Reviewerly/exHarmony huggingface dataset:
A Performance Study of the Impact of Different&#x0D; Perturbation Methods on the Efficiency of GVNS for&#x0D; Solving TSP

The purpose of this paper is to assess how three shaking procedures affect the performanceof a metaheuristic GVNS algorithm. The first shaking procedure is generally known in the literatureas intensified shaking method. The second is a quantum-inspired perturbation method, and thethird is a shuffle method. The GVNS schemes are evaluated using a search strategy for both Firstand Best improvement and a time limit of one and two minutes. The formed GVNS schemes wereapplied on Traveling Salesman Problem (sTSP, nTSP) benchmark instances from the well-knownTSPLib. To examine the potential advantage of any of the three metaheuristic schemes, extensivestatistical analysis was performed on the reported results. The experimental data shows that for aTSPinstances the first two methods perform roughly equivalently and, in any case, much better thanthe shuffle approach. In addition, the first method performs better than the other two when usingthe First Improvement strategy, while the second method gives results quite similar to the third.However, no significant deviations were observed when different methods of perturbation were usedfor Symmetric TSP instances (sTSP, nTSP).
Digital pedagogy: features of the term evolution in the framework of categories and concepts of pedagogy

Е. Ю. Илалтдинова, Т
Mapping ilmenite deposit in Pulmudai, Sri Lanka using a hyperspectral imaging-based surface mineral mapping method

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.Cover :Leatherback (a), green (b - adult &amp; f - hatchling), hawksbill (c) and olive ridley (d) turtles who nest in Sri Lankan beaches, green turtle crawl marks (e) and by-catch of sea turtles in fisheries (c &amp; d)Photo credits: SJ Perera (a &amp; c), WPN Perera (b &amp; e), K Ekaratne (d &amp; f)
Full Cut Elimination and Interpolation for Intuitionistic Logic with Existence Predicate

In previous work by Baaz and Iemhoff, a Gentzen calculus for intuitionistic logic with existence predicate is presented that satisfies partial cut elimination and Craig's interpolation property; it is also conjectured that interpolation fails for the implication-free fragment. In this paper an equivalent calculus is introduced that satisfies full cut elimination and allows a direct proof of interpolation via Maehara's lemma. In this way, it is possible to obtain much simpler interpolants and to better understand and (partly) overcome the failure of interpolation for the implication-free fragment.
The Effects of Augmented Training Dataset on Performance of Convolutional Neural Networks in Face Recognition System

Nowadays, deep learning methods have been used in many areas such as big data analysis, speech and image processing with the increasing processing power and the development of graphics processors.In particular, face recognition systems have become one of the most important research topics in biometry.Light direction, reflection, emotional and physical changes in facial expression are the main factors in face recognition systems that make recognition difficult.Training of the system with the available data in small data sets is an important factor that negatively affects the performance.The Convolutional Neural Network (CNN) model is a deep learning architecture used for large amounts of training data.In this study, a small number of employee images set of a small-scale company has been increased by applying different filters.In addition, it has been tried to determine which data augmentation options have more effect on face recognition.Thus, non-real-time face recognition has been performed by training with new augmented dataset of each picture with many features.
Dynamic Group Recommendation Based on the Attention Mechanism

Group recommendation has attracted significant research efforts for its importance in benefiting group members. The purpose of group recommendation is to provide recommendations to group users, such as recommending a movie to several friends. Group recommendation requires that the recommendation should be as satisfactory as possible to each member of the group. Due to the lack of weighting of users in different items, group decision-making cannot be made dynamically. Therefore, in this paper, a dynamic recommendation method based on the attention mechanism is proposed. Firstly, an improved density peak clustering ( DPC ) algorithm is used to discover the potential group; and then the attention mechanism is adopted to learn the influence weight of each user. The normalized discounted cumulative gain (NDCG) and hit ratio (HR) are adopted to evaluate the validity of the recommendation results. Experimental results on the CAMRa2011 dataset show that our method is effective.
Improved Piecewise Linear Transformation for Precise Warping of Very-High-Resolution Remote Sensing Images

