Reviewerly/exHarmony huggingface dataset:
Analysis of Tissue Abnormality in Mammography Images Using Gray Level Co-occurrence Matrix Method

Abstract One of the dangerous diseases is breast cancer, which threatens women and men to the same extent. But women are more affected by this disease. Computer-Aided Diagnosis (CAD) is the optimal method for the early detection of breast cancer. It can reduce the false positives in radiologist diagnosis, which leads to reduce the death-rate. This paper presents a feature extraction technique with mammography images to breast mass recognition. Then, distinguishing normal tissue and abnormal breast masses. The mini-MIAS database of mammograms was used in this paper. Gray Level Co-occurrence Matrix is the method that was used to extract features from the region of interest. The best sensitivity, specificity, and accuracy are observed with a k nearest neighbor classifier.
Joint Multi-object Detection and Segmentation from an Untrimmed Video

In this paper, we present a novel method for jointly detecting and segmenting multiple objects from an untrimmed video. Unlike most existing video object segmentation methods that can only handle a trimmed video in which all video frames contain the target objects, we address a more practical and difficult problem, i.e., joint multi-object detection and segmentation from an untrimmed video where the target objects do not always appear per frame. In particular, our method consists of two modules, i.e., object decision module and object segmentation module. The object decision module is used to detect the objects and decide which target objects need to be separated out from video. As there are usually two or more target objects and they do not always appear in the whole video, we introduce the data association into object decision module to identify their correspondences among frames. The object segmentation module aims to separate the target objects identified by object decision module. In order to extensively evaluate the proposed method, we introduce a new dataset named UNVOSeg dataset, in which $$7.2\%$$ of the video frames do not contain objects. Experimental results on four datasets demonstrate that our method outperforms most of the state-of-the-art approaches.
Case Study of How to Help Manufacturing Enterprises Obtain Loan Through Supply Chain Documents on Blockchain Platform

Recent years, it has produced one trend to update the traditional business with the developing internet thinking and technology. In order to build the industrial ecosphere, which can attract more related partners, many companies try to push the layout of the bulk supply chain platform based on blockchain technology. The papers studies how to make full use of supply chain documents in digital assets. Based on risk and economic analysis, it makes research on the key points in the processes and provides the control measures to push the project innovation in order to help enterprises to obtain loan. At last, it compares the differences before and after the implementation. It shows the validity of receipts, storage and industry finance effectiveness can be further improved after implementation.
Exposing Bot Activity with PARAFAC Tensor Decompositions

Russian disinformation tactics via social media have been very topical in Western countries over the past year, and one of the specific tactics that has been spotlighted is the deceptive use of ‘bots’, or automated accounts, by Russia’s Internet Research Agency, particularly on Twitter. In a sense, bots hide in plain sight, purporting to be something that they are not. A useful function is therefore served if we can use data analytics techniques to automate the process of exposing bot activity in a way which helps direct an analyst’s attention towards the signature activity of bots within crowds of ‘genuine’ users. However, the problem is non-trivial because adversaries may deliberately introduce obfuscation in the form of slight differences between bots’ posts. Notwithstanding this, we show how the problem may be solved using the tensor decomposition method PARAFAC.
Supporting the Design and Analysis of HCI Experiments

Researchers in HCI commonly use controlled experiments to evaluate artifacts and interaction techniques. However, experiment design and statistical analysis are complex tasks that are prone to errors, especially for novice researchers. In part, this is because researchers need to make numerous decisions about the design and analysis while it is hard to immediately anticipate their effect. In this dissertation, I aim to study how interactive systems that provide real-time feedback, enable direct manipulation, and facilitate exploration positively influence decision making and reproducibility. My previous work, Touchstone2, shows how researchers can benefit from comparing trade-offs among experimental designs. I contribute an empirical understanding of how researchers design and analyze experiments, as well as a set of tools that support researchers during that process.
Image enhancement with DAGNet-based artificial neural network

Abstract The article is devoted to an artificial neural network that increases image resolution. According to the meaning of the article, two parts can be distinguished. The first part of the article discusses the process of designing an artificial neural network using DAGNet technology, during which its main parameters and structure are described. The second part is devoted to assessing the quality of functioning of the developed artificial neural network. To evaluate the performance of ANNs a comparative analysis with the bicubic interpolation method was used.
Android based learning application for Wudhu and Tayamum using augmented reality technology

