Reviewerly/exHarmony huggingface dataset:
Introducing Requirements Engineering In An Undergraduate Software Engineering Curriculum: Lessons Learned

Requirements Engineering (RE) is the process of determining, analyzing, documenting, validating and maintaining the services and constraints of the systems that needs to be designed.Research indicates that on a typical software project, the percentage of time spent on RE and Design, Implementation, and Testing are 40%, 20% and 40% respectively, whereas for successful projects these numbers are 60, 15 and 25%.Because of the high importance of RE in the design of software systems, the need to introduce RE as a required course in the undergraduate Software Engineering (SE) and Computer Science (CS) curricula is getting more attention.This paper summarizes the author's experiences in developing and teaching a RE course to juniors in the new Software Engineering degree program offered at Milwaukee School of Engineering (MSOE).One of the major issues holding back broad acceptance of RE in the software industry today is the widespread belief that major effort in software development lies in programming and testing.Our students have similar perceptions and beliefs and like some seasoned practitioners, are more interested in "how" to solve problems instead of discovering "what" to solve, i.e. gathering the requirements.This paper details how working on "real" industrial projects with external clients for the first time in unfamiliar domains, spending an entire term writing documents (instead of programming), being cognizant of ethical issues, and having to deal with ambiguous and conflicting customer requirements made this course very different and challenging for students.The challenges faced by the instructor in developing and teaching this course are also summarized.Heeding to their criticisms and various discussions on learning styles [10] [11] which demonstrate that retention and learning is better when students work on the problems themselves -in the second offering of the course (Winter 2001(Winter -2002) ) I have decided to take one case study and tackle it in depth.I let the students work on a particular aspect of the process (generating contextual diagram, brainstorming on the list of stakeholders, business events, use cases etc, writing a use case, enumerating functional and non-functional requirements) and then discuss it in class.Typically, they put their solutions on the board and then we discuss each solution.Obviously, the time spent on each aspect has increased tremendously (which means certain topics have been eliminated) but the student retention and class participation has increased and the students seem to be having more fun!
Sentiment Analysis On Tweets

In this current era, social media plays a vital role in information exchange, sharing their thoughts. Emotional result of an individual maintains a vital role on their day-to day life. Sentiment Analysis could be a procedure of analysing the opinions and polarity of thoughts of the person. Twitter could be a main platform on sharing the thought's, opinion and sentiments on completely different occasions. Twitter Sentimental Analysis is methodology of analysing the emotions from tweets (message denote by user in twitter). Tweets are useful in extracting the Sentimental values from the user. the info offering the Polarity indication like positive, negative or unbiased values. it's targeted on the person’s tweets and also the hash tags for understanding the things in every facet of the standards. The paper is to analyse the famed person’s id’s or hash tags for understanding the mind-set of individuals in every scenario once the person has tweeted or has acted upon some incidents. When the person has tweeted or has acted upon some incidents.
Fuzzy Rules In Assessing Student Learning Outcomes

In this paper, it is shown how fuzzy rules can be used as a modeling and evaluation tool for the achievement of the learning outcomes in information systems (IS) courses.In an outcome-based educational model (OBE), all courses in an IS college are required to clearly demonstrate the experiences that students can gain upon achieving a learning outcome.Consequently, master course syllabi describing the integration of the desired learning outcomes into IS courses are developed.The IS college provides a map between passing a given course and achieving a particular learning outcome at a certain level.Four different levels of achievements are identified: Beginning, Developmental, Achieved and Exemplary.Though, the map shows a clear relationship between courses and learning outcomes, it is not easy to define the boundaries between these four achievement levels or to combine all of the achievement results into one final assessment.In this case, the use of fuzzy logic is suitable to represent the complexities and vagueness in modeling the students learning outcomes achievements.Fuzzy membership functions are developed to model the achievement levels and define their overlaps and Fuzzy rules are generated to model the relationship between course grade (input) and the expected achievement level of learning outcomes (output).The aggregation of all learning outcome achievement levels for a sequence of courses that a student has to take provides an approximate indication of the experiences learned.Moreover, an overall analysis of all students' performances can identify the inherent strengths and weaknesses in the outcome-based educational model.Furthermore, theses results can be used by faculty members to assess the effectiveness of the integration of the learning outcomes into their courses.
The New Version of Government Research Bulletin (GRB) Website Applying Visualization and Text-mining Tools

