Reviewerly/exHarmony huggingface dataset:
Cross-Resolution Deep Features Based Image Search

Deep Learning models proved to be able to generate highly discriminative image descriptors, named deep features, suitable for similarity search tasks such as Person Re-Identification and Image Retrieval. Typically, these models are trained by employing high-resolution datasets, therefore reducing the reliability of the produced representations when low-resolution images are involved. The similarity search task becomes even more challenging in the cross-resolution scenarios, i.e., when a low-resolution query image has to be matched against a database containing descriptors generated from images at different, and usually high, resolutions. To solve this issue, we proposed a deep learning-based approach by which we empowered a ResNet-like architecture to generate resolution-robust deep features. Once trained, our models were able to generate image descriptors less brittle to resolution variations, thus being useful to fulfill a similarity search task in cross-resolution scenarios. To asses their performance, we used synthetic as well as natural low-resolution images. An immediate advantage of our approach is that there is no need for Super-Resolution techniques, thus avoiding the need to synthesize queries at higher resolutions.
Workload Optimization and Energy Consumption Reduction Strategy of Private Cloud in Manufacturing Industry

The private cloud of manufacturing industry whose business forms are rich and business chains long has numerous legacy systems. According to the analysis of the historical monitoring data of a private cloud system in manufacturing industry and interviews with people concerned, it is found in this paper that the private cloud in manufacturing industry could not only cope with the problems of limited capacity but also the issues of low resource utilization and excessive energy consumption in data centers caused by resource provisioning modes and characteristics of user psychology and behavior. Based on the creation of configuration management database (CMDB) and monitoring system, and combined with the features of private cloud resources provisioning, the paper tried to propose different strategies to optimize the private cloud for the platform administrator and resource users. Moreover, through optimization, sound economic and social values, such as workload improvement, energy consumption reduction, and longer expansion cycle for the private cloud platform, could be obtained.
Effects of Motion Picture Frame Rate on Material and Texture Appearance

High frame rate film should decrease motion blur and temporal aliasing allowing viewers to see more detail. Here we assessed how frame rate affected perception of detail in fabrics and costume ornamentation in three experiments. Live action footage with a fashion show theme was captured at all combinations of two resolutions (2k and 4k), three frame rates (24, 48 and 60 fps), and two shutter angles (180° and 358°). In the first experiment, participants rated the sharpness of a clip of a moving garment relative to a stationary clip. In the second experiment, participants rated (1) image sharpness and (2) quality of motion of 20-s sequences of costumed actors walking on a catwalk. In the last experiment, observers viewed pairs of image sequences and made direct pairwise comparisons while attending to the quality of motion, realism and detail in the garments. As expected, fabric detail became noticeably less distinct when in motion. Motion quality and image sharpness ratings improved with increasing frame rate, especially from 24 to 48 fps. Sharpness ratings were higher for 180° than for 358° shutter angle but the effect was small except at the lowest frame rate. Given the relatively weak effects of shutter angle, we conclude that aliasing and judder were stronger determinants of perceived detail than motion blur. Our results show that naïve observers perceive enhanced detail in HFR film sequences of moving fabrics. We argue that this improved perception of detail could underlie both the positive and negative reactions to HFR film, depending on the nature of the content and whether it lends itself to such high fidelity.
Modeling and visualizing two‐way contingency tables using compositional data analysis: A case‐study on individual self‐prediction of migraine days

Two‐way contingency tables arise in many fields, such as in medical studies, where the relation between two discrete random variables or responses is to be assessed. We propose to analyze and visualize a sample of 2 × 2 tables in the context of single‐subject repeated measurements design by means of compositional data (CoDa) methods. First, we propose to visualize the tables in a quaternary diagram. Second, we show how to represent these tables by means of logratios indicating the relationship between the two variables as well as their strength and direction of dependency. Finally, we describe a technique to model those tables with a simplicial regression model. Data from a real‐world study of self‐prediction of migraine attack onset is used to illustrate this methodology. For each individual, the 2 × 2 table of their migraine expectation vs next day migraine occurrence is computed, generating a sample of tables. Then we visualize and interpret the prediction ability of individuals both in the simplex and in terms of logratios of components. Finally, we model the self‐prediction ability with respect to demographic variables, days tracked and disease characteristics. Our application demonstrates that CoDa can be a useful tool for visualizing, modeling, and interpreting the components of 2 × 2 tables.
Decoupled Variational Embedding for Signed Directed Networks

Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification, and node recommendation. The challenge lies in how to adequately encode the complex topological information of the networks. Recent studies mainly focus on preserving the first-order network topology that indicates the closeness relationships of nodes. However, these methods generally fail to capture the high-order topology that indicates the local structures of nodes and serves as an essential characteristic of the network topology. In addition, for the first-order topology, the additional value of non-existent links is largely ignored. In this article, we propose to learn more representative node embeddings by simultaneously capturing the first-order and high-order topology in signed directed networks. In particular, we reformulate the representation learning problem on signed directed networks from a variational auto-encoding perspective and further develop a decoupled variational embedding (DVE) method. DVE leverages a specially designed auto-encoder structure to capture both the first-order and high-order topology of signed directed networks, and thus learns more representative node embeddings. Extensive experiments are conducted on three widely used real-world datasets. Comprehensive results on both link sign prediction and node recommendation task demonstrate the effectiveness of DVE. Qualitative results and analysis are also given to provide a better understanding of DVE.
Identifying Usability Issues of Software Analytics Applications in Immersive Augmented Reality

Software analytics in augmented reality (AR) is said to have great potential. One reason why this potential is not yet fully exploited may be usability problems of the AR user interfaces. We present an iterative and qualitative usability evaluation with 15 subjects of a state-of-the-art application for software analytics in AR. We could identify and resolve numerous usability issues. Most of them were caused by applying conventional user interface elements, such as dialog windows, buttons, and scrollbars. The used city visualization, however, did not cause any usability issues. Therefore, we argue that future work should focus on making conventional user interface elements in AR obsolete by integrating their functionality into the immersive visualization.
Molecular Data Visualization with Augmented Reality (AR) on Mobile Devices

Augmented reality (AR) allows a computer-generated 3D model to be superimposed onto a real-world environment in real time. The model can then be manipulated or probed interactively as if it is part of the real world. The application of AR in visualizing macromolecular structures is growing, primarily in showing preset collections of scenes for education purpose. Here, our emphasis is, however, on exploiting AR as a tool to facilitate scientific communication on the go. We have searched for freely available mobile software and custom-built tools which allow the display of user-specified protein structures. We provide step-by-step guides on a standalone app Ollomol (iOS and Android), as well as an in-browser web app, WebAR-PDB. Both of them allow users to specify entries from the Protein Data Bank (PDB) for an elementary AR experience. The application of AR enhances interactivity and imaginativity in macromolecular visualization.
Cache-in-the-Middle (CITM) Attacks: Manipulating Sensitive Data in Isolated Execution Environments

The traditional usage of ARM TrustZone has difficulty on solving the conflicts between the manufacturers that want to minimize the trusted computing base by constraining the installation of third-party applications in the secure world and the third-party application developers who prefer to have the freedom of installing their applications into the secure world. To address this issue, researchers propose to create Isolated Execution Environments (called IEEs) in the normal world to protect the security-sensitive applications. In this paper, we perform a systematic study on the IEE data protection models and the ARM cache attributes, and discover three cache-based attacks called CITM that can be leveraged to manipulate the sensitive data protected in IEEs. Specifically, due to the inefficient and incoherent security measures on the cache that maps to the IEE memory (i.e., memory designated for IEEs), attackers in the normal world may compromise the security of IEE data by manipulating the IEE memory during concurrent execution, bypassing the security measures enforced when a security-sensitive application is suspended or finished, or misusing the incomplete security measures during IEE's context switching processes. We conduct case studies of CITM attacks on three well-known IEE systems including SANCTUARY, Ginseng, and TrustICE to illustrate the feasibility to exploit them on real hardware testbeds. Finally, we analyze the root causes of the CITM attacks and propose a countermeasure to defeat them. The experimental results show that our defense scheme has a small overhead.
Multiobjects Association and Abnormal Behavior Detection for Massive Data Analysis in Multisensor Monitoring Network

