Reviewerly/exHarmony huggingface dataset:
Moral Standards in the Psychological Structure of the Personality of Students of Higher Education Institutions

In this article's scientific space, one can observe the differentiation of the definition of the terms "value", "value orientations" because it does not yet have a clear standard definition. Many researchers have dealt with this topic, researched, analyzed, observed, and made conclusions. However, there is still a rich scope for research of such phenomena of personal structure as value orientations. Outstanding psychologists-researchers who, in their scientific, practical, and theoretical works, dealt with the topic of values and value orientations and came to the general conclusion that values are a structural component of a personality, with the help of which a person achieves a goal, sets this goal, and characterizes position in life. The article saw the relationship between values and the basic structures of the personality, including value orientations—considered in values a system of orientation and personality attitudes.
Facial Expression Recognition System Using Different Methods

Facial expressions are one of the nonverbal channels through which the emotional state of a human being can be known. These convey the emotional state of an individual to others. There are four basic emotions—happy, sad, surprise and neutral. Facial expressions are identified using various parameters, e.g., distance measured when eyebrows are raised gives us a particular facial expression. Likewise, thickness of lower lip, upper lip, width of mouth, etc., give us different types of facial expressions. The proposed system identifies these expressions using convolutional neural network (CNN) and facial action coding system (FACS) with AdaBoost method. The proposed work has used FER2013 data set and also has created own data set consisting of 80 images each of happy and sad expressions [].
Exploring the diversity and invariance in yourself for visual pre-training task

Recently, self-supervised learning methods have achieved remarkable success in the visual pre-training task. By simply pulling the different augmented views of each image together or other novel mechanisms, they can learn much unsupervised knowledge and significantly improve the transfer performance of pre-training models. However, these works still cannot avoid the representation collapse problem, i.e., they only focus on limited regions or the extracted features on totally different regions inside each image are nearly the same. Generally, this problem makes the pre-training models cannot sufficiently describe the multi-grained information inside images, which further limits the upper bound of their transfer performance. To alleviate this issue, this paper introduces a simple but effective mechanism, called Exploring the Diversity and Invariance in Yourself (E-DIY). By simply pushing the most different regions inside each augmented view away, E-DIY can preserve the diversity of extracted region-level features. By pulling the most similar regions from different augmented views of the same image together, E-DIY can ensure the robustness of extracted region-level features. Benefiting from the above diversity and invariance exploring mechanism, E-DIY better extracts the multi-grained visual information inside each image compared with previous self-supervised learning approaches. Extensive experiments on various downstream tasks have demonstrated the superiority of our method, e.g., there is 1.9% improvement (compared with the recent state-of-the-art method, BYOL) on AP50 metric of detection task on VOC.
PoshakNet: Framework for Matching Dresses from Real-Life Photos Using GAN and Siamese Network

Online garment shopping has gained many customers in recent years. Describing a dress using keywords does not always yield the proper results, which in turn leads to dissatisfaction of customers. A visual search based system will be enormously beneficent to the industry. Hence, we propose a framework that can retrieve similar clothes that can be found in an image. The first task is to extract the garment from the input image (street photo). There are various challenges for that, including pose, illumination, and background clutter. We use a Generative Adversarial Network for the task of retrieving the garment that the person in the image was wearing. It has been shown that GAN can retrieve the garment very efficiently despite the challenges of street photos. Finally, a siamese based matching system takes the retrieved cloth image and matches it with the clothes in the dataset, giving us the top k matches. We take a pre-trained inception-ResNet v1 module as a siamese network (trained using triplet loss for face detection) and fine-tune it on the shopping dataset using center loss. The dataset has been collected inhouse. For training the GAN, we use the LookBook dataset [14], which is publically available.
The LRA Workbench: an IDE for efficient REST API composition through linked metadata

