Reviewerly/exHarmony huggingface dataset:
Relationship between Region of Origin and Activity of Student Organizations

Background Problems: Student maturity or maturity is not only seen in intra-campus activities (organizations), but also seen by the activity of students in extra-campus (organization). However, the activity of students in organization is influenced by the origin of the student area because each region has a different cultural culture. As for the region, the student determines the organization's choice and activeness of the organization that is followed.Research Objective: This study aims to determine the relationship between regional origin and the activeness of student organizations in the tarbiyah faculty of STAI At-Taqwa Bondowoso.Method: this study uses a type of field research (field). The approach used through a quantitative approach. This data is obtained from the distribution of questionnaires or Likert scale questionnaires that contain regional origin variables and activeness in organization. The data is tabulated using the Likert scale questionnaire tabulation. The population of this research is the Tarbiyah faculty students STAI At-Taqwa MPI study program and PGMI study program. Sampling was carried out by proportional random sampling using questionnaires and documentation. The spread of the Likert scale questionnaire was conducted in room 2E with the MPI study program and room 3C with the PGMI study program with a total of 25 respondents each. Data analysis using Correlation Test with Crosstabulation using SPSS.Result: Based on the Chi-square test table, the Asymp column appears. Sig is 0.673 or significance> 0.05, meaning Ho is accepted and Ha is rejected. This means that there is no relationship between the origin of the student area and the activeness of the organization. Whereas in the Symmetric Measures table, where the price of the contingency coefficient (contingency coefficient) seen in the Value column is 0.060, and in the Approx column. Sig is 0.670, or significance> 0.05, meaning Ho is accepted and Ha is rejected. This means that there is no relationship between the origin of the student area and the activeness of the organization.Conclusion: the closer to the origin of the area students are not necessarily active in the organization and the farther away from the region is not necessarily also not active in organizing. Then there is no relationship between the origin of the region and the activeness of the organization.
Personal Credit Rating System Based on the Logistic Regression Method

Credit card rating has become an increasingly crucial problem in the society as credit card system greatly improves the efficiency and convenience of people's life. The rating of credit card is an important part of risk control. In this research we focus on predicting credit card holder's default rate. Classical classifiers are implemented in this research to find the optimal model and method of data preprocessing.
Generating Synthetic Samples to Improve Small Sample Learning with Mixed Numerical and Categorical Attributes

The small data learning issue has existed for over one hundred years (since 1908) when the Student's t-distribution was first developed. Few statistical tools can evaluate a population appropriately if the sample size is too small; small samples can be remedied through virtual sample generation (VSG) methods, which are widely used in industry and machine learning. However, most VSG methods were developed for data having only numerical attributes, very few studies have dealt with nominal attributes and cause domain estimation limitations. Therefore, this paper proposes a method that generates virtual samples based on the discrete degrees of nominal attributes, and then estimates the general population domains by fuzzy membership functions. A backpropagation neural network model and a support vector regression model are used to test the efficiency of the proposed method, while the Wilcoxon-sign test is used to test the difference with raw data sets. The result shows that the proposed method can reduce the mean absolute error and enhance classification accuracy by generating virtual samples that have nominal attributes.
Modelling and Designing the IMS-LD Models for Evaluation Space of a New Learning Management System

The context of this work is that of designing an IMS-LD model for collaboration space of a learning management system (LMS). The work is specifically in the field or seeking to promote by means of information technology from a distance. The approach is to first think about the conditions for creating a real LMS between learners and designing the IT environment that supports this LMS. In this chapter, the authors try to adapt the IMS-LD model with a collaboration model for learning management system based on the social constructivism.
Research of Low-Quality Laser Security Code Enhancement Technique

The laser security code has been widely used for providing guarantee for ensuring quality of productions and maintaining market circulation order. The laser security code is printed on the surface of the productions, and it may be disturbed by printing method, printing position, package texture and background, which will make the laser security code cannot work normally. The image enhancement algorithm combining with bilateral filter and contrast limited adaptive histogram equalization is provided, which can realize the enhanced display of laser security code in strong interference background. The performance of this algorithm is analyzed and evaluated by experiments, and it can prove that the indexes of this algorithm are better than others.
Research on C-E Fingerprint Extraction of Emitter

