Reviewerly/exHarmony huggingface dataset:
Education in System Engineering: Digital Competence

At the beginning it is not known which are the factors thatmotivate the development of digital competence in students. as requirement to recover the most critical and ideological approaches to education. the university develops appropriate strategies that allow its advancement. The research analyzes the students' perception of the Systems Engineering career at the Catholic University of Cuenca in Ecuador. about factors associated with digital competence. This report details the various aspects of digital competence manifested by the students. For this purpose, an instrument is applied that collects quantitative data whose objective is to analyze the influence of the following variables: "sense of belonging." "interaction." "privacy." and "online tag" on the "digital competence" in the student. This is accomplished through a system of four hypotheses. whose causal relationship induces the conformation of a model of structural equations_the representative sample size is made up of students from different training stages. The output of this project determines that there is a direct influence on the "digital competence" of the university student of the "interaction" variable. On the other hand, there is not a direct influence on the "online tag", "sense of belonging" and “privacy” variables.
Dynamic Working Memory for Context-Aware Response Generation

In human-to-human conversations, the context generally provides several backgrounds and strategic points for the following response. Therefore, many response generation approaches have explored the methodologies to incorporate the context into the encoder-decoder architecture, to generate context-aware responses that are remarkably relevant and cohesive to the given context. However, most approaches pay less attention to semantic interactions implicitly existing within contextual utterances, which are of great importance to capture semantic clues of the given dialog context, indeed. This paper proposes a dynamic working memory mechanism to model long-term semantic hints in the conversation context, by performing semantic interactions between utterances and updating context representation dynamically. Then, the outputs of the dynamic working memory are employed to provide helpful clues for the encoder-decoder architecture to generate responses to the given dialog. We have evaluated the proposed approach on Twitter Customer Service Corpus and OpenSubtitles Corpus, with several automatic evaluation metrics and the human evaluation, and the empirical results show the effectiveness of the proposed method.
Neural Sequence Labeling for Vietnamese POS Tagging and NER

This paper presents a neural architecture for Vietnamese sequence labeling tasks including part-of-speech (POS) tagging and named entity recognition (NER). We applied the model described in [[1]] that is a combination of bidirectional Long-Short Term Memory and Conditional Random Fields, which rely on two sources of information about words: character-based word representations learned from the supervised corpus and pre-trained word embeddings learned from other unannotated corpora. Experiments on benchmark datasets show that this work achieves state-of-the-art performances on both tasks - 93.52% accuracy for POS tagging and 94.88% F1 for NER. Our sourcecode is available at hear.
3D Shape Classification Using 3D Discrete Moments and Deep Neural Networks

In this paper, we propose a new model for 3D shape classification based on 3D discrete orthogonal moments and deep neural network (DNN) to enhance the classification accuracy of 3D objects under geometric transformations such as scale and rotation. The proposed model is derived by introducing image moments as an input vector in DNN, frequently utilized in many tasks of pattern recognition. Discrete orthogonal moments have the ability to capture global information from an image in lower orders. The aim of this work is to investigate the robustness of the proposed model to geometric transformations like rotation and scale. The simulations are performed on constructed dataset by applying some geometric transformations on selected objects from the McGill database to evaluate the performance of our proposed model. The obtained results show that the proposed model with Hahn moments achieves high classification rates and robust to geometric transformations than Krawtchouk moments.
Open traffic lights: a strategy for publishing and preserving traffic lights data

For better traffic flow and making better policy decisions, the city of Antwerp is connecting traffic lights to the Internet. The live “time to green” only tells a part of the story: also the historical values need to be preserved and need to be made accessible to everyone. We propose (i) an ontology for describing the topology of an intersection and the signal timing of traffic lights, (ii) a specification to publish these historical and live data with Linked Data Fragments and (iii) a method to preserve the published data in the long-term. We showcase the applicability of our specification with the opentrafficlights.org project where an end-user can see the live count-down as well as a line chart showing the historic “time to green” of a traffic light. We found that publishing traffic lights data as time sorted Linked Data Fragments allow synchronizing and reusing an archive to retrieve historical observations. Long-term preservation with tape storage becomes feasible when archives shift from byte preservation to knowledge preservation by combining Linked Data Fragments.
TCON - A lightweight Trust-dependent Consensus framework for blockchain

