Reviewerly/exHarmony huggingface dataset:
A Virtual Reality Game Design for Collaborative Team-Building: A Proof of Concept

In this study, we propose a proof of concept of a Virtual Reality system able to provide a high degree of engagement and activation to multiple users. Specifically, we designed a game for a collaborative Team-Building experience, we tested the system during a slow rogaining activity and we assessed the participants' appreciation level. Our results show that several persons can be entertained and are able to maintain a high activation, even though only one of them is actually immersed inside a VR experience wearing a Head Mounted Display
Reseña del "Nuevo Seminario": Experiencias de Aula con GeoGebra"

We describe the development, main contributions and ideas arising in the Nuevo Seminario: Experiencias de Aula con that took place at the Centro Internacional de Encuentros Matematicos (CIEM) of Castro-Urdiales, Cantabria, Spain, on December 14-16, 2018, sponsored both by the Federacion Espanola de Sociedades de Profesores de Matematicas (FESPM) and the Instituto GeoGebra de Cantabria. It focused on presenting the most interesting and recent didactical experiences in Spain concerning the use of GeoGebra, on the teaching of the different parts of the curriculum: Numbers and Algebra, Geometry, Functions, Statistics and Probability
Exploring Immersive and Non-Immersive Techniques for Geographic Data Visualization

Analyzing multi-dimensional geospatial data is difficult and immersive analytics systems are used to visualize geospatial data and models. There is little previous work evaluating when immersive and non-immersive visualizations are the most suitable for data analysis and more research is needed.
Studying the Relationship Between the Usage of APIs Discussed in the Crowd and Post-Release Defects

Software development nowadays is heavily based on libraries, frameworks and their proposed Application Programming Interfaces (APIs). However, due to challenges such as the complexity and the lack of documentation, these APIs may introduce various obstacles for developers and common defects in software systems. To resolve these issues, developers usually utilize Question and Answer (Q&A) websites such as Stack Overflow by asking their questions and finding proper solutions for their problems on APIs. Therefore, these websites have become inevitable sources of knowledge for developers, which is also known as the crowd knowledge. However, the relation of this knowledge to the software quality has never been adequately explored before. In this paper, we study whether using APIs which are challenging according to the discussions of the Stack Overflow is related to code quality defined in terms of post-release defects. To this purpose, we define the concept of challenge of an API, which denotes how much the API is discussed in high-quality posts on Stack Overflow. Then, using this concept, we propose a set of products and process metrics. We empirically study the statistical correlation between our metrics and post-release defects as well as added explanatory and predictive power to traditional models through a case study on five open source projects including Spring, Elastic Search, Jenkins, K-8 Mail Android Client, and OwnCloud Android client. Our findings reveal that our metrics have a positive correlation with post-release defects which is comparable to known high-performance traditional process metrics, such as code churn and number of pre-release defects. Furthermore, our proposed metrics can provide additional explanatory and predictive power for software quality when added to the models based on existing products and process metrics. Our results suggest that software developers should consider allocating more resources on reviewing and improving external API usages to prevent further defects.
Four Arithmetic Operations on the Quantum Computer

Abstract Quantum computer has been proved to be superior to electronic computer in solving some NP problems. Based on the former quantum adder, this paper proposes a new quantum adder and quantum subtracter, and then designs a fixed-point quantum multiplier and a fixed-point quantum divider based on fixed-point number operations. Four arithmetic units are presented using quantum gate circuits. These researches lay a foundation for the quantum implementation of digital filters.
Semantic Relevance Feedback Based on Local Context

Semantic similarity is a measure based on the meaning of the word and represented by the type of semantic relationships between the meanings of two words. The semantic similarity between words or sentences can be calculated by following the method, WordNet-based similarity. In our suggested method, the proposed solution depends on the two-step to return the target document collection. Once the top d1, d2..., dn documents have been retrieved using the initial query. The second compares these retrieved documents with the query, this step takes the most frequent senses of sentences in d1, d2..., dn with the senses of the query sentences. Query enrichment is then carried out adding terms semantically related to these senses the aim is to resolve the issue of incompatibility and the ambiguity problem affecting the results of information retrieval through the use of techniques to Word sense disambiguation (WSD) the word and the results be more semantically related with the query. Our experiment, carried out using part of the fire2011 data set.
Activity Recognition Based on Latent Knowledge Mining in Smart Home

