Reviewerly/exHarmony huggingface dataset:
The New Concept of Guaranteeing Confidence in the E-Voting System

There is no doubt about the urgency of ensuring trust in e-voting systems since elections without trust cannot be acceptable in a democratic society. To solve this problem, it was proposed to use blockchain technology, however, in voting systems this leads to significant delays in the execution of transactions, and the software solution is accessible only to a narrow circle of specialists, which clearly does not help to show confidence on the part of voters. This paper proposes a different approach, which is based on the widely available verification of all means and processes that may cause distrust in the accuracy of the vote count or in ensuring their confidentiality. We offer a solution to the problem of transparency of all hardware and software that cause distrust. Voting systems that use this approach are implemented in three leading Ukrainian universities for student elections to self-government bodies. The conceptual model of the e-voting system for state elections on the example of Ukraine is presented. Links to Internet resources are provided where you can not only get acquainted with the principles of building the system, but also conduct an experimental vote in Ukrainian, English or Russian. You can also copy and test e-voting server software on your computers.
Sequential Fusion via Bounding Box and Motion PointPainting for 3D Objection Detection

Due to the complementary characteristics of camera and LiDAR data, recent research efforts have been focused on designing 3D object detectors capable of fusing images and point clouds. However, LiDAR-based detectors currently achieve better performance on KITTI and Waymo benchmark datasets [1], [2] when compared to fusion methods. This result is counter-intuitive, as fusing information from the two modalities should result in performance that at least matches the performance of LiDAR-only methods. Pointpainting [3] attempts to address this gap by sequential fusion, which solves the issue of misalignment between image view and LiDAR BEV. In this paper, we propose class-aware and class-agnostic point painting methods which employ predicted bounding boxes from image-based 2D object detectors to extract coarse image semantics instead of full scene semantic segmentation used in [3]. In addition, a motion point painting method is proposed to fuse motion cues as a way to focus attention on dynamic objects when they can be reliably distinguished from the scene, as is the case when the sensors are static. Our experiments on KITTI [1] show a 3% mAP improvement on car class for bounding box methods compared to PointPainting [3]. In addition, motion painting shows an improvement of 1.45% mAP for car class and 2.99% for pedestrian class on our proprietary traffic dataset. Finally, we conduct a range-binned evaluation on KITTI dataset using two different LiDAR stream and show that relative gain of sequential fusion methods is dependent on the selected LiDAR stream.
FPGA Implementation of Unimodal Thresholding

Thresholding is a very simple image processing procedure that is commonly used for segmentation or edge detection. The final result of thresholding is significantly affected by the used threshold value. The several methods for determining the optimal threshold have been developed. Most often, the threshold value is determined using the Otsu's or Rosin's method. In this paper we propose modification of Rosin's algorithm based on the geometric transformation of the histogram. Our approach simplifies calculations and allows FPGA implementation of Rosin's method.
Demo: Security of Camera-based Perception for Autonomous Driving under Adversarial Attack

Robust perception is crucial for autonomous vehicle security. In this work, we design a practical adversarial patch attack against camera-based obstacle detection. We identify that the back of a box truck is an effective attack vector. We also improve attack robustness by considering a variety of input frames associated with the attack scenario. This demo includes videos that show our attack can cause endto-end consequences on a representative autonomous driving system in a simulator.
Task-based parameter isolation for foreground segmentation without catastrophic forgetting using multi-scale region and edges fusion network

Foreground segmentation of moving objects is widely used in different computer vision applications; however, existing deep learning-based methods generally suffer from overall degraded F-measure performance. The two main sources that degrade the F-measure are under-segmentation and catastrophic forgetting. Under-segmentation is the problem of misdetecting objects' fine details. The catastrophic forgetting problem occurs when training on a large number of video sequences that leads to forgetting information learned from early video sequences. This paper proposes a novel multi-scale region and edges fusion network with task-based parameter isolation (REFNet-TBPI) to overcome these two problems. The proposed method consists of a novel multi-scale region and edges fusion network (REFNet) to capture the moving objects' boundary details by extracting regions and boundary edges of each object at different feature scales and fusing them to produce high-detailed segmented objects. REFNet is trained using a novel continual learning technique called task-based parameter isolation (TBPI) to overcome the catastrophic forgetting problem. The proposed method (REFNet-TBPI) is extensively evaluated on three benchmarks, namely CDnet2014, DAVIS2016, and SegTrack. By comparing REFNet-TBPI with current state-of-the-art methods, the proposed method outperforms the best-reported state-of-the-art by 4.4% on average. • A novel multi-scale region and edge fusion network (REFNet). • REFNet detects the region and edges of objects to provide accurate detection. • A novel task-based parameter isolation (TBPI) continual learning technique. • TBPI improves the performance of any CNN-based model.
House price prediction using clustering and genetic programming along with conducting a comparative study

