Reviewerly/exHarmony huggingface dataset:
LCA-ABE: Lightweight Context-Aware Encryption for Android Applications

The evolving of context-aware applications are becoming more readily available as a major driver of the growth of future connected smart, autonomous environments. However, with the increasing of security risks in critical shared massive data capabilities and the increasing regulation requirements on privacy, there is a significant need for new paradigms to manage security and privacy compliances. These challenges call for context-aware and fine-grained security policies to be enforced in such dynamic environments in order to achieve efficient real-time authorization between applications and connected devices. We propose in this work a novel solution that aims to provide context-aware security model for Android applications. Specifically, our proposition provides automated context-aware access control model and leverages Attribute-Based Encryption (ABE) to secure data communications. Thorough experiments have been performed and the evaluation results demonstrate that the proposed solution provides an effective lightweight adaptable context-aware encryption model.
Page-Turning Techniques for Reading Interfaces in Virtual Environments

Virtual Reality (VR) environments offer new ways and formats to consume and process information. Despite multimedia offerings, most information remains to be presented via text. VR has the potential to deliver immersive reading experiences while compensating for some of the drawbacks of rather static e-books. To allow readers to step into virtual books, we developed a 3D reading environment with three page-turning techniques for VR. Readers either move the camera position from page to page or control the page flow as positioned in a sequential or radial arrangement. Results from a user study with 18 participants show that moving pages is perceived as more comfortable than moving the camera position while allowing for higher fluency and reading speeds. Linear page movements support readers’ focus on a single page whereas the radial arrangement enables readers to jump between pages quickly. Our findings inform the design of immersive reading experiences in VR.
Noise-Aware Texture-Preserving Low-Light Enhancement

A simple and effective low-light image enhancement method based on a noise-aware texture-preserving retinex model is proposed in this work. The new method, called NATLE, attempts to strike a balance between noise removal and natural texture preservation through a lowcomplexity solution. Its cost function includes an estimated piece-wise smooth illumination map and a noise-free texture-preserving reflectance map. Afterwards, illumination is adjusted to form the enhanced image together with the reflectance map. Extensive experiments are conducted on common low-light image enhancement datasets to demonstrate the superior performance of NATLE.
Numerically stable multi-channel depth scene flow with adaptive weighting of regularization terms

Scene flow is a three-dimensional (3D) vector field with velocity in the depth direction and optical flow that represents the apparent motion, which can be estimated from RGB-D videos. Scene flow can be used to estimate the 3D motion of objects with a camera; thus, it is used for obstacle detection and self-localization. It can potentially be applied to inter prediction in 3D video coding. The scene-flow estimation method based on the variational method requires numerical computations of nonlinear equations that control the regularization strength to prevent excessive smoothing due to scene-flow regularization. Because numerical stability depends on multi-channel images and computational parameters such as regularization weights, it is difficult to determine appropriate parameters that satisfy the stability requirements. Therefore, we propose a numerical computation method to derive a numerical stability condition that does not depend on the color of the image or the weight of the regularization term. This simplifies the traditional method and facilitates the setting up of various regularization weight functions. Finally, we evaluate the performance of the proposed method.
Enhancing Large Vocabulary Continuous Speech Recognition System for Urdu-English Conversational Code-Switched Speech

This paper presents first step towards Large Vocabulary Continuous Speech Recognition (LVCSR) system for Urdu-English code-switched conversational speech. Urdu is the national language and lingua franca of Pakistan, with 100 million speakers worldwide. English, on the other hand, is official language of Pakistan and commonly mixed with Urdu in daily communication. Urdu, being under-resourced language, have no substantial Urdu-English code-switched corpus in hand to develop speech recognition system. In this research, readily available spontaneous Urdu speech corpus (25 hours) is revised to use it for enhancement of read speech Urdu LVCSR to recognize code-switched speech. This data set is split into 20 hours of train and 5 hours of test set. 10 hours of Urdu BroadCast (BC) data are collected and annotated in a semi-supervised way to enhance the system further. For acoustic modeling, state-of-the-art DNN-HMM modeling technique is used without any prior GMM-HMM training and alignments. Various techniques to improve language model using monolingual data are investigated. The overall percent Word Error Rate (WER) is reduced from 40.71% to 26.95% on test set.
Solar Radiation Forecasting based on Neural Network in Guangzhou

