Reviewerly/exHarmony huggingface dataset:
R2D2: A Dbpedia Chatbot Using Triple-Pattern Like Queries

Chatbots, also known as conversation agents, are programs that are able to simulate and reproduce an intelligent conversation with humans. Although this type of program is not new, the explosion of the available information and the rapid increase of the users seeking this information have renewed the interest in their development. In this paper, we present R2D2, an intelligent chatbot relying on semantic web technologies and offering an intelligent controlled natural language interface for accessing the information available in DBpedia. The chatbot accepts structured input, allowing users to enter triple-pattern like queries, which are answered by the underlying engine. While typing, an auto-complete service guides users on creating the triple patterns, suggesting resources available in the DBpedia. Based on user input (in the form of triple-pattern like queries), the corresponding SPARQL queries are automatically formulated. The queries are submitted to the corresponding DBpedia SPARQL endpoint, and then the result is received by R2D2 and augmented with maps and visuals and eventually presented to the user. The usability evaluation performed shows the advantages of our solution and its usefulness.
Research on GAN-based Image Super-Resolution Method

Super-Resolution (SR) refers to the reconstruction of high-resolution image from low-resolution image, which has important application value in object detection, medical imaging, satellite remote sensing and other fields. In recent years, with the rapid development of deep learning, the image super-resolution reconstruction method based on deep learning has made remarkable progress. In this paper, R-SRGAN (Residual Super-Resolution Generative Adversarial Networks) is used to build the model and realize image super-resolution. By adding residual blocks between adjacent convolutional layers of the GAN generator, more detailed information is retained. At the same time, the Wassertein distance is used as a loss function to enhance the training effect and achieve image super-resolution.
Day-Ahead Prediction of PV Generation Using Weather Forecast Data: a Case Study in the UK

With the ever-increasing capacity of PV installations, the requirement for accurate and computationally efficient day-ahead forecasts is becoming more evident, while the solution remains a real challenge. Different techniques are being employed to transform a combination of weather forecasts and historical measurements into PV generation predictions. In this paper, the weather forecast data, provided by the UK Met Office, and the historical measurements are used to construct three different prediction models, based on linear least square regression (LSR), artificial neural network (ANN), and fuzzy. All the models can learn from new available data while running the forecasts. The results of an almost one-year study show that the best permutation (for the under-study case) is achieved by averaging the forecasts from LSR and ANN.
Optimization of Watermarking in Image by Using Particle Swarm Optimization Algorithm

The current digital era is trending with a secure and advances optimized watermarking schemes for digital images. For optimization of the result that has been obtained by applying the DCT-DWT on the original image is being accomplished by Particle Swarm Optimization (PSO). In this paper, the watermark is inserted in the DWT-DCT coefficients, where the value of the coefficients is larger than some specific value. The embedding of the watermark is in the region with low-frequency coefficients. This paper mainly focuses on DCT-DWT but SVD is also tried on the image watermarking. The PSO is first compared with proposed modified inertia weight-based PSO on standard test function and its effectiveness is proposed. Later, to check the undetectability and to gain the optimized result modified PSO is applied in the image watermarking process. It is also checked that it is robust or not by applying some attacks on the image. Simulation results show that there is a marginal difference between the original and watermarked image. The Optimization technique is successfully implemented to achieve an optimal watermarking solution.
A Virtual Assistant for Cybersickness Care

We present an avatar and task-oriented dialog agent for monitoring user discomfort during a virtual reality (VR) cognitive exercise and providing personalized information and advice on its relief. The goal of this approach is to provide instantaneous assistance to users for a more comfortable VR experience, thereby enabling them to spend more time on cognitive tasks. We developed an avatar in a VR environment with which users may communicate verbally, and a dialog agent in a machine-learning based conversational AI platform. We performed a technical evaluation of the natural language understanding (NLU) component by comparing 2 models (BERT and StarSpace) using a train-test split, showing a significant benefit of BERT with smaller data sets. We validated the turn prediction using a train-test split and using randomly generated conversations. Both validations showed acceptable conversation-level accuracy. We undertook a usability study at two sites, showing effectiveness at both and good acceptability at one of the two. The framework outlined can be used to develop other virtual agents for cognitive self-care. Suggested improvements include validating the avatar with integrated BERT and reducing reliance on data augmentation, offline voice interaction modules, improved UX design, clinically validating the effect of the dialog agent on user discomfort and on cognitive performance, and increasing the ubiquity of the avatar within the VR cognitive care environment.
Fuzzy-as-a-Service for Real-Time Human Activity Recognition Using IEEE 1855-2016 Standard

