Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Privacy-Preserving Certificateless Provable Data Possession Scheme for Cloud Storage

Cloud computing is a burgeoning paradigm that offers reliable and adaptive infrastructure to the data owner who shares his data with the data user through the cloud server. In the data sharing process, the integrity of the data stored in the cloud faces serious threats. The provable data possession schemes ensure that a cloud server provider proves to a third party auditor that it is truthfully storing data from the data owner. However, the PKI-based provable data possession scheme suffers the management issue of the certificates and the identity-based provable data possession scheme causes an inherent key escrow problem. In this paper, we put forward an efficient privacy-preserving certificateless provable data possession scheme based on certificateless cryptography and elliptic curve cryptography, which has been proved to be unforgeable against adaptive chosen message attacks in the random oracle model. At the same time, the data integrity can be audited by the third party auditor without downloading the whole data. The proposed scheme gives away no information of the stored data to the third party auditor during the audit phase and the data owner’s identity privacy is protected. The experiment results show that the proposed scheme is of efficiency and feasibility as far as computation and communication costs are concerned.
Prediction of Crime Occurrence using Information Propagation Model and Gaussian Process

Prediction and prevention of crime have long been one of the main concerns of public security and safety. Due to the emergence of available data and analytic tools, research for crime prediction has been attracting more attention recently. In essence, the current techniques are based on either the analysis of discrete crime event locations or the theory with aggregated crime data. However, it is difficult to estimate the probability of future crimes based on the direct interpretation of the past crime rate. Therefore, existing methods are not good at adapting to different environment and trends of crime occurrence. Currently, there is no standard method that can simultaneously address all challenges posed by different crime data sets. A more universal solution, which can cope with the changes in the environment and the diversity of crime occurrence would be highly desirable. In this paper, we present a novel approach to crime prediction and establishes a model flexible enough to apply to different circumstances. To achieve our goal, we build an information propagation model which incorporates a concept of information entropy. This research helps security organizations to address or react to crime occurrence proactively and helps local policy-makers to prevent or manage crime risks, which would eventually improve public security and safety.
Time-to-Digital Converter with Pseudo-Segmented Delay Line

This paper presents a time-to-digital converter (TDC) with pseudo-segmented delay line implemented in 28 nm field-programmable gate array (FPGA) device (Kintex-7 XC7K160T, Xilinx). The TDC employs a carry chain based delay line wherein each tap is connected to multiple flip-flops. Proposed solution gives the same measurement resolution and comparable precision improvement as using multiple time coding lines (TCL) but allows to save logical resources available in FPGA chip. The TDC was tested in timestamps based time interval counter operating at 700 MHz clock and provides 1.1 ps resolution and 5 ps precision.
Autoencoder-Based Transceiver Design for OWC Systems in Log-Normal Fading Channel

In this paper, we construct the autoencoder (AE) for optical wireless communication (OWC) systems with non-negativity and peak power constraints, which provides effective transceiver design in log-normal channel. We consider the cases where perfect channel state information (CSI) or noisy CSI can be obtained under three kinds of communication rate, which is defined as the ratio of channel use number to bit number. Meanwhile, we present the block error rate (BLER) performance to further demonstrate our transceivers’ superior performance than common model-based methods. The learned constellation points distribution is provided to understand the transmitter's performance. Numerical simulations are conducted to ensure the best convergence. The results indicate that AE-based transceivers can achieve model-based optimal BLER performance or provide significantly better BLER performance.
Measurement of Relative Position and Orientation using UWB

This paper introduces a new approach to measure relative positioning and orientation (RPO), by instrumenting mobile equipment with ultra-wideband (UWB) distance measurements. In this experiment RPO is tested without a surrounding stationary UWB anchor network; all necessary UWB devices are directly mounted on the machinery. This results in a simplified implementation in the industry, but also challenges the RPO determination. Due to this, the precision and uncertainty of the UWB measurements were characterized in a real application environment, i.e., on a quay to determine if a large body of water would influence the high frequency signals. It was determined that the UWB distance measurements had an uncertainty of approximately 20mm when measuring orthogonal to the antenna and 35mm at large angles; both results for 95% confidence. Based on these results a new approach to calculating the RPO was implemented and tested. The goal was to minimize the effects of horizontal dilution of precision. Three new algorithms were implemented and tested, the best results were obtained with a non-linear covariance weighted least squares computation with self calibration. With this approach the standard deviation of the x and y positions were 64mm and 13mm respectively. This is a very significant improvement in performance with respect to trilateration, which is currently the standard approach.
A Kalman Filter Approach for the Application of Electrical Capacitance Tomography in Dynamic Processes using a State Reduction

