Reviewerly/exHarmony huggingface dataset:
Development of Trust-Integrated Technology Acceptance Model for eHealth Based on MetaAnalytic Findings

eHealth program has been proposed by World Health organization to support universal health coverage for all people using information and communication technology (ICT). This can enable people to easily access health treatment anywhere and everywhere. However, the adoption of eHealth is still limited due to many factors and one of the important elements is trust as the underlying construct for acceptance. This study aims to analyze factors affecting trust in eHealth and to propose the trust integrated technology acceptance model in eHealth based on the meta-analytic findings. Meta-analysis procedure was conducted involving article selection, study coding, and effect size calculation. Affinity diagram approach was adopted to cluster the factors resulted from the meta analysis. There were three main aspects affecting trust in and use of eHealth obtained from the meta-analysis, namely personal, system, and interaction factors. The meta-analysis findings revealed that internet use, perceived usefulness, and baseline trust as the factors having higher effect than other factors. From the results of meta-analysis findings, we proposed a TAM model for eHealth considering trust construct and the factors influencing it. This model can be used as a suggestion for policy maker to enhance the acceptance and utilization of eHealth.
Simulation Development of Multi-Axis PV System Tracker

Solar energy is the energy generated by harnessing the power of solar radiation. In order to extract maximum energy from the sun, the solar panel installed must be positioned as much as possible perpendicular to the sun's incident radiation. However, due to the continuous change in relative angle of the sun with reference to earth and other constraints, for instance; shadow of long buildings and trees around the PV plant, reduces the yield power delivered by solar panel is reduced considerably. To reduce the loss and to increase the efficiency of the photovoltaic panel solar trackers are best alternative. Various types of tracking systems were developed. However, most of them were done with the emphasis of the unceasing change of relative angle of the sun. In this research work, inspired by a previous collaboration with the University of the District of Columbia, in Washington DC, USA, we, at the faculty of Electrical and Computer Engineering at Bahir Dar University, Ethiopia, proposed a novel multi axis tracking system aimed at reducing shadow effect and improve PV panel efficiency by extending solar extraction daily hour in small scaled system for the selected rural locations.
Detecting and Mitigating Low-and-Slow DoS Attacks in NoC-based MPSoCs

As Multi-Processor Systems-on-Chip (MPSoCs) permeate the Internet by powering IoT devices, they are exposed to new threats. One major threat is Denial-of-Service (DoS) attacks, which make communication services slow or even unavailable. While mainly studied on desktop and server systems, some DoS attacks on mobile devices and Network-on-Chip (NoC) platforms have also been considered. In the context of NoC-based MPSoC architectures, previous works have explored flooding DoS attacks and their countermeasures, however, these protection techniques are ineffective to mitigate new DoS attacks. Recently, a shift of the network attack paradigm from flooding DoS to Low-and-Slow DoS has been observed. To this end, we present two contributions. First, we demonstrate, for the first time, the impact of Low-and-Slow DoS attacks in NoC environments. Second, we propose a lightweight online monitor able to detect and mitigate these attacks. Results show that our countermeasure is feasible and that it effectively mitigates this new attack. Moreover, since the monitors are placed at the entry points of the network, both, single- and multi-source attacks can be neutralized.
Parallelizing the Bounded K-Nearest Neighbors Algorithm for Distributed Computing Systems

The need for data collection and analysis in recent years has given rise to the field of Big Data. Organizations place high importance in this area because it could potentially lead to an improved quality of life for consumers and large amounts of profits. The K Nearest Neighbors (KNN) algorithm has been used to extract meaningful information from datasets. However, its performance suffers when it's applied to large datasets due to a bottleneck issue. We previously proposed a Bounded KNN algorithm, which alleviates this bottleneck and improves performance without sacrificing prediction accuracy. Although it is more efficient than the traditional KNN algorithm, the Bounded KNN is still not ideal for handling the massive amounts of data which are prevalent in today's world. In this paper, we present a parallelized algorithm which further improves performance by distributing the workload across a cluster of machines. Our experimental results show that there is some overhead time involved with distributing the workload, but when the datasets are increasingly larger, the benefits of parallelization eventually outweigh this limitation.
Towards Robust Speech Interfaces for the ISS

The International Space Station ISS is a scientific laboratory in which astronauts conduct a great variety of experiments on a tight schedule. In order to fulfill their tasks efficiently and correctly, astronauts need assistance, which (at least partially) can be provided by IT systems on board, among them robotic assistants like the Crew Interactive Mobile Companion CIMON. However, the creation of user interfaces for such systems is a challenge, because astronauts often have to interact hands-free or cannot direct their attention to a visual user interface. These challenges can be met by providing multimodal user interfaces that enable speech interaction, among other modalities. We describe the use context for speech interfaces on the ISS, specific requirements and possible solutions. Our concepts rely on previous work carried out in acoustically demanding environments.
Participatory Data Physicalization: A New Space to Inform

