Reviewerly/exHarmony huggingface dataset:
Cosmos-Loss: A Face Representation Approach With Independent Supervision

Cost function is one of the most important topics in face recognition. Classic methods based on anchor-positive-negative sample pairs directly or indirectly have been proved to be effective. Taking advantage of information from sample pair with labels is the key point to make intra-class samples more centralized and inter-class ones more scattered. However, there are some problems to constrain model in these classical approaches, such as sample-pair-combination and the poor information used in parameter updating. This paper proposes a novel method named Cosmos-Loss based on the principle of feature center. It divides the feature space into many subspaces. There is a one-to-one match between each subspace and face identity. All samples belonged to the same subspace decide their feature center. This proposed method uses the information between sample and its own feature center to make inner features more similar and the information between sample and other center to make intra features more dissimilar, instead of the poor information between only two samples. This supervision method could use the information of all samples which belonged to the same center indirectly and make feature distribution smoother. And all experiments show that this proposed method is an effective way to supervise model training independently.
Secure and Verifiable Data Access Control Scheme With Policy Update and Computation Outsourcing for Edge Computing

Edge computing means that computing tasks are executed on edge devices closer to the data source. It can effectively improve system response speed and reduce the risk of user data leakage. However, current data access control schemes usually focus on cloud computing and rarely on edge computing. Although attribute-based encryption (ABE) scheme can realize flexible and reliable access control, computing cost is too high with the increase of access policy complexity. Therefore, combining computation outsourcing technology with dynamic policy updating technology, we propose a data access control scheme based on ciphertext-policy ABE (CP-ABE) for edge computing. We outsource part of storage service and part of decryption computing to edge nodes, effectively reducing the computing pressure of users. When data owner requires a new access policy, policy update key is generated timely and transmitted to cloud service provider, which is used to update the access policy, reducing the risk of bandwidth consumption and leakage of the ciphertext back and forth transmission. Finally, security analysis and experiment results verify the safety and effectiveness of our scheme.
Why We Should Build Robots That Both Teach and Learn

In this paper, we argue in favor of creating robots that both teach and learn. We propose a methodology for building robots that can learn a skill from an expert, perform the skill independently or collaboratively with the expert, and then teach the same skill to a novice. This requires combining insights from learning from demonstration, human-robot collaboration, and intelligent tutoring systems to develop knowledge representations that can be shared across all three components. As a case study for our methodology, we developed a glockenspiel-playing robot. The robot begins as a novice, learns how to play musical harmonies from an expert, collaborates with the expert to complete harmonies, and then teaches the harmonies to novice users. This methodology allows for new evaluation metrics that provide a thorough understanding of how well the robot has learned and enables a robot to act as an efficient facilitator for teaching across temporal and geographic separation.
Improving Users Engagement Detection using End-to-End Spatio-Temporal Convolutional Neural Networks

The ability to infer latent behaviours such as the degree of engagement of humans interacting with social robots is still considered one challenging task in the human-robot interaction (HRI) field. Data-driven techniques based on machine learning were recently shown to be a promising approach for tackling the users' engagement detection problem, however, the resolution often involves multiple consecutive stages. This in return makes these techniques either incapable of capturing the users' engagement especially in a dynamic environment or un-deployable because of their inability to track engagement in real-time. This study is based on a data-driven framework, and we propose an end-to-end technique based on a unique 3D convolutional neural network architecture. Our proposed framework was trained and evaluated using a real-life dataset of users interacting spontaneously with a social robot in a dynamic environment. The framework has shown promising results over three different evaluation metrics when compared against three baseline approaches from the literature with an F1-score of 76.72. Additionally, our framework has achieved a resilient real-time performance of 25 Hz.
Accelerating PageRank in Shared-Memory for Efficient Social Network Graph Analytics

PageRank has a wide applications in online social networks and serves as an important benchmark to examine graph processing frameworks. Many efforts have been made to improve the computation efficiency of PageRank in shared-memory platforms, where a single machine can be sufficiently powerful to handle a large-scale graph. Existing methods, however, still suffer from synchronization issues and irregular memory accesses, which will deteriorate their overall performance. In this paper, we present an accelerated parallel PageRank computation approach, named APPR. By investigating the characteristics of parallel PageRank computation and degree distributions of social network graphs, APPR proposes a series of optimization techniques to improve the efficiency of PageRank computation. Specifically, a destination-centric graph partitioning scheme is designed to avoid the synchronization issues when concurrently updating the common vertex data. By exploiting power-law structure of social network graphs, APPR can intelligently schedule the computations of vertices to save computing operations. The vertex messages are adjusted by APPR for transmission to further improve the locality of memory accesses. Empirical evaluations are performed based on a set of large real-world graphs. Experimental results show that APPR significantly outperforms the state-of-the-art methods, with on average 2.4x speedup in execution time and 16.4x reduction in DRAM communication.
Corrective Feedback in Mobile Technology-Mediated Contexts