Government Research Bulletin (GRB) (www.grb.gov.tw) is the official platform to provide up-to-date information regarding the government funded research projects in Taiwan. Since GRB was launched in 1997, it has been operated as a data warehouse to collect and store research information from research institutes. To increase the discoverability and accessibility of the research information in GRB, the website services of GRB have been improved recently by applying visualization tools and text-mining tools. The new GRB provides multi-dimensional search engine for users to search for research projects, researchers and institutes. Users can explore the search results through not only the related record list, but also the word cloud graphs derived from text-mining results of those related contents. Word cloud graphs can help users to form concepts in mind about what researches have been done recently. The new GRB also applies an API provided by Lucene to calculate the similarity between every two projects, and to provide the list of similar projects ranking by their similarity to the main project interested by users. Users can also easily find a group of researchers working on similar topics and their cooperation network graphs by typing in a researcher’s name or just a keyword in the researcher search engine. Furthermore, to help users locate the institutes which are conducting projects on a specific topic, geographic visualization tool is applied to display the location of those institutions. Besides the improvement of search engine, a data-oriented charting tool called Highcharts is also applied to display the real-time calculated results of the data collected by GRB. Users can interact with the charts online to explore the number of projects and the amount of research budgets that government has already input every year since 1993. To further serve users interested in some specific hot topics (such as biotech medicine, green energy, and artificial intelligent), the new GRB also provides analysis reports and value-added analyses using GRB data to reveal the research trends about those hot topics. Now GRB are still evolving for providing better research information services.
Sliding variance and data range for lightweight sports activity recognition with fusion of modalities

Abstract This research develops a novel lightweight sport activity detection system using time series data of the accelerometer sensors embedded in two modalities, the smartphone and smartwatch. This research focuses on the lightweight exercises, more specifically, jumping jack, push up, sit up, and squat jump. Such activities are chosen for two reasons; the smartphone is now accessible for many persons, and the lightweight exercises are deemed to be easily completed in daily basis for everyone. Our proposed algorithm includes two novel feature extraction methods, i.e. sliding variance and data range, combined with a digital filter and data clipping methods. The results of feature extraction processes were classified using a combination of k-NN and DTW algorithms. The classification results are subsequently compared with the-state-of-the-art algorithms, i.e. LMNN and Naive Bayes algorithms. The final results imply the merge of k-NN and DTW algorithms (k = 1) with data range method achieves the highest accuracy. The average accuracy for this method is 97.4%, with the processing time of 0.86 s. Thus, the result of counting activity method was acceptable with average values 80% for the whole movement by using two sensor accelerometers.
How stage theorizing can improve recommendations against phishing attacks

Purpose Phishing remains a major cybersecurity problem. Mainly adopting variance approaches, researchers have suggested several recommendations to help users avoid being victimized in phishing attacks. However, the evidence suggests that anti-phishing recommendations are not very effective. The purpose of this paper is threefold: first, to analyze why the existing anti-phishing recommendations may not be very effective; second, to propose stage theorizing as an additional approach for studying phishing that can contribute toward more effective recommendations; and third, to demonstrate using a stage theory, how IS researchers can utilize the concept of stages in phishing research. Design/methodology/approach The study draws on findings from previous empirical phishing research to assess whether the reasons why people are victimized in phishing attacks can be categorized into stages. The criteria for stages of the Transtheoretical Model (TTM) are used as an example. Findings Analysis indicates support for the existence of stages of phishing victims. The criteria for stages of the TTM were applied to the reasons that subjects in previous studies gave for clicking on phishing links and to the anti-phishing recommendations proposed in previous studies. There was overall support for four of the five criteria of the TTM. The results from the current study indicate that a targeted approach is a better approach to proposing anti-phishing recommendations. Practical implications The analysis identified the stages of phishing victims and the processes of change for each stage. It is suggested that recommendations against phishing should target individuals based on their resident stages. Moreover, the processes of change should be applied to the correct stage for the recommendations to be effective. Originality/value From a phishing perspective, there is a lack of research based on stage theorizing. The current study presents stage theorizing as an additional approach to the existing approaches and demonstrates how a stage theory can be used to make more effective recommendations against phishing. The study has thrown light on the benefits of stage theorizing and how its approach to targeted recommendations can be useful in IS security research.
An investigation of the game of Defend the Island