The consensus algorithm played a significant role in successful adoption of Blockchain technology. The security of bitcoin blockchain hugely depends on the complexity and reliability of the Proof-of-Work (PoW) consensus. However, a non-financial application, whose security requirements are less demanding, requires faster operation and so PoW is not suitable for many of the commercial applications. In this paper, we propose a lightweight Trust-dependent Consensus scheme (TCON) for a blockchain based IoT access control framework. TCON ensures that the blocks are added at a faster rate, spending minimal energy compared to PoW based systems. To the best of our knowledge, this is the first time the notion of trust is quantified and used as a means to simplify the consensus problem in blockchain. A comparison of the various metrics to understand the differences in overhead involved suggest that the proposed scheme outperforms other non-trust-based consensus schemes.
An Efficient Framework for a Third Party Auditor in Cloud Computing Environments

Abstract Cloud Service Providers supply services to clients in terms of their demands. They need to be constantly under monitoring for their services with respect to consensus agreements between clients and service providers. A Third Party Auditor or TPA as a trusted organization appears to be necessary to monitor executing agreements of cloud services. Using a third party as an extra component creates cost overheads for clients in a cloud environment. Thus, introducing a cost efficient framework for a cloud environment which includes a third party is an eminent achievement to make a TPA feasible and practical in cloud environments. In this paper, we propose a TPA framework for monitoring service level agreements between cloud service providers and cloud clients using several cloud resources. This framework employs different types of service deployments from various cloud service providers excluding the cloud service provider which is being monitored. Then, we demonstrate that the framework can mitigate costs of a third party auditor in a cloud environment. Simulations of trends for costs exhibits cost efficiency of at least forty percent over ten years when a TPA follows our proposed framework in comparison to other frameworks. Finally, we provide an analysis to compare characteristics of our framework with other frameworks and discuss the advantages of our proposed framework. Our results indicate that TPA as a component of the framework not only reduces overall costs of its presentation in a cloud environment but additionally improves management efficiency and security.
An Implementation of Text Hiding in Medical Images based on Graph Coloring for Android Devices

Medical images are representations of highly vulnerable digital images and useful objects that help diagnose a patient. However, it is not easy to maintain the authenticity of a medical image because there are many ways that it can be manipulated. Therefore, several methods have been developed to protect the authenticity of medical images. One way is to add a watermark onto the medical images. However, digital image watermarking techniques are usually widely implemented using software running on a computer. In this paper, the authors built a system to hide the text on medical images using an Android device. The system consists of an embedding and extracting process. In the embedding process, the method used is vector quantization and graph coloring based on a Genetic Algorithm. The test result shows that the scheme has good imperceptibility and a fast enough time consumption to be implemented on an Android device.
Automatic Modulation Classification: Investigation for Millimeter Wave Over Fiber Channels

With the rapid development of intelligent communication systems, classical problems, such as automatic modulation classification (AMC), have gained extensive research interest. This is due to the significant role that AMC plays in many civilian and military applications. In this letter, we consider AMC for millimeter wave-over-fiber (MMWoF) communication. This type of communication is of practical interest because it enables centralized analysis and processing, taking the advantages of low transmission loss of MMW signals over fiber optic channels. In this letter, we use autoencoder neural networks for automatic features extraction and classification, preceded by a pre-processing step applied to the samples of the input signal. The performance of the system under consideration has been thoroughly investigated by simulation and verified experimentally under different impairments, including fiber chromatic dispersion and amplified spontaneous emission noise. The results are presented in terms of the probability of correct classification for different values of optical signal-to-noise ratio and different lengths of fiber channels. The results from simulation are in good match to those obtained experimentally.
A Hybrid Cascade Neural Network with Ensembles of Extended Neo-Fuzzy Neurons and Its Deep Learning

This research contribution instantiates a framework of a hybrid cascade neural network rest on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that gives the possibility to operate with nonstationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined which suits for handling nonstationary objects. Added to everything else, there is always an occasion to set up (increase) an inference order and a quantity of membership relations inside the extended neo-fuzzy neuron.
Design and Prototyping of Container-Enabled Cluster for High Performance Data Analytics

With the rapid spread of cloud-native computing paradigm, the utilization of cloud has been maturing to accommodate versatile microservices-based application services with agility and scalability. However, in case of HPDA (high-performance data analytics) workload, the transition toward cloud-native-style computing infrastructure is still facing several hurdles to realize container-enabled clusters. To address technical constraints around bottlenecked inter-connections for overlay networking and storage access, in this paper, we design and prototype a container-enabled cluster that effectively and seamlessly integrate the hardware and software pieces of HPDA cluster.
A Multi-Factor Authentication Framework for Secure Access to Blockchain