This chapter discusses the role of corrective feedback in mobile technology-mediated contexts. It begins by briefly discussing the role of technology in second language learning and teaching, focusing on mobile technology and explaining its affordances. It then examines the role that mobile-assisted language learning (MALL) can play in providing and monitoring corrective feedback in language learning. The chapter concludes with a discussion of the pedagogical implications as well as directions for future research on MALL-supported corrective feedback.
Steganographic algorithm based on chaotic random system on Raspberry Pi hardware

The paper presents a novel steganographic algorithm. It is based on a least significant bit method and a chaotic random system. The designed scheme is realized by the Raspberry Pi hardware. It is proved that the novel algorithm shows good statistical results.
The immersive environment as a driver for environmental change; addressing the Out of Sight, Out of Mind impacts of the Anthropocene on the Mar Menor, Spain

Out of Sight, Out of Mind is a unique interactive 360° video experience of the Mar Menor that manifests the Anthropocene effects on the landscape as augmented, surreal and metaphysical interpretations of the artist’s experiences, during their residency and available secondary scientific data of the Mar Menor ecosystem. Through environmental, social, economic and cultural observations and encounters the team developed an immersive 360° environment that incorporates video and audio recordings with augmented, imaginary and predicted realities transformed from scientific data in obscure and profound guises. This 360° telematic installation incorporating live audience interaction within the original 360° video experience was exhibited at the Centro Cultural Puertas de Castilla in Murcia in May 2019. This collaborative project was developed following a ten-day residency on the Mar Menor, a 170 km² saltwater lagoon on the south-east coast of Spain in September 2018, where the majority of the primary research took place by gathering 360° video material from observations, experiences and interviews. The project has been developed by a team of three UK artists, each bringing specialist experience and knowledge of 360° video to undertake the research and create a unique understanding and manifestation of the changing ecosystem of the Mar Menor. This collaborative project includes and combines Paul Sermon’s co-located telematic experiences in 360° live video environments, Charlotte Gould’s immersive 360° animated augmented reality and Jeremiah Ambrose’s gaze-controlled navigation through 360° video narratives. This practice-based team of artists undertook this research using a range of video and gaming software and advanced hardware devices, including Insta360 Pro 8K video cameras and Oculus Rift head-mounted-displays in conjunction with live video switchers. This has produced a range of ultra HD 360° outputs involving stereo 8K and real-time 4K environments with augmented live 360° video and animation sequences through live chroma-keying effects.
A Comprehensive Study of VoIP Security

Voice over Internet Protocol (VoIP) Service has been the implementation over the past few years. Most of the Organizations which have implemented VoIP Services over there are either unaware or ignoring the security and Vulnerabilities issues with VoIP. a VoIP network is also susceptible to abuse and easy to exploit. Here, I would like to discuss various Exploitation techniques followed by Pentesting and Security of VoIP Machine. I will not go to protocol level as this report is aimed at Penetration Testers who want to learn the basics first, though it is strongly encouraged to understand the protocols which is used in VoIP Networks. Here performing attacks against VoIP Denial of Service (DoS) attacks Registration Manipulation and Hijacking Authentication Attacks Caller ID Spoofing Man-in-the-middle attacks Virtual Lan Hopping passive and Active Eavesdropping Spamming over Internet Telephony (SPIT) VoIP Phishing (Vishing).
User Satisfaction Analysis for Event Management Systems Using RAD and PIECES Framework

Abstract The National University Information System Student Association often holds events or activities every period it takes place but has not been integrated and well managed. Many problems were caused, such as: large costs, data loss, long queuing services, and large human resources. Seeing from these conditions, the authors conducted a study that aims to determine the level of user satisfaction analysis of the event management information system that has been designed for the activities of the National University Information System Student Association. This study uses 3 methods, namely Rapid Application Development (RAD), Waiting Line and PIECES. The average result of the analysis of respondents on questions on the performance aspect was 4.35, the information aspect was 3.6, the economic aspect was 4.45, the control &amp; security aspect was 3.50, the efficiency aspect was 3.64 and the services aspect was 3,825. From the results of the average assessment of the total score for the PIECES aspect, it was obtained 3,894 or 77.88% of respondents said they were satisfied with this system. This research contributes to the governance of activities organized by the Information Systems Student Association so that they can be well integrated and managed.
White Pulp Segmentation Algorithm for Mouse Spleen Cryo-imaging Data Using U-Net

