Reviewerly/exHarmony huggingface dataset:
A genetic algorithm based task scheduling system for logistics service robots

The demand for autonomous logistics service robots requires an efficient task scheduling system in order to optimise cost and time for the robot to complete its tasks. This paper presents a Genetic algorithm (GA) based task scheduling system for a ground mobile robot that is able to find a global near-optimal travelling path to complete a logistics task of pick-and-deliver items at various locations. In this study, the chromosome representation and the fitness function of GA is carefully designed to cater for a single load logistics robotic task. Two variants of GA crossover are adopted to enhance the performance of the proposed algorithm. The performance of the scheduling is compared and analysed between the proposed GA algorithms and a conventional greedy algorithm in a virtual map and a real map environments that turns out the proposed GA algorithms outperform the greedy algorithm by 40% to 80% improvement.
La indagación guiada como estrategia metodológica para el desarrollo de competencias científicas en estudiantes de Educación Media

Technological advances require the development of scientific thinking in which students can access and transform their environment. This tends to teach an updated, contextualized and motivating science which awakens interest in students and that emphasizes in the development of scientific competences. Under this premise, the objective of this article is to present the results obtained when implementing a methodological strategy based on a guided inquiry, on the adjustment of physics laboratory practices. The strategy was carried out in three schools of Bogotá (Colombia) in a research consisting of 145 students from secondary education, monitoring the interactions of students employing the follow-up process in the interaction whit laboratory practices model, and analyzing the progress of their performances inherent to the competences suggested in the research. From a mixed methodology, analytical descriptive, were implemented ad hoc designed input and output tests, which establish the level of appropriation of competencies, before and after the implementation of the methodological strategy. The results obtained show the effectiveness of the strategy within the educational contexts considered. It is concluded that the strategy presents an innovative and relevant character, since it proposes guiding and didactic elements for the teaching of science, bringing students closer to the new technological and scientific developments of today.
A Cognitive Assistant Functional Model and Architecture for the Social Media Victim Behavior Prevention

Nowadays the facilities of a parental control are not sufficient due to the numerous mobile devices and Internet access points. Another approach to control the children’s behavior in the Social Media is to augment the human capabilities by the usage of a cognitive assistant. Cognitive assistants usually provide such features as self-learning, adaptive behavior, big data processing, etc. This paper focuses on the design of the cognitive assistant based on the authorship attribution methods. The major functions of the cognitive assistant as an online service are highlighted, the authorship attribution methods are analyzed and the base model and architecture of the cognitive assistant service are presented. The cognitive map usage as a data analysis intelligent technique is proposed. Also some issues of the service (i.e. scalability, dependability, integration) are considered and discussed.
Geometrically invariant watermarking for medical images based on digital holography and three-level DWT-SVD

Digital imaging and communications in medicine are being applied widely to improve health care for people, and their security attracted our attention when they are transmitted through the Internet. The paper proposes a digital watermarking method for medical images based on digital holography and three-level discrete wavelength transform (DWT) singular value decomposition (SVD), where a live code including privacy information is transformed into digital holography by an improved computer-generated hologram method. The holography then is embedded into the singular values of the LL3 subband wavelet of the medical image. Experimental results show that the proposed method provides sufficient security results against various attacks, especially geometrical attacks.
RETRACTED CHAPTER: U-Control Chart Based Differential Evolution Clustering for Determining the Number of Cluster in k-Means

The automatic clustering differential evolution (ACDE) is one of the clustering methods that are able to determine the cluster number automatically. However, ACDE still makes use of the manual strategy to determine k activation threshold thereby affecting its performance. In this study, the ACDE problem will be ameliorated using the u-control chart (UCC) then the cluster number generated from ACDE will be fed to k-means. The performance of the proposed method was tested using six public datasets from the UCI repository about academic efficiency (AE) and evaluated with Davies Bouldin Index (DBI) and Cosine Similarity (CS) measure. The results show that the proposed method yields excellent performance compared to prior researches.
Augmenting Password Strength Meter Design Using the Elaboration Likelihood Model: Evidence from Randomized Experiments

