Reviewerly/exHarmony huggingface dataset:
VRChairRacer

Locomotion in Virtual Reality (VR) is an important topic as there is a mismatch between the size of a Virtual Environment and the physically available tracking space. Although many locomotion techniques have been proposed, research on VR locomotion has not concluded yet. In this demonstration, we contribute to the area of VR locomotion by introducing VRChairRacer. VRChairRacer presents a novel mapping the velocity of a racing cart on the backrest of an office chair. Further, it maps a users' rotation onto the steering of a virtual racing cart. VRChairRacer demonstrates this locomotion technique to the community through an immersive multiplayer racing demo.
An exploratory study on cooccurrence of design patterns and bad smells using software metrics

Summary A design pattern is a general reusable solution to commonly recurring problems in software projects. Bad smells are symptoms existing in the source code that possibly indicate the presence of a structural problem that requires code refactoring. Although design pattern and bad smells be different concepts, literature has shown that they may be related and cooccur during the evolution of a software system. This paper presents an empirical study that investigates cooccurrences of design patterns and bad smells as well as identifies the main factors that contribute to the emergence of the relationship between them. We carried out a case study with five Java systems to: (1) investigate if the use of design pattern reduces bad smell occurrence, (2) identify cooccurrences of design patterns and bad smells, and (3) identify situations that contribute for the cooccurrence emergence. As the main result, we found that the application of design pattern not necessarily avoid bad smell occurrences. The results also show that some design patterns such as composite, factory method, and singleton, are intrinsically modular and might be useful in creating high‐quality systems. However, other design patterns such as adapter‐command, proxy, and state‐strategy, have presented high cooccurrence frequency with bad smells; therefore, they require attention in their implementation. Finally, via manual inspection in the components with cooccurrence, we found that the identified cooccurrences appeared due to poor planning and inadequate application of design patterns.
Automated Invigilation System for Detection of Suspicious Activities during Examination

Cheating and abnormal activities like- whispering, impersonation or hand contact are widely involved in offline examination around the globe, this hinders the integrity and morality of fair conduction of examination. Video Surveillance systems embeds image and video processing algorithms to monitor, analyse and process various activities and even detection of crime, suspicious activities. The purpose of this research is to design a robust video surveillance system model to monitor and analyse activities during examination, this will allow the academic institutes to detect and identity unfair or suspicious activities during examination. This system model will use automated video as feed to monitor students activities during examination in real-time. We have discussed about various video analytics or video processing and image processing methods and tools involved in surveillance model. We throughout the paper have walk through about the various processes- pre-processing, segmentation, classification, feature extraction and its related video processing algorithm in sequential manner. The proposed model is effective, efficient and requires relatively less processing power.
A Multiuser Identification Algorithm Based on Internet of Things

With the rapid development of the Internet of Things (IoT) in 4G/5G deployments, the massive amount of network data generated by users has exploded, which has not only brought a revolution to human’s living, but also caused some malicious actors to utilize these data to attack the privacy of ordinary users. Therefore, it is crucial to identify the entity users behind multiple virtual accounts. Due to the low precision of user identification in the many-to-many mechanism of user identification, a random forest confirmation algorithm based on stable marriage matching (RFCA-SMM) is proposed in this study. It consists of three key steps: we first employ the stable marriage matching model to calculate the similarity between multiple users and utilize a scoring model to calculate the overall similarity of the users, after which candidate matching pairs are selected; second, we construct the random forest model that exploits a user similarity vector training set; afterward, the candidate matching pairs combine the secondary confirmation of the random forest model, which both improve the precision of the many-to-many user identification and protect private user data in the IoT. Extensive experiments are provided to demonstrate that the proposed algorithm improves precision rate, recall rate, and F-Measure (F1), as well as Area Under Curve (AUC).
Sketching in HCI

Freehand sketching is a valuable process, input, output, and tool, often used by people to communicate and express ideas, as well as document, explore and describe concepts between researcher, user, or client. Sketches are fast, easy to create, and - by varying their fidelity - can be used in all areas of HCI. Sketching in HCI will explore and demonstrate themes around sketching in HCI with the aim of producing tangible outputs. Attendees will leave the course with the confidence to engage actively with sketching on an everyday basis in their research practice.
Solving class imbalance problem using bagging, boosting techniques, with and without using noise filtering method

