Reviewerly/exHarmony huggingface dataset:
PanoMan: Sparse Localized Components–based Model for Full Human Motions

Parameterizing Variations of human shapes and motions is a long-standing problem in computer graphics and vision. Most of the existing methods only deal with a specific kind of motion, such as body poses, facial expressions, or hand gestures. We propose PanoMan (sParse locAlized compoNents based mOdel for full huMAn motioNs) to handle shape variation and full-motion across body, face, and hand in a unified framework. Like previous approaches, we factor shape variation into principal components to obtain a human shape space that approximates the shape of arbitrary identity. We then analyze sparse localized components in terms of relative edge length and dihedral angle to capture full motions of body poses, facial expressions, and hand gestures. The final piece of our model is a multilayer perceptron (MLP) that fits the residual between the ground truth and the aforementioned two-level approximation. As an application, we employ the discrete-shell deformation to drive the model to fit sparse constraints such as joint positions and surface feature points. We thoroughly evaluate PanoMan on body, face, and hand motion benchmarks as well as scanned data. The existing skinning-based techniques suffer from joint collapsing when encountering twisting motion of joints. Experiments show that PanoMan can capture all kinds of full human motions with high quality and is easier than the state-of-the-art models in recovering poses with wide joint twisting and complex hand gestures.
Clustering and presorting for parallel burrows wheeler-based compression

We describe practical improvements for parallel BWT-based lossless compressors frequently utilized in modern day big data applications. We propose a clustering-based data permutation approach for improving compression ratio for data with significant alphabet variation along with a faster string sorting approach based on the application of the [Formula: see text] complexity counting sort with permutation reindexing.
Gesture Recognition Based on YCbCr Color Space and Neural Network

Aiming at the problem of gesture recognition in complex background, a convolutional neural network gesture recognition algorithm based on improved YCbCr color space is proposed. Firstly, 8000 images of four gestures in the complex background are collected as the data set of this study. Then, the data set is preprocessed based on YCbCr color space, and the adaptive threshold method is used to improve it. Finally, a shallow convolutional neural network is built and trained with the preprocessed data set. The experimental results show that the gesture recognition accuracy of this method can reach 98.2% on the collected data set, which is higher than 85.7% and 90.6% using AlexNet and VGG-16.
Volumetric Human Reconstruction from a Single Depth Map

We present an efficient approach to reconstruct a human body from a single depth map, captured by a commercial depth camera or a stereo depth sensor. The underlying idea is to predict the rear side depth map through the deep network because the rear side depth map tends to symmetric to the front depth map and the shape variation is lesser than the front. One the rear side depth map is predicted, we construct a signed distance volume and extract a human as the form of 3D meshes through the Marching Cubes method. We experimentally show that the proposed method can effectively predict the rear side depth map.
Level of comprehension in poetry of the grade five pupils of private schools in Surigao city

Mindful of the need to articulate the level of comprehension in poetry of the learners, the study on level of comprehension in poetry of the Grade 5 pupils of the private schools in Surigao City was conducted.The study made use of the descriptive research using survey technique, with a researcher-made questionnaire containing 4 poems with 30-item test, pilot tested to 30 pupils from Clementino V. Diez Memorial Central Elementary School (Cronbach Alpha= .91).A population of 133 Grade 5 pupils from St. Paul University Surigao, Surigao Education Center, and Surigao Sun Yat Sen Elementary School were taken as participants.T-test was used in testing differences which found no significant differences between the profile and the levels of comprehension in poetry, thus, being boys or girls does not affect the level of poetry comprehension.However, teacher should undergo trainings, seminars, and workshops on comprehension strategies in poetry focusing on the levels found to be low and school administrators are encouraged to support programs-such as the Saturday Remedial Classes on reading comprehension in poetry.
Small data assisting face image illumination normalization

Image transfer based on deep learning methods can achieve good results in face illumination processing. However, data constraints and generalization ability restrict the further development of these methods in this field. In this paper, we propose small data assisting face image illumination normalization. For data constraints, we train the network model on a small number of image pairs. In terms of generalization ability, the proposed normalization network parameters are different for processing different face images. Small data learning can provide prior knowledge, and the reconstruction process can guide detail generation. Therefore, the small data learning network and the reconstruction network are complementary to each other in image generating mode when only a small quantity of data is available. We use this network mode to normalize the illumination and reconstruct the super-resolution of face images. After illumination normalization, super-resolution reconstruction can obtain more precise face information and further improve the face recognition rate. Experiments show that the proposed method has good normalization performance when only 500 face image pairs are used for training.
Google Controlled Car

