Reviewerly/exHarmony huggingface dataset:
Participatory data collection and management in low-resource contexts

As access to and control of data becomes increasingly democratized, understanding the potential and constraints for low resource contexts has important implications for system design as well as practice. Our research pushes the bounds of current system deployment by proposing and testing an ICT-based participatory data management system to transform participants from data providers to data consumers. This tool begins with participatory design, which engages participants in deciding which types of data to collect. Then, it involves training them in data collection, analysis and management. This enables participants to gain basic data science skills to make informed decisions. Our study uses mixed methods to explore the feasibility and effects of this system with urban refugees living in Rwanda. The quantitative results indicate refugees' perceived effectiveness in using the system to build communities is directly influenced by system usability assessments, which in turn are influenced by education levels but not ICT self-efficacy. Qualitative results highlight the community-based interactions experienced by all participants and highlight important differences in pursuing projects with urban versus camp-based refugees.
Real-Time Vehicle Detection Using Cross-Correlation and 2D-DWT for Feature Extraction

Nowadays, real-time vehicle detection is one of the biggest challenges in driver-assistance systems due to the complex environment and the diverse types of vehicles. Vehicle detection can be exploited to accomplish several tasks such as computing the distances to other vehicles, which can help the driver by warning to slow down the vehicle to avoid collisions. In this paper, we propose an efficient real-time vehicle detection method following two steps: hypothesis generation and hypothesis verification. In the first step, potential vehicles locations are detected based on template matching technique using cross-correlation which is one of the fast algorithms. In the second step, two-dimensional discrete wavelet transform (2D-DWT) is used to extract features from the hypotheses generated in the first step and then to classify them as vehicles and nonvehicles. The choice of the classifier is very important due to the pivotal role that plays in the quality of the final results. Therefore, SVMs and AdaBoost are two classifiers chosen to be used in this paper and their results are compared thereafter. The results of the experiments are compared with some existing system, and it showed that our proposed system has good performance in terms of robustness and accuracy and that our system can meet the requirements in real time.
Shape monitoring of morphing wing using micro optical sensors with different embedded depth

Considering the complex and variability of flight environment of the morphing aircraft, usually it is difficult to solve the real-time wing shape monitoring problem when the aircraft changes its motion state or suffering the wind and waves. In this paper, with the goal of supporting real-time wing shape monitoring of morphing aircraft, three-dimensional (3D) shape monitoring of polyimide film skin for morphing wing based on the embedded Fiber Bragg Grating (FBG) sensor is designed and analyzed. Firstly, the sensitively of FBG sensors with different embedded depth in the polyimide film was analyzed, and the finding is that when the ratio value of upper/lower embedded depth is larger, the sensitively of FBG sensor is higher. Secondly, the wavelength of FBG sensors at different states were obtained, and the center wavelength shift of FBG sensors can be used to measure the bending curvatures of the wing, besides, according to the obtained bending curvatures, the 3D shape of polyimide skin of the morphing wing at different deformation states are reconstructed through interpolation and curve fitting functions. Moreover, several shape reconstruction experiments of the morphing wing were carried out with precise visual measurement, and the maximum error between FBG sensing and visual measurement is 5%. Consequently, the effectiveness of FBG sensing method for wing shape monitoring of morphing aircraft is demonstrated, as well as more potentiality and applicability for the shape monitoring in the field of soft robotics and flexible biosensor monitoring.
Knowledge Extraction and Improved Data Fusion for Sales Prediction in Local Agricultural Markets

In this paper, a monitoring system of agricultural production is modeled as a Data Fusion System (data from local fairs and meteorological data). The proposal considers the particular information of sales in agricultural markets for knowledge extraction about the associations among them. This association knowledge is employed to improve predictions of sales using a spatial prediction technique, as shown with data collected from local markets of the Andean region of Ecuador. The commercial activity in these markets uses Alternative Marketing Circuits (CIALCO). This market platform establishes a direct relationship between producer and consumer prices and promotes direct commercial interaction among family groups. The problem is presented first as a general fusion problem with a network of spatially distributed heterogeneous data sources, and is then applied to the prediction of products sales based on association rules mined in available sales data. First, transactional data is used as the base to extract the best association rules between products sold in different local markets, knowledge that allows the system to gain a significant improvement in prediction accuracy in the spatial region considered.
Heartbeat design for energy-aware IoT: Are your sensors alive?

