Reviewerly/exHarmony huggingface dataset:
Leveraging conditional generative models in a general explanation framework of classifier decisions

With the increase in use of machine learning classifiers in several fields, providing human- understandable explanation of their outputs has become an imperative. It is essential to generate trust for day-to-day tasks, especially in the sensible domains as medical imaging. Although many works have addressed this problem by generating visual explanation maps, they often provide noisy and inaccurate results forcing heuristic regularization unrelated to the classifier in question. In this paper, we propose a general perspective of the visual explanation problem overcoming these limitations. We show that visual explanation can be produced as the difference between two generated images obtained via two specific conditional generative models. Both generative models are trained using the classifier to explain and a database to enforce the following properties: (i) All images generated by the first generator are classified similarly to the input image, whereas the second generator's outputs are classified oppositely. (ii) All generated images belong to the distribution of real images. (iii) The distances between the input image and the corresponding generated images are minimal so that the difference between the generated elements only reveals relevant information for the studied classifier. Using symmetrical and cyclic constraints, we present two different approximations and implementations of the general formulation. Experimentally, we demonstrate significant improvements with respect to the state-of-the-art on three different public data sets. In particular, the localization of regions influencing the classifier is consistent with human annotations.
Distributed Density Peaks Clustering Revisited (Extended Abstract)

Density Peaks (DP) Clustering organizes data into clusters by finding peaks in dense regions. This involves computing density (ρ) and distance (δ) of every point, and the time complexity is O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) where N is the number of data points. In this paper, we propose a fast distributed density peaks clustering algorithm, FDDP, based on the z-value index. We also propose FC, an efficient algorithm that employs a forward computing strategy to calculate ρ linearly, and CB, which uses a caching and efficient searching strategy to compute δ. Our experimental results show that FDDP outperforms the state-of-the-art algorithms significantly.
Video Searching and Retrieval using Scene Classification in Multimedia Databases

Image and video indexing techniques play a key role in content-based searching in multimedia databases. A novel efficient video similarity search approach for content-based video retrieval in a large storage device is proposed. A compressed domain feature is used to classify the scenes of a video sequence. The performance of the said feature in classifying the scenes is evaluated by considering different feature lengths. Subsequently, a novel searching algorithm is proposed based on entropy of the image. This method incorporates temporal information of the video during video retrieval. Additionally, the proposed system has the facility to query both by video and by image. The 92.3% accuracy of the proposed method exceeded that of the VA-file 81.0% and OVA-file 76.4% methods and the computational complexity was lower than the VA-file method. This demonstrates the efficiency and effectiveness of the proposed method.
Data-Driven Personas: Expanding DHM for a Holistic Approach

In this paper, we detail research and development of data-driven personas in the IPS-IMMA digital human modelling system. Semi-automatically generating personas for working with user experience (UX) aspects of the Operator 4.0 side of Industry 4.0 is suggested as a viable approach for contributing to operator well-being and diversity by supporting workstation designers to take these factors into account early in the workstation design process. These data-driven personas are being developed to be generated using anthropometric data from the manikin family generation module of IPS-IMMA. Specific design suggestions are presented, what should be taken into account and how that will be implemented, and the current state of development of the data-driven personas module is discussed. Prototypes are planned under the coming year.
Cloud Computing Aided Model Design of Urban and Rural Planning and Design Based on OBE Results Oriented Theory

Hybrid teaching mode is an inevitable choice for colleges and universities to carry out teaching reform and cultivate applied talents under the background of education informatization . Based on the concept of “OBE”, this paper takes the training and development course of human resource management as an example, optimizes the hybrid teaching mode through preset learning results, reverse design and positive implementation, and makes a preliminary discussion on the optimization of the hybrid teaching mode.
Gooey Gut Trail :Demystifying Human Gut Health Through a Board Game

Human gut health is influenced by a range of factors; however, it is often perceived through a single lens-the food we eat. With the increase in auto-immune disorders, having a holistic understanding of gut health factors is imperative. In an attempt to extend this perception of gut health beyond just food to other major factors that influence it, we designed a board game, called Gooey Gut Trail (GGT). GGT aims to create awareness and enable self-reflection amongst players through the introduction of real-world human actions and interactions that influence gut health. The game involves the use of natural accessories of a physical dining context to enable a contextual understanding of gut health. In this pictorial, we present our design process involving the synthesis and presentation of scientific literature through play; offer a critical reflection on key design decisions and the challenges addressed; elaborate on the iterative process involved in the development of a domestic science-based research product and our future plans.
Research on Wireless Video Monitoring System Based on Hisilicon Platform

