Reviewerly/exHarmony huggingface dataset:
CPO: A Crow Particle Optimization Algorithm

Particle swarm optimization (PSO) is the most well known of the swarm-based intelligence algorithms and is inspired by the social behavior of bird flocking.However, the PSO algorithm converges prematurely, which rapidly decreases the population diversity, especially when approaching local optima.Recently, a new metaheuristic algorithm called the crow search algorithm (CSA) was proposed.The CSA is similar to the PSO algorithm but is based on the intelligent behavior of crows.The main concept behind the CSA is that crows store excess food in hiding places and retrieve it when needed.The primary advantage of the CSA is that it is rather simple, having just two parameters: flight length and awareness probability.Thus, the CSA can be applied to optimization problems very easily.This paper proposes a hybridization algorithm based on the PSO algorithm and CSA, known as the crow particle optimization (CPO) algorithm.The two main operators are the exchange and local search operators.It also implements a local search operator to enhance the quality of the best solutions from the two systems.Simulation results demonstrated that the CPO algorithm exhibits a significantly higher performance in terms of both fitness value and computation time compared to other algorithms.
Optical Inspection System for Gear Tooth Surfaces Using a Projection Moiré Method

The demand for rapid online optical inspection of gear tooth surfaces is increasing, especially for precision gears. In this study, a non-contact optical measurement method was established for the inspection of gear tooth surfaces. For the system architecture, a halogen lamp was selected as the light source, and a collimated beam was produced by an autocollimator. Subsequently, moiré fringes were formed as the collimated beam went through the two linear gratings. The moiré fringes projected on the gear tooth surface were recorded with a charge-coupled device (CCD) camera, and the contour of the gear tooth surface was estimated and reconstructed from the phase information of the fringes by our developed computer codes. To verify the accuracy of the system, a spur gear tooth surface measured by a commercial coordinate measuring machine (CMM) was defined as the reference tooth profile. The tooth topography, involute profile deviation, and axial-direction deviation were successfully calculated by measuring the deviation of the optically measured surface based on the reference gear tooth profiles measured using the CMM.
Attenuation of Pain Perception Induced by the Rubber Hand Illusion

Adaptive behavior usually requires accurate representations of body positions and ownership, which rely on integration of multiple sources of sensory information. The rubber hand illusion (RHI) presents a compelling example demonstrating that the combination of visual and tactile signals strongly influences the subjective experience of body ownership. However, it still remains unclear how the perception of body ownership in turn alters other aspects of sensory processing, such as pain perception. In the present study, we examined whether the RHI could modulate the subjective experience of pain. We set three conditions corresponding to different levels of ownership of the rubber hand: the synchronous condition in which the rubber and the real hand were simultaneously stroked; the asynchronous condition in which the two hands were asynchronously stroked; the own-hand-only condition in which only the real hand was stroked. Results from our pilot experiment indicated that subjects experienced the stronger RHI in the synchronous condition, compared with the strength of RHI in the other two conditions. In the main experiment, subjects were requested to report the intensity and unpleasantness of pain evoked by laser stimuli under the three stroking conditions. Results showed that pain ratings were significantly lower under the synchronous condition than those under the other two conditions, suggesting the RHI could induce a significant analgesic effect. Furthermore, the correlation analysis showed that the degree of the analgesic effect was positively correlated with the RHI strength across individuals. Taken together, these results suggest an analgesic effect of the RHI and support the potential usage of visual illusions in future translational research on pain.
Structural Similarity-Based Nonlocal Variational Models for Image Restoration

In this paper, we propose and develop a novel nonlocal variational technique based on structural similarity (SS) information for image restoration problems. In the literature, patches extracted from images are compared according to their pixel values, and then nonlocal filtering can be employed for image restoration. The disadvantage of this approach is that intensity-based patch distance may not be effective in image restoration, especially for images containing texture or structural information. The main aim of this paper is to propose using SS between image patches to develop nonlocal regularization models. In particular, two types of nonlocal regularizing functions are studied: an SS-based nonlocal quadratic function (SS-NLH1) and an SS-based nonlocal total variation function (SS-NLTV) for regularization of image restoration problems. Moreover, we employ iterative algorithms to solve these SS-NLH1 and SS-NLTV variational models numerically and discuss the convergence of these algorithms. The experimental results are presented to demonstrate the effectiveness of the proposed models.
High-Definition Video Compression System Based on Perception Guidance of Salient Information of a Convolutional Neural Network and HEVC Compression Domain

