japanese language online content consisting of 6 lessons. The average score of the Japanese language proficiency test of the sample group before the study of Japanese online content was 50.89 points, which is higher than the average of the 6 points before the study of Japanese language content which is equal to 24.23 points and the average difference between the after and before the study of the 6 Japanese lessons equal to 24.23 points (Sig.=0.000 &lt; 0.01) indicating that the knowledge on the Japanese language of the sample group after the experiment was significantly higher than that before the experiment with statistical significance of 0.01.
Deep learning-based breast cancer disease prediction framework for medical industries

Breast cancer is one among the dreadful cancer which is caused due to formation in breast cells. Earlier recognition of breast cancer is most required in the medical field to avoid the dangerous threat to human life. This is carried out in the existing work, namely Predictive Modeling Technique (PMT). Existing work cannot handle the database with noises properly which might lead to inaccurate prediction outcome. These problems are addressed by introducing Deep Learning-based Breast Cancer Disease Prediction Framework (DLBCDPF). The proposed research framework objective is to present the structures for the disease diagnosis in a further accurate way. In this work, feature selection is achieved through optimization algorithm, namely ranking-based bee colony approach by which the most optimal feature is chosen from the training dataset. The fitness values considered in this work for optimal feature selection are F-score values. Each feature’s F-score and N numbers of feature’s F-score are arranged in a descending manner; in addition, feature subset with more than one feature are produced. In this phase, diagnosis of various stomach-related problems is done through a unique hybridized classification methodology. In this hybridization methodology, clustering is accomplished before classification, and data pruning is attained in every classification iteration. This leads to improved classification accuracy owing to efficient diagnosis. The clustering is attained by fuzzy C-means clustering, and classification is done using the improved deep neural network. The entire research analysis is carried out in python platform for breast cancer dataset from which it is substantiated that the suggested research work tends to outperform in an enhanced way than prevailing work.
An efficient approach for spammer detection on Twitter and their behavior analysis

Twitter is the 4th most popular social networking site and almost 353 million are using Twitter worldwide. The main problems faced by Twitter users are spam, malicious automation, and platform manipulation. Twitter’s developer API is exposed to make it easy to interact. So, the anti-spam system on Twitter is known by spammers. Here, we are using some features that are related to account details of Twitter users with two different data sets having one lakh data for each. We propose a method to classify spam and non-spam users using five machine learning classifiers. We evaluated the performance using accuracy, precision, and recall. Then we are proposing a fuzzy-based approach for user behavior analysis, inspired by the Five-factor model of behavior theory. Only a few works are discussing the behavior of the spammer, and we found that there exists a relationship between the traits of a person and spamming.
Extended imaging applications

Going back to the very basics of imaging techniques, all imaging requires a source, a target, and a receiver. Modern technology has expanded these capabilities in ways that might not be expected. A few basic principles still apply though. Taking a measurement affects the system is a well-known concept illustrated by the life or death of Heisenburg’s cat. Tom Clancy’s executive orders also defines a way to trace information pathways by using unique enticing differentiability. In 2015, OPM was hacked twice, compromising assumedly everyone’s information—is there thus a way to determine who is now intimately familiar with all of our information that wasn’t previously? If everything is compromised, are there ways to defend privacy anyway and compromise those believed to be incognito while compromising others? How does this apply to acoustics? A person practices a speech by themselves in an empty room. They are determined to be crazy talking to themselves by the person monitoring the room discreetly onsite. Then the person practicing a speech gets a critique of their speech by someone discreetly monitoring offsite unknown to the onsite personnel. Knowledge of the actual audience, both as the source speaking, and the receiver in the audience, is impactful.
ARM-NMS: SHAPE BASED NON-MAXIMUM SUPPRESSION FOR INSTANCE SEGMENTATION IN LARGE SCALE IMAGERY

Abstract. Detecting objects in aerial scenes is a fundamental and critical task in remote sensing. However, state-of-the-art object detectors are susceptible to producing correlated scores in neighboring detections resulting in increased false positives. In addition, detection on large-scale images requires a tiling scheme with usually overlapping windows, consequently creating more double detections. Therefore, a non-maximum suppression (NMS) approach can be exploited as integral to the detection pipeline. NMS suppresses overlapping detections in regards to their scores. Current NMS algorithms filter detections by utilizing their corresponding bounding boxes. However, one can assume that comparing bounding boxes to determine the overlap of non-rectangular objects involves a certain degree of inaccuracy. Therefore, we propose Area Rescoring Mask-NMS (ARM-NMS), which uses object shapes for filtering. ARM-NMS exploits instance masks instead of the conventional boxes to eliminate detections and does not require retraining for instance segmentation pipelines. To exhibit the effectiveness of our approach, we evaluate our method on the large-scale aerial instance segmentation dataset iSaid. Our approach leads to considerable improvements for the COCO-style mAP metric of 3.3 points for segmentations and 3.5 points for boxes.
Keynote: Blockchain consensus protocols, from Bitcoin to Ethereum 2.0

