Reviewerly/exHarmony huggingface dataset:
Arabic text summarization via Knapsack balancing of effective retention

This paper presents a new extractive Arabic text summarization approach based on the Knapsack balancing of effective retention. Effective retention refers to maximizing retention while prioritizing salient themes. First, text segments are mapped to salient topics, and an effective retention score is computed for every sentence. Next, the summarization task is formulated as a combinatory optimization problem. The final output is generated through the maximization of effective retention. Aka selected sentences to be part of the summary are the best ones to encode salient ideas while covering most central themes. Experimental results show that the proposed approach outperforms three state-of-the-art Arabic summarization protocols.
Forensic Analysis of Fitbit Versa: Android vs iOS

Fitbit Versa is the most popular of its predecessors and successors in the Fitbit faction. Increasingly data stored on these smart fitness devices, their linked applications and cloud datacenters are being used for criminal convictions. There is limited research for investigators on wearable devices and specifically exploring evidence identification and methods of extraction. In this paper we present our analysis of Fitbit Versa using Cellebrite UFED and MSAB XRY. We present a clear scope for investigation and data significance based on the findings from our experiments. The data recovery will include logical and physical extractions using devices running Android 9 and iOS 12, comparing between Cellebrite and XRY capabilities. This paper discusses databases and datatypes that can be recovered using different extraction and analysis techniques, providing a robust outlook of data availability. We also discuss the accuracy of recorded data compared to planned test instances, verifying the accuracy of individual data types. The verifiable accuracy of some datatypes could prove useful if such data was required during the evidentiary processes of a forensic investigation.
Cybersecurity behavior under covid-19 influence

The aim of the article is to study the impact of the stress related to covid-19 on the cybersecurity behaviors of students. Based on a survey questionnaire, we will stress that cyber security behavior (CSB) is not directly affected by the stress related to covid-19. The influence of the covid stress is mainly on cybersecurity self-efficacy, but only marginally. Further, the CBS is explained by the ease of use, the response cost, self-efficacy, different protective neutralization techniques, social guardianship, but not perceived susceptibility and severity to digital threat.
Robust Registration of Multispectral Satellite Images Based on Structural and Geometrical Similarity

Accurate registration of multispectral satellite images is a challenging task due to the significant and nonlinear radiometric differences between these data. To address this problem, this letter explores the strategy of geometrical similarity between triplets of feature points, and it is combined with the structural similarity between images in a feature-based image registration framework. The underlying principle is that the structural and geometrical similarities generally preserve across the images being registered. In this feature-based image registration framework, a set of control points (CPs) are first detected. Then, the geometric similarity between triplets of CPs is defined, followed by a ranking operation of these triplets of CPs. The highly ranked triplets are used to estimate a spatial transformation between images. Finally, initial matches obtained by a benchmark registration technique are refined by the estimated transformation. The experimental results demonstrate the great effectiveness of the proposed technique for registering multispectral satellite images.
Free-viewpoint image based rendering with multi-layered depth maps

• A novel depth refinement algorithm that respects photo-consistency and edge preservation to correct misalignment between color-and-depth image pairs and fill missing depth information. • A novel adaptive view selection approach that effectively avoids selecting redundant and useless input views to improve the quality of synthesized images and the rendering speed. • A novel rendering algorithm providing high-quality free-viewpoint synthesized images, which is based on layered 3D warping to deal with occluded elements and lower the rendering complexity. Our aim is to give free-viewpoint photo-realistic rendering of real indoor scenes, using a sparse collection of RGB-D image as input. Image based rendering (IBR) is an effective way to achieve both realism and interactivity. However, there are several challenges for IBR: misalignment of object boundaries between color-and-depth image pairs often leads to ghost contours; projection errors result in the visibility failure; and useless and redundant input views often produce blurring images. To address these issues, we propose a pixel-to-pixel multi-view depth refinement method to produce pixel-accurate alignment between color-and-depth image pairs, and an adaptive view selection approach to avoid choosing redundant or useless input views. Furthermore, we propose a layered 3D warping to handle occluded elements. These components are designed to work together, reducing visual artifacts and providing plausible free-viewpoint synthesized images. The evaluation results indicate that our method achieves good performance on a wide variety of challenging scenes and performs best among popular IBR algorithms designed for dynamic scenes.
Container Placement for Resource Utilization in Cyber Physical Cloud Systems

