Reviewerly/exHarmony huggingface dataset:
Periocular Recognition in the Wild with Orthogonal Combination of Local Binary Coded Pattern in Dual-stream Convolutional Neural Network

In spite of the advancements made in the periocular recognition, the dataset and periocular recognition in the wild remains a challenge. In this paper, we propose a multilayer fusion approach by means of a pair of shared parameters (dual-stream) convolutional neural network where each network accepts RGB data and a novel colour-based texture descriptor, namely Orthogonal Combination-Local Binary Coded Pattern (OC-LBCP) for periocular recognition in the wild. Specifically, two distinct late-fusion layers are introduced in the dual-stream network to aggregate the RGB data and OC-LBCP. Thus, the network beneficial from this new feature of the late-fusion layers for accuracy performance gain. We also introduce and share a new dataset for periocular in the wild, namely Ethnic-ocular dataset for benchmarking. The proposed network has also been assessed on one publicly available dataset, namely UBIPr. The proposed network outperforms several competing approaches on these datasets.
U-Net cascaded with dilated convolution for medical image registration

Registration is a critical step in many biomedical image analysis tasks. In this work, we propose a novel cascaded 3D U-Net with dilated convolution for volumetric image registration. We implement a coarse-to-fine registration strategy that commonly used in traditional registration methods by cascading two 3D U-Net based networks. In the first registration stage, a 3D U-Net network is employed to predict the large and coarse displacements. In order to capture more precise pixel-level displacement field, in the second stage, we propose to substitute the down-sampling operations in the encoding stage of 3D U-Net by dilated convolutions. As result, the resolution of feature map can be maintained as well as its field-of-view and semantic information. In addition, to obtain a more parameter-efficient network that is essential for volumetric image analysis, we replace the 3D convolution kernel by 3D dense separated convolution (3DDSC) module. We evaluated the proposed method on two commonly used datasets, and the results demonstrated that our method could obtain better performance than state-of-the-art image registration algorithms with short execution time.
3D Measurement of Human Chest and Abdomen Surface Based on 3D Fourier Transform and Time Phase Unwrapping

Monitoring respiratory movements is an effective way to improve radiotherapy treatments of thoracic and abdominal tumors, but the current approach is limited to measuring specific points in the chest and abdomen. In this paper, a dynamic three-dimensional (3D) measurement approach of the human chest and abdomen surface is proposed, which can infer tumor movement more accurately, so the radiotherapy damage to the human body can be reduced. Firstly, color stripe patterns in the RGB color model are projected, then after color correction, the collected stripe image sequences are separated into the three RGB primary color stripe image sequences. Secondly, a fringe projection approach is used to extract the folded phase combined 3D Fourier transform with 3D Gaussian filtering. By the relationship between adjacent fringe images in the time sequence, Gaussian filter parameters with individual characteristics are designed and optimized to improve the accuracy of wrapped phase extraction. In addition, based on the difference between the fractional parts of the folded phase error, one remainder equation can be determined, which is used for time-phase unwrapping. The simulation model and human experiments show that the proposed approach can obtain the 3D image sequences of the chest and abdomen surface in respiratory motion effectively and accurately with strong anti-interference ability.
A Taxonomy of Social Engineering Defense Mechanisms

Humans have become the weakest point in the information security chain, and social engineers take advantage of that fact. Social engineers manipulate people psychologically to convince them to divulge sensitive information or to perform malicious acts. Social engineering security attacks can be severe and difficult to detect. Therefore, to prevent these attacks, employees and their organizations should be aware of relevant defense mechanisms. This research develops a taxonomy of social engineering defense mechanisms that can be used to develop educational materials for use in various kinds of organizations. To develop the taxonomy, the authors conducted a systematic literature review of related research efforts and extracted the main target points of social engineers and the defense mechanisms regarding each target point.
Rethinking Decentralised Identifiers and Verifiable Credentials for the Internet of Things