Abstract Nowadays, wudhu and tayamum are widely found in various media such as books, articles, tutorial videos and internet. Muslims who are new to Islam as children often have difficulty reading books to understand how to learn and practice tayamum, one of the lessons learned in technology development using Augmented Reality based learning. The learning method used in Augmented Reality is the marker method. The marker method step is that the program will read an object from the image marker, the marker uses a downloadable image by clicking the Download Marker button in the main menu, and then it will be displayed as a moving object or an output in the data. This app is implemented for Android-based smartphones, with the help of Blender 3D and Unity 3D software. The results of the questionnaire count conclude that augmented reality learning media applications are easy to use, practical, useful, and effective in supporting children’s learning activities as children become more engaged in learning. The attractive interface design makes kids feel comfortable and engaging as they use it and develop their understanding. In addition, this application is also used as an interactive learning medium.
Cat swarm optimization and Levenberg-Marquardt for model identification and prediction identification and prediction tuberculosis disease spreading

Abstract Tuberculosis (TB) is an infectious disease caused by the bacterium Mycobacterium tuberculosis and can cause death. Based on the prevalence rate, Indonesia ranks third after India and China as the countries with the highest number of TB sufferers in the world. Models identification and prediction are needed to minimize and anticipate problems that can occur. In applying the model, Cat Swarm Optimization and the Levenberg-Marquardt are used. The purpose of this study is to obtain the prediction result based on the model identification in TB disease spreading using a swarm optimization algorithm and Levenberg-Marquardt. The identification is intended to analyze the spread of TB disease based on actual data. The process begins with estimating the parameters in the model using the Cat Swarm Optimization algorithm. After getting the optimal parameters, the acquisition of the model uses Levenberg-Marquardt. Based on the implementation of the program on the spread of TB disease in East Java in the period of the first quarter of 2002 to the fourth quarter of 2017, the MSE of training and prediction process were 0.000225563 and 0.0085307, respectively. The MSE value from the prediction process shows that the Cat Swarm Optimization and Levenberg-Marquardt can be used to identify models and predict the spread of tuberculosis.
gCodex: A tool to analyze software repositories over time (Visualization)

gCodex is a novel tool for analyzing and visualizing the code base in a manner that it allows its users to get an idea of the insights of the codebase. This tool was built to analyze code bases and it supports any language. In addition, it provides a visualization of the file structure, rate of change of complexity and defects rate. In order to improve the quality of the software and the controllability of the project, it is necessary to control the complexity of the software by measuring the associated aspects and visualize those in a descriptive and attractive dashboard. Using the existing tools, it is not possible to visualize the rate of change of code complexity with the time. This tool uses Cyclomatic complexity, line of code and Halstead complexity metric and their impact on the software quality, and visualize those in a descriptive dashboard which provides analytics that describes and summarize past trends.
Organizational Factors that Affect the Software Quality A Case Study at the Engineering Division of a Selected Software Development Organization in Sri Lanka

The objective of this study is to identify the critical factors affecting the software quality at the engineering division of a selected software development organization in Sri Lanka. The purpose of the study is to identify the critical organizational factors that affect the software quality in the selected organization, determine the relationship and impact of those factors to software quality and provide recommendations and strategies to improve software quality. Having conducted a critical literature review, four independent variables have been identified which are staff training & development, coworker relationships, diversity of the team and knowledge sharing. A quantitative survey was carried out with a population of 110 and sample size of 86 senior engineers. The data analysis has been conducted using SPSS 19.0 tool and hypothesis validation was carried out using Pearson correlation, regression and significance values. The research findings showcase that, knowledge sharing has the strongest relationship towards software quality. Staff training and development and co-worker relationships also hold a relationship on software quality whereas diversity of the team does not claim a relationship on software quality. This research would help the software organizations to uplift the software quality by various strategies such as improving existing knowledge management systems with new features, introducing certification process for staff trainings and team building activities. This research could be further enhanced with a larger sample size with the use of other qualitative survey techniques.
Management of container-based genetic algorithm workloads over cloud infrastructure

This paper proposes two approaches to managing the workload of multiple instances of genetic algorithms (GAs) running as containers over a cloud environment. The aim of both approaches is to obtain, for as many instances as possible, a GA output which achieves a user-defined fitness level by a user-defined deadline. To reach such a goal, the proposed approaches allocate the GA containers to cloud nodes and carefully control the execution of every GA instance by forcing them to run in stages. The paper proposes two approaches, fitness tracking (FT) and fitness prediction (FP), with both approaches compared against state-of-the-art container-based orchestration approaches.
Effects of Wartime Crisis Perceptions on the Effectiveness of Political Advertising: The Moderating Role of Political Involvement