Activity recognition in smart home is one pattern recognition problem. Many activity recognition algorithms have appeared so far to recognize activities in smart home. Past researches have proved that dynamic and deep knowledge mining algorithms will help improve the accuracy. But because of the uncertainty of sensors and the complexity of the user activities, existing activity recognition methods still have a lot of room for improvement. Considering there is some latent knowledge existed in sensors or user activities, this paper proposes to recognize activities by exploring latent knowledge. Firstly, this paper improves activity recognition by extracting latent knowledge between sensors and activities, thereby proposed one feature preprocessing method. Then, it proves one new multi-resident activity recognition method based on latent knowledge in multi-resident activities. Simulations conclude that extracting latent knowledge can greatly enhance activity recognition.
Artificial immune system as a high parallel intelligent information processing and protection system

Abstract The article discusses the possibilities of applying the artificial immune system to ensure the protection of information. It is described that, according to studies, processing of the immune system of external signals is carried out using a highly parallel process.
HECC based anti-replay attack model for securing data in Cloud

IT world of today is being much revolutionized by cloud computing where the services are outsourced to be benefitted by both the users as well owners. Benefits of cloud includes less computational cost and reducing the infrastructural costs. Other than the advantages mentioned, cloud computing suffers from certain limitations such as privacy, security, access control and so on. Security of data has earned many researchers to identify robust algorithm to prevent the attackers from invading the sensitive information. Hyperelliptic curve cryptography is used to encrypt and decrypt data. Its discrete logarithm principle prevents the attackers from resending the messages thereby preventing the replay attacks. The experimental results showcase the model efficiency as HECC uses reduced key size compared to other asymmetric algorithms.
Changing Climate, Changing Data: Exposing Climate Data to New Users Through GeoPlatform.gov’s Resilience Community

Over 700 climate related datasets were curated by subject matter experts into 9 thematic areas as a part of the Climate Data Initiative (CDI). NASA was tasked with maintaining the collection’s data inventory and supporting web pages at data.gov/climate. Today, the Data Curation for Discovery (DCD) team at MSFC continues to support the CDI collection. In order to expose the collection to a new and growing user community, the DCD team has partnered with GeoPlatform.gov to develop the Resilience community. The Resilience community serves as an interactive, topically-focused web portal that further promotes and shares CDI web content, datasets, services, maps, and other tools relevant to global resilience and change. This poster focuses on the team’s efforts to leverage GeoPlatform’s semantic applications to link CDI objects within the platform to improve discoverability. This poster also provides insights as to how this effort may serve as an example for building and expanding future Geoplatform.gov communities..
Stepwise Iterative Maximum Likelihood Clustering Based on Kompetisi Sains Madrasah’ Scores for Identifying Quality of Junior High School Grading Distribution

Kompetisi Sains Madrasah (KSM) is a science competition organized by the Ministry of Religious Affairs Indonesia. KSM scores can help to determine the quality distribution of students based on KSM data. Clustering is a method that can group data based on the similarity of data. One method of student clustering that has never been done is Stepwise Iterative Maximum Likelihood. SIML does clustering based on the average value and standard deviation of each cluster. SIML grouping is done several times by creating several different clusters. The best grouping experimental results are in the number of clusters 3. Cluster data is divided into 3 groups consisting of very good, good, and fair students. Silhouette values indicate the best grouping using 3 clusters. The silhouette value is 0.423745 for natural sciences subject data. Social sciences data has a silhouette value of 0.415654, and a silhouette value of 0.487071 for mathematics subjects. Three silhouette values are the best silhouette values when compared to other experiments.
Formal Specification and Validation of a Gas Detection System in the Industrial Sector

In gas concentrations greater than the allowable amounts, these become an imminent danger. It is true that there are devices that already read information, but are intended exclusively for the mining sector and are very expensive. That is why we propose to model and validate a new system for other industrial sectors. Our proposal, The Gas Detection System is based on The Explosive Discussion Triangle method developed by Coward and Jones. We use this method to develop a control system that will allow gas concentrations to be detected in a given environment and send an alarm if a risk situation arises. Formal Specifications allows the use of mathematical notations that help in the process of implementing critical systems and helps to reduce the potential ambiguities that occur in the interpretation of traditional graphic models. This work uses the VDM ++ formal specification language to describe system properties for its subsequent modeling and validation through the VDMToolBox tool. The System architecture is based on sensors, a control module and a set of alarms. Our proposal makes use of formal specifications in order to validate the main properties of the functional requirements.
Deep learning for recommending subscription-limited documents