The new generation of the high-efficiency video coding (HEVC) video coding standard has improved compression performance and brought considerable coding complexity. Therefore, reducing the perception redundancy of video to obtain a better compression effect is the new direction of video development at present. In this paper, the HEVC is improved and enhanced from the aspects of a video saliency algorithm based on an attention mechanism and a video compression algorithm based on perception priority. In terms of video saliency, this paper proposes a spatial saliency algorithm based on a convolutional neural network and a temporal saliency algorithm based on motion vector. The saliency algorithm can combine the motion estimation results of each block during the HEVC compression on the basis of convolutional neural network and carry out adaptive dynamic fusion of the two, to complete the saliency map of the input video. In the aspect of the video compression algorithm with perception priority, this paper proposes a more flexible QP selection method, which selects its corresponding QP according to the saliency value of CU. At the same time, we propose a new rate-distortion optimization algorithm, which integrates the current block's saliency feature into the traditional rate-distortion calculation method, to guide the allocation of bits and achieve the purpose of perception priority. The experimental results proved the superiority of the proposed method over the state-of-the-art perceptual coding algorithms in terms of saliency detection and perceptual compression quality.
A Novel Cooperative Path Planning for Multi-robot Persistent Coverage with Obstacles and Coverage Period Constraints

In this paper, a multi-robot persistent coverage of the region of interest is considered, where persistent coverage and cooperative coverage are addressed simultaneously. Previous works have mainly concentrated on the paths that allow for repeated coverage, but ignored the coverage period requirements of each sub-region. In contrast, this paper presents a combinatorial approach for path planning, which aims to cover mission domains with different task periods while guaranteeing both obstacle avoidance and minimizing the number of robots used. The algorithm first deploys the sensors in the region to satisfy coverage requirements with minimum cost. Then it solves the travelling salesman problem to obtain the frame of the closed path. Finally, the approach partitions the closed path into the fewest segments under the coverage period constraints, and it generates the closed route for each robot on the basis of portioned segments of the closed path. Therefore, each robot can circumnavigate one closed route to cover the different task areas completely and persistently. The numerical simulations show that the proposed approach is feasible to implement the cooperative coverage in consideration of obstacles and coverage period constraints, and the number of robots used is also minimized.
Active learning technologies in distance education of gifted students

The article reveals the notion of distance education, meaning formation, meaningful technologies. The modern concept of psychological and pedagogical support of talent is associated with the concept of the child’s abilities disclosure in education. Discussion about the developing potential of distance education, where, on the one hand, the student’s development potential is flattened, compressed and translated into “one-dimensional space” in the process of education; on the other hand, there is a whole range of opportunities to initiate independent activity of students, to include mechanisms of cognitive and personal development of a modern student. The psychological bases of active learning technologies in distance education of gifted students are described. The author’s classification of interactive learning technologies using remote technologies is considered, where each technology finds several concrete embodiments. The results of an experimental study of gifted students’ independent activity initiation in distance education are presented.
Design and evaluation of a prototype of augmented reality applied to medical devices

According to current legislation, medical devices have to incorporate all the necessary information to eliminate or greatly minimise any problem associated with their use. However, the physical capacity of the actual device's packaging may frequently not be enough to contain all this information. To address this limitation, this study aimed to design and evaluate a prototype app for mobile devices applying augmented reality technology. The main feature of this kind of technology is combining virtual images with images from the real world. This work, carried out in Spain, was developed in three different phases. 1) Assessment of users’ needs: Through a focus group and an online questionnaire, information was obtained about the following aspects: type of medical devices likely to be included in the app, relevant information that should be included and format in which this information should be presented. 2) Development of the prototype: Considering all the functional features identified in the previous phase, the software was developed by a team of professionals specialised in AR technology and applying a user-centred model. 3) Evaluation of the software: functionality and usability were assessed by means of the think-aloud method. 1) Assessment of users’ needs: a total of 11 nurses participated in the focus group and 280 healthcare professionals answered the questionnaire. Their findings showed that users consider that information about the following aspects of medical devices should be included in the app: instructions for use, indications for use, brief description of the device, special precautions and biocompatibility, image of the content with its components and meaning of icons. 2) Description of the prototype: Once the app has been launched, when the user scans the medical device with the mobile device camera, access to the home screen is activated, where three sections can be found: name of the medical device, image of the device and four icons which provide access to: a brief description of the device, a detailed description of it, the packaging iconography and a video about use of the device. 3) Evaluation of the software: the app was defined by users as “very intuitive”. They highlighted, as one of its main positive aspects, the chance to obtain information about the medical device just by scanning the object. Additionally, the evaluation performed through the think-aloud method identified potential improvements in the app. These improvements were subsequently implemented to make the prototype more functional. Working with potential prototype users made it possible to identify information considered relevant for these users and to delve into the format which they consider more appropriate to show this information in the prototype. Our results show that AR technology can be used as support for clinical practice.
Robust Cyberbullying Detection with Causal Interpretation