Cloud computing is considered a convenient and promising solution to handle the fast and large growth of Cyber Physical Systems (CPSs). Cloud platform can provide networking access to resources such as Virtual Machines and Containers for data storage, communication, processing, and decision making for the services provided by CPSs. Placement of the virtual resources at the cloud platform plays a key role on the quality of the service and utilization of the resources. Data analysis show that many of the requested resources are wasted and not utilized properly due to inappropriate placement of the virtual computing resources. This paper tackles Container placement problem in CPSs from resource requested and utilization perspectives. The present problem is mapped as an optimization Integer Linear Programming (ILP) model. A heuristic approach that uses deep learning Artificial Neural Networks (ANN) is proposed to solve the ILP model. To evaluate the ILP model, we use a large dataset collected from Alibaba group computing cluster. The results of the current study show that a large number of the resources can be saved and utilized using the proposed solution.
A Differentially Private Task Planning Framework for Spatial Crowdsourcing

Spatial crowdsourcing has stimulated various new applications such as taxi calling and food delivery. A key enabler for these spatial crowdsourcing based applications is to plan routes for crowd workers to execute tasks given diverse requirements of workers and the spatial crowdsourcing platform. Despite extensive studies on task planning in spatial crowdsourcing, few have accounted for the location privacy of tasks, which may be misused by an untrustworthy platform. In this paper, we explore efficient task planning for workers while protecting the locations of tasks. Specifically, we define the Privacy-Preserving Task Planning (PPTP) problem, which aims at both total revenue maximization of the platform and differential privacy of task locations. We first apply the Laplacian mechanism to protect location privacy, and analyze its impact on the total revenue. Then we propose an effective and efficient task planning algorithm for the PPTP problem. Extensive experiments on both synthetic and real datasets validate the advantages of our algorithm in terms of total revenue and time cost.
Real-Time AI-Based Informational Decision-Making Support System Utilizing Dynamic Text Sources

Unstructured data from the internet constitute large sources of information, which need to be formatted in a user-friendly way. This research develops a model that classifies unstructured data from data mining into labeled data, and builds an informational and decision-making support system (DMSS). We often have assortments of information collected by mining data from various sources, where the key challenge is to extract valuable information. We observe substantial classification accuracy enhancement for our datasets with both machine learning and deep learning algorithms. The highest classification accuracy (99% in training, 96% in testing) was achieved from a Covid corpus which is processed by using a long short-term memory (LSTM). Furthermore, we conducted tests on large datasets relevant to the Disaster corpus, with an LSTM classification accuracy of 98%. In addition, random forest (RF), a machine learning algorithm, provides a reasonable 84% accuracy. This research’s main objective is to increase the application’s robustness by integrating intelligence into the developed DMSS, which provides insight into the user’s intent, despite dealing with a noisy dataset. Our designed model selects the random forest and stochastic gradient descent (SGD) algorithms’ F1 score, where the RF method outperforms by improving accuracy by 2% (to 83% from 81%) compared with a conventional method.
Demonetization in India: Good or Bad in Context of Social Media

Indian government tweaked 500 and 1000 rupees notes on 8th November 2016 without any prior notification. There was a huge struggle for cash, so people shared their experience and opinion related to demonetization on online platforms such as Twitter, Facebook, etc. One group of people supported demonetization by giving their arguments while the other group of people strongly opposed it. In this paper, we used tweets to find the following: (i) the sentiments of the people across the country, (ii) major topics discussed during demonetization such as black money and, (iii) the effect of the demonetization on Indian financial market viz Sensex and Nifty. This study might be useful to political parties in knowing the opinion of people, and the financial situation so that prior precaution can be taken in such cases. We found slightly negative sentiment of the people across the country regarding demonetization.
Image Steganography using Dynamic Threshold based on Discrete Cosine Transform

Abstract The art of preventing the detection of hidden information messages is the way that steganography work. Several algorithms have been proposed for steganographic techniques. A major portion of these algorithms is specified for image steganography because the image has a high level of redundancy. This paper proposed an image steganography technique using a dynamic threshold produced by the discrete cosine coefficient. After dividing the green and blue channel of the cover image into 1*3-pixel blocks, check if any bits of green channel block less or equal to threshold then start to store the secret bits in blue channel block, and to increase the security not all bits in the chosen block used to store the secret bits. Firstly, store in the center of the block and then store another bit in the write or left bit depended on differences between them. The proposed method was applied to many color images and many measurement terms used to show the efficiency of it. The experiment result showed good result that the PSNR = 53.76, MSE = 0.273, SSIM= 0.999, with embedding rates 0.55
A Comparative Study on Lossless compression mode in WebP, Better Portable Graphics (BPG), and JPEG XL Image Compression Algorithms