A number of algorithms now exist for using model-based prediction at the sensor node of a wireless sensor network (WSN) to enable a dramatic reduction in transmission rates, and thus save energy at the sensor node. These approaches, however, sometimes reduce the rate so substantially as to make the health state of the network opaque. One solution is to include a regular heartbeat transmission whose receipt or otherwise informs the sink about the health state of the node. However, given that a large period increases the probability that dead nodes go unnoticed at the sink, while a small period likely increases the energy cost of communication, what should be the period of the heartbeat transmission? In this paper, we examine the use of heartbeats in WSN design. We derive a general protocol for optimal and dynamic heartbeat transmission by minimising the Bayes risk, which is the expected cost of missing data from dead nodes plus the energy cost of heartbeat transmissions. Our proposed algorithm is dynamic in the sense that the heartbeat period is updated as time goes on and node failures become more probable. We validate our design experimentally using three real-world datasets, and show a 36% reduction in the total heartbeat operational cost over a heartbeat transmission with a fixed period; the results also highlight the superiority of our algorithm over arbitrarily chosen heartbeat periods in different WSN settings, thus promising significant cost savings in WSN applications.
Dissecting the performance of YouTube video streaming in mobile networks

Summary Video streaming applications constitute a significant portion of the Internet traffic today, with mobile accounting for more than half of the online video views. The high share of video in the current Internet traffic mix has prompted many studies that examine video streaming through measurements. However, streaming performance depends on many different factors at different layers of the TCP/IP stack. For example, browser selection at the application layer or the choice of protocol in transport layer can have significant impact on the video performance. Furthermore, video performance heavily depends on the underlying network conditions (eg, network and link layers). For mobile networks, the conditions vary significantly, since each operator has a different deployment strategy and configuration. In this paper, we focus on YouTube and carry out a comprehensive study investigating the influence of different factors on streaming performance. Leveraging the Measuring Mobile Broadband Networks in Europe (MONROE) test bed that enables experimentation with 13 different network configurations in four countries, we collect more than 1800 measurement samples in operational mobile networks. With this campaign, our goal is to quantify the impact of parameters from different layers on YouTube's streaming quality of experience (QoE). More specifically, we analyze the role of the browser (eg, Firefox and Chrome), the impact of transport protocol (eg, TCP or QUIC), the influence of network bandwidth, and signal coverage on streaming QoE. Our analysis reveals that all these parameters need to be taken into account jointly for network management practices, in order to ensure a high end‐user experience.
Dense Stereo Matching Based on Multiobjective Fitness Function—A Genetic Algorithm Optimization Approach for Stereo Correspondence

Dense stereo image matching in remotely sensed images is a challenging problem, though it has been studied for more than two decades, due to occlusions, discontinuities, geometric, and radiometric distortions. A novel multiobjective fitness function-based dense stereo matching approach using genetic algorithms (GAs) is proposed in this paper. The proposed method is useful for estimating dense disparity map with an improved number of inliers for a stereo image pair, despite the constraint of finding correct disparity at depth discontinuities. In this paper, the steps of GA, such as initialization of the population, fitness function, and crossover and mutation operation, are designed and implemented to effectively deal with the problem of dense stereo image matching. To initialize the population, a Scale Invariant Feature Transform (SIFT) descriptor is computed for each pixel and multiple-size window-based matching is performed, using the similarity measures: 1) Euclidean distance and 2) spectral angle mapper. The generated disparity maps are pruned to choose a suitable subset using the designed fitness functions, considering the constraints related to stereo image pair, such as epipolar constraint, which encodes the epipolar geometry and the similarity measure that is useful to decide accuracy of the correspondences. The two objective functions are the number of inliers computed using the fundamental matrix and an energy minimization function, considering discontinuities and occlusions. The usefulness of this approach for remotely sensed stereo image pairs is demonstrated by improving the number of inliers and favorably comparing with state-of-the-art dense stereo image matching methods.
Large Scale Characterization of Software Vulnerability Life Cycles