Motivated by the problem of “Defend the Roman Empire”, we propose a variant of graph pebbling, named ( d , t )-pebbling, and use this idea to devise a two-player game of imperfect information, named “Defend the Island”. On an island, the target castle attacked by the opponent is securable if t Field Armies (FAs) can reach the target castle from neighboring castles within a distance of d. The aim is to obtain more points than the opponent before an insecure castle on the island is attacked. There is a minimum number of FAs initially deployed on the island so that each castle is securable. In graph pebbling, this number is called the optimal ( d , t )-pebbling number of a graph G, denoted by π ( d , t ) ∗ ( G ). When the number of FAs is less than π ( d , t ) ∗ ( G ), there exists at least one insecure castle. This property is the core idea to devise the new game. In addition, when the castles forms a cycle, that is, the graph is a cycle, we give a lower bound of π ( d , t ) ∗ ( G ) for d = 1 , 2 and determine the exact value of π ( d , t ) ∗ ( G ) for the ordered pairs ( d , t ) = ( 2 , 2 ) , ( 1 , 4 m ) and ( 2 , 10 m ), where m is any positive integer.
Secure Outsourced Principal Eigentensor Computation for Cyber-Physical-Social Systems

Cyber-physical-social systems (CPSS) are revolutionizing the relationships between humans, computers, and things. Outsourcing computation to the cloud can offer resources-constrained enterprises and consumers sustainable computing in CPSS. However, ensuring the security of data in such an outsourced environment remains a research challenge. Principal eigentensor computation has emerged as a powerful tool dealing with multidimensional cyber-physical-social systems data. In this paper, we present two novel secure principal eigentensor computation (SPEC) schemes for sustainable CPSS. To the best of our knowledge, this is the first effort to address SPEC over encrypted data in the cloud without the interaction need between multiple users and cloud. More specifically, we leverage cloud server and trusted hardware component to design a collaborative cloud model. Using the model, we propose (1) a basic SPEC scheme based on homomorphic computing and (2) an efficient SPEC scheme that combines the advantages of homomorphic computing and garbled circuits, and exploits packing technology to reduce computational cost. Finally, we theoretically and empirically analyze the security and efficiency of our SPEC schemes. Findings demonstrate that the proposed schemes provide a secure and efficient way of outsourcing computation for CPSS. In addition, from the cloud user's perspective, our proposal is lightweight.
Las nuevas tecnologías en el desarrollo académico universitario

The university education faces the changes propitiated by the new technologies which should be seen as an opportunity to take a leap into the world of digital communication and thus favor a direct interaction between students and teachers. This topic has been raised to analyze the role of new technologies in the university academic development of the city of Quito. In this study, several mechanisms were used for the analysis: participant observation and the survey to students and teachers from private and public universities in the city. The selection to make the sample was random so it is representative of the different economic and urban levels of the city. The results reflect that students from public and private universities use «smartphones» and social networks for the exchange of information, teachers from private institutions use virtual platforms more frequently while their peers from public institutions do not use them. Resources such as books in digital format are preferred by younger teachers. These technologies are an active part of everyday life, even educational, but its influence is not yet as expected. In this environment the university teacher becomes a facilitator of learning, teachers and students interact in a new educational space that adapts according to their interests and personal possibilities.
The Association Rules Algorithm Based on Clustering in Mining Research in Corn Yield

With the popularization of agricultural information technology, the use of data mining techniques to analyze the impact of different types of soil nutrient content and yield of corn has become a hot topic in the field of agriculture. Association rule mining is an important part of the field in Data mining, association rules can be found associated with agricultural data attributes. This article will use cluster analysis and association rule to analysis correlation between corn yield and soil nutrient. Firstly compare different clustering algorithm to chooses the optimal algorithm, make data collected in scientific classification, and based on expert knowledge of the collected data into different levels; then determine the type and content of different soil by association rules corn yield and soil nutrient; final inspection algorithm is correct. The results showed that: comparing K-means, hierarchical clustering analysis, and PAM, K-means algorithm to determine the optimal clustering; K value can be determined at selected intervals. K is equal to 3, 4 or 6, clustering effect is good according to Sil value when K from 3 to 10. Based on the principle of association rules, clustering algorithm to select a K value associated with the combination of rule 6; After clustering algorithm of association rules, support and credibility and improve degree of accuracy is better than not clustering; by mining association rules after clustering, a great influence on the different levels of soil nutrients in corn yield. The results for the corn yield provides intelligent decision support data.
Visual System Examination Using Synthetic Scenarios