A large number of evenly distributed conjugate points (CPs) in entirely overlapping regions of the images are required to achieve successful co-registration between very-high-resolution (VHR) remote sensing images. The CPs are then used to construct a non-linear transformation model that locally warps a sensed image to a reference image’s coordinates. Piecewise linear (PL) transformation is largely exploited for warping VHR images because of its superior performance as compared to the other methods. The PL transformation constructs triangular regions on a sensed image from the CPs by applying the Delaunay algorithm, after which the corresponding triangular regions in a reference image are constructed using the same CPs on the image. Each corresponding region in the sensed image is then locally warped to the regions of the reference image through an affine transformation estimated from the CPs on the triangle vertices. The warping performance of the PL transformation shows reliable results, particularly in regions inside the triangles, i.e., within the convex hulls. However, the regions outside the triangles, which are warped when the extrapolated boundary planes are extended using CPs located close to the regions, incur severe geometric distortion. In this study, we propose an effective approach that focuses on the improvement of the warping performance of the PL transformation over the external area of the triangles. Accordingly, the proposed improved piecewise linear (IPL) transformation uses additional pseudo-CPs intentionally extracted from positions on the boundary of the sensed image. The corresponding pseudo-CPs on the reference image are determined by estimating the affine transformation from CPs located close to the pseudo-CPs. The latter are simultaneously used with the former to construct the triangular regions, which are enlarged accordingly. Experiments on both simulated and real datasets, constructed from Worldview-3 and Kompsat-3A satellite images, were conducted to validate the effectiveness of the proposed IPL transformation. That transformation was shown to outperform the existing linear/non-linear transformation models such as an affine, third and fourth polynomials, local weighted mean, and PL. Moreover, we demonstrated that the IPL transformation improved the warping performance over the PL transformation outside the triangular regions by increasing the correlation coefficient values from 0.259 to 0.304, 0.603 to 0.657, and 0.180 to 0.338 in the first, second, and third real datasets, respectively.
VINCENT: A visual analytics system for investigating the online vaccine debate

This paper reports and describes VINCENT, a visual analytics system that is designed to help public health stakeholders (i.e., users) make sense of data from websites involved in the online debate about vaccines. VINCENT allows users to explore visualizations of data from a group of 37 vaccine-focused websites. These websites differ in their position on vaccines, topics of focus about vaccines, geographic location, and sentiment towards the efficacy and morality of vaccines, specific and general ones. By integrating webometrics, natural language processing of website text, data visualization, and human-data interaction, VINCENT helps users explore complex data that would be difficult to understand, and, if at all possible, to analyze without the aid of computational tools.The objectives of this paper are to explore A) the feasibility of developing a visual analytics system that integrates webometrics, natural language processing of website text, data visualization, and human-data interaction in a seamless manner; B) how a visual analytics system can help with the investigation of the online vaccine debate; and C) what needs to be taken into consideration when developing such a system. This paper demonstrates that visual analytics systems can integrate different computational techniques; that such systems can help with the exploration of public health online debates that are distributed across a set of websites; and that care should go into the design of the different components of such systems.
Improved Model for Micro-UAV Propulsion Systems: Characterization and Applications

An improved propulsion model for multirotor small unmanned aerial vehicles is developed by incorporating the effects of airflow speed, direction, rotor interaction, and other phenomena related to particular configurations, which are elements that are lacking in typical models. Two applications are presented. The first is a propulsion envelope, which helps determining safe operating conditions for particular propulsion systems. The second application allows elucidating important dynamical phenomena that traditional models lack, including possible instability with closed-loop controllers.
An Overview of Natural Language Processing

Natural Language Processing is branch of machine learning that deals with text and speech.Natural Language Processing is a way for computers to analyze, understand, and derive meaning from human language in a smart and useful way.By utilizing Natural Language Processing, developers can organize and structure knowledge to perform tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.
Quality attributes use in architecture design decision methods: research and practice

Over the past 10 years software architecture has been perceived as the result of a set of architecture design decisions rather than the elements that form part of the software design. As quality attributes are considered major drivers of the design process to achieve high quality systems, the design decisions that drive the selection and use of specific quality properties and vice versa are closely related. Consequently, quality attributes must play a role for decision making processes and be documented alongside the decisions captured. Consequently, we conduct a systematic literature review to study the importance and impact of the relationships between quality attributes and architecture design decisions and to what extent existing architecture knowledge management methods and tools deal with the decisions that affect the quality of a system. We also report on the challenges and future research paths for architectural knowledge management methods and tools. Our results reveal important explicit relationships between both software artifacts, the role of uncertainty in decision making and empirical studies reporting the use of quality attributes in architecture knowledge management activities.
Risk Prediction for Imbalanced Data in Cyber Security : A Siamese Network-based Deep Learning Classification Framework

