Reviewerly/exHarmony huggingface dataset:
An Improved Watershed Segmentation Algorithm of Medical Tumor Image

Abstract Aiming at the problem that the tumor is difficult to be accurately segmented due to noise and edge discontinuity in the chest breast MRI image, a medical image segmentation algorithm based on gradient edge detection and control marked watershed is proposed. Firstly, the mathematical morphology reconstruction filter is used to remove the small texture and noise of the MRI image of the breast tumor, and then the local minimum associated with the object is extracted from the low-frequency components of the gradient image, and they are formed into a binary marker image; The reconstructive technique is forced to be the local minimum of the original gradient image, the adaptive correction of the gradient image is realized, and finally the watershed segmentation is performed. The experimental results show that the algorithm can automatically and quickly extract the breast tumor region in the series of MRI images and realize the automatic segmentation of the region of interest in the medical image.
EMPIR: Ensembles of Mixed Precision Deep Networks for Increased Robustness Against Adversarial Attacks

Ensuring robustness of Deep Neural Networks (DNNs) is crucial to their adoption in safety-critical applications such as self-driving cars, drones, and healthcare. Notably, DNNs are vulnerable to adversarial attacks in which small input perturbations can produce catastrophic misclassifications. In this work, we propose EMPIR, ensembles of quantized DNN models with different numerical precisions, as a new approach to increase robustness against adversarial attacks. EMPIR is based on the observation that quantized neural networks often demonstrate much higher robustness to adversarial attacks than full precision networks, but at the cost of a substantial loss in accuracy on the original (unperturbed) inputs. EMPIR overcomes this limitation to achieve the “best of both worlds”, i.e., the higher unperturbed accuracies of the full precision models combined with the higher robustness of the low precision models, by composing them in an ensemble. Further, as low precision DNN models have significantly lower computational and storage requirements than full precision models, EMPIR models only incur modest compute and memory overheads compared to a single full-precision model (<25% in our evaluations). We evaluate EMPIR across a suite of 3 different DNN tasks (MNIST, CIFAR-10 and ImageNet) and under 4 different adversarial attacks. Our results indicate that EMPIR boosts the average adversarial accuracies by 43.6%, 15.3% and 11.9% for the DNN models trained on the MNIST, CIFAR-10 and ImageNet datasets respectively, when compared to single full-precision models, without sacrificing accuracy on the unperturbed inputs.
Sample Efficient Policy Gradient Methods with Recursive Variance Reduction

Improving the sample efficiency in reinforcement learning has been a long-standing research problem. In this work, we aim to reduce the sample complexity of existing policy gradient methods. We propose a novel policy gradient algorithm called SRVR-PG, which only requires $O(1/\epsilon^{3/2})$\footnote{$O(\cdot)$ notation hides constant factors.} episodes to find an $\epsilon$-approximate stationary point of the nonconcave performance function $J(\boldsymbol{\theta})$ (i.e., $\boldsymbol{\theta}$ such that $\|\nabla J(\boldsymbol{\theta})\|_2^2\leq\epsilon$). This sample complexity improves the existing result $O(1/\epsilon^{5/3})$ for stochastic variance reduced policy gradient algorithms by a factor of $O(1/\epsilon^{1/6})$. In addition, we also propose a variant of SRVR-PG with parameter exploration, which explores the initial policy parameter from a prior probability distribution. We conduct numerical experiments on classic control problems in reinforcement learning to validate the performance of our proposed algorithms.
URNet: User-Resizable Residual Networks with Conditional Gating Module

Convolutional Neural Networks are widely used to process spatial scenes, but their computational cost is fixed and depends on the structure of the network used. There are methods to reduce the cost by compressing networks or varying its computational path dynamically according to the input image. However, since a user can not control the size of the learned model, it is difficult to respond dynamically if the amount of service requests suddenly increases. We propose User-Resizable Residual Networks (URNet), which allows users to adjust the computational cost of the network as needed during evaluation. URNet includes Conditional Gating Module (CGM) that determines the use of each residual block according to the input image and the desired cost. CGM is trained in a supervised manner using the newly proposed scale(cost) loss and its corresponding training methods. URNet can control the amount of computation and its inference path according to user's demand without degrading the accuracy significantly. In the experiments on ImageNet, URNet based on ResNet-101 maintains the accuracy of the baseline even when resizing it to approximately 80% of the original network, and demonstrates only about 1% accuracy degradation when using about 65% of the computation.
A Comprehensive Survey on Image Aesthetic Quality Assessment

