Reviewerly/exHarmony huggingface dataset:
Lucas-Kanade-based Face Detection Network: An Unsupervised Approach to Improve the Precision and Stability of video-based Face Detector

Though significant works have been made in image-based face detection, precise and stable video-based face detection remains a big challenge. What's more, the jittering of bounding boxes has an important impact on the stability of face attributes analysis. To address these issues, this paper presents an unsupervised single-stage face detector, named Lucas-Kanade-based face detection network (LK-FDN). Our key idea is that the detections of the same landmark in adjacent frames should be coherent with optical flow registration. By combining RetinaFace and the optical flow algorithm in a multi-task learning framework, LK-FDN does not require any video-level annotation, because the coherency of optical flow is a source of supervision and can be leveraged during detector training. Essentially, LK-FDN augments the training loss function with a registration loss. Supervised by the registration loss, the retraining process is conducted, which computes optical flow registration in the forward pass, and back-propagates gradients that ensure temporal coherency in the detector. The output of LK-FDN is a more precise and stable video-based face detector. Specifically, the advantages of LK-FDN are summarized in the following three aspects: (1) Compared with RetinaFace(baseline), LK-FDN improves the average precision (AP) by 0.43%, 0.23%, and 4.1% respectively on the easy, medium, and hard group of WIDER FACE. (2) Compared with RetinaFace, LK-FDN improves the precision without spending any extra inference time in test time, because LK is conducted during the retraining backward process. (3) LK-FDN significantly shrinks the jittering in video detection on the self-built ElevatorFace dataset without any video-level annotation. (4) According to the self-built evaluation criterion, the score of stability is improved from 88.409 to 97.119, which verifies the effectiveness of the LK-FDN.
Research on 3D face measurement for facial virtual plastic surgery

Abstract Calculating the key data of human face through three-dimensional face model can help doctors predict the surgical results before plastic surgery, guide the formulation of surgical plan and reduce the surgical risk. In this paper, 3D face measurement technology is studied, and the area of plastic area is calculated by slice method. A large number of experimental tests show that the average errors of facial geometric feature measurement, area measurement are 0.458mm and 0.65mm 2 respectively. The rating of simulation results provided by plastic surgeons shows that facial measurement technology can effectively improve the success rate of plastic surgery. Compared with the original face, the edited 3D face is more attractive in respecting the following beauty standards: “three courts and five eyes”, golden section, etc.
Text Mining, Clustering and Sentiment analysis: A systematic Literature Review

The objective of this study is to provide a systematic literature review related to text mining, clustering, and sentiment analysis by trying to find more appropriate technics, methods, algorithms, and procedures in this field with special emphasis on lower resource languages such as Albanian language. In this study, we have analyzed more than 170 papers that are published in the last decade from relevant databases such as IEEE, ACM, Scopus, and Springer among others. This paper aims to identify the advantages and disadvantages of each approach and underline some of the major remaining obstacles and challenges in this area.
Quantum Computing Algorithms

Abstract Alice and Bob introduce and explain in detail some of the algorithms that convinced physicists and computer scientists that there might be something to this crazy idea of quantum computing. Each algorithm provides a case study of how the properties of quantum states can be used to provide an advantage for using a quantum computer compared to a classical computer. Alice and Bob describe the Deutsch algorithm, designed to answer a simple question about the properties of functions of binary digits (0 and 1), and the Simon algorithm, which finds a “secret code.” The Simon algorithm, like many other quantum algorithms, is a mix of quantum state algorithms and classical computational methods. Alice and Bob also introduce Hadamard gates that act on multi-qubit systems. Those gates play an important role in many quantum algorithms.
Gesture Recognizing Technology Development for Interaction with Mobile Devices

The article presents the implementation of gesture recognizing technology for interaction with mobile devices, describes approaches to the design of the user interface and approaches to the implementation of gesture recognizing technology. The software implementation of the palm recognizing function and an example of a developed software product for interacting with mobile devices through gesture recognizing are considered in detail.
Supervised learning of explicit maps with ability to correct distortions in the target output for manifold learning

