Reviewerly/exHarmony huggingface dataset:
Agent-Based Behavior Precursor Model of Insider IT Sabotage

Insider IT sabotage can be defined as the use of information technology to cause harm to an organization or an individual. Behavioral precursors are usually observable during the evolution of the threat. These precursors include personal predispositions such as rule and policy violations or mental health disorders, expressed disgruntlement due to unmet expectations or stressful events experienced by highly skilled employees with access to administrate systems, networks, and data in the organization. This research uses an agent-based modeling and simulation approach for modeling behavior precursors of insider IT sabotage within an organization using a risk scale. The specific behavioral precursors include the individual’s predisposition, disgruntlement, stress levels, technical skill levels and the level of access to the computer systems. The simulation provides a framework for exploring the emergence and development of insider IT sabotage within organizations for different turnover rates.
Research on Asynchronous Registration Method Based on Improved Unequal-interval Federated Filter

In this paper, an improved unequal-interval federated filter algorithm is proposed for the integrated navigation registration process, which can solve the asynchronous information problem and the engineering problem under large multiplier measurement period for the traditional unequal-interval federated filter algorithm. By introducing fusion information, the applicability and practicability of the algorithm are expanded, and the engineering application of the algorithm is improved. The simulation results prove that the improved unequal-interval federated filter algorithm can expand the selection of fusion moments and avoid the interrupt zone effectively, which shown a better result compared with the traditional method.
An Iterative Robust Kernel-Based Regression Method for Simultaneous Single Image Super-Resolution and Denoising

In this paper, we present a uniform mathematical framework based on a robust kernel-based regression for the task of simultaneous single-image super-resolution and denoising. The given model is formulated as a convex ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> sparse optimization problem, which can be efficiently solved by the alternating direction method of multipliers (ADMM). Especially, the proposed method is applied to image patches to reduce computational time. Additionally, an iterative strategy is also incorporated into the approach to refine more image details. The extensive experiments on simulated natural images with additional sparse noise and real time-of-flight (ToF) images demonstrate the ability of simultaneously removing sparse noise and enhancing image resolution.
Pengaruh Teknologi Informasi terhadap Proses Pembelajaran dan Prestasi Akademik Mahasiswa

This study aimed to obtain empirical evidence regarding the correlation of the use of information technology to the learning process (lectures) and its impact on student academic achievement. This study used path analysis or analysis that not only tested the direct influence of independent variable to the dependent variable, but also explained the presence or absence of indirect influence given by the independent variable through the intervening variable to the dependent variable. This study uses questionnaires media that are disseminated through electronic media. The statements contained in the questionnaire were measured using the Likert scale of 5 (five) points, then processed using Path Analysis, and interpreted. Total respondents in this study as many as 204 students of Master of Accounting who have been through the course of information technology management. The results of this study indicate that information technology influences student academic achievement and influences the learning process and has an impact on student academic achievement. Keywords : Information t echnology; Learning process; Student academic achievement.
Risk Assessment Model of Expressway under Adverse Weather Based on Dynamic Bayes Network

According to the results of risk source identification, this paper constructs the discrete DBN network structure by selecting the influential factors which have great influence on the expressway operation, then chooses timeliness as the evaluation index of dynamic DBN. Based on the discrete DBN network, this paper finally establishes a real-time expressway risk evaluation model. The validity of the above risk assessment model is demonstrated by examples.
Progressive back-projection networks for large-scale super-resolution

Recently, experiments show that deep super-resolution (SR) networks with error feedback mechanisms can achieve better accuracies than purely feed-forward networks. However, such error feed-back networks use one-step mapping, which increases the difficulty for training with large-scale factors. We first propose a stage-progressive back-projection network to progressively reconstruct images by simply dividing the entire back-projection stage into several levels. We convert one-step large-scale sampling into multiple samplings with a moderate scale factor through such division. At each level, we process low-resolution-to-high-resolution/high-resolution-to-low-resolution mapping with a scale factor of 2. In order to enhance the effect of dense connections and to further improve the performance, we propose the unit-progressive back-projection network, in which we construct progressive projection units to avoid one-step mappings with large-scale factors. Additionally, we recommend a subpixel convolutional layer and its inverse transform as the mapping layer since it computes each pixel of the outputs covering more pixels from the inputs. Extensive quantitative and qualitative evaluations on benchmark datasets show that our algorithms substantially improve performance on large-scale SR tasks.
An improved public auditing protocol for cloud storage integrity checking