Electrical capacitance tomography (ECT) refers to an imaging technique, which provides information about the spatial material distribution within an object. Since the image reconstruction forms a non-linear ill-posed inverse problem, prior information about the occurring material distributions is required in order to obtain stable reconstruction results. For the monitoring of dynamic industrial processes by means of ECT, recursive Bayesian estimation such as the extended Kalman filter (EKF) were shown to be suitable reconstruction algorithms. Though due to the usually high dimension of the inverse problem, computational costs are an immanent issue for the tracking of fast changes in the material distribution. In this paper we present a sample based state reduction approach for the extended Kalman filter. This approach not only allows stable reconstruction results but also involves significantly decreased reconstruction times due to the reduced state space.
Pilot evaluation of a persuasive mobile application to change individuals’ behavior during recurrent myocardial infarction

Objective: The objective of the current study was to develop and evaluate a persuasive decision-support aiming at reducing pre-hospital delay and increasing ambulance service use for patients who suffer from a recurrent myocardial infarction. Materials and methods: The system was developed as a prototype mobile application for smartphones. The system was evaluated by four end-users with previous experience of myocardial infarction. The user tests were complemented with cognitive walkthroughs and heuristic evaluation. Results: A total of 14 persuasive design principles were used to guide the design of the system. The usability was regarded as high with an average score of 82 on the System Usability Scale. The users reported that they found the system highly persuasive. Conclusions: User-centered design together with persuasive design principles through iterative testing and development has resulted in a prototype app with potential to improve patients’ care-seeking behavior. The content in the prototype will be further evaluated before it is integrated in educational interventions.
PartsSweeper: interactive workbench to casually organize electronic parts and tools

Electronics workbenches often becomes messy, as various electronic parts and tools are used repeatedly. To solve this problem, we propose a system called "PartsSweeper", which cleans up both parts and tools on electronics workbenches. The PartsSweeper mainly consists of a customized XY plotter and GUI software on a tablet. We attached several magnets, servomotors, and lift mechanisms on a head of the XY plotter. As most electronic parts and tools are ferromagnetic, the system can move them along with the head while the magnet is lifted. Moreover, the system can selectively move parts and tools using two magnets of different force; while the strong magnet can move both tools and parts, the weak magnet moves only small parts.
Towards an empirically developed scale for measuring trust

New technologies, data, and algorithms impact nearly every aspect of daily life. Unfortunately, many of these algorithms operate like black boxes and cannot explain their results even to their programmers, let alone to end-users. As more and more tasks get delegated to such intelligent systems and the nature of user interactions with them becomes increasingly complex, it is important to understand the amount of trust that a user is willing to place on such systems. However, attempts at quantifying trust have either been limited in their scope or not empirically thorough. To address this, we build on prior work which empirically modelled trust in user-technology interactions and describe the development and evolution of a human computer trust scale. We present results of two studies (N=118 & N=183) which were undertaken to assess the reliability & validity of the proposed scale.
Switching it Up: Designing Adaptive Interfaces for Virtual Reality Exergames

Adaptive interfaces offer new opportunities to enhance motivation and intensity of physical exercise. We developed VeRitas, a physiologically controlled exergame in which the player's exertion influences their avatar's performance. Three adaptive interventions are identified, varying display immersion, the level of physical resistance, and the accuracy of physiological data. A study examined players’ heart rates, rate of perceived exertion, physical power and workload. Our work reveals that enhanced immersion clearly increases physical exertion, and that complex interactions between adaptive physical resistance and falsified physiological data occur during gameplay. New directions for adaptive interfaces are proposed that enhance training and gaming performance. We suggest that users may benefit from delegating control and precision to exergaming systems, but this requires careful consideration around the design of awareness mechanisms to communicate performance.
An Empirical Study of Wireless Security in City Environment

In nowadays, many users use wireless networks, which are usually based on the IEEE 802.11 standard (Wi-Fi). Because of broadcast technology used, they are easily susceptible to security attacks. It is very important to study the risks and potential vulnerabilities of Wi-Fi networks to recommend users how to improve their security. This paper presents a research of the security of wireless networks in city of Varna, Bulgaria, and methodology based on war-driving technique. The obtained results are summarized and compared with those from previous research. The recommendations for users are formulated.
Ensemble-based domain adaptation for transport mode recognition with mobile sensors