Image aesthetic quality assessment has demonstrated tremendous success in variety of application domains in recent years. This field has been growing so rapidly that various approaches have been proposed trying to solve this challenging problem. This report presents a comprehensive survey on image aesthetic quality assessment, mainly focus on the contributions and novelties of the existing approaches recently. In this work, we firstly illustrate datasets related to image aesthetics and investigate feature extractions. Then five different aesthetic tasks are reviewed, including aesthetic classification, aesthetic regression, aesthetic distribution, aesthetic factors and aesthetic description. In addition, we reviewed recent applications concerning image aesthetics. Finally, different evaluation criterions in different literatures are summarized. We hope the survey could serve as a comprehensive reference and be useful for those who are interested in exploiting image aesthetic for their research.
Generic Decoding in the Sum-Rank Metric

We propose the first non-trivial generic decoding algorithm for codes in the sum-rank metric.The new method combines ideas of well-known generic decoders in the Hamming and rank metric.For the same code parameters and number of errors, the new generic decoder has a larger expected complexity than the known generic decoders for the Hamming metric and smaller than the known rank-metric decoders.Furthermore, we give a formal hardness reduction, providing evidence that generic sum-rank decoding is computationally hard.As a by-product of the above, we solve some fundamental coding problems in the sum-rank metric: we give an algorithm to compute the exact size of a sphere of a given sum-rank radius, and also give an upper bound as a closed formula; and we study erasure decoding with respect to two different notions of support.
Real-Time Aspect-Based Sentiment Analysis on Consumer Reviews

The rise of e-commerce websites, as new shopping channels, led to an upsurge of review sites for a wide range of services and products. This provides an opportunity to use aspect-based sentiment analysis and mine opinions expressed from text which can help consumers decide what to purchase and businesses to better monitor their reputation and understand the needs of the market. Aspect-based sentiment analysis (ABSA) is a technique aimed to foster research beyond sentence or text-level sentiment classification. The goal is to identify opinions expressed about specific entities (e.g., laptops) and their aspects (e.g., price, performance, build quality, etc.). There exist very few techniques which can generate such results based on customer ratings, however usually for a limited set of pre-defined aspects and not from free-text reviews. The other challenge in this process is cold start problem because of the lack of enough review data for a product. In this paper, a methodology is proposed to automatically compute sentiments of dynamic aspects from user-generated reviews from the web scraping from multiple sources to overcome the cold start problem. Therefore, this methodology is devising a better solution for understanding sentiments in e-commerce than existing methods.
Too little too late: can we control browser fingerprinting?

Purpose Browser fingerprinting is increasingly being used for online tracking of users, and, unlike the use of cookies, is almost impossible for users to control. This has a major negative impact on online privacy. Despite the availability of a range of fingerprinting countermeasures as well as some limited attempts by browser vendors to curb its effectiveness, it remains largely uncontrolled. The paper aims to discuss this issue. Design/methodology/approach This paper provides the first comprehensive and structured discussion of measures to limit or control browser fingerprinting, covering both user-based and browser-based techniques. Findings This study discusses the limitations of counter browser fingerprinting measures and the need for browser vendor support in controlling fingerprinting. Further, a somewhat counterintuitive possible new browser identifier is proposed which could make cookies and fingerprint-based tracking redundant; the need for, and possible effect of, this feature is discussed. Originality/value This study provides the first comprehensive and structured discussion of measures to limit or control browser fingerprinting. Also, it proposes a new browser identifier that could make cookies and fingerprint-based tracking redundant.
Parallel Genetic Algorithms with GPU Computing

Genetic algorithms (GAs) are powerful solutions to optimization problems arising from manufacturing and logistic fields. It helps to find better solutions for complex and difficult cases, which are hard to be solved by using strict optimization methods. Accelerating parallel GAs with GPU computing have received significant attention from both practitioners and researchers, ever since the emergence of GPU-CPU heterogeneous architectures. Designing a parallel algorithm on GPU is different fundamentally from designing one on CPU. On CPU architecture, typically data or tasks are distributed across tens of threads or processes, while on GPU architecture, more than hundreds of thousands of threads run. In order to fully utilize the computing power of GPUs, the design approaches and implementation strategies of parallel GAs should be re-probed. In the chapter, a concise overview of parallel GAs on GPU is given from the perspective of GPU architecture. The concept of parallelism granularity is redefined, the aspect of data layout is discussed on how it will affect the kernel performance, and the hierarchy of threads is examined on how threads are organized in the grid and blocks to expose sufficient parallelism to GPU. Some future research is discussed. A hybrid parallel model, based on the feature of GPU architecture, is suggested to build up efficient parallel GAs for hyper-scale problems.
Usability of Mobile Assisted Language Learning App