Many systems use visual devices to detect, inspect and analyze persons, scenes, and properties of objects. Often, they use samples to learn relevant indicators to reach a high level of quality of the appropriated operation. Nevertheless, collecting samples and annotate the relevant parts may be a hard, expensive and error prone task in same fields of use. To overcome this problem we create a system to generate synthetic scenarios based on predefined and exact definitions of the content as well as the sample production process. To demonstrate the usability we apply a scenario with a humanoid with known activity and with various environment objects to different systems for visual detection and analysis.
Show Me How You Click, and I’ll Tell You What You Can: Predicting User Competence and Performance by Mouse Interaction Parameters

Automatically detecting and adapting to user competence is a promising approach for advancing human-technology interaction. With the present work, we demonstrate that perceived user competence and performance can be predicted by easily ascertainable low-level mouse interaction parameters with considerable amounts of explained variance. N = 71 users with varying competence interacted with a statistical software while mouse interaction parameters were recorded. Results showed that perceived task competence could best be predicted by clicks per second, maximum mouse velocity, and average duration of pauses > 150 ms (R2 = .39). Perceived system competence could best be predicted by clicks per second, maximum mouse acceleration, and average number of pauses > 150 ms (R2 = .28). Performance could best be predicted by clicks per second, maximum mouse velocity, and average number of pauses > 150 ms (R2 = .50). Results imply that assessing low-level mouse interaction parameters could be a feasible approach for automatic detection of user competence and performance.
Partial-duplicate image retrieval based on HSV colour space for coverless information hiding

In conventional image steganography approaches, modification traces will be left in the designated cover images. Consequently, the secret information could be possibly detected by steganalysis tools. In this paper, a coverless image steganography approach based on partial-duplicate image retrieval is proposed to hide images without modification. First, we divide equally any given query or database image into a set of image blocks. Then, for a given query image, we search for its partial-duplicate images in a large-scale database according to the similarity between the HSV colour features of image blocks. Finally, these retrieved partial-duplicate images are used to represent and hide the secret image. Moreover, we propose an encryption algorithm based on chaotic maps to ensure the security of our approach. Both the theoretical analysis and experimental results demonstrate that the proposed coverless image steganography approach has good security, high robustness and strong resistance to the existing steganalysis tools.
Automated Analytical Model for Content Based Selection of Web Services

There are various inbound web services which prescribe services to clients. Specialists are more engaged in making framework for proposal of web service (WS) which limit the intricacy of selection process and improve the quality of service (QOS) suggestion. Our work implements a framework which recommends web services using an analytical model based on the contextual information provided by the service providers. This system helps users obtain high quality service automatically. Adaptive work performs feature reduction, similarity and ranking of WS. The important feature reduction process helps identify attribute values with maximum accuracy which results in proper evaluation of data. Efficient selection of WS for service composition requires better methods which properly calculate the similar values. A similarity helps to identify the closest services as per the requirement in the process of service composition. Ultimately, the system automatically selects the set of web services with highest similarity scores from the optimized set of web service description.
Mobile Human Shape Superimposition: An Initial Approach using OpenPose

When it comes to visitors at museums and heritage attractions objects speak for themselves. Nevertheless, it is important to give those visitors the best experience possible as, this will lead to an increase in the visits number, enhance the perception and value of an organisation, and boost the sales. With the aim of enhancing a traditional museum visit, a mobile Augmented Reality (AR) framework is being developed as part of the Mobile Five Senses Augmented Reality (M5SAR) system for museums project. This paper presents an initial approach to human shape detection and AR content superimposition, achieved by combining information of human body joints with shape segmentation or texture overlapping. The OpenPose model was used to compute the body joints and the GrabCut algorithm for person segmentation, allowing to fit clothes segments to persons moving in real environments. The initial results and proof-of-concept are shown.
Evaluating Index Systems of High Energy Physics

Nowadays, more and more scientific data has been produced through high-energy physics (HEP) facilities. Even in one particle physics experiment, the generated data reaches to petabytes scale. Retrieving data from massive data occupies a large proportion of data processing in HEP. Hence, the data query latency and throughput are the most important metrics for HEP data management. Inspired by the indexing technology of databases, the technology that improves the performance of data retrieval through the HEP data indexing, becomes the mainstream in the HEP data management. In this paper, focusing on two typical index systems–MySQL and HBase–for HEP data management, which are the typical SQL and NoSQL system respectively, we evaluate them from the perspectives of overall performance, system and micro-architecture behaviors. We find that HBase achieves higher performance than MySQL with the data scale increasing.