In numerous real-world applications/domains, the class imbalance problem is prevalent/hot topic to focus. In various existing work, for solving class imbalance problem, almost data is labeled as one class called majority class, while fewer data is labeled as the other class, called minority class ( more important class to focus). But, none of the work has performed efficiently (in terms of accuracy). This work presents a comparison of the performance of several boosting and bagging techniques from imbalanced datasets. The wide range of application of data mining and machine learning encounters class imbalance problem. An imbalanced datasets consists of samples with skewed distribution and traditional methods show biased towards the negative (majority) samples. Note that popular pre-processing technique for handling class imbalance problems is called over-sampling. It balances the datasets to achieve a high classification rate and also avoids the bias towards majority class samples. Over-sampling technique takes full minority samples in the training data into consideration while performing classification. But, the presence of some noise (in the minority samples and majority samples) may degrade the classification performance. Hence, the work presents a performance comparison using boosting and bagging (i.e., with both techniques) with and without using noise filtering. This work evaluates the performance with the state of-the-art methods based on ensemble learning like AdaBoost, RUSBoost, SMOTEBoost, Bagging, OverBagging, SMOTEBagging on 25 imbalance binary class datasets with various Imbalance Ratios (IR). The experimental results show that our approach works as promising and effective for dealing with imbalanced datasets using metrics like F-Measure and AUC.
Informal STEAM Education Case Study

STEAM education fuses arts with traditional STEM fields so that the diverse disciplines can broaden and inform each other. Our eight-week STEAM after school program exposed elementary school children to social robotics and musical theater. Approximately 25 children grades K-5 participated over the course of the program with an average of 12 children attending each week. The program covered acting, dancing, music, and drawing with the robots in two-week modules based around the fairy tale, "Beauty and the Beast". The modular design enabled children who could come to only a few sessions to participate actively. The children demonstrated enthusiasm for both the robots and the musical theater activities and were engaged in the program. Efforts such as this can provide meaningful opportunities for children to explore a variety of arts and STEM fields in an enjoyable manner. The program components and lessons learned are discussed with recommendations for future research.
Application of Computer Vision and Deep Learning in Breast Cancer Assisted Diagnosis

In the general process of breast cancer diagnosis, doctors mainly analyze and judge B-mode ultrasound images through vision, which depends heavily on doctors' operational experience and technical level. Artificial intelligence methods represented by machine learning algorithms have made rapid progress in recent years, especially natural image classification, target detection, semantics segmentation based on computer vision technology have been relatively mature, and have been widely used successfully in various fields. So as to improve the automation ability and reduce human errors, etc. By using artificial intelligence technology such as computer vision and in-depth learning, an automated method is established to diagnose breast cancer B-mode ultrasound images. This method can quickly strengthen the correct diagnostic rate of front-line medical staff and reduce the difference of operation level between urban and rural doctors. It has obvious medical needs and wide social significance.
Efficient Algorithms for Text Lines and Words Segmentation for Recognition of Arabic Handwritten Script

A new methodology for Arabic handwritten document images segmentation is done in this paper to segment the documents into distinct entities as words and text lines. Based on features of Arabic scripts, the document images are divided into three main subsets of connected components where the Hough transform method is applied to them to achieve text lines segmentation. To enhance the result by avoiding the Hough transform text line detection failure, the authors used a method in postprocessing stage based on skeletonization that covers the possible false correction alarms to create proficiency vertical connected characters’ segmentation. The segmentation of the Arabic words is pointed as a two-class problem. The authors used fusion of convex and Euclidean distance metrics to calculate the distance between neighboring overlapped components, which in the Gaussian mixture modeling framework is classified as a distance of an intra-word or as an inter-word. The proposed method performance is depended on a constant and particular evaluation method that appropriate measures of the performance used to compare the segmentation of our result against the other strong researcher result. The proposed method showed higher efficiency and accuracy in the experimentation, which was conducted on two various Arabic handwriting datasets that are IFN/ENIT and AHDB.
Autonomous Robot Navigation System Without Grid Maps Based on Double Deep Q-Network and RTK-GNSS Localization in Outdoor Environments

