Reviewerly/exHarmony huggingface dataset:
GPU-based parallel Shadow Features generation at neural system for improving gait human activity recognition

Abstract In this paper, we propose a new method for improving human activity recognition (HAR) datasets in order to increase their classification accuracy when trained with a certain classifier like a Neural Network. In this paper a novel training/testing process for building/testing a classification model for human activity recognition (HAR) is proposed. Traditionally, HAR is done by a classifier that learns what activities a person is doing by training with skeletal data obtained from a motion sensor such as Microsoft Kinect or accelerometer sensors. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. In addition to the spatial features that describe current positions in the skeletal data, new features called Shadow Features are used to improve the supervised learning efficiency and accuracy of Neural Network classifiers. Shadow Features are inferred from the dynamics of body movements, thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterizing activities in the classification process and thus significantly improving the accuracy. These Shadow Features are generated based on the existing features obtained from sensor datasets. In this paper we show that the accuracy of a neural network classifier can be significantly improved by the addition of Shadow Features and we also show that the generation of Shadow Features can be achieved with little time cost, on the fly, with the NVIDIA GPU technology and the CUDA programming model, this way we can improve the Neural Network accuracy at almost no time cost. GPUs are particularly suitable for generating Shadow Features, since they possess multiple cores which can be taken advantage of, in order to generate Shadow Features for multiple data columns in parallel, therefore reducing a lot of processing time, especially when dealing with huge datasets.
Conceptual processing system for a companion robot

Companion robots should perceive speech, recognize objects in the real world, and further react with speech utterances and nonverbal communicative cues. Robots should also remember the interaction history and accumulate knowledge from external text sources: news, blogs, and e-mails. We have designed a conceptual representation system for a companion robot, able to support this list of interactive tasks. The system includes a speech processing component and operates with semantic representations: sets of semantic markers, assigned to valencies within sentence predications. The reaction support system inherits a classic production architecture and consists of scripts, sensitive to rational or emotional stimuli. The general architecture is based on parallel processing of scripts, it may trigger several behavioral reactions in response to a stimulus, and after that it combines speech output and nonverbal reactions of these scripts on the robot, thus, constructing compound and rich behavior. Semantic representations and scripts are also used to index incoming utterances in a memory base.
Neuroevolution Architecture Backbone for X-ray Object Detection

Most of the developments in Convolutional Neural Networks come from creating innovative designs in the architecture and hyperparameters. Architecture designs often grow in depth and usually outperform their predecessors. However, these deep networks are not optimized for a specific application, especially with limited datasets such as X-ray dataset. Moreover, training these networks require considerable GPU resources. YOLOv3 is an object detection network iterated to include the recent developments in the deep learning research field. Thus, it also has a deep backbone network similar to Residual Network. The developments incorporated are effective in general datasets and benchmarks but needlessly big for a specialized application. Hence, this paper proposes methods to create a suitable backbone for YOLOv3 with Neuroevolution. Neuroevolution is an optimization algorithm based on genetic algorithm, which does not require gradients to optimize. Using Neuroevolution, a small network for limited dataset can be designed without human expertise. Experimental results show that it can compete with the larger networks in terms of accuracy.
Exploring Cyber Security Measures in Smart Cities

Technologies which enhance the quality of everyday lifestyle are increasing rapidly. During their initial development, Internet and network-based services were not built by taking cyber security measures into consideration. The cost of this is still being paid to this day. Nowadays, there are certainly clear need for cyber security requirements for almost all Internet and network-based systems. Several kinds of such systems are normally connected together in unprecedented ways in smart cities. This in fact will complicate the security requirements that should be considered when building smart cities. Yet, the studies on security requirements in smart cities remain to be one of the fields that needs further exploration. This article explores the essential cyber security requirements in smart cities by considering various previous research studies in the field. On top of that, several other recommended security requirements will be introduced as a response to the gaps that continues to be predominating the field.
Improved Bell–LaPadula Model With Break the Glass Mechanism