Aiming at the problem of limited presentation ability and easy drift of results for a single feature, a joint fingerprint feature extraction algorithm based on the complexity and entropy of instantaneous parameter (C-E) is proposed. From the perspective of signal information integrity, this paper multi-extract the secondary features of instantaneous amplitude, frequency and phase, and obtain the box dimension, information dimension, information entropy, as well as the Hilbert envelope spectrum information entropy newly proposed in this paper. Finally, the features are fused into C-E features to identify the individual radiation source. Compared with single class features, the recognition rate of the joint features is greatly improved at little time cost. In simulation experiment, the accuracy increases by 15.2% and 19.7% than fractal dimension and entropy respectively. And features have good independence and noise resistance under different environments.
The Effective Method to Elicit Requirements of Scientific Research

How to acquire correct and sufficient requirements at the beginning of project approval is an important factor for a successful project. By explaining the common problems in the process of requirement acquisition, analyzing the causes of requirement defects, and further analyzing the significance of requirements of scientific research projects, especially of the software development projects, this paper finally puts forward the method of deep requirement mining, which provides effective reference and guidance for requirement mining in scientific research projects.
SRGNet: A GRU Based Feature Fusion Network for Image Denoising

Image denoising is an essential pretreatment for most of image processing pipelines, which has been extensively studied for several decades. Recently, convolutional neural networks with skip connections show promising performances on image denoising due to their discriminative feature modeling and utilizing of features from former layers. However, they only apply coarse feature fusion strategies like pixel-wise addition or concatenation which are insufficient in image denoising. In this work, we propose a novel network architecture to exploit finer feature fusion. Specifically, a module based on gate recurrent unit is introduced into the architecture, fusing features from different layers and adopting finer feature selection at the same time. Experiments on multiple challenging datasets show the effectiveness of the proposed network.
A Novel Method to Enhance Recommendation Systems via Leveraging Multiple Types of Implicit Feedbacks

Recommendation system is one of the most widely applied information filtering techniques. In recent years, more and more studies on recommendation systems have shifted from explicit behaviors to implicit behaviors. Although a lot of existing implicit recommendation systems have been proven to have excellent performance, these implicit recommendation systems have two major issues. First, most of studies only consider to utilize one implicit behavior (e.g. click / no click) to learn user preference and help improve recommendation performance. Second, almost all studies neglect the important role of co-occurrence among different implicit behaviors. In this paper, to address the aforementioned challenges, we propose a novel implicit recommendation model Bayesian Personalized Ranking by leveraging Multiple types of Implicit Feedbacks (BPR-MIF), which can distinguish user's favorite degree and make full use of user behaviors. We further leverage the significant role of co-occurrence to highlight implicit behavior combinations that better reflect user preference. In addition, an effective objective function which is suitable for multiple types of implicit behaviors recommendation systems is adopted in our model. And we extend the usage of co-occurrence to a specific item. Ultimately, extensive experiments are conducted on three real-world datasets, including Retailrocket, Douban Book and Jobs datasets. And experimental results have demonstrated that our model outperforms several state-of-the-art implicit recommendation systems in terms of recommendation performance on Retailrocket and Douban datasets.
The psychology of working lifeToon W.TarisNew York, NY: Routledge, Taylor & Francis Group, 2018, 114 pages, $12.76 paperback

Personnel PsychologyVolume 73, Issue 1 p. 229-231 BOOK REVIEW SECTION The psychology of working life Toon W. Taris , New York, NY: Routledge, Taylor & Francis Group, 2018, 114 pages, $12.76 paperback Alexander T. Jackson, Alexander T. Jackson Assistant Professor Psychology Department, Middle Tennessee State University, Murfreesboro, TennesseeSearch for more papers by this authorStacey M. Stremic, Stacey M. Stremic Graduate Student Psychology Department, Middle Tennessee State University, Murfreesboro, TennesseeSearch for more papers by this author Alexander T. Jackson, Alexander T. Jackson Assistant Professor Psychology Department, Middle Tennessee State University, Murfreesboro, TennesseeSearch for more papers by this authorStacey M. Stremic, Stacey M. Stremic Graduate Student Psychology Department, Middle Tennessee State University, Murfreesboro, TennesseeSearch for more papers by this author First published: 09 February 2020 https://doi.org/10.1111/peps.12373Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Volume73, Issue1Spring 2020Pages 229-231 RelatedInformation
Robust data retrieval from high-security structural colour QR codes via histogram equalization and decorrelation stretching

