Reviewerly/exHarmony huggingface dataset:
New Interfaces for Classifying Performance Gestures in Music

Interactive machine learning (ML) allows a music performer to digitally represent musical actions (via gestural interfaces) and affect their musical output in real-time. Processing musical actions (termed performance gestures) with ML is useful because it predicts and maps often-complex biometric data. ML models can therefore be used to create novel interactions with musical systems, game-engines, and networked analogue devices. Wekinator is a free open-source software for ML (based on the Waikato Environment for Knowledge Analysis – WEKA - framework) which has been widely used, since 2009, to build supervised predictive models when developing real-time interactive systems. This is because it is accessible in its format (i.e. a graphical user interface – GUI) and simplified approach to ML. Significantly, it allows model training via gestural interfaces through demonstration. However, Wekinator offers the user several models to build predictive systems with. This paper explores which ML models (in Wekinator) are the most useful for predicting an output in the context of interactive music composition. We use two performance gestures for piano, with opposing datasets, to train available ML models, investigate compositional outcomes and frame the investigation. Our results show ML model choice is important for mapping performance gestures because of disparate mapping accuracies and behaviours found between all Wekinator ML models.
Critical Dimension of Word2Vec

Word embeddings are an efficient way of representing text such that they can be used by different Machine Learning Algorithms. Word2Vec is one such word embedding model. Although it is highly efficient, this model can take up a lot of space to store the vector representations. So defining the appropriate dimension of this model is very important to improve its performance in memory restricted devices. In this work, we present an empirical approach to decide the dimension of the word embeddings for a specific set of documents (corpora) to a critical value such that the representation of the words still preserve their original semantic and syntactic meanings. In this context, principal component analysis (PCA) has been used to compare the precision of vectors represented in different dimensions.
Big Data Contextual Analytics Study on Arabic Tweets Summarization

Twitter represents a source of information as well as a free space for people to express their opinions on diverse topics. The use of twitter is rapidly increasing and generates a massive amount of data from several types and forms, in which searching for relevant tweets in a specific topic is hard manually due to irrelevant tweets. There has been much research on English tweets for understanding context; however, in spite of the fact that the Twitter active Arabic users are over hundreds of millions, there are very limited studies that have investigated Arabic tweets to produce an automatic summarization. This article proposes a multi-conversational Arabic tweets summarization approach, with a new concept of tweet classification based on influence factor. Such an approach is able to analyze Arabic tweets and provide a readable, informative, precise, concise, and diversified summary. The evaluation metrics of precision, recall, and f-measure have shown good results of the system compared to related Arabic summarization studies.
Recent Developments in Recommender Systems

With greater penetration of online services, the use of recommender systems to predict users' propensity for continuous engagement becomes crucial in ensuring maximum revenue. There are many challenges, such as the cold start problem and data sparsity, that are continuously being addressed by a myriad of techniques in recommender systems. This paper provides insights into the trends of the techniques used for recommender systems and the challenges they address. With the insights; deep learning, matrix factorization or a combination of both can be used in addressing the data sparsity challenge.
Driving the paradigm shift of near surface exploration geochemistry using ultrafine soils

SummarySoils are widely used as a geochemical sample medium. These samples are sieved to <250 μm or <180 μm to remove larger gravels and coarse sand and later pulverized for analysis, but no other refinement is commonly used. Why is this the case? In transported cover, the soils that host/adsorb the mobile element signature are the smallest size fractions, so we should consider concentrating the clay size fraction (<2 μm) as an improved sample medium. Industry is unlikely to fully adopt these changes without proof of the benefit of refining the size fraction so a series of experiments were conducted to demonstrate the value of using <2 μm fractions for exploration geochemistry. Twenty-seven composite reference soils were collected in the vicinity of known mineral deposits (including background areas) that reflect the common soil types of Western Australia. These soils were used in replicate testing to assess differences in particle size, sample weight, dispersants and how this relates to the geochemistry with intent to optimise the extraction and speed of ultra-fine fraction recovery. Following the refinement of the technique, we commercialised and then applied the new UltraFine+ workflow to numerous orientation site studies, including reprocessed archived regional soil samples from the Geological Survey of Western Australia. Orientation results were promising. Analysing fine fractions (<2 μm) generated reproducible, reliable results, with bigger concentrations. Key benefits were the removal of nugget effects (for Au) and the challenges with detection limits in materials that are dominantly quartz sand. Importantly, the study revealed a marked decrease in censored results for Au (63% to 10% below detection limit) and less variation with sampling depth in soil profiles. The UltraFine+ workflow demonstrates the additional value from (re-) assaying regional soil and sediment samples to generate new targets and improve regional geochemical maps.
Insights Into The Behaviour Of Multi-Task Deep Neural Networks For Medical Image Segmentation