Cyberbullying poses serious threats to preteens and teenagers, therefore, understanding the incentives behind cyberbullying is critical to prevent its happening and mitigate the impact. Most existing work towards cyberbullying detection has focused on the accuracy, and overlooked causes of the outcome. Discovering the causes of cyberbullying from observational data is challenging due to the existence of confounders, variables that can lead to spurious causal relationships between covariates and the outcome. This work studies the problem of robust cyberbullying detection with causal interpretation and proposes a principled framework to identify and block the influence of the plausible confounders, i.e., p-confounders. The de-confounded model is causally interpretable and is more robust to the changes in data distribution. We test our approach using the state-of-the-art evaluation method, causal transportability. The experimental results corroborate the effectiveness of our proposed algorithm. The purpose of this study is to provide a computational means to understanding cyberbullying behavior from observational data. This improves our ability to predict and to facilitate effective strategies or policies to proactively mitigate the impact of cyberbullying.
Linguistically-Informed Specificity and Semantic Plausibility for Dialogue Generation

Wei-Jen Ko, Greg Durrett, Junyi Jessy Li. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
STUDIES ON STATISTICAL ANALYSIS AND PERFORMANCE EVALUATION FOR SOME STREAM CIPHERS

This paper presents the results of the comparative analysis of safety statistics and performance of encryption, the Strumok stream symmetric cipher (proposed for the national encryption standard of Ukraine) with other known cryptographic transformation algorithms, such as SALSA20, SNOW2.0, HC, AES with usages in stream mode, etc. They are accepted as national, international standards or are presented by the New European Schemes for Signatures, Integrity, and Encryptions (NESSI), Cryptography Research and Evaluation Committees (CRYPTREC) and others. The result of safety statistics is an analysis of the cryptographic properties of the output sequences using statistical test sets developed by the National Institute of Standards and Technology (NIST STS) and the DIEHARD tests. The result of the study of performance is the evaluation of the use of central processing unit (CPU) time to convert one octet of data to 64-bit computing platforms, following the test profile used in the eSTREAM contest.
A new reversible watermarking scheme using the content-adaptive block size for prediction

Reversible watermarking aims to embed more messages in a cover image with less distortion. For reversible watermarking schemes (RWSs) using the prediction error expansion and histogram shifting (PEE-HS), the key point is to predict image pixels accurately. To obtain more accurate prediction, by taking into account the fact that there exist edges and textures in natural images, this paper designs a local predictor exploiting the content-adaptive block size for each to-be-predicted pixel. Specifically, in constructing the prediction context, the original to-be-predicted pixel is replaced by its rhombus-averaged version, aiming to result in the same prediction context for the embedder and decoder. A number of candidate block sizes are then set and the same number of prediction errors between the rhombus-averaged and predicted values are generated. Considering that the watermarking performance is measured with respect to the original pixels, a mechanism is thus devised to find a preferable content-adaptive block size leading to the minimum prediction error close to the difference between the original and predicted values. After generating prediction errors, we deploy the PEE-HS to design a new RWS exploiting the content-adaptive block size. Experimental results show that the proposed scheme outperforms the-state-of-the-art using the least-square-based predictor.
Rapidly Deployable IoT Architecture with Data Security: Implementation and Experimental Evaluation

