Reviewerly/exHarmony huggingface dataset:
Stochastic Evaluation of Landscapes Transformed by Renewable Energy Installations and Civil Works

Renewable energy (RE) installations and civil works are beneficial in terms of sustainability, but a considerable amount of space in the landscape is required in order to harness this energy. In contemporary environmental theory the landscape is considered an environmental parameter and the transformation of the landscape by RE works has received increasing attention by the scientific community and affected societies. This research develops a novel computational stochastic tool the 2D Climacogram (2D-C) that allows the analysis and comparison of images of landscapes, both original and transformed by RE works. This is achieved by a variability characterization of the grayscale intensity of 2D images. A benchmark analysis is performed for art paintings in order to evaluate the properties of the 2D-C for image analysis, and the change in variability among images. Extensive applications are performed for landscapes transformed by RE works. Results show that the 2D-C is able to quantify the changes in variability of the image features, which may prove useful in the landscape impact assessment of large-scale engineering works.
Optical frequency comb-based multichannel parallel continuous-variable quantum key distribution

Continuous-variable quantum key distribution (CVQKD) provides an approach for secure communication in optical fiber communication systems. However, its practical implementation has been hindered by low secret key bit rates that are usually limited to several bits/s to hundreds of kbits/s at distances of more than 25 kilometers. In this paper, we use a pair of optical frequency combs (OFCs) for both multiple parallel transmission and coherent reception, which assign multiple sub-channels involving multiple independent secret keys in a single fiber to increase the key bit rate. The first and last sub-channels are selected for propagating phase references to compensate the phase offset between two free-running combs. We analyze possible excess noise caused by dispersive walk-off in the transmission, imperfect phase compensation in the reception and photon leakage from the phase references. Compared to the previous single-channel CVQKD method, simulation results show more than a factor of 20 increase in the secret key rate at a transmission distance of 35 km and the number of comb lines of 35.
Analyzing Phonetic and Graphemic Representations in End-to-End Automatic Speech Recognition

End-to-end neural network systems for automatic speech recognition (ASR) are trained from acoustic features to text transcriptions. In contrast to modular ASR systems, which contain separately-trained components for acoustic modeling, pronunciation lexicon, and language modeling, the end-to-end paradigm is both conceptually simpler and has the potential benefit of training the entire system on the end task. However, such neural network models are more opaque: it is not clear how to interpret the role of different parts of the network and what information it learns during training. In this paper, we analyze the learned internal representations in an end-to-end ASR model. We evaluate the representation quality in terms of several classification tasks, comparing phonemes and graphemes, as well as different articulatory features. We study two languages (English and Arabic) and three datasets, finding remarkable consistency in how different properties are represented in different layers of the deep neural network.
Predicting Resource Locations in Game Maps Using Deep Convolutional Neural Networks

We describe an application of neural networks to predict the placements of resources in StarCraft II maps. Networks are trained on existing maps taken from databases of maps actively used in online competitions and tested on unseen maps with resources (minerals and vespene gas) removed. This method is potentially useful for AI-assisted game design tools, allowing the suggestion of resource and base placements consonant with implicit StarCraft II design principles for fully or partially sketched heightmaps. By varying the thresholds for the placement of resources, more or fewer resources can be created consistently with the pattern of a single map. We further propose that these networks can be used to help understand the design principles of StarCraft II maps, and by extension other, similar types of game content.
Development of the model of the antagonistic agents behavior under a cyber conflict

The results of the development of the model of the antagonistic agents behavior in a cyber conflict are presented. It is shown that the resulting model can be used to analyze investment processes in security systems, taking into account the assumption that investment processes are significantly influenced by the behavior of parties involved in a cyber conflict.General approaches to model development are presented. First of all, the system of concepts, assumptions and limitations is formed, within the framework of which a mathematical model of behavior must be developed. Taking this into account, the mathematical model of the conflicting agents behavior, presented in the form of algebraic and differential equations, is developed. The developed model presents both the technical characteristics of the security system and the psychological characteristics of the participants in the cyber conflict, which affect the financial characteristics of the investment processes in cybersecurity systems. A distinctive feature of the proposed model is the simultaneous consideration of the behavior of the parties to a cyber conflict not as independent parties, but as agents mutually interacting with each other. The model also makes it possible to simulate the destabilizing effect of the confrontation environment disturbances on the behavior of the conflicting parties, changing the degree of vulnerability of the cybersecurity system along various attack vectors and the level of their success.Using the developed model, simulation modeling of the interacting agents behavior in a cyber conflict is performed. The simulation results showed that even the simplest behavior strategies of the attacking side ("the weakest link") and the defense side ("wait and see") make it possible to ensure information security of the business process loop.The developed model of interaction between the attacker and the defender can be considered as a tool for modeling the processes of the conflicting parties behavior when implementing various investment scenarios. The simulation results enable decision-makers to receive support regarding the direction of investment in the security of the business process loop.
Image Enhancement Based on Histogram Equalization