On daily basis, millions of images related to the fields of medicine, astronomy, and remote sensing are generated. According to the sensitivity of the information, the generated images require storing them without any pictorial information loss. The lossless mode in image compression algorithms didn't introduce any loss to the pictorial information while compressing the image size by a ratio called the compression ratio (CR). In this study, the most recent and promising general image compression algorithms are compared side by side in terms of CR. The tested algorithms are WebP, BPG, and JPEG XL. The dataset used in this study is the TESTIMAGES archive which has been used due to its futuristic features such as high dynamic range (HDR) and high resolution as well as the availability of both natural and synthetic images. The results indicate that JPEG XL has the best CR on average compared to the other two algorithms when images with 8 bits per channel are used. Unlike the BPG and WebP, the JPEG XL offered real compression when HDR images, i.e., with 16 bits per channel, are feed for compression while the other algorithms didn't support such bit depth and generated images with 8 bits per channel.
FedFog: Network-Aware Optimization of Federated Learning Over Wireless Fog-Cloud Systems

Federated learning (FL) is capable of performing 1 large distributed machine learning tasks across multiple edge 2 users by periodically aggregating trained local parameters.To 3 address key challenges of enabling FL over a wireless fog-cloud 4 system (e.g., non-i.i.d.data, users' heterogeneity), we first propose 5 an efficient FL algorithm based on Federated Averaging (called 6 FedFog) to perform the local aggregation of gradient parameters 7 at fog servers and global training update at the cloud.Next, 8 we employ FedFog in wireless fog-cloud systems by investigating 9 a novel network-aware FL optimization problem that strikes the 10 balance between the global loss and completion time.An iterative 11 algorithm is then developed to obtain a precise measurement 12 of the system performance, which helps design an efficient 13 stopping criteria to output an appropriate number of global 14 rounds.To mitigate the straggler effect, we propose a flexible 15 user aggregation strategy that trains fast users first to obtain 16 a certain level of accuracy before allowing slow users to join 17 the global training updates.Extensive numerical results using 18 several real-world FL tasks are provided to verify the theoretical 19 convergence of FedFog.We also show that the proposed co-design 20 of FL and communication is essential to substantially improve 21 resource utilization while achieving comparable accuracy of the 22 learning model.23
Real-Time Area-Based Traffic Density Calculation Using Image Processing for Smart Traffic System

Traffic is one major problem in metropolitan cities and capital cities of all the states. As more people moving to the cities, the number of vehicles on the road is increasing rapidly. The traffic signals controlled by the traffic police manually have not proved to be efficient. Also the static predefined set time for traffic signals at all conditions whether the traffic density is heavy or low has not solved this problem. One solution for this problem is to develop a smart traffic management system which works by calculating the density of the vehicles on the road using real time video processing technique. We have used Mixture of Gaussian (MoG) algorithm for background subtraction technique and foreground detection, then generated capacity maps using which we found the density of traffic at that particular instant. The traffic lights at junctions are controlled dynamically based on the traffic density detected from the video feeds.
ChainOps for Smart Contract-Based Distributed Applications

Three of the key impediments that need to be addressed to unlock the true potential of smart contract-based applications include: (i) a lack of operational capabilities to leverage trustworthiness, (ii) limited ability for reuse in volatile and heterogenous application contexts, and (iii) inherent coding complexity that hinders not only involvement of non-technical business experts but also widely used DevOps practices. This paper reports on the core intermediate results of the development of a model-driven DevOps approach -labelled ChainOps- that ultimately will enable involvement of non-experts, promote reuse and allow for automatic semantic model checking and reasoning to ascertain improved trustworthiness. In addition, a preliminary architecture of an experimental prototype -that is currently under development- is discussed. Finally, this paper plots a roadmap for much needed future research to further explore, validate and extend our initial findings.
Higher-dimensional open quantum walk in environment of quantum Bernoulli noises

Open quantum walks (OQWs) (also known as open quantum random walks) are quantum analogs of classical Markov chains in probability theory, and have potential application in quantum information and quantum computation. Quantum Bernoulli noises (QBNs) are annihilation and creation operators acting on Bernoulli functionals, and can be used as the environment of an open quantum system. In this paper, by using QBNs as the environment, we introduce an OQW on a general higher-dimensional integer lattice. We obtain a quantum channel representation of the walk, which shows that the walk is indeed an OQW. We prove that all the states of the walk are separable provided its initial state is separable. We also prove that, for some initial states, the walk has a limit probability distribution of higher-dimensional Gauss type. Finally, we show links between the walk and a unitary quantum walk recently introduced in terms of QBNs.
PQA: Perceptual Question Answering