The paper proposes a reflection on how to analyze, assess and design Participatory Data Physicalization (PDP) by taking into consideration a PDP project designed by one of the authors of the paper and commissioned by Prevention For You, a startup specialized in prevention education. For Data Physicalization we intend the translation of data into tangible and, in any case, perceptible-with-senses-other-than-vision artifacts. Data Physicalization allows for an engagement of the recipient of information which differ from that of Data Visualization. By exploiting and exploring these different forms of engagement, which often entail the involvement of the entire body, Data Physicalization can give way to artifacts that dispose and afford participation. PDP can be then a way to promote participation and to democratize data beyond a broader diffusion and deeper understanding of information. The paper discusses all these issues by taking into account the PDP project “KnowAndBe.live – more knowledge, less fear” about cancer awareness.
Feature Space Exploration for Motion Classification Based on Multi-Modal Sensor Data for Lower Limb Exoskeletons

In this paper, we address the problem of finding a minimal multi-modal sensor setup for motion classification in lower limb exoskeleton applications while maintaining the classification performance. We present an approach for a systematic exploration of the feature space and feature space dimensionality reduction for motion recognition using Hidden Markov Models (HMMs). We evaluated our approach using IMU and force sensor data with 10 subjects performing 14 different daily activities. We perform a dimensionality reduction on sensor feature level with single- and multi-subjects and we explore the feature space using fine-grained features such as the force value of a single direction. Additionally, we investigate the influence of physical characteristics on the classification quality. Our results show that a subject specific and general reduction of the sensors is possible while still achieving the same classification performance.
GAUSSIAN MIXTURE NOISED RANDOM FRACTALS WITH ADVERSARIAL LEARNING FOR AUTOMATED CREATION OF VISUAL OBJECTS

Because of the self-similarity properties of nature, fractals are widely adopted as generators of natural object multimedia contents. Unfortunately, fractals are difficult to control due to their iterated function systems, and traditional researches on fractal generating visual objects focus on mathematical manipulations. In Generative Adversarial Nets (GANs), visual object generators can be automatically guided by a single image. In this work, we explore the problem of guiding fractal generators with GAN. We assume that the same category of fractal patterns is produced by a group of parameters of initial patterns, affine transformations and random noises. Connections between these fractal parameters and visual objects are modeled by a Gaussian mixture model (GMM). Generator trainings are performed as gradients on GMM instead of fractals, so that evaluation numbers of iterated function systems are minimized. The proposed model requires no mathematical expertise from the user because parameters are trained by automatic procedures of GMM and GAN. Experiments include one 2D demonstration and three 3D real-world applications, where high-resolution visual objects are generated, and a user study shows the effectiveness of artificial intelligence guidances on fractals.
A Multi-server ORAM Framework with Constant Client Bandwidth Blowup

Oblivious Random Access Machine (ORAM) allows a client to hide the access pattern when accessing sensitive data on a remote server. It is known that there exists a logarithmic communication lower bound on any passive ORAM construction, where the server only acts as the storage service. This overhead, however, was shown costly for some applications. Several active ORAM schemes with server computation have been proposed to overcome this limitation. However, they mostly rely on costly homomorphic encryptions, whose performance is worse than passive ORAM. In this article, we propose S 3 ORAM, a new multi-server ORAM framework, which features O (1) client bandwidth blowup and low client storage without relying on costly cryptographic primitives. Our key idea is to harness Shamir Secret Sharing and a multi-party multiplication protocol on applicable binary tree-ORAM paradigms. This strategy allows the client to instruct the server(s) to perform secure and efficient computation on his/her behalf with a low intervention thereby, achieving a constant client bandwidth blowup and low server computational overhead. Our framework can also work atop a general k -ary tree ORAM structure ( k ≥ 2). We fully implemented our framework, and strictly evaluated its performance on a commodity cloud platform (Amazon EC2). Our comprehensive experiments confirmed the efficiency of S 3 ORAM framework, where it is approximately 10× faster than the most efficient passive ORAM (i.e., Path-ORAM) for a moderate network bandwidth while being three orders of magnitude faster than active ORAM with O (1) bandwidth blowup (i.e., Onion-ORAM). We have open-sourced the implementation of our framework for public testing and adaptation.
Syntax-ignorant N-gram embeddings for dialectal Arabic sentiment analysis