The present day scenario is actively moving ahead towards smarter things. Smart things signify of connected devices, objects and more. Its feasibility is achievable by using IoT (Internet of Things) technology. Driver-less vehicles has been a very hot topic in the market. This paper focuses on using IoT technology towards developing driverless cars using Google Assistant. The car is operated via a MCU which functions according to commands given by Google Assistant. Exploring the current situations of pollution and congestions that take place this car can be of utmost help to mankind thus-making the society smarter.
Experience of Using Information Resources While Teaching the Topic of "The World's Population» in the Course of Geography for the 10th – 11th Grades

The article considers the main elements of the emerging information and educational environment. The reasons for the decline in the attractiveness of geography among schoolchildren are determined. Various approaches to the organization of remote classes with the use of a computer and the use of geoinformation technologies are proposed. As an alternative source of geographical information, images of the Earth from space are presented, made both for the world as a whole, and for individual regions and countries. The authors offer options for tasks using information resources in the context of distance learning of geography in grades 10-11. The techniques of using photos of the world's regions making from space, online statistics on the topics "World population" and "World Regions" are considered. The main types of activities and planned results when working with these resources are analyzed. Information about the use of online population counters in real time is of great interest. Such variants of tasks make it possible to simultaneously analyze natural and socio-economic processes, forming a higher level of spatial thinking.
Building a Pos Tagger and Lemmatizer for the Italian Language

In this work, we present two modules for a python open-source library for the analysis of the Italian language. The modules include a Pos tagger based on Averaged Perceptron Tagger and a Lemmatizer, based on the vast collection of linguistic data held by the Department of Politics and Communication Science of the University of Salerno. While the Averaged Perceptron Tagger algorithm is mostly used for the the English language from famous python libraries such as NLTK or Spacy, the Lemmatizer represents an entirely original module that relies on a vast electronic dictionary characterized by the presence of syntactic, morphological, and semantic tags. We present our approach and a preliminary experiment in which we compare our module results with the results of another widely used Pos-tagger and Lemmatizer as Tree-Tagger.
Neural Network Quantization Methods For Voice Wake Up Network

Abstract Quantifying the pre-trained neural network model can reduce its storage size and speed up the forward-inference process. Previous researches for the quantization of voice wake up networks couldn’t reach our anticipation for applying it to applications. The voice wake up network is mainly composed of recurrent neural network and classification function. Recurrent neural network can take preorder words into consideration and it widely used in voice network. In this paper, we propose method to quantize the structure of recurrent neural networ including RNN, GRU and LSTM by optimizing the method of quantifying the activation function. At the same time, using the translation invariance of the classification function to improve classification accuracy on Speech commands dataset version 2. Using the method proposed in this paper, we can make the quantized model achieve a better voice wake up rate.
Research and Application in Human Resources Information System of Electric Power Enterprises Based on Blockchain Integration

As an important task for power companies, the construction of talent team requires high-quality and high-efficiency talent selection based on huge human resources information. The emerging block “Blockchain” is favored by various industries because of its decentralization and trustworthiness. This paper takes electric power enterprises as the research object, aims at realizing the common development of enterprises and employees, analyzes and designs the human resources information system including the blockchain underlying structure and human resources information application layer, and realizes the database construction of human resources information. And the transaction records are all stored, creating an efficient and orderly development environment for people.
Design of intelligent bedroom system based on pure off-line speech recognition technology

Due to the popularity of the network, most smart home [1] and intelligent products need to be connected to the Internet to improve the degree of intelligence. However, these products are excessively dependent on the Internet. In order to avoid the excessive reliance of intelligent products on the Internet and improve the life quality and Intelligent degree of majority of users, this paper proposes an intelligent bedroom system based on pure off-line speech recognition technology, namely: the system does not need to connect the network and will can run well and complete the work of the intelligent bedroom system. The system has the characteristics of good identification effect, stable, reliable performance, low customer development difficulty, short product development cycle and high customer satisfaction.
MLDRL: A multi-layer distributed reinforcement learning framework with multiple trainers