Glandular morphology is used by pathologists to assess the malignancy of different adenocarcinomas. This process involves conducting gland segmentation task. The common approach in specialised domains, such as medical imaging, is to design complex architectures in a multi-task learning setup. Generally, these approaches rely on substantial postprocessing efforts. Moreover, a predominant notion is that general purpose models are not suitable for gland instance segmentation. We analyse the behaviour of two architectures: SA-FCN and Mask R-CNN. We compare the impact of post-processing on the final predictive results and the performance of generic and specific models for the gland segmentation problem. Our results highlight the dependency of post-processing on tailored models as well as comparable results when using a generic model. Thus, in the interest of time, it is worth considering to use and improve generic models as opposed to design complex architectures when tackling new domains.
High Dynamic Range Point Clouds for Real‐Time Relighting

Abstract Acquired 3D point clouds make possible quick modeling of virtual scenes from the real world. With modern 3D capture pipelines, each point sample often comes with additional attributes such as normal vector and color response. Although rendering and processing such data has been extensively studied, little attention has been devoted using the light transport hidden in the recorded per‐sample color response to relight virtual objects in visual effects (VFX) look‐dev or augmented reality (AR) scenarios. Typically, standard relighting environment exploits global environment maps together with a collection of local light probes to reflect the light mood of the real scene on the virtual object. We propose instead a unified spatial approximation of the radiance and visibility relationships present in the scene, in the form of a colored point cloud. To do so, our method relies on two core components: High Dynamic Range (HDR) expansion and real‐time Point‐Based Global Illumination (PBGI). First, since an acquired color point cloud typically comes in Low Dynamic Range (LDR) format, we boost it using a single HDR photo exemplar of the captured scene that can cover part of it. We perform this expansion efficiently by first expanding the dynamic range of a set of renderings of the point cloud and then projecting these renderings on the original cloud. At this stage, we propagate the expansion to the regions not covered by the renderings or with low‐quality dynamic range by solving a Poisson system. Then, at rendering time, we use the resulting HDR point cloud to relight virtual objects, providing a diffuse model of the indirect illumination propagated by the environment. To do so, we design a PBGI algorithm that exploits the GPU's geometry shader stage as well as a new mipmapping operator, tailored for G‐buffers, to achieve real‐time performances. As a result, our method can effectively relight virtual objects exhibiting diffuse and glossy physically‐based materials in real time. Furthermore, it accounts for the spatial embedding of the object within the 3D environment. We evaluate our approach on manufactured scenes to assess the error introduced at every step from the perfect ground truth. We also report experiments with real captured data, covering a range of capture technologies, from active scanning to multiview stereo reconstruction.
Jaya-Neural Network for Server Room Temperature Forecasting Through Sensor Network

The server room temperature is the primary variable that influenced the server performance. Therefore, forecasting the temperature environment in a server room is necessary to prevent the worst condition. The conventional neural network has been widely used to the forecasting problem even though it can easily trap in the local solution, which is the leading research topic recently. The other ways to adjust the weight of the multi-layer neural network is using intelligence optimization algorithm. However, in the existing optimization algorithm, the parameter setting is the main topic to get the best optimization result. In this paper, the implementation of the parameterless optimization algorithm is discussed. Jaya algorithm is proposed to adjusting the weight of the multi-layer neural network in the forecasting of server room temperature. The experimental result shows that Jaya-NN has outperformed than the other optimization, and it can forecast temperature data accurately.
A Dynamic Software Startup Competency Model

Current literature suggests that engineering activities of software engineering and software startup engineering differ. Thus, there is the need to elicit competencies specific for software startup engineering. This paper proposes a model that provides the various types of competencies and their respective relevance at the various stages of software startup evolution.
Drone-Steering: A Novel VR Traveling Technique

This paper presents a novel technique of navigation in Virtual Reality (VR) called Drone-Steering. This technique has been designed to facilitate path learning and traveling in VR by reducing both cybersickness and disorientation. We compared this technique to traditional Hand-Steering in a landmark-free environment. Our first experiment confirmed a significantly lower level of cybersickness during traveling and significantly better path learning. We believe that our technique constitutes a promising alternative to current VR navigation techniques, and will especially interest researchers and developers targeting large VR environments.
End-to-End Secure Mobile Group Messaging with Conversation Integrity and Deniability