This paper proposes an autonomous mobile robot navigation system without grid maps in outdoor environments. The system integrates local navigation based on deep reinforcement learning and localization using RTK-GNSS. Local navigation is to travel between waypoints by using a learned policy. Localization is required for state of learning-based navigation and arrival evaluation of waypoints. First, a robot learns a policy traveling between waypoints in an environment that imitates an actual outdoor environment and avoiding collision with obstacles in our original simulator. DDQN(Double Deep Q-Network) is applied as an learning algorithm. We aim for learning that a robot can take an adequate action from obstacle positions obtained from 2D-LiDAR, a relative distance and a relative angle to a destination. Then, a robot performs navigation in outdoor environments based on the learned policy. Experimental results include learning in several environments, accuracy of RTK-GNSS and the integrated navigation system in an actual outdoor environment. Especially, our proposed system could travel approximately 600[m] in a general urban environments.
Agile Release Planning Using Natural Language Processing Algorithm

Once the requirement is gathered in agile, it is broken down into smaller pre-defined format called user stories. These user stories are then scoped in various sprint releases and delivered accordingly. Release planning in Agile becomes challenging when the number of user stories goes up in hundreds. In such scenarios it is very difficult to manually identify similar user stories and package them together into a release. Hence, this paper suggests application of natural language processing algorithms for identifying similar user stories and then scoping them into a release This paper takes the approach to build a word corpus for every project release identified in the project and then to convert the provided user stories into a vector of string using Java utility for calculating top 3 most occurring words from the given project corpus in a user story. Once all the user stories are represented as vector array then by using RV coefficient NLP algorithm the user stories are clustered into various releases of the software project. Using the proposed approach, the release planning for large and complex software engineering projects can be simplified resulting into efficient planning in less time. The automated commercial tools like JIRA and Rally can be enhanced to include suggested algorithms for managing release planning in Agile.
Neural arbors are Pareto optimal

Neural arbors (dendrites and axons) can be viewed as graphs connecting the cell body of a neuron to various pre- and post-synaptic partners. Several constraints have been proposed on the topology of these graphs, such as minimizing the amount of wire needed to construct the arbor (wiring cost), and minimizing the graph distances between the cell body and synaptic partners (conduction delay). These two objectives compete with each other-optimizing one results in poorer performance on the other. Here, we describe how well neural arbors resolve this network design trade-off using the theory of Pareto optimality. We develop an algorithm to generate arbors that near-optimally balance between these two objectives, and demonstrate that this algorithm improves over previous algorithms. We then use this algorithm to study how close neural arbors are to being Pareto optimal. Analysing 14 145 arbors across numerous brain regions, species and cell types, we find that neural arbors are much closer to being Pareto optimal than would be expected by chance and other reasonable baselines. We also investigate how the location of the arbor on the Pareto front, and the distance from the arbor to the Pareto front, can be used to classify between some arbor types (e.g. axons versus dendrites, or different cell types), highlighting a new potential connection between arbor structure and function. Finally, using this framework, we find that another biological branching structure-plant shoot architectures used to collect and distribute nutrients-are also Pareto optimal, suggesting shared principles of network design between two systems separated by millions of years of evolution.
Variable-domain fuzzy sets—Part II: Apparatus

This is the second part of a two-part paper on operations on fuzzy sets that may not share the same domain of definition. Part I described several ways of representing variable-domain fuzzy sets and introduced a few basic variable-domain fuzzy set operations. The present (largely self-contained) Part II further develops the theory of variable-domain fuzzy sets, based primarily on the representation of variable domains by a dummy membership degree that indicates the out-of-domain assignment error. We first describe several families of algebraic operations on the extended set of membership degrees; these are later employed in the investigation of basic notions of variable-domain fuzzy set theory. The fuzzy set operations studied in this paper include variable-domain variants of unions and intersections, kernels and supports, heights and plinths, equalities and inclusions, Cartesian products, and fuzzy relational compositions. Besides the initial examination of these variable-domain notions, our aim is to demonstrate the viability of variable-domain fuzzy set theory and highlight the similarity as well as differences between the fixed- and variable-domain treatments of fuzzy sets.
Security Assured Vehicle Data Collection Platform by Blockchain: Service Provider’s Perspective