In digital era of Internet of Things (IoT), internet-connected devices are becoming smart and autonomous. In this distributed world IoT device operate not only with the owner of the device but also need to operate with third parties, which leads to have identification for each IoT device. In the coming years, deploying IoT solutions at large scale will have to address complex issues of managing digital identity, security and identity ownership. Most of the current IoT solutions rely upon centralized client-server mechanism for providing connectivity and access to devices and services. However, when billions of devices will begin connecting simultaneously, then existing centralized solution will not suffice. Now days, we rely on federated identities however in this user need to depend on third party as users are not owner of their own identity, this leads to have Self-Sovereign Identities (SSI). In the sequel, there is a need of decentralized method for managing identities in near future. In this chapter, various identification methods are analyzed. Further, Decentralized Identifiers and Verifiable Credentials are discussed, the discussion focus on whether DID and VC are applicable to the IoT as IoT includes resource constrained devices at base level. It also presents smart home use case utilizing DID and VCs in order to evaluate its applicability. In the same line threat analysis is presented to analyze applicability of the DID and VC for the IoT applications.
Tutor Support as a Tool of Social-Cultural Adaptation ofInternational Students in Russian Universities

In the conditions of increasing internationalization of higher education, attracting students from abroad is considered as one of the means of strengthening the country's position in the world educational space. At the same time, the implementation of this indicator is complicated by the need to systematically, comprehensively change the educational and educational process and create favorable conditions for students from among foreign citizens. The purpose of this article is to consider tutor support as a tool of sociocultural adaptation of international students and its application in Russian universities. The authors analyze the concepts of "socio-cultural adaptation", "tutor support" and show their relationship. The article considers such principles of modern education as openness, variability, dialog, individualization, creation of open information environment, and shows compliance with these principles of tutor support practice. The program of training of student tutors is presented and the content and forms of implementation of educational modules of this program are revealed. As a result of the analysis of the experience of the School of tutors and volunteers in Siberian Institute of management, the branch of RANEPA it is concluded about the prospects and opportunities of using student tutoring as a tool for adapting international students to study in Russian universities and an effective way of their involvement in joint sociocultural activities, allowing to reveal and develop their existing personal and educational potential. The experience of training students-tutors is aimed at creating favorable conditions for socio-cultural adaptation of international students in Russia.
Analysis of Instance Segmentation using Mask-RCNN

Object detection has been one of the greatest achievement in the field of Machine learning. Most of the models in the domain carry out the process of identifying the character or object using a bounding box. In the recent years, detection of the object using Instance segmentation has been in limelight. However, no interest has been showed towards the data present in the field of Entertainment. The focus of this paper is to carry out pixel level comparison and to study the behaviour of the model by analysing the result obtained at various different instances.
Evaluation of the implementation of a subset of ISO/IEC 29110 Software Implementation process in four teams of undergraduate students of Ecuador. An empirical software engineering experiment

The competitiveness of software development companies depends on their ability to offer software products with quality attributes within approved budget and schedule. Most Very Small Entities (VSEs) that develop software do not see the benefits of implementing software standards. Consequently, they limit their potential to be recognised as quality software development entities. In this study, the authors present results obtained through the application of empirical software engineering in an experiment in which the ISO/IEC TR 29110–5–1–2 "Software engineering – Lifecycle profiles for Very Small Entities (VSEs) – Part 5–1–2: Management and engineering guide: Generic profile group: Basic profile" was used. The guide includes two processes: Project Management (PM) process and Software Implementation (SI) process. The objective of the project was the development of a software product for the scheduling of medical appointments for the Student Wellness Center of a university of Ecuador. Four teams of undergraduate students were involved. Two of them (controlled teams) implemented a subset of the SI process, while the other two (non-controlled teams) had freedom to choose development activities that were subsequently mapped with the activities of the standard. All teams developed the software product using the SCRUM framework within the same timeframe. Although the experiment was focused on the SI process, the teams also used a tailored version of the PM process defined by the professors. The experiment execution encountered several difficulties. For example, the timeframe of six weeks established in the design of the experiment was too short since students worked part time in the project. All the teams experienced this difficulty, especially when they had to construct and test the software components. Overall, the teams that used the ISO/IEC TR 29110–5–1–2 guide achieved better scores in the quality evaluation of their software processes.
Blockchain Technology Implementation In Raspberry Pi For Private Network

This research tries to prove low-cost computer devices such as mini-computers can be used in the development of blockchain technology. Mini-computer technology is used because of many problems that occur due to the use of hardware resources that are discussed large, specifically in the pharmaceutical industry in the process drug makers, packing drugs (drug packaging identities), checking drugs, installations (printing drug identities), and this use to label drug to read posts on drug strips, to make the products used by customers better. This study uses an experimental design. Experiments were carried out with devices to use and blockchain technology. The device used is a Raspberry Pi mini-computer, and blockchain technology is the Ethereum platform. The results of this study indicate the installation process until the blockchain system can run. The results show that the raspberry pi device can be implemented with Ethereum blockchain technology correctly and adequately.
Data Augmentation Method of SAR Image Dataset Based on Wasserstein Generative Adversarial Networks

