Reviewerly/exHarmony huggingface dataset:
Facilitating Physical-Computer System Design through Data-Driven Natural-Language Interaction

Designing and creating physical computing system can be challenging for novice user.In this paper, we present FritzBot, an intelligent conversational agent offering assistance for novice users on constructing physical-computing systems through natural-language interaction. We create a lexical circuit-event database based on 152 student reports from the undergraduate physical-computing course in a local art school. The LSTM-CRF network of FrzitBot is trained on that database, and is able to extract the input and the output events from the user's description, and generate the circuit and the code along with the construction guidelines. A user study shows that FritzBot can significantly reduce the construction effort and time spent for novice users on physical-computing task.
Analysis on Affected Factors of Employment in Wuhan City Based on Vector-Auto-Regression

The development and progress of cities cannot be separated from talents. It is well-known that a series of policies such as 20% off college students' purchases and graduation certificates were occurred in Wuhan City from the year of 2017, which is especially significant to measure and quantify the impact of these related policies in the next few years. Based on this, we select the variables such as urban employment, total GDP, total real estate investment and graduates from 1990 to 2016 about Wuhan City. Then the Vector-Auto-Regression (VAR) model was established; furthermore, the impulse response function and variance decomposition were used to analyze the impact of the other three variables on employment. The results showed that the gross output of Wuhan and the number of graduates of the university were positive for the employment, and the number of graduates changed from the long run, which can explain 18.19% of the change in employment..
Wastewater preparation for irrigation based on the sorption filtering technology

Abstract The article presents the results of scientific research in the field of wastewater purification from heavy metals using natural sorbents as a filter load for a sorption filter. Based on the obtained experimental data, the particle size distribution of the natural sorbent is correctly selected, which in turn directly affects the quality of wastewater purification from various harmful impurities. During the experiments, the required filtering time intervals were established and the absorption effect of the sorbent in relation to heavy metals and ammonium was determined. Filtering was carried out at various intervals for 0.2, 1, 12, and 24 hours. It was found that the achievement of the maximum permissible concentration of impurities in wastewater was reached in 24 hours of filtration. The conducted studies will allow one to make the more reliable calculations and choose the optimal filter load for wastewater purification from heavy metals in the sorption filter, which in turn will allow for the further use of treated wastewater in irrigation systems and increase the economic effect when irrigating crops.
Semantics-Preserving XML Query

XML query such as the query language XQuery leads to incomplete query results since XML focuses on data structure rather than semantics. In this paper we propose semantics-preserving XML query that uses SPARQL query to access over XML data based on the transformation of XML data to RDF ontology. Namely, we manually map XML data to RDF ontology in semantics-preserving way. We can then automatically translate SPARQL query into semantics-preserving XQuery query to access over XML data. We thus provide an interoperability between the existing legacy XML data and the RDF ontology.
The Power of Bounds: Answering Approximate Earth Mover’s Distance with Parametric Bounds (Extended abstract)

The Earth Mover's Distance (EMD) is a robust similarity measure between two histograms (e.g., probability distributions). It has been extensively used in a wide range of applications, e.g., multimedia, data mining, computer vision, etc. As EMD is a computationally intensive operation, many efficient lower and upper bound functions of EMD have been developed. However, they provide no guarantee on the error. In this work, we study how to compute approximate EMD value with bounded error, using these bound functions. First, we propose an approximation framework that leverages on lower and upper bound functions to compute approximate EMD with error guarantee. Then, we present three solutions to solve our problem. Experimental results on real data demonstrate the efficiency of our proposed solutions.
Analyzing Balancing of Heterogeneous Load on Cluster-Based Web Server

With increasing workload experienced by many websites, distributed web-server cluster has become a popular choice for site managers to expand the capacity of the webserver system. A web-server cluster consists of multiple web servers with a load distribution scheme to spread client requests among servers. This report focuses on dispatcher-based architecture. We study the performances of different scheduling algorithms by means of trace-driven simulation and then propose a more time-efficient approach for heterogeneous loads.
Practical automatic information authoring system for information guidance

In this paper, we propose a method to automatically assign attributes to guidance information and automatic authoring method of information according to user attributes. This makes it possible to provide an information guidance system with low cost. Also, we describe the problem of "reading" occurring in Japanese and its solution. Our work has not completed yet. We just completed the system development. We are planning to carry out evaluation of our system from now on.
QKD Protocol To Enhance The Southbound Security Of Software Defined Network