Most manifold learning algorithms are non-parametric, use unsupervised approaches, and hence lack prediction capability. Some of these methods have been adapted for out of sample points, but often they require the training data. We propose a framework to estimate an explicit map for manifold learning in a supervised setting. Although different modeling tools can be used, we study the performance with polynomials and neural networks . The quality of output of any regression system depends on the quality of the target data. We consider outputs from an unsupervised manifold learning method as the target. However, even for simple data, often the outputs (target) get severely distorted. For high-dimensional data, it is difficult to assess and correct if the target data are distorted. Our approach can predict as well as eliminate such distortions to a great extent in the output. We suggest three regularizers, each of which can be used to augment the loss function. For image data, even for a low order polynomial , the number of free parameters becomes large demanding a large training set. For this, we propose a scheme exploiting the spatial characteristics of neighboring pixels . The effectiveness of our framework is demonstrated using synthetic and real data for both polynomial and neural network-based models.
Android-based feasibility study transformation analysis case study of the RASI development in Cirendeu-Kampung Cimahi

Abstract Development of rasi or rice made from cassava flour was developed in Cirendeu Village-Cimahi City. The activity requires a feasibility study analysis that is mobile and fast so that it can be implemented more easily in the field. Nowadays, information technology is developing very fast to support decision support systems that are mobile. Analysis of economic-based feasibility studies has been widely developed in the field, but often is still based on virtual laptops. The purpose of this research activity is to conduct a trial-based Android-based feasibility study for the development of constellations in Cirendeu Village, Cimahi City. The research activity method includes several stages including optimizing and developing constellation technology in the field, analyzing the laptop-based feasibility study, transforming the results of an Android-based feasibility study and testing the implementation of performance in the field. Based on the results of the analysis in the field, the results were obtained that the software was able to identify the feasibility and optimum capacity of the production business based on the results of input data for several parameters that had been identified based on the system. The feasibility of the constellation development efforts developed in Cirendeu Village-Cimahi City can be identified more easily by using android for further implementation at the field level. This technology can be used and replicated as a mobile software-based decision-making system approach.
Password generation using array generating interval timed colored Petri net (AGITCPN) for effective security

Proportions of web users are becoming more and more. To secure their information they create their own login identification and password. Secret word confirmation is a cycle that includes a client contributing an ID and key that is then checked against certifications. They are probably been approached to make or potentially enter a secret key to access an individual record, regardless of whether that is a web-based portal or applications. In general the password will be in the form of string with the characters. Securing the information has become a challenging factor for web users. When the user creates a highly secured password it takes time for the hackers to crack it with the help of available tools. Cracking the password has created a serious challenge to the users to provide a highly secured password. Many authentication methods have been proposed to secure their data. Password authentication using Petri nets, Colored Petri nets have been used to create a secured login password. In this paper, we proposed and developed the application of Interval Timed colored Petri nets to generate password which will be more secured. This research work provides a new platform for the web users to create a matrix type password in a secured way. This helps them to secure their information more than ever before. To create this matrix type password, applications of Interval Timed colored Petri nets are used.
Mnemonic: A Parallel Subgraph Matching System for Streaming Graphs

Finding patterns in large highly connected datasets is critical for value discovery in business development and scientific research. This work focuses on the problem of subgraph matching on streaming graphs, which provides utility in a myriad of real-world applications ranging from social network analysis to cybersecurity. Each application poses a different set of control parameters, including the restrictions for a match, type of data stream, and search granularity. The problem-driven design of existing subgraph matching systems makes them challenging to apply for different problem domains. This paper presents Mnemonic, a programmable system that provides a high-level API and democratizes the development of a wide variety of subgraph matching solutions. Importantly, Mnemonic also delivers key data management capabilities and optimizations to support real-time processing on long-running, high-velocity multi-relational graph streams. The experiments demonstrate the versatility of Mnemonic, as it outperforms several state-of-the-art systems by up to two orders of magnitude.
Online modulation classification with multiple receivers under unknown symbol timing and flat fading

Likelihood based techniques present a viable approach to modulation classification (MC) since they are based on sound probabilistic models for the transmitted signal and the channel. However, as more uncertainties are taken into account in these models, they begin to suffer from increased computational complexity and memory requirements due to parameter estimation. In this paper, we propose an online MC technique based on Titterington’s expectation–maximization algorithm and apply it to centralized and distributed networks of multiple receivers that operate under unknown independent flat-block fading and symbol timing uncertainty. Compared to existing methods that yield high classification accuracy based on batch processing of the data, simulation results indicate that our sequential algorithm delivers comparable performance without the need to oversample the waveforms intercepted by the receivers.
A System for Evaluating the Correctness of Physical Exercises Execution