With the rapid increase in the number of large-scale distributed cameras and the rapid increase in the monitoring range of the camera network, how to accurately recognize and analyze abnormal behavior is still a challenging problem. In addition, the appearance of moving objects between different cameras without overlapping fields of view undergoes significant changes, making it difficult to obtain accurate association Therefore, multiobjects association and abnormal behavior detection for massive data analysis in multisensor monitoring network are proposed in this paper, which firstly uses belief propagation to associate multiple objects, extracts the object’s behavior trajectory characteristics, and then builds a long short-term memory classification network to realize automatic classification of abnormal behaviors. Multiobject association fully considers the timing correlation and object detection probability, as well as the statistical dependence of the measurement on the association matrix. The experimental results show that our proposed method can achieve a high classification accuracy and sensitivity, which meets the requirements of automatic classification of abnormal behavior in complex monitoring network. This further shows that this research has practical application value.
Adaptively Secure Threshold Symmetric-Key Encryption

In a threshold symmetric-key encryption ( $$\text {TSE}$$ ) scheme, encryption/decryption is performed by interacting with any threshold number of parties who hold parts of the secret-keys. Security holds as long as the number of corrupt (possibly colluding) parties stay below the threshold. Recently, Agrawal et al. [CCS 2018] (alternatively called DiSE) initiated the study of $$\text {TSE}$$ . They proposed a generic $$\text {TSE}$$ construction based on any distributed pseudorandom function (DPRF). Instantiating with DPRF constructions by Naor, Pinkas and Reingold [Eurocrypt 1999] (also called NPR) they obtained several efficient TSE schemes with various merits. However, their security models and corresponding analyses consider only static (and malicious) corruption, in that the adversary fixes the set of corrupt parties in the beginning of the execution before acquiring any information (except the public parameters) and is not allowed to change that later. In this work we augment the DiSE $$\text {TSE}$$ definitions to the fully adaptive (and malicious) setting, in that the adversary is allowed to corrupt parties dynamically at any time during the execution. The adversary may choose to corrupt a party depending on the information acquired thus far, as long as the total number of corrupt parties stays below the threshold. We also augment DiSE's DPRF definitions to support adaptive corruption. We show that their generic $$\text {TSE}$$ construction, when plugged-in with an adaptive DPRF (satisfying our definition), meets our adaptive $$\text {TSE}$$ definitions. We provide an efficient instantiation of the adaptive DPRF, proven secure assuming decisional Diffie-Hellman assumption (DDH), in the random oracle model. Our construction borrows ideas from Naor, Pinkas and Reingold's [Eurocrypt 1999] statically secure DDH-based DPRF (used in DiSE) and Libert, Joye and Yung's [PODC 2014] adaptively secure threshold signature. Similar to DiSE, we also give an extension satisfying a strengthened adaptive DPRF definition, which in turn yields a stronger adaptive $$\text {TSE}$$ scheme. For that, we construct a simple and efficient adaptive NIZK protocol for proving a specific commit-and-prove style statement in the random oracle model assuming DDH.
Data Oriented Blockchain: Off-Chain Storage with Data Dedicated and Prunable Transactions

Blockchain is a rising technology mainly characterized by its decentralization, persistency, anonymity, and auditability. Due to these properties it is nowadays increasingly used in IoT and smart cities. However, the amount of generated data in these areas is large, and the size of the blockchain increases steadily as transactions and data are stored. To deal with this drawback, the blockchain technology must be adapted, in particular regarding its size. Indeed, after a few years of operation, Bitcoin and Ethereum blockchains already weigh more than 200 GB, which is impossible to manage for resource limited devices. Several solutions have been proposed, each with a different approach. In this paper, we try to overcome many of their limitations, by offering a solution that allows to greatly reduce the size of the blockchain by proposing an architecture specifically dedicated to the storage and manipulation of data, while allowing a free control and sharing of data by their owner.
Introducing a road interaction-focused approach towards autonomous driving cargo bikes as a new urban mobility offer

The project AuRa (autonomes Rad, engl. autonomous bicycle) is motivated by the real-world problem of availability and accessibility of bicycles in bike-sharing schemes in urban environments. Autonomous driving offers completely new possibilities to overcome it. With autonomous cargo-bikes as an efficient and ecological mobility solution, we can take a big step towards mobility transition. However, one of the major challenges they will be facing is the ability to interact with other road users as the most vulnerable - pedestrians. Yet, especially in terms of bikes, few studies investigated the timely topic of interaction concepts for autonomous vehicles. With our research in progress, we aim to contribute to research on the interaction of humans and autonomous cargo-bikes and gain findings helpings us design a communication interface for the autonomous bike. To this end, we designed a road interaction-focused approach involving three scientific sections: computer science, design engineering, and environmental psychology. This paper is introducing the overall interdisciplinary approach design including a series of studies for developing an interaction concept via visual and auditive signaling for the autonomous bike. It presents the current research state and some first findings of the research practice.
GreenLife: A Persuasive Social Robot to Enhance the Sustainable Behavior in shared Living Spaces