This research examines how political advertising is operated in a volatile context, such as a state of war or instability. The study employed a self-completing cross-sectional survey to gather the data in the period of the 2016 Syrian elections for members of parliament. The research tested the hypothetical model and its equivalency related to political involvement by structural equation modelling. The outcomes of the tests revealed the structure of belief as a four-dimensional variate. The four dimensions encapsulate information, veracity, sarcasm, and cynicism. In addition, perceptions during conflicts had a negative effect on attitude through sarcasm displayed by voters with low political involvement. The results also found that negative attitude had a link with lower degrees of veracity with regards to voters who are less involved but to greater degrees of cynicism highly politically active individuals. We found that less favourable attitudes to political advertising lowered the likelihoods for voters to watch political ads or announcements, support a runner, and be willing to go to the poll. We found no relation between the fact of paying attention to political advertising and intention of voters to use their ballot.
Illumination Compensation And Image Denoising for Low-Light Images Based on Deep Learning

Image denoising is one of the basic low-level computer vision problems, but low-light denoising is challenging due to low photon count and low SNR. Therefore, we propose an end-to-end encoded and decoded network of illumination compensation and image denoising in low-light condition based on deep learning, which is used to denoise low-light images and adaptively brighten images without over-amplifying the brighter part of the images with high dynamic range.In the network, the illumination compensation branch network eliminates the disadvantage that the magnification must be selected externally. Different simulation gain and exposure time are used to train the multi-light compensation coefficient, which can eliminate the residual errors caused by inaccurate gain and various exposure time effectively. The results show that the model is suitable for the recovery and reconstruction of natural low-light images with different degrees of degradation due to the advantages of flexibility and data driving.
Defining AR: Public Perceptions of an Evolving Landscape

This paper presents findings from an ongoing investigation into the public understanding of augmented reality (AR) technologies. Despite AR technologies becoming increasingly available to the general public, perceptions of its use and capabilities still vary based on a number of factors. To explore this concept, a survey was conducted into individual's definition of AR and classification of AR and traditional technologies. The themes elicited from responses indicated that digital and real components were both perceived as key characteristics, but the synthesis of these components was not significant. Responses also indicated that the public is still relatively unfamiliar with AR technologies, but familiarity does lend itself to a better understanding of what is or isn't AR. Trends in public perceptions of AR are presented, but also identify the need for more investigation into the public understanding of AR technologies.
A Fast Filtering Algorithm for Massive Context-free Grammars

All non-statistical context-free parsers are plagued by a worst case parse time, O(|G|xn3), linear in the size of the grammar, |G|, which in many applications can cause major slowdowns [6]. In this paper, we pursue general purpose pre-processing intended to speed up the run time of context-free parsers by selecting a smaller fragment of the grammar prior to parsing. Our work provides an ad-hoc method to perform subgrammar selection, increasing the practicality of parsing with very large context-free grammars. We present an algorithm we call 'Terminal-tree filtering' (TTF), a new variant of the 'b-filtering' algorithm presented in [6], which finds the same filtered set of grammar rules as the 'b-filter' for a given input, but which achieves new state-of-the-art run time and grammar size performance within in this problem space. Our work boasts remarkable performance boosts across a range of workloads, including a 10-20 fold speedup on a real world English grammar of size |G| ≈ 115, 000, 000. Furthermore, we achieve far greater scale, parsing sentences using grammars ≈ 10 times larger than those previously studied. The TTF algorithm filters quickly enough to provide feasible parsing times on our example English grammar. On this very same test suite [6] 's algorithms, which are the broadest, most recent contributions to the field, demonstrate unacceptable run times. We also provide a theoretical analysis which elucidates under which conditions worst-case behavior and best-case behavior can be expected, and show that even under worst-case performance, variants of the TTF still show some empirical advantages to the b-filter.
Fuzzy-PI algorithm application to controlling servo motor position using microcontroller AVR

Abstract Understanding implementation of control process in real application is not easy for common student that their study of control application. This research purpose to describe how algorithm Fuzzy-PI implementation in control position servo motor. Realisation of this research using circuit of microcontroller with before programmed with algorithm fuzzy-pi and then implemented to control rotation position servo motor. Method and step of process the research start with make module circuit microcontroller as circuit controlling servo motor position, and then make script program for to upload to circuit microcontroller. After that do experiment for get data with entry several parameters Fuzzy and PI and analysed result experiment. From result of experiment using microcontroller as controlling servo motor with fuzzy algorithm result controlling to arise very fast to state of stability. With using fuzzy algorithm parameter of Kp and Ki with autotune at optimum value. Result from this research a prototype device for experiment facility students for understanding algorithm Fuzzy PI in the real device and make easier for learning and understanding concept control system application in a device electronic.
Evaluation of mobile applications for disaster responses through personas and scenarios