Nowadays, cloud storage is a popular service for many data owners, they prefer to outsource their datum to the cloud servers. However the cloud servers maybe sometimes loss datum due to accidents. Thus, the integrity of the outsourced datum need to be ensured by the data owners or even any other third parties publicly. Recently in the mobile cloud computing setting, Chen et al. proposed a public auditing protocol for data integrity based on adjacency-hash-table. However we find the data blocks' tags can be easily forged in their proposal, and thus the cloud servers can loss datum but still has the ability to give correct proof for data position, which breaks the security of their proposal. We show two concrete attacks to their proposal and give an improved public auditing protocol for cloud storage integrity checking and roughly analysis its security.
Quality Index for Benchmarking Image Inpainting Algorithms with Guided Regional Statistics

This letter presents an objective quality index for benchmarking image inpainting algorithms. Under the guidance of the masks of damaged areas, the boundary region and the inpainting region are first located. Then, the statistical features are extracted from the boundary and inpainting regions respectively. For the boundary region, we utilize Weibull distribution to fit the gradient magnitude histograms of the exterior and interior regions around the boundary, and the Kullback-Leibler Divergence (KLD) is calculated to measure the boundary distortions caused by imperfect inpainting. Meanwhile, the quality of the inpainting region is measured by comparing the naturalness factors between the inpainted image and the reference image. Experimental results demonstrate that the proposed metric outperforms the relevant state-of-the-art quality metrics.
Part-Time Learners of Higher Education: Training and After Graduation

Analysis based on state statistics and survey data of RLMS for 2016 on 30–39-year-olds with higher education, who completed part-time and completed full-time education in universities, allows to detect the degree of commonality/differences in their socio-demographic characteristics, educational path, employment status and subjective evaluations. “Part-timers” are older, among them the proportion of women, residents of villages and settlements, who are married and have children, is higher. They study more often in rural schools and graduate from 9 rather than 11 grades; more than a half go to university after receiving a secondary vocational education. “Part-timers” are very slightly different from “Full-timers” in main indicators of labor activity: share of employed is slightly higher, one of the unemployed is lower; more often work in formally organized jobs. The first ones are distributed on somewhat lower statuses: the share of highly qualified specialists (which is formally provided by diploma) is lower and the share of workers is slightly higher. There is much more unity than differences in subjective assessments of their work by “Parttimers” and “Full-timers “. Almost three quarters are satisfied with working conditions and 7–10% are not; the ratio of satisfied to unsatisfied wages is 45 to 30%. At the same time, “Part-timers” have a small margin of positive ratings. Ideas about their place in some social hierarchies are also very close in both groups.
Predicting Co-Changed Files: An External, Conceptual Replication

A software project can be comprised of several, highly connected files. A software developer may not know the files that are connected to which are developed or that are changed by another developer. This may induce faults by missing necessary edits on all related files. We build a prediction model for identifying files that should be edited together during a code change, and evaluate the performance of our model on two Apache projects’ development history over more than 10 years. We conduct an external, conceptual replication study based on Wiese et al.'s prior work on predicting co-changed files. Our study shares the same goal but differentiates the experimental design in terms of data set construction, selection of file pairs, feature selection and the model output. Our prediction model’s results, although the same performance measures are used, are much lower than what is reported in Wiese et al.’s study, mainly due to the differences in calculating these measures. The models evaluated at commit granularity could achieve 20% and 45% lower recall and precision rates, respectively, than those aggregated over all file-pairs. Although it is practically more useful, predicting all files that will be co-changed together during a commit is more challenging than predicting whether a particular file will be changed in that commit. More information about the context of a co-change, the degree of centrality of a file in the project, or project characteristics could reveal more insights in building such predictors in the future.
Variable-dimensional Flower Pollination obstacle avoidance algorithm on autonomous walking of NAO robot in dynamic environment

This paper proposes a novel dynamic obstacle avoidance method based on the Variable-dimensional Flower Pollination (VFP) algorithm, which can avoid dynamic obstacles under an unknown environment. The grid map is used to describe the environment and the shearing map refreshment strategy is used to improve refreshment efficiency. The fitness function is designed by combining Chebyshev distance with Euclidean distance, which can reasonably evaluate the planned path. Simulation results with traditional Flower Pollination algorithm and VFP algorithm are compared. The simulation results show that the VFP algorithm has a faster learning speed than the traditional Flower Pollination algorithm. To verify the simulation result, the VFP algorithm is implemented on the NAO robot, and the experiment result demonstrates that the Variable-dimensional Flower Pollination algorithm is feasible and effective.
End-to-End Personalized Humorous Response Generation in Untrimmed Multi-Role Dialogue System