The combination of quantum encryption and internet technology has become an inevitable development trend. To improve the security performance of communication between control layer and forwarding layer in the software defined network (SDN), we propose a security scheme based on quantum encryption technology. The proposed scheme can realize two-way authentication and quantum key distribution between the controller and the switch. The security performance analysis shows that this scheme can effectively prevent eavesdropping attacks, man-in-the-middle attacks and replay attacks. It effectively improves the southbound security of SDN and provides a theoretical basis for the healthy development of SDN.
Demo of a Visual Gaze Analysis System for Virtual Board Games

We demonstrate a system for the visual analysis of eye movement data of competitive and collaborative virtual board games two persons play. Our approach uses methods to temporally synchronize and spatially register gaze and mouse recordings from two possibly different eye tracking devices. Analysts can then examine such fused data with a combination of visualizations. We demonstrate our methods for the competitive game Go, which is especially complex for the analysis of strategies of individual players.
Optimization of a Robotic Manipulation Path by an Evolution Strategy and Particle Swarm Optimization

This research work focusses on the optimization of a robotic manipulation problem. The problem is modeled with the robot simulation software V-REP. The objectives are the optimization movement path of the robot and its robotic arm for certain positions and orientations with respect to energy consumption. The paper compares an evolution strategy with particle swarm optimization to minimize deviations of position and orientation while using forward kinematics. Experiments to evaluate the algorithms are presented and discussed.
Eye-tracking Data Analyser (EDA): Web Application and Evaluation

Eye tracking is a growing field of research that is widely used to understand user behaviours and cognitive strategies. Eye-trackers typically collect an enormous amount of data that needs to be analysed, processed and interpreted to draw some conclusions. Collected data is typically examined with either commonly used statistical tools such as SPSS which are not designed specifically for eye-tracking data analysis, or bespoke tools provided by eye-tracker vendors. However, these tools may require extensive experience or they may be extremely expensive. To address these limitations, we propose an open-source web application called EDA (Eye-Tracking Data Analyser) which can be used to analyse eye-tracking data, in particular, to conduct comparative statistical tests between two groups. In this paper, we first present the overall architecture and implementation of this application and then present its evaluation conducted with ten people who are experts in eye-tracking research. The evaluation shows that the EDA application is easy to use, and its workload measure is low in terms of NASA NLX.
University’s digital transformation: A case study for providing e-services via Moodle

Abstract The evolution of digitization reaches a new stage with a focus on the overall transformation, where the so called “Intelligent Environment” such as “Smart City”, “Smart Campus“ is in the heart. Today’s institutions, which have not yet started the transformation towards a Smart Campus, are capable to exploit untapped ICT resources that could potentially lead to the optimization of the processes and services. In order to verify that a new service can be offered with the available ICT resources, a method of finding a subset satisfying some condition is used. The paper reports approach where elements of the main set include own resources and their functionalities, while needed resources to provide a new service build the subset elements. This work presents examples of developed e-services by using the open source Learning Management System (LMS) Moodle. The study demonstrates that Moodle and advanced ICTs are just as so an alternative for improving personal management and information systems in the means of e-services offering. The paper analyzes the results and evaluate the benefits from the digital transformation point of view. The conclusion summarizes features of the applications will contribute the way university interacts with students and staff.
The Role of Artificial Intelligence in Decision Making in Small Businesses

Background/Objectives: The science of support for decision-making has undergone many changes thanks to the remarkable development in the science of networks and Artificial Intelligence.Decision support was born through the integration of management science, operational research and cognitive science.Now this science is growing in line with the results of advanced research in the field of database management, Internet networks, intranet networks, extranet networks, good systems and neural networks, genetic algorithms and gas logic.Method: Management Science, Operations Research and Cognitive Science are the basic concepts of decision support.We find the concept of decision, the decision process and the concept of different models to study the decision-making process.We find how to simulate and simulate real systems (modeling, descriptive models and methods).Mathematical models used in solving these models, such as linear and nonlinear programming methods and the theory of frames and rows.Artificial Intelligence (AI) became one of the hottest topic and a promising methodology trending in many industries and businesses.Artificial Intelligence is a computer-based science developed to analyze data, tackle problems, and discover trends and similarities.Findings: This study shed the light on the importance of using AI in supporting the big and small businesses through adapting the novel advantages gained by Artificial Intelligence to overcome routine or complicated problems that usually take a lot of effort and resources such as data analysis and forecasting markets and customers' preferences.Also, in this article, the experience of several entrepreneurs who have augmented us with their own knowledge on how they used Artificial Intelligence in their corporations will be demonstrated.Application/Improvements: The application of this study will be on internet technology that has been greatly contributed to the development of collective decision support systems through virtual meeting sessions and Brainstorming sessions without the need to be physically present in one place.
Edge detection in non-linear scalable space

