Reviewerly/exHarmony huggingface dataset:
A Design Thinking Techniques Recommendation Tool: An Initial and On-going Proposal

Putting the user at the center of the process has been considered key to achieve quality in modern software development. Discovering and framing the user needs are necessary activities to produce better quality software that fits the customer’s needs and their satisfaction. User-centered Design approaches such as Design Thinking (DT) help to establish a creative mindset through convergent and divergent working spaces that allow for the team to better understand, to create empathy, to brainstorm ideas, to prototype, and to test innovative solutions in software development. However, navigating in this world is still challenging. Literature reports on the use of a large spam of techniques to support DT. Selecting an appropriate set of techniques considering the project context, the stakeholders’ profiles, and other characteristics may be challenging, mainly to novice professionals. Thus, as part of an on-going long-term research project, we propose Helius, a collaborative recommender system for DT techniques in software development. Helius provides features supporting recommendations of DT Techniques based on the user previous experiences, techniques’ evaluation ratings, and community feedback. This paper focuses on our preliminary results upon the tool proposal–on a comparison of Helius features with other related tools using the DESMET method. This comparison shows that Helius brings new features for the recommendation of DT techniques, thus, contributing to process quality improvement.
Investigation on the Effect of the Feature Extraction Backbone for Small Object Segmentation using Fully Convolutional Neural Network in Traffic Signs Application

Abstract Small objects are widely found in different applications such as traffic signs and to segment those objects make it difficult to extract features due to the small number of pixels. Previous research has been done to show how error prone the semantic segmentation networks to small objects in variance of application such as medical images and remote sensing and how it leads to class imbalance. However, small object segmentation seems to be tricky and making the network struggle. Recently there are small amount of research has been done in the effect of the feature extraction backbone to the small object datasets. In this paper we investigate the effect of different backbone feature extraction such as AlexNet, VGGNet, GoogleNet on an imbalanced small objects dataset after grouping them by shape and colour in the Fully Convolutional Networks (FCN). We measure the performance on PASCAL VOC and Malaysian Traffic Sign Dataset (MTSD) showing the pixel accuracy, mean accuracy per class, mean IoU and frequency weighted IoU for each backbone and FCN. The results show that VGGNet as a backbone with Cross Entropy (CE) combined with Dice Loss (DL) achieves the highest score in mean IoU for imbalanced dataset but not for balanced dataset. However, in the imbalanced dataset major classes have a higher probability to confuse with minor classes due to the class imbalance. In conclusion we investigate different backbone networks with grouped labels dataset in shape and colour and we recommend using VGGNet FCN with CE combined with DL for imbalanced datasets.
Prediction of student graduation accuracy using decision tree with application of genetic algorithms

Abstract Many of students who can’t complete the study promptly is a problem that needs to be faced with a fast and effective way. In the student’s education system is an important asset to the note the graduation rate of students promptly. Predicting graduation is very important for the institution to determine the strategic policies for the institution. The scope of the study is limited in performing accurate comparison between studies that only use Genetic Algorithm method and the research-based Genetic Algorithm using forward selection. Here we can conclude that the value of the highest accuracy is there in the Genetic Algorithm-based method Forward selection. Thus the Genetic Algorithm method with the selection of attribute-based Forward Selection is the best method for solving a problem in the prediction accuracy of graduation.
Innovation in Data Visualisation for Public Policy Making

Abstract In this contribution, we propose a reflection on the potential of data visualisation technologies for (informed) public policy making in a growingly complex and fast changing landscape—epitomized by the situation created after the outbreak of the Covid-19 pandemic. Based on the results of an online survey of more than 50 data scientists from all over the world, we highlight five application areas seeing the biggest needs for innovation according to the domain specialists. Our main argument is that we are facing a transformation of the business cases supporting the adoption and implementation of data visualisation methods and tools in government, which the conventional view of the value of Business Intelligence does not capture in full. Such evolution can drive a new wave of innovations that preserve (or restore) the human brain’s centrality in a decision making environment that is increasingly dominated—for good and bad—by artificial intelligence. Citizen science, design thinking, and accountability are mentioned as triggers of civic engagement and participation that can bring a community of “knowledge intermediaries” into the daily discussion on data supported policy making.
Novel Adaptive Binarization Method for Degraded Document Images