Abstract Arabic sentiment analysis models have recently employed compositional paragraph or sentence embedding features to represent the informal Arabic dialectal content. These embeddings are mostly composed via ordered, syntax-aware composition functions and learned within deep neural network architectures. With the differences in the syntactic structure and words’ order among the Arabic dialects, a sentiment analysis system developed for one dialect might not be efficient for the others. Here we present syntax-ignorant, sentiment-specific n-gram embeddings for sentiment analysis of several Arabic dialects. The novelty of the proposed model is illustrated through its features and architecture. In the proposed model, the sentiment is expressed by embeddings, composed via the unordered additive composition function and learned within a shallow neural architecture. To evaluate the generated embeddings, they were compared with the state-of-the art word/paragraph embeddings. This involved investigating their efficiency, as expressive sentiment features, based on the visualisation maps constructed for our n-gram embeddings and word2vec/doc2vec. In addition, using several Eastern/Western Arabic datasets of single-dialect and multi-dialectal contents, the ability of our embeddings to recognise the sentiment was investigated against word/paragraph embeddings-based models. This comparison was performed within both shallow and deep neural network architectures and with two unordered composition functions employed. The results revealed that the introduced syntax-ignorant embeddings could represent single and combinations of different dialects efficiently, as our shallow sentiment analysis model, trained with the proposed n-gram embeddings, could outperform the word2vec/doc2vec models and rival deep neural architectures consuming, remarkably, less training time.
Attentional Adversarial Variational Video Generation via Decomposing Motion and Content

Predicting future frames sequentially for a video is considered a challenging generative modeling task. Our work is among the few works for complete video generation. A video is about objects (content) performing actions (motion). Based on this, we utilize paired inputs including human skeleton information as motion embedding and a human appearance image as the content information, to generate novel motion frames. Our approach is based on a video prediction model using a combination of the Variational Autoencoder and Generative Adversarial Network (VAE-GAN). On top of that, we applied two attentional mechanisms to obtain the important regions of interest in a video, aimed at improving the visual representation of the human motion in the generated output. To evaluate the performance of the proposed method, we conduct quantitative and qualitative experiments including comparisons with state-of-the-arts. As the result of experiments suggest, the proposed method shows improved performance in comparison with other widely used methods and performs favorably under the metrics PSNR, SSIM, LPIPS, and FVD on Human3.6M dataset.
Assistive sensing technology for the elderly health monitoring

World population of elderly is growing rapidly. Due to aging, independent living of older adults becomes difficult because of their cognitive decline, increase in chronic diseases, deterioration of physical activity, and impairment of hearing and vision system. To assist the elderly for their independent and comfortable safe living, there is a rapid progress of assisted living technologies. These technologies are developed to monitor in real time the ambient environment and daily residential activities of elderly. Assistive technologies are also developed to monitor the salient health parameters with the help of wearable sensors. Over the years, many research articles, including review papers, are published to discuss about this technology. Most of these technologies are based on different types of the sensors. However, working and construction of the sensors are available in different literature in distributed manner. Here, an effort is made to provide detailed information of the wearable and nonwearable sensors for the desired application of the assistive living. Effort is also made to point out future scope of research in this area.
Using Eye Tracking Data to Improve Requirements Specification Use

[Context and motivation] Software requirements specifications are the main point of reference in traditional software projects. Especially in large projects, these documents get read by multiple people, multiple times. [Question/problem] Several guidelines and templates already exist to support writing a good specification. However, not much research has been done in investigating how to support the use of specifications and help readers to find relevant information and navigate in the document more efficiently. [Principal ideas/results] We used eye tracking data obtained from observing readers when using specifications to create three different attention transfer features to support them in this process. In a student experiment, we evaluated if these attention visualizations positively affect the roles software architect, UI-designer and tester when reading a specification for the first time. The results show that the attention visualizations did not decrease navigation effort, but helped to draw the readers’ attention towards highlighted parts and decreased the average time spent on pages. [Contribution] We explored and evaluated the approach of visualizing other readers’ attention focus to help support new readers. Our results include interesting findings on what works well, what does not and what could be enhanced. We present improvement suggestions and ideas on where to focus follow-up research on.
Interactive exploration of large time-dependent bipartite graphs

Bipartite graphs are typically visualized using linked lists or matrices, but these visualizations neither scale well nor do they convey temporal development. We present a new interactive exploration interface for large, time-dependent bipartite graphs. We use two clustering techniques to build a hierarchical aggregation supporting different exploration strategies. Aggregated nodes and edges are visualized as linked lists with nested time series. We demonstrate two use cases: finding advertising expenses of public authorities following similar temporal patterns and comparing author-keyword co-occurrences across time. Through a user study, we show that linked lists with hierarchical aggregation lead to more insights than without.
Application of Deep Learning Methods for Automation of Leukocyte Microscopy