We present our submission (team S304) to the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge 2019. The goal is to recognize 8 modes of transport and locomotion from 5 second frames of inertial sensor data of a smartphone carried in the hand, while most of the labelled data provided for classifier training consists of data from three other smartphone placements: hips, torso and bag. Only a small dataset from a smartphone carried in the hand was provided. Model training is complicated by the fact that the data distribution differs between the phone positions. To optimize classification performance for data from the Hand phone, we employ an ensemble of Multilayer Perceptrons, each trained with data from a different particular smartphone placement, including the small dataset of the Hand phone. We propose an iterative re-weighting scheme for combining the classifiers that takes their agreement with the specialized Hand classifier into account. The proposed method achieves 74% average per-class Recall, significantly improving the performance achieved when training with mixed data from all phone placements (59%) and training with data from the Hand phone only (66%). The ensemble-based method also outperforms domain adaptation by Feature Augmentation, which achieves 70% average Recall.
Towards Local Alignment of Multiple Networks: Multi-GLAlign

Pairwise Local Alignment of biological and biomedical net- works has proven to be a useful instrument for network anal- ysis. Nevertheless, the comparative analysis of multiple net- works is still a challenge, despite its potential. Multiple net- work analysis may reveal hidden knowledge that pairwise aligners may miss. Therefore we extended our GL-Align al- gorithm to analyse multiple networks and we propose Multi- GLAlign, a novel algorithm for multiple local alignment of networks. Multi-GLAlign is based on the building of a multi- ple alignment graph by using a multiple global aligner and on the subsequent mining of this graph. We show the proposed software framework and some preliminary results.
GeoLifecycle

As Location-Based Social Networks (LBSNs) have become widely used by users, understanding user engagement and predicting user churn are essential to the maintainability of the services. In this work, we conduct a quantitative analysis to understand user engagement patterns exhibited both offline and online in LBSNs. We employ two large-scale datasets which consist of 1.3 million and 62 million users with 5.3 million reviews and 19 million tips in Yelp and Foursquare, respectively. We discover that users keep traveling to diverse locations where they have not reviewed before, which is in contrast to "human life" analogy in real life, an initial exploration followed by exploitation of existing preferences. Interestingly, we find users who eventually leave the community show distinct engagement patterns even with their first ten reviews in various facets, e.g., geographical, venue-specific, linguistic, and social aspects. Based on these observations, we construct predictive models to detect potential churners. We then demonstrate the effectiveness of our proposed features in the churn prediction. Our findings of geographical exploration and online interactions of users enhance our understanding of human mobility based on reviews, and provide important implications for venue recommendations and churn prediction.
InDexMo: exploring finger-worn RFID motion tracking for activity recognition on tagged objects

This work explores and evaluates the designs of finger-worn radio-frequency identification (RFID) motion tracking for activity recognition on tagged objects. We propose an index-finger-worn device that consists of a short-range (~2cm) RFID reader and a pair of two inertial measurement units (IMUs), which are mounted at the locations where an artificial nail and a ring are worn. The short-range RFID reader recognizes the tagged object on finger touch, and then the IMU data are used for activity recognition. Data collected from the user of this device allows for a post-hoc analysis, which informs the activity recognition performance in various RFID+IMU and IMU-only configurations on the same task. The results of a ten-participant user study show that when the objects have similar physical form factors, the hybrid RFID motion tracking significantly outperforms the IMU-only tracking, especially in a larger-number set of objects. In our test, three IMU configurations (i.e., NailOnly, RingOnly, and Nail+Ring) achieved comparable action recognition performances, i.e., ≥90% accuracy, with 500 ms recognition time, though the NailOnly RFID+IMU configuration provided the highest wearability. The practical challenges toward a real-world deployment of a finger-worn RFID motion tracking system are also discussed.
Smartphone interaction and survey data as predictors of snapchat usage

Snapchat is a highly popular smartphone app that allows personalised multimedia communication for spontaneous experiences, where the shared content disappears after a short period of time. In this paper, we examine the predictors of Snapchat usage based on a range of data collected through surveys and from interaction with the handset, using a cohort of 64 recruited participants. The results show that age, Smartphone Addiction, happiness and the use of the popular chatting apps WhatsApp and Facebook Messenger are significant predictors for Snapchat usage. We discuss the implications of these findings against the related literature, and also against the design of the app itself.
Listening space: Satellite Ikats

Listening Space is an artistic research that was born during the eTextile Spring Break camp that took place in upstate New York at the beginning of April 2019. Following their previous explorations of ecologies of transmissions and wanting to experiment with Software-Defined Radio, the authors, setup a DIY satellite tracking station and aimed at intercepting the NOAA weather satellite audiovisual transmissions. During the course of three days, they observed five satellite passes, intercepted successfully three transmissions and decoded the audio signals into images which they later knitted in order to create a textile archive of the transmissions. Conceptually the project seeks to explore transmissions ecologies as raw material for artistic exploration, to understand and re-imagine in poetic means, representations of audio and images broadcasted from space, while regarding knitted textiles as a physical medium for memory storage and archiving.
Impacts of Disruption on Secondary Task Knowledge: Recovery Modes and Social Nuances