Achieving a good recognition rate for degraded document images is difficult as degraded document images suffer from low contrast, bleedthrough, and nonuniform illumination effects.Unlike the existing baseline thresholding techniques that use fixed thresholds and windows, the proposed method introduces a concept for obtaining dynamic windows according to the image content to achieve better binarization.To enhance a low-contrast image, we proposed a new mean histogram stretching method for suppressing noisy pixels in the background and, simultaneously, increasing pixel contrast at edges or near edges, which results in an enhanced image.For the enhanced image, we propose a new method for deriving adaptive local thresholds for dynamic windows.The dynamic window is derived by exploiting the advantage of Otsu thresholding.To assess the performance of the proposed method, we have used standard databases, namely, document image binarization contest (DIBCO), for experimentation.The comparative study on well-known existing methods indicates that the proposed method outperforms the existing methods in terms of quality and recognition rate.
Predicting Software Defects for Object-Oriented Software Using Search-based Techniques

Development without any defect is unsubstantial. Timely detection of software defects favors the proper resource utilization saving time, effort and money. With the increasing size and complexity of software, demand for accurate and efficient prediction models is increasing. Recently, search-based techniques (SBTs) have fascinated many researchers for Software Defect Prediction (SDP). The goal of this study is to conduct an empirical evaluation to assess the applicability of SBTs for predicting software defects in object-oriented (OO) softwares. In this study, 16 SBTs are exploited to build defect prediction models for 13 OO software projects. Stable performance measures — GMean, Balance and Receiver Operating Characteristic-Area Under Curve (ROC-AUC) are employed to probe into the predictive capability of developed models, taking into consideration the imbalanced nature of software datasets. Proper measures are taken to handle the stochastic behavior of SBTs. The significance of results is statistically validated using the Friedman test complied with Wilcoxon post hoc analysis. The results confirm that software defects can be detected in the early phases of software development with help of SBTs. This paper identifies the effective subset of SBTs that will aid software practitioners to timely detect the probable software defects, therefore, saving resources and bringing up good quality softwares. Eight SBTs — sUpervised Classification System (UCS), Bioinformatics-oriented hierarchical evolutionary learning (BIOHEL), CHC, Genetic Algorithm-based Classifier System with Adaptive Discretization Intervals (GA_ADI), Genetic Algorithm-based Classifier System with Intervalar Rule (GA_INT), Memetic Pittsburgh Learning Classifier System (MPLCS), Population-Based Incremental Learning (PBIL) and Steady-State Genetic Algorithm for Instance Selection (SGA) are found to be statistically good defect predictors.
Challenges to the development of teachers’ professional digital competences – Bulgarian perspective

The paper presents and analyzes teachers’ views and attitudes on their participation in continuous professional development (CPD) training courses and their usefulness for the improvement of digital knowledge and skills which are part of teacher digital competence – so much necessary for meeting the educational challenges of the 21st century. The findings discussed in this paper were obtained within a national research project carried out with the support of the Ministry of Education and Science at the end of 2019.The analysis of some of the research findings shows a relatively contradictory picture in terms of the relation between continuous professional development of teachers and the development of teachers’ digital competences.On the one hand, a positive tendency is observed related to a very active participation in CPD training of Bulgarian teachers in the field of ICT implementation in the educational context for the past 5 years. On the other hand, the educators themselves give positive but not very high evaluations of the degree to which they have developed basic aspects of their digital competence as a result of the training courses. The way the Bulgarian students evaluate different aspects of their own digital competence developed as a result of the learning experience at school is also not markedly positive.The comparison between the teachers’ self-evaluations of their discrete digital competences and the self-evaluations of students regarding their skills for using ICT acquired in the learning process is an adequate research approach through which an authentic idea could be construed about the efficiency of the CPD trainings of educational specialists in the field of digital and technological competence as well as about the existing challenges in this respect in Bulgaria.
Analysis of Gaussian and Cauchy Mutations in K-Means Particle Swarm Optimization Algorithm for Data Clustering

Clustering is an important unsupervised classification process. Some researchers have suggested K-means PSO algorithm to solve clustering problems. But many complex problems require further improvement of the solution. However, in this paper two types of popular mutation techniques are being used with the k-means PSO algorithm for improvement of the clustering problems. In this correspondence Gaussian mutation and Cauchy mutation are two common mutation techniques that are used to mutate the k-means PSO algorithm. The primary objective is to have better convergence as well as accurate results in the solutions of complex problems. The evaluation of the performance of the planned algorithm is done with six datasets and the results have been analyzed well with the existing k-means PSO clustering algorithm. The simulation results showed that both mutation techniques will give better results as compared to the existing k-means PSO clustering algorithm. But Cauchy mutation having stronger ability to escape local optima, effectiveness in results as well as robustness will dominate over Gaussian mutation. This paper verifies that the overall performance of Cauchy mutation operator combining with k-means PSO clustering algorithm is better.
Methodological Methods to Improve the Efficiency of Cloud Storage by applying De-duplication Techniques in Cloud Computing

