Reviewerly/exHarmony huggingface dataset:
Information Visualization Applied to Computer Network Security

Computer networks are becoming increasingly vital to the activities of organizations, and their monitoring is necessary to ensure proper functioning. The use of the human cognitive process in decision-making through information visualization (IV) is a viable option for large amounts of data, such as those generated in network monitoring. Considering the need to monitor modern computer networks and the quality gain when using visualization techniques, the objective was to conduct a review study to understand the process of building a monitoring tool using information visualization resources and, from this review, follow with a case study through a tool for visualization application in a university wireless network management. To this end, a systematic literature review was performed, and then a survey of requirements was conducted with the university network managers. With the analysis of the data from the review and the survey, a tool was specified and developed, evaluated in several units of the university. In this way, the results from the review and the requirements survey are observed in time, which allowed the development of a solution using the observed trends, validating them in the use and evaluation of the tool. The main contribution of the work is the resulting tool and its impact on the management of the university wireless network, facilitating the activities of managers.
Two-Level System of on-Line Risk Assessment in the National Cyberspace

The paper presents a hierarchical, two-level approach to on-line cyber risk assessment at the national level. It takes into account cyber threats and vulnerabilities identified at the lower level formed by essential service operators and digital service providers. A computational algorithm is proposed, making use of the local measurements and assessments and its asynchronous convergence is proved. At the end a case study concerning a system consisting of four entities is presented.
Pengaruh Penggunaan Jarimatika Terhadap Kemampuan Berhitung Perkalian Peserta Didik Kelas IV Sekolah Dasar

This study aims to determine the effect of the Jarimatika method on the ability of multiplication in fourth grade of elementary school students. This research focused on the use of Jarimatika to assist students in solving numeracy multiplication problem questions. This is based on factual condition where students have not been able to work on the number multiplication and also the monotonous learning methods implemented made learning mathematics feels difficult. This research employed an experimental method with a quasi experimental design in the form of nonequivalent control group design. The experimental group treated by Jarimatika, while the control group used the conventional methods of expository, question and answer and rote method. Data collection techniques in this study used tests. Research data are presented using descriptive statistical data analysis techniques. The research shows that there is an increase in the average of pretest and posttest score of the experimental group. Based on the t-test results obtained, the t obtained value of 6.966 is greater than t table of 2,056 (6,966 2,056) and a significance value of 0,000 is less than 0.05 (0,000 0.05) at a significance level of 5%. Based on the results of the t test, it showed a significant influence on the use of Jarimatika on multiplication learning outcomes in forth grade students of elementary schools.
ManiGen: A Manifold Aided Black-Box Generator of Adversarial Examples

From recent research work, it has been shown that neural network (NN) classifiers are vulnerable to adversarial examples which contain special perturbations that are ignored by human eyes while can mislead NN classifiers. In this paper, we propose a practical black-box adversarial example generator, dubbed ManiGen. ManiGen does not require any knowledge of the inner state of the target classifier. It generates adversarial examples by searching along the manifold, which is a concise representation of input data. Through extensive set of experiments on different datasets, we show that (1) adversarial examples generated by ManiGen can mislead standalone classifiers by being as successful as the state-of-the-art white-box generator, Carlini, and (2) adversarial examples generated by ManiGen can more effectively attack classifiers with state-of-the-art defenses.
The Effect of Geography Teaching Curriculum Enriched with Virtual Reality Applications on Teacher Candidates’ Interest for the Course, Achievement and the Tendencies to Utilise Information Technologies

In this study, the effect of the geography curriculum enriched with virtual reality applications on the teacher candidates' interest towards the course, academic achievement and tendency to use information technologies were examined.In the study in which both face-to-face and online learning environments were employed, in-class activities were conducted with virtual reality (VR) applications, and extracurricular activities were carried out with Google Classroom.In the study designed with single group pretest-posttest experimental design, a mixed-method approach in which quantitative and qualitative methods are used together was adopted.A total of 27 teacher candidates participated in the study that lasted for six weeks.The results obtained from the study revealed that the curriculum created a significant difference in teacher candidates' interest in the course, their academic success and their tendencies to use information technologies in their future classes.Teacher candidates stated that they were satisfied with the activities carried out with virtual reality and Google Classroom applications and that the geography curriculum supported by technology is effective in their learning processes.
Genre-based Analysis of Syntactic Complexity in L2 College Students’ Writing: Pedagogic Scope and Directions