The Bell-LaPadula (BLP) model is a widely used access control model for the multilevel security system. The researchers proposed many modified BLP models to express privileges that cannot be expressed by the BLP model. However, these models are not compatible with the BLP model, leading to the transportation cost-prohibitive and difficult to be practically applied. In this article, an improved BLP model incorporated the break the glass (BTG) mechanism is proposed to overcome the limitations of the standard BLP and other modified BLP models. The improved model inherits some of the advantages of BTG, such as policy dynamic modification and fine-grained access control, which gives it wide availability. Additionally, in the implementation, BTG is used as an independent function attached to the original BLP; the proposed BLP model can be easily implemented in systems where BLP models have been implemented. The results of the analysis and simulations showed that the proposed BLP model improves the ability of expressing policy of BLP and achieves fine-grained access control without compromise in security. Compared with other modified BLP models, the proposed BLP model could express policy more effectively and is compatible with the original BLP model.
Deep multi-shot network for modelling appearance similarity in multi-person tracking applications

The automatization of Multi-Object Tracking becomes a demanding task in real unconstrained scenarios, where the algorithms have to deal with crowds, crossing people, occlusions, disappearances and the presence of visually similar individuals. In those circumstances, the data association between the incoming detections and their corresponding identities could miss some tracks or produce identity switches. In order to reduce these tracking errors, and even their propagation in further frames, this article presents a Deep Multi-Shot neural model for measuring the Degree of Appearance Similarity (MS-DoAS) between person observations. This model provides temporal consistency to the individuals' appearance representation, and provides an affinity metric to perform frame-by-frame data association, allowing online tracking. The model has been deliberately trained to be able to manage the presence of previous identity switches and missed observations in the handled tracks. With that purpose, a novel data generation tool has been designed to create training tracklets that simulate such situations. The model has demonstrated a high capacity to discern whether a new observation corresponds to a certain track or not, achieving a classification accuracy of 97% in a hard test that simulates tracks with previous mistakes. Moreover, the tracking efficiency of the model in a Surveillance application has been demonstrated by integrating that into the frame-by-frame association of a Tracking-by-Detection algorithm.
Probabilistic model based path planning

Path planning shows great potential for exploring indoor and outdoor environments. In this paper, a probabilistic method is proposed to design path planners based on transition probabilistic matrices and signed distance functions. The transition probabilistic matrix is constructed by collecting path sequence data generated by performing a modified RRT with many times. Moreover, the signed distance function is introduced to simulate the safety coefficient which can guarantee a suitable distance between robots and obstacles. By combining the transition probability and the safety coefficient, our path planning task is modeled as a maximal probability sequence decision problem which in essence is equivalent to a minimal cost path problem, and then the dynamic programming solver is achieved by using the push-based efficient implementation of Bellman–Ford’s algorithm Kleinberg and Tardos (2006). Several path evaluation criteria are also used to evaluate path planning results, and plenty of experimental results illustrate the effectiveness of the proposed method.
Research trends in Sentiment Analysis and Opinion Mining from Knowledge Management approach: A science mapping from 2007 to 2020

The internationalization of markets and the increasing intensification of competition, forces managers of organizations or companies to make precise decisions in the shortest possible time. When the organization promotes the exchange of knowledge, it is oriented towards the sustained improvement of processes and internal communication. In the same way, relations with the outside environment improve: customers and suppliers. The birth of the Internet and later of social networks led to public ideas, debates and opinions; which constitute an interesting source to detect opinion trends among users. This complex data analysis is carried out by different disciplines of artificial intelligence with the implementation of algorithms. One of those disciplines is the Analysis of Feelings, which through an opinion, emotion or attitude that is mainly inferred from a text, analyzes and classifies them. The approach of these methodologies assumes the interest of this work for the extraction of information that allows its later use in the decision-making of organizations. Finally, to learn more about its evolution, trends, research areas, authors and publications, a Bibliometric Analysis was applied from 2007 to date on Sentiment Analysis and Opinion Mining from Knowledge Management.
SodsMPC: FSM based Anonymous and Private Quantum-safe Smart Contracts

SodsMPC is a quantum-safe smart contract system. SodsMPC permissioned servers (verification nodes) execute contracts by secure multi-party computation (MPC) protocols. MPC ensures the contract execution correctness while trivially keeping the data privacy. Moreover, SodsMPC accomplishes the contract business logic privacy while protecting the contract user anonymous identity simultaneously. We express the logic of a contract by a finite state machine (FSM). A state transition of the FSM is represented by a blind polynomial with secret-shared coefficients. When using MPC to compute this blind polynomial, the contract business logic privacy is obtained. These coefficients which control the logic are binary secret shares. We also propose a base conversion method among binary and integer secret shares by MPC. Our contract anonymity comes from the “mixing-then-contract” paradigm. The online phase of the SodsMPC mixing is a multiplication between a preprocessed permutation matrix and an input vector in the form of secret sharing, which accomplishes a fully randomized shuffle of the inputs and keeps the secret share form for the following contract execution. All SodsMPC components, including a verifiable secret sharing scheme, are quantum-safe, asynchronous, coping with t <; n/3 compromised servers, and robust (tolerates Byzantine servers) in both preprocessing and online phases.
Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning

Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent. In this work, we propose UVIT, a novel unsupervised video-to-video translation model. Our model decomposes the style and the content, uses the specialized encoder-decoder structure and propagates the inter-frame information through bidirectional recurrent neural network (RNN) units. The style-content decomposition mechanism enables us to achieve style consistent video translation results as well as provides us with a good interface for modality flexible translation. In addition, by changing the input frames and style codes incorporated in our translation, we propose a video interpolation loss, which captures temporal information within the sequence to train our building blocks in a self-supervised manner. Our model can produce photo-realistic, spatio-temporal consistent translated videos in a multimodal way. Subjective and objective experimental results validate the superiority of our model over existing methods.
Core dataset extraction from unlabeled medical big data for lesion localization

With the advancement of technology in the big data era, the amount of data in the medical field has increased considerably, which has promoted the rapid development of intelligent medical diagnoses. Lesion localization plays an indispensable role in the medical field. However, this approach has not been widely applied in realizing intelligent diagnoses. The effect of lesion localization depends on the training of a convolutional neural network, for which a large amount of medical image data with lesion location labeling is required. Although a considerable amount of medical image data is available, the quality varies and most of the data are not labeled in terms of the lesion location. This labeling process is not only time-consuming and laborious but also requires professional knowledge. To solve this problem and facilitate the development of lesion localization, we propose a novel core dataset extraction architecture, which is a general architecture for extracting the core dataset from unlabeled medical big data for lesion localization. In the architecture, the comprehensive core degree of the images is computed by using three evaluation indicators and an indicator fusion algorithm. In addition, we propose an iterative optimization selection module to enhance the performance of the subsequent batch extraction. The experimental results show that the proposed method only needs to extract 30% of the training data to achieve the training effect of the entire training data, thereby considerably reducing the amount of required human resources.
Touch Behavior Based Age Estimation Toward Enhancing Child Safety

Adult content on the Internet may be accessed by children with only a few keystrokes. While separate child-safe accounts may be established, a better approach could be incorporating automatic age estimation capability into the browser. We envision a safer browsing experience by implementing child-safe browsers combined with Internet content rating similar to the film industry. Before such a browser is created it was necessary to test the age estimation module to see whether acceptable error rates are possible. We created an Android application for collecting biometric touch data, specifically tapping data. We arranged with an elementary school, a middle school, a high school, and a university and collected samples from 262 user sessions (ages 5 to 61). From the tapping data, feature vectors were constructed, which were used to train and test 14 regressors and classifiers. Results for regression show the best mean absolute errors of 3.451 and 3.027 years, respectively, for phones and tablets. Results for classification show the best accuracies of 73.63% and 82.28%, respectively, for phones and tablets. These results demonstrate that age estimation, and hence, a child-safe browser, is feasible, and is a worthwhile objective.
Fully Bayesian Human–Machine Data Fusion for Robust Online Dynamic Target Characterization

This work examines the problem of fusing human operator observations with probabilistic information extracted by an automated data fusion system, in the context of dynamic multitarget track characterization for large-scale surveillance. This soft data fusion problem is challenging because human operator observation errors are difficult to calibrate a priori and in general exhibit subtle conditional dependencies. Furthermore, the efficacy of combining inputs from operators depends heavily on operator error characteristics as well as track characterization uncertainty. A hierarchical fully Bayesian probabilistic model is developed to explicitly account for human sensor quality and Markovian dependencies in human reports. This model is used to perform online Bayesian inference via Gibbs sampling to simultaneously update the data fusion system’s knowledge of human sensor characteristics and target type probabilities. A strategy is also developed for value of information assessments to automatically query human operators for inputs as needed. Efficient approximations of high-dimensional human sensor parameter posterior distributions via Dirichlet probability density function (pdf) moment matching and parameter tying are also developed to provide significant computational speedups with little information loss. Results for different simulated operator profiles and automated target characterization baselines show that fully Bayesian fusion improves track characterization performance, while intelligently accounting for uncertain operator characteristics.
Machine translation system to convert Sinhala and English Braille documents into voice