Risk prediction plays an important role in network security which can be used to predict riskiest parts and then proactive measures can be adopted to avoid potential damage. Most existing literature model risk prediction problems as binary classification problems by using machine learning methods. However, these traditional machine learning models have poor performance - tending to misclassify the risky ones into the category of risk-free - on risk prediction task when the datasets are imbalanced or small in size. In this paper, we propose a Siamese Network Classification Framework (SNCF) that can map the Siamese network to a classification based on the similarity to alleviate imbalance for risk prediction. Experimental results on imbalanced data in risk prediction verify that the deep learning-based classification architecture SNCF has better efficiency when compared with other algorithms.
Robust Multi-Feature Learning for Skeleton-Based Action Recognition

Skeleton-based action recognition has advanced significantly in the past decade. Among deep learning-based action recognition methods, one of the most commonly used structures is a two-stream network. This type of network extracts high-level spatial and temporal features from skeleton coordinates and optical flows, respectively. However, other features, such as the structure of the skeleton or the relations of specific joint pairs, are sometimes ignored, even though using these features can also improve action recognition performance. To robustly learn more low-level skeleton features, this paper introduces an efficient fully convolutional network to process multiple input features. The network has multiple streams, each of which has the same encoder-decoder structure. A temporal convolutional network and a co-occurrence convolutional network encode the local and global features, and a convolutional classifier decodes high-level features to classify the action. Moreover, a novel fusion strategy is proposed to combine independent feature learning and dependent feature relating. Detailed ablation studies are performed to confirm the network’s robustness to all feature inputs. If more features are combined and the number of streams increases, performance can be further improved. The proposed network is evaluated on three skeleton datasets: NTU-RGB + D, Kinetics, and UTKinect. The experimental results show its effectiveness and performance superiority over state-of-the-art methods.
Neural Machine Translation between Vietnamese and English: an Empirical Study

Machine translation is shifting to an end-to-end approach based on deep neural networks. The state of the art achieves impressive results for popular language pairs such as English - French or English - Chinese. However for English - Vietnamese the shortage of parallel corpora and expensive hyper-parameter search present practical challenges to neural-based approaches. This paper highlights our efforts on improving English-Vietnamese translations in two directions: (1) Building the largest open Vietnamese - English corpus to date, and (2) Extensive experiments with the latest neural models to achieve the highest BLEU scores. Our experiments provide practical examples of effectively employing different neural machine translation models with low-resource language pairs.
A Fast 4K Video Frame Interpolation Using a Multi-Scale Optical Flow Reconstruction Network

Recently, video frame interpolation research developed with a convolutional neural network has shown remarkable results. However, these methods demand huge amounts of memory and run time for high-resolution videos, and are unable to process a 4K frame in a single pass. In this paper, we propose a fast 4K video frame interpolation method, based upon a multi-scale optical flow reconstruction scheme. The proposed method predicts low resolution bi-directional optical flow, and reconstructs it into high resolution. We also proposed consistency and multi-scale smoothness loss to enhance the quality of the predicted optical flow. Furthermore, we use adversarial loss to make the interpolated frame more seamless and natural. We demonstrated that the proposed method outperforms the existing state-of-the-art methods in quantitative evaluation, while it runs up to 4.39× faster than those methods for 4K videos.
A survey on the use of humanoid robots in primary education: Prospects, research challenges and future research directions

In parallel with the significant and exciting advancements in robot technologies, the use of humanoid robots to support teaching strategies and learning goals has become a popular topic. Different from the traditional instructional or learning tools, humanoid robots can exhibit mobile behaviours and numerous repetitions and are very helpful to the students in developing problem-solving and collaboration abilities. Presently, the roles of humanoid robots in classrooms fall into four main categories: learning materials, learning companions, teaching assistants and communication mediators to support group learning, respectively. With the humanoid appearance, anthropomorphism, interaction, flexibility, repeatability and digital data representation, humanoid robots have great potential to be useful especially in preschool and primary school education. In this paper, limitations and challenges of the use of humanoid robots as teaching assistants are presented in addition to exploring the relationship between humanoid robots and performance in learning.&#x0D; Keywords: Humanoid robot, teaching, social interaction, learning tool, assisted learning.
Gaussian states as minimum uncertainty states