Bitcoin revealed to the world a consensus protocol able to validate transactions without the need of a trusted third party. But why this protocol is so amazing? One of the reasons is its peer-to-peer nature : a fully decentralized protocol that allows transactions to be securely validated in an open network where participants can join and leave at will. Actually, before Bitcoin, Byzantine Fault Tolerant (BFT) consensus protocols already emerged in the distributed computing field, but in the scope of closed networks with a fixed number of participants.
unifir: A Unifying API for Working with Unity in R

unifir is an R package representing a first step towards an open-source, fully reproducible approach for constructing immersive virtual environments (IVEs).Users are able to construct IVEs from real or simulated spatial data by writing simple, idiomatic R code, allowing the production of IVEs without needing users to be highly familiar with the complicated video game engines used to power these environments.unifir also provides a simple set of classes which are easily extended, enabling future efforts to provide users with a consistent API for IVE design while relying upon unifir as a low-level interface to the Unity game engine and extending its capabilities to new data formats or engine capabilities.
A Causal Relationship Model of English Language Learning Strategies and Achievement Motivation among Physical Education Students at Thailand National Sports University

The objectives of this research were 1) to study the level of English language learning strategies, 2) to study the level of English language learning achievement motivation, and 3) to establish and validate a causal relationship model of English language learning strategies and achievement motivation with empirical data. The population of this study was 1,597 second-year physical education students from 17 campuses of Thailand National Sports University. The sample size was determined for 20 people per 1 observed variable (Hair, et al., 2010), in which there were 22 observed variables in this research. The sample size was calculated to be 440 people (20 x 22) and the sample proportion was determined according to the population of each campus. The questionnaire used in the research was a 5-rating scale questionnaire with a content validity ranged between 0.67-1.00 and the reliability value of the English learning strategy questionnaire, the reliability value of the English language learning achievement motivation questionnaire and the reliability value of the whole questionnaire were 0.936, 0.958 and 0.967, respectively. The results of this research indicated that 1) The English language learning strategies of physical education students at Thailand National Sports University were at a moderate level in both overall and individual strategies. 2) The level of English language learning achievement motivation of physical education students at Thailand National Sports University was at a high level and 3) the causal relationship model of English language learning strategies and achievement motivation among physical education students at Thailand National Sports University was consistent with empirical data.
Multi-Vector Embedding on Networks with Taxonomies

A network can effectively depict close relationships among its nodes, with labels in a taxonomy describing the nodes' rich attributes. Network embedding aims at learning a representation vector for each node and label to preserve their proximity, while most existing methods suffer from serious underfitting when dealing with datasets with dense node-label links. For instance, a node could have dozens of labels describing its diverse properties, causing the single node vector overloaded and hard to fit all the labels. We propose HIerarchical Multi-vector Embedding (HIME), which solves the underfitting problem by adaptively learning multiple 'branch vectors' for each node to dynamically fit separate sets of labels in a hierarchy-aware embedding space. Moreover, a 'root vector' is learned for each node based on its branch vectors to better predict the sparse but valuable node-node links with the knowledge of its labels. Experiments reveal HIME’s comprehensive advantages over existing methods on tasks such as proximity search, link prediction and hierarchical classification.
An Efficient K-means Algorithm: Generating Clusters Dynamically in MapReduce Framework

Background: K-Means is a widely used partition based clustering algorithm which organizes input dataset into predefined number of clusters. Simplicity and speed in clustering of massive data are two features which have made K-Means a very popular algorithm. The generation of huge amount of electronic data has resulted in modifications in data clustering algorithms to process the huge data. The performance of the K-Means can further be enhanced if we use distributed computing environment to deal with the big data. MapReduce paradigm can be used with the K-Means to give it a distributed computing environment and make it more efficient in terms of time. K-Means has a major limitation -- the number of clusters, ‘K’, need to be pre-specified as an input to the algorithm. In absence of thorough domain knowledge, or for a new and unknown dataset, this advance estimation and specification of cluster number typically leads to “forced” clustering of data, and proper clustering does not emerge. Method: In this paper, we introduce a new algorithm based on the K-Means that takes only the numerical dataset as an input and generates appropriate number of clusters on the run using MapReduce programming style. Findings: The new algorithm not only overcomes the limitation of providing the value of K initially but also reduces the computation time using MapReduce framework.
Elder Assist - An IoT Based Fall Detection System

