Reviewerly/exHarmony huggingface dataset:
GraphOpt: Constrained-Optimization-Based Parallelization of Irregular Graphs

Sparse, irregular graphs show up in various applications like linear algebra, machine learning, engineering simulations, robotic control, etc.These graphs have a high degree of parallelism, but their execution on parallel threads of modern platforms remains challenging due to the irregular data dependencies.The execution performance can be improved by efficiently partitioning the graphs such that the communication and thread synchronization overheads are minimized without hurting the utilization of the threads.To achieve this, this paper proposes GRAPHOPT, a tool that models the graph parallelization as a constrained optimization problem and uses the open Google OR-Tools solver to find good partitions.Several scalability techniques are developed to handle large real-world graphs with millions of nodes and edges.Extensive experiments are performed on the graphs of sparse matrix triangular solves (linear algebra) and sum-product networks (machine learning), respectively, showing a mean speedup of 2.0× and 1.8× over previous state-of-the-art libraries, demonstrating the effectiveness of the constrained-optimization-based graph parallelization.
Understanding Software Quality Metrics for Virtual Reality Products - A Mapping Study

Virtual Reality (VR) Software is becoming more mainstream in recent years. It has provided an opportunity for VR practitioners to explore new domains and deliver cutting edge products. The success of the VR products depends primarily on the product contextual relevance and qualities exhibited. However, it is unclear how VR practitioners curb software quality challenges and improve the essence of the VR product over every release. In this paper, we present a Systematic Mapping Study of the software quality metrics adopted by VR practitioners for assessing the quality of their VR products. The study showed that practitioners used unique metrics to measure the quality of their VR products in addition to adopting some of existing enterprise software metrics. Further, we consolidate these metrics into different themes that future practitioners may use for developing VR products.
Implementation of User-Centered Design (UCD) Method in Product Development of Traveling Wheelchair

Abstract Persons with disabilities often difficulties when traveling and mobility in daily activities, especially with movement disorders. Many tools have been developed for people with disabilities to support mobility. One of them is a traveling wheelchair. Based on preliminary studies, it is known that the dimensions of traveling wheelchairs tend to be smaller than wheelchairs in general and have limited features that affect the comfort of its users. This study tries to evaluate a traveling wheelchair using the user-centered design (UCD) method and usability testing and developing this product. The standard UCD method used is based on ISO 9241-210, which focuses on users’ needs and interests by applying ergonomic criteria and knowledge of usability techniques. The evaluation results show that the overall usability level of the wheelchair product tested has excellent standards with usability values ranging from 89.12%-94.56%. However, there are still several problems, especially in the usability satisfaction criteria. Based on the evaluation results and the user’s thinking aloud, the development of this traveling wheelchair product was carried out. Development is based on several parameters such as stability, wheelchair dimensions, seat, postural support, and adjustable and ergonomic factors.
Analysis and Evaluation of Template Based Methods Against Geometric Attacks: A Survey

Template based methods have been identified as one of the acknowledged methods in image watermarking consisting the influential techniques to prevent geometric attacks. These are added features in the watermarked image by the aim of synchronization recognition without including sensitive information. However, up to now, there is no activity to narrow down each of these methods. This paper, categorized the solutions for prevention and detection of geometric attacks based on predefined templates considered in digital images. Due to this classification in the proposed solutions, a state of the art investigation is performed to evaluate and compare the improvements in this area during 2003 to 2020. The goal is to find the critical factors to make an optimum balance between robustness, imperceptibility, capacity, and to increase security of the watermarking techniques planned in template based methods in order to assist the future researchers to achieve an optimum watermarking algorithm for protection of images against Geometric attacks. The experimental results prove the efficacy of utilizing Pyramidal Just Noticeable Difference (PJND) with high accuracy to increase robustness against more attacks in comparison to the other methods while employing curvelet to hide templates is appropriate to make the capacity of method independent from robustness and imperceptibility.
Search with Discretion

This paper describes and assesses the value sensitive design (VSD) of a test collection: data used to train and evaluate a machine learning system for information retrieval. The project used the VSD framework and methods to design a test collection annotated for discretion. We conducted qualitative stakeholder interviews to develop values personas, which guided annotation of a collection of corporate emails for contextual notions of sensitivity. Both qualitative and quantitative evaluations of the method reveal that the values personas concretely shaped annotators' sensitivity judgments, and analysis of the test collection itself demonstrates that the sensitivity annotations have utility for identifying features that may correlate with email sensitivity. Values personas for training data annotation expand the toolkit of methods for value-sensitive machine learning.
Dakter Bari