In this research, we study an effective method to encourage users to generate stronger passwords. Specifically, we propose a novel design of password strength meters that incorporates contextual information to help users digest the message generated by the password strength meter. We evaluate our design by leveraging three independent and complementary methods: a survey-based experiment using students to evaluate the saliency of our conceptual design (proof of concept), a controlled laboratory experiment conducted on Amazon Mechanical Turk to test the effectiveness of the proposed design (proof of value), and a randomized field experiment conducted in collaboration with an online forum in Asia to establish proof of use. In each study, we observe that users exposed to the proposed password strength meter are more likely to change their passwords, leading to a new password that is significantly stronger. Our findings suggest that the proposed design of augmented password strength meters is an effective method for promoting secure password behavior among end users. Our design also requires minimal computational resources and technical capabilities.
Digital Generation: Between Myth and Reality

The article is devoted to the actively discussed question of the uniqueness of Net generation. The digital natives have been credited with the ability to multitask and high-speed information processing, greater efficiency in online work. According to many researchers, the high technological skills of digital generation require an educational approach radically different from that of previous generations. According to S. Benett and K. Maton, these appeals for revolutionary changes in educational policy and practice turn into “moral panic.” The analysis of contemporary empirical researches show that the digital skills and competencies attributed to the new generation are significantly overvalued, they need to be shaped and developed. The question regarding the ability of digital generation to adopt and adapt digital technologies remains controversial. The main characteristics of digital generation are distractibility, low attentional control and memorization ability, the problem with cognitive control, and, as a result, reduced educational achievements. The modified reward system and reduced self-control may lead to the Internet addiction formation. This article presents scientific evidence showing that designing education that assumes the presence of these abilities hinders rather than helps learning. However, this does not mean that we have to abandon the idea of technological modernization in education, this only means that new technologies should be used at the right time in the right place.
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation

Disentangling the content and style in the latent space is prevalent in unpaired text style transfer. However, two major issues exist in most of the current neural models. 1) It is difficult to completely strip the style information from the semantics for a sentence. 2) The recurrent neural network (RNN) based encoder and decoder, mediated by the latent representation, cannot well deal with the issue of the long-term dependency, resulting in poor preservation of non-stylistic semantic content. In this paper, we propose the Style Transformer, which makes no assumption about the latent representation of source sentence and equips the power of attention mechanism in Transformer to achieve better style transfer and better content preservation.
The dark side of licensing cryptocurrency exchanges as payment institutions

The ultimate objective of cryptocurrencies is to become a payment system substituting, complementing, or competing with the conventional payment systems. Irrespective of whether such an objective could be accomplished, the functional similarities between certain cryptocurrencies and fiat money has persuaded competent authorities of certain EU Member States to grant payment institution licenses to cryptocurrency exchanges. At first blush, granting such an authorization would seem to be a step forward as it would bring otherwise unregulated cryptocurrency exchanges within the scope of the existing payment regulatory framework. However, this authorization effectively applies payment laws to new payment infrastructures that rely on volatile settlement assets with probabilistic finality. Since the volatility and finality risks cannot be fully addressed under the existing payment laws, an alternative policy option would be granting a special license to cryptocurrency businesses or introducing ring-fencing mechanisms to protect the conventional payment systems from the risks of cryptocurrency payments.
Leveraging Machine-Executable Descriptive Knowledge in Design Science Research – The Case of Designing Socially-Adaptive Chatbots

In Design Science Research (DSR) it is important to build on descriptive (Ω) and prescriptive (Λ) state-of-the-art knowledge in order to provide a solid grounding. However, existing knowledge is typically made available via scientific publications. This leads to two challenges: first, scholars have to manually extract relevant knowledge pieces from the data-wise unstructured textual nature of scientific publications. Second, different research results can interact and exclude each other, which makes an aggregation, combination, and application of extracted knowledge pieces quite complex. In this paper, we present how we addressed both issues in a DSR project that focuses on the design of socially-adaptive chatbots. Therefore, we outline a two-step approach to transform phenomena and relationships described in the Ω-knowledge base in a machine-executable form using ontologies and a knowledge base. Following this new approach, we can design a system that is able to aggregate and combine existing Ω-knowledge in the field of chatbots. Hence, our work contributes to DSR methodology by suggesting a new approach for theory-guided DSR projects that facilitates the application and sharing of state-of-the-art Ω-knowledge.
Eliminating the Effect of Image Border with Image Periodic Decomposition for Phase Correlation Based Remote Sensing Image Registration