Documents recommendation for a commercial, subscription-based online platform is important due to the difficulty in navigation through a large volume and diversity of content available to clients. However, this is also a challenging task due to the number of new documents added every day and decreasing relevance of older contents. To solve this problem, we propose deep neural network architecture that combines autoencoder with multilayer perceptron in a hybrid recommender system. We train our model using real-world historical data from commercial platform using interactions to capture user similarity and categorical document features to predict the probability of a user-document interaction. Our experimental results demonstrate the effectiveness of the proposed architecture. We plan to release our model in a commercial online platform to support a personalized user experience.
On the Peripherals of Peripherals: Exploring a Holistic Augmented Reality Product System

Substantial advances in technology have consistently been developed alongside new methods of interaction. With the recent advances in the areas of both augmented and virtual reality (AR, VR), there exists a strong need to explore what new interaction paradigms should be implemented to ensure the best possible user experience for those engaging with these technologies. With VR having far more definition in this space then AR, the focus of this research is on defining types of functionality best suited for a product that can assist its user in better navigating an AR-based experience. Research areas were selected based upon the information most relevant to AR-wearable products, including: use-cases, wearable technology, and virtual reality. Each of these subjects occupied a distinct phase of research (1–3). Insight gained from the present study could serve as a useful guide for the design of a concept peripheral product with the goal of improving the user experience of those unfamiliar with AR systems. Results showed that there is certainly a potential opportunity for an AR product in the right environment, that the general public is ready and willing to adopt wearable technology so long as it supplies enough functionality to justify its use, and that adopting affordances of technology that users are familiar with such as touch screens and haptic vibrations greatly improves the comfort and ease of use of AR products.
Training with Reduced Precision of a Support Vector Machine Model for Text Classification

This paper presents the impact of using quantization on the efficiency of multi-class text classification in the training process of a support vector machine (SVM). This work is focused on comparing the efficiency of SVM model trained using reduced precision with its original form. The main advantage of using quantization is decrease in computation time and in memory footprint on the dedicated hardware platform which supports low precision computation like GPU (16-bit) or FPGA (any bit-width). The paper presents the impact of a precision reduction of the SVM training process on text classification accuracy. The implementation of the CPU was performed using the OpenMP library. Additionally, the results of the implementation of the GPGPU using double, single and half precision are presented.
A long-term visual tracking algorithm based on tracking-evaluation-learning-detection

Long-term visual objec tracking is an open and challenging research topic. There is still a large gap between the existing research and the actual application requirements. This paper studies the long-term tracking algorithm, draws on the ideas of the TLD framework, and pays more attention to the role of confidence evaluation in long-term tracking. Taking confidence evaluation as an important part of long-term tracking, this paper proposes a tracking-evaluation-based -Learn-detect long-term tracking algorithm. The main work of this paper can be summarized into three aspects: First, an improved fully-correlation filtering re-detection algorithm is proposed. This algorithm can make full use of the output resources of the confidence evaluation model, effectively reduce the calculation amount during re-detection, and improve Detection efficiency. Secondly, the interaction and update strategies of the short-term tracker, confidence evaluation model, and re-detector in the framework are designed. The proposed strategy can effectively reduce the number of re-detections. Combined with the improvement of the short-term tracking model in the previous article, the reset efficiency of the tracker can be effectively improved after the target is lost. Finally, the validity of the framework is tested on the long-term tracking evaluation dataset.
Organ Detection in Surgical Videos Using Neural Networks

Kumar, Amit Gupta, Anshu Pramanik, AnkitaThe automatic surgical video processing for anomaly detection is becoming important nowadays since manual feedback from surgeons is subject to errors and time-consuming. It is necessary to have more precise and detailed detection and recognition of organs and tissues as we move into more complete understanding of the surgical videos. Thus, automatic localization and detection of organs have now become a prerequisite in surgical video analysis. In this paper, a convolutional neural network (CNN) is designed to evaluate laparoscopic and endoscopic surgical videos. The neural network will detect and localize the organs for the given videos which lay the path for automatic post-surgical processing.
From Smart Concept to Desired Experiences