With the development of Internet of Things and artificial intelligence technology, people have higher requirements for the reliability and flexibility of video surveillance systems. In order to enhance the reliability of video surveillance systems and the flexibility during installation, we designed and implemented a wireless intelligent video surveillance system based on the Hisilicon platform. The system used the H.264 encoding protocol to compress and encode video streams. The encoded streams are transmitted in real time through the RTCP protocol. Wi-Fi is used as the network transmission. The H.264 streams are packed into MP4 format files for storage. On the platform, we implement the MOSSE algorithm to track and detect the moving objects in the video. Experiments show the usability of the platform.
Review on applications of quantum image processing

Quantum image processing and its various applications developed in the last few years. These applications utilize different quantum algorithms for higher accuracy and reduced time complexity compared to classical computing. This paper reviews some applications of quantum image processing like edge extraction, Security, and denoising of images. Applications like feature extraction, machine vision, and feature detection use edge extraction. In this paper, we observe the field of Security by dividing it into two sub-areas, i.e., cryptography and information hiding, used for authentication purposes. For removal of noise signals from input images, the method of denoising is used.
Computational Methods and Techniques for Case-Based Reasoning (CBR)

Case-based reasoning (CBR) is a computational model of problem solving. It uses a representation of specific episodes of problem solving to learn to solve a new problem. CBR uses the experience of past problem solving when solving a new problem. CBR systems store past experience as individual problem solving episodes. These two parts of case-based reasoning are modeling as a computational model. In this context, we propose to present a methodology allowing the performance of the CBR system. The methodology consists of designing experiments to control the variability in the behavior of the CBR system and obtaining empirical performance data.
Constructing a quasi-professional educational environment as a condition of future teachers’ professional and communicative competence formation

The article examines the conditions of constructing future teachers’ (currently B.A. students’) professional and communicative competence in the context of modernization of the present-day system of higher education, which stipulate the need to rethink the concept of future professionals’ competence and the transition from sector-specific professional competencies to meta-competencies of a wider scope associated with the peculiarities of professional and communicative communication. Following on from the theoretical and methodological analysis, it is concluded that the construction of a quasi-professional educational environment reconstituting the conditions of professional communication can be organized in the form of solving situational case problems using such technologies as subject-language integrated learning, blended learning visualization and podcasting. The methodological validity of the proposed scientific research results on constructing a quasi-professional educational environment to form future teachers’ professional and communicative competence has been confirmed by the results of their evaluation at the Humanitarian and Pedagogical Academy (branch) of Vernadsky Crimean Federal University (Yalta) and Krasnoyarsk State Pedagogical University named after V.P. Astafiev (Krasnoyarsk).
Data Processing and Development of Big Data System: A Survey

At present, data are generated all the time in the world, and these data contain great value. However, these data have problems such as huge scale, complex type, fast flow rate and low value density. Therefore, the research of big data analysis means and tools has increasingly become a research hotspot. On the basis of research at home and abroad, this paper attempts to analyze the definition, framework and typical big data processing systems of big data, especially focuses on the analysis and comparison of batch data processing system, stream data processing system, hybrid processing system and graph processing system, and obtains the characteristics, processing mechanism and applicable occasions of each system. The paper hopes to provide some references for understanding big data systems, solving problems in the process of big data processing and developing big data applications, and provide reference for improving the effectiveness and efficiency of data processing.
Android Based E-Module Development on Computer Learning and Basic Networks Class X TKJ

The existence of new subjects that are included in the 2013 curriculum in the computer and network engineering department makes teachers have to find teaching materials that can be used. Teachers still use modules in hardfile form which are less attractive so that they affect the teaching and learning process. The purpose of this study was to determine the development of android-based e-modules on computer and basic network subjects for class X TKJ SMK Ar-Roudhoh and the feasibility of developing android-based e-modules on computer and basic network subjects for class X TKJ SMK Ar-Roudhoh. The method used is the Research and Development method using the ADDIE development model. Data collection techniques are carried out by means of observation, questionnaires or questionnaires and instruments. The media validation test was carried out by a media expert lecturer from a PTI lecturer and one material expert teacher from a computer and basic network subject teacher. The instrument for assessing the feasibility of learning media in the form of a questionnaire was carried out by class XI TKJ students at SMK Ar-Roudhoh. The android-based interactive e-module has met the validity criteria by obtaining an average score from all aspects of the assessment, the media expert validator is 81.25% which is in the category worthy of use with revision, and 80% material expert validator in the category worthy of use with revision so that based on the judgment of the experts it is appropriate and suitable to be used as teaching material in Basic Computer and Networking subjects
Quantum-Correlated Noise Radar with Phase-Sensitive Amplification