Abstract Automatic image processing and computer vision applications often include object extraction and recognition. To extract object boundaries better, high-quality methods of edge detection are required. Objects in real-world photos usually have complex structure, and discovering their edges with higher precision is a very important and tough task. Better edge detection drastically increases the quality of the entire process. Unfortunately, edge detection problem still has no satisfactory solution in the case of real-world color images, because efficient approaches exist for grayscale images only. Known edge detection techniques usually either consist of special filters applied to a grayscale image, or implement rather unpredictably behaving artificial intelligence solutions. We propose a new powerful mathematical tool to extract edge information not only from intensity but from color distribution as well. Correctness of edge detection gets better by means of an adaptive mathematical model of non-linearly scalable color space. In our research, we consider flexible non-linear transformation of the color space with a few parameters. It helps reliably extracting otherwise faded edges delimiting the objects. We compare the results of edge detection process based on this new technique to well-known base-line approaches of Canny, Prewitt, Sobel and Wallace filters.
Bring Your Own Data to X-PLAIN

Exploring and understanding the motivations behind black-box model predictions is becoming essential in many different applications. X-PLAIN is an interactive tool that allows human-in-the-loop inspection of the reasons behind model predictions. Its support for the local analysis of individual predictions enables users to inspect the local behavior of different classifiers and compare the knowledge different classifiers are exploiting for their prediction. The interactive exploration of prediction explanation provides actionable insights for both trusting and validating model predictions and, in case of unexpected behaviors, for debugging and improving the model itself.
Authentication Beyond Desktops and Smartphones

Much of the research on authentication in the past decades focused on developing authentication mechanisms for desktop computers and smartphones with the goal of making them both secure and usable. At the same time, the increasing number of smart devices that are becoming part of our everyday life creates new challenges for authentication, in particular since many of those devices are not designed and developed with authentication in mind. Examples include but are not limited to wearables, AR and VR glasses, devices in smart homes, and public displays. The goal of this workshop is to develop a common understanding of challenges and opportunities smart devices and environments create for secure and usable authentication. Therefore, we will bring together researchers and practitioners from HCI, usable security, and specific application areas (e.g., smart homes, wearables) to develop a research agenda for future approaches to authentication.
The Concrete Complexity of LWE without Gaussian Sampling

The LWE problem is a widely used tool in cryptography because of the well hardness and cryptographic versatility. In consideration of efficiency, a variant of LWE---LWE uniform errors, is proposed with some related work. However, no conclusion has been given to the complexity analysis for the LWE without Gaussian sampling of restricted samples.
Factors influencing the volatility price of bitcoin / Nur Faridza Roslan

The purpose to conduct this study is to investigate the relationship between supply of Bitcoin, interest rate, exchange rate and oil price toward the volatility price of Bitcoin in United States. It covers the period from 1st quarter 2011 to 3rd quarter 2019, a period of 8 years. Simply, those factors are considered as influencer for volatility price of Bitcoin in United States and it has been investigated quarterly. The researcher want to investigate the factors that contribute the price of Bitcoin becomes volatile in some of period in United States. Also, the researcher want to estimate whether the independent variables have the significant or insignificant relationship toward the volatility price of Bitcoin. The researcher used secondary data in this study. In addition, the researcher also used Multiple Linear Regression to test the data collected. The researcher used this method in order to get strong result at the end of the analysis. Finally, we have come out with the conclusion and recommendation as a suggestion for a good and better decision for the price of Bitcoin in the cryptocurrency market in United States.
Methodological Views on Agile Testing