Abstract The number of Web APIs for accessing information and services is continuously increasing, and yet, no tools exist to automate the time-consuming and error-prone process of invoking those APIs and composing their responses. The recent emergence of widely-adopted, standardized, Web-API description formats and the development of Linked Data technologies for data integration have motivated our work on the LRA (Linked REST APIs) methodology [1, 2]. LRA relies on RDF service specifications to automate the development process around the usage of Web APIs. This automation represents a great opportunity to systematize and improve the quality of service-oriented application development. However, LRA’s reliance on SPARQL as the user-interaction model may hinder its adoption, because it requires developers to learn the intricacies of the unconventional graph data model and its associated datasets. In this paper we have developed the LRA Workbench ( $$LRA_{Wbench}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>L</mml:mi> <mml:mi>R</mml:mi> <mml:msub> <mml:mi>A</mml:mi> <mml:mrow> <mml:mi>Wbench</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> ), which takes advantage of the emergent schema of Web-API specifications, in order to simplify the formulation of LRA-compliant SPARQL queries. Our empirical evaluation of the $$LRA_{Wbench}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>L</mml:mi> <mml:mi>R</mml:mi> <mml:msub> <mml:mi>A</mml:mi> <mml:mrow> <mml:mi>Wbench</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> usability demonstrates that our tool significantly improves the performance of developers formulating SPARQL queries for LRA. A subsequent study on the effectiveness of the $$LRA_{Wbench}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>L</mml:mi> <mml:mi>R</mml:mi> <mml:msub> <mml:mi>A</mml:mi> <mml:mrow> <mml:mi>Wbench</mml:mi> </mml:mrow> </mml:msub> </mml:mrow> </mml:math> demonstrated that developers using LRA tend to produce code with considerable better structural complexity, in less time, than developers manually composing APIs.
Learning Invariance in Deep Neural Networks

One of the long-standing difficulties in machine learning involves distortions present in data – different input feature vectors may represent the same entity. This observation has led to the introduction of invariant machine learning methods, for example techniques that ignore shifts, rotations, or light and pose changes in images. These approaches typically utilize pre-defined invariant features or invariant kernels, and require the designer to analyze what type of distortions are to be expected. While specifying possible sources of variance is straightforward for images, it is more difficult in other domains. Here, we focus on learning an invariant representation from data, without any information of what the distortions present in the data, only based on information whether any two samples are distorted variants of the same entity, or not. In principle, standard neural network architectures should be able to learn the invariance from data, given sufficient numbers of examples of it. We report that, somewhat surprisingly, learning to approximate even a simple types of invariant representation is difficult. We then propose a new type of layer, with a richer output representation, one that is better suited for learning invariances from data.
Design of optoelectronic computing circuits with VCSEL-SA based neuromorphic photonic spiking

A detailed theoretical analysis of spiking dynamics of VCSEL-SA based Photonic Laser Neuron (PLN) has been presented, under continuous and pulsed current injection. The maximum output spiking frequency of laser neuron is 9 GHz on continuous input current injection. Optimization of the amplitude and width tuned with threshold has been carried out that are crucial input parameters to control the behavior of PLN. Based on the results, neuromorphic photonic OR, AND, NOT, XOR and the universal NAND, NOR logic gates have been designed using a single PLN. Higher computing circuits such as half-adder, half-subtractor, full-adder and full-subtractor have also been designed that highlight the potential of brain-inspired scalable ultrafast neuromorphic photonic computing circuits for a wide range of applications.
The Practical Confusions and Countermeasures of the Individualized Teaching of University Teachers

Individualized teaching meets the individual needs of students, conforms to the social trend of cultivating innovative talents, and is the inevitable way for the professional development of university teachers and the generation of students' personality. As the primary practitioners of personalized teaching in universities, university teachers face many obstacles before generating individual teaching systems. Aiming at the realistic dilemma faced by university teachers’ personalized teaching, try to solve the following paths: create a relaxed teaching environment to make up for the vacancies in the teaching system; focus on self-improvement and break the impression of traditional university teachers’ roles; insist on teaching reflection to form an individual teaching system for university teachers .
Detection of Deep Video Frame Interpolation via Learning Dual-Stream Fusion CNN in the Compression Domain

Deep learning-based Video Frame Interpolation (Deep VFI) diminishes the visual traces of the conventional one such that it is challenging for the current VFI detectors. Therefore, it is necessary to identify the presence of deep interpolated frames (DIF) in a video. This paper proposed a hybrid neural network to localize the DIF by learning spatio-temporal representations from the residual and motion vector information in the compression domain. Firstly, the residual and motion vector of motion regions are maintained by an intra-prediction constraints. Then, inherent tampering traces are further highlighted through subtracting the estimate of the residual or motion vector by virtue of residual modulation or MV refinement network. Finally, an attention-based dual-stream network is designed to jointly learn discriminative representations from the enhancement traces. Deep VFI video datasets created by the state-of-the-art deep VFI methods, have been evaluated, and extensive experimental results clearly demonstrate that our approach can achieve state-of-the-art performance compared with conventional methods.
The Visualization Analysis of Internet of Things Research in China Based on the Bibliometrics Method

