Reviewerly/exHarmony huggingface dataset:
A virtual and augmented reality platform for the training of first responders of the ambulance bus

AmBus is a bus-sized ambulance that EMS personnel utilize during large-scale emergencies. Although EMS personnel receive annual training, evidence shows current training efforts leave some personnel unfamiliar with the AmBus system and unprepared to respond to an emergency. This work presents a novel interactive training application, utilizing emerging technologies in virtual and augmented reality, that can be delivered remotely to the distributed EMS personnel before they assemble, or as they are assembling. Our initial findings show that such an application can better prepare first responders to be as effective as possible in using the life-saving features of the AmBus. The methodology described in this work can be expanded to include other first responders, and, ultimately, lives may be saved because personnel are better prepared.
CarChain: A Novel Public Blockchain-based Used Motor Vehicle History Reporting System

Blockchain Technology has been proposed to tackle author, centrality and storage issues. It converts centralized applications into dynamic distributed ones. Many researchers have proposed protocols and applications for mapping the old central applications into blockchain based distributed application. In this work, new system framework for Public World Wide Used Motor Vehicle History Reporting System, named CarChain, is proposed and designed. The new framework has not been constructed based on any of the popular well-known public blockchain networks. The framework constructs a peer-to-peer (P2P) overlay network that broadcasts transactions as any end system multicasting system in P2P live streaming applications. The framework allows car owners, repairing companies and insurance agencies to register and add new histories for cars in a simple method. Four different smart contracts control block updates in CarChain. In addition, database technology has been leveraged to cache intermediate data. We show in this paper the challenges and research opportunities that encounter blockchain based applications, such as CarChain.
Application of Data Analytics Techniques to Establish Geometallurgical Relationships to Bond Work Index at the Paracutu Mine, Minas Gerais, Brazil

Analysis of geometallurgical data is essential to building geometallurgical models that capture physical variability in the orebody and can be used for the optimization of mine planning and the prediction of milling circuit performance. However, multivariate complexity and compositional data constraints can make this analysis challenging. This study applies unsupervised and supervised learning to establish relationships between the Bond ball mill work index (BWI) and geomechanical, geophysical and geochemical variables for the Paracatu gold orebody. The regolith and fresh rock geometallurgical domains are established from two cluster sets resulting from K-means clustering of the first three principal component (PC) scores of isometric log-ratio (ilr) coordinates of geochemical data and standardized BWI, geomechanical and geophysical data. The first PC is attributed to weathering and reveals a strong relationship between BWI and rock strength and fracture intensity in the regolith. Random forest (RF) classification of BWI in the fresh rock identifies the greater importance of geochemical ilr balances relative to geomechanical and geophysical variables.
A Fog-Centric Secure Cloud Storage Scheme

Cloud computing is now being utilized as a prospective alternative for catering storage service. Security issues of cloud storage are a potential deterrent in its widespread adoption. Privacy breach, malicious modification, and data loss are emerging cyber threats against cloud storage. Recently, a fog server based three-layer architecture has been presented for secure storage employing multiple clouds. The underlying techniques used are Hash-Solomon code and customized hash algorithm in order to attain the goal. However, it resulted in loss of smaller portion of data to cloud servers and failed to provide better modification detection and data recoverability. This paper proposes a novel fog-centric secure cloud storage scheme to protect data against unauthorized access, modification, and destruction. To prevent illegitimate access, the proposed scheme employs a new technique <inline-formula><tex-math notation="LaTeX">$Xor - Combination$</tex-math></inline-formula> to conceal data. Moreover, <inline-formula><tex-math notation="LaTeX">$Block - Management$</tex-math></inline-formula> outsources the outcomes of <inline-formula><tex-math notation="LaTeX">$Xor - Combination$</tex-math></inline-formula> to prevent malicious retrieval and to ensure better recoverability in case of data loss. Simultaneously, we propose a technique based on hash algorithm in order to facilitate modification detection with higher probability. We demonstrate robustness of the proposed scheme through security analysis. Experimental results validate performance supremacy of the proposed scheme compared to contemporary solutions in terms of data processing time.
A new service broker algorithm optimizing the cost and response time for cloud computing

Cloud Computing has emerged as a new paradigm that permits to deliver Information Technology resources on demand and outsource software and hardware infrastructure. To support dynamic load, the system should select the best location for serving the request to achieve best performance. Selection of data center plays an important role to improve the performance as well as to reduce the cost. Service broker controls the traffic routing between user bases and data centers based on different service broker policies. In this paper we propose a new service broker algorithm which combines the cost and efficiency for optimized the cost and response time.
Bitcoin Price Prediction Through Opinion Mining