With the ever-growing population, there is an urgent need for the development of fall detection systems that can help individuals in falling which is one of the most damaging events elderly people may experience. Thanks to the rapid development of sensors and the development of the Internet of Things (IoT), human-computer interaction using sensor fusion has been considered an efficient method to deal with the problem of fall detection and can help in aiding immediately. In this paper, we discuss about a solution to this problem by proposing an IoT based device which can be implemented on a walking stick which is used by elderly people. This device is made using sensor networks and IoT. The development of technology requires the innovation of a device that can be used to help the blind &amp; senior citizen as a road guide. The project describes walking stick. Senior citizen &amp; blind people must ask guidance to reach their destination. They must face more struggles in their life daily life. Using this stick, a person can walk more confidently. This stick detects the object in front of the stick and gives response to the user. So, the person can walk without any fear. This system will be best solution to overcome their difficulties. We are going to upgrade the project by increasing its application. In this project, we are going to use ultrasonic sensor to sense any obstacle. The smart walking stick helps people to perform navigation and to do their work easily and comfortably. In smart walking stick, the object is detected with the help of ultrasonic sensor which detects the distance between object and the stick. If any obstacle comes in front of stick, he/she can know about the obstacle by indication.
Analysis of Buzzwords from the Perspective of Sociolinguistics—Take “Waste Sorting” as an Example

With the popularity of the Internet and the increasing number of netizens, the Internet has long become an inseparable part of people's daily lives. Every year, new online buzzwords will appear on the Internet. The buzzwords released by the National Language Resource Monitoring and Research Flat Media Center are based on the Beijing Language and Culture University Dynamic Circulation Corpus (DCC), which are obtained through computer extraction and humancomputer interaction processing. This paper will use the buzzwords “waste sorting” as a case to analyze the social phenomenon mapped by this word.
Influencing Neutrosophic Factors of Speech Recognition Technology in English Collection

Based on the research and analysis of speech recognition system and neural network principle, combined with English related decision tree, this paper completes the construction and design of English speech recognition based on hybrid frame and series frame neural system network. Combined with relevant information, this paper analyzes the influencing factors of language recognition technology in English collection under neural network. This paper proposes a method of English Corpus collection. Through the speech recognition technology under the neural network, experiments are carried out by using the neural network algorithm, K-means clustering algorithm and HMM / Ann cascade system to analyze the influencing factors of speech recognition technology based on the neural network in English collection. Finally, from the English accent, the amount of speech information make a detailed analysis of the proportion of speech fuzziness, speech speed and environmental interference, so as to draw a conclusion.
Survey on Sign Language Detection Application

Neural networks, as its name suggests, is a machine learning technique which is modeled after the brain structure. It comprises of a network of learning units called neurons. These neurons learn how to convert input signals (e.g. picture of a cat) into corresponding output signals (e.g. the label “cat”), forming the basis of automated recognition. A convolutional neural network (CNN, or ConvNet) is a type of feed¬forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex.
Solo-chef

Thierry De Mey’s Light Music for solo conductor, projection, and interactive device was written in 2004. In 2020, Centre Henri Pousseur, De Mey, and myself decided to rethink and revise the work, updating both the electronics and implementing a more instrumentalized deployment of both the conductor’s role and conductors’ generally recognizable movement repertoire. This paper will delve into the specific manner in which De Mey casts the solo conductor in Light Music and how that role and its sub-roles evolve throughout the piece.
Superconducting Qubits

Quantum computing is a novel paradigm for information processing that promises computational speedup for certain computational tasks while not being driven by Moore's law. Superconducting circuits based on Josephson junctions are one of the leading approaches for implementing the quantum bits, or qubits, in a quantum processor. Most superconducting qubit architectures involve circuit quantum electrodynamics, where the qubits are analogous to artificial atoms that are coupled to superconducting linear resonant cavities. This scheme allows for the readout of individual qubits, the manipulation of single-qubit states, and the generation of entanglement between qubits. The performance of current state-of-the-art systems is limited by decoherence of the qubits and associated gate errors. Nonetheless, experiments have been performed that already demonstrate a significant computational speedup compared to classical processing. Future implementations of fault-tolerant quantum computers will require the development of quantum error correction. Initial tests of elements of error correction in superconducting qubit arrays are underway.