Software systems inherently contain vulnerabilities that have been exploited in the past resulting in significant revenue losses. The study of various aspects related to vulnerabilities such as their severity, rates of disclosure, exploit and patch release, and existence of common vulnerabilities in different products can help in improving the development, deployment, and maintenance process of software systems. It can also help in designing future security policies and conducting audits of past incidents. Furthermore, such an analysis can help customers to assess the security risks associated with software products of different vendors. In this paper, we conduct an exploratory measurement study of a large software vulnerability data set containing 56077 vulnerabilities disclosed since 1988 till 2013. We investigate vulnerabilities along following eight dimensions: (1) phases in the life cycle of vulnerabilities, (2) evolution of vulnerabilities over the years, (3) functionality of vulnerabilities, (4) access requirement for exploitation of vulnerabilities, (5) risk level of vulnerabilities, (6) software vendors, (7) software products, and (8) existence of common vulnerabilities in multiple software products. Our exploratory analysis uncovers several statistically significant findings that have important implications for software development and deployment.
Characterizing and predicting downloads in academic search

Numerous studies have been conducted on the information interaction behavior of search engine users. Few studies have considered information interactions in the domain of academic search. We focus on conversion behavior in this domain. Conversions have been widely studied in the e-commerce domain, e.g., for online shopping and hotel booking, but little is known about conversions in academic search. We start with a description of a unique dataset of a particular type of conversion in academic search, viz. users’ downloads of scientific papers. Then we move to an observational analysis of users’ download actions. We first characterize user actions and show their statistics in sessions. Then we focus on behavioral and topical aspects of downloads, revealing behavioral correlations across download sessions. We discover unique properties that differ from other conversion settings such as online shopping. Using insights gained from these observations, we consider the task of predicting the next download. In particular, we focus on predicting the time until the next download session, and on predicting the number of downloads. We cast these as time series prediction problems and model them using LSTMs. We develop a specialized model built on user segmentations that achieves significant improvements over the state-of-the art.
New particle filtering phase unwrapping algorithm for wrapped fringe patterns

• A novel robust phase unwrapping algorithm based on particle filter is presented for wrapped fringe patterns. • An efficient local slope estimator is applied to efficiently and accurately obtain phase slope information needed for the state estimate model based on particle filter to enhance the efficiency and accuracy of the unwrapping procedure. • An efficient quality-guided strategy is used to guide the unwrapping paths. This paper presents a novel phase unwrapping algorithm by combining a particle filter with an efficient local slope estimator based on amended matrix pencil model, and an efficient quality-guided strategy based on heap-sort. The proposed method formulates the phase unwrapping problem of wrapped phase images as an optimal state estimation problem under the framework of a particle filter, and gains the estimate of the unwrapped phase at each pixel using a particle filter. The efficient local slope estimator based on amended matrix pencil model is applied to rapidly obtain the phase slope information needed for the state estimation model based on particle filter, and the efficient quality-guided strategy based on heap-sort guides the particle filter to efficiently and accurately unwrap wrapped pixels along the path from the high-reliance regions to the low-reliance regions of the wrapped phase images. The results obtained using synthetic data and experimental data demonstrate the effectiveness of the proposed method, and show that this new approach can obtain satisfactory solutions from noisy wrapped phase images.
Ensuring High-Quality Private Data for Responsible Data Science

High-quality data is critical for effective data science. As the use of data science has grown, so too have concerns that individuals’ rights to privacy will be violated. This has led to the development of data protection regulations around the globe and the use of sophisticated anonymization techniques to protect privacy. Such measures make it more challenging for the data scientist to understand the data, exacerbating issues of data quality. Responsible data science aims to develop useful insights from the data while fully embracing these considerations. We pose the high-level problem in this article, “How can a data scientist develop the needed trust that private data has high quality?” We then identify a series of challenges for various data-centric communities and outline research questions for data quality and privacy researchers, which would need to be addressed to effectively answer the problem posed in this article.
DeepShoe: An improved Multi-Task View-invariant CNN for street-to-shop shoe retrieval

The difficulty of describing a shoe item seeing on street with text for online shopping demands an image-based retrieval solution. We call this problem street-to-shop shoe retrieval, whose goal is to find exactly the same shoe in the online shop image (shop scenario), given a daily shoe image (street scenario) as the query. We propose an improved Multi-Task View-invariant Convolutional Neural Network (MTV-CNN+) to handle the large visual discrepancy for the same shoe in different scenarios. A novel definition of shoe style is defined according to the combinations of part-aware semantic shoe attributes and the corresponding style identification loss is developed. Furthermore, a new loss function is proposed to minimize the distances between images of the same shoe captured from different viewpoints. In order to efficiently train MTV-CNN+, we develop an attribute-based weighting scheme on the conventional triplet loss function to put more emphasis on the hard triplets; a three-stage process is incorporated to progressively select the hard negative examples and anchor images. To validate the proposed method, we build a multi-view shoe dataset with semantic attributes (MVShoe) from the daily life and online shopping websites, and investigate how different triplet loss functions affect the performance. Experimental results show the advantage of MTV-CNN+ over existing approaches.
Deriving architectural models from requirements specifications: A systematic mapping study