The Bitcoin protocol and its underlying cryptocurrency have started to shape the way we view digital currency, and opened up a large list of new and interesting challenges. Amongst them, we focus on the question of how is the price of digital currencies affected, which is a natural question especially when considering the price rollercoaster we witnessed for bitcoin in 2017-2018. We work under the hypothesis that price is affected by the web footprint of influential people, we refer to them as crypto-influencers.
Curation as “Interoperability With the Future”: Preserving Scholarly Research Software in Academic Libraries

This article considers the problem of preserving research software within the wider realm of digital curation, academic research libraries, and the scholarly record. We conducted a pilot study to understand the ecosystem in which research software participates, and to identify significant characteristics that have high potential to support future scholarly practices. A set of topical curation dimensions were derived from the extant literature and applied to select cases of institutionally significant research software. This approach yields our main contribution, a curation model and decision framework for preserving research software as a scholarly object. The results of our study highlight the unique characteristics and challenges at play in building curation services in academic research libraries.
A high-frequency mobile phone data collection approach for research in social-environmental systems: Applications in climate variability and food security in sub-Saharan Africa

Collecting high-frequency social-environmental data about farming practices in sub-Saharan Africa can provide new insight into environmental changes that farmers face and how they respond within smallholder agro-ecosystems. Traditional data collection methods such as agricultural censuses are costly and not useful for understanding intra-annual and real-time decisions. Short-message service (SMS) has the potential to transform the nature of data collection in coupled social-ecological systems. We present a system for collecting, managing, and synthesizing weekly data from farmers, including data infrastructure for management of big and heterogeneous datasets; probabilistic data quality assessment tools; and visualization and analysis tools such as mapping and regression techniques. We discuss limitations of collecting social-environmental data via SMS and data integration challenges that arise when linking these data with other social and environmental data. In combination with high-frequency environmental data, such data will help ameliorate issues of scale mismatch and build resilience in environmental systems.
Region growing segmentation optimized by evolutionary approach and Maximum Entropy

In this paper, we propose a segmentation method based on region growing and evolutionary algorithms. Before segmentation, the number of classes is determined by the principle of maximum entropy. The proposed approach is validated on some synthetic and real images and, it shows to be very interesting as decision support in quality control.
Application and visualization of typical clustering algorithms in seismic data analysis

Earthquake brings enormous loss of lives and properties to human beings due to its suddenness, destructiveness and inscrutability. The new techniques for analyzing seismic data can reveal the distribution of earthquakes, which helps us master the laws of earthquake disasters and reduce the risks brought by them. In this paper, we applied K-means and DBSCAN clustering algorithms to the analysis of seismic data. Their performances in fitting seismic belts with seismic datasets are compared. First, we map the positional parameters in the seismic data to coordinate points on a two-dimensional plane and then cluster them with the DBSCAN algorithm. In addition, we combine the magnitude and depth properties of seismic data, use the Elbow method to find the best K value, and then classifies the dataset by K-means algorithm. We visualize the results, and the distinction of each classification is clear. The experimental results show that the DBSCAN algorithm has a better effect on fitting the seismic belt, and the classification results of K-means algorithm for earthquakes are also in line with expectations.
Efficiently using contextual influence to recommend new items to ephemeral groups

Group recommender systems suggest items to groups of users that want to utilize those items together. These systems can support several activities that can be performed together with other people and are typically social, like watching TV or going to the restaurant. In this paper we study ephemeral groups, i.e., groups constituted by users who are together for the first time, and for which therefore there is no history of past group activities. Recent works have studied ephemeral group recommendations proposing techniques that learn complex models of users and items. These techniques, however, are not appropriate to recommend items that are new in the system, while we propose a method able to deal with new items too. Specifically, our technique determines the preference of a group for a given item by combining the individual preferences of the group members on the basis of their contextual influence, the contextual influence representing the ability of an individual, in a given situation, to guide the group's decision. Moreover, while many works on recommendations do not consider the problem of efficiently producing recommendation lists at runtime, in this paper we speed up the recommendation process by applying techniques conceived for the top-K query processing problem. Finally, we present extensive experiments, evaluating: (i) the accuracy of the recommendations, using a real TV dataset containing a log of viewings performed by real groups, and (ii) the efficiency of the online recommendation task, exploiting also a bigger partially synthetic dataset.
Parallel RDF generation from heterogeneous big data

