Reviewerly/exHarmony huggingface dataset:
Review of Blockchain Enabled Decentralized Energy Trading Mechanisms

With special emphasis being put upon renewable sources of energy day-by-day, there is a growing demand for this new technology to be integrated into the existing framework. To be widely accepted and made sustainable, such a technology should be profitable for all participating entities; giving them incentive enough to continue using it. What remains to be answered is the question of how that will be done. We break down the traditional power grid into smaller clusters, called microgrid and discuss its properties as potential candidates for implementing Blockchain powered energy trading mechanisms. Blockchain enabled technology is chosen as it brings with it, a degree of security, transparency and automation; and cuts out the established monopoly with the decentralized network and peer-to-peer trading. Two such working algorithms are reviewed for their implementation into the microgrid. One algorithm works on an auction model while another is a calculative data-dependent model but both the models function using smart contracts. Pre-existing projects in this field and their implementation around the world are reviewed by us in the later section of the paper. We conclude this paper by encapsulating the deployment considerations and limitations of employing Blockchain technology in a microgrid.
Towards Automatic Diagnosis from Multi-modal Medical Data

Many healthcare applications would significantly benefit from the processing and analyzing of multi-modal data. In this paper, we propose a novel multi-task, multi-modal, and multi-attention framework to learn and align information from multiple medical sources. Based on experiments on a public medical dataset, we show that combining features from images (e.g. x-rays) and texts (e.g. clinical reports), sharing information among different tasks (e.g. x-rays classification, autoencoder, and diagnosis generation) and across domains boost the performance of diagnosis generation (86.0% in terms of BLEU@4).
Lexicon information in neural sentiment analysis: a multi-task learning approach.

This paper explores the use of multi-task learning (MTL) for incorporating external knowledge in neural models. Specifically, we show how MTL can enable a BiLSTM sentiment classifier to incorporate information from sentiment lexicons. Our MTL set-up is shown to improve model performance (compared to a single-task set-up) on both English and Norwegian sentence-level sentiment datasets. The paper also introduces a new sentiment lexicon for Norwegian.
Mobile Robot Position Control Using Computer Vision

This paper describes computer vision for controlling board cutter mobile robot position in a known environment. This technique develops camera calibration to control the position of the robot that is directed at a certain position in a known wide area. The system calculate x, y, and orientation of mobile robot position from pixel coordinate to world scale coordinate by changing the pixel scale displayed on the monitor screen in millimeters using camera calibration, therefore the actual position in world coordinate could be known. Fixed camera is placed above environment to make it available to view mobile robot clearly while the mobile robot placed in work area. To find out the actual position of the robot in world scale units, it can be done by using focal length as one of the camera's intrinsic parameters. The experiment results show that the best technique is measuring the distance between the camera and the work area as far as 1500 millimeters. The developed method gives a command to the controller by serial communication to set the track length and also the direction of the robot as input by user. The experiment results show that the accuracy is in average 5% - 10%.
FPVRGame: Deep Learning for Hand Pose Recognition in Real-Time Using Low-End HMD

Head Mounted Display (HMD) became a popular device, drastically increasing the usage of Virtual, Mixed, and Augmented Reality. While the systems’ visual resources are accurate and immersive, precise interfaces require depth cameras or special joysticks, requiring either complex devices or not following the natural body expression. This work presents an approach for the usage of bare hands to control an immersive game from an egocentric perspective and built from a proposed case study methodology. We used a DenseNet Convolutional Neural Network (CNN) architecture to perform the recognition in real-time, from both indoor and outdoor environments, not requiring any image segmentation process. Our research also generated a vocabulary, considering users’ preferences, seeking a set of natural and comfortable hand poses and evaluated users’ satisfaction and performance for an entertainment setup. Our recognition model achieved an accuracy of 97.89%. The user’s studies show that our method outperforms the classical controllers in regards to natural interactions. We demonstrate our results using commercial low-end HMD’s and compare our solution with state-of-the-art methods.
Using the Random Forest Algorithm for Searching Behavior Patterns in Electronic Health Records