Abstract Image enhancement is an important technology in the process of image processing. It can improve the image effect, enhance the overall brightness and clarity of the image. Histogram equalization is a very effective image enhancement technology. On the basis of probability theory, it uses gray point calculation to transform histogram. This paper mainly studies the basic theory of histogram equalization for image processing. At the same time, the image is experimentally analyzed by matlab. The results show that histogram equalization can improve the image effect and enhance the contrast of the image.
Mind the Gap

Recent research in high-throughput networked systems has established the need for centralized and preemptive request scheduling in order to achieve good hardware utilization and low tail latency for a wide variety of workloads. However, this approach is expensive to scale as it requires an increasing number of CPU cores dedicated to scheduling. Moreover, passing every request through a scheduling core introduces latency for inter-core communication and reduces the effectiveness of data preloading and caching optimizations.
FWFS: Selecting Robust Features Towards Reliable and Stable Traffic Classifier in SDN

Real-time Internet traffic flow classification is important in managing network resources in accordance to Quality of Service (QoS) requirements.The centralized network's control in Software Defined Networking (SDN) provides a platform for Internet Service Provider (ISP) to perform specific actions on the classified flows through routing and scheduling.Though machine learning (ML) can be the alternative to Deep Packet Inspection (DPI) in classifying SDN traffic flows, several problems, such as classifier's accuracy, computational complexity, multi-class imbalanced data, and concept drift, need to be addressed in order to have a reliable solution.Therefore, this work has proposed a hybrid filter-wrapper feature selection (FS) algorithm, named Filter-Wrapper Feature Selection (FWFS).The algorithm selects robust features that represent minority classes and resistant to concept drift and is also computationally inexpensive by discarding irrelevant features before further processing with wrapper function.Based on the performance evaluation, the feature selection process of FWFS is computationally inexpensive; i.e. 59.6s, which produces a classifier with an overall accuracy of 98.9%.The result is better than state-of-the-art FS algorithm, Efficient Feature Optimization Approach (EFOA) which requires >400s to select features which can produced a classifier with 97.7% accuracy.In addition to the high overall accuracy, the classifier trained with features selected by FWFS has better F-measure values for each classes including minority classes; i.e. >0.8 in MULTIMEDIA and INTERACTIVE which consist only 0.15% and 0.03% instances, respectively, of the total 377,526 instances in the dataset.Furthermore, the classifier is stable and reliable for classifying new data; i.e. 98.7% accuracy for classifying new data and F-measure of more than 0.8 in every class.The classifier model will be embedded in the SDN-ISP traffic classification solution which provides insights for resource allocations and traffic scheduling in the network.
An Empirical Study of AML Approach for Credit Card Fraud Detection–Financial Transactions

Credit card fraud is one of the flip sides of the digital world, where transactions are made without the knowledge of the genuine user. Based on the study of various papers published between 1994 and 2018 on credit card fraud, the following objectives are achieved: the various types of credit card frauds has identified and to detect automatically these frauds, an adaptive machine learning techniques (AMLTs) has studied and also their pros and cons has summarized. The various dataset are used in the literature has studied and categorized into the real and synthesized datasets.The performance matrices and evaluation criteria have summarized which has used to evaluate the fraud detection system.This study has also covered the deep analysis and comparison of the performance (i.e sensitivity, specificity, and accuracy) of existing machine learning techniques in the credit card fraud detection area.The findings of this study clearly show that supervised learning, card-not-present fraud, skimming fraud, and website cloning method has been used more frequently.This Study helps to new researchers by discussing the limitation of existing fraud detection techniques and providing helpful directions of research in the credit card fraud detection field.
Kecanduan Game Online pada Remaja: Dampak dan Pencegahannya

