Reviewerly/exHarmony huggingface dataset:
Performance Enhancement Method for Machine Learning Algorithm

Machine learning is programming computer or a mobile device that learns from experience. Machine learning classification methods are helpful in various fields of Computer Science like driverless cars, product recommendation systems, dynamic pricing, Google translate, online video streaming, internet and mobile fraud detection systems and much more. The present work proposes a method augClassifier to enhance the performance of Simple Logistics machine learning algorithm. The performance assessment of machine learning algorithm is conducted on a Mobile device using Android Environment. The work also presents the comparative performance investigations of Simple Logistics machine learning algorithm using correlation based feature selection method with respect to performance measures Precision, Sensitivity, F-Measure and ROC. The present work conforms that the augClassifier enhances the performance of Simple Logistics machine learning algorithm.
On The Development of an Iot Infrastructure to Perform Behavioural Change Towards Low Energy Habits

Building simulation allows to understand on a very precise manner the way in which the energy flows are occurring within a building.This, apart from being necessary to evaluate the energy performance, offers the possibility of tackling several aspects of the building operation.In most buildings, occupants are capable of modifying operation parameters such as switching off the lights, or changing the set point temperatures.A poor selection of these operational parameters may lead to a larger energy consumption that one may expect.Although some buildings have amperimetric clamps and other sensors that can inform about energy consumption, it is practically impossible to measure all the energy flows that can be given by a model.Making use of this, a building has been modelled and calibrated, to get an informative model that was then used for evaluating the potential of energy behaviour interventions in demand reduction.The work has used more than 200 sensors to ensure that the results were close to those in reality, and the model has been seen to represent the demands accurately.The savings obtained have been seen to be substantial, especially for winter where a poor mental model of occupants is making them select set point temperatures that are too high, even affecting negatively their comfort.
Content Based Image Retrieval using Transform Domain Features and Algorithms

In this paper, Content Based Image Retrieval using Transform domain features and algorithms has been implemented. The image can be decomposed by Discrete Wavelet Transform (DWT) to extract the features based on DC coefficients. Each sub-image is calculated by mean, variance and standard deviation to get more efficient recognition. The database image also applied in the domain of Stationary Wavelet Transform (SWT) and Integer Wavelet Transform (IWT) by using different distance measures. The proposed algorithm is the combination of DWT, SWT and IWT has been implemented using COREL database. This proposed method has more efficient recognition and less computational time over existing methods
IMPROVING STUDENTS’ READING COMPREHENSION THROUGH RECIPROCAL TEACHING STRATEGY ON REPORT TEXT

The objective of this research is to know how to use of reciprocal methods to teaching reading comprehension of the senior high school in second grade. The background of the research is to help students’ to cope the difficulties in reading report text, because there are still many students who find it difficult to read English and the lack of reading comprehension of students in class XI Animation of SMK TI Garuda Nusantara Cimahi with the aim to know whether there is an increase in student scores after using the Reciprocal Learning method. The author uses the reciprocal method to increase the ability of students to read comprehension. This research is quantitative by using experimental research methods. After conducting the research obtained the data pre-test (80) and post-test (95). Based on these data, it can be concluded that the use of reciprocal methods in the report text material can improve reading comprehension in students of class XI Animation 2 Vocational School of TI Garuda Nusantara Cimahi.
Teaching flamenco zapateado: a new notation-based methodology

Dance has traditionally been taught using a style based on teacher-pupil oral transmission, imitation and repetition. While there are notation methods for dance, few proposals for zapateado notation exist. This research develops a critical analysis not only of the currently existing notation systems for flamenco zapateado, but also of rhythmic notation systems for percussion based on the traditional Western system of musical notation, which form the basis of the foundations of the system of the method of notation presented here. The article shows that this flamenco zapateado notation system is the first to combine how the foot strikes the floor with the rhythmic aspects using notation with a clear visual appearance and a sequence of didactic content which takes into account motor aspects as well as rhythmic ones.
Extrusioncutter : a novel system for generating interactive context-preserving cutaways of anatomical surface meshes

This thesis presents ExtrusionCutter - a novel 3D visualization tool that enables users to create complex, context-preserving cutaways of anatomical surface meshes. To accomplish this, a "paint-roller" interaction metaphor has been developed that allows users to extrude an editable cutting mesh along the natural geometry of an occluding surface. This virtual analogy of a familiar real-world action not only facilitates the removal of occluding surfaces, but also creates a user-defined region parameterization which makes it possible to also generate an effective contextual outline view of the removed material. This thesis will demonstrate how the paint roller interaction metaphor has been implemented to facilitate the creation of multiple editable cutaway types on 3D anatomical surface meshes. Additionally, it will show that the resulting cutaway views are capable of exposing occluded parts while still maintaining their context in the visualization.
OFGIM: A New Algorithm to Mine Generalized-Itemsets

