Reviewerly/exHarmony huggingface dataset:
Deep Multi-Stage Approach For Emotional Body Gesture Recognition In Job Interview

Abstract Affective computing is a key research topic in artificial intelligence which is applied to psychology and machines. It consists of the estimation and measurement of human emotions. A person’s body language is one of the most significant sources of information during job interview, and it reflects a deep psychological state that is often missing from other data sources. In our work, we combine two tasks of pose estimation and emotion classification for emotional body gesture recognition to propose a deep multi-stage architecture that is able to deal with both tasks. Our deep pose decoding method detects and tracks the candidate’s skeleton in a video using a combination of depthwise convolutional network and detection-based method for 2D pose reconstruction. Moreover, we propose a representation technique based on the superposition of skeletons to generate for each video sequence a single image synthesizing the different poses of the subject. We call this image: ‘history pose image’, and it is used as input to the convolutional neural network model based on the Visual Geometry Group architecture. We demonstrate the effectiveness of our method in comparison with other methods in the state of the art on the standard Common Object in Context keypoint dataset and Face and Body gesture video database.
A Study on the Difference between Social Network Analysis and Text Network Analysis

Recruitment corruption makes people demand fair and objective hiring procedures. The development of IT technology has promoted the emergence of recruitment system based on AI which make interview conduct without human intervention. As companies can reduce costs and efforts through the AI recruitment system, more and more companies are using the system. As the result, the research for figuring out the user intention on the system is needed. We designed the research model to find factors affecting the intention to use of AI-based recruitment system utilizing constructs of TAM and TOE. Also the moderating effect by the number of employees in the corporation will be estimated.
CURRICULAR PERSPECTIVES AND STRATEGIES FOR DEVELOPING COGNITIVE SKILLS IN EARLY EDUCATION

Starting from the assumption that in early education the curricular strategies are selected and developed according to the curricular context and the specificity of general and particular cognitive development, we decided to engage in an experimental research focused on identifying, validating and developing some curricular strategies that are as relevant in the development of cognitive skills, as well as of other areas of development and experience. Our research fulfils several functions: 1) The systematization of the conceptual analyses regarding the problem of the cognitive skills and of the curricular strategies of their development, accompanied by personal appreciations and interpretations; 2) Of retesting and developing a previous micro-research; 3) Of methodological research, based on the development and validation of relevant investigation tools; 4) The development of a methodological framework and operational landmarks for the development of cognitive skills in early education. The article is conceived in the spirit of scientific consistency and comprehensive analysis of conceptual models, strategies and methodological models that outline the terminological code of the problem, but which also constitute premises for theoretical development and design of some experimental approaches. The article is structured on two dimensions: a) theoretical foundation regarding the conceptual analysis of cognitive competence in early education, from the perspective of the skills-centred educational approach, of the curriculum for early education, with implications for lifelong learning; b) addressing curricular strategies, which facilitate the process of developing cognitive skills in preschoolers, with an emphasis on child-centred training.
Joint Extraction of Entities and Relations for Chinese Text of Tea

In view of the problems of polysemy and overlapping relations of Chinese tea text. In this paper, we present a joint model BERT-LCM-Tea for extraction of entities and relations, which combines the Bidirectional Encoder Representations from Transformers (BERT) and the last character matching (LCM) algorithm. This model uses BERT to fine-tuning character embedding through contextual information, the problem of polysemy is solved and the performance of entity recognition of Chinese tea text is improved. In addition, the model uses last character matching algorithm, the problem of overlapping relations is solved and the accuracy of relation extraction of Chinese tea text is improved. The experimental results show that BERT-LCM-Tea F1 score to 86.8% in entity recognition task and F1 score to 77.1% in relation extraction task, which is higher than the currently popular Bi-RNN-CRF, Bi-LSTM-CRF and Bi-GRU-CRF. Thus, the BERT-LCM-Tea is more suitable for the entity recognition and relation extraction of Chinese tea text, and provides a basis for future research on the construction of tea knowledge graph.
ATINEGAR-KOOSHA: A Genetic-algorithm-based method for feasibility of face recognition in CCTV cameras

Today, by development of technology in CCTV systems, their applications have become more widespread and specialized. In this article at first, CCTV systems are introduced. After that the components of a CCTV camera and the function of them are described. One of the most popular applications of CCTV systems is their ability to recognize the faces of suspects. What is presented in this article is the introduction of a method based on genetic algorithm by which the feasibility of this event can be investigated. The feasibility has been investigated in different resolution conditions, in sensors with different sizes and at the required variable distances from the camera. This feasibility study has been done with the approach of increasing the pixel density assigned to people's faces for better detection as well as increasing the transverse field of view of the camera. Also in this method based on genetic algorithm, which is called Atinegar-Koosha method, all practical limitations can be detected, including the limitation in the focal length of the camera lens, the minimum and maximum transverse angle of view of the camera, the minimum required width for face detection and also level of importance coefficient of the required pixel density - to further enhance the ability to recognize people's faces - they are applied quite accurately.
A Gesture Controlled Music Playback System Using Convolutional Neural Network