In bosonic fields, Gaussian states, which consist of a rather wide family of states including coherent states, squeezed states, thermal states, etc., have many classical-like features, and are usually defined from the mathematical perspective in terms of characteristic functions. It is well known that some special Gaussian states, such as coherent states, are minimum uncertainty states for the conventional Heisenberg uncertainty relation involving canonical pair of position and momentum observables. A natural question arises as whether all Gaussian states can be characterized as minimum uncertainty states. In this work, we show that indeed Gaussian states coincide with minimum uncertainty states for an information-theoretic refinement of the conventional uncertainty relation established in Luo (2005) [40]. This characterization puts Gaussian states on a novel basis of physical significance.
The Asymmetric Effect of Volatility Spillover in Global Virtual Financial Asset Markets: The Case of Bitcoin

In this paper, we measure the asymmetric volatility spillover among six Bitcoin markets from 1 January 2014 to 30 September 2017, using the volatility spillover index based on Markov regime switching VAR model, and provide the static and dynamic analysis under different regime.The static results show that asymmetric effects of volatility spillover exist in total, own and net volatility spillover of different Bitcoin markets under different regime.The dynamic results show that there are significantly asymmetric effects in total, directional and net volatility spillover.Thus, the authorities should monitor the specific regime of VFAs and improve market mechanisms.
Wavelet-based Semi-supervised Adversarial Learning for Synthesizing Realistic 7T from 3T MRI

Ultra-high field 7T magnetic resonance imaging (MRI) scanners produce images with exceptional anatomical details, which can facilitate diagnosis and prognosis. However, 7T MRI scanners are often cost prohibitive and hence inaccessible. In this paper, we propose a novel wavelet-based semi-supervised adversarial learning framework to synthesize 7T MR images from their 3T counterparts. Unlike most learning methods that rely on supervision requiring a significant amount of 3T-7T paired data, our method applies a semi-supervised learning mechanism to leverage unpaired 3T and 7T MR images to learn the 3T-to-7T mapping when 3T-7T paired data are scarce. This is achieved via a cycle generative adversarial network that operates in the joint spatial-wavelet domain for the synthesis of multi-frequency details. Extensive experimental results show that our method achieves better performance than state-of-the-art methods trained using fully paired data.
Omnidirectional depth segmentation using orthogonal fringe patterns and multi-scale enhancement

Phase-shifted depth segmentation, which is facilitated by the phase-shift invariance of the singular points in different wrapped phase maps, has the advantages of being immune to color, texture, and camera exposure. However, because the phase sensitivity is distributed unevenly, it is difficult to perform this type of depth segmentation properly in some special regions. Instead, this paper presents an omnidirectional method for depth segmentation without the need for precalibration or complementary cues. During data acquisition, two groups of orthogonal phase-shifted patterns are cast sequentially onto the target objects. A least-squares algorithm is then applied to these fringe patterns, and two sets of phase sequences are calculated separately. Using the proposed multi-scale enhancement technology, objects with abruptly changing surfaces can be segmented into different parts. Simulations show that even under a noisy environment (namely a signal-to-noise ratio of 10), the correct rate of the proposed framework still reaches 98.97%. Verification experiments show that the proposed method performs omnidirectional depth segmentation effectively, thereby offering a solution to the problem of mis-segmentation in phase-insensitive regions.
Malicious Websites Detection via CNN based Screenshot Recognition

Traditional malicious website detection schemes no longer work effectively, for miscellaneous Web spam techniques are widely used and relentlessly innovative. This paper analyzes typical spam techniques and proposes a novel detection scheme, which origins from end-user perspective and adopts Convolutional Neural Network as a key element for precise image recognition.
Ontology Completion Using Graph Convolutional Networks

Many methods have been proposed to automatically extend knowledge bases, but the vast majority of these methods focus on finding plausible missing facts, and knowledge graph triples in particular. In this paper, we instead focus on automatically extending ontologies that are encoded as a set of existential rules. In particular, our aim is to find rules that are plausible, but which cannot be deduced from the given ontology. To this end, we propose a graph-based representation of rule bases. Nodes of the considered graphs correspond to predicates, and they are annotated with vectors encoding our prior knowledge about the meaning of these predicates. The vectors may be obtained from external resources such as word embeddings or they could be estimated from the rule base itself. Edges connect predicates that co-occur in the same rule and their annotations reflect the types of rules in which the predicates co-occur. We then use a neural network model based on Graph Convolutional Networks (GCNs) to refine the initial vector representation of the predicates, to obtain a representation which is predictive of which rules are plausible. We present experimental results that demonstrate the strong performance of this method.