Hematology is data-rich discipline and at the same time the hematological data are complex and often difficult to understand. Using deep learning methods, it is possible to solve a range of problems related to analyzing a large number of hematological preparations. This is particularly relevant for such activities as mass medical examination and distance microscopy as part of telemedicine system. This article discusses the possibility of using deep learning models based on convolutional neural networks to automate leukocyte microscopy. An approach to solving the problem of detecting and classifying cells in microscope images is proposed. The key components of biomedical image analysis pipeline are implemented including data preprocessing module, segmentation and image classification neural network models. The proposed pipeline is evaluated, and further directions of development are considered according to the obtained performance characteristics.
Benchmarking quantum processors with a single qubit

The first generation of small noisy quantum processors have recently become available to non-specialists who are not required to understand specifics of the physical platforms and, in particular, the types and sources of noise. As such, it is useful to benchmark the performance of such computers against specific tasks that may be of interest to users, ideally keeping both the circuit depth and width as free parameters. Here, we benchmark the IBM quantum experience using the deterministic quantum computing with 1 qubit (DQC1) algorithm originally proposed by Knill and Laflamme in the context of liquid-state NMR. In the first set of experiments, we use DQC1 as a trace estimation algorithm to benchmark performance with respect to circuit depth. In the second set, we use this trace estimation algorithm to distinguish between knots, a classically difficult task which is known to be complete for DQC1. Our results indicate that the main limiting factor is the depth of the circuit and that both random and systematic errors become an issue when the gate count increases. Surprisingly, we find that at the same gate count wider circuits perform better, probably due to randomization of coherent errors.
Detecting Defects in Sanitary Wares Using Deep Learning

Anomaly detection is an important task and has a wide range of applications, e.g. fraud, disease, and damage detection. Currently, the fault detection procedures in the industry of sanitary ware often rely on the senses of the test operators. One of the standard failure tests consists of hitting the sanitary ware with a small hammer and analyzing the sound emitted. Products with good quality emit a characteristic sound pattern. On the other hand, wares with defect emit sounds with slightly different patterns, and the goal is to determine when the emitted sound does not follow the typical pattern. There are two crucial problems in this procedure: its efficiency can be compromised due to fatigue and lack of attention of the human being performing the test, and the environment is noisy. This work proposes the development of an automatic system to aid the operator to identify defects in sanitary ware. The proposed system deploys a supervised deep learning technique. The trained models presented an average precision superior to 99%, and an execution time inferior to 0.018 s.
Comparative study of face detection methods in spontaneous videos

Finding faces in the images or in real time situation is a challenging image processing problem. Hence, new approaches have achieved accurate real time detection. This paper aims to find the best face detector and specifically proper for real time detection for further work. Different face detection algorithms are analyzed and compared namely: (1) Haar-like features, (2) Local Binary Pattern and (3) Histogram of oriented object. For this aim, the multimodal RECOLA database is used. The test is implemented using the python language with Opencv and the dlib libraries. After applying and evaluating the face detectors on each video, the obtained experimental results proved that the Histogram of oriented object algorithm has the efficient detection rate of 62.16% out of the three methods while the LBP algorithm has the fastest frame rates of 33.79 frames per second.
Robust long-term object tracking with adaptive scale and rotation estimation

In this article, a robust long-term object tracking algorithm is proposed. It can tackle the challenges of scale and rotation changes during the long-term object tracking for security robots. Firstly, a robust scale and rotation estimation method is proposed to deal with scale changes and rotation motion of the object. It is based on the Fourier–Mellin transform and the kernelized correlation filter. The object’s scale and rotation can be estimated in the continuous space, and the kernelized correlation filter is used to improve the estimation accuracy and robustness. Then a weighted object searching method based on the histogram and the variance is introduced to handle the problem that trackers may fail in the long-term object tracking (due to semi-occlusion or full occlusion). When the tracked object is lost, the object can be relocated in the whole image using the searching method, so the tracker can be recovered from failures. Moreover, two other kernelized correlation filters are learned to estimate the object’s translation and the confidence of tracking results, respectively. The estimated confidence is more accurate and robust using the dedicatedly designed kernelized correlation filter, which is employed to activate the weighted object searching module, and helps to determine whether the searching windows contain objects. We compare the proposed algorithm with state-of-the-art tracking algorithms on the online object tracking benchmark. The experimental results validate the effectiveness and superiority of our tracking algorithm.