The search for information associated with qualitative data is usually done using data mining algorithms, the presented research analyzes data of patients with essential hypertension (HTA), patients who have developed hypertension but there is no clear reason why it has occurred. In this research, a search of behavioral patterns was performed in the data associated with the clinical records of 8470 patients using the Random Forest algorithm. As a case study, the proposal focuses on finding the relationship between the different pathologies or factors associated to Hypertensive patients (other diseases for example). The findings validate the right use of the algorithm due to the results obtained agrees with the knowledge defined and validated in the literature. Thus, trivial knowledge can be obtained with the algorithm used. However, non-trivial knowledge was also obtained given the analysis performed on a total of 4408 data of female patients and 4062 of male patients showed a great difference between the factors or pathologies that a patient presents when classified according to their sex, thus another deep study must be carried out closely with experts in the area of the health as future research.
An Unsupervised Real-Time Framework of Human Pose Tracking From Range Image Sequences

Pose tracking from range image sequences remains a difficult task due to strong noise and serious self-occlusion of human body. Existing work either rely on extremely large and precisely annotated datasets, or rely on accurate human mesh model and GPU acceleration. In this paper, we propose an unsupervised real-time framework of pose tracking from range image sequences. Our framework consists of a visible hybrid model (VHM), a componentwise correspondence optimization (CCO) and a dynamic database lookup (DDL). VHM consists of component sphere sets and component visible spherical point sets which exhibits both simplicity and high accuracy. CCO converts the matching between VHM and input point cloud into several subproblems regarding local rotations of components and a global translation of body abdominal joint, each of which has an efficient closed form solution. DDL is designed to recover correct pose when tracking fails, which effectively mitigates accumulative error during tracking. Experiments on SMMC, PDT, EVAL datasets indicate that our framework not only achieves better or competitive precision compared with state-of-the-art methods, but also produces real-time efficiency in personal computers without GPU acceleration.
Benchmarking the Performance and Power of AI Accelerators for AI Training

Deep learning has become widely used in complex AI applications. Yet, training a deep neural network (DNNs) model requires a considerable amount of calculations, long running time, and much energy. Nowadays, many-core AI accelerators (e.g., GPUs and TPUs) are designed to improve the performance of AI training. However, processors from different vendors perform dissimilarly in terms of performance and energy consumption. To investigate the differences among several popular off-the-shelf processors (i.e., Intel CPU, NVIDIA GPU, AMD GPU, and Google TPU) in training DNNs, we carry out a comprehensive empirical study on the performance and energy efficiency of these processors by benchmarking a representative set of deep learning workloads, including computation-intensive operations, classical convolutional neural networks (CNNs), recurrent neural networks (LSTM), Deep Speech 2, and Transformer. Different from the existing end-to-end benchmarks which only present the training time, We try to investigate the impact of hardware, vendor's software library, and deep learning framework on the performance and energy consumption of AI training. Our evaluation methods and results not only provide an informative guide for end-users to select proper AI accelerators, but also expose some opportunities for the hardware vendors to improve their software library.
Non-Volatile Memory Forensic Analysis in Windows 10 IoT Core

The increase in the number of cybersecurity incidents in which internet of things (IoT) devices are involved has called for an improvement in the field of computer forensics, which needs to provide techniques in order to perform complete and efficient investigations in this new environment. With the aim of doing so, new devices and systems are being studied in order to offer guidelines for investigators on how to examine them. This papers follows this approach and presents a forensic analysis of the non-volatile memory of Windows 10 IoT Core. It details how the investigation should be performed and highlights the relevant information that can be extracted from storage. In addition, a tool for the automation of the retrieval of the pieces of evidence detected is provided.
Self-Supervised Flow Estimation using Geometric Regularization with Applications to Camera Image and Grid Map Sequences

We present a self-supervised approach to estimate flow in camera image and top-view grid map sequences using fully convolutional neural networks in the domain of automated driving. We extend existing approaches for self-supervised optical flow estimation by adding a regularizer expressing motion consistency assuming a static environment. However, as this assumption is violated for other moving traffic participants we also estimate a mask to scale this regularization. Adding a regularization towards motion consistency improves convergence and flow estimation accuracy. Furthermore, we scale the errors due to spatial flow inconsistency by a mask that we derive from the motion mask. This improves accuracy in regions where the flow drastically changes due to a better separation between static and dynamic environment. We apply our approach to optical flow estimation from camera image sequences, validate on odometry estimation and suggest a method to iteratively increase optical flow estimation accuracy using the generated motion masks. Finally, we provide quantitative and qualitative results based on the KITTI odometry and tracking benchmark for scene flow estimation based on grid map sequences. We show that we can improve accuracy and convergence when applying motion and spatial consistency regularization.
Implementation of Association Rule Mining Algorithms on Distributed Data Processing Platforms