Abstract: The Internet of Things (IoT) has become the central issue and research focus of the global academic community and industry. Because of the lack of research in the field of IoT and in order to understand the development direction of the IoT in the future. This paper uses the CSSCI in CNKI (China National Knowledge Infrastructure) database as a data source, using the software such as VOSviewer, Citespace, Bicomb, Ucinet, and SPSS to get statistical visualization, co-occurrence analysis, cluster analysis, social network analysis, and multidimensional scale analysis. We find that research in the field of IoT is rich and well-defined. But there is a lack of effective communication and cooperation among researchers. It's remarkable to promote communication among scholars in the field of IoT and provide research directions for researchers.
Refining Skip Connections by Fusing Multi-scaled Context in Neural Network for Cardiac MR Image Segmentation

Applying convolutional neural network (CNN) for medical image segmentation has been well known for several years. The classical method normally is based on encoder-decoder architecture. The main drawback of encoder-decoder architecture is the long-range feature dependencies that are not preserved when model goes deeper and deeper. One way to overcome this problem is using complementary layers, called skip layers, from contracting path. However, the use of only skip layers that have the similar shape in contracting path seems to be insufficient and inefficient. Therefore, some skip methods have been released to boost the performance such as Unet++, Mask R-CNN++, … In this study, we concentrate on improving skip layer method by applying attention mechanism and multi-scaled context fusion. This approach is able to associate the local features with global dependencies, weighting information between layers, so reducing unnecessary and noisy information, and simultaneously illuminate important features for segmentation. We evaluate our proposed method on 2017 ACDAC dataset. The results show that our model achieves the remarkable performance in term of Dice coefficient and Jaccard index. This demonstrates the efficiency of our approach to precisely segment the target regions in medical images.
Vehicular Networks Applications Based on Blockchain Framework

In recent years, the number of autonomous smart vehicles deployments has continued to increase. In the vehicular ad hoc networks (VANET), driver’s life depends on the efficiency of the vehicles communication. There are several reality, security, privacy and mutual trust concerns in the current VANETs architecture. Since inaccurate and untrusted information could be sent by malicious vehicles, legitimate vehicles cannot distribute sensitive information accurately in real time. Also, the dissemination of critical event messages in a targeted area under a dynamic VANET characteristic remains a challenge. A popular distributed ledger technology, Blockchain, could be a solution to eliminate these vulnerabilities. Blockchain technology is now spreading rapidly across all areas of IoT technology, as well as providing a transparent, scalable, secure and privacy-friendly environment. Blockchain is a distributed, decentralized network and digital public ledger used to store the transaction or services that is performed through the linked nodes. This is why building the VANETs over the Blockchain technology is under research at the moment. The objective of this paper is to illustrate the potential work in the field of Blockchain-based vehicle networks by addressing the Blockchain technology integration with vehicle networks. Also, present and compare the existing research that works to overcome the VANETs challenges and satisfy the vehicular network demands.
Testing the Program Determining the Impact of Teaching Methodology on a Student’s Formation as a Subject of Educational Activity

The article deals with the ability of students to self-realization and self-development in the process of learning activity which is a condition for the intensification of university education. Attention is drawn to the indispensable condition comprising the priority of a holistic, socially mature, highly intellectual personality and creative self-expression. The developed program reflects the logic of the teacher's activities in the formation of a student's personal qualities [participating in subject-subject relations; participating in a joint, discursive practice on the part of all participants, which leads to the active transformation of the students' "Image of the World"; immersion in another culture and another language as a means of transmitting information and an instrument of thinking; the allocation of the educational task and its transformation into an independent goal of activity; development of self-control and self-esteem skills; definition of life strategies]. This program is intended by the authors for estimating the accomplished results with the use of any textbook and course in any subject on condition of changing test materials for such a factor attribute as the quality of knowledge in the subject.
How to Quantify the Security Level of Embedded Systems? A Taxonomy of Security Metrics

Embedded Systems (ES) development has been historically focused on functionality rather than security, and today it still applies in many sectors and applications. However, there is an increasing number of security threats over ES, and a successful attack could have economical, physical or even human consequences, since many of them are used to control critical applications. A standardized and general accepted security testing framework is needed to provide guidance, common reporting forms and the possibility to compare the results along the time. This can be achieved by introducing security metrics into the evaluation or assessment process. If carefully designed and chosen, metrics could provide a quantitative, repeatable and reproducible value that would reflect the level of security protection of the ES. This paper analyzes the features that a good security metric should exhibit, introduces a taxonomy for classifying them, and finally, it carries out a literature survey on security metrics for the security evaluation of ES. In this review, more than 500 metrics were collected and analyzed. Then, they were reduced to 169 metrics that have the potential to be applied to ES security evaluation. As expected, the 77.5% of them is related exclusively to software, and only the 0.6% of them addresses exclusively hardware security. This work aims to lay the foundations for constructing a security evaluation methodology that uses metrics so as to quantify the security level of an ES.
A Data-Driven Storage Assignment Strategy for Automated Pharmacy