Abstract Synchronous distributed sampling is widely used in various distributed reinforcement learning algorithms. However, it is difficult to scale to a large case due to significant synchronization and communication costs. In this paper, we propose a scalable layered distributed reinforcement learning framework by introducing multiple trainers. We use a parameter processor to coordinate parameters obtained from multiple trainers and introduce worker nodes to reduce the communication cost among the parameter processor and the trainers. We do experiments in a multi-agent environment, and the results show that the proposed framework significantly accelerates the training efficiency of the reinforcement learning algorithm.
Design of S-Box Based on Chao Initialized RC4

S-box is an important component of block ciphers because it is the only nonlinear component for most of the block ciphers such as AES, DES and IDEA. In this paper a new method of generating S-box based on chao initialized RC4 stream cipher is presented. Pomeau-Manneville chaotic map is employed to generate initial values for RC4. Then S-box is constructed by using RC4 stream cipher. The security analysis of the proposed method is evaluated using criteria such as bijective property, nonlinearity, strict avalanche criterion, bit independence criterion and differential probability. The proposed method satisfies all the criteria and differential probability result is better than the existing schemes.
IBRE: An Incremental Bootstrapping Approach for Chinese Appointment and Dismissal Relation Extraction

In the field of government affairs, Appointment and Dismissal Relation Extraction (ADRE) of officials from personnel news is crucial for updating government knowledge. However, ADRE faces great challenges, including extremely high accuracy demand, lack of data, tuple sparsity and conflict, and requiring incremental update. To address these challenges, we propose an Incremental Bootstrapping approach for Relation Extraction (IBRE) and apply it to real-time updating of personnel knowledge in government-affair Knowledge Graphs. IBRE starts with few seeds and trains with pattern generation, pattern evaluation, tuple prediction and seed augmentation in an iterative and incremental manner. First, we design a new strategy for defining seeds as document-tuple pairs to eliminate the effects of tuple sparsity and conflict. Then, a new definition of patterns with both word and part-of-speech is proposed to get high accuracy. Finally, we augment seeds with corrected tuples and apply incremental learning to continually improve performance with least training cost. We build a dataset called ADNP (Appointment and Dismissal News from People.cn) and compare our approach with baselines. Comparison results show that our approach performs the best. Moreover, experimental results demonstrate that incremental learning continuously improves the performance of IBRE.
Concept of the Ring Enclosing Docking System for Spherical Shaped Free-Flying Robot

This paper presents the concept of the Ring Enclosing Docking System for Spherical Shaped Free-flying Robot. This docking system captures the robot with caging-based capturing and form closure technology. A tendon actuation mechanism with an elastic element is selected to realize light-weighted, faster actuation and contact force reduction. The validity of the proposed docking system’s acceptable range for capturing and the method of contact force reduction are experimentally evaluated.
Students’ Reading Anxiety in Reading Aloud at The Second Year of MA Pondok Pesantren Sultan Hasanuddin

Based on the finding and discussion of the research, the researcher drew a conclusion that students’ anxiety in reading was from learners’ beliefs about language learning as well as personal and interpersonal anxiety. Students’ ways to overcome their anxiety were preparation, relaxation, positive thinking, and resignation.
Design and Implementation of Cryptographic Technique Employing Intricate Table and Rubik Rotation Algorithm

Malicious intrusions, executing hackers, and snowballing upshot of artificial intelligence have made network security as sturdy as aged boots. The indispensability of formulating cryptographic techniques would dent the safekeeping matters quite briskly. The paper demonstrates a cryptographic archetypal in aid of Rubik’s cube scrambling and fragmentary ciphering the text, with the assistance of intricate table prior to scrambling, for concealing the treasured message, amended by means of brisk tackling with safekeeping matters and to have a bang at impending encounters. Elucidating explicitly, employment of a Rubik’s cube of order 3 × 3 and an intricate table for ciphering the text, dependent on the proposed algorithm through scrambling procedure has been adhered. Escorted by the usage of keys through the ciphering process for endowing with innumerable stratum of safekeeping would inspire apiece end user to preserve their facts in innocuous repository. With enactment and implementation of the proposed cryptographic technique, dark Web could be sheltered with numerous strata of safekeeping, which in go would furnish the pro-stratum safekeeping to apiece individual and consequently benefiting the society in entirety. The fineness of the formulated ploy is split in the paper amid diverse segments. The afresh cryptographic modus operandi scheming is conversant in the attempt and credible in its incorporation.
Human Strategic Decision Making in Parametrized Games