Most databases mined by a single layer/ crisp association rules algorithms are not flat but contain data in hierarchal/ generalized format. In spite of this fact, a few algorithms available to mine generalized itemsets to produce generalized association rules, GAR, which escort to mine more specific and concrete knowledge for decision makers. This research presents a new algorithm, (Optimized Frequent Generalized Itemsets Miner (OFGIM)), to mine generalized itemsets. Simply, it depends on extending the transactions of a database. The extension is done by adding the parent of an item to the transaction containing the item. The mining process is accomplished by the union of itemsets and the intersection of the tidsets. The algorithm requires two database scans only; the first one is for extending operation and the second scan is for mining process. The proposed algorithm does not need a specified data structure such as hash tree and prunes the apriori-based pruning steps.OFGJM was tested by using six synthetic databases. OFGJM overcomes apriori based algorithm in a ratio of ¼ in all the experiments, but it exhibitsits RAMappealing.
Introduction to topiCS Volume 13, Issue 3

With the current issue of Topics in Cognitive Science, we are proud to present award-winning research again, starting off with a scholar's lifetime achievements distinguished by the Rumelhart Prize, then introducing the Best Papers from the 18th International Conference on Cognitive Modeling. The first topic honors Michelene (“Micki”) T. H. Chi (Arizona State University), the 19th recipient of the David E. Rumelhart Prize. With a background in mathematics and a strong interest in the science of education, Chi has conducted pioneering, widely cited, and highly influential work on the active role of learners in the learning process, from self-explanations to the development of expertise. Having been active in the society for almost her entire academic life, and elected one of its inaugural fellows in 2003, she received the award also referred to as “the Nobel Prize in Cognitive Science” in 2019, for having “challenged basic assumptions about the mind” more than once and for having “defined new approaches that have shaped a generation of cognitive and learning scientists.” Following a pithy introduction by former topiCS editor Wayne Gray, we are happy to present the paper based on her Rumelhart Lecture, on “Translating a theory of active learning,” in which she attempts to close the gap between research and practice by outlining a multistep translation research framework. The second part of this issue comprises revised and expanded versions of the four best papers presented at the 18th International Conference on Cognitive Modeling, which took place last year fully virtually as was the case for so many other conferences. This batch has been curated by Topic Editors Terrence C. Stewart (National Research Council Canada) and Christopher Myers (Air Force Research Laboratory), who also introduce this selection of papers in more detail. Congratulations to all our authors for their awards––we hope you continue the outstanding work you are doing! topiCS encourages letters and commentaries on all topics, as well as proposals for new topics. Letters are not longer than two published pages (ca. 400–1000 words). Commentaries (between 1000 and 2000 words) are often solicited by Topic Editors prior to the publication of their topic, but they may also be considered after publication. Letters and commentaries typically come without abstract and with few references, if any. The Executive Editor and the Senior Editorial Board (SEB) are constantly searching for new and exciting topics for topiCS. Feel free to open communications with a short note to the Executive Editor ([email protected]) or a member of the SEB (for a list, see the publisher's homepage for topiCS: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1756-8765/homepage/EditorialBoard.html).
An Automated Tool for Inspection of Requirements Engineering Techniques

From last few decades, researchers and practitioners have well recognized the significance of Requirements Engineering. Requirements Engineering stage is the foundation stone on which the entire building named software can be built. There are several Requirements Engineering (RE) techniques exists but requirements engineer choose a specific technique for a particular software project with their own preferences or organization standards. There is not only little guidance available for analyzing Requirements Engineering techniques but also all the existing researches focus on qualitative measures. There is no consideration of physical measures while analyzing and accepting a technique for a particular project. Nowadays customers satisfaction is also gaining great importance so customer perspective should also be taken into account. We have performed deep literature review and noted that analysis and selection of Requirements Engineering technique should consider all relevant attributes of each techniques and their mapping with project, people or other factors. There is a need to thoroughly comprehend and evaluate all the existing techniques with respect to analyst preferences, client experiences, project attributes, software process model characteristics. To do so, fuzzy clustering method is implemented in MATLAB. The key emphasis of this paper is to study and list all possible Requirements Engineering techniques related to Elicitation, Prioritization, Documentation, Verification and Validation, etc. The research work also analyzes attributes of each RE technique using Fuzzy C mean clustering and K mean clustering methods. The results of clustering provide a set of techniques, from which requirements engineer can select for specific phase of Requirements Engineering. The substantiation of the research work is done with the help of a case study that is having some known problem domain characteristics
On the use of hidden Markov model to predict the time to fix bugs