Solar power generation occupies a leading position in new energy generation technology, however output power fluctuations caused by the uncertainty of solar radiation changes may seriously affect the safety and stability of the power grid. Therefore, solar radiation data is the main basis for evaluating the output of photovoltaic (PV) power generation. Considering the influence of various meteorological factors on the intensity of solar radiation, accurate prediction of solar radiation intensity is particularly important in the development of the new energy industry. In this paper, we first analyze the relationship between various meteorological factors and solar radiation intensity; secondly, we built a BP neural network to construct a model; then we proposed to use rainfall, humidity, and clear-air index as inputs to the solar radiation intensity prediction system. We verified that the predicted and actual output values of the model have a root mean square error (RMSE) of less than 0.5. Finally, the training and testing samples of different months and regions were selected to test the model which verified the universal applicability of the proposed model.
Action Detection and Anomaly Analysis Visual System using Deep Learning for Robots in Pandemic Situation

In this paper a visual system equipped with state of the art in Deep Learning is presented which could be employed in the robotics platforms for the pandemic situation where human - human contact needs to be limited in order to perform various detection and anomaly analysis tasks. The developed detection and anomaly analysis system deals with human and environmental hazards and disasters especially for pandemic prevention. The system could detect whether the person of interest is wearing mask or not, social distancing is followed, person or environment are in normal condition for example if a window is open to keep ventilation in a closed environment. This research is a part of our project to develop a specific robot for pandemics to use advanced in artificial intelligence to make systems which could keep us safe and healthy.
WITHDRAWN: Smart jacket for health monitoring using LabVIEW

This article describes the designing and applications of smart jacket equipped with health monitoring sensors using lab view software tool. In today’s life it is very essential to have quality of improvement in health care at home and at hospital for patients and society at large. As many innovations are going on in micro technologies, telecommunication, low-power design, new textiles, and flexible sensors new user friendly devices got developed which will be giving comfort to patients. As 90% of clothes and textiles are in direct contact with the skin surface, smart sensors and smart clothes with non invasive sensors this will be a great solution for home-based as well as ambulatory health monitoring. These systems can provide a safe and comfortable environment for home healthcare, illness prevention, pharmaceuticals to citizens, data analysis and wireless transmission in order to collect, save, analyze the wireless transmit physiological signals such as human body temperature and pulse rate and Blood Pressure. As we know that these health monitoring sensors can be operated by using Arduino or Raspberry pi tool with a specific computer programming dumped in it which makes the whole process complex. To overcome the complexity and the many limitations as per design and specifications of these tools Lab VIEW is used. It is a tool that uses graphical programming languages. These are coded by selecting objects, connecting them and adding functionality. The coding or building a circuit in Lab VIEW in one operating system can work in another operating system which makes it reliable.
Parallel Multi-Population Technique for Meta-Heuristic Algorithms on Multi Core Processor

This paper investigates parallelization method for meta-heuristic algorithms such as Particle Swarm optimization (PSO) and Differential Evolution (DE) on multicore processor to reach eventually fast execution and stable result. In PSO or DE algorithm, all of member in initial population are created to search the best place in which the value of member in that place is satisfied the output criteria. As the parallelization method, the searching region is separated into many sub-regions which are executed with optimized algorithm on multi-core processor. The structure of meta-heuristic algorithms is rebuilt to execute in parallel multi population mode. The benchmark functions such as Rosenbrock, Griewank, Ackley and Michalewicz are used to test those proposed algorithms. The results show that the proposed parallel multi-population technique applied on PSO and DE algorithm has a competitive performance compared to the standard ones. The parallel multi-population technique shows better result which proves more precise and stable. Especially, meta-heuristic algorithms running in parallel multipopulation mode execute quite convincingly faster than standard ones.
A Comparative Study of Named Entity Recognition on Myanmar Language

This paper represents the development of the Myanmar Named Entity Recognition (NER) system using Conditional Random Fields (CRFs). In order to develop the system, a manually annotated Named Entities (NEs) corpus - collected from Myanmar news websites and Asia Language Treebank(ALT)-Parallel-Corpus has been used. We compare the performance of the system getting syllable-based input to the one getting character-based input. We observed that training data has more impact on the performance of the system. The experimental results show that the syllable-based system performs better than the character-based system. It achieves that Precision, Recall and F1-score values of 93.62%, 91.64% and 92.62% respectively.
Myanmar Text-to-Speech System based on Tacotron-2