Multi-role dialogue is challenging in natural language processing (NLP), which needs not only to understand sentences but also to simulate interaction among roles. However, the existing methods assume that only two speakers are present in a conversation. In real life, this assumption is not always valid. More often, there are multiple speakers involved. To address this issue, we propose a multi-role interposition dialogue system (MIDS) that generates reasonable responses based on the dialogue context and next speaker prediction. The MIDS employs multiply role-defined encoders to understand each speaker and an independent sequence model to predict the next speaker. The independent sequence model also works as a controller to integrate encoders with weights. Then, an attention-enhanced decoder generates responses based on the dialogue context, speaker prediction, and integrated encoders. Moreover, with the help of unique speaker prediction, the MIDS is able to generate diverse responses and allow itself to join (interpose) the conversation when appropriate. Furthermore, a novel reward function and an updating policy of reinforcement learning (RL) are applied to the MIDS, which further enable MIDS the ability to write drama scripts. The experimental results demonstrate that the MIDS offers a significant improvement to the accuracy of speaker prediction and the reduction of response generation perplexity. It is also able to interact with users without cues during real-life online conversations and avoid meaningless conversation loops while generating scripts. This paper marks the first step toward multi-role humorous dialogue generation.
Entropy Based Illumination-Invariant Foreground Detection

Background subtraction algorithms generate a background model of the monitoring scene and compare the background model with the current video frame to detect foreground objects. In general, most of the background subtraction algorithms fail to detect foreground objects when the scene illumination changes. An entropy based background subtraction algorithm is proposed to address this problem. The proposed method adapts to illumination changes by updating the background model according to differences in entropy value between the current frame and the previous frame. This entropy based background modeling can efficiently handle both sudden and gradual illumination variations. The proposed algorithm is tested in six video sequences and compared with four algorithms to demonstrate its efficiency in terms of F-score, similarity and frame rate.
An Optimized Predictive Coding Algorithm for Medical Image Compression

This article proposes a novel algorithm which helps in efficient transmission and storage of medical images. The conventional prediction algorithm is modified in such a way that they provide high compression without any further degradation in the image quality. A binary mask is generated based on the optimized threshold value for the image data. Then prediction is done for the masked coefficients to eliminate high error values caused by lower range of coefficients. An appropriate prediction function which gives less entropy for the input image is selected and encoded. The experimental results showed a maximum of 45% improvement in compression ratio compared to the normal prediction process. The proposed modified prediction algorithm can efficiently replace the prediction step in any lossy or lossless compression algorithms. They can also be utilized as a part of compression in any contextual compression techniques. Any kind of transformation approach can be used in hybridization with this proposed optimized prediction model to perform better.
DEvIANT: Discovering Significant Exceptional (Dis-)Agreement Within Groups

We strive to find contexts (i.e., subgroups of entities) under which exceptional (dis-)agreement occurs among a group of individuals, in any type of data featuring individuals (e.g., parliamentarians, customers) performing observable actions (e.g., votes, ratings) on entities (e.g., legislative procedures, movies). To this end, we introduce the problem of discovering statistically significant exceptional contextual intra-group agreement patterns. To handle the sparsity inherent to voting and rating data, we use Krippendorff’s Alpha measure for assessing the agreement among individuals. We devise a branch-and-bound algorithm, named DEvIANT, to discover such patterns. DEvIANT exploits both closure operators and tight optimistic estimates. We derive analytic approximations for the confidence intervals (CIs) associated with patterns for a computationally efficient significance assessment. We prove that these approximate CIs are nested along specialization of patterns. This allows to incorporate pruning properties in DEvIANT to quickly discard non-significant patterns. Empirical study on several datasets demonstrates the efficiency and the usefulness of DEvIANT.
Development and Validation of an Attitude Assessment Scale for the Use of 3D Printing in Education

The purpose of this study was to develop and validate a scale aiming to assess pre-service teachers’ attitudes towards 3D printing for teaching and learning processes. The data were collected from 250 pre-service teachers in different departments at a public state university. In order to test the validity of the scale, the exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were carried out. As a result of EFA, the scale included three factors; perceived ease of use, perceived usefulness, and intention to use, with 18 items in 5 point Likert type. The fit values from the CFA confirmed the three-factor structure of the scale. The correlations among the factors were positive, and collinearity among the factors was not calculated. The scale and the factors demonstrated a satisfactory level of internal reliability, ranging from 0.68 to 0.94. The findings of this study showed that the scale was valid and reliable, and could be used by researchers interested in technology integration in teaching and learning environments.
The Process of Academization of the Art of Russian Folk Instrumental Music