Spleen is one of the important organs for studying graft-versus-host disease development using cryo-imaging modality. Up to this point, there is no algorithm developed to segment spleen tissues in the cryo-imaging data. In this study, we developed a new, automatic segmentation algorithm for spleen tissues in the cryo-images for the first time. The algorithm consisted of (1) pre-processing, (2) predicting, and (3) post-processing. The predicting model consisted of two U-Nets to separate spleen from background and white pulp from red pulp. The results generated by the algorithm were compared against the ground truth generated by cryo-imaging experts. The Dice similarity coefficients were about 86% for red pulp class and 87% for white pulp class. We also used the algorithm to measure spleen volumes and performed the T-cells proliferation assays on the results. We found that there was a significant difference between ratios of white pulp volume to spleen volume between allogeneic spleen and syngeneic spleen. The results were consistent with the previous biological reports. In conclusion, the algorithm accurately and objectively segmented the spleen tissues with similar quality to the experts. By incorporating the algorithm into the T-cell proliferation assay workflow, it should greatly increase throughput of the process as compared to manual segmentation by humans. We expect that this algorithm will help accelerate the research for further understanding of graft-versus-host disease and development of the cure.
An Ontology for Fire Building Evacuation

Guiding the building occupants under fire emergency to a safe place is an open research problem. Finding solutions to address the problem requires a perfect knowledge of the fire building evacuation domain. The use of ontologies to model knowledge of a domain allows a common and shared understanding of that domain, between people and heterogeneous systems. This paper presents an ontology that aims to build a knowledge model to better understand the referred domain and to help develop more capable building evacuation solutions and systems. The herein proposed ontology considers the different variables and actors involved in the fire building evacuation process. We followed the Methontology methodology for its developing, and we present all the development steps, from the specification to its implementation with the Protégé tool.
A Novel Computer-Aided Cloud Type Classification Method Based on Convolutional Neural Network with Squeeze-And- Excitation

Abstract Clouds have a huge impact on the energy balance, climate and weather of the earth. Cloud types have different cloud radiation effects, which is an important indicator of cloud radiation effects. Therefore, determining the type of cloud is of great significance in meteorology. In this paper, the Convolutional neural network with Squeeze &amp; Excitation Networks (SENet) are mainly used to solve this probelm. CNN can automatically learn the filters that need to be manually set before, and can learn complex edge, spatial and texture information in the image which are difficult for traditional methods to learn and extract. Moerover, a website and a deep learning framework are established to showcase the results of this article and to further develop our models and methods through open source methods.
Bengali Abusive Speech Classification: A Transfer Learning Approach Using VGG-16

Swear words used in speech to abuse someone is frowned upon in every society. Abusive speeches can destroy the victim's morale, mental strength, and the will to live. Abusing others through social media, video streaming sites, and over voice calls are becoming a common problem. There are laws to punish the offenders. However, without proper surveillance, stopping abusive speech is tough. Machine learning can help to create surveillance methods by detecting abusive speech from human conversation. There have been a few works in the relevant field to detect abusive speech. However, detecting abusive speech in the Bengali language remains an unexplored area. This paper aims at providing an approach towards the classification of abusive and non-abusive Bengali speech. The authors collected 960 voice recordings of native Bengali speakers. The authors used Transfer Learning for extracting features from the data. Then, the authors used different methods for classification. The proposed approach achieves high accuracy (98.61%) in classifying abusive and non-abusive Bengali speech.
Mixed Reality Interaction Techniques

This chapter gives an overview of the interaction techniques for mixed reality with its variations of augmented and virtual reality (AR/VR). Various modalities for input and output are discussed. Specifically, techniques for tangible and surface-based interaction, gesture-based, pen-based, gaze-based, keyboard and mouse-based, as well as haptic interaction are discussed. Furthermore, the combinations of multiple modalities in multisensory and multimodal interaction as well as interaction using multiple physical or virtual displays are presented. Finally, interactions with intelligent virtual agents are considered.
Digital Transformation of the Educational Process in University in the Context of Globalization