The published Synthetic Aperture Radar (SAR) samples are not abundant enough, which is not conducive to the application of deep learning methods in the field of SAR automatic target recognition. Generative Adversarial Nets (GANs) is one of the most effective ways to generate image samples. In this manuscript, the gradient penalty method was used on the traditional DCGAN to solve the mode collapse problem. The improved model was used for data augmentation of the MSTAR dataset. All the experimental results show that the method was an effective way to generate SAR images with high quality.
Multi-label Aspect Categorization with Convolutional Neural Networks and Extreme Gradient Boosting

Aspect categorization is a subtask in aspect-based sentiment analysis. This task is a multi-label classification task since each document may belong to more than one aspect categories. In this paper, we divided the aspect categorization task into two subtasks, feature extraction and multi-label classification. We use Convolutional Neural Network (CNN) as feature extractor and Extreme Gradient Boosting (XGBoost) as the top-level classifier. Dataset used in this paper consists of 9450 hotel reviews. Our model achieved higher F1-score, 0.9316, and lower Hamming Loss, 0.02667, compared to our baselines which are CNN-LSTM (F1 0.9198 & loss 0.03217) and CNN-SVM (F1 0.9295 & loss 0.02719).
NIR-to-VIS Face Recognition via Embedding Relations and Coordinates of the Pairwise Features

NIR-to-VIS face recognition is identifying faces of two different domains by extracting domain-invariant features. However, this is a challenging problem due to the two different domain characteristics, and the lack of NIR face dataset. In order to reduce domain discrepancy while using the existing face recognition models, we propose a 'Relation Module' which can simply add-on to any face recognition models. The local features extracted from face image contain information of each component of the face. Based on two different domain characteristics, to use the relationships between local features is more domain-invariant than to use it as it is. In addition to these relationships, positional information such as distance from lips to chin or eye to eye, also provides domain-invariant information. In our Relation Module, Relation Layer implicitly captures relationships, and Coordinates Layer models the positional information. Also, our proposed Triplet loss with conditional margin reduces intra-class variation in training, and resulting in additional performance improvements.Different from the general face recognition models, our add-on module does not need to pre-train with the large scale dataset. The proposed module fine-tuned only with CASIA NIR-VIS 2.0 database. With the proposed module, we achieve 14.81% rank-1 accuracy and 15.47% verification rate of 0.1% FAR improvements compare to two baseline models.
Face recognition method based on sparse representation and feature fusion

In order to improve the accuracy of face recognition, a face recognition algorithm based on sparse representation and feature fusion is proposed. Firstly, the training samples and test samples are pre-processed by gray image conversion, scale scaling, histogram equalization, smooth filtering and so on, and the LBP, Gabor and HOG features of face images are extracted. And then the RSC classification test is carried on the partial samples. A loss function is defined according to the recognition result and the classification residual, then the weight vector is obtained by using the regularized least square method to minimize the loss function. Finally, the final residual is calculated according to the weight vector so as to obtain the final classification result. The experimental results on AR face dataset and LFW face dataset show that the recognition rate of our algorithm is obviously higher than the single feature recognition method, and it is robust to illumination, occlusion and expression.
Deep Learning based Moving Object Detection for Video Surveillance

This paper proposes a new two-stream neural network which combines the traditional background modeling method with a deep learning network to detect moving objects. The input for the deep neural network is the original image and its corresponding foreground image, while the output is the bounding boxes of the moving objects in the image. Traditional CNN methods cannot distinguish moving objects from static objects, but the method in this paper successfully solves this problem.
Multi-Stage Residual Hiding for Image-Into-Audio Steganography

The widespread application of audio communication technologies has speeded up audio data flowing across the Internet, which made it a popular carrier for covert communication. In this paper, we present a cross-modal steganography method for hiding image content into audio carriers while preserving the perceptual fidelity of the cover audio. In our framework, two multi-stage networks are designed: the first network encodes the decreasing multilevel residual errors inside different audio subsequences with the corresponding stage sub-networks, while the second network decodes the residual errors from the modified carrier with the corresponding stage sub-networks to produce the final revealed results. The multi-stage design of proposed framework not only make the controlling of payload capacity more flexible, but also make hiding easier because of the gradual sparse characteristic of residual errors. Qualitative experiments suggest that modifications to the carrier are unnoticeable by human listeners and that the decoded images are highly intelligible.
A Deep Learning based Approach for Precise Video Tagging