The art of Russian folk instrumental music in the 20th century underwent an active stage of academization. Scholars have consentaneously interpreted it as the process of achievement of an academic level of performance and study. The author of the present article criticizes this kind of interpretation and proposes the conception of academization based on the theory of evolution. What is understood under the term academization here is the evolution of thinking and creative activity of humanity from authentic folklore to contemporary music of the academic type. Two types of academization are brought out: the natural and the artificial, demonstrating a single substance – the aspiration towards an ideal artistic paradigm. However, in the natural process the paradigm is formed spontaneously during the course of the artists’ creative search and the selective activity of society, and in the artificial it is borrowed from another culture, and so in the artificial process the reproductive function predominates over the creative. Both types are subjected to the “postacademic syndrome” – negative tendencies, which demonstrate themselves more acutely in the artificial process. Overcoming the “post-academic syndrome” is possible through the integration of academic art with the analogous tradition of the folklore variety, the development of the theory of national instrumentalism, removal of dualism of thinking from it and assertion of the integrity of the phenomenon of the folk trait of artistic creativity. Keywords: the art of folk instrumental music, the academization of art and education, the types of academization, evolutionary and synergetic natural laws.
Cooperative Control Technology of Electromechanical Facilities Under Emergencies in Freeway Tunnel Group

Due to the increasing number of freeway tunnels, it becomes more and more important to ensure the tunnel safety. The paper presents a cooperative control technology under typical emergencies in freeway tunnel groups, which is proposed to achieve coordinated cooperation between Lane Indicators, Video Monitors, Variable Message Signs, and other electromechanical facilities. The method is aimed at reducing the negative impact of emergencies in freeway tunnels.
An Axiomatization of the d-logic of Planar Polygons

We introduce the modal logic of planar polygonal subsets of the plane, with the modality interpreted as the Cantor-Bendixson derivative operator. We prove the finite model property of this logic and provide a finite axiomatization for it.
Clustering by Using the Way of Atomic Fission

Cluster analysis, which focuses on the grouping and categorization of similar elements, is widely used in various fields of research. Inspired by the phenomenon of atomic fission, this paper proposes a novel density-based clustering algorithm, called fission clustering (FC). It focuses on mining the dense families of clusters in the dataset and utilizes the information of the distance matrix to fissure the dataset into subsets. A K-nearest neighbor (KNN) local density indicator is applied to identify and remove the points of sparse areas so as to obtain a dense subset that consists of the dense families of clusters. The algorithm, denoted as FC-KNN, is achieved by merging FC and KNN local density indicator. Several frequently-used datasets were applied to test the performance of the proposed clustering approach and to compare the results with those of other algorithms. The comprehensive comparisons indicate that the proposed method has advantages over other common methods.
Campur Kode dan Alih Kode pada Mahasiswa S1 Sastra Jepang Angkatan 2017/2018 Universitas Ngudi Waluyo

Code switching and code mixing is one phenomenon in social interaction. Thisphenomenon often arises in bilingual or people who speak more than two languages(multilingual). Japanese Literature Students of Universitas Ngudi Waluyo, 2017/2018 aspart of a multi-lingual society, also experienced events called code switching and codemixing. Mastery of Japanese is certainly inﬂuential on student interaction outside theclassroom. This research needs to be done to fnd out the events of code switchingand code mixing that occur. This research is based on the idea that the language usedby Japanese Literature students at Universitas Ngudi Waluyo in class of 2017/2018 ininteracting with each other outside the classroom also includes the Japanese languageas a second language, even the third language that students master. Besides that, it alsolooks at how the 2017/2018 Japanese literature students enter Japanese in interacting, asa choice of language that they think is appropriate.Keywords: code, code switching, code mixing
Evaluation of Streamer Apparition Processing Results

Summary This paper presents the processing results for a field test of triple-source streamer apparition, a blending technique for marine airgun sources that uses periodic codes in contrast with the more commonly used natural or artificial random dithers. The paper discusses the processing steps taken and the issues encountered while processing apparition data. We also compare migrated results to similarly processed data from a conventionally acquired triple source line. Conclusions are drawn on the benefits and opportunities of towed-streamer apparition for multiple source marine surveys.