In the modern world all the peoples having a smart device to access the whole world beyond the limit with the use of the internet, so cloud computing takes the major part to store the real world processed information like WhatsApp, Facebook, Twitter, Amazon, online ticket booking web sites, etc., Major problem in now a day is a storage issue because trillion and trillions of people are storing the information, due that setup in cloud storage has increased in space, performance, time and cost. It leads to many drawbacks like emitting CO2 and CO3 storage centre, consumes a lot of energy, heat penetration and increases in setup costs, etc. This drawback can be avoided by proposing efficient storage algorithm and De-duplication method, which leads to Green Computing and dynamic access to Cloud Storage also. Doing a methodological Survey to improve the cloud Storage by applying the de-duplication techniques, this methodological survey is useful for identifying several de-duplication techniques based on text, audio, video, image files.
Mapping Materials to Curriculum Standards for Design, Alignment, Audit, and Search

Computing proficiency is an increasingly vital component of the modern workforce, and computer science programs are faced with the challenges of engaging and retaining students to meet the growing need in that sector. However, administrators and instructors often find themselves either reinventing the wheel or relying too heavily on intuition, despite the availability of national curriculum standards. To address these issues, we present CS Materials, an open-source resource targeted at computing educators for designing and analyzing courses for coverage of recommended guidelines, and alignment between the various components within a course, between sections of the same course, or course sequences within a program. The system works by facilitating mapping educational materials to national curriculum standards. A side effect of the system is that it centralizes the design of the courses and the materials used therein. The curriculum guidelines act as a lingua franca that allows examination of and comparison between materials and courses. More relevant to instructors, the system enables a more precise search for materials that match particular topics and learning outcomes, and dissemination of high quality materials and course designs. This paper discusses the system, and analyzes the costs and benefits of its features and usage. While adding courses and materials requires some overhead, having a centralized repository of courses and materials with a shared structure and vocabulary serves students, instructors, and administrators, by promoting a data-driven approach to rigor and alignment with national standards.
Research on the Use of Computer Music in Modern Musical Composition

Abstract In the ever-changing society, disruptive technology not only causes a tremendous impact on the instruments that musicians play, but also how they make music and how we perceive them. Music algorithms and software as well as computational models are evolving as critical tools to give life to original musical ideas, diversify the nature of music, and create magnificent works of art. Even artificial intelligence can be applied to this vibrant field because they both deal with intellectual and emotional human activities. This paper investigates the current developments of computer music, its influence on the elements of modern music composition, and best practices of their applications in the music industry. The main purpose of this paper is to provide an overview of the use of computer music on music creation, analyze the latest trends of technical developments in music, to recognize the enormous potential of computer music along with its practical applications.
Multilanguage Speech-Based Gender Classification Using Time-Frequency Features and SVM Classifier

Speech is the most significant communication mode among human beings and a potential method for human-computer interaction (HCI). Being unparallel in complexity, the perception of human speech is very hard. The most crucial characteristic of speech is gender, and for the classification of gender often pitch is utilized. However, it is not a reliable method for gender classification as in numerous cases, the pitch of female and male is nearly similar. In this paper, we propose a time-frequency method for the classification of gender-based on the speech signal. Various techniques like framing, Fast Fourier Transform (FFT), auto-correlation, filtering, power calculations, speech frequency analysis, and feature extraction and formation are applied on speech samples. The classification is done based on features derived from the frequency and time domain processing using the Support Vector Machines (SVM) algorithm. SVM is trained on two speech databases Berlin Emo-DB and IITKGP-SEHSC, in which a total of 400 speech samples are evaluated. An accuracy of 83% and 81% for IITKGP-SEHSC and Berlin Emo-DB have been observed, respectively.
Multimodal Dialogue Data Collection and Analysis of Annotation Disagreement

We have been collecting multimodal dialogue data [1] to contribute to the development of multimodal dialogue systems that can take a user’s non-verbal behaviors into consideration. We recruited 30 participants from the general public whose ages ranged from 20 to 50 and genders were almost balanced. The consent form to be filled in by the participants was updated to enable data distribution to researchers as long as it is used for research purposes. After the data collection, eight annotators were divided into three groups and assigned labels representing how much a participant looks interested in the current topic to every exchange. The labels given among the annotators do not always agree as they depend on subjective impressions. We also analyzed the disagreement among annotators and temporal changes of impressions of the same annotators.
Video Tampering Detection Using Difference-Hashing Algorithm