A significant amount of time is spent by software developers in investigating bug reports. It is useful to indicate when a bug report will be closed, since it would help software teams to prioritize their work. Several studies of this problem have been conducted during the past decade. Most of these studies have used the frequency of occurrence of certain developer activities as input attributes in building their prediction models. However, these approaches tend to ignore the temporal nature of the occurrence of these activities. In this thesis, a novel approach using Hidden Markov Models and temporal sequences of developer activities is introduced. The approach is empirically demonstrated using eight years of bug reports collected from the Firefox project. The model correctly identifies bug reports with expected bug fix times. The approach is also compared against the frequency based classification approaches. The results indicate around 10% higher accuracy .
Twitter Sentiment Recognition using Support Vector Machine

In this we explore the effectiveness of language features to identify Twitter messages ' feelings. We assess the utility of existing lexical tools as well as capturing features of informal and innovative language knowledge used in micro blogging. We take a supervised approach to the problem, but to create training data, we use existing hash tags in the Twitter data. We Using three separate Twitter messaging companies in our experiments. We use the hash tagged data set (HASH) for development and training, which we compile from the Edinburgh Twitter corpus, and the emoticon data set (EMOT) from the I Sieve Corporation (ISIEVE) for evaluation. Twitter contains huge amount of data . This data may be of different types such as structured data or unstructured data. So by using this data and Appling pre processing techniques we can be able to read the comments from the users. And also the comments will be classified into three categories. They are positive negative and also the neutral comments.Today they use the processing of natural language, information, and text interpretation to derive and classify text feeling into pos itive, negative, and neutral categories. We can also examine the utility of language features to identify Twitter mess ages ' feelings. In addition, state-of - the-art approaches take into consideration only the tweet to be classified when classifying the feeling; they ignore its context (i.e. related tweets).Since tweets are usually short and more ambiguous, however, it is sometimes not enough to consider only the current tweet for classification of sentiments.Informal and innovative microblogging language. We take a sup ervised approach to the problem, but to create training data, we use existing hashtags in the Twitter data.This paper also contrasts sentiment analysis approaches in evaluating political views using Naïve Bayes supervised machine learning algorithm which performs in better analysis compared to other techniques Paper
Visual Data Mining for Multimedia Databases

With computers and communication dominating technology in different fields, the need to look for media-based information processing, MBIP –rather than data-based information processing, DBIP- is increasingly being felt and this is compounded by the explosive developments in cellular communication, which brought computing and interaction on the move. The basis is to explore possibilities of using conventional data-mining approaches with visualization and object orientation so that human interaction is easier. Data Mining involves exploring databases to try and discover data relationships which are not explicitly stored with in the databases. Traditional techniques involve statistical analysis, clustering and pattern matching. Many current efforts are underway to integrate visualization in to this process. Visual data mining is a novel approach to data mining. The aim is to combine traditional data mining algorithms with information visualization techniques to utilize the advantages of both approaches. The utilization of both automatic analysis methods and human perception/understanding promises better and more effective data exploration. Visualization is a key process in visual data mining. Here the focus is on the presentation of all aspect of multimedia objects, their identification, their analysis and relationships.
The Digital Game ‘Artesaga’ as a Methodological Strategy for Teaching Art History

Digital games have been presented as mediation tools in theLearning Process area in educational contexts.As such, this article aims to present the educational digital game 'ArteSaga' which allows the learning of Art History in high school.For that to happen, a survey was conducted in IFRN -Campus Parnamirim, in four second grade classes of high school, allowing for the development of software, whose conductive approach is the Design Methodology.This investigation started with a bibliographic research, followed by a questionnaire applied to the students, with the purpose to determine the digital game shaping, and it is developed in C# language and its graphic design created with Raster graphics.The game 'ArteSaga' was created and validated presenting the content related to the Prehistoric and Ancient Art periods, and the final prototype was exhibited at scientific research expositions and scientific and technological events in the area. I.
Marketing Knowledge Management and Innovation Performance: Examining the Moderating Role of Business Environmental Volatility