Fuzzy Logic Systems (FLSs) have shown their potentials in Ambient Intelligence (AmI) applications. However, the implementation of FLSs is typically linked to dedicated and non-scalable hardware/software systems. As a result, some specific AmI requirements such as web communications and Service-Oriented Architecture (SOA), which can be found in many modern systems, are rarely adapted for FLSs. Sharing FLSs accessibility as web services (called fuzzy-as-a-service), in which the service is developed independently from a specific FLS, allows for system autonomy, openness, load balancing, efficient resource allocation and eventually cost-efficiency, particularly for computationally intense FLSs. In a wider context, such features can open new dimensions for FLSs' applicability in Cloud Computing and Internet of Things devices. Recent advances in standardising Fuzzy Mark-up Language (IEEE 1855-2016) and its associated software libraries (such as JFML) has made this even more achievable. This paper proposed fuzzy-as-a-service architecture based on IEEE 1855-2016, JFML and SOA. Through a simulated experiment, this paper concerned the collection, processing and monitoring the distributed data over the web, thus a real-time human activity recognition simulated scenario using a rule based FLS is demonstrated.
Chi-BD-DRF: Design of Scalable Fuzzy Classifiers for Big Data via A Dynamic Rule Filtering Approach

Big data classification problems are known to be no longer addressable by sequential algorithms. Therefore, it is necessary to design and develop novel solutions to provide accurate yet interpretable models in a tolerable elapsed time. In this area, Fuzzy Rule-Based Classification Systems are very advantageous due to their intrinsic interpretable and accurate capabilities. However, when these systems are applied in Big Data scenarios, the size of the rule set can become too large to be useful, whereas many of the generated rules could be associated with the non-dense areas or outliers. The presence of such rules in the rule base not only increases the running time and computation overheads but also affects on the interpretability of the fuzzy system. In this contribution, we propose a novel approach to obtain compact and accurate fuzzy models for Big data problems in a linearly scalable complex time. To do so, a dynamic filtering approach is applied to remove low supporting rules. Moreover, an efficient computation of the rules' weights is presented to improve the accuracy of the predictions. This model is developed for Big Data analytics by using Apache Spark framework. This allows taking advantage of the built-in resources and directives for a transparent distributed computing, as well as the machine learning pipeline to ease the complete processing. Experimental results, using different Big Data problems, confirmed the goodness of the proposed algorithm with respect to the baseline fuzzy classifier.
A Practical System for Privacy-Preserving Video Surveillance

Video surveillance on a massive scale can be a vital tool for law enforcement agencies. To mitigate the serious privacy concerns of wide-scale video surveillance, researchers have designed secure and privacy-preserving protocols that obliviously match live feeds against a suspects’ database. However, existing approaches provide stringent privacy guarantees and, as a result, they do not scale well for ubiquitous deployment. To this end, we introduce a system that relaxes the underlying privacy requirements by giving away some information when a face is compared against the law enforcement’s database. Specifically, our protocol reveals a random permutation of obfuscated similarity scores, where each obfuscated score discloses minimal information about the actual similarity score. We show that, despite the relaxed security definitions, our system protects the privacy of the underlying faces, while offering significant improvements in terms of performance. In particular, our protocol necessitates a single round of communication between the camera and the server and, for a database of 100 suspects, the online computation time at the camera and the server is 155 ms and 34 ms, respectively, while the online communication cost is only 12 KB.
Online Multi-object Tracking with Siamese Network and Optical Flow

Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) is a method of multi-object tracking combined appearance features with motion state of objects estimated by Kalman Filter which has a promising performance. However, maintaining the identity of targets becomes formidable when the objects have a similar appearance and complex patterns of the movement. To address these issues, a novel Online Multi-object Tracking with Siamese Network and Optical Flow is proposed. We utilize the Siamese network structure to obtain our appearance feature extractor. Furthermore, optical flow is introduced into the scheme to promote the accuracy of motion prediction from the Kalman filter. Our approach combines appearance and motion features in a tracking framework. The experimental results evaluated on the public MOT dataset illustrate that our method has the better performance in comparison with the DeepSORT algorithm.
Deep Learning Architectures for Medical Image Segmentation