The problem of online game addiction demands public attention. Currently, many studies tend to be focused on efforts to reduce the level. However, there are very limited researches centered on discussing efforts to prevent this occurrence. This paper aims to examine various alternatives, in order to prevent this form of addiction, especially in adolescent’s that tend to be the most affected age group. In addition, there have been reports on its impact on several aspects of life, including health, psychologically, academically, socially and financially. Therefore, there is a need for preventive approaches, due to the impacts that disrupt the life of adolescents, which this paper is expected to identify. Furthermore, future research ought to focus on in depth examination of other alternatives to prevent online game addiction.
Reinforcing an Image Caption Generator Using Off-Line Human Feedback

Human ratings are currently the most accurate way to assess the quality of an image captioning model, yet most often the only used outcome of an expensive human rating evaluation is a few overall statistics over the evaluation dataset. In this paper, we show that the signal from instance-level human caption ratings can be leveraged to improve captioning models, even when the amount of caption ratings is several orders of magnitude less than the caption training data. We employ a policy gradient method to maximize the human ratings as rewards in an off-policy reinforcement learning setting, where policy gradients are estimated by samples from a distribution that focuses on the captions in a caption ratings dataset. Our empirical evidence indicates that the proposed method learns to generalize the human raters' judgments to a previously unseen set of images, as judged by a different set of human judges, and additionally on a different, multi-dimensional side-by-side human evaluation procedure.
VIREO @ Video Browser Showdown 2020

In this paper, we present the features implemented in the 4th version of the VIREO Video Search System (VIREO-VSS). In this version, we propose a sketch-based retrieval model, which allows the user to specify a video scene with objects and their basic properties, including color, size, and location. We further utilize the temporal relation between video frames to strengthen this retrieval model. For text-based retrieval module, we supply speech and on-screen text for free-text search and upgrade the concept bank for concept search. The search interface is also re-designed targeting the novice user. With the introduced system, we expect that the VIREO-VSS can be a competitive participant in the Video Browser Showdown (VBS) 2020.
Super-Resolution Reconstruction Method of Remote Sensing Image Based on Multi-Feature Fusion

The acquisition of remote sensing images is affected by imaging equipment and environmental conditions.Usually on lower performance devices, the resolution of the acquired images is also low.Among many methods, the super-resolution reconstruction method based on generative adversarial networks has obvious advantages over previous network models in reconstructing image texture details.However, it is found in experiments that not all of these reconstructed textures exist in the image itself.Aiming at the problem of whether the texture details of the reconstructed image are accurate and clear, we propose a super-resolution reconstruction method combining wavelet transform and generative adversarial network.Using wavelet multi-resolution analysis, training wavelet decomposition coefficients in the generative adversarial network can effectively improve the local detail information of the reconstructed image.Experimental results show that our method can effectively reconstruct more natural image textures and make the images more visually clear.In the remote sensing image test set, the four indicators of the algorithm, peak signal to noise ratio (PSNR), structural similarity (SSIM), Feature Similarity (FSIM) and Universal Image Quality (UIQ) are slightly better than the algorithms mentioned in the article.
Assessing Students’ Digital Literacy Skill in Senior High School Yogyakarta

This study aimed at investigating students’ digital literacy skill of students Grade X and XI in Senior High School in Yogyakarta. There were three steps of this research. First, we constructed the item format and aspect of digital literacy skill. Second, the administration of this research to 193 students of science class in Yogyakarta which were selected randomly. The last, analysis data of students’ digital literacy skill based on educational level. For the profile of students’ digital we used descriptive and quantitative statistics (frequency and percent). To determine the significant difference of the students’ level we also used independent sample t-test. Result of this study showed that the students’ digital literacy skill of all students was very low level. In addition, there was significant difference of students’ digital literacy skill based on educational level. The conclusion was students’ digital literacy skill in Yogyakarta was very low and there must an effort to force these skills. Recommendation for the future study is to develop a learning model that can enhance students’ digital literacy skill. For the policy makers, learning based on digital literacy skill must be integrated on the curriculum. ICT learning should be thought to students in Indonesia, especially in Yogyakarta.
A comparison of Arabic sign language dynamic gesture recognition models

Arabic Sign Language (ArSL) is similar to other sign languages in terms of the way it is gestured and interpreted and used as a medium of communication among the hearing-impaired and the communities in which they live in. Research investigating sensor utilization and natural user interfaces to facilitate ArSL recognition and interpretation, is lacking.Previous research has demonstrated that there is not a single classifier modeling approach that can be suitable for all hand gesture recognition tasks, therefore, this research investigated which combination of algorithms, set with different parameters used with a sensor device, produce higher ArSL recognition accuracy results in a gesture recognition system.This research proposed a dynamic prototype model (DPM) using Kinect as a sensor to recognize certain ArSL gestured dynamic words. The DPM used eleven predictive models of three algorithms (SVM, RF, KNN) based on different parameter settings. Research findings indicated that highest recognition accuracy rates for the dynamic words gestured were achieved by the SVM models, with linear kernel and cost parameter = 0.035.
Boosting Factual Correctness of Abstractive Summarization with Knowledge Graph.