In the remote sensing community, accurate image registration is the prerequisite of the subsequent application of remote sensing images. Phase correlation based image registration has drawn extensive attention due to its high accuracy and high efficiency. However, when the Discrete Fourier Transform (DFT) of an image is computed, the image is implicitly assumed to be periodic. In practical application, it is impossible to meet the periodic condition that opposite borders of an image are alike, and image always shows strong discontinuities across the frame border. The discontinuities cause a severe artifact in the Fourier Transform, namely the known cross structure composed of high energy coefficients along the axes. Here, this phenomenon was referred to as effect of image border. Even worse, the effect of image border corrupted its registration accuracy and success rate. Currently, the main solution is blurring out the border of the image by weighting window function on the reference and sensed image. However, the approach also inevitably filters out non-border information of an image. The existing understanding is that the design of window function should filter as little information as possible, which can improve the registration success rate and accuracy of methods based on phase correlation. In this paper, another approach of eliminating the effect of image border is proposed, namely decomposing the image into two images: one being the periodic image and the other the smooth image. Replacing the original image by the periodic one does not suffer from the effect on the image border when applying Fourier Transform. The smooth image is analogous to an error image, which has little information except at the border. Extensive experiments were carried out and showed that the novel algorithm of eliminating the image border can improve the success rate and accuracy of phase correlation based image registration in some certain cases. Additionally, we obtained a new understanding of the role of window function in eliminating the effect of image border, which is helpful for researchers to select the optimal method of eliminating the effect of image border to improve the registration success rate and accuracy.
No‐reference image quality metric based on multiple deep belief networks

The last decade has witnessed great advances in digital images. These images are subjected to many processing stages during storing, transmitting, or sharing over a network connection. Unfortunately, these processing stages could potentially add visual degradation to original image. These degradations reduce the perceived visual quality which leads to an unsatisfactory experience for human viewers. Therefore, image quality assessment (IQA) has become a topic of high interest and intense research over the last decade. This study mainly focuses on the most challenging category of IQA general-purpose No-Reference Image Quality Assessment (NR-IQA), where the goal is to assess the quality of images without information about the reference images and without prior knowledge about the types of distortions in the tested image. A novel NR-IQA approach is presented, by utilizing multiple deep belief networks (DBNs) with multiple regression models. It consists of four DBNs. Each DBN is associated with one type of distortion. The authors have evaluated the performance of the proposed and some existing models on a fair basis. The obtained results show that their model gives better results and yield a significant improvement.
Deep Siamese Networks toward Robust Visual Tracking

Recently, Siamese neural networks have been widely used in visual object tracking to leverage the template matching mechanism.Siamese network architecture contains two parallel streams to estimate the similarity between two inputs and has the ability to learn their discriminative features.Various deep Siamese-based tracking frameworks have been proposed to estimate the similarity between the target and the search region.In this chapter, we categorize deep Siamese networks into three categories by the position of the merging layers as late merge, intermediate merge and early merge architectures.In the late merge architecture, inputs are processed as two separate streams and merged at the end of the network, while in the intermediate merge architecture, inputs are initially processed separately and merged intermediate well before the final layer.Whereas in the early merge architecture, inputs are combined at the start of the network and a unified data stream is processed by a single convolutional neural network.We evaluate the performance of deep Siamese trackers based on the merge architectures and their output such as similarity score, response map, and bounding box in various tracking challenges.This chapter will give an overview of the recent development in deep Siamese trackers and provide insights for the new developments in the tracking field.
Towards Dynamic Answer Set Programming over Finite Traces

Our ultimate goal is to conceive an extension of Answer Set Programming with language constructs from dynamic (and temporal) logic to provide an expressive computational framework for modeling dynamic applications. To address this in a semantically well founded way, we generalize the definition of Dynamic Equilibrium Logic to accommodate finite linear time and extend it with a converse operator in order to capture past temporal operators. This results in a general logical framework integrating existing dynamic and temporal logics of Here-and-There over both finite and infinite time. In the context of finite time, we then develop a translation of dynamic formulas into propositional ones that can in turn be translated into logic programs.
A Sparse Analysis-Based Single Image Super-Resolution

In the current study, we were inspired by sparse analysis signal representation theory to propose a novel single-image super-resolution method termed “sparse analysis-based super resolution” (SASR). This study presents and demonstrates mapping between low and high resolution images using a coupled sparse analysis operator learning method to reconstruct high resolution (HR) images. We further show that the proposed method selects more informative high and low resolution (LR) learning patches based on image texture complexity to train high and low resolution operators more efficiently. The coupled high and low resolution operators are used for high resolution image reconstruction at a low computational complexity cost. The experimental results for quantitative criteria peak signal to noise ratio (PSNR), root mean square error (RMSE), structural similarity index (SSIM) and elapsed time, human observation as a qualitative measure, and computational complexity verify the improvements offered by the proposed SASR algorithm.
Evaluation Metrics for Unsupervised Learning Algorithms