This study aims to examine the degree of syntactic complexity in Korean L2 college students' writing of four different genres.Using 14 syntactic complexity measures as indices of L2 language development of writing, this study investigates how syntactic patterns are different among four different genres and English proficiency levels.Sixty-one participants in A university in South Korea participated in this study, and their writings were scored by two raters.All 244 essays including four different genres were collected, and these data were analyzed by using the computer program of syntactic complexity analyzer.The results have shown that genre difference has an impact on syntactic complexity in Korean students' English written products.Details of results showed strong evidence of genre-specific features among narrative, expository (comparison and cause-effect essays), and argumentative genres.Based on the overall findings of the study, syntactic complexity showed significant genre differences, but there were not significant group differences of syntactic complexity among L2 proficiency levels.
Reuse-trap: Re-purposing Cache Reuse Distance to Defend against Side Channel Leakage

Modern computing systems typically have multiple users sharing hardware resources. While such shared hardware have typically been performance boosters, they have also led to inadvertent side-effects such as side channels. Caches, that present the largest attack surface, have been popular among adversaries for side channel attacks. In this work, we repurpose a classic cache performance metric namely, reuse distance, to capture the activity of an adversary in cache timing channels. We design Reuse-trap, an efficient cache side channel mitigation framework to record reuse distances during victim accesses and carefully inject noise to mislead the spy from inferring the victim’s activity. Our experimental results show that we can identify adversaries with zero false positives and make timing channels suffer from over 50% bit error rate on average.
Recommender systems for sensor-based ambient control in academic facilities

Academic spaces are an environment that promotes student performance not only because of the quality of its equipment, but also because of its ambient comfort conditions, which can be controlled by means of actuators that receive data from sensors. Something similar can be said about other environments, such as home, business, or industry environment. However, sensor devices can cause faults or inaccurate readings in a timely manner, affecting control mechanisms. The mutual relationship between ambient variables can be a source of knowledge to predict a variable in case a sensor fails. Moreover, the relationship between these variables and the occupation of spaces by students over time also contains an adequate knowledge of the context for prediction. In this article we propose to predict ambient variables by means of recommendation systems based on collaborative filtering, which are fed with data from sensors over time in different academic rooms. For this purpose, we applied two different algorithms: Probabilistic Matrix Factorization and Bayesian Non-negative Matrix Factorization. The accuracy of the algorithms when comparing actual and predicted values and the performance comparison between the two collaborative filtering implementations lead us to propose Probabilistic Matrix Factorization as a good approach for supporting ambient control systems.
How Different Are Full and Short Papers in Word-Usage?

Academic papers submitted to a conference are assessed by reviewers and judged if they deserve to be presented at the conference.The accepted papers are often classified into full papers, short papers, and other types, according mainly to the reviewers' assessment.The major aim of the study presented in this paper is to find tips which are effective for a paper to be improved so that a paper supposed to be classified as a short paper becomes a full paper.In this study, we investigate a scenario for finding the differences between full and short papers on the usage of words/terms.Then, we extract words which are characteristic for either full or short papers through an experimental study.In order to find these words, we introduce a couple of indexes of a word.The results inspire that we can obtain practical tips in this approach by refining this method.
Selective information passing for MR/CT image segmentation

Automated medical image segmentation plays an important role in many clinical applications, which however is a very challenging task, due to complex background texture, lack of clear boundary and significant shape and texture variation between images. Many researchers proposed an encoder–decoder architecture with skip connections to combine low-level feature maps from the encoder path with high-level feature maps from the decoder path for automatically segmenting medical images. The skip connections have been shown to be effective in recovering fine-grained details of the target objects and may facilitate the gradient back-propagation. However, not all the feature maps transmitted by those connections contribute positively to the network performance. In this paper, to adaptively select useful information to pass through those skip connections, we propose a novel 3D network with self-supervised function, named selective information passing network. We evaluate our proposed model on the MICCAI Prostate MR Image Segmentation 2012 Grant Challenge dataset, TCIA Pancreas CT-82 and MICCAI 2017 Liver Tumor Segmentation Challenge dataset. The experimental results across these datasets show that our model achieved improved segmentation results and outperformed other state-of-the-art methods. The source code of this work is available at https://github.com/ahukui/SIPNet .
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework

Transfer Learning has shown great potential to enhance the single-agent Reinforcement Learning (RL) efficiency, by sharing learned policies of previous tasks. Similarly, in multiagent settings, the learning performance can also be promoted if agents can share knowledge between each other. However, it remains an open question of how an agent should learn from other agents' knowledge. In this paper, we propose a novel multiagent option-based policy transfer (MAOPT) framework to improve multiagent learning efficiency. Our framework learns what advice to give to each agent and when to terminate it by modeling multiagent policy transfer as the option learning problem. MAOPT provides different kinds of variants which can be classified into two types in terms of the experience used during training. One type is the MAOPT with the Global Option Advisor which has the access to the global information of the environment. However, in many realistic scenarios, we can only obtain each agent's local information due to the partial observation. The other type contains MAOPT with the Local Option Advisor and MAOPT with the Successor Representation Option (SRO) which are suitable for this setting and collect each agent's local experience for the update. In many cases, each agent's experience is inconsistent with each other which causes the option-value estimation to oscillate and to become inaccurate. SRO is used to handle the experience inconsistency by decoupling the dynamics of the environment from the rewards to learn the option-value function under each agent's preference. MAOPT can be easily combined with existing deep RL approaches. Experimental results show it significantly boosts the performance of existing deep RL methods in both discrete and continuous state spaces.
Joint and individual effectiveness of galvanic cutaneous stimulation and tactile stimulation at decreasing Simulator Adaptation Syndrome

This research was focused on investigating the effectiveness of galvanic cutaneous stimulation and tactile stimulation jointly and individually at mitigating Simulator Adaptation Syndrome. Forty drivers (mean age = 23.1 ± 3.4 years old, twenty women) participated in a driving simulation experiment. Total scores of the Simulator Sickness Questionnaire, head movements (an index of body balance), and driving performance variables were compared across four different stimulation conditions: i) baseline (where no stimulation was presented), ii) galvanic cutaneous stimulation and iii) tactile stimulation deployed individually, and iv) both techniques deployed jointly. The results showed that both techniques presented in conjunction alleviate Simulator Adaptation Syndrome and improve driving performance more effectively than when they are presented in isolation. Importantly, reduced head movements were only revealed when galvanic cutaneous stimulation was applied. We concluded that the reduction of this syndrome is due to an improvement of body balance (elicited by galvanic cutaneous stimulation), and a distraction from the symptoms (elicited by tactile stimulation). We encourage the use of both techniques simultaneously to decrease Simulator Adaptation Syndrome.
On the Performance of Deep Learning Models for Time Series Classification in Streaming

Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are the state-of-the-art for many machine learning tasks, their performance in real-time data streaming scenarios is a research area that has not yet been fully addressed. Nevertheless, there have been recent efforts to adapt complex deep learning models for streaming tasks by reducing their processing rate. The design of the asynchronous dual-pipeline deep learning framework allows to predict over incoming instances and update the model simultaneously using two separate layers. The aim of this work is to assess the performance of different types of deep architectures for data streaming classification using this framework. We evaluate models such as multi-layer perceptrons, recurrent, convolutional and temporal convolutional neural networks over several time-series datasets that are simulated as streams. The obtained results indicate that convolutional architectures achieve a higher performance in terms of accuracy and efficiency.
Biomedical Image Compression Techniques for Clinical Image Processing

Image processing is widely used in the domain of biomedical engineering especially for compression of clinical images. Clinical diagnosis receives high importance which involves handling patient’s data more accurately and wisely when treating patients remotely. Many researchers proposed different methods for compression of medical images using Artificial Intelligence techniques. Developing efficient automated systems for compression of medical images in telemedicine is the focal point in this paper. Three major approaches were proposed here for medical image compression. They are image compression using neural network, fuzzy logic and neuro-fuzzy logic to preserve higher spectral representation to maintain finer edge information’s, and relational coding for inter band coefficients to achieve high compressions. The developed image coding model is evaluated over various quality factors. From the simulation results it is observed that the proposed image coding system can achieve efficient compression performance compared with existing block coding and JPEG coding approaches, even under resource constraint environments.
Improved Artificial Fish Swarm Algorithm Approach to Robot Path Planning Problems