As many people work from home, the effects of a sedentary lifestyle are more prominent and there is need to promote and support at-home workout sessions. In this paper we present an instrument that can partially replace the supervision of a professional coach by evaluating the correctness of physical exercises. Our objective was to use a smart phone as a sensor to monitor the physical activity of the user and machine learning techniques to classify the type of movement and to assess the execution of the workout session. The experiments show good results despite using data collected from just one set of sensors.
GA-MKB:A Multi-kernel Boosting Learning Method based on Normalized Kernel Target Alignment and Kernel Difference

Abstract Concentrates on the problem that the traditional kernel target alignment(KTA) is not invariance under data translation in the feature space, a cosine matrix alignment method is proposed for kernel selection, which is called normalized kernel target alignment(NKTA). On the basis of normalized kernel target alignment and kernel difference, we propose a new multi-kernel boosting. Firstly, the value of NKTA is taken as the election rarget of the kernel function in each iteration of algorithm, which leads to a selective kernel fusion. Secondly, the kernel difference measure is used to construct the combination coefficient to increase the diversity of weak classifiers, and then improve the generalization performance of integrated strong classifiers. Finally, among the 6 data sets, the GA-MKB performed better than MKBoost-D1 under the accuracy of classification, and can improve the generalization performance of the integrated classifier compared with MKBoost-D2.
The Routledge handbook of language testing, 2nd edition Edited by GlennFulcher | LukeHardingRoutledge, 2021. xviii + 656 pp. ISBN: 9781138385436

International Journal of Applied LinguisticsEarly View BOOK REVIEW The Routledge handbook of language testing, 2nd edition Edited by Glenn Fulcher | Luke Harding Routledge, 2021. xviii + 656 pp. ISBN: 9781138385436 Rujun Pan, Rujun Pan orcid.org/0000-0001-5388-766X Institute of International Students Educational Policy and Evaluation, Beijing Language and Culture University, Beijing, ChinaSearch for more papers by this authorJimin Wang, Corresponding Author Jimin Wang blculing26@outlook.com Institute of International Students Educational Policy and Evaluation, Beijing Language and Culture University, Beijing, China Correspondence Jimin Wang, Institute of International Students Educational Policy and Evaluation, Beijing Language and Culture University, Beijing, China. Email: blculing26@outlook.comSearch for more papers by this author Rujun Pan, Rujun Pan orcid.org/0000-0001-5388-766X Institute of International Students Educational Policy and Evaluation, Beijing Language and Culture University, Beijing, ChinaSearch for more papers by this authorJimin Wang, Corresponding Author Jimin Wang blculing26@outlook.com Institute of International Students Educational Policy and Evaluation, Beijing Language and Culture University, Beijing, China Correspondence Jimin Wang, Institute of International Students Educational Policy and Evaluation, Beijing Language and Culture University, Beijing, China. Email: blculing26@outlook.comSearch for more papers by this author First published: 23 June 2022 https://doi.org/10.1111/ijal.12429Read 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 onFacebookTwitterLinked InRedditWechat Open Research PEER REVIEW The peer review history for this article is available at https://publons.com/publon/10.1111/ijal.12429. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
Free-Space Continuous-Variable Quantum Key Distribution with Imperfect Detector against Uniform Fast-Fading Channels

Free-space continuous-variable quantum key distribution based on atmospheric laser communications is expected to play an important role in the global continuous-variable quantum key distribution network. The practical homodyne detector model is applied in free-space continuous-variable quantum key distribution which models the imperfect characteristics including the detection efficiency and the electronic noise. In the conventional model, we must calibrate them simultaneously. In the modified model, only one of the imperfections needs to be calibrated to simplify the calibration process of the practical experiments, also known as one-time calibration. The feasibility of the modified detector model against the fast-fading channel is proved. The results of the symmetry operations are considered when presenting detailed security analysis. Some remarkable features of the uniform fast-fading channel were found from the simulation results. The performances of the conventional model and the modified model are similar but the modified model has the advantage of achieving one-time calibration.
The Use of Networking for Community Development: A Survey

This paper presents a survey on the role of ICT community networks in the development of communities. We summarize here the work done in the field of community networking around the world and describe its evolution. We describe how e-networks are formed and explain the models on which communities are developed along with their type of interest. We further bring into account the benefits of community networks accrued on societies encompassing social and economic development, awareness, collective working, education/learning and training, civic participation, policy making, emotional impacts and overall sustenance of a community. We also identify the research issues that still remain a concern while implementing community networks.
Network Layer Analysis for a RL-Based Robotic Reaching Task