Myanmar is one of the developing countries situated in South-East Asia, and there are still many areas that have been under-developed with respect to advanced natural language processing technologies, where text-to-speech is one of them. The main motivation of this paper is to improve the naturalness of Myanmar text-to-speech system that is able to generate human-like speech. In this paper, we apply the neural network architecture based on Tacotron-2 that generates a mel spectrogram for speech synthesis directly from the sequence of text. Our proposed method is composed of three steps. In the first step, we create a speech corpus of 5k sentences of text and audio pair of Myanmar text from a large set of news articles, novel books, daily usages and travel-related expressions. We segment the Myanmar text into a sequence of characters by using a syllable segmenter and text normalizer. In the second step, we utilize the recurrent sequence-to-sequence feature prediction network that maps character embedding to mel-scale spectrograms. In the final step, we use Griffin-Lim algorithm to convert the corresponding text into generate Myanmar speech output. We compare our proposed method with an end-to-end generative model based on Tacotron. Furthermore, we investigate the subjective evaluation for both methods in speech synthesis by using mean opinion score (MOS). The experimental results show that our proposed method obtains an improvement over Tacotron based speech synthesis in terms of naturalness and intelligibility.
Spatial-Channel Context-Based Entropy Modeling for End-to-end Optimized Image Compression

End-to-end optimized image compression has emerged as a disruptive technique to reduce the spatial redundancies with an improved reconstruction quality. However, existing entropy model for latent representations cannot sufficiently exploit their spatial and channel-wise correlations. In this paper, we propose a novel entropy model based on spatial-channel contexts for end-to-end optimized image compression. The proposed model jointly leverages spatial structural dependencies and channel-wise correlations to improve the probabilistic estimation of latent representations. Instead of complex autoregressive hyperprior network, shallow artificial neural networks (ANNs) incorporating 3-D masks are developed to efficiently realize the entropy model with a guarantee of causality. Experimental results demonstrate that the proposed model achieves competitive rate-distortion performance and reduces model complexity in comparison to recent end-to-end optimized methods for image compression.
Categorization of process names

Abstract The categorization of processes according to their functionality is a key point to understand, how the user spent his or her time working with a computer. This task is not trivial due to the limited textual information available with applications. The existing approaches of this area commonly use predefined categories or clusterization techniques for applications, not processes. These techniques also do not describe the choice of these particular categories. In this paper, we describe our approach for the categorization of process names and explain, why we choose the set of categories we used. We evaluated the proposed approach on our collected data and got 81% correctly classified processes.
Dynamic Reference Information: Formalising Contextual Actionable Information for Contested Environments

Acquiring situation awareness and understanding within contested and dynamic environments requires the most up-to-date information that is both actionable and contextual. Information fusion systems currently use static a-priori information and knowledge bases, and in some cases, incomplete or stale symbolic information, which is not situational or contextual. This paper formalises Dynamic Reference Information (DRI), which is defined as temporal, contextual, situational and actionable information that can update a-priori information. Furthermore, DRI semantically represents new objects, attributes and their values from situations within an observable space. The formalisation of DRI was used on unstructured information generated during the Heimdall Experiment. During the analysis, two main results were reported: i) novel Objects of Interest within an Observable Space were able to be defined from unstructured information ii) the implicit nature of context in DRI allowed an enhanced situational understanding of all a-priori and novel objects perceived in a situation represented within a tactical display.
University Music Students’ Choice of Music Listening Sources: Use of Library Resources as Compared with Non-Academic Streaming Services

The rise of streaming services and decline of analog media have affected academic music libraries in their traditional role as a resource of listening materials. This study examines the listening-source preferences of college-level music students across multiple institutions through a survey in which students compared electronic commercial streaming services and both electronic and analog library multimedia collections to determine the factors that lead to the use of one source over the other. Findings indicate a strong preference for commercial streaming services over library materials, emphasizing the importance of convenience. However, the perceived quality of library materials remains high among participants.
Quality of Context in the internet of things: Parametres and models

In the internet of things word, the ability of a system to adapt and react to the changes accruing in the environment is the most important feature in a context aware system. This system relays on gathered context information that is imperfect due to the limitation of the sensors and the dynamicity of the environment, therefore, considering the quality of the context (QoC) is curial and important to ensure the right system performance. In this paper, we present the concepts of context, context awareness, and QoC. We define the most relevant QoC parameters, present a comparison of their relevant denomination and introduce our QoC parameters definition. Finally, we highlight the main QoC evaluation method and models that been suggested in the literature.
HPME: A High-Performance Hardware Memory Encryption Engine Based on RISC-V TEE