Path planning is one of the research hotspots in the field of robotics and it has certain difficulties. A novel intelligent optimization algorithm, artificial fish swarm algorithm (AFSA) is employed to solve this problem. When establishing the environmental model by polyhedral terrain technique, we present a method to determine the number of fence lines. Especially to overcome the shortcomings of original AFSA, such as low convergence rate and poor ability of balancing exploitation and exploration, an improved artificial fish swarm algorithm (IAFSA) is proposed. The basic idea of proposed algorithm is to divide the whole population into two sub groups, and different adaptive strategies are applied to each of them respectively. These two subpopulations evolve independently and individual migrations are conducted regularly to increase the population diversity and improve convergence rate of the algorithm. The decision probability is also introduced to reduce its computational complexity. In addition, differential evolution is hybridized with AFSA in order to make proposed algorithm escape from local optima. Then IAFSA is employed to solve robot path planning problems. Compared with the original AFSA, results show that proposed IAFSA can improve overall optimization performance. And the feasibility and effectiveness of IAFSA are verified.
Hidden Conflicts of Belief Functions

In this paper, hidden conflicts of belief functions are observed in the case where the sum of all multiples of the conflicting belief masses equals zero.Degrees of hidden conflicts and a degree of non-conflictness are defined and analyzed, including full non-conflictness.The notion of a hidden conflict extends the traditional approach to conflicts of belief functions.A hidden conflict between two belief functions is distinguished from internal hidden conflict(s) of the individual belief function(s).Upper bounds for both degrees of hidden conflict of two belief functions and degrees of hidden internal conflict of a belief function are presented, together with other properties of both types of hidden conflicts.The presented results include the relationship of hidden conflicts and of Martin's auto-conflicts together with an extended overview of properties of auto-conflicts.Finally, computational issues of hidden conflicts and non-conflictness are presented.
Image-to-Level: Generation and Repair

Procedural content generation via machine learning (PCGML) has recently gained research attention due to its ability to generate new game content with minimal user input. However, thus far those without machine learning expertise have been largely unable to use PCGML to generate content to fit their needs. This paper proposes the use of images as the input for a PCGML process to generate game levels. Intuitively, a user can submit an image, with the system returning the closest valid Super Mario Bros. game level. Our results indicate that at least for domains like Super Mario Bros. we can recreate a target level with high fidelity.
Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

Vehicle detection in aerial videos often requires post-processing to eliminate false detections. This paper presents a spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification. The proposed scheme also performs spatial post-processing, which includes morphological opening and closing to shape and prune the detected objects, and temporal post-processing to further reduce false detections. We evaluate the performance of the proposed spatial processing on two local aerial video datasets and one parking vehicle dataset, and the performance of the proposed spatio-temporal processing scheme on five local aerial video datasets and one public dataset. Experimental evaluation shows that the proposed schemes improve vehicle detection performance for each of the nine algorithms when evaluated on seven datasets. Overall, the use of the proposed spatio-temporal processing scheme improves average F-score to above 0.8 and achieves an average reduction of 83.8% in false positives.
Foundations of a Computational Science of Game Design: Abstractions and Tradeoffs

This paper presents the foundations of a computational science of game design—a model of abstraction: the outcome of a situated design process that conceptualizes something in terms of something else for a specific purpose. The outcome of abstraction is modeled via an abstraction scheme, a step toward modeling the process of game design abstraction from a human-centered perspective. A scheme’s purpose is couched relative to a scheme’s properties, also defined herein; collectively, the properties represent the space of tradeoffs designers must navigate for engineering virtual worlds. This model’s analytical traction is evidenced by applying it to the design of pathfinding, a core behavior in artificial intelligence for games. More broadly, it is a foundation for shared progress because it affords directly comparing particular abstractions of concepts and phenomena across the gamut of research on intelligent systems in entertainment.
NASE:

Link prediction is the task of predicting missing connections between entities in the knowledge graph (KG). While various forms of models are proposed for the link prediction task, most of them are designed based on a few known relation patterns in several well-known datasets. Due to the diversity and complexity nature of the real-world KGs, it is inherently difficult to design a model that fits all datasets well. To address this issue, previous work has tried to use Automated Machine Learning (AutoML) to search for the best model for a given dataset. However, their search space is limited only to bilinear model families. In this paper, we propose a novel Neural Architecture Search (NAS) framework for the link prediction task. First, the embeddings of the input triplet are refined by the Representation Search Module. Then, the prediction score is searched within the Score Function Search Module. This framework entails a more general search space, which enables us to take advantage of several mainstream model families, and thus it can potentially achieve better performance. We relax the search space to be continuous so that the architecture can be optimized efficiently using gradient-based search strategies. Experimental results on several benchmark datasets demonstrate the effectiveness of our method compared with several state-of-the-art approaches.