Association rule mining algorithms are a frequently used data mining technique. It is aimed to find the items that are frequently found from the data. Nowadays, large data processing and analysis platforms are not focused on data mining, so they do not offer large-scale libraries for association rule mining algorithms. In the scope of this research, a library has been developed for association rule mining algorithms on a large data processing platform. The Apache Spark platform has been preferred in terms of common usage for the research case study. Implementation methods of different algorithms have been implemented on this platform to benefit from the Map-Reduce programming model. In this context, Apriori, Eclat and Pascal algorithms are implemented for large data platform. The library created by the implementation method we suggest is comparatively analyzed in terms of performance metrics on big data processing platforms with single and multiple nodes. The methods implemented within the scope of the research are also compared with the performance of the FpGrowth algorithm implemented by the Spark platform. The results of our research show that when tested on large scale data, the Apriori algorithm gives much better performance values than the other algorithms when switching from single-node cluster environment to multi-node cluster environment.
Intelligent Library System Based on RFID Technology

Abstract Radio Frequency Identification (RFID) is a kind of automatic identification technology. It uses radio frequency to communicate two-way non-contact data, and uses radio frequency to read and write recording media (electronic tags or radio frequency cards) so as to achieve identification objectives and data transfer. It is considered as one of the most promising information technologies in the 21st century. In the field of Library management, it can improve the overall efficiency of libraries and reduce the cost of library management of mass books. In this way, libraries can put more funds and manpower into improving the quality of Library services.
Multi-feature Fusion Action Recognition Based on Key Frames

As an important technology in computer vision, human action recognition has a great commercial value, which has attracted extensive attention in both academia and industry. However, data redundancy and single feature were largely limited the accuracy of human action recognition. In this paper, adopting the Key frame extraction and multi-feature fusion techniques, a novel action recognition method was proposed, which can improve the recognition accuracy. The main works are as follows: 1) in order to solve the problem of data redundancy, a key frame extraction method based on node contribution weighting is proposed to extract video key frames; 2) different kinds of information flows are extracted from the obtained key frame sequences, and different convolutional neural networks are used to obtain corresponding classification results and merge, so as to better complement the information in different flows. Lastly, the experimental results show that our method improves the accuracy of action recognition.
A critical comparison of methods to estimate solar rooftop photovoltaic potential in Switzerland

Abstract To understand the opportunities and challenges of large shares of solar photovoltaics (PV) in our electricity mix, various large-scale studies of PV potential on building roofs have been conducted in recent years. The use of different datasets, methods and spatio-temporal resolutions leads to widely varying results, making a comparison across different studies intrinsically difficult. In this work, six studies carried out in Switzerland are compared in a quantitative way, in order to understand how different methods impact the potential estimates. We observe a strong trend towards increasing spatial and temporal resolutions, using larger and more accurate datasets for the analysis. While the earliest study relies on rules of thumb, later studies use data-driven estimations, enabled by the use of Machine Learning, scalable algorithms and powerful computational engines. Our analysis shows that the largest differences are caused by the source of the solar radiation input data, the computation of shading effects on rooftops and the estimation of available roof area for PV panel installation. The latter is the most uncertain parameter in the presented studies and offers opportunities for future work.
Improving the Identification of Code Smells by Combining Structural and Semantic Information

In software engineering, a code smell is an indication of a deeper problem in the source code, hindering the maintainability and evolvability of the system. In the literature, there is a significant emphasis on the detection of code smells because of its importance as a maintenance task. Most of previous studies focus in their analyses on one source of information, i.e. structural, historical or semantic information. However, some instances of bad smells could be identified by a type of information but missed by another one. In this paper, we propose an improved detection approach that combines structural and semantic information in order to fully exploit their complementarity in the identification of code smells. Both information are extracted separately using conventional and deep learning methods. For the evaluation, we have selected five open source projects which are JHotDraw, Apache Karaf, Freemind, Apache Nutch and JEdit. In order to optimize our performance results, we have set up four different experiments and compare between them. The obtained accuracy results confirm the effectiveness of combining structural and semantic information in improving the detection of code smells.
Cognitive Science-Based Security Framework in Consumer Electronics