In this paper, we propose an application of applying hand gesture recognition using Convolutional Neural Network(CNN) for music playback control. Our system begins with live image capturing from a USB camera; subsequently, followed by the initialization, calibration, and motion detection stages. To train the neural network, a set of segmented and tagged hand gesture images were used. According to our experimental results, our method has the advantages of low latency and high accuracy, which is capable of detecting a set of six gestures in real-time.
Software Methodology for Estimating the Efficiency of the Hardware Composition of Deep Packet Inspection System Using the Modernized Hooke ‒ Jeeves Method

Deep packet inspection systems on communication networks are used to identify the application generating a specific traffic flow. The issues related to modeling and design of deep packet inspection systems remain poorly understood. In this paper, a software technique for evaluating the effectiveness of the hardware composition of the servers of the deep packet inspection system is presented, using a mathematical model of such a system and software search methods. The description of the program search by the maximum element method and the Hook - Jeeves method is given. A modernization of the Hook-Jeeves method for a monotonically decreasing function is proposed. Comparison of the methods by the number of search steps is performed.
Supporting Efficient Dynamic Update in Public Integrity Verification of Cloud Data

Cloud storage is an increasingly popular service of cloud computing, which can provide convenient on-demand data outsourcing services and release the burden of maintaining local data for both individuals and organizations. However, the cloud service providers are not fully trusted by the users. The reason is that the users lose physical control of their cloud data, and the cloud service providers may conceal the status of the data when encountering data loss accidents for reputation. Therefore, it is critical for users to efficiently verify the integrity of cloud data.In this paper, we propose a public integrity verification scheme to support efficient dynamic update of cloud data based on Merkle Hash Tree linked list (MHT-list), which is a novel two-dimensional data structure we designed. This structure utilizes multiple merkle hash trees (MHTs) and a linked list to record data information at the cloud service provider side. Meanwhile, we exploit the structural advantages of the MHT-list to make our scheme more efficient in dynamic update and integrity verification than existing works. Moreover, we formally prove the security of the proposed scheme and evaluate the performance of our scheme by concrete extensive experiments. The results demonstrate that our proposed scheme achieves dynamic update effectively in public integrity verification of cloud data, and outperforms the previous works in computation and communication overhead.
On Approximation of the Tails of the Binomial Distribution with These of the Poisson Law

A subject of this study is the behavior of the tail of the binomial distribution in the case of the Poisson approximation. The deviation from unit of the ratio of the tail of the binomial distribution and that of the Poisson distribution, multiplied by the correction factor, is estimated. A new type of approximation is introduced when the parameter of the approximating Poisson law depends on the point at which the approximation is performed. Then the transition to the approximation by the Poisson law with the parameter equal to the mathematical expectation of the approximated binomial law is carried out. In both cases error estimates are obtained. A number of conjectures are made about the refinement of the known estimates for the Kolmogorov distance between binomial and Poisson distributions.
Analyzing digital discourse: New insights and future directions, PatricaBou‐Franch, PilarGarcés‐Conejos Blitvich, Cham, Switzerland: Palgrave Macmillan, 2019, xviii+398 pp. ISBN: 978‐3‐319‐92662‐9

International Journal of Applied LinguisticsVolume 31, Issue 2 p. 308-311 BOOK REVIEW Analyzing digital discourse: New insights and future directions, Patrica Bou-Franch, Pilar Garcés-Conejos Blitvich, Cham, Switzerland: Palgrave Macmillan, 2019, xviii+398 pp. ISBN: 978-3-319-92662-9 Jie Xia, Corresponding Author jiexia@just.edu.cn orcid.org/0000-0002-3219-5928 School of Foreign Languages, Jiangsu University of Science and Technology, Zhenjiang, China Correspondence Jie Xia, School of Foreign Languages, No. 2 Mengxi Road, Jiangsu University of Science and Technology, Zhenjiang, China, 212003. Email: jiexia@just.edu.cnSearch for more papers by this author Jie Xia, Corresponding Author jiexia@just.edu.cn orcid.org/0000-0002-3219-5928 School of Foreign Languages, Jiangsu University of Science and Technology, Zhenjiang, China Correspondence Jie Xia, School of Foreign Languages, No. 2 Mengxi Road, Jiangsu University of Science and Technology, Zhenjiang, China, 212003. Email: jiexia@just.edu.cnSearch for more papers by this author First published: 11 April 2021 https://doi.org/10.1111/ijal.12347Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Open Research PEER REVIEW The peer review history for this article is available at https://publons.com/publon/10.1111/ijal.12347. Volume31, Issue2Supplement: Corpus‐based language teaching and learning: applications and implicationsJuly 2021Pages 308-311 RelatedInformation
Deep Neural Network Gaussian Process Regression Method for End-to-end Driving Behavior Learning