Bangladesh (a low-income country) has a significant number of people dependent on alms for daily survival. These people, who we address as extremely impoverished people (EIP) are deprived of even basic healthcare. Their extreme levels of poverty, coupled with low literacy skills, and complete lack of access to technology means that they are unaware of existing low-cost/free healthcare services (as arranged by local hospitals) available for EIPs. In this paper, we address this gap by means of a carefully-crafted solution, Dakter Bari (a term in Bengali that translates to "Doctor's Home'' in English), that is contextually tailored to enable healthcare access to impoverished people. Extracting critical insights from our field study with (N=70) EIPs, we create a pathway for availing lower-cost healthcare solutions using intermediaries for information dissemination. These intermediaries are small businesses that impoverished people visit often. We also conduct field studies with (N=71) intermediary partners and (N=10) hospitals to identify challenges and realities of such intermediary-based solutions. Based on our findings, we design, iteratively develop, deploy, and user-test our system in real cases and collect feedback from related stakeholders. Preliminary analysis on usage of our system (deployed at intermediaries) revealed 255 healthcare requests made by EIPs via our system in six months. We connect our finding to the broader interests of CSCW around contextualized intermediation, inclusive healthcare, and sustainability of deployed systems.
Game Theory-Based Parameter-Tuning for Path Planning of UAVs

Modern UAV s are incredibly complex systems with numerous tunable knobs such as the battery capacity, camera settings, sampling rate, constraints on the route, etc. The area of theoretical exploration of the optimization problems that arise in such settings is dominated by traditional approaches that use regular nonlinear optimization often enhanced with AI-based techniques such as genetic algorithms. These techniques are sadly rather slow, have convergence issues, and are typically not suitable for use at runtime. In this paper, we leverage recent and promising research results that propose to convert the optimization problem into a game and then find the set of equilibrium strategies of different players. The strategies can then be mapped to the optimal values of the tunable parameters. With simulation studies in virtual worlds, we show that our solutions are 5-21% better than those produced by traditional methods, and our approach is 10-100 times faster.
A Novel Random Forest Dissimilarity Measure for Multi-View Learning

Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.
The Challenge of Information and Communications Technology in Education

The dramatic impact of Information and Communications Technology (ICT) on society suggests the potential for an analogous effect on education. The prospects for some benefit from the affordances offered by ICT are appealing but not entirely understood by many educators. The challenges of design, implementation, assessment, and analysis of ICT supported education are considerable. These challenges include how ICT can support traditional learning approaches, add new educational opportunities, and reduce resistance to introducing disruptive technologies such as smartphones. The affordances of ICT in education open many possibilities to integrate with and support existing curriculums. However, many educational institutions have not fully embraced these opportunities. This paper will explore some barriers to ICT adoption in the educational context. The first section is a brief introduction followed by a section on the history of educational theory to illustrate the considerable body of knowledge available on this topic. The third section introduces a type of case study of the challenges faced by a nation in providing the infrastructure needed to implement many of these technologies. Section four gives a summary of many, but not all, barriers to technology adoption faced by educational administrators, instructional designers, educators, and learners. And the final section is a brief conclusion.
Deep Learning-Enabled Variational Optimization Method for Image Dehazing in Maritime Intelligent Transportation Systems

Image dehazing has become a fundamental problem of common concern in computer vision-driven maritime intelligent transportation systems (ITS). The purpose of image dehazing is to reconstruct the latent haze-free image from its observed hazy version. It is well known that the accurate estimation of transmission map plays a vital role in image dehazing. In this work, the coarse transmission map is firstly estimated using a robust fusion-based strategy. A unified optimization framework is then proposed to estimate the refined transmission map and latent sharp image simultaneously. The resulting constrained minimization model is solved using a two-step optimization algorithm. To further enhance dehazing performance, the solutions of subproblems obtained in this optimization algorithm are equivalent to deep learning-based image denoising. Due to the powerful representation ability, the proposed method can accurately and robustly estimate the transmission map and latent sharp image. Numerous experiments on both synthetic and realistic datasets have been performed to compare our method with several state-of-the-art dehazing methods. Dehazing results have demonstrated the proposed method’s superior imaging performance in terms of both quantitative and qualitative evaluations. The enhanced imaging quality is beneficial for practical applications in maritime ITS, for example, vessel detection, recognition, and tracking.
Improved SRCNN for super-resolution reconstruction of retinal images