There is rapid utilization of social networking services (SNSs) in the consumer electronics (CE) industries for marketing. Unfortunately, with this rise of SNSs use, the traditional SNSs security techniques fail to address the unique security demands of the SNSs, such as the identification of malicious users that can temper the algorithms used to determine the consumer purchasing decisions on SNSs. To handle such security requirements, we propose a cognitive identification framework that relies on the concept of cognitive science to distinguish malicious user from legitimate user on SNSs. The framework takes benefit of social sensing and data processing competency of apache sparks to analyze real-time SNSs data and make identification decision using machine-learning techniques.
Confront Phishing Attacks — from a Perspective of Security Education

Recently, targeted attacks are drastically increasing in both indivduals and companies. For technical countermeasure against such a targeted attack, various methods such as email/web contents analysis etc., are developed and realized. However, as it is often said, attackers precisely exploit the most vulnerable part in order to achieve their goals.Therefore, spear phishing against human user is employed for such attacks in a large propotion. Moreover, in order to increase success probability of such attacks, attackers often adopt social engineering technique.In this paper, we present a current effort of our research group on combating targeted attacks employing spear phishing with using social engineering, through user education.Specifically, at first we present relationship between human psychological characteristics and vulnerability against social engineering. The result can be used for testing whether a user has vulnerability on some social engineering technique, and the testing result can be utilized for countermeasure or user's training.Secondly, we present development of a web-based self learning material for countermeasure against social engineering which employs interactive motion picture contents.
IRS: A Large Synthetic Indoor Robotics Stereo Dataset for Disparity and Surface Normal Estimation.

Indoor robotics localization, navigation and interaction heavily rely on scene understanding and reconstruction. Compared to monocular vision which usually does not explicitly introduce any geometrical constraint, stereo vision based schemes are more promising and robust to produce accurate geometrical information, such as surface normal and depth/disparity. Besides, deep learning models trained with large-scale datasets have shown their superior performance in many stereo vision tasks. However, existing stereo datasets rarely contain the high-quality surface normal and disparity ground truth, which hardly satisfy the demand of training a prospective deep model for indoor scenes. To this end, we introduce a large-scale synthetic indoor robotics stereo (IRS) dataset with over 100K stereo RGB images and high-quality surface normal and disparity maps. Leveraging the advanced rendering techniques of our customized rendering engine, the dataset is considerably close to the real-world captured images and covers several visual effects, such as brightness changes, light reflection/transmission, lens flare, vivid shadow, etc. We compare the data distribution of IRS with existing stereo datasets to illustrate the typical visual attributes of indoor scenes. In addition, we present a new deep model DispNormNet to simultaneously infer surface normal and disparity from stereo images. Compared to existing models trained on other datasets, DispNormNet trained with IRS produces much better estimation of surface normal and disparity for indoor scenes.
Knowledge of Uncertain Worlds: Programming with Logical Constraints

Programming with logic for sophisticated applications must deal with recursion and negation, which have created significant challenges in logic, leading to many different, conflicting semantics of rules. This paper describes a unified language, DA logic, for design and analysis logic, based on the unifying founded semantics and constraint semantics, that support the power and ease of programming with different intended semantics. The key idea is to provide meta-constraints, support the use of uncertain information in the form of either undefined values or possible combinations of values, and promote the use of knowledge units that can be instantiated by any new predicates, including predicates with additional arguments.
Developing Creative Thinking in Future Teachers as a Topical Issue of Higher Education.

Professionally significant qualities of a modern teacher include his/her ability to think and act outside the box, to invent and develop new ways of addressing professional issues. Today, there is a contradiction between the society's need for and resourceful teachers and the inability of the vocational pedagogical training system to create the necessary conditions for organizing the process of forming thinking in students-future teachers. The analysis of the scientific literature on the problem under consideration leads to the following definition of creative thinking: it is a type of thinking resulting in a creation of a subjectively new product, which, in turn, implies new formations in the cognitive activity. The researchers of thinking phenomenon define certain characteristics and properties, which distinguish this type of thinking from other types. Formation of thinking in students becomes possible under several subjective and objective conditions in the educational environment of a higher educational institution (HEI). To access the formed level of thinking in future teachers, the authors conducted an ascertaining experiment that suggested testing students by J. Bruner’s method and questioning them according to the authorial method. The analysis of the data obtained showed that the conditions created in a HEI are not sufficient for formation of high-level creativity. It was found that only a third of the surveyed students have a high level of thinking. In addition, the study confirmed the hypothesis that regular leisure fiction reading contributes to a higher level of creativity in future teachers. The study of the problem of developing thinking in future teachers requires further examination of factors and conditions, which determine the effectiveness of this process in a HEI.