The development and implementation of ICT solutions changed the built environment. Objects from daily human routines and services, while aiming for sustainable and inclusive qualified solutions, challenged the countless possibilities of interaction between individuals, individuals and products, and products and products. Smart cities associate intelligence to environments and products to boost functionalities, uses, and experiences. The design of smart cities, from the spatial relations, objects interactions, and services availability must be user oriented to satisfy users' needs and expectations, while promoting qualified experiences. From real and questionable solutions, due to technical or functional orientation, emerges the need to observe human perception, reaction, and behavior to understand how it is possible to improve the experience. The aim of this chapter is to highlight the need of a different approach to the conceptualization of smart cities, environments, products, and services, emphasizing an emotionally appealing experience.
Research on Chinese Sentence Compression Automatic Evaluation Method based on Sentence Hierarchical Information Extraction

Abstract People often feel incomprehensible and difficult to understand in the face of huge amounts of information. They instinctively produce the natural need to condense information to save time. Sentence compression technology has been born. The research on sentence compression evaluation methods has developed with the rise of sentence compression technology, and has gradually become a hot issue for sentence compression tasks. The method of sentence compression evaluation is to judge a good or bad cornerstone of compression results. influences. Under the current practical background, the performance evaluation of sentence compression mainly includes two methods: manual evaluation and automatic evaluation. The main problems encountered are: First, most of the research work uses manual evaluation indicators. The compression results under manual evaluation have high accuracy and meet the requirements. People’s daily language habits have the disadvantages of inflexibility and poor reusability. Second, there are few automatic evaluation methods for Chinese sentences, and the existing traditional automatic evaluation methods only consider the morphological similarity between the compressed sentence and the original sentence, and cannot well represent the compression effect. Based on the above practical problems, this paper proposes an automatic evaluation method for Chinese sentence compression based on sentence hierarchical information extraction. Compared with the manual evaluation method and the traditional automatic evaluation method, the Chinese sentence compression automatic evaluation method based on sentence hierarchical information extraction overcomes the disadvantages of high manual evaluation cost and low reusability. It uses the similarity of sentence hierarchical information to diversify. Ground characterization and evaluation of compression effects have significantly improved the rationality and accuracy of traditional automatic evaluation methods Introduction.
Non-periodic Noisy Signals Denoising Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Abstract Signal always occurs with noise. Since noise acts as an unwanted signal, we must clear or reduce it with some denoising method. It is relatively easy to denoise the normal distribution noise-contaminated the periodic signal. The problem ascends if a non-Gaussian noise intrudes into a non-periodic signal. The standard filter, such as DWT (discrete wavelet transforms), cannot overcome this directly and blindly. In this research, we proposed ANFIS (Adaptive Neuro-Fuzzy Inference System) as a non-periodic noisy signal denoising method. Foremost, the ANFIS trained to mimic or estimate the interfered noise, then this noise estimation used as a subtractive signal in a non-periodic noisy signal. As a result, the ANFIS can reduce the non-Gaussian noise in the various noisy non-periodic signals with minimum error better than standard DWT (Discrete Wavelet Transform).
Enhanced UML Use Case Meta-model Semantics from Cognitive and Utility Perspectives

Unified Modeling Language (UML) formalized by Object Management Group (OMG) to express analysis and design models is a general-purpose graphical language for visualization and documentation of software system artifacts. UML diagrams are interdependent and hence a change in one diagram at a level would introduce changes in the entire related diagrams. Since UML divides the system model into functional requirement capture views modeled by use case diagrams, static structural views modeled by class diagrams, and dynamic behavior views modeled by interaction and state-machine diagrams. As domain consists of concepts, the higher-order views can be formed from the recognized concepts so that the structuring is visible at the initial development efforts. The models are required to be platform-independent so that they can be mapped to any available platform using migrations. From the model semantics, a metalanguage representing the model language can be created so that the model transformations can be applied vertically and horizontally. In this regard, an attempt to narrate enhanced semantics for use cases and its relationship has been made.
Location-Based Sentiment Analysis of the Revocation of Article 370 Using Various Recurrent Neural Networks

This study aims to determine the public sentiment on the revocation of Article 370 by the Indian Government. Article 370 gave special privileges to the Indian state of Jammu and Kashmir, allowing it to have a separate constitution and autonomous internal administration. The textual data for arbitrating this sentiment is extracted from Twitter—a social media platform for sharing content, in the form of tweets, which include the geographical location of the users posting them. With over 96,000 tweets obtained in a span of three weeks, this study finds that the location of the individual influences their opinion toward this revocation. This paper uses three neural network architectures, namely LSTM, bidirectional LSTM, and CNN-LSTM, where the CNN-LSTM model is used to classify the sentiments since it achieves the highest test-set accuracy.