One of the most important tasks in machine learning is prediction. Data scientists use different regression methods to find the most appropriate and accurate model for each type of datasets. This study proposes a method to improve accuracy in regression and prediction. In common methods, different models are applied to the whole data to find the best model with higher accuracy. In our proposed approach, first, we cluster data using different methods such as K-means, DBSCAN, and agglomerative hierarchical clustering algorithms. Then, for each clustering method and for each generated cluster we apply various regression models including linear and polynomial regressions, SVR, neural network, and symbolic regression in order to find the most accurate model and study the genetic programming potential in improving the prediction accuracy. This model is a combination of clustering and regression. After clustering, the number of samples in each created cluster, compared to the number of samples in the whole dataset is reduced, and consequently by decreasing the number of samples in each group, we lose accuracy. On the other hand, specifying data and setting similar samples in one group enhances the accuracy and decreases the computational cost. As a case study, we used real estate data with 20 features to improve house price estimation; however, this approach is applicable to other large datasets.
Stance Detection: Concepts, Approaches, Resources, and Outstanding Issues

Stance detection (also known as stance classification and stance prediction) is a problem related to social media analysis, natural language processing, and information retrieval, which aims to determine the position of a person from a piece of text they produce, towards a target (a concept, idea, event, etc.) either explicitly specified in the text, or implied only. The output of the stance detection procedure is usually from this set: Favor, Against, None. In this tutorial, we will define the core concepts and research problems related to stance detection, present historical and contemporary approaches to stance detection, provide pointers to related resources (datasets and tools), and we will cover outstanding issues and application areas of stance detection. As solutions to stance detection can contribute to significant tasks including trend analysis, opinion surveys, user reviews, personalization, and predictions for referendums and elections, it will continue to stand as an important research problem, mostly on textual content currently, and particularly on social media. Finally, we believe that image and video content will commonly be the subject of stance detection research soon.
Exploring Plausible Patches Using Source Code Embeddings in JavaScript

Despite the immense popularity of the Automated Program Repair (APR) field, the question of patch validation is still open.Most of the present-day approaches follow the socalled Generate-and-Validate approach, where first a candidate solution is being generated and after validated against an oracle.The latter, however, might not give a reliable result, because of the imperfections in such oracles; one of which is usually the test suite.Although (re-) running the test suite is right under one's nose, in real life applications the problem of over-and underfitting often occurs, resulting in inadequate patches.Efforts that have been made to tackle with this problem include patch filtering, test suite expansion, careful patch producing and many more.Most approaches to date use post-filtering relying either on test execution traces or make use of some similarity concept measured on the generated patches.Our goal is to investigate the nature of these similarity-based approaches.To do so, we trained a Doc2Vec model on an open-source JavaScript project and generated 465 patches for 10 bugs in it.These plausible patches alongside with the developer fix are then ranked based on their similarity to the original program.We analyzed these similarity lists and found that plain document embeddings may lead to misclassification -it fails to capture nuanced code semantics.Nevertheless, in some cases it also provided useful information, thus helping to better understand the area of Automated Program Repair.
DPNCT: A Differential Private Noise Cancellation Scheme for Load Monitoring and Billing for Smart Meters

Highly accurate profiles of consumers daily energy usage are reported to power grid via smart meters which enables smart grid to effectively regulate power demand and supply. However, consumer's energy consumption pattern can reveal personal and sensitive information regarding their lifestyle. Therefore, to ensure users privacy, differentially distributed noise is added to the original data. This technique comes with a trade off between privacy of the consumer versus utility of the data in terms of providing services like billing, Demand Response schemes, and Load Monitoring. In this paper, we propose a technique - Differential Privacy with Noise Cancellation Technique (DPNCT) - to maximize utility in aggregated load monitoring and fair billing while preserving users' privacy by using noise cancellation mechanism on differentially private data. We introduce noise to the sensitive data stream before it leaves smart meters in order to guarantee privacy at individual level. Further, we evaluate the effects of different periodic noise cancelling schemes on privacy and utility i.e., billing and load monitoring. Our proposed scheme outperforms the existing scheme in terms of preserving the privacy while accurately calculating the bill.
Examining Average and Discounted Reward Optimality Criteria in Reinforcement Learning