A form of quantum-correlated noise radar that uses phase-sensitive amplification is analyzed. Both the transmitted field and the retained reference field are subject to amplification of a single field quadrature. The receiver uses homodyne detection to correlate the quadratures of the retained field and received radiation. This approach is intended to avoid issues in previous implementations of quantum noise radar such as a weak signal field or quantum noise associated with phase-insensitive amplification and heterodyne detection. The performance of quantum-correlated noise radar with phase-sensitive amplification is quantified in terms of the Bhattacharyya distance which sets a bound on the probability of detection errors in a symmetric detection scenario. The resulting bound is compared to some classical-state radar benchmarks. Despite making best-case-scenario assumptions for the quantum radar and ignoring practical difficulties with implementation, no advantage over classical noise radar with a bright retained field appears in the Bhattacharyya distance.
A Grid Partition-Based Local Outlier Factor by Reachability Distance for Data Stream Processing

In the field of big data, outlier detection is an important issue in many applications, such as communications, health, and network intrusion detection. Data streams present a challenge to the traditional outlier detection methods due to their unique characteristics, i.e., large volume and sequential structure. The Local Outlier Factor (LOF) is a popular algorithm used in anomaly detection to find outliers in a data stream. However, the LOF algorithm has a drawback. When a new point is added to the data stream, the algorithm needs to reprocess the measurement from the beginning of any change in the dataset. Additionally, LOF requires the whole dataset to be stored in memory. These issues were solved in the Grid Partition-based Local Outlier Factor (GP-LOF) algorithm. A new algorithm is introduced in this paper to improve the GP-LOF algorithm. It is based on the reachability distance measurement in LOF, and we name it Grid Partition-based Local Outlier Factor by Reachability distance (GP-LOFR) algorithm. GP-LOFR has slightly improved the accuracy rate of local outlier detection in our experiments with several real-world datasets.
Models Of Integration Of Information Systems In Higher Education Institutions

At present a lot of automated systems are developing and implementing to support the educational and research processes in the universities. Often these systems duplicate some functions, databases, and also there are problems of compatibility of these systems. The most common educational systems are systems for creating electronic libraries, access to scientific and educational information, a program for detecting plagiarism, testing knowledge, etc. In this article, models and solutions for the integration of such educational automated systems as the information library system (ILS) and the anti-plagiarism system are examined. Integration of systems is based on the compatibility of databases, if more precisely in the metadata of different information models. At the same time, Cloud technologies are used - data processing technology, in which computer resources are provided to the user of the integrated system as an online service. ILS creates e-library of graduation papers and dissertations on the main server. During the creation of the electronic catalog, the communication format MARC21 is used. The database development is distributed for each department. The subsystem of anti-plagiarism analyzes the full-text database for the similarity of texts (dissertations, diploma works and others). Also it identifies the percentage of coincidence, creates the table of statistical information on the coincidence of tests for each author and division, indicating similar fields. The integrated system was developed and tested at the Tashkent University of Information Technologies to work in the corporate mode of various departments (faculties, departments, TUIT branches).
Fair Visual Recognition in Limited Data Regime using Self-Supervision and Self-Distillation

Deep learning models generally learn the biases present in the training data. Researchers have proposed several approaches to mitigate such biases and make the model fair. Bias mitigation techniques assume that a sufficiently large number of training examples are present. However, we observe that if the training data is limited, then the effectiveness of bias mitigation methods is severely degraded. In this paper, we propose a novel approach to address this problem. Specifically, we adapt self-supervision and self-distillation to reduce the impact of biases on the model in this setting. Self-supervision and self-distillation are not used for bias mitigation. However, through this work, we demonstrate for the first time that these techniques are very effective in bias mitigation. We empirically show that our approach can significantly reduce the biases learned by the model. Further, we experimentally demonstrate that our approach is complementary to other bias mitigation strategies. Our approach significantly improves their performance and further reduces the model biases in the limited data regime. Specifically, on the L-CIFAR-10S skewed dataset, our approach significantly reduces the bias score of the baseline model by 78.22% and outperforms it in terms of accuracy by a significant absolute margin of 8.89%. It also significantly reduces the bias score for the state-of-the-art domain independent bias mitigation method by 59.26% and improves its performance by a significant absolute margin of 7.08%.
Antiderivative of gradient data by spline model integration