For dynamic complex driving tasks of autonomous vehicle, traditional driving behavior learning methods still need to be improved in terms of learning accuracy and generalization performance. A convolutional neural network gaussian process regression (CNN-GPR) method for driving behavior learning is proposed to tackle these problems that full connection layers of end-to-end convolutional neural networks (CNN) have limited generalization ability and easily converge to local optimization. At the same time, the PilotNet-GPR algorithm based on the CNN-GPR method is designed. The gaussian process regression (GPR) method with global mapping capability and better generalization performance is used to improve fully connected layers of the end-to-end CNN in order to complete the mapping from features extracted to driving actions more efficiently for the proposed CNN-GPR method. In addition, the long short-term memory network (LSTM) is added into CNN-GPR method, and a convolutional long short-term memory network gaussian process regression (CNN-LSTM-GPR) method of driving behavior learning with time-sequential images is proposed in order to further improve the accuracy of driving behavior learning through time-sequential information. This method utilises the gaussian process regression method to improve structures of fully connected layers in the cascaded deep neural network (CNN-LSTM) in order to make a more efficient learning approximation between features extracted by the CNN-LSTM and driving actions. Verification experiments on the Apollo end-to-end dataset for autonomous driving show that the proposed CNN-GPR method can further improve the imitation accuracy for end-to-end driving behavior learning and promote generalization performance of learned models compared with the PilotNet method. compared with related end-to-end driving behavior learning methods based on single image under the same condition, the proposed CNN-LSTM-GPR method can make full use of time-sequential information of images, which leads to smaller imitation errors. Additionally, it can further enhance the learning accuracy and demonstrates a more satisfying generalization performance compared with the CNN-LSTM method.
Bayesian Meta Regression

This work extends Bayesian regression as an adaptive that augmented by deep neural networks (the probabilistic encoder) to obtain the posterior probability distributions of the regression coefficients. We use variational inference to obtain the conditional distribution over the regression coefficients, which are the latent space given the observed data. Therefore, our model can recognize local conditional probability distribution of the regression coefficients for each observation as well as it can measure the uncertainty of the model locally. Experimental results on the benchmark datasets prove that our Bayesian Meta Regression (BMR) significantly outperforms baseline regression techniques and precisely measures the model uncertainty for each observation.
Robust Lightweight Image Encryption Technique Using Crossover Operator

In the current scenario, most of the communications via smart devices in the form of images and these devices are having less memory and require high-speed communications. For such types of communications, there is a need for a lightweight encryption technique. This work proposed a robust lightweight image encryption technique using the crossover operator of a genetic algorithm. This work uses 64-bits block cipher and 64-bit key for encryption. Here, crossover operator is used to creating confusion and diffusion among attackers. This work uses a unique key for each image encryption. The proposed work is implemented on MATLAB 2015 software and tested on various parameters for checking the efficiency of proposed work.
Pengenalan Gangguan Ginjal Melalui Iridologi Menggunakan Hidden Markov Model (HMM)

ABSTRACTIridology has not been widely applied for the recognition of kidney disorders. identification of kidney disorders through iris image using iridology chart, can make it easier to make diagnosis to find out about kidney disorders. The method used in the process of recognition of kidney disorders through iridology is the Hidden Markov Model (HMM) method, with a HMM parameter determination system using the calculation of the koefisien Singular Value Decomposition (SVD) coefficient. The size of the codebook used is 7, i.e. 16, 32, 64, 128, 256, 512 and 1024. Different sizes of codebooks will result in different recognition times. The time needed will be longer when the size of the codebook is getting bigger. The accuracy of the process of recognition of kidney disorders through iridology using the HMM method in this study is 68.75% for codebook 16, 87.5% for codebook 32, 100% for codebook 128 and 100% for codebook 512. Keywords : iridology, codebook, image processing, singular value decomposition (SVD), Hidden Markov Model (HMM).
A Novel Adaptive Differential Privacy Algorithm for Empirical Risk Minimization