To unlock the value of increasingly available data in high volumes, we need flexible ways to integrate data across different sources. While semantic integration can be provided through RDF generation, current generators insufficiently scale in terms of volume. Generators are limited by memory constraints. Therefore, we developed the RMLStreamer, a generator that parallelizes the ingestion and mapping tasks of RDF generation across multiple instances. In this paper, we analyze what aspects are parallelizable and we introduce an approach for parallel RDF generation. We describe how we implemented our proposed approach, in the frame of the RMLStreamer, and how the resulting scaling behavior compares to other RDF generators. The RMLStreamer ingests data at 50% faster rate than existing generators through parallel ingestion.
User preference modeling based on meta paths and diversity regularization in heterogeneous information networks

Recommendation methods based on heterogeneous information networks (HINs) have been attracting increased attention recently. Meta paths in HINs represent different types of semantic relationships. Meta path-based recommendation methods aim to use meta paths in HINs to evaluate the similarity or relevancy between nodes to make recommendations. In previous work, the meta paths have usually been selected manually (based on experience), and the path weight optimization methods usually suffer from overfitting. To solve these problems, we propose to automatically select and combine the meta paths through weight optimization. Diversity is introduced into the objective function as a regularization term to avoid overfitting. Inspired by the ambiguity decomposition theory in ensemble learning, we present a new diversity measure and use it to encourage diversity among meta paths to improve recommendation performance. Experimental results on item recommendation and tag recommendation tasks confirm the effectiveness of the proposed method compared with traditional collaborative filtering and state-of-the-art HIN-based recommendation methods.
Trimmed Robust Loss Function for Training Deep Neural Networks with Label Noise

Deep neural networks obtain nowadays outstanding results on many vision, speech recognition and natural language processing-related tasks. Such deep structures need to be trained on very large datasets, what makes annotating the data for supervised learning, particularly difficult and time-consuming task. In the supervised datasets label noise may occur, which makes the whole training process less reliable. In this paper we present a novel robust loss function based on categorical cross-entropy. We demonstrate its robustness for several amounts of noisy labels, on popular MNIST and CIFAR-10 datasets.
Hybrid Neural Network to Impute Missing Data for IoT Applications

The new development of the Internet of Things (IoT) depends on reliable data delivery, where transferring data between devices should be accurate and fast to ensure high performance for IoT applications. IoT applications could suffer from low quality of data delivery due to several factors such as connection errors, sensor faults, or security attacks. Low quality of data delivery reduces the performance of IoT applications since if the collected data is incomplete it could eventually be useless. In this paper, we propose a hybrid neural network with genetic algorithm to impute the missing data for medical IoT applications. A deep learning neural network (Jordan network) is used as a model to predict the missing data, while the genetic algorithm is adopted to optimize the weights of the neural network. The obtained results show that the proposed algorithm is able to impute missing data with high classification value based on Area Under the Curve (AUC) and improve the final performance of IoT application by up tp 5%.
Understanding NFC-Net: a deep learning approach to word-level handwritten Indic script recognition

This paper presents a deep learning architecture modified for resource-constrained environments, called Non-Fully-Connected Network or NFC-Net, based on convolutional neural network architecture in order to solve the problem of Indic script recognition from handwritten word images. NFC-Net mainly targets resource constraint environment where there is a limited computation power or inadequate training samples or restricted training time. Previous approaches to handwritten script recognition included handcrafted features such as structure-based features and texture-based features. In contrast, here our model learns relatively different features from raw input pixels using NFC-Net. Various parameters of the NFC-Net are adjusted to present a vast and comprehensive study of the neural net in the domain of handwritten script recognition. In order to evaluate the performance of the NFC-Net with suitable parameter estimation, a dataset of 18,000 handwritten multiscript word images consisting of 1500 text words from each of the 12 officially recognized Indic scripts has been considered and a maximum script recognition accuracy of 96.30% is noted. Our proposed model also performs better than some of the recently published script recognition methods in bi-script, tri-script, tetra-script and 12-script scenarios. It has been additionally tested on the RaFD and BHCCD datasets with improved results to prove dataset independency of our model.
Comparing DBpedia, Wikidata, and YAGO for Web Information Retrieval