In this work, robust readout of the data (232 English characters) stored in high-security structural colour QR codes, was achieved by using multiple image processing techniques, specifically, histogram equalization and decorrelation stretching. The decoded structural colour QR codes are generic diffractive RGB-pixelated periodic nanocones selectively activated by laser exposure to obtain the particular design of interest. The samples were imaged according to the criteria determined by the diffraction grating equation for the lighting and viewing angles given the red, green, and blue periodicities of the grating. However, illumination variations all through the samples, cross-module and cross-channel interference effects result in acquiring images with dissimilar lighting conditions which cannot be directly retrieved by the decoding script and need significant preprocessing. According to the intensity plots, even if the intensity values are very close (above ~200) at some typical regions of the images with different lighting conditions, their inconsistencies (below ~100) at the pixels of one representative region may lead to the requirement for using different methods for recovering the data from all red, green, and blue channels. In many cases, a successful data readout could be achieved by downscaling the images to ~300-pixel dimensions (along with bilinear interpolation resampling), histogram equalization (HE), linear spatial low-pass mean filtering, and gamma function, each used either independently or with other complementary processes. The majority of images, however, could be fully decoded using decorrelation stretching (DS) either as a standalone or combinational process for obtaining a more distinctive colour definition.
Double Window Concept Drift Detection Method Based on Sample Distribution Statistical Test

Aiming at the problem of data stream drift due to its time attribute, this paper proposes a double window concept drift detection method based on sample distribution statistical test. In this method, support vector regression (SVR) is used to detect data anomalies in the outlier detection window. The Euclidean distance between samples is statistically tested in the distribution detection window to reflect the change of data distribution. This method essentially explores the concept drift phenomenon and applies it to the regression problem. This paper verifies the performance of this method based on the benchmark data set of concrete compressive strength.
Development and Evaluation of a Character Social Network Relationship Map Tool in an Ancient Book Digital Humanities Research Platform

This study aims to develop a character social network relationship map tool (CSNRMT) for supporting an ancient book digital humanities research platform to semi-automatically assist humanists through human-computer interaction in more efficiently and accurately exploring the character social network relationships from texts for useful research findings. With a quasi-experimental design, the experimental results reveal that the use of the ancient book digital humanities research platform with the CSNRMT appears higher effectiveness on the interpretation of characters and character social network relationships than the one without the CSNRMT, but does not achieve significantly statistical difference. Using the CSNRMT to interpret characters and character social network relationships presents remarkably higher technology acceptance of perceived usefulness and perceived ease of use than the one without the CSNRMT. Furthermore, use behaviors analyzed by lag sequential analysis reveal that the CSNRMT could actually assist in the interpretation of character social network relationships. The interview results present positive opinions on the integration of system interface, the smoothness of operation, and the external search function. The CSNRMT developed in this study would be more practical when the offered information about character names and character social network relationships is more accurate and broad.
A fire equipment enterprise performance game management system based on SaaS cloud platform

In order to improve performance assessment participation, we need to add a new module for management performance appraisal system. However, the traditional performance appraisal is too programmed, the lack of interest. In order to solve this problem, this paper combines the advantages of performance cloud and mobile game, and designs and develops a performance game management software. It is based on the SaaS cloud platform. According to business needs assessment indicators to quantify, for the completion of basic performance appraisal module. Then, choose a mature SaaS vendors, designed a fusion of performance and gaming cloud platform. Then, scene settings are made for the newly added game module, and a performance game credit system and a challenge system are added respectively. Finally, with the mobile phone as the carrier, the system is spread through APP. In order to test the practicability of the system, the author put the application into a fire-fighting equipment enterprise for testing, and collected and analyzed the data of the sales department of the fire-fighting enterprise. The results showed that the system improved sales performance in the sector, and improve employee participation.
Ant Colony Optimization Parameters Control Based on Evolutionary Strength

Ant Colony Optimization (ACO) was developed to be a metaheuristic and widely used for combinatorial optimization problems. It is confirmed that the parameters setting is of vital importance for performance of ACO. This paper proposed a strategy to perform ACO Parameters Control based on Evolutionary Strength (ACOP_ES). First, we transform four parameters setting problem to decision making problem. Then, by learning the evolutionary strength curve, we set sum, peak, and kurtosis of evolutionary strength curve as the objective function, such that the continuous ant colony algorithm can be used to optimize this decision problem. Further, via characterizing an excellent evolutionary curve, a relatively optimal parameters setting can be found. Compared with existing methods, our proposed method focuses on the performance of the running process rather than the results only. To the best of our knowledge, we are among the first to introduce process performance evaluation of ACO to guide parameters control, and determine the relatively optimal parameters of ACO. At last, we apply our method to Multi-dimensional Knapsack Problem (MKP), the results look promising that parameters returned by ACOP_ES are better than the ones returned by other methods on datasets: WEISH*.DAT and WEING*.DAT.
Color Visual Cues Evaluation Method for Decision-oriented Multiple Coordinated Views