The article provides a literature review on agile testing, which encompasses the most important factors in conducting software testing in all aspects of its practice. This is a growing area of academic research due to the increasing role of conducting professional software testing in very short amounts of time. This article maps the main strands of research on agile software testing by showing the different types of models developed and the key takeaways for further research using five articles derived from western literature. The review indicates that much of the literature is framed in terms of comparing the existing agile models or comparing the agile model to other older software development models used such as the waterfall model. Researchers investigating agile testing draw predominantly on multivariate analysis using surveys, questionnaires, documentary analysis and interviews. The theme has gained more and more importance through the last years as more and more companies are adopting the agile software development and testing methodology. A stronger focus is needed in developing a professional testing concept and in determining the key dimension on which the productivity of such approaches is based on.
Verifiable Self-Selecting Secret Sharing Based on Elliptic Curves

In distributed systems, as any network architecture, cryptography has a vital role in communication security, and sharing a secret represents a jump in this field where the secret depends on a group instead of a single person. In this article, the authors propose a method to share a multi secrets matrix represented by an image, that could be reconstructed without any loss by an access structure over a distributed system. The presented approach has a verifiable property, where each candidate possesses the advantage to verify the validity of his shadow. The security level of the scheme is based on elliptic curve discrete logarithm problem and the opportunity of allowing each side to generate a private sub-secret key for the sharing. The benefit of this method is justified by the absence of information loss and a lower timing results.
Predicting engineering students' optimal group size using socio-educational features

Socio-educational background plays an influential role in the success of a studentś engineering schooling. These socio-educational backgrounds are of more diverse nature in developing countries like Bangladesh. The fact that, tertiary education is given in a foreign language adds another dimension to challenge of imparting a successful engineering education. If the students could be grouped according to their socio-educational features, then it would have been easier to anticipate the needs of students coming from diverse backgrounds. In this work, we classify the students (N=237) of the department of Computer Science and Engineering of a university in the Bangladeshi capital of Dhaka based on their socio-educational features using K-means clustering and then propose a classifier that could work as a predictor that could work as a predictor for predicting student needs coming from different backgrounds.
Research on Innovation Model of Sharing Economy based on Blockchain Technology

Sharing economy has innovated the economic development model and created new employment opportunities, which has attracted widespread attention. But it has also been accompanied by problems in identity authentication, credit system and supervision and management. Based on the analysis of the current situation and problems of shared economic development and the advantages of Blockchain technology, this paper puts forward a new sharing model based on the alliance Blockchain, and establishes a shared economic platform (SEPBB) framework based on Blockchain, which provides a new idea and model for the development of sharing economy and the application of Blockchain technology.
Application of Monitoring Database for Accreditation Instrument UKI PAULUS

Accreditation is a form of quality and feasibility evaluation of higher education institutions or study programs conducted by independent organizations or bodies outside of tertiary institutions. To achieve the target of accreditation scores, universities and study programs must adjust their internal conditions in accordance with the standards given by the National Accreditation Board for Higher Education (BAN-PT). In accordance with Permenristekdikti No 32/2016, BAN-PT develops accreditation instruments that are relevant to the development of the higher education sector in Indonesia and follows the global developments. Study Program Accreditation Instrument (IAPS 4.0) BAN-PT uses 9 criteria. The Accreditation Instrument of Monitoring Database of the Study Program UKI Paulus was built using a PHP interface and MySql database that can integrate the entire contents of the data and other supporting documents so that during the evaluation processes, the information of achievement determined by BAN-PT from each standard is obtained. The system development steps, uses the Waterfall method which starts from the analysis phase, system design, program code writing, system testing and system maintenance. From 30 respondents who joined the test using black box method, the user accessibility level reached 95.52%. It shows that the system run optimally. This system is able to provide easy access and management of study program accreditation documents.
A Fast Detecting Method for Clone Functions Using Global Alignment of Token Sequences

In large software projects, proper source code reuse can make development more efficient, but a lot of duplicate code and error code reuse can be a major cause of difficult system maintenance. Efficient clone code detection for large project can help manage the project. However, most of the clone detection methods are difficult to perform on adaptive analysis that adjusts specificity or sensitivity according to the type of clone to be detected. Therefore, when a user wants to find a particular type of clone in a large project, they must analyze it repeatedly using various tools to adjust the options. In this study, we propose a clone detection system based on the global sequence alignment. Lex based token analysis models and global alignment algorithm-based clone detection models were able to detect not only exact matches but also various types of clones by setting lower bound scores. Using features of the global alignment score calculation method to eliminate functions that cannot be clone candidates in advance, alignment analysis was possible even for large projects, and the execution time was predicted. For clone functions, we visualized the matching area, which is the result of alignment analysis, to represent clone information more efficiently.