In this paper, we describe Mobile CoWPI, a deployable, end-to-end secure mobile group messaging application with proofs of security. Mobile CoWPI allows dynamic groups of users to participate in, join, and leave private, authenticated conversations without requiring the participants to be simultaneously online or maintain reliable network connectivity. We identify the limitations of mobile messaging and how they affect conversational integrity and deniability. We define strong models of these security properties, prove that Mobile CoWPI satisfies these properties, and argue that no protocol that satisfies these properties can be more scalable than Mobile CoWPI. We also describe an implementation of Mobile CoWPI and show through experiments that it is suitable for use in real-world messaging conditions.
The urgency of Interactive Animated Learning Media Development for Facilitating Literate Skills for The Student of Primary School

Abstract The purpose of this article is to remind the importance of developing an interactive animated learning medium to facilitate literacy skills in elementary school students. One of the problems faced by education in Indonesia is the low level of literacy ability of students in Indonesia. Data states that the results of the research Program for International Student Assessment (PISA) mentioned, the literacy culture of Indonesian society in 2012 the second worst of 65 countries studied in the world. Education in primary school as the first foundation in improving education world, it is necessary to take a role by doing an interactive animative learning media development which is adjusted to the level of psychological development of elementary school children who are still in a concrete operational stage to build their literacy skills early on. A culture of early literacy in elementary school children will be able to empower and improve the quality of individuals, families, communities and even the nation itself. Based on the above explanation shows that the world of education Indonesia needs a real and sustainable solution to improve the literacy skills of the nation’s children.
Ontology-Based Modelling for Cyber Security E-Learning and Training

The Conceptual Framework for e-Learning and Training (COFELET) constitutes a design standard for the enhancement of cyber security education by guiding the development of effective game-based approaches (e.g., serious games). The COFELET framework envisages cyber security serious games as highly organized and parameterized learning environments which monitor learner’s actions, evaluate their efforts and adapt to their needs. To this end, the COFELET framework employs well known cyber security standards (e.g., MITRE’s CAPEC, Lockheed Martin’s Cyber Kill Chain model or CKC) as a vehicle for organizing educational environments which model learners’ actions and strategies. In this light, the COFELET ontology is proposed aiming at providing a foundation for the development of a universal knowledge base for modeling such environments. The COFELET ontology provides an analytical description of the key elements of COFELET’s compliant serious games along with the appropriate classes and their properties. These elements include the cyber security domain elements that model the actions attackers perform to unleash cyber security attacks (i.e., the tasks) and the strategies they employ to achieve their malicious objectives (e.g., CAPEC’s attack patterns, the CKC model). The cyber security domain elements are associated with the educational elements (e.g., hints, utilized knowledge, exercised skills) that provide the means to infuse the didactics in the COFELET compliant approaches. A set of instances is presented to provide a better appreciation of the COFELET ontology rational, usage and usefulness. The proposed ontology is a cause and effect of the design and development process of a prototype COFELET compliant game.
A Self-Pruning Classification Model for News

News aggregators are on-line services that collect articles from numerous reputable media and news providers and reorganize them in a convenient manner with the aim of assisting their users to access the information they seek. One of the most important tools offered by news aggregators is based on the classification of the articles into a fixed set of categories. In this article, we introduce a supervised classification method for news articles that analyzes their titles and constructs multiple types of tokens including single words and n-grams of variable sizes. In the sequel, it employs several statistics, such as frequencies and token-class correlations, to assign two importance scores to each token. These scores reflect the ambiguity of a token; namely, how significant it is for the classification of an article to a category. The tokens and their scores are stored in a support structure that is subsequently used to classify the unlabeled articles. In addition, we propose a dimensionality reduction approach that reduces the size of the model without significant degradation of its classification performance. The algorithm is experimentally evaluated by employing a popular dataset of news articles and is found to outperform standard classification methods.
System Deterioration Detection and Root Cause Learning on Time Series Graphs

System deterioration detection and root cause analysis is crucial for today's industrial society. However, the design and operation of mechanic system is getting more and more complex, which makes it hard at identifying deterioration with noisy data. Our research focuses on solving such problem on time-evolving sensor graphs in a streaming setting. Given a sequence of graphs, the ability to identify 1) any gradual and stable structured change and 2) the root cause components is of importance for early warning and system diagnosis. Existing methods either raise too many false alerts on instant changes or are too sensitive to noise. To address these problems, we propose Robust Failure Detection and Diagnosis (RoFaD). RoFaD can capture failure propagation given a time series of graph. By optimizing a matrix-based Taylor expansion, RoFaD can identify system deterioration in the presence of noise and immediate changes, and diagnose the root cause components. Experiments on both synthetic and real world datasets demonstrate that RoFaD is more effective than the popular baselines.
Mathematical Models Used in Intelligent Assistive Technologies: Response Surface Methodology in Software Tools Optimization for Medical Rehabilitation