The advancements in information and communications technology have connected the disconnected, and changed the daily lives of everybody. The automobile industry is comparatively a laggard to connect its disconnected mobility, but new efforts are introduced to put the car on the network as so called connected cars. A single vehicle contains numerous parts to be assembled just as the numerous types of data which can be collected. Like all the connected things being exposed to a cybersecurity concern, the connected cars are also being exposed to cyber-attacks which can exploit your physical safety from privacy. This research proposes security-assured vehicle data platform through closed blockchain for the service provider who facilitates the data for business. The paper is composed of following. First, the paper identifies the type of data which contains the privacy concerns that can be collected from the vehicle. Second, the authors present how the data collected from the vehicle is valuable to multiple parties. Third, the study reviews why the blockchain is an appropriate technology to collect and redistribute the vehicle collected data. Last, the research proposes a vehicle data platform with a blockchain application which assures confidentiality, integrity and accessibility of the data. The expected contributions of this research are following: First, the research identifies and proposes the value of the vehicle collected data. Second, the study determines and tackles the potential exploits from cyber-attacks to the vehicle data platform. Third, the technical extensions from blockchain to the related industries and potential participants of the platform. The study also expects to extend the technical applications with actual vehicle collected data to closed blockchain.
Proposal of an insider sabotage detection method for nuclear security using deep learning

Following the Fukushima Daiichi nuclear accident, fears have arisen that terrorists can cause a similar accident by acts of sabotage against nuclear facilities; as a result, the importance of nuclear security has increased. In particular, sabotage by insiders is a distinct threat to nuclear power plants. In response to limitations of physical protection system (PPS) in nuclear facilities, in this paper, we propose a behavior recognition method that is based on hand motion time-series data analysis and use deep learning to explore insider sabotage detection.
Artist Recommendation System Using Hybrid Method: A Novel Approach

Recommendation systems have a wide range of applications today in the digital world. The recommender system must be able to accurately predict the users’ tastes as well as broaden their horizon about the available products. There are various dimensions in which recommendation systems are created and evaluated. Accuracy and diversity play an important role in the recommendation systems and a trade-off must be identified between the two parameters to suit the business requirements. The proposed system makes use of various recommendation approaches to give a wide range of recommendations to users. The recommendations are provided based on similarity of the selected artist, top artists in a genre, using a hybrid model and artists listened by users’ friends. Some recommendations would include the most popular ones, and some would be randomly picked for a diverse range of recommendations.
A Type-Driven Vector Semantics for Ellipsis with Anaphora Using Lambek Calculus with Limited Contraction

We develop a vector space semantics for verb phrase ellipsis with anaphora using type-driven compositional distributional semantics based on the Lambek calculus with limited contraction (LCC) of Jäger (Anaphora and type logical grammar, Springer, Berlin, 2006). Distributional semantics has a lot to say about the statistical collocation based meanings of content words, but provides little guidance on how to treat function words. Formal semantics on the other hand, has powerful mechanisms for dealing with relative pronouns, coordinators, and the like. Type-driven compositional distributional semantics brings these two models together. We review previous compositional distributional models of relative pronouns, coordination and a restricted account of ellipsis in the DisCoCat framework of Coecke et al. (Mathematical foundations for a compositional distributional model of meaning, 2010. arXiv:1003.4394 , Ann Pure Appl Log 164(11):1079–1100, 2013). We show how DisCoCat cannot deal with general forms of ellipsis, which rely on copying of information, and develop a novel way of connecting typelogical grammar to distributional semantics by assigning vector interpretable lambda terms to derivations of LCC in the style of Muskens and Sadrzadeh (in: Amblard, de Groote, Pogodalla, Retoré (eds) Logical aspects of computational linguistics, Springer, Berlin, 2016). What follows is an account of (verb phrase) ellipsis in which word meanings can be copied: the meaning of a sentence is now a program with non-linear access to individual word embeddings. We present the theoretical setting, work out examples, and demonstrate our results with a state of the art distributional model on an extended verb disambiguation dataset.
Preliminary analysis of edupark fluid learning tool in Mifan water park in Padang Panjang city

Visiting Geopark (natural park) or natural attractions has become a community lifestyle, but the visit is still limited to tourist visits and their activity is nothing more to capture the moment (selfie) or for recreation only. Geopark or tourism object as a source of learning has not been fully utilized, including Mifan Water Park Padang Panjang. A prelude to clicking earthy analysis learning device Edupark Fluid needs in Mifan Water Park Padang Panjang. The type of research is developing research using the development model of Plomp at the preliminary research stage. The data in this study were taken with questionnaires and interviews of teachers and learners in SMAN 2 Padang Panjang. While the concept of Fluid Concepts to be discussed, obtained by way of direct observation to Mifan Water Park. Data analysis techniques used descriptive percentages. The results of research at a preliminary research stage this will reveal whether or not the development of Edupark is necessary Fluid in Mifan Water park Padang Panjang based on a tailored learning model.