Recently, blockchain has been a disruptive technology for many systems, such as finance, e-health, supply-chain, and etc. Secure access to blockchain is the grand challenge for many systems. Key management is one of challenges to ensure secure access to blockchain. In this paper, we propose a framework for secure accessing to blockchain via multi-factor authentication. We combine both biometric and password authentications to secure private keys of users. The framework contains a secure device which has a biometric sensor to ensure secure access to private keys that extends the usability for secure accessing to blockchain.
Security Testing Approach for IaaS Infrastructure

Cloud computing represents a new computing model that poses many demanding security issues at all levels. The variety of delivery models presents different security challenges depending on the model and the quality of service (QoS) requirements of consumers. Confidentiality, integrity, and availability are key concerns for cloud computing providers and consumers. In this paper, we present a novel secure architecture-Based Conformance Testing in order to check not only if the behavior of the cloud environment is conforms to its specification but also if respects some security policies. The proposed approach shows how to cope with problems encountered in cloud distributed testing using distributed testing rules.
Privacy and Data Protection Concerns Regarding the Use of Blockchains in Smart Cities

In this work we investigate which aspects of data protection regulation must be carefully observed when implementing Blockchain-based projects in smart cities. This technology provides interesting properties and allows governments to develop flexible and innovative data management systems. Nevertheless, realizing the benefits of using Blockchains requires understanding the government processes along with the legal framework and political setting imposed on government. Though it is a buzzword, Blockchain may not always be the best solution for data processing, and carrying out a Data Protection Impact Assessment could allow an analysis of the necessity and proportionality of the mechanism. Furthermore, principles relating to security of data remain applicable to Blockchains. We discuss points of interaction between Blockchain technology and the European Union data protection framework, and provide recommendations on how to better develop Blockchain-based projects in smart cities. The findings of the study should provide public sector actors with a guideline to assess the real necessity and better format of a Blockchain-based application.
Online probabilistic theory revision from examples with ProPPR

Handling relational data streams has become a crucial task, given the availability of pervasive sensors and Internet-produced content, such as social networks and knowledge graphs. In a relational environment, this is a particularly challenging task, since one cannot assure that the streams of examples are independent along the iterations. Thus, most relational learning systems are still designed to learn only from closed batches of data. Furthermore, in case there is a previously acquired model, these systems either would discard it or assuming it as correct. In this work, we propose an online relational learning algorithm that can handle continuous, open-ended streams of relational examples as they arrive. We employ techniques of theory revision to take advantage of the previously acquired model as a starting point, by finding where it should be modified to cope with the new examples, and automatically update it. We rely on the Hoeffding’s bound statistical theory to decide if the model must, in fact, be updated in accordance with the new examples. The proposed algorithm is built upon ProPPR statistical relational language, aiming at contemplating the uncertainty inherent to real data. Experimental results in social networks and entity co-reference datasets show the potential of the proposed approach compared to other relational learners.
Activity Gesture Recognition on Kinect Sensor Using Convolutional Neural Networks and FastDTW for the MSRC-12 Dataset

In this paper, we use data from the Microsoft Kinect sensor that processes the captured image of a person, thus, reducing the number of data in just joints on each frame. Then, we propose a creation of an image from all the frames removed from the movement, which facilitates training in a convolutional neural network. Finally, we trained a CNN using two different forms of training: combined training and individual training using the MSRC-12 dataset. Thus, the trained network obtained an accuracy rate of 86.67% in combined training and 90.78% of accuracy rate in the individual training, which is a very good performance compared to related works. This demonstrates that networks based on convolutional networks can be effective for the recognition of human actions using joints.
Implementation Security Certification of Decoy‐BB84 Quantum Key Distribution Systems

Abstract One of the most important obstacles for the social deployment of Quantum Key Distribution (QKD) systems is the lack of standards for security certification. Proper selection of application will circumvent other obstacles, such as cost, key generation distance, and rate. Nevertheless, security certification of real systems, or “implementation security certification” is indispensable. Such certification should be clear and tractable for non‐experts and should be performed with standardized characterization methods. Otherwise, the potential users cannot adopt the QKD system with confidence, because the highest level of security is what QKD appeals. This report discusses the process of the implementation security certification and the requirements for device characterization. It also presents several case studies on the requirements and experimental characterization of QKD devices for the security certification.
Integration of datamining techniques into the CBR cycle to predict the result of immunotherapy treatment