In reinforcement learning (RL), the goal is to obtain an optimal policy, for which the optimality criterion is fundamentally important. Two major optimality criteria are average and discounted rewards. While the latter is more popular, it is problematic to apply in environments without an inherent notion of discounting. This motivates us to revisit a) the progression of optimality criteria in dynamic programming, b) justification for and complication of an artificial discount factor, and c) benefits of directly maximizing the average reward criterion, which is discounting-free. Our contributions include a thorough examination of the relationship between average and discounted rewards, as well as a discussion of their pros and cons in RL. We emphasize that average-reward RL methods possess the ingredient and mechanism for applying a family of discounting-free optimality criteria to RL.
Research on Unmanned Ship Route Planning Based on The Smoothed DGWW Algorithm

This paper proposes a smoothed DGWW global planning method based on the BAS algorithm to optimize the GWO algorithm to solve the problem of the poor local path and low convergence speed in a complex environment of unmanned ships route planning. This method integrates the method of using the two tentacles on the head of the beetle to perform the optimal search in the BAS algorithm into the GWO algorithm so local optimization can be avoided. Then, the Bezier curve is used in the acute angle removing in the path and realistic path matching. The simulation results show that: the DGWW algorithm has a smoother path, fewer turning points, and a larger search range compared with the GWO algorithm; it can produce a better path, and the local exploration is more detailed compared with the BAS algorithm.
Modulating scalable Gaussian processes for expressive statistical learning

For a learning task, Gaussian process (GP) is interested in learning the statistical relationship between inputs and outputs, since it offers not only the prediction mean but also the associated variability. The vanilla GP however is hard to learn complicated distribution with the property of, e.g., heteroscedastic noise, multi-modality and non-stationarity, from massive data due to the Gaussian marginal and the cubic complexity. To this end, this article studies new scalable GP paradigms including the non-stationary heteroscedastic GP, the mixture of GPs and the latent GP, which introduce additional latent variables to modulate the outputs or inputs in order to learn richer, non-Gaussian statistical representation. Particularly, we resort to different variational inference strategies to arrive at analytical or tighter evidence lower bounds (ELBOs) of the marginal likelihood for efficient and effective model training. Extensive numerical experiments against state-of-the-art GP and neural network (NN) counterparts on various tasks verify the superiority of these scalable modulated GPs, especially the scalable latent GP, for learning diverse data distributions.
HERMES: Scalable, Secure, and Privacy-Enhancing Vehicular Sharing-Access System

We propose HERMES, a scalable, secure, and privacy-enhancing system for users to share and access vehicles. HERMES securely outsources operations of vehicle access token (AT) generation to a set of untrusted servers. It builds on an earlier proposal, namely, SePCAR, and extends the system design for improved efficiency and scalability. To cater to system and user needs for secure and private computations, HERMES utilizes and combines several cryptographic primitives with secure multiparty computation (MPC) efficiently. It conceals secret keys of vehicles and transaction details from the servers, including vehicle booking details, AT information, and user and vehicle identities. It also provides user accountability in case of disputes. Besides, we provide semantic security analysis and prove that HERMES meets its security and privacy requirements. Last but not least, we demonstrate that HERMES is efficient and, in contrast to SePCAR, scales to a large number of users and vehicles, making it practical for real-world deployments. We build our evaluations with two different MPC protocols: 1) HtMAC-MiMC and 2) CBC-MAC-AES. Our results demonstrate that HERMES is in the range of milliseconds for generating an AT, whether it operates for a single-vehicle owner or a large rental-company branch with over 1000 vehicles; handling 546 and 84 AT generations per second, respectively. As a result, HERMES is an order of magnitude faster compared to SePCAR. Specifically, it delivers 696 (with HtMAC-MiMC) and 42 (with CBC-MAC-AES) more ATs compared to in SePCAR for a single-vehicle owner AT generation. Furthermore, we show that HERMES is practical on the vehicle side, too, as AT operations performed on a prototype vehicle on-board unit take only <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 62 $ </tex-math></inline-formula> ms.
Cooking up engagement during a pandemic: The international Great Rare Books Bake Off between the Penn State and Monash University Libraries