Abstract Videos plays an important role in the judgement of many criminal cases. It can be considered as an evidence in every step in the criminal justice process. Since it is involved in the judgement, the integrity of the video is very much important. If anything gets tampered in the video, it leads to misconception in the process. Video tampering detection is a technique of finding whether the video content is modified or not. Alteration of video contents is rising speedily because of increasing video procurement gadgets and enormous programming devices to alter the video content. This integrity check can be done by either active or passive techniques. Along these lines our zone of worry in our research work is to display our perspectives on various passive video tampering detection techniques to check its correctness. Passive techniques are assembled into ensuing three arrangements relying upon the kind of counterfeiting. There are different methods to perform video tampering detection but passive methods prove more effective in terms of maintaining the originality of the video. Even in passive methods, hashing has been the sort after approach because of its accuracy and so we have adopted Difference hashing algorithm (D-Hash) to perform tampering detection.
Sketch and Scale Geo-distributed tSNE and UMAP

Running machine learning analytics over geographically distributed datasets is a rapidly arising problem in the world of data management policies ensuring privacy and data security. Visualizing high dimensional data using tools such as t-distributed Stochastic Neighbor Embedding (tSNE) and Uniform Manifold Approximation and Projection (UMAP) became a common practice for data scientists. Both tools scale poorly in time and memory. While recent optimizations showed successful handling of 10,000 data points, scaling beyond million points is still challenging. We introduce a novel framework: Sketch and Scale (SnS). It leverages a Count Sketch data structure to compress the data on the edge nodes, aggregates the reduced size sketches on the master node, and runs vanilla tSNE or UMAP on the summary, representing the densest areas, extracted from the aggregated sketch.We show this technique to be fully parallel, scale linearly in time, logarithmically in memory and communication, making it possible to analyze datasets with many millions, potentially billions of data points, spread across several data centers around the globe. We demonstrate the power of our method on two mid-size datasets: cancer data with 52 million 35-band pixels from multiplex images of tumor biopsies; and astrophysics data of 100 million stars with multi-color photometry from the Sloan Digital Sky Survey (SDSS).
Comparison of K-Medoids and Self Organizing Maps Algorithm in Grouping Hydrometeorological Natural Disasters in Java Island

Abstract Indonesia is a country that is prone to disasters. Based on data from the National Disaster Management Authority, there are 6868 disasters in the last 3 years. Changes in weather, temperature, and wind direction that are quite extreme in Indonesia lead to one of the disasters namely hydrometeorological disasters such as floods, landslides, tornadoes, forest fires, and drought. According to the National Coordinating Agency for Disaster Management, this type of disaster is quite common in Indonesia. Over the past 3 years, Java Island received the highest number of disasters. The National Disaster Management Authority states that tornadoes contribute 21% of all disasters in Indonesia. Therefore, the researcher wants to grouping the cases of hydrometeorological natural disaster in Indonesia, especially in Java. This research is to determine the areas that are the focus of the government in disaster mitigation efforts using K-Medoids Clustering and Self Organizing Maps. Based on the results of this study, it was found that the suitable method used was the Self Organizing Maps Method because it was seen based on a smaller standard deviation value. Cluster one there are 108 members who are groups with relatively low disaster vulnerability, while cluster two there are 10 members are city and district groups that have relatively high disaster vulnerability.
Selective Imaging of File System Data on Live Systems

In contrast to the common habit of taking full bitwise copies of storage devices before analysis, selective imaging promises to alleviate the problems created by the increasing capacity of storage devices. Imaging is selective if only selected data objects from an image that were explicitly chosen are included in the copied data. While selective imaging has been defined for post-mortem data acquisition, performing this process live, i.e., by using the system that contains the evidence also to execute the imaging software, is less well defined and understood. We present the design and implementation of a new live Selective Imaging Tool for Windows, called SIT, which is based on the DFIR ORC framework and uses AFF4 as a container format.
Interactive Animation Media of Sea Biota Design for Young Learners

Abstract Interactive animation media could provide children good experience and imagination, which serve as important aspects in their intelligence development. However such media that incorporate children’s multiple intelligences and specifically focus on marine biota are relatively limited in number. To have such media is particularly relevant for Indonesia to build awareness among children towards marine environment preservation. Within that framework, the current study attempts to develop interactive animation media on marine lives through waterfall System Development Life Cycle (SDLC). Although the study is still in its analyzing and designing stages, to this point it has shown that the architectural design of the media might benefits classroom activities related to sea biota.