The aim of this study is to evaluate the usability of Mobile Assisted Language Learning i.e.Literacy and Numeracy Drive (LND) which is smartphone application to learn language and mathematics in public sector primary schools of Punjab, the biggest province of Pakistan.In this study, usability tests were conducted which included surveys of questionnaires from teachers and students.The user experience, reliability, and performance of mobile application assessed, along with user satisfaction.The LND mobile application has not been found to be successful, with a poor user interface and requires improvement.The "Using Experience," "Ease of Use" and "Usefulness" variables have been the lowest scorers in terms of user experience.Mobile device specifications were not simple and confusing; the services provided by the LND were not appealing and effective for students or teachers.This research suggested several improvements in the usability and functionality of this LND application based on assessed user experience.Many schools have chosen to use mobile apps for the teaching and evaluation of language at school.The use of mobile-assisted learning at public sector schools in Punjab, invites us to gauge the usability and effectiveness of this approach at such a huge scale which will make it more effective.
The Effect of Jigsaw Technique in Reading Comprehension on Recount Text

The objective of this research is to find out the effect of Jigsaw technique on reading comprehension at grade ten of SMA 4 Negeri Pematangsiantar. The research used descriptive quantitative research design by applying Quasi-Experimental design. In conducting this design, the subjects of the research were divided into two groups: experimental group and control group. The treatment is introduced only to the experimental subject. The experimental group is treated by using Jigsaw technique while the control group is treated without Jigsaw technique. The subject of this research was the grade ten students (X PMIA 4 and X PMIA 5) in SMA Negeri 4 Pematangsiantar. The researcher used mobile phone as instrument which recorded video teacher and students activity during teaching and learning process in the class.&#x0D; The result of the research showed that in significance degree of 5%, the value of t-observe &gt; t-table that was (5.55 &gt; 1.669). It could be said that t-observe was higher than t-table. So, the null hypothesis is rejected while the alternative hypothesis is accepted. In other words it can be concluded that the application of Jigsaw teaching technique significantly affects the students’ ability in reading comprehension of recount text at grade ten of SMA Negeri 4 Pematangsiantar.
Online Collision Avoidance for Human-Robot Collaborative Interaction Concerning Safety and Efficiency

With the development of robot technology and the arrival of industry 4.0 era, society pays more attention to collaboration and interaction between human and robots. However, safety is still main concern in the development of human-robot collaboration. In this paper, a novel real-time collision avoidance approach for manipulator is proposed by considering the motion status of the human, which includes the relative minimum distance and velocity (both magnitude and direction) between the robot and the human. The distance and velocity of the human hand are first estimated online using a vision sensor, and then defined as danger factors in the potential function of the potential field. The novel potential function proposed in this paper considers not only the safety problem, but also the efficient problem, i.e., the manipulator can make smart control decision to avoid the collision according to the relative velocity in case of the cross over. To overcome the local minimum problem and choose a best motion direction, we propose a motion sampling mechanism for motion planning. For each sample, the robot calculates the potential function to evaluate the safety and efficiency, and chooses a direction which is best for avoidance. We finally demonstrate our idea on a real manipulator platform in a human co-existance environment.
Estimating Children’s Personalities Through Their Interaction Activities with a Tele-Operated Robot

Based on the little big-five inventory, we developed a technique to estimate children’s personalities through their interaction with a tele-operated childcare robot. For personality estimation, our approach observed not only distance-based but also face-image-based features when a robot interacted with a child at a close distance. We used only the robot’s sensors to track the child’s positions, detect its eye contact, and estimate how much it smiled. We collected data from a kindergarten, where each child individually interacted for 30 min with a robot that was controlled by the teachers. We used 29 datasets of the interaction between a child and the robot to investigate whether face-image-based features improved the performance of personality estimation. The evaluation results demonstrated that the face-image-based features significantly improved the performance of personality estimation, and the accuracy of the personality estimation of our system was 70% on average for the personality scales.
Social Creation and Media Publicity

The article discusses the problems of formation of a model of informational impulse-wave interaction of society and mass media within a system of social creativity, which were suggested by A.P. Sukhodolov and co-authors, as well as characterizes systematic properties of media publicity as a factor of social creativity on the current civilization stage. Media publi­city is analyzed as an informational phenomenon closely related to social art, to creativity of social practice, to interaction within society providing unity of thought and action, to intellectual interaction within a society, to enrichment of the intellect of audience groups based on continuously updated media reality in the informational potential of mass media.
Survey of image denoising methods for medical image classification