This research aims to gain understanding of interactive technology, which could persuade building users to behave sustainably inside shared living spaces. By utilizing the research through design approach, we conducted a three-phase qualitative study to explore persuasive technologies for sustainability. First, we ran three focus groups (n=14) to explore what form of interactive technology could persuade building users to behave sustainably, and to investigate their related needs. Second, based on the focus group findings, we designed an application concept called GreenLife for the social robot Pepper. Third, we evaluated the GreenLife concept represented by video scenarios using an online survey (n=20). Most participants believed that social robots can persuade building users to behave sustainably by providing social feedback, valuable rewards, and reliable information on sustainability. A list of design implications was formulated to support the future design of social robots as motivators of sustainability in shared living spaces.
Deep Learning Systems: Algorithms, Compilers, and Processors for Large-Scale Production

This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications.
Detecting Items with the Biggest Weight Based on Neural Network and Machine Learning Methods

The article considers the development of a program for predicting areas on the design of the graphical interface, where users’ attention will be greatest. Current methods of improving graphic design in accordance with UX and software for getting UX information are considered. In this project the problem in complexity of creation of the graphic user interface for modern programs, web resources, and applications is considered. Namely, the problem is related to UX design, with errors that occur at the stage of product launch. Also considered methods to help avoid errors, software, using which, will help avoid difficulties and errors. The concept of developing a program for recognizing areas to which the user is most likely to pay attention in the first place is considered. This program aims to help designers to create a modern graphical user interface design that will guide the user to the right places and the user will spend a minimum of time and effort to interact and use the program. A method has developed to collect data on the behavior of the user’s eyes when using the program, during user interaction with the program interface. This method will create a database on the basis of which the program to predict the most visible areas will return the result.
Comparison of Chinese Character Correct and Error Classifier for Overseas Students Based on Handwriting Motion Characteristics

Abstract The quality of Chinese characters written by foreign students is reflected not only in the result of writing, but also in the writing movement. 25 characteristics of handwriting movement were selected from 39 parameters, such as time, space, movement and pressure. Based on the feature of handwriting motion, 6 classifiers, such as Decision Tree and Artificial Neural Network, are selected to compare the performance of them. Experiments show that the accuracy of DT algorithm is the best regardless of whether the target characters are known or not. Handwriting movement features are effective representations of the quality of Chinese characters written by foreign students.
Trustworthy Cloud Computing

Abstract Trustworthy cloud computing has been a central tenet of the European Union cloud strategy for nearly a decade. This chapter discusses the origins of trustworthy computing and specifically how the goals of trustworthy computing—security and privacy, reliability, and business integrity—are represented in computer science research. We call for further inter- and multi-disciplinary research on trustworthy cloud computing that reflect a more holistic view of trust.
A Dual-Path Deep Neural Network for Sonar Image Quality Evaluation

Sonar technology plays an important role in the development of marine resources and military strategy. Due to the bad underwater acoustic channel, the sonar image collected by sonar technology equipment is affected by various kinds of distortions easily. To obtain high-quality sonar image, we devise a novel dual-path deep neural network (DPDNN) to measure the quality of sonar image. In these two paths, we use the batch normalization layer to reduce the training time and take the skip operation to speed up the feature extraction. Based on the above two operations, we extract the micro-scopic and macro-scopic structure of sonar image, respectively. Finally, the global average pooling layer and the fully connection layer are used to connect the above two paths. Experiments show that our DPDNN has a significant improvement in prediction performance and efficiency, respectively.
Designing an Interactive Multimedia Application for the Course “Communication Circuits”

The paper describes a multimedia interactive application elaborated in English and Bulgarian, that will be utilized for teaching the course “Communication Circuits” of Bachelors in the specialty “Internet and Mobile Communications” in the University of Ruse, and mostly for Erasmus students (in English) attending the course. With the help of the application based on varied patterns of exercises students will be able to learn the basic concepts and issues related to the subject, including resonant circuits, electrical filters and modulations. It is planned to be multilingual, incorporating English, Spanish, French, Russian and Bulgarian.