Medical image segmentation is a bottleneck for physicians and radiologists in diagnosis of diseases. Deep learning based convolutional neural networks (CNNs) is used to support decision making in medical diagnosis. Three architectures are analyzed for segmentation of affected tissues. CNN with contraction path classifies the each pixel in the image into region of interest and region of non interest. The affected region is then segmented from RoI. Second architecture is developed with contraction and expansion paths (auto-encoders) to extract the spatial information of the pixels from the input image. The deconvolutional layer extracts the spatial information related to corresponding features but still fails to capture contextual dependent information of high-level features. In the third architecture, attention module with U-Net captures the contextual dependent information. Filter size, learning rate and k-fold cross validation are tuned to improve the accuracy and dice similarity coefficient (DSC). Filter size and k-fold cross validation are varied as 3x3, 5x5 and 7x7 and 3-fold, 5-fold and 10-fold respectively. The attention module helps to extract the spatial information of the high level features which are related to low level features. This gives better segmentation output. U-Net with attention module provides an accuracy 99.07 %, sensitivity 98.57 %, specificity 99.5 % and DSC 91.7%.
Multiatlas‐based segmentation of female pelvic organs: Application for computer‐aided diagnosis of cervical cancer

Abstract Atlas‐based segmentation is a high level segmentation technique which has become a standard paradigm for exploiting prior knowledge in image segmentation. Recent multiatlas‐based methods have provided greatly accurate segmentations of different parts of the human body by propagating manual delineations from multiple atlases in a data set to a query subject and fusing them. The female pelvic region is known to be of high variability which makes the segmentation task difficult. We propose, here, an approach for the segmentation of magnetic resonance imaging (MRI) called multiatlas‐based segmentation using online machine learning (OML). The proposed approach allows separating regions which may be affected by cervical cancer in a female pelvic MRI. The suggested approach is based on an online learning method for the construction of the dataset of atlases. The experiments demonstrate the higher accuracy of the suggested approach compared to a segmentation technique based on a fixed dataset of atlases and single‐atlas‐based segmentation technique.
An Efficient Blockchain Based Authentication Scheme to Secure Fog Enabled IoT Devices

IoT Devices have revolutionized how people interact with each other and their surroundings, with Gartner predicting 50 billion devices by 2020. IoT devices emerging from WSN to RFIDs are capable of sensing, actuating and sharing data but are resource-constrained devices meaning these devices have limited memory, storage, and computation power. Thus, IoT devices are incapable of securing themselves from various security and privacy threats. Moreover, current Cloud-IoT architectures are centralized; managed and controlled by Trusted Third parties also individuals have no control over their data. Fog-IoT integration has better latency, response time and is much secure compared to Cloud-IoT integration. Recently, Blockchain emerged as a decentralized, highly trusted and tamper-proof technology. In this paper, we proposed a blockchain-based scheme for secure User authentication access to Fog enabled IoT devices. The proposed scheme utilizes the Ethereum Smart Contract. We developed our smart contract using Solidity language with Remix-IDE and tested its functionalities on two test networks Test RPC (Ganache) and Rinkeby Test Network. We also analyzed security vulnerabilities of our smart contract on the ChainSecurity analyzing tool.
Automatic Chinese Font Generation System Reflecting Emotions Based on Generative Adversarial Network

Manual font design is difficult and requires professional knowledge and skills to perform. Therefore, how to automatically generate the required fonts is a very challenging research task. On the other hand, there are few people who have studied the relationship between fonts and emotions, and common fonts generally cannot reflect emotional information. This paper proposes an Emotional Guidance GAN: an automatic Chinese font generation framework based on Generative Adversarial Network (GAN), which enables the generated fonts to reflect human emotional information. First, an elaborated questionnaire system was developed from Tencent company, which aims to quantitatively figure out the relationship between fonts and emotions. A visual expression recognition part is designed based on the trained model to provide a font generation module with conditional information. Moreover, the Emotional Guidance GAN (EG-GAN) with EM Distance and Gradient Penalty, as well as classification strategies, is proposed to generate new fonts with combined multiple styles that infer by an expression recognition module. The results of the evaluation experiments and the resolution of the synthesized font characters show the credibility of our model.
Understanding the human in the design of cyber-human discovery systems for data-driven astronomy

High-quality, useable, and effective software is essential for supporting astronomers in the discovery-focused tasks of data analysis and visualisation. As the volume, and perhaps more crucially, the velocity of astronomical data grow, the role of the astronomer is changing. There is now an increased reliance on automated and autonomous discovery and decision-making workflows rather than visual inspection. We assert the need for an improved understanding of how astronomers (humans) currently make visual discoveries from data. This insight is a critical element for the future design, development and effective use of cyber-human discovery systems, where astronomers work in close collaboration with automated systems to gain understanding from continuous, real-time data streams. We discuss how relevant human performance data could be gathered, specifically targeting the domains of expertise and skill at visual discovery, and the identification and management of cognitive factors. By looking at other disciplines where human performance is assessed and measured, we propose four early-stage applications that would: (1) allow astronomers to evaluate, and potentially improve, their own visual discovery skills; (2) support just-in-time coaching; (3) enable talent identification; and (4) result in user interfaces that automatically respond to skill level and cognitive state. Throughout, we advocate for the importance of user studies and the incorporation of participatory design and co-design practices into the planning, implementation and evaluation of alternative user interfaces and visual discovery environments.
Deconstructing Human-assisted Video Transcription and Annotation for Legislative Proceedings