Automated pharmacy (AP) with random storage location assignment (RSLA) strategy has been widely applied in large hospitals and retail pharmacies. In this paper, an integrated optimization problem of storage location assignment (SLA) and robot arm path planning (RAPP) is considered. For the trade-offs, a Hungarian method (HM)-based storage location assignment (HMSLA) method is proposed for further improving the operation efficiency of AP. Two phases are involved in the proposed method. At phase one, AP is divided into four areas based on BP neural network, and then medicine storage area and specific location are determined through data mining of drug delivery frequency and common combinations. At phase two, HM is applied for optimal scheduling of dispensing multiple medicines. Numerical studies show the proposed method outperforms significantly the traditional RSLA strategy.
Answer Graph-based Interactive Attention Network for Question Answering over Knowledge Base

Question answering over knowledge base is one of the promising tasks to access knowledge in knowledge bases. Existing information retrieval based methods mainly map candidate answer into a vector space by aggregating different answer aspects (i.e., entity types, relation paths and context). However, these answer aspects are simply embedded uniformly, while neglecting both question-related and candidate-related answer aspects are dominant to find the optimal answer. To address the above issue, we propose a novel Answer Graph-based Interactive Attention Network, which explicitly constructs an answer graph for each candidate answer. The answer graph consists of most of the possible answer aspects, and we selectively find partial answer aspects that are most relevant to the question using Gated Graph Neural Networks. With the guidance of both question-related and candidate-related answer aspects, the optimal candidate answer can greatly approximate the question, and hence can answer the question more accurately. We conduct extensive experiments on the WebQuestions dataset. Results demonstrate that our approach outperforms the previous state-of-the-art methods.
Digital humanities and web archives: Possible new paths for combining datasets

This article discusses the importance of web archives making their collections available as data and not only as sources seen through the Wayback Machine’s interface where only individual web pages are displayed. This will help unlock the full potential of the treasure trove that web archives constitute, and thereby also open up for methods from the wider field of digital humanities. Based on a case study of the entire Danish web domain .dk the article discusses methodological challenges involved in combining large extracted datasets from web archives, namely metadata about the size of websites and data about hyperlinks from the same websites. The aim is to answer the following two questions: 1) How to combine two different types of datasets extracted from a web archive, in this case the Danish Netarkivet? 2) What can the result of such a combination teach us about the structural characteristics of the Danish web domain from 2006 to 2015? The article shows that, indeed, it is possible to go beyond the Wayback Machine as the prime interface to web archives by combining two distinct datasets, and that such a venture can provide valuable knowledge about the overall structure of the Danish web domain, thus highlighting that websites of the same size tend to constitute isolated ‘link islands’, and that big websites are also the most important in the hyperlink network, which is more clearly the case in 2015 than in 2006.
Collision-aware formation assignment of quadrotors

Formation flight of multiple unmanned aerial vehicles (UAV) is in an active development and spreads more widely in many areas of human activity nowadays. One of the basic problems in their application is a formation assignment and formation control. Many authors work in the direction of coordination control and multiple UAV path planning, discarding problems of collision avoidance when following the trajectories to the assigned target positions in dense formations. A novel formulation of the collision-aware formation assignment problem and a centralized turn-based algorithm to solve the problem of realizability of assignment and provide the optimal solution by several criteria is proposed in this paper. Besides the formation assignment solution, the priority of movement to avoid deadlocks is obtained. The approach consists of three stages: 1) obtaining of solution of assignment problem; 2) validation of realizability of optimal solution with simultaneous determination of acceptable turn order; 3) in case of unrealizability the next optimal solution is being validated. The proposed algorithm is validated with simulation results, which confirm the possibility of real time application.
A Sentiment-Based Recommender System Framework for Social Media Big Data Using Open-Source Tech Stack

The large volume of data getting generated on the internet has become an opportunity for data analysts to retrieve information and perform decision-making. However, the extraction of relevant information has turned out to be an extremely difficult task, especially when dealing with big data sources like social media. Intelligent approaches like recommendation systems came into existence to deal with such situations. These systems require the capability to deal with various big data sources like user-generated content—reviews, comments, ratings, likes, etc. This work proposes a four-stage recommender system architecture for big data processing using open-source technology stack. The key objective of our proposed architecture is to ensure an efficient and robust recommendation system with excellent efficiency.