This paper explores the visual navigation and cognitive navigation effects of the color visual cues on the user's decision-making task under multiple coordinated views display. In this paper, the concept of information scent is proposed to put forward a method for evaluating color visual cues under the premise of multiple coordinated views display. The eye movement index and its parameters obtained by eye tracking technology are quantitatively evaluated to analyze and explore the visual navigation and cognitive navigation effects of the users in completing different types of decision tasks whether there are color cues and whether the color cues are valid or not. It can provide effective theoretical support for the information representation and multiple coordinated views display. Technical support is provided for the practice of network security monitoring engineering projects, crime data monitoring and biological experiment projects.
The Metropolis-Hastings Algorithm To Simulate Fluxes of Bitcoins Volumes in Developing Countries: Are you the winner or loser?

Once bitcoins are standardized as unit of official currency in any country, people might opt a different perception about the worth of buy any product since the usage of bitcoin is seen as a bit different of the case of traditional money. It might be interesting in all those business models that would adjust their products in the ones that are of enormous attraction to people that have decided to expend their volumes of bitcoins. We applied the Metropolis-Hastings algorithm to extract information on users belonging to a developing country. Our results would indicate the electronics market are strongly a choice of customers.
AdapTimer: Hardware/Software Collaborative Timer Resistant to Flush-Based Cache Attacks on ARM-FPGA Embedded SoC

ARM-FPGA embedded SoCs have been widely used in the fields of drones, embedded and IoT devices due to its high performance and hardware design flexibility. However, ARM-FPGA embedded SoC suffers various types of security threats, one of which is flush-based cache attack. The proposed defense schemes either lead to a high false positive rate or a large performance loss. Due to the importance of high resolution time APIs in the system, schemes that permanently reduce the resolution of time APIs can only be implemented in specific applications such as browsers. Moreover, the method of protecting high resolution timers in software cannot defend against an attacker with root privileges. In this paper, we propose a more secure timer which is a hardware/software co-design on ARM-FPGA embedded SoC. When a software process calls the flush operation, the timer adaptively reduces its resolution and recover after a short period of time. In the case that the flush operation is not called, the impact of the timer on system performance is almost negligible. This hardware/software co-design guarantees the availability of a high resolution time API while defend against attackers with root privileges. The results of the attack experiments show that the success rates of Flush+Reload and flush-based Spectre attacks can be reduced to less than 1% when using the timer. Performance test results show that the timer access latency is 9.5% slower than the fastest PMCCNTR but 5% faster than the global timer of Cortex-A9 MPCore. The modified flush operation API for the design only increases the time consumption by about 12%.
An Approach to Estimate Skew Angle in Printed Document Images

In this paper, a new approach to assess the skew angle for scanned/printed document images has been proposed. This is significant for an automatic document processing system (as text and image segmentation) to avert errors in auxiliary stages. The proposed tactic is based on the statistical analysis of the slope of the connected lines in the document. The proposed technique detects skew and corrects it by initial letter (X1, Y1+150) from left margin of the resized (800X800) image. Final letter (X2, Y2-150) were chosen from right margin of the same image. The skew angle estimation is done for standard skewed dataset and effective correction of the same is performed with minimum errors.
Automated Detection of Complex System Operator's Condition by Using Non-Contact Vital Sensing

This study is focused on the automated detection of a complex system operator's condition. For example, in this study a person's reaction while listening to music (or not listening at all) was determined. For this purpose, various well-known data mining tools as well as ones developed previously were used. To be more specific, the following techniques were applied for the mentioned problems: support vector machines, artificial neural networks, fuzzy logic systems and others. However, firstly each person's state was monitored using non-contact vital sensing. Experimental results demonstrated that automatically generated fuzzy rule-based classifiers can properly determine the human condition (and reaction) based on data obtained by non-contact vital sensing using the Doppler sensors introduced earlier. Besides, these fuzzy logic systems outperformed alternative well-known data mining tools. Thus, more complex problems related to the automated detection of an operator's condition can be solved in the same manner.