Legislative proceedings present a rich source of multidimensional information that is crucial to citizens and journalists in a democratic system. At present, no fully automated solution exists that is capable of capturing all the necessary information during such proceedings. Even if professional-quality automated transcriptions existed, other tasks such as speaker or rhetorical position identifications are not fully automatable. This work focuses on improving and evaluating the transcription software used by the Digital Democracy initiative, named Transcription Tool. Human transcribers work to up-level state legislative proceedings using this tool. Five phases of tool improvements are introduced and for each phase, the resulting change in efficiency is measured. We investigate over 12,000 individual transcription sessions (2,300 hours of video), where each session is the record of one bill discussion. A set of about 3,200 sessions belonging to a single cohort of 20 transcribers is further evaluated. Through introduction of new tool features, human-assisted transcription efficiency can be improved by 19.4% over five phases. Furthermore, investigation into transcriber usage patterns reveals that transcription time is composed of passive time, speaker identification, text correction, tool startup, as well as splitting and merging utterances. We analyze and rank these as a contribution.
Program Synthesis in a Continuous Space Using Grammars and Variational Autoencoders

An important but elusive goal of computer scientists is the automatic creation of computer programs given only input and output examples. We present a novel approach to program synthesis based on the combination of grammars, generative neural models, and evolutionary algorithms. Programs are described by sequences of productions sampled from a Backus-Naur form grammar. A sequence-to-sequence Variational Autoencoder (VAE) is trained to embed randomly sampled programs in a continuous space – the VAE’s encoder maps a sequence of productions (a program) to a point z in the latent space, and the VAE’s decoder reconstructs the program given z. After the VAE has converged, we can engage the decoder as a generative model that maps locations in the latent space to executable programs. Hence, an Evolutionary Algorithm can be employed to search for a vector z (and its corresponding program) that solves the synthesis task. Experiments on the program synthesis benchmark suite suggest that the proposed approach is competitive with tree-based GP and PushGP. Crucially, code can be synthesised in any programming language.
Digitalization of Russian higher education: educational process technologies (experience of universities of the Ural Federal District of Russian Federation)

В. Д. Перевалов, А
Hospital readmission prediction based on long-term and short-term information fusion

The hospital readmission prediction becomes a significant task for healthcare systems and patients. Many predictive models have been developed and make progress in this task. However, most of them roughly combine the patient’s long-term(e.g., the history of present illness) and short-term(e.g., the performed laboratory test results when the patients are discharged) information without considering the inner distinction between them. In this paper, we propose a new approach for hospital readmission prediction based on transformation from numerical features to natural language, which makes better fusion of these two kinds of information. Through a rule-based transformation, the original numerical features are transformed into corresponding descriptive short sentences based on medical knowledge. Meanwhile, with the help of public well pre-trained character embeddings, our model can incorporate the prior semantic knowledge into the data. Moreover, by using the long-term information as the query of short-term feature attention mechanism, our model can capture the effective information in the short-term features from a more global perspective, and better incorporates the long-term and short-term information. Extensive experiment results on a real dataset demonstrate the effectiveness and superiority of our proposed model compared with the baseline methods.
Modified Critical Point – A Bug Algorithm for Path Planning and Obstacle Avoiding of Mobile Robot

Path planning is one of the basic problems of Autonomous Mobile Robot. The mobile robot supposed to be able to work in an unfamiliar situation using an automatic plan determined by locally sensed information. In the case of real-time environments speed calculation and rescheduling of path is essential to bypass the moving obstacle and make collision free path of robot. Robot movement planning in dynamic condition requires the actions to be selected under real-time control. To avoid run-time obstacles a new approach is presented in this paper based on Bug Algorithm. This proposed ModifiedCriticalPointBug(MCPB) algorithm, is a new Bug algorithm for path planning of mobile robots. This algorithm is carried out by the robot after regular interval, thus permitting the robot to correct its path if a new obstacle comes into the path or the old one move in a new route. As a result, the robot not only bypasses collision but also makes almost optimal path by making a sequence of run time modification in its path.
Discriminating qubit amplitude damping channels

Abstract We address the issue of the discrimination between two-qubit amplitude damping channels by exploring several strategies. For the single-shot, we show that the excited state does not always give the optimal input, and that side entanglement assistance has limited benefit. On the contrary, feedback assistance from the environment is more beneficial. For the two-shot, we prove the in-utility of entangled inputs. Then focusing on individual (local) measurements, we find the optimal adaptive strategy.