Privacy-preserving empirical risk minimization model is crucial for the increasingly frequent setting of analyzing personal data, such as medical records, financial records, etc. Due to its advantage of a rigorous mathematical definition, differential privacy has been widely used in privacy protection and has received much attention in recent years of privacy protection. With the advantages of iterative algorithms in solving a variety of problems, like empirical risk minimization, there have been various works in the literature that target differentially private iteration algorithms, especially the adaptive iterative algorithm. However, the solution of the final model parameters is imprecise because of the vast privacy budget spending on the step size search. In this paper, we first proposed a novel adaptive differential privacy algorithm that does not require the privacy budget for step size determination. Then, through the theoretical analyses, we prove that our proposed algorithm satisfies differential privacy, and their solutions achieve sufficient accuracy by infinite steps. Furthermore, numerical analysis is performed based on real-world databases. The results indicate that our proposed algorithm outperforms existing algorithms for model fitting in terms of accuracy.
Neural Network Based Scope Positioning System for First-Person Shooters

In this paper, we present a new approach to assessing the position of the sight in first-person shooters. In such games, one of the most difficult tasks is to accurately aim the weapon. And if the game has a bullet drop, you have to take into account the properties of the bullet and weapon and shoot higher. Position of the scope depends also on the distance to the target and angle. Traditional methods of software-assisted aiming require access to the game files or processes. In our approach, we use only screen captured images.We present our system of automatic aiming based on neural networks. The main advantage of this approach is that our system for accurate definition of scope position requires only information about what is happening on the screen. To achieve this goal, we use technologies of convolutional neural networks that allow detecting objects in the image, as well we used Feed-Forward neural networks for adjusting the sight.
Automatic Recognition of Identification Schemes for IoT Identifiers via Sequence-to-Sequence Model

In Internet of Things (IoT), each object requires a unique identifier to identify itself and index its detailed profile to support mutual recognitions among multiple objects. However, existing IoT identifiers belonging to different identification schemes are heterogeneous from each other, which create a great challenge for the applications that need to resolve the heterogeneous identifiers. To address this challenge, we propose an algorithm to automatically recognize the heterogeneous identification schemes used by various IoT identifiers, based on a sequence-to-sequence (seq2seq) model consisting of an encoder and a decoder. The encoder uses one Long Short-Term Memory (LSTM) to map the identifier sequence to a vector of fixed dimensionality, and the decoder uses another LSTM to unfold the vector into a target sequence representing the identification scheme of this identifier. To evaluate our algorithm, we create a new dataset named ID-20 with 20 categories of IoT identifiers and conduct experiments on it. The results demonstrate the superiority of our algorithm against other state-of-the-art methods, with an identifier recognition accuracy of up to 94.57%.
Auto-Encoder Based Wavelet and Extreme Learning Machine for Face Recognition

Face recognition is a popular topic in biometrics. It has received increasing interests. Face recognition is widely used in many applications. Such as surveillance, identification in login system and personalized technology. Recognizing multiple face in real-time on the embedded system is very challenging due to the complexity computation of the processing. Extreme learning machine (ELM) has garnered tremendous success and attention as an efficient learning algorithm due to its good generalization performance in various problems such as face recognition. In this paper we present a new method for face recognition using an auto encoder based wavelet analysis and extreme learning machine. For more details, we have used a composite wavelet activation function at the hidden nodes of ELM and the learning step is accomplished by the Deep ELM. For the data collection we have used the Face Recognition Grand Challenge (FRGC) v.2 database.
Control strategies for adaptive resource allocation in cloud computing

Using a compute infrastructure efficiently to execute jobs while respecting Service Level Agreements (SLAs) and thereby guaranteeing Quality of Service (QoS) poses a number of challenges. One such challenge lies in the fact that SLAs are set prior to the execution of a job, but the execution environment is subject to a number of possible disturbances, such as poor knowledge about actual resource necessity, demand peaks and hardware malfunctions, amongst others. Thus by using a fixed resource allocation, the manager of a shared computing environment risks violating user SLAs. Furthermore, the complexity of managing several workload executions increases with the number of workloads, implying the need for an automatic method to manage and control the execution of workloads. The execution time SLA is specially important in streaming scenarios such as web applications and continuous video processing, and is the focus of this paper. A method based on adaptive model predictive control (aMPC) is proposed here to adapt the amount of allocated resources to iterative workloads. The methodology is tested applied to Deep Learning Workloads, in standalone and multi-workload versions. The results show that using adaptive optimal control with a linearized model improves performance with respect to simpler control laws as well as reinforcement learning approaches.