Determining the quality of the results obtained by clustering techniques is a key issue in unsupervised machine learning. Many authors have discussed the desirable features of good clustering algorithms. However, Jon Kleinberg established an impossibility theorem for clustering. As a consequence, a wealth of studies have proposed techniques to evaluate the quality of clustering results depending on the characteristics of the clustering problem and the algorithmic technique employed to cluster data.
A Deep Dive into Bitcoin Mining Pools: An Empirical Analysis of Mining Shares

Miners play a key role in cryptocurrencies such as Bitcoin: they invest substantial computational resources in processing transactions and minting new currency units. It is well known that an attacker controlling more than half of the network's mining power could manipulate the state of the system at will. While the influence of large mining pools appears evenly split, the actual distribution of mining power within these pools and their economic relationships with other actors remain undisclosed. To this end, we conduct the first in-depth analysis of mining reward distribution within three of the four largest Bitcoin mining pools and examine their cross-pool economic relationships. Our results suggest that individual miners are simultaneously operating across all three pools and that in each analyzed pool a small number of actors (&lt;= 20) receives over 50% of all BTC payouts. While the extent of an operator's control over the resources of a mining pool remains an open debate, our findings are in line with previous research, pointing out centralization tendencies in large mining pools and cryptocurrencies in general.
Local Dominant Directional Symmetrical Coding Patterns for Facial Expression Recognition

To overcome the shortcomings of inaccurate textural direction representation and high-computational complexity of Local Binary Patterns (LBPs), we propose a novel feature descriptor named as Local Dominant Directional Symmetrical Coding Patterns (LDDSCPs). Inspired by the directional sensitivity of human visual system, we partition eight convolution masks into two symmetrical groups according to their directions and adopt these two groups to compute the convolution values of each pixel. Then, we encode the dominant direction information of facial expression texture by comparing each pixel’s convolution values with the average strength of its belonging group and obtain LDDSCP-1 and LDDSCP-2 codes, respectively. At last, in view of the symmetry of two groups of direction masks, we stack these corresponding histograms of LDDSCP-1 and LDDSCP-2 codes into the ultimate LDDSCP feature vector which has effects on the more precise facial feature description and the lower computational complexity. Experimental results on the JAFFE and Cohn-Kanade databases demonstrate that the proposed LDDSCP feature descriptor compared with LBP, Gabor, and other traditional operators achieves superior performance in recognition rate and computational complexity. Furthermore, it is also no less inferior to some state-of-the-art local descriptors like as LDP, LDNP, es-LBP, and GDP.
Scaling up quantum simulations

It is difficult to carry out and verify digital quantum simulations that use many quantum bits. A hybrid device based on a digital classical computer and an analog quantum processor suggests a way forward. A programmable analog quantum simulator.
Neural Bug Finding: A Study of Opportunities and Challenges

Static analysis is one of the most widely adopted techniques to find software bugs before code is put in production. Designing and implementing effective and efficient static analyses is difficult and requires high expertise, which results in only a few experts able to write such analyses. This paper explores the opportunities and challenges of an alternative way of creating static bug detectors: neural bug finding. The basic idea is to formulate bug detection as a classification problem, and to address this problem with neural networks trained on examples of buggy and non-buggy code. We systematically study the effectiveness of this approach based on code examples labeled by a state-of-the-art, static bug detector. Our results show that neural bug finding is surprisingly effective for some bug patterns, sometimes reaching a precision and recall of over 80%, but also that it struggles to understand some program properties obvious to a traditional analysis. A qualitative analysis of the results provides insights into why neural bug finders sometimes work and sometimes do not work. We also identify pitfalls in selecting the code examples used to train and validate neural bug finders, and propose an algorithm for selecting effective training data.
A Recent Trend in Individual Counting Approach Using Deep Network

The density maps technique exhibits great success in tackling the issue of individual counting in comparison to regression-based methods, specifically where the scene consists of a few complications such as heavy inter-occlusion between individuals, unclear resolution in videos 22232425.Additionally, these methods can specify spatial