Assistive Technology (AT) refers to any commercially, modified, or customized item, equipment, or system that is used to enhance, maintain or improve the functional capabilities of people with disabilities. Assistive technologies should be viewed as a series of products and services through which people with different types of disabilities achieve their own goals by facilitating an independent life. The scope of support technologies extends to those products and services for three categories of people: people with disabilities, the elderly and people with chronic illnesses. Ambient Assistance Living (AAL) is a subcategory of environmental intelligence, which refers to the use of intelligent environmental techniques, processes and technologies to enable the elderly to live independently for as long as possible without intrusive behaviors. The Exergaming Platform presented in this chapter is a recovery application, designed for upper limb rehabilitation, that helps patients with locomotor disabilities and not only, transforming the unpleasant physical therapy into a fun game. The platform transforms the traditional games into video game-based exercises and drives patients to exercise correctly, while monitoring them. A RSM tool for Medical Rehabilitation based on an Exergaming Platform that uses a Microsoft Kinect Sensors, is presented in this chapter. The development of this application was made in cooperation between a research team from Institute de Telecommunication from Lisbon, Portugal and a research team from the University of Suceava, Romania. In order to find the score for a subject with a locomotor system leisure or neurodegenerative disorders, we used RSM Methodology and we optimized the exergame using statistical and mathematical. During the model developing, the data analysis has shown that the RSM can be a good candidate for optimization the application. The application has demonstrated that the Response Surface Methodology (RSM) is a useful instrument in the prediction of the patients variable scores.
On the Assessment of Security and Performance Bugs in Chromium Open-Source Project

An individual working in software development should have a fundamental understanding of how different types of bugs impact various project aspects. This knowledge allows one to improve the quality of the created software. The problem, however, is that previous research typically treats all types of bugs as similar when analyzing several aspects of software quality (e.g. predicting the time to fix a bug) or concentrates on a particular bug type (e.g. performance bugs) with little comparison to other types. In this paper, we look at how different types of bugs, specifically performance and security bugs, differ from one another. Our study is based on a previous study done by Zaman et al. [1] in which the study was performed on the FireFox project. Instead of Firefox, we will be working with the Chrome web-browser. In our case study, we find that security bugs are fixed faster than performance bugs and that the developers who were assigned to security bugs typically have more experience than the ones assigned to performance bugs. Our findings emphasize the importance of considering the different types of bugs in software quality research and practice.
Enterprise Knowledge Graph From Specific Business Task to Enterprise Knowledge Management

Data driven Knowledge Graph is rapidly adapted by different societies. Many open domain and specific domain knowledge graphs have been constructed, and many industries have benefited from knowledge graph. Currently, enterprise related knowledge graph is classified as specific domain, but the applications span from solving a narrow specific problem to Enterprise Knowledge Management system. With the digital transform of traditional industry, Enterprise knowledge becomes more and more complicated, it involves knowledge from common domain, multiple specific domains, and corporate-specific in general. This tutorial provides an overview of current Enterprise Knowledge Graph(EKG). It distinguishes the EKG from specific domain according to the knowledge it covers, and provides the examples to illustrate the difference between EKG and specific domain KG. The tutorial further summarizes EKG into three types: Specific Business Task Enterprise KG, Specific Business Unit Enterprise KG and Cross Business Unit Enterprise KG, and illustrates the characteristics, steps, challenges, and future research in constructing and consuming of each of these three types of EKG .
Human Body’s Orientation Estimation Based On Depth Image

In human-robot interactions, robots that often interact with humans need some information from humans to be able to communicate. In human-oriented robots, the robots must pay attention wherever the human will go. Therefore, the human-oriented robots are more flexible. To estimate wherever the human will go, it can use the direction of the human or what is called intention. Intention can be taken from the perspective of the human body. An algorithm is needed to estimate the orientation of the human body. In this study, using image processing from depth image is expected to be able to estimate the orientation of the human body when a human is not moving (static orientation) so that it can be used by the robots to predict the intention direction of a human. One method that can be used to classify the orientation of a human body is the Support Vector Machine (SVM) so that it gets a classified orientation value. Then from the classified orientation value to get the static orientation value, Kalman Filter is used. Orientation classification with SVM method has accuracy of 96.528%. Static orientation estimation using this method produces precision value of 0.097 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> .