This study empirically examines the link between marketing knowledge management and innovation performance focusing on the moderating role of business environmental volatility.We define marketing knowledge management as the integration of knowledge generation, knowledge dissemination and knowledge storage.Using a unique data set that consists of 439 employees at 156 firms in China, we find that knowledge dissemination and knowledge storage have a positive effect on innovation performance.Also found is the negative moderating effect of business environmental volatility on innovation performance.Our findings suggest that firms should strengthen their marketing knowledge management to improve innovation performance and stay flexible to cope with the ever-changing and often volatile market environments.
Deduction of Efficient Scanning Machines using Big Data Technology

Nowadays, the advancement in the field of information technology has witnessed stupendous growth in various industries, especially the medical imaging technologies in the healthcare industry. However, these advancements in the different technologies have not only made the data bigger but also a bit difficult to process and handle it. Though, these advancements may have resulted in huge amount of unnecessary data, it still cannot be considered as a major problem in today’s world as nowadays, the various advancements in technologies such as Big Data Analytics, Cloud Computing and several others, have made it really easy and effortless for storing huge amount of datasets and handling them. One of the boon that the advancement in technology has given to the world in the field of healthcare industry is the evolution of the scanning machines which can be used for the diagnosis of different diseases and to assemble the conclusions in the form of various medical reports for different scans such as ECG (Electrocardiogram), MRI(Magnetic Resonance Imaging) Brain scans, Ultrasounds, X-Rays, CT-Scanners and much more. But, the interesting part here is that though these scanning machines have their own advantages, one of the main disadvantages of them is that the efficiency of the results produced by them are yet to be known when comparing their performance’s to justify their enormous costs. Therefore, in the paper, the key challenges and various methodologies are being investigated in the healthcare industry with prime focus on comparing the scanning machines such as ECG, MRI, and Ultrasoundetc. by using Big Data Analytics. The various manufacturers of the scanning devices which are used by the hospitals or diagnostic centers have already fixed their price to such a high level that, even the hospitals have to spend lots of money to buy those machines and install them. Therefore, as a management side it becomes difficult to cope up with the performance related cost effectiveness of machines, which even shatters the trust of patients related to technical issues with a particular hospital. The prime aim is to focus on the precise implementation, performance efficiency and cost effectiveness of all the medical scans. The idea can also be implemented in improving theperformance along with the cost effectiveness of machines and devices other than the medical industry as well.
Preface

The Summer Workshop on Interval Methods (SWIM) is an annual meeting initiated in 2008 by the French MEA working group on Set Computation and Interval Techniques of the French research group on Automatic Control. A special focus of the MEA group is on promoting interval analysis techniques and applications to a broader community of researchers, facilitated by such multidisciplinary workshops. Since 2008, SWIM has become a keystone event for researchers dealing with various aspects of interval and set-based methods.&#x0D; In 2019, the 12th edition in this workshop series was held at ENSTA Paris, France, with a total of 25 talks.&#x0D; Traditionally, workshops in the series of SWIM provide a platform for both theoretical and applied researchers who work on the development, implementation, and application of interval methods, verified numerics, and other related (set-membership) techniques.For this edition, given talks were in the fields of&#x0D; &#x0D; the verified solution of initial value problems for ordinary differential equations, differential-algebraic system models, and partial differential equations,&#x0D; scientific computing with guaranteed error bounds,&#x0D; the design of robust and fault-tolerant control systems,&#x0D; the implementation of corresponding software libraries, and&#x0D; the usage of the mentioned approaches for a large variety of system models in areas such as control engineering, data analysis, signal and image processing.&#x0D; &#x0D; Seven papers were selected for submission to this Acta Cybernetica special issue. After a two turn peer-review process, six high-quality articles were selected for publication in this special issue. Three papers propose a contribution regarding differential equations, two papers focus on robust control, and one paper considers fault detection.
Timestamp Injected Cryptographic Hash Function to Reduce Fabrication of Hash Collisions

Cryptographic hash functions are used in many applications. One important application is to ensure data integrity. Although there are many different types of hashing algorithms, MD5 is widely used to ensure data integrity in digital evidence. However, a weakness, where collisions can occur, has been found in the MD5 algorithm. With regards to digital evidence, this is a big issue. The integrity of the digital evidence becomes questionable due to collisions and hence it is not admissible in court. Many methods were used to find collisions, such as the Chosen-Prefix Collision and researchers have been improving collision finding algorithms. This paper concentrates on reducing the chances of collision by chopping the last 16 bits of the MD5 algorithm and injecting timestamp into the chopped parts. Experiments are performed to test this algorithm and the results show that the time taken to find collisions is longer using the MD5 with an injected timestamp. The chopping construction and the timestamp disrupt the iterative property of the hash function thus when dealing with digital evidence, there are less chances of hash collision and therefore the probability of the admissibility of the digital evidence in court is higher.