Many real-world games contain parameters which can affect payoffs, action spaces, and information states. For fixed values of the parameters, the game can be solved using standard algorithms. However, in many settings agents must act without knowing the values of the parameters that will be encountered in advance. Often the decisions must be made by a human under time and resource constraints, and it is unrealistic to assume that a human can solve the game in real time. We present a new framework that enables human decision makers to make fast decisions without the aid of real-time solvers. We demonstrate applicability to a variety of situations including settings with multiple players and imperfect information.
Implementation of the New Student Admission Policy Based on Zoning Systems in Indonesia

In 2018, the Minister of Education released Education Regulation (Permendikbud) No. 14 of the year 2018. The purpose of this regulation is that schools must accept new students who live within a certain radius of the school. The implementation of the new student admission policy at the elementary, junior high, and senior high school levels in Surabaya has led to protests and even demonstrations of the rejection of new student admissions based on zoning that is considered discriminatory, unfair, and does not pay attention to the academic achievements of prospective new students. For this reason, the purpose of this study is to analyze why the implementation of the registration of the new student admission (PPDB) policy was rejected in Surabaya, and to analyze any efforts that can be made to implement the PPDB policy effectively. The analytical method used is qualitative by using a focus group discussion (FGD) approach with three main groups: parents, teachers, and officers of Surabaya City Education (Dispendik Surabaya). The results indicate that the implementation of the zoning-based PPDB policy was rejected because each school did not yet have quality teachers and infrastructure, and thus lacked opportunities for students with high academic achievements. Also, not all schools had a good culture and environment, which is called culture lag. In order for the student admission policy based on zoning to be implemented effectively, the central government must work together with the local government to improve the quality of each school. School quality includes the provision of hardware and software. Provision of hardware includes learning space facilities, learning facilities, and teacher competencies. Provision of software includes internet access, online learning modules, and school culture systems. Dispendik Surabaya must also increase the intensity of communication to the public, achieve adequate, socialization and improve coordination between the Ministry of Education and Culture and the local governments. Keywords: student admission policies, preparing infrastructure, communication, socialization.
An efficient multi-scaled super resolution framework for image enhancement

In recent years, the super-resolution (SR) technique achieved impressive results by using a deep convolutional neural network (CNN). Generally, deep CNNs networks can extract better feature maps and reconstruct high-resolution (HR) images. However, a wider and deeper network increases complexity and computation time for any real-time applications. It is essential to develop a lightweight network for useful SR images that have less computation time and complexity. In this paper, we proposed multi-scale residual blocks based network for single image super-resolution. The multi-scale layers are effectively utilized in the model for feature extraction and reconstruct high-resolution(HR) images with less computation time and complexity. We try to find out an optimal point for image quality and computational time. In comparison to others, the optimal point of our model is (100.7) greater than other state-of-the-art CNN models.
Face Recognition Using Genetic Algorithm - A Unique Approach With Pre-Trained Models For Low Power Microcontrollers

This paper proposes a unique approach for recognizing faces with pre-trained models using Genetic Algorithm and 2D Fast Fourier Transform in microcontrollers. Conventional real-time face recognition algorithms on low-end hardware have three limitations: a) complex algorithms are difficult to implement; b) recognizing new faces requires retraining which in-turn requires a lot of processing power, RAM and time; and c) pre-trained models (using architectures such as Neural Networks) need a lot of memory. These factors limit its usage in low-end devices where training time is restricted and the face database size is variable. Hence, most of the offline machine learning algorithms in microcontrollers are limited to text and character recognition. We propose a robust and memory-efficient system that works with pre-trained models and does not require retraining for recognizing new faces. The trained data is in the order of a few kilo-bits and the feature vector size for each image is in the order of a few kilobytes. This enables it to run on low-end Microcontrollers (MCUs) and Node MCUs. This algorithm has been evaluated using standard face datasets. Tests with these datasets show that the proposed solution using Genetic algorithm performs as good or better than other widely available techniques.