Knowledge graphs serve as the primary sources of structured data in many Semantic Web applications. In this paper, the three most popular cross-domain knowledge graphs (KGs), namely, DBpedia, YAGO, and Wikidata were empirically explored and compared. These knowledge graphs were compared from the perspectives of completeness of the relations, timeliness of the data and accessibility of the KG. Three fundamental categories of named entities were queried within the KGs for detailed analysis of the data returned. From the experimental results and findings, Wikidata scores the highest in term of the timeliness of the data provided owing to the effort of global community update, with DBpedia LIVE being the next. Regarding accessibility, it was observed that DBpedia and Wikidata gave continuous access using public SPARQL endpoint, while YAGO endpoints were intermittently inaccessible. With respect to completeness of predicates, none of the KGs have a remarkable lead for any of the selected categories. From the analysis, it is observed that none of the KG can be considered complete on its own with regard to the relations of an entity.
Risk Management in Projects Based on Open-Source Software

Reusing software components from third-party vendors is one of the key technologies to gain shorter time-to-market and better quality of the software system. These components, also known as OTS (Off-the-Shelf) components, come in two types: COTS (Commercial Off-The-Shelf) and OSS (Open-Source Software). To utilize OSS components effectively, it is necessary to figure out how the development processes and methods to be adapted. Most current studies are either theoretical proposals without empirical assessment or case studies in similar project contexts. It is therefore necessary to conduct more empirical studies on how process improvement and risk management can be performed and what are the results in various project contexts.
Population Diversity of Nonelitist Evolutionary Algorithms in the Exploration Phase

This paper discusses the genetic diversity of real-coded populations processed by an evolutionary algorithm (EA). Diversity is expressed as a variance or a covariance matrix of individuals contained in the population, in one- or multi-dimensional cases, respectively. We focus on the exploration stage of the optimization, therefore, the fitness function is modeled as noise. We prove that the expected value of genetic diversity achieves a level proportional to the mutation covariance matrix. The proportionality coefficient depends solely on the EA parameters. Formulas are derived to predict the diversity for fitness proportionate, tournament, and truncation selection, with and without arithmetic crossover and with Gaussian mutation. Experimental validation of the multidimensional case shows that prediction accuracy is satisfactory in a broad spectrum of settings of EA parameters.
Vulnerabilities in Continuous Delivery Pipelines? A Case Study

More and more companies are in the process of adopting modern continuous software development practices and approaches like continuous integration (CI), continuous delivery (CD), or DevOps. These approaches can support companies in order to increase the development speed, the frequency of product increments, and the time to market. To be able to get these advantages, especially the tooling and infrastructure need to be reliable and secure. In case CI/CD is compromised or even unavailable, all mentioned advantages are at stake. Potentially, this could also even hinder the forthcoming of the software development. Therefore, our goal was to identify which vulnerabilities are present in industry CD pipelines and how they can be detected. In this paper, we present our results of an industry case study which includes a qualitative survey of agile project teams regarding the awareness of security in CI/CD, the analysis and abstraction of two CD pipelines, and a threat analysis based on the deducted CD pipeline to identify vulnerabilities. In this case study, we found that the team members that work with the CD pipeline in different roles do not have a strong security background but are aware of security attributes in general. Furthermore, two CD pipelines from industry projects were analyzed using the STRIDE threat analysis approach. In total, we identified 22 vulnerabilities that have been confirmed by the project teams.
An intelligent approach for the design and development of a personalized system of knowledge representation

This article proposes a generic presentation system for hypermedia systems of adaptive teaching that is highly independent from the representation of domain knowledge and the application state maintenance. Generality is achieved by providing an application framework for the definition of ontologies that best fit a domain or a specific author. The presentation of the pages to be generated is described in terms of classes and relationships of the ontology. For this purpose, a web page ranking algorithm based on automatic learning is used, specifically, the algorithm for Advanced Cluster Vector Page Ranking (ACVPR). This algorithm provides the user a powerful meta-search tool that presents a ranking order of the web page to quickly meet custom needs, especially when the search is erroneous or incomplete.
ScoringNet: Learning Key Fragment for Action Quality Assessment with Ranking Loss in Skilled Sports

Nowadays, scoring athletes’ performance in skilled sports automatically has drawn more and more attention from the academic community. However, extracting effective features and predicting reasonable scores for a long skilled sport video still beset researchers. In this paper, we introduce the ScoringNet, a novel network consisting of key fragment segmentation (KFS) and score prediction (SP), to address these two problems. To get the effective features, we design KFS to obtain key fragments and remove irrelevant fragments by semantic video segmentation. Then a 3D convolutional neural network extracts features from each key fragment. In score prediction, we fuse the ranking loss into the traditional loss function to make the predictions more reasonable in terms of both the score value and the ranking aspects. Through the deep learning, we narrow the gap between the predictions and ground-truth scores as well as making the predictions satisfy the ranking constraint. Widely experiments convincingly show that our method achieves the state-of-the-art results on three datasets.