Internet of Things (IoT) has brought about a new horizon in the field of pervasive computing and integration of heterogeneous objects connected to the network. The broad nature of its applications requires a modular architecture that can be rapidly deployed. Alongside the increasing significance of data security, much research has focused on simulation-based encryption algorithms. Currently, there is a gap in the literature on identifying the effect of encryption algorithms on timing and energy consumption in IoT applications. This research addresses this gap by presenting the design, implementation, and practical evaluation of a rapidly deployable IoT architecture with embedded data security. Utilizing open-source off-the-shelf components and widely accepted encryption algorithms, this research presents a comparative study of Advanced Encryption Standards (AES) with and without hardware accelerators and an eXtended Tiny Encryption Algorithm (XTEA) to analyze the performance in memory, energy, and execution time. Experimental results from implementation in multiple IoT applications has shown that utilizing the AES algorithm with a hardware accelerator utilizes the least amount of energy and is ideal where timing is a major constraint, whereas the XTEA algorithm is ideal for resource constrained microcontrollers. Additionally, software implementation of AES on 8-bit PIC architecture required 6.36x more program memory than XTEA.
Ginseng: Keeping Secrets in Registers When You Distrust the Operating System

Many mobile and embedded apps possess sensitive data, or secrets.Trusting the operating system (OS), they often keep their secrets in the memory.Recent incidents have shown that the memory is not necessarily secure because the OS can be compromised due to inevitable vulnerabilities resulting from its sheer size and complexity.Existing solutions protect sensitive data against an untrusted OS by running app logic in the Secure world, a Trusted Execution Environment (TEE) supported by the ARM TrustZone technology.Because app logic increases the attack surface of their TEE, these solutions do not work for third-party apps.This work aims to support third-party apps without growing the attack surface, significant development effort, or performance overhead.Our solution, called Ginseng, protects sensitive data by allocating them to registers at compile time and encrypting them at runtime before they enter the memory, due to function calls, exceptions or lack of physical registers.Ginseng does not run any app logic in the TEE and only requires minor markups to support existing apps.We report a prototype implementation based on LLVM, ARM Trusted Firmware (ATF), and the HiKey board.We evaluate it with both microbenchmarks and real-world secret-holding apps.Our evaluation shows Ginseng efficiently protects sensitive data with low engineering effort.For example, a Ginsengenabled web server, Nginx, protects the TLS master key with no measurable overhead.We find Ginseng's overhead is proportional to how often sensitive data in registers have to be encrypted and decrypted, i.e., spilling and restoring sensitive data on a function call or under high register pressure.As a result, Ginseng is most suited to protecting small sensitive data, like a password or social security number.
Enhancing the crowdsourced live streaming

With the growing demand for crowdsourced live streaming (CLS), how to schedule the large-scale dynamic viewers effectively among different Content Delivery Network (CDN) providers has become one of the most significant challenges for CLS platforms. Although abundant algorithms have been proposed in recent years, they suffer from a critical limitation: due to their inaccurate feature engineering or naive rules, they cannot optimally schedule viewers. To address this concern, we propose LTS (Learn to schedule), a deep reinforcement learning (DRL) based scheduling approach that can dynamically adapt to the variation of both viewer traffics and CDN performance. After the extensive evaluation the real data from a leading CLS platform in China, we demonstrate that LTS improves the average quality of experience (QoE) over state-of-the-art approach by 8.71%-15.63%.
Learning Dependency Structures for Weak Supervision Models

Labeling training data is a key bottleneck in the modern machine learning pipeline. Recent weak supervision approaches combine labels from multiple noisy sources by estimating their accuracies without access to ground truth labels; however, estimating the dependencies among these sources is a critical challenge. We focus on a robust PCA-based algorithm for learning these dependency structures, establish improved theoretical recovery rates, and outperform existing methods on various real-world tasks. Under certain conditions, we show that the amount of unlabeled data needed can scale sublinearly or even logarithmically with the number of sources $m$, improving over previous efforts that ignore the sparsity pattern in the dependency structure and scale linearly in $m$. We provide an information-theoretic lower bound on the minimum sample complexity of the weak supervision setting. Our method outperforms weak supervision approaches that assume conditionally-independent sources by up to 4.64 F1 points and previous structure learning approaches by up to 4.41 F1 points on real-world relation extraction and image classification tasks.
Path Planning Method for AUV Docking Based on Adaptive Quantum-Behaved Particle Swarm Optimization

AUV docking requires the platform to have both wide range cruising and accurate operating abilities, to against challenges of ocean currents, obstacles, and constraints. This paper proposed an evolutionary-based method, for the purpose of docking path optimization. First, the ocean environment and constraints are analyzed and modeled. Next, the control points are designed to satisfy the model constraints. Then, the adaptive law and quantum behavior are introduced in particle swarm optimization (PSO), to achieve global time-optimization. Finally, the proposed approach is evaluated via Monte-Carlo trials, which demonstrates a significant improvement with respect to the state-of-the-art approaches.