With the increase in smart devices and abundance of video contents, efficient techniques for the indexing, analysis and retrieval of videos are becoming more and more desirable. Improved indexing and automated analysis of millions of videos could be accomplished by getting videos tagged automatically. A lot of existing methods fail to precisely tag videos because of their lack of ability to capture the video context. The context in a video represents the interactions of objects in a scene and their overall meaning. In this work, we propose a novel approach that integrates the video scene ontology with CNN (Convolutional Neural Network) for improved video tagging. Our method captures the content of a video by extracting the information from individual key frames. The key frames are then fed to a CNN based deep learning model to train its parameters. The trained parameters are used to generate the most frequent tags. Highly frequent tags are used to summarize the input video. The proposed technique is benchmarked on the most widely used dataset of video activities, namely, UCF-101. Our method managed to achieve an overall accuracy of 99.8% with an F1- score of 96.2%.
Subpopulation initialization driven by linkage learning for dealing with the Long-Way-To-Stuck effect

The maintenance of many subpopulations is an important technique employed in evolutionary methods. However, the use of a multi-population approach has its drawbacks. Among all, it requires spending high amounts of available resources. Therefore, the methods that dynamically manage the number of subpopulations gain an increasing interest due to their capability of adjusting the subpopulation number to their current state. They are shown to be capable of reaching excellent results. In this paper, we identify the Long-Way-To-Stuck effect and show it on the base of the practical, NP-complete problem. Such a phenomenon occurs when a randomly initialized population of an evolutionary method must be processed through many iterations before it is incapable of improving the best-found solution. If so, then a large amount of computational resources must be spent on subpopulation initialization, which may turn multi-population methods ineffective. Therefore, in this paper, we propose the Linkage Learning-Driven Subpopulation Initialization (LLDSI) that limits the costs of subpopulation initialization and significantly improves the effectiveness methods dynamically managing the subpopulation number.
Investigating Expectations for Voice-based and Conversational Argument Search on the Web

Millions of arguments are shared on the web. Future information systems will be able to exploit this valuable knowledge source and to retrieve arguments relevant and convincing to our specific need---all with an interface as intuitive as asking your friend "Why ...". Although recent advancements in argument mining, conversational search, and voice recognition have put such systems within reach, many questions remain open, especially on the interface side. In this regard the paper at hand presents the first study of argument search behavior. We conduct an online-survey and a focused user study, putting emphasis on what people expect argument search to be like, rather than on what current first-generation systems provide. Our participants expected to use voice-based argument search mostly at home, but also together with others. Moreover, they expect such search systems to provide rich information on retrieved arguments, such as the source, supporting evidence, and background knowledge on entities or events mentioned. In observed interactions with a simulated system we found that the participants adapted their search behavior to different types of tasks, and that up-front categorization of the retrieved arguments is perceived as helpful if this is short. Our findings are directly applicable to the design of argument search systems, not only voice-based ones.
Codebooks of Complex Lines Based on Binary Subspace Chirps

Motivated by problems in machine-type wireless communications, we consider codebooks of complex Grassmannian lines in N = 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> dimensions. Binary Chirp (BC) codebooks of prior art are expanded to codebooks of Binary Subspace Chirps (BSSCs), where there is a binary chirp in a subset of the dimensions, while in the remaining dimensions there is a zero. BSSC codebooks have the same minimum distance as BC codebooks, while the cardinality is asymptotically 2.38 times larger. We discuss how BC codebooks can be understood in terms of a subset of the binary symplectic group Sp(2m, 2) in 2m dimensions; Sp(2m, 2) is isomorphic to a quotient group of the Clifford group acting on the codewords in N dimensions. The Bruhat decomposition of Sp(2m, 2) can be described in terms of binary subspaces in m dimensions, with ranks ranging from r = 0 to r = m. We provide a unique parameterization of the decomposition. The BCs arise directly from the full-rank part of the decomposition, while BSSCs are a group code arising from the action of the full group with generic r. The rank of the binary subspace is directly related to the number of zeros (sparsity) in the BSSC. We develop a reconstruction algorithm that finds the correct codeword with O(N log <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> N) complexity, and present performance results in an additive white Gaussian noise scenario.