Numerous optical techniques describe the local slope of the functions at their discrete positions but do not report the actual functions. However, many applications require the description of the functions, which must be retrieved from the gradients by an integration process. This study shows a spline model function-based integration technique that can construct original functions from irregularly measured gradient data over general shape domains with high accuracy and speed.
Graphical-Based Authentication System and Its Applications

Authentication systems are the protective barrier of any software. They make sure that the right people enter the system and access the right information. As per available literature, there are basically three authentication techniques. This research study is based on the knowledge-based authentication, since it is the most widely accepted technique for securing resources from unauthorized access. As an initial step, the existing knowledge-based authentication system has been studied for its highlights, comparative facts, advantages, and disadvantages. For acceptance of any secure system, the usability aspect is the first step in the authentication process. The various usability evaluation parameters of the existing systems and an approach towards developing a modified usable authentication system have been briefly discussed. An initial lab study for the proposed system was conducted to analyze the comparative results. The future scope of other dimensions, namely randomness and security, as well as a thorough investigation, has been highlighted as the research work's concluding remark.
Show, don’t tell: Using Go-along Interviews in Immersive Virtual Reality

Go-along interviewing is an emerging qualitative research method where researcher and interviewee go together to a location relevant for the research. Usually employed in ethnographic studies, the method is used to provide a contextualized understanding of a participant’s experience. This paper explores performing Go-along interviews in Immersive Virtual Reality (VR). Through an analysis of ten interviews conducted inside our participants’ Virtual Mind Palaces we show how the interlocutors’ shared presence in the virtual environment established a common ground beneficial for communication. Being in VR enabled our participants to demonstrate interactions spontaneously, and, by providing a guided tour, show us relevant objects and locations in their Virtual Mind Palace. Benefits and challenges of adapting this method to VR are discussed and recommendations for researchers who want to conduct VR Go-along interviews are provided. Finally, we argue the method as an effective tool for eliciting contextual, phenomenological accounts of virtual environments.
A Modulation Recognition Algorithm for Analog and Digital Mixed Signals Based on Multidimensional Features

Aiming at the problem of mixed signal recognition, this paper proposes a modulation recognition algorithm based on multidimensional features of all digital receiver. Use the features of demodulated signal, such as constellation quadrant distribution, frequency tracking distribution distance and baseband data distribution distance, realized the recognition of A/D mixed signal in dense environment. Theoretical analysis and simulation results show that this algorithm has better performance than the widely used modulation recognition algorithm based on cyclic spectrum and higher-order spectrum. It has fewer requirements for prior information and synchronization, and it is easy to be implemented.
Efficient Multi-receiver Certificate-Based Proxy Re-encryption Scheme for Secure Cloud Data Sharing

Sharing data through clouds has never been more economical and easier than now. To guarantee the confidentiality of the data stored in the cloud storages, the data owners should encrypt their sensitive data before uploading them to the clouds. But, traditional encryption paradigm makes it difficult for flexibly sharing encrypted data between different users. The paradigm of proxy re-encryption (PRE), which can securely delegate the decryption right from one user to another, offers an effective solution to the encrypted data sharing in the clouds. To share the encrypted data with multiple users efficiently and securely, we extend certificate-based PRE into the multi-receiver setting and put forward the notion of multi-receiver certificate-based PRE (MR-CBPRE). By using MR-CBPRE, a data owner can securely distribute his encrypted data to a group of users though public clouds in an efficient manner. We first formalize the syntax and security definition of MR-CBPRE, and then design a concrete MR-CBPRE scheme. In the random oracle model, the proposed scheme is proven to be chosen-ciphertext secure. To demonstrate the merits of our scheme, we analyze its performance by comparing it with the previous certificate-based PRE schemes which consist of only single receiver. As far as we know, it is the first certificate-based PRE scheme in the multi-receiver setting to date.