Image super-resolution reconstruction means to recover high resolution image from low resolution image. It is widely used in satellite image, city monitoring, medical treatment and other fields. Medical image processing usually requires a high level of image detail. We designed an improved SRCNN for super-resolution reconstruction of retinal images (ISRCNN). In the image reconstruction part of this network, deconvolution was adopted for up-sampling to reconstruct high-resolution retinal images. Secondly, we deepened the layers of the network and adopted dense connections to improve the ability of network feature extraction. Finally, we adopted ReZero residue learning method, which could not only avoid the gradient disappearance or explosion caused by excessively deep network, but also help to reconstruct the detailed information of retinal image. We test models on DRIVE data sets, the results showed that our approach was effective in reconstructing retinal images.
A Model for the Evaluation of Critical IT Systems Using Multicriteria Decision-Making with Elements for Risk Assessment

One of the important objectives and concerns today is to find efficient means to manage the information security risks to which organizations are exposed. Due to a lack of necessary data and time and resource constraints, very often it is impossible to gather and process all of the required information about an IT system in order to properly assess it within an acceptable timeframe. That puts the organization into a state of increased security risk. One of the means to solve such complex problems is the use of multicriteria decision-making methods that have a strong mathematical foundation. This paper presents a hybrid multicriteria model for the evaluation of critical IT systems where the elements for risk analysis and assessment are used as evaluation criteria. The iterative steps of the design science research (DSR) methodology for development of a new multicriteria model for the objectives of evaluation, ranking, and selection of critical information systems are delineated. The main advantage of the new model is its use of generic criteria for risk assessment instead of redefining inherent criteria and calculating related weights for each individual IT system. That is why more efficient evaluation, ranking, and decision-making between several possible IT solutions can be expected. The proposed model was validated in a case study of online banking transaction systems and could be used as a generic model for the evaluation of critical IT systems.
Optimized stereo digital image correlation setup for miniature round specimen: framework development and implementation

For the advancement of micro- and nano-technologies, multiaxial material testing at a small length scale is imperative. A novel multiaxial miniature testing system (MMTS) is under development for testing a tubular specimen of outer diameter (OD) as small as 1 mm. Because of the small specimen size of MMTS, stereo-Digital Image Correlation (DIC) is the preferred strain measurement technique. Although theoretically, stereo-DIC is length-scale independent, the implementation of stereo-DIC, particularly for miniature testing, faces experimental setup related challenges. For this reason, although stereo-DIC is strongly recommended over 2D DIC, researchers are sometimes compelled to use the latter. It is shown in the present study that the experimental setup related difficulties, particularly for miniature round specimen testing, can be overcome by a systematic development of a mathematical framework for stereo-DIC implementation. This framework addresses all setup decisions concerning stereo-DIC implementation, such as selections of stereo angle, speckle size, camera position, camera sensor size, lens focal length, dimensions of camera and lens bodies, calibration grid size, etc., as well as stereo-DIC analysis parameters, such as subset and step size. Besides serving the need of building a stereo-DIC setup for MMTS, since the developed mathematical framework treats all stereo-DIC setup decisions as variables, it can be used to develop an optimized stereo-DIC setup for any application. Examples of two general cases are reported. Since this general framework serves as a tool to solve the stereo-DIC experimental setup related challenges, the developed framework will contribute to the wider adoption of stereo-DIC over 2D DIC.
Learning to Prune in Training via Dynamic Channel Propagation

In this paper, we propose a novel network training mechanism called “dynamic channel propagation” to prune the neural networks during the training period. In particular, we pick up a specific group of channels in each convolutional layer to participate in the forward propagation in training time according to the significance level of channel, which is defined as channel utility. The utility values with respect to all selected channels are updated simultaneously with the error back-propagation process and will adaptively change. Furthermore, when the training ends, channels with high utility values are retained whereas those with low utility values are discarded. Hence, our proposed scheme trains and prunes neural networks simultaneously. We empirically evaluate our novel training scheme on various representative benchmark datasets and advanced convolutional neural network (CNN) architectures, including VGGNet and ResNet. The experiment results verify the superior performance and robust effectiveness of our approach.
AntiPhiMBS-Auth: A New Anti-phishing Model to Mitigate Phishing Attacks in Mobile Banking System at Authentication Level