Disasters are constantly affecting Asia. To minimize the damage, it is important to have an immediate and effective response, which requires multiple stakeholders of the disaster scenarios to communicate. After identifying stakeholders and their needs through personas and scenarios illustrating earthquake situations, we evaluated existing mobile applications with the needs for disaster responses, earthquake in particular. The findings of this study suggest design implications for creating mobile applications to support multiple stakeholders for disaster responses.
Ultrasound Pupil Image Segmentation Based on Edge Detection and Detection Operators

Ultrasound imaging technology is developing rapidly and ultrasound images can provide a lot of information for doctors to diagnose. However, the same tissues and organs show large differences in edge strength in ultrasound images which may cause difficulty in segmentation and analysis. A new method is proposed in this paper, which integrates edge detection operator with our proposed detection operators. We first use canny edge detection and our proposed operators to find three points on the pupil, and then use these three points to draw a circle, which is the contour of the pupil. Such an algorithm can eliminate the effect of edge blur on ultrasound pupil segmentation. The proposed algorithm was tested on 100 ultrasound pupil images. Experimental results show its effectiveness for ultrsound pupil image segmentation.
Deriving Explicit Control Policies for Markov Decision Processes Using Symbolic Regression

In this paper, we introduce a novel approach to optimizing the control of systems that can be modeled as Markov decision processes (MDPs) with a threshold-based optimal policy. Our method is based on a specific type of genetic program known as symbolic regression (SR). We present how the performance of this program can be greatly improved by taking into account the corresponding MDP framework in which we apply it.
Blockchain Materialization as a General Purpose Technology: A Research Framework

Blockchain, often treated as a next transforming general-purpose technology (GPT), might have the potential to revolutionize economic, political and social structures in the subsequent years. However, the actual impact of Blockchain will ultimately depend on the pace and direction of its diffusion process throughout the economy. Being currently in the nascent phase, the diffusion process of Blockchain remained largely neglected in the academic literature so far. The study at hand covers this research gap by investigating the forces affecting Blockchain diffusion leading to its materialization as a GPT. Identified from the broad literature on technological change, the various factors influencing diffusion patterns of GPTs are conceptualized in a framework of three interrelated groups: advancements in the GPT itself, advancements in its application sectors, and environmental factors. This conceptual framework is then applied to structure the current studies of Blockchain technology and reveal unexplored blind spots. In such a way, the present paper serves as a basis for further research on diffusion patterns of Blockchain and its materialization as a general-purpose technology.
Research on Position Advantage of Gaze Interactive Control in Eye Control System

In the design of digital interface of eye control system, interactive control is an important element, and it's convenience directly affects the operation efficiency of the system. In order to reduce the cognitive load of users in the process of eye movement interaction and improve the user experience of eye control system. An ergonomic experiment is designed to explore the input characteristics of gaze interactive controls. The main purpose is to determine the position advantages of the interactive controls. By comparing the scanning velocity between interactive controls, the priority order between interactive controls is obtained. It provides a scientific theoretical basis for the arrangement of the position of interactive controls in the eye control system interface design.
Sieve: An Ensemble Algorithm Using Global Consensus for Binary Classification

In the field of machine learning, an ensemble approach is often utilized as an effective means of improving on the accuracy of multiple weak base classifiers. A concern associated with these ensemble algorithms is that they can suffer from the Curse of Conflict, where a classifier’s true prediction is negated by another classifier’s false prediction during the consensus period. Another concern of the ensemble technique is that it cannot effectively mitigate the problem of Imbalanced Classification, where an ensemble classifier usually presents a similar magnitude of bias to the same class as its imbalanced base classifiers. We proposed an improved ensemble algorithm called “Sieve” that overcomes the aforementioned shortcomings through the establishment of the novel concept of Global Consensus. The proposed Sieve ensemble approach was benchmarked against various ensemble classifiers, and was trained using different ensemble algorithms with the same base classifiers. The results demonstrate that better accuracy and stability was achieved.
MARDU

Defense techniques such as Data Execution Prevention (DEP) and Address Space Layout Randomization (ASLR) were role models in preventing early return-oriented programming (ROP) attacks by keeping performance and scalability in the forefront, making them widely-adopted. As code reuse attacks evolved in complexity, defenses have lost touch with pragmatic defense design to ensure security, either being narrow in scope or providing unrealistic overheads.