In July 2020, during the COVID-19 pandemic, the Monash University Library in Melbourne, Australia, and the Penn State University Libraries in Pennsylvania, USA, leveraged their previously established international sister-library partnership to host a virtual engagement event focused on promoting the partnership and selected special collections at both institutions. The Monash–Penn State Great Rare Books Bake Off, a friendly competition collaboratively developed by the two academic libraries, engaged both institutions’ communities with their collections and resources by inviting participants to bake featured recipes from their collections and post evidence of their experiences on social media platforms. In addition to promoting awareness of the institutions’ international partnership and their respective collections, a primary goal of the collaborative project was to offer their local communities an enjoyable and creative outlet during a difficult time. This illustrative case study describes how the two institutions planned, executed, and assessed the project.
From general to specific: Online updating for blind super-resolution

Most deep learning-based super-resolution (SR) methods are not image-specific: 1) They are trained on samples synthesized by predefined degradations (e.g.bicubic downsampling), regardless of the domain gap between training and testing data. 2) During testing, they super-resolve all images by the same set of model weights, ignoring the degradation variety. As a result, most previous methods may suffer a performance drop when the degradations of test images are unknown and various (i.e.the case of blind SR). To address these issues, we propose an online SR (ONSR) method. It does not rely on predefined degradations and allows the model weights to be updated according to the degradation of the test image. Specifically, ONSR consists of two branches, namely internal branch (IB) and external branch (EB). IB could learn the specific degradation of the given test LR image, and EB could learn to super resolve images degraded by the learned degradation. In this way, ONSR could customize a specific model for each test image, and thus get more robust to various degradations. Extensive experiments on both synthesized and real-world images show that ONSR can generate more visually favorable SR results and achieve state-of-the-art performance in blind SR.
Capitalizing the database cost models process through a service‐based pipeline

Abstract Designing a database cost model is one of the main research topics related to the physical design phase. It follows the evolution of database technology in order to evaluate and quantify the performance metrics (e.g., response time, energy consumption, etc.). Therefore, it makes the community researchers sensitive to the generated results. However, reusing and comparing database cost models require extracting related information manually from the research publications. This process is error‐prone and time‐consuming. Unfortunately, many researchers claim the difficulty of surveying and reproducing cost models already published in several/journal articles and/or reports. This difficulty is due to the absence of a process describing the cost model itself formally as well as the context of its utilization. This article presents an approach enabling the extraction of cost models information (context, parameters, features, etc.) as a set of orchestrated services. These services are implemented using natural language processing and machine‐learning techniques via a work‐flow pipeline inspired by DevOps practices. We illustrate our approach on a case study to stress the feasibility and benefits of our proposal by emphasizing the reproduction and automatization facilities.
Practical Wavelet Tree Construction

We present new sequential and parallel algorithms for wavelet tree construction based on a new bottom-up technique. This technique makes use of the structure of the wavelet trees—refining the characters represented in a node of the tree with increasing depth—in an opposite way, by first computing the leaves (most refined), and then propagating this information upwards to the root of the tree. We first describe new sequential algorithms, both in RAM and external memory. Based on these results, we adapt these algorithms to parallel computers, where we address both shared memory and distributed memory settings. In practice, all our algorithms outperform previous ones in both time and memory efficiency, because we can compute all auxiliary information solely based on the information we obtained from computing the leaves. Most of our algorithms are also adapted to the wavelet matrix , a variant that is particularly suited for large alphabets.
Assessing the Technology Self-Efficacy of Maritime Instructors: An Explorative Study

Maritime Education and Training (MET) is an integral part of the global maritime industry, playing an essential role in ensuring that the sector is supplied continuously with a skilled workforce. The successful outcomes of the educational content delivery in MET institutes depend, to a certain extent, on the maritime instructor’s ability to create conducive learning environments utilizing all of the resources available. The self-efficacy of maritime instructors in various facets, most notably their proficiency with the use of technology in classrooms, can lead to the introduction of transformative learning practices. Accurately measuring their self-reported technological proficiency could be the initial step in this direction. This study aimed to measure the self-reported technology proficiency of maritime instructors using an established and validated scale: Technology Proficiency Self-Assessment for the 21st century (TPSA-C21). The scale was administered, using an online survey, to a sample of MET instructors within Europe and the UK, with n = 62 valid responses received. Using descriptive statistics and the evaluation of the measurement model, the study highlighted the perceived level of proficiency of the MET instructors along dimensions such as email, world-wide web use, emerging tools, teaching with technology, integrated applications, and teaching with emerging technologies. The survey also measured the perceived level of technology integration for maritime instructors according to the Concerned-Based Adoption Model–Level of Use (CBAM–LoU) classification. The results indicate a potential area of improvement for maritime instructors with regard to their self-reported proficiency, namely in the dimension of teaching with emerging technology. The implications for the MET domain, the respondent demographics and the future research directions are discussed.