Medical imaging devices, such as X-ray machines, inherently produce images that suffer from visual noise. Our objectives were to (i.) determine the effect of image denoising on a medical image classification task, and (ii.) determine if there exists a correlation between image denoising performance and medical image classification performance. We performed the medical image classification task on chest X-rays using the DenseNet-121 convolutional neural network (CNN) and used the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics as the image denoising performance measures. We first found that different denoising methods can make a statistically significant difference in classification performance for select labels. We also found that denoising methods affect fine-tuned models more than randomly-initialized models and that fine-tuned models have significantly higher and more uniform performance than randomly-initialized models. Lastly, we found that there is no significant correlation between PSNR and SSIM values and classification performance for our task.
International Network Conference as an Efficient Way to Integrate Universities and Businesses in the Context of Digital Economy

Development of engineering education is closely related to the global development processes of post-industrial society in the context of Industry 4.0, digitalization of economy, and the vigorous growth of technology and media. The ideas become dominating that education must serve the goals of a sustainable and dynamic society experiencing serious crises of technogenic and social/political nature. Accordingly, the goal of higher engineering/technology education be-comes turning out engineers that are ready to overcome the above challenges. This goal cannot be achieved without exchanging best practices and ideas among researchers and teachers representing the universities of various countries, involving the interested representatives of leading corporations. International engineering education forums and conferences may offer great opportunities for that. Issues and problems of engineering education are currently dis-cussed at many global forums and conferences, including the ICL/IGIP. Since 2016, the International Science and Practice SYNERGY Network Conference has become a large discussion site in Russia. The conference applies best global practices, as well as identifies and accomplishes important goals, such as developing public-private partnership, modernizing curricula, upgrading the skills of faculty members, managing staff, and other conference participants, spreading new educational techniques and technology, using digital formats and education digitalization being especially important given the scale of Russia, and expanding partnership links to companies being the customers of universities and participating in the conference. Outcomes of the conferences already held demonstrate a significant effect pro-vided on the university teachers who adopt best practices and teaching methods, learn from each other, cross-pollinate, and get feedback from employers regarding the quality of professional training and necessary improvements.
An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance

The novel global harmony search (NGHS) algorithm is proposed in 2010, and it is an improved harmony search (HS) algorithm which combines the particle swarm optimization (PSO) and the genetic algorithm (GA). One of the main differences between the HS and NGHS algorithms is that of using different mechanisms to renew the harmony memory (HM). In the HS algorithm, in each iteration, the new harmony is accepted and replaced the worst harmony in the HM while the fitness of the new harmony is better than the worst harmony in the HM. Conversely, in the NGHS algorithm, the new harmony replaces the worst harmony in the HM without any precondition. However, in addition to these two mechanisms, there is one old mechanism, the selective acceptance mechanism, which is used in the simulated annealing (SA) algorithm. Therefore, in this paper, we proposed the selective acceptance novel global harmony search (SANGHS) algorithm which combines the NGHS algorithm with a selective acceptance mechanism. The advantage of the SANGHS algorithm is that it balances the global exploration and local exploitation ability. Moreover, to verify the search ability of the SANGHS algorithm, we used the SANGHS algorithm in ten well-known benchmark continuous optimization problems and two engineering problems and compared the experimental results with other metaheuristic algorithms. The experimental results show that the SANGHS algorithm has better search ability than the other four harmony search algorithms in ten continuous optimization problems. In addition, in two engineering problems, the SANGHS algorithm also provided a competition solution compared with other state-of-the-art metaheuristic algorithms.
Soft skills, their development and mastering among post graduate students

The article deals with the issues of the content, assessment of the current state and the main tendencies in the development of the so-called “soft skills”. At present soft skills are essential to have good job opportunities and university curriculum should be focused on developing and mastering them. The article analyses the results of the research of the soft skills development among post graduate students majoring in Professional Education of Alfred Nobel University, Dnipro. These data prove that postgraduate students realize the importance of mastering soft skills to make a successful career. Moreover, innovative teaching methods and techniques aimed at mastering soft skills are to be introduced into the educational process.
Radical disruptions of self-consciousness

This special issue is about something most of us might find very hard to conceive: states of consciousness in which self-consciousness is radically disrupted or altogether missing.