Recent experiments indicate that pretraining of end-to-end reinforcement learning neural networks on general tasks can speed up the training process for specific robotic applications. However, it remains open if these networks form general feature extractors and a hierarchical organization that can be reused as in, for example, convolutional neural networks. In this study, we analyze the intrinsic neuron activation in networks trained for target reaching of robot manipulators with increasing joint number and analyze the individual neuron activation distribution within the network. We introduce a pruning algorithm to increase network information density and depict correlations of neuron activation patterns. Finally, we search for projections of neuron activation among networks trained for robot kinematics of different complexity. As a result, we show that the input and output network layers entail more distinct neuron activation in contrast to inner layers. Our pruning algorithm reduces the network size significantly and increases the distance of neuron activation while keeping a high performance in training and evaluation. Our results demonstrate that robots with small difference in joint number show higher layer-wise projection accuracy, whereas more distinct robot kinematics reveal dominant projections to the first layer.
Effects of the Pandemic on Education

The pandemic, as a super-problem, caused the greatest educational challenge of the last decades. The educational response of different countries was unplanned and led to what has been called emergency online learning. This chapter describes the effects of this educational response on the different agents involved teachers, students, and educational organizations. Finally, the analysis focuses on Spain, where the conclusions are the lack of planning, the lack of teacher training, and the lack of infrastructure and technology both in the education system and at home. At the same time, it has been an opportunity to test the potential of technology, the generation of educational innovation, and different alternative ways to solve problems such as teacher training.
Designing a VR Lobby for Remote Opera Social Experiences

Several social VR platforms support virtual entertainment events, however their value for post-show activities remains unclear. Through a user-centered approach, we design a social VR lobby experience to enrich four motivations of theatre-goers: social, intellectual, emotional, and spiritual engagement. We ran a context-mapping focus group session with professionals (N=6) to conceptualize the social VR space for digital opera experiences. Based on our findings, we propose a social VR lobby consisting of four rooms: 1) a Bar for social engagement, 2) an Info Booth for intellectual engagement, 3) a Photo Zone for emotional engagement, and 4) an Interactive Stage for spiritual engagement. Based on this work, we plan to experimentally evaluate audience experiences in each room in order to create a social VR lobby template for theater experiences.
AI-Based Interactive Agent for Health Care Using NLP and Deep Learning

Artificial intelligence (AI)-based interactive agent plays a vital role in health care. In this paper, we have implemented AI-based Interactive agent using natural language processing (NLP) and deep learning (DL) which deals with resolving simple queries and provide health care services. This work helps to build a system for providing better health care service for user and increase user interaction. This work builds a chatbot using DL and NLP pipeline. We conclude that chatbots build with NLP and neural network are more efficient than human resource and predefined chatbots that are not efficient. It is necessary to discuss potential research directions that can improve capabilities.
Improved state estimator for linear-Gaussian systems subject to initialization errors

This paper proposes an improved state estimator for linear-Gaussian systems subject to initialization errors. A one-step prediction function depicted by the Student-t is reconstructed artificially by inserting an auxiliary variable into the original Gaussian distribution. The variational Bayesian (VB) technique is then employed to obtain the approximated posterior joint distribution of the additional variable and the state. A fixed-point iteration is used for recursions to calculate the necessary moments of state with the updated distribution of the auxiliary variable. Simulation and experiment verify the performance of the proposed algorithm. It shows that the proposed method yields significant improvements over the existing initialization approaches, such as the practical Kalman filter (KF) and the recently-developed Bayesian initialization algorithm.
Research on Fault Diagnosis of Switchgear Based on Temperature Cloud Image Technology

Abstract In the process of power system operation, contact aging, partial discharge and overcurrent faults are the main causes of switch cabinet faults. At the same time, it is difficult to install sensors inside the switch cabinet due to its closed operation. In this paper, in order to solve the over current problem of switchgear, according to the principle of high current heating, the thermal flow model of power switchgear is established, and the characteristics of heat flow inside the switchgear are studied. The infrared temperature detector is designed to collect the surface temperature cloud map of the switchgear. Combined with the internal heat flow law of the switchgear, the over current phenomenon in the switchgear is judged, so as to judge the operation state of the switchgear. In order to verify the correctness of the theory proposed in this paper, the operation state of the fault switch cabinet is designed for detection and comparative analysis. The test results show that the heat change caused by the over-current fault can be accurately fed back to the surface of the switch cabinet, which can be used as an effective means to diagnose the fault of the switch cabinet.