A commonly observed problem with abstractive summarization is the distortion or fabrication of factual information in the article. This inconsistency between summary and original text has led to various concerns over its applicability. In this paper, we propose to boost factual correctness of summaries via the fusion of knowledge, i.e. extracted factual relations from the article. We present a Fact-Aware Summarization model, FASum. In this model, the knowledge information can be organically integrated into the summary generation process via neural graph computation and effectively improves the factual correctness. Empirical results show that FASum generates summaries with significantly higher factual correctness compared with state-of-the-art abstractive summarization systems, both under an independently trained factual correctness evaluator and human evaluation. For example, in CNN/DailyMail dataset, FASum obtains 1.2% higher fact correctness scores than UniLM and 4.5% higher than BottomUp.
Analysis and Findings of Social Engineering Industry Experts Explorative Interviews: Perspectives on Measures, Tools, and Solutions

Social engineering is one of the biggest threats organizations face today, as more and more organizations are adopting digitalization.In the context of cyber security, social engineering is the practice of taking advantage of human weaknesses through manipulation to accomplish a malicious goal.For better implementation methods against social engineering, this qualitative study will attempt to provide measures against information security challenges faced by organizations.The analysis is then provided by the answers of interviewed experts in the field of cyber security and social engineering.The research herein focuses on the human element of cyber security threats, recognizing that hackers exploit the vulnerabilities and lack of awareness of staff.Then using these issues to create security loopholes and engineer cyber-attacks that include the interruption or infection of information systems, transfer of unauthorized funds, and stealing of credentials.The results of this qualitative study highlight that there is a positive relationship between social engineering and user awareness.The findings build upon the researchers' ongoing work, which postulates that as an increase in contextual social engineering knowledge leads to a decrease in being victims of social engineering and is, therefore, one of the most effective mechanisms for managing social engineering.
A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm

One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM.
Imitating by Generating: Deep Generative Models for Imitation of Interactive Tasks

To coordinate actions with an interaction partner requires a constant exchange of sensorimotor signals. Humans acquire these skills in infancy and early childhood mostly by imitation learning and active engagement with a skilled partner. They require the ability to predict and adapt to one's partner during an interaction. In this work we want to explore these ideas in a human-robot interaction setting in which a robot is required to learn interactive tasks from a combination of observational and kinesthetic learning. To this end, we propose a deep learning framework consisting of a number of components for (1) human and robot motion embedding, (2) motion prediction of the human partner and (3) generation of robot joint trajectories matching the human motion. \reviewOne{As long-term motion prediction methods often suffer from the problem of regression to the mean, our technical contribution here is a novel probabilistic latent variable model which does not predict in joint space but in latent space. To test the proposed method,} we collect human-human interaction data and human-robot interaction data of four interactive tasks "hand-shake", "hand-wave", "parachute fist-bump" and "rocket fist-bump". We demonstrate experimentally the importance of predictive and adaptive components as well as low-level abstractions to successfully learn to imitate human behavior in interactive social tasks.
Semantic Segmentation With Low Light Images by Modified CycleGAN-Based Image Enhancement

In recent years, the importance of semantic segmentation has been widely recognized and the field has been actively studied. The existing state-of-the-art segmentation methods show high performance for bright and clear images. However, in low light or nighttime environments, images are blurred and noise increases due to the nature of the camera sensor, which makes it very difficult to perform segmentation for various objects. For this reason, there are few previous studies on multi-class segmentation in low light or nighttime environments. To address this challenge, we propose a modified cycle generative adversarial network (CycleGAN)-based multi-class segmentation method that improves multi-class segmentation performance for low light images. In this study, we used low light databases generated by two road scene open databases that provide segmentation labels, which are the Cambridge-driving labeled video database (CamVid) and Karlsruhe Institute of Technology and Toyota Technological Institute at Chicago (KITTI) database. Consequently, the proposed method showed superior segmentation performance compared with the other state-of-the-art methods.