The working memory of the human brain has always had a strong influence on the design of Human-Computer Interaction. Yet, it is limited in capacity and loses its contents over time. Research regarding these constraints typically focused on single user tasks and systems. Collaborative settings and systems introduce the need for the secondary task of coordination which shares the same conditions and constraints as the knowledge of the primary task. Additionally, it is easily compromised by interruption and interference. Our approach seeks to understand the impacts of disruption on secondary task knowledge, but from a different angle than previous related work. Since it is hard to avoid disruption entirely, it aims to understand how users recover from disruptions in order to help them recover the best way possible from different types of interruptions using appropriate mechanisms and cues. This paper reports on the results of one of the first experiments along the way, observing the effects of four types of interruptions revealing different modes of recovery and social nuances that inform the design of adaptive coordination support systems.
Novel Role of the JAK-STAT Pathway in Mediating the Effects of Atypical Antipsychotics on 5-HT2A Receptor Signaling

The therapeutic benefits of atypical antipsychotics are proposed to be mediated by antagonism and subsequent desensitization of 5-HT2A receptor signaling; however, the mechanisms underlying this desensitization response are not yet understood. We hypothesize that the desensitization of 5-HT2A receptors induced by atypical antipsychotics is dependent on activation of the JAK-STAT pathway. To test this hypothesis, we used a cell line, A1A1v cells, that natively expresses 5-HT2A receptor signaling system, and further confirmed the findings in rats. In A1A1v cells, we confirmed that treatment with both olanzapine and clozapine desensitizes 5-HT2A receptor signaling. Furthermore, olanzapine treatment also increased RGS7 mRNA and protein levels which were dependent on activation of JAK-STAT pathway. Similar results were found with MDL100907, a specific 5HT2A receptor antagonist; RGS7 protein and mRNA levels were increased along with activation of the JAK-STAT pathway, suggesting that antagonism of 5-HT2A receptors is sufficient to induce these changes. In addition, we also found an increase in STAT3 binding to the putative RGS7 promoter region with olanzapine treatment suggesting that the increase in RGS7 expression could be directly mediated by the JAK-STAT pathway. An increase in RGS protein could mediate desensitization of 5-HT2A receptor signaling by its GAP activity. Lastly, inhibition of the JAK-STAT pathway significantly attenuated olanzapine-induced desensitization of 5-HT2A receptor signaling in A1A1v
Kubow: an architecture-based self-adaptation service for cloud native applications

This paper presents Kubow, an extensible architecture-based self-adaptation service for cloud native applications. Kubow itself was implemented by customizing and extending the Rainbow self-adaptation framework with support for Docker containers and Kubernetes. The paper highlights Kubow's architecture and main design decisions, and illustrates its use and configuration through a simple example. An accompanying demo video is available at the project's web site: https://ppgia-unifor.github. io/kubow/.
Using sensing technologies, self-reported information, and interpersonal observations to promote health and well-being in the workplace

This paper outlines a pilot study aimed at identifying salient themes in the awareness of health and well-being and the effects that this can have on one's home life and work behavior. We also sought to identify the factors that lead to lapses in one's awareness of health and well-being issues. Interviews were conducted with individuals who are known to work for extended durations (taxi / Uber drivers). A number of proven methods were used for the collection of pertinent data (i.e., sensing technologies, self-reported information, and interpersonal observations). Finally, design guidelines are presented for the development of technology-based solutions aimed at raising awareness of health and well-being among workers in high-stress occupations.
Forecasting Models of Chinese, Malaysian and South Korean Tourists Visiting Thailand

This research presents a comparative study of two different forecasting methods based on the monthly Chinese, Malaysian, and South Korean tourists visiting Thailand. Holt-Winters method and Box-Jenkins method are compared. The data are taken from the Tourism Authority of Thailand, Ministry of Tourism and Sports starting from January, 2007 to December, 2018. The data are divided into 2 sets. The first set from January, 2007 to December, 2017 is used for constructing and selection the forecasting models. The second set from January, 2018 to December, 2018 is used for computing the accuracy of the forecasting model. The forecasting models are chosen by considering the smallest root mean square error (RMSE). The mean absolute percentage error (MAPE) is used to measure the accuracy of the model. The results show that Additive Holt-Winters method obtains the smallest RMSE for Chinese tourists and Box-Jenkins method gain the smallest RMSE for both Malaysian and South Korean tourists in the modeling process. While MAPE in the forecasting process for China, Malaysia and South Korea tourists are 10.22%, 11.71% and 5.14% respectively.