Perceptual organization remains one of the very few established theories on the human visual system. It underpinned many pre-deep seminal works on segmentation and detection, yet research has seen a rapid decline since the preferential shift to learning deep models. Of the limited attempts, most aimed at interpreting complex visual scenes using perceptual organizational rules. This has however been proven to be sub-optimal, since models were unable to effectively capture the visual complexity in real-world imagery. In this paper, we rejuvenate the study of perceptual organization, by advocating two positional changes: (i) we examine purposefully generated synthetic data, instead of complex real imagery, and (ii) we ask machines to synthesize novel perceptually-valid patterns, instead of explaining existing data. Our overall answer lies with the introduction of a novel visual challenge – the challenge of perceptual question answering (PQA). Upon observing example perceptual question-answer pairs, the goal for PQA is to solve similar questions by generating answers entirely from scratch (see Figure 1). Our first contribution is therefore the first dataset of perceptual question-answer pairs, each generated specifically for a particular Gestalt principle. We then borrow insights from human psychology to design an agent that casts perceptual organization as a self-attention problem, where a proposed grid-to-grid mapping network directly generates answer patterns from scratch. Experiments show our agent to outperform a selection of naive and strong baselines. A human study however indicates that ours uses astronomically more data to learn when compared to an average human, necessitating future research (with or without our dataset).
Remote Virtual Counseling and Effects of Embodied Cues: Toward Casual On-Line Counseling Under COVID-19 Situation

Some embodied cognition literature has shown that some environmental factors contribute to reducing client protective attitude. We have applied this aspect to counseling in VR. The aims of this paper are (1) to confirm VR environmental factors affect the subject's sense of openness, (2) explore the relationship between self-disclosure attitudes and environmental factors, and (3) investigating remote and semi-automatic counseling possibilities in this COVID-19 affected stressed world via virtual psychological experiment. We proposed the hypothesis: the large room would promote self-disclosure to a NPC virtual counselor more than the small room. We have made a "VRChat" based counseling rooms with the NPC virtual counselor. On self-disclosure attitude, the results of the Mann-Whitney U test showed that the large room promotes self-disclosure more than in the small room (U = 216, p = .01, d = −.657). These results suggest the potential for a remote and easy-to-participate virtual environment for casual counseling.
Introduction

introduction Introduction: Performing Rurality with Computing Share on Authors: Norman Makoto Su Indiana University Bloomington Indiana University BloomingtonView Profile , Jean Hardy Michigan State University Michigan State UniversityView Profile , Morgan Vigil-Hayes Northern Arizona University Northern Arizona UniversityView Profile , Tiffany Veinot University of Michigan University of MichiganView Profile , Rob Comber KTH Royal Institute of Technology KTH Royal Institute of TechnologyView Profile Authors Info & Claims ACM Transactions on Computer-Human InteractionVolume 28Issue 3July 2021 Article No.: 16epp 1–13https://doi.org/10.1145/3461832Online:03 July 2021Publication History 0citation126DownloadsMetricsTotal Citations0Total Downloads126Last 12 Months126Last 6 weeks17 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Real-Time Pose Estimation from Images for Multiple Humanoid Robots

Pose estimation commonly refers to computer vision methods that recognize people's body postures in images or videos. With recent advancements in deep learning, we now have compelling models to tackle the problem in real-time. Since these models are usually designed for human images, one needs to adapt existing models to work on other creatures, including robots. This paper examines different state-of-the-art pose estimation models and proposes a lightweight model that can work in real-time on humanoid robots in the RoboCup Humanoid League environment. Additionally, we present a novel dataset called the HumanoidRobotPose dataset. The results of this work have the potential to enable many advanced behaviors for soccer-playing robots.
Using reinforcement learning for tuning genetic algorithms

Genetic algorithms (GAs) are a subclass of evolutionary algorithms often used to solve difficult combinatorial or non-linear problems. However, most GAs have to be configured for a particular problem type, and even then, the performance depends on many hyperparameters and reproduction operators. In this paper, a reinforcement learning (RL) approach is designed to adaptively set parameters for a GA used for solving a Capacitated Vehicle Routing Problem (CVRP). An RL agent interacts with the GA environment by taking actions that affect the parameters governing its evolution, starting from a given initial point. The results obtained by this RL-GA procedure are then compared with those obtained by alternate static parameter values. For a set of benchmark problems, the solutions obtained by the RL-GA are better (up to 11% improvement) than those obtained for the set as compared to the alternate approach. Examination of the results shows that the RL-GA maintains great diversity in the population pool, especially as the iterations accrue. Computational runs are traced to show how the RL agent learns from population diversity and solution improvements over time, leading to near-optimal solutions.