Reading Braille documents are a time-consuming and labor-intensive task. A blind person should touch every Braille letter by his or her fingers. Therefore, high sensitivity in fingers and memorizing every Braille letters are key factors in Braille reading. Due to enhancement in technology, several Optical Character Recognition (OCR) systems have been introduced for different languages in different parts of the world. However, in Sri Lanka, there are no systems that extract Sinhala or English Braille characters using OCR and convert those Braille codes to sound output. The main purpose of the research is to create a system that extracts both Sinhala and English Braille segments from a given Braille document and makes the Braille content into voice. At the beginning Embossed Sinhala or English Braille image which took from webcam or a high-resolution phone will use for image processing techniques such as gray scaling, thresholding, erosion, and dilation. Erosion and gray scaling help to eliminate the noise and the noise-free image used to detect contour. After pre-processing, the image using an OpenCV library do the segmentation and character extraction. Braille character recognition has done by taking binary to decimal equivalent numbers. Before generating voice output, recognized Braille letters should convert to corresponding language letters (either Sinhala letter or English) and mapping English letters can directly generate English words but in Sinhala need an additional step called Unicode Mapping to generate a Sinhala word. When a Braille document is in high quality, the system will reach for 95 percent accuracy level and generally, results have around 85.30 percent success rate. This software provides many usability characteristics to increase simplicity when it's using OpenCV and related technologies. Even teachers can use this to improve their teaching terminologies and explain things more clearly in their lessons.
Evaluation of Network File System as a Shared Data Storage in Serverless Computing

Fully-managed cloud and Function-as-a-Service (FaaS) services allow the wide adoption of serverless computing for various cloud-native applications. Despite the many advantages that serverless computing provides, no direct connection support exists between function run-times, and it is a barrier for data-intensive applications. To overcome this limitation, the leading cloud computing vendor Amazon Web Services (AWS) has started to support mounting the network file system (NFS) across different function run-times. This paper quantitatively evaluates the performance of accessing NFS storage from multiple function run-times and compares the performance with other methods of sharing data among function run-times. Despite the great qualitative benefits of the approach, the limited I/O bandwidth of NFS storage can become a bottleneck, especially when the number of concurrent access from function run-times increases.
University Students’ Online Social Presence and Digital Competencies in the COVID-19 Virus Situation

2020 is the year of change and adaptability. The COVID19 pandemic immensely influences our everyday routines. Education is no exception as schools have to transform their classroom teaching to distance online tutoring. It is not easy on either side as educators and students encountered gaps in digital capabilities. Moreover they suddenly happen to be an online medium where different social behavior pattern immerged. We were very much interested how young adults endure and overcome this situation. In the present study the first result of our ongoing research that aimed to explore the digital competence and social presence among the students of the University of Pannonia are presented. The main objective was to see how students perceive their social situation online with their digital abilities, knowledge and level of competences. The results indicated that although on average perceived online social presence is not affected by individuals' digital competences, significant relationships were found between some factors.
A method of forensic authentication of audio recordings generated using the Voice Memos application in the iPhone

Considering the widespread use of mobile phones, audio recordings of crime scenes are widely used as digital evidence; however, it is important to authenticate the audio recordings before consideration as legal evidence. This study aimed to develop a method to authenticate audio recordings generated using the iPhone through three steps: 1) bitrate/audio latency time analysis of audio recordings, 2) comparison of the file structure/timestamp on audio recordings, and 3) device-based log history examinations for the provenance of audio recordings. Herein, we analyzed audio recording samples from ten different models of mobile handsets of the iPhone with Advanced Audio Coding (AAC) or Apple Lossless Audio Codec (ALAC), through the Voice Memos application depending on the iPhone Operating System (iOS). To analyze the characteristics of these audio recordings, we compared features including audio latency, file format/structure, and timestamps between the audio recordings generated in the iPhone and those edited through the built-in audio editing function. Furthermore, we investigated the log history registered in devices during the generation of the audio recordings. Differences in the audio latency, file size, timestamps, bitrate, and log history were confirmed on the iPhone when manipulating the audio recordings. The present results show that it is possible to verify the authentication of audio recordings generated using the Voice Memos application on iPhone.