The functioning of the medical diagnostic process is very comparable to the pattern of the CBR cycle. The doctor often starts to analyze the whole situation and takes advantage of previous situations resolved successfully in order to efficiently diagnose a new situation. More generally, this mode of operation, based on experience and analogy, can be found practically in all diagnostic fields (medical, industrial, etc.). In this work, we propose the integration of datamining techniques in the CBR cycle for medical decision support. This integration aims to improve the performance of the retrieval phase by reducing the number of attributes considered in the similarity calculation through the selection of the most relevant attributes. To evaluate our approach, we have customized the jCOLIBRI 2.1 framework with a real case base to predict the response to immunotherapy treatment for patients who have plantar and common warts disease.
The FAIR TRADE Framework for Assessing Decentralised Data Solutions

Decentralised data solutions bring their own sets of capabilities, requirements and issues not necessarily present in centralised solutions. In order to compare the properties of different approaches or tools for management of decentralised data, it is important to have a common evaluation framework. We present a set of dimensions relevant to data management in decentralised contexts and use them to define principles extending the FAIR framework, initially developed for open research data. By characterising a range of different data solutions or approaches by how TRusted, Autonomous, Distributed and dEcentralised, in addition to how Findable, Accessible, Interoperable and Reusable, they are, we show that our FAIR TRADE framework is useful for describing and evaluating the management of decentralised data solutions, and aim to contribute to the development of best practice in a developing field.
A Distributed PCM Clustering Algorithm Based on Spark

With the large-scale growth of data, traditional single-machine data processing methods are difficult to deal with massive data, especially iterative clustering algorithms that require frequent reading and writing operations. On the basis of Spark framework, this paper proposes a distributed possibilistic c-means algorithm based on memory computing, called Spark-PCM. The proposed method improves the related processing of distributed matrix operation and is implemented on the Spark platform. Experimental results show that the proposed Spark-PCM algorithm runs in a linear relationship with the number of nodes and has a good scalability, which indicates that it has higher scalability and adaptability to large-scale data.
Component Comparison, Evaluation, and Selection: A Continuous Approach

Early visions of component-based software development have been realized, with software projects now composed mostly of other peoples code. However, the challenge of selecting the best components, with speed and confidence in the result, has only become more difficult. Previous work has focused on systematic approaches to component selection, but in continuous- <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> and agile settings, the increase in confidence from being systematic is not worth the cost of delay. In this emerging ideas paper, we present early results on work to balance speed with confidence in component selection. Our idea is to define a scorecard for components based on high-level quality attribute indicators, project health measures, and a context-specific aggregation function for producing a single yes/no decision for integrators. We present preliminary results showing how this scorecard approach works on computer vision components.
C<i>k</i>LR Algorithm for Improvement of Data Prediction and Accuracy Based on Clustering Data

In this paper, we proposed the CkLR algorithm to build a model to analyze the response variables of clustered outcomes with a K-means algorithm to categorize and analyze unstructured data such as SNS and assess the prediction rate of clustering and its accuracy rate after the entry of new data. CkLR performs a neural network algorithm based on the clustering outcomes of the data classified in the previous stage. The CkLR model applies a neural network to reflect the entire form of data learning methods rather than stepwise data learning based on clustering data, and conducts research to select a neural network model and analyze variables through learning by clustering. The datasets of the input clustering outcomes are adjusted at certain percentages (learning, testing, and evaluation) to check the prediction rate of input outcomes. The study also proposed a CkLR algorithm based on an LSTM recurrent neural network to compensate for the problem of determining the size of input and output value data for each repetition in a single training session. The training structure of the proposed prediction model, based on a neural network, would calculate the loss values of training, minimize them, and select an optimal model reflecting weights in case of clustering sequential data. The performance evaluation results demonstrate that it recorded prediction and accuracy rates that were 3.65% higher than the old single neural network structure through individual neural networks.
Modelling the Provenance of Linked Data Interlinks for the Library Domain

As the Web of Data grows, so does the need to establish the quality and trustworthiness of its contents. Increasing numbers of libraries are publishing their metadata as Linked Data (LD). As these institutions are considered authoritative sources of information, it is likely that library LD will be treated with increased credibility over data published by other sources. However, in order to establish this trust, the provenance of library LD must be provided.