In the era of digital banking, the advent of the latest technologies, utilization of social media, and mobile technologies became prime parts of our digital lives. Unfortunately, phishers exploit digital channels to collect login credentials from users and impersonate them to log on to the victim systems to accomplish phishing attacks. This paper proposes a novel anti-phishing model for Mobile Banking System at the authentication level (AntiPhiMBS-Auth) that averts phishing attacks in the mobile banking system. This model employs a novel concept of a unique id for authentication and application id that is known to users, banking app, and mobile banking system only. Phishers and phishing apps do not know the unique id or the application id, and consequently, this model mitigates the phishing attack in the mobile banking system. This paper utilized a process meta language (PROMELA) to specify system descriptions and security properties and built a verification model of AntiPhiMBS-Auth. The verification model of AntiPhiMBS-Auth is successfully verified using a simple PROMELA interpreter (SPIN). The SPIN verification results prove that the proposed AntiPhiMBS-Auth is error-free, and financial institutions can implement the verified model for mitigating the phishing attacks in the mobile banking system at the authentication level.
A differentially private location generalization approach to guarantee non-uniform privacy in moving objects databases

Recently there has been much interest in moving objects databases because of their applications in many domains, such as location-based services and traffic management. Moving objects databases store and manage information representing changes in the spatial properties of moving objects over time. Meanwhile, privacy protection has been one of the most important concerns in these databases. In this paper, we study this problem by presenting DPLG, a location generalization approach for moving objects databases that preserves the strong guarantee of differential privacy. Our main goal is to guarantee non-uniform privacy for locations with different privacy protection requirements while being scalable for spatial domains with a large number of locations. For this purpose, we use location generalization in such a way that locations with higher privacy protection requirements are generalized to larger ones. Location generalization also has the advantage that it enables DPLG to reduce the number of locations and, thus, to keep the running time and space requirements as reasonable as possible. We also present two post-processing techniques, namely, consistency constraints enforcement and quality improvement, to have consistent query answers and to reduce query errors caused by location generalization. The quality improvement technique divides the noisy count of each generalized location among the reference locations it contains homogeneously or heterogeneously. Extensive experiments demonstrate that, in addition to keeping reasonable the running time and space requirements, DPLG improves the utility of query answers for locations with lower privacy protection requirements in comparison to those with higher privacy protection requirements while satisfying differential privacy.
Adversarial attacks on a lexical sentiment analysis classifier

Social media has become a relevant information source for several decision-making processes and for the definition of business strategies. As various sentiment analysis techniques are used to transform collected data into intelligence information, the sentiment classifiers used in these collection environments must be carefully studied and observed before being considered trustful and ready to be installed in decision support systems. An important research area concerns the robustness of sentiment classifiers in view of new adversarial attacks, in which small perturbations may be created by malicious users to deceive the sentiment classifiers, generating a perception different from the one that should be observed in the environment. Thus, it is important to identify and analyze the vulnerabilities of these classifiers under different strategies of adversarial attacks to propose countermeasures that can be used to mitigate such attacks. In this context, this work presents adversarial attacks related to a lexical natural language classifier. Being the target of the attacks, this classifier is used to calculate the sentiment of collected data as posted by users in various social media applications. The results indicate that the found vulnerabilities, if exploited by malicious users in applications that use the same lexical classifier, could invert or cancel the classifiers' perception, thus generating perceptions that do not correspond to the reality for decision making. This work also proposes some countermeasures that might mitigate the implemented attacks.
Geometric discord for multiqubit systems

Radhakrishnan et al. (Phys. Rev. Lett. 124:110401, 2020) proposed quantum discord to multipartite systems and derived explicit formulae for any states. These results are significant in capturing quantum correlations for multiqubit systems. In this paper, we evaluate the geometric measure of multipartite quantum discord and obtain the results for a large family of multiqubit states. Furthermore, we investigated the dynamic behavior of geometric discord for the family of two-, three- and four-qubit states under phase noise acting on the first qubit. And we discover that sudden change of multipartite geometric discord can appear when phase noise act only on one part of the two-, three- and four-qubit states.