As a result of the research the following spheres of influence of virtual environment on the university were defined: professional, environmental, functional. Exogenous and endogenous factors of information and communication technologies affecting universities were identified. In order to increase the efficiency of the educational process in the university it was proposed to introduce SMART-complexes of disciplines. The structure of SMART-complexes of disciplines is established, consisting of the following modules: definition of the level of learning, assessment of learning efficiency, content for adaptive learning, tasks for adaptive learning, a set of databases for storing learning profiles, a knowledge base with a mechanism for logical output. Detailed components of the educational SMART-complexes guide students in their learning activities to correctly find the tasks that are most appropriate and necessary at present, to effectively define strategies and methods of learning, taking into account their personal factors. A methodology for assessing the digitalization level of a university based on fuzzy logic as a tool for managing processes of the digital transformation of the educational process is proposed. The following groups of indicators were identified to assess the digitalization level of the university: digital literacy of teachers, digital literacy of students and digitalization of the educational process. Assessing the digitalization level of the university will reveal the strengths and weaknesses of the educational process, justify the necessary effective management decisions. The main directions of state policy for digitalization of education have been defined. The need to create a comprehensive national system for the development of digital literacy, combining the efforts of academic and non-formal education on the basis of public-private partnership has been stressed. The most important function of traditional higher education institutions remains the socialization of students, so in order to improve the quality of education, it is appropriate to use mixed learning through various forms of education.
A Joint Model of Entity Recognition and Predicate Mapping for Chinese Knowledge Base Question Answering

Knowledge base question answering(KBQA) is the key technology of natural language processing. How to understand the semantic information of the natural language problem and capture the semantic relationship between the problem and the structured triples are the problems that KBQA needs to solve. The boundary of subject entities in Chinese questions is not as clear as English, which increases the difficulty of entity recognition. Besides, the variable Chinese grammar makes predicate mapping more difficult for semantic analysis. Existing KBQA is usually implemented using a pipeline model, which has two disadvantages: (1) Errors caused by entity recognition will be propagated to predicate mapping. (2) Neither entity recognition nor predicate mapping can benefit from the information available to each other. So we propose a BERT-based KBQA to joint entity recognition and predicate mapping tasks that use their dependencies to improve model performance. BERT can solve the semantic ambiguity of the Chinese Q&A databases and improve the accuracy of Chinese Knowledge Base Question Answering(CKBQA). The model achieved an F1 score of 92.04% on the NLPCC 2016 KBQA dataset.
ADMMiRNN: Training RNN with Stable Convergence via an Efficient ADMM Approach

It is hard to train Recurrent Neural Network (RNN) with stable convergence and avoid gradient vanishing and exploding, as the weights in the recurrent unit are repeated from iteration to iteration. Moreover, RNN is sensitive to the initialization of weights and bias, which brings difficulty in the training phase. With the gradient-free feature and immunity to unsatisfactory conditions, the Alternating Direction Method of Multipliers (ADMM) has become a promising algorithm to train neural networks beyond traditional stochastic gradient algorithms. However, ADMM could not be applied to train RNN directly since the state in the recurrent unit is repetitively updated over timesteps. Therefore, this work builds a new framework named ADMMiRNN upon the unfolded form of RNN to address the above challenges simultaneously and provides novel update rules and theoretical convergence analysis. We explicitly specify essential update rules in the iterations of ADMMiRNN with deliberately constructed approximation techniques and solutions to each sub-problem instead of vanilla ADMM. Numerical experiments are conducted on MNIST and text classification tasks, where ADMMiRNN achieves convergent results and outperforms compared baselines. Furthermore, ADMMiRNN trains RNN more stably without gradient vanishing or exploding compared to the stochastic gradient algorithms. Source code has been available at https://github.com/TonyTangYu/ADMMiRNN.
Computer Vision-Based Vehicle Detection and Tracking

To briskly detect people and vehicle on the road in a video sequence is a challenging problem. Most researches focus on detecting or tracking of specific targets only. Detection and tracking of vehicles have a great demand in video surveillance and traffic management applications. Each and every vehicle in the scene must be observed, while dealing with the traffic scenarios, which solves the problem occurred due to the traffic density in an area, is high due to occlusion caused by the large number of vehicles being observed. This paper portrays the computer vision-based vehicle detection and tracking for real-time scenarios. The proposed algorithm uses the blob analysis and tracking based on a correlation filter. Here, HOG is used for feature extraction, improvised correlation filter is used for tracking and AdaBoost classifier used for classifying the vehicle. The proposed system successfully tracks and counts the vehicles during and after occlusion with other vehicles which is shown in the result.
Data-Driven Requirements Engineering: A Guided Tour

Data-driven approaches are becoming dominant in almost every single software engineering activity, and requirements engineering is not the exception. The analysis of data coming from several sources may indeed become an extremely useful input to requirements elicitation and management. However, benefits do not come for free. Techniques such as natural language processing and machine learning are difficult to master and require high-quality data and specific competences from different fields, whilst their generalization remains as a challenge. This paper introduces the main concepts behind data-driven requirements engineering, provides an overview of the state of the art in the field and identifies the main challenges to be addressed.