Encryption and integrity verification of memory content is very important, especially for IoT devices. In this paper, a high-performance hardware memory encryption engine integrated into RISC-V SoC is proposed. By applying this scheme, we achieved a nearly 41.8% cycle reduction over the reference design. The proposed design can help provide more safety mechanisms for TEE such as Keystone[1] by providing hardware-implemented memory encryption and integrity verification.
Countering Anti-forensics of SIFT-based Copy-Move Detection

Forensic analysis is used to detect image forgeries e.g. the copy move forgery and the object removal forgery, respectively. Counter forensic techniques (aka anti-forensic methods to fool the forensic analyst by concealing traces of manipulation) have become popular in the game of cat and mouse between the analyst and the attacker. Classical anti-forensic techniques targeting on SIFT keypoints have been established with particular emphasis on keypoint removal in the context of copy move forgery detection. In this paper we propose a forensic approach countering SIFT keypoint removal by changing to a different type of keypoints in forensic analysis, clearly demonstrating benefits over traditional SIFT keypoint oriented techniques.
Random disclosure in confidential statistical databases

As part of statistical disclosure control National Statistical Offices can only deliver confidential data being sufficiently protected meeting national legislation. When releasing confidential microdata to users, data holders usually apply what are called anonymisation methods to the data. In order to fulfil the privacy requirements, it is possible to measure the level of privacy of some confidential data file by simulating potential data intrusion scenarios matching publicly or commercially available data with the entire set of confidential data, both sharing a non-empty set of variables (quasi-identifiers). According to real world microdata, incompatibility between data sets and not unique combinations of quasi-identifiers are very likely. In this situation, it is nearly impossible to decide whether or not two records refer to the same underlying statistical unit. Even a successful assignment of records may be a fruitless disclosure attempt, if a rationale data intruder would keep distance from that match. The paper lines out that disclosure risks estimated thus far are overrated in the sense that revealed information is always a combination of both, systematically derived results and non-negligible random assignment.
Predictive model for elderly dependency assessment in ambient assisted living

A significant proportion of elderly population suffers from age-related health issues and different chronic diseases which leads to a progressive decline in physical and cognitive skills which prevents them to live independently in their home and to perform basic Activities of Daily Living (ADL). In this context, the integration of information and communication technologies (ICT) has resulted in a rapid emergence of pervasive healthcare systems such as Health Smart Homes which aim to monitor and evaluate the person's health condition and their behavior in performing ADL and to avoid, as long as possible, the delays of recourse to healthcare institutions (e.g. nursing homes and hospitals). In this research, we propose a predictive model for detecting behavioural and health-related changes in a patient who is monitored continuously in an assisted living environment. We focus on keeping track of the evolution of the dependency level and detecting the loss of autonomy for elderly person using a Hidden Markov Model based approach. We were interested in including blood pressure in this predictive process as it is considered the primary risk factor for cardiovascular disease, stroke, kidney failure and many other diseases. Our simulation was applied on a empirical dataset that concerned 3046 old persons monitored over 9 years. The results show that our model accurately evaluates person's dependency, follows its evolution of autonomy over the time and thus predicts moments of important changes.
Distortion Specific Contrast Based No-Reference Quality Assessment of DIBR-Synthesized Views

In the literature, many 3D-Synthesized Image Quality Assessment (IQA) algorithms are proposed, which are based on predicting the geometric and structural distortions present in the synthesized datasets. With the exponential growth of accurate inpainting algorithms, certain types of distortions, such as Blackholes, has become obsolete. Unfortunately, the existing IQA algorithms are mainly concentrating on efficiently identifying these black holes and subsequently predicting the perceptual quality of 3D synthesized views. The performance of these algorithms is quite weak in the recently proposed IETR dataset. Towards this end, we propose a new completely blind IQA algorithm, which is based on the following key observations: 1. Distortions such as blurriness, blockiness (compression artifacts), and fast fading (object shifting) primarily affect the perceptual quality of 3D-synthesized views. 2. The perceptual characteristics of natural and synthetic synthesized views are quite different; distortions in natural views are perceptually more sensitive than the former. 3. Human Visual System's (HVS) ability to access the perceptual quality of an image also depends on some other properties of the images, such as contrast. All these observations are integrated into the proposed algorithm named Distortion-Specific Contrast-Based (DSCB) IQA. Various experiments validate that the proposed DSCB IQA efficiently competes with human perception and exhibits substantially better results (at least 17% gain in terms of PLCC) when compared to the existing NR IQAs.