Software architecture design creates and documents the high-level structure of a software system. Such structure, expressed in architectural models, comprises software elements, relations among them, and properties of these elements and relations. Existing software architecture methods offer ways to derive architectural models from requirements specifications. These models must balance different forces that should be analyzed during this derivation process, such as those imposed by different application domains and quality attributes. Such balance is difficult to achieve, requiring skilled and experienced architects. The purpose of this paper is to provide a comprehensive overview of the existing methods to derive architectural models from requirements specifications and offer a research roadmap to challenge the community to address the identified limitations and open issues that require further investigation. To achieve this goal, we performed a systematic mapping study following the good practices from the Evidence-Based Software Engineering field. This study resulted in 39 primary studies selected for analysis and data extraction, from the 2575 initially retrieved. The major findings indicate that current architectural derivation methods rely heavily on the architects’ tacit knowledge (experience and intuition), do not offer sufficient support for inexperienced architects, and lack explicit evaluation mechanisms. These and other findings are synthesized in a research roadmap which results would benefit researchers and practitioners.
Opportunities and challenges in connecting care recipients to the community health feedback loop

This paper explores the design space of feedback systems that connect care recipients to the community health feedback loop. While related work in this vein has often emphasized gathering feedback for the sake of transparency alone, our study emphasizes opportunities to integrate the collection and use of feedback in ways that may improve the quality or equity of routine health services. We conducted a qualitative study using semi-structured interviews and focus groups with 23 participants in Kenya. Our field study makes current feedback practices visible; and reveals barriers faced by beneficiaries, community health workers, and their supervisors. Our findings identify relevant socio-technical complexities, and we outline concrete opportunities to design feedback systems that support and augment current practices. These contributions to the ICTD literature hold potential to inform the design of feedback systems that engage underserved populations in a systematic and equitable manner.
Improving User Performance in Haptics-Based Rehabilitation Exercises by Colocation of User's Visual and Motor Axes via a Three-Dimensional Augmented-Reality Display

Serious games are recently becoming a common sight in rehabilitation settings to provide motivation for patients undergoing therapy to regain upper limb function after disability. These are often presented using a two-dimensional (2-D) monitor to the patient who uses a robotic device (haptic user interface) as the game controller. In this letter, we develop a 3-D spatial augmented reality (AR) display to colocate visual and haptic feedback to the user in three rehabilitative games. The same games are also displayed in a 2-D nonimmersive virtual reality (VR) and are compared against their AR counterpart in terms of user task performance to evaluate the benefit of the 3-D AR system. To simulate a rehabilitation scenario, able-bodied participants are put under cognitive load (CL) for simulating disability-induced cognitive deficiencies when performing the tasks. A within-subjects analysis of ten participants was carried out for the rehabilitative games. The results show that AR leads to the best user performance with or without cognitive loading. This result is most evident in dynamic exercises where the participants are required to have quick reaction times and fast movement. Furthermore, even while AR had a significant difference over VR, one of the tasks showed that the performance in AR between non-CL and CL cases was similar, thereby showing how AR can alleviate the negative effects of CL.
Recognizing Bangla Handwritten Numeral Utilizing Deep Long Short Term Memory

Handwritten numeral recognition (HNR) has gained much attention in present days as it can be applied in range of applications.Research on unconstrained HNR has shown impressive progress in few scripts but is far behind for Bangla although it is one of the major languages.Bangla contains similar shaped numerals which are difficult to distinguish even in printed form and this makes Bangla HNR (BHNR) a challenging task.Our goal in this study is to build up a superior BHNR framework and consequently explore the profound design of Long Short Term Memory (LSTM) method.LSTM is a variation of Recurrent Neural Network and is effectively used for sequence ordering with its distinct features.This study considered deep architecture of LSTM for better performance.The proposed BHNR with deep LSTM (BNHR-DLSTM) standardizes the composed numeral images first and then utilizes two layers of LSTM to characterize singular numerals.Benchmark dataset with 22000 handwritten numerals having various shapes, sizes and varieties are utilized to examine the proficiency of BNHR-DLSTM.The proposed method indicates agreeable recognition precision and beat other conspicuous existing methods.