Reviewerly/exHarmony huggingface dataset:
Cognitive Aspects-Based Short Text Representation with Named Entity, Concept and Knowledge

Short text is widely seen in applications including Internet of Things (IoT). The appropriate representation and classification of short text could be severely disrupted by the sparsity and shortness of short text. One important solution is to enrich short text representation by involving cognitive aspects of text, including semantic concept, knowledge, and category. In this paper, we propose a named Entity-based Concept Knowledge-Aware (ECKA) representation model which incorporates semantic information into short text representation. ECKA is a multi-level short text semantic representation model, which extracts the semantic features from the word, entity, concept and knowledge levels by CNN, respectively. Since word, entity, concept and knowledge entity in the same short text have different cognitive informativeness for short text classification, attention networks are formed to capture these category-related attentive representations from the multi-level textual features, respectively. The final multi-level semantic representations are formed by concatenating all of these individual-level representations, which are used for text classification. Experiments on three tasks demonstrate our method significantly outperforms the state-of-the-art methods.
Semantic - based service discovery in grid environment

In the process of informatization, there are also some new problems, mainly information can’t be shared and integrated, distributed resources can’t be used effectively, these problems make the industry face new challenges. The goal of this paper is to combine the grid technology and ontology organically, to build a unified information system integration and interoperation platform based on semantics, to realize information sharing and accelerate the pace of informatization. The method is to construct the whole structure of the system according to the actual needs of the system. This paper firstly analyzes the current research status and existing problems of semantic grid service matching, and proposes a semantic layered matching algorithm based on Massimo Paolucci elastic matching algorithm. To verify the feasibility and effectiveness of the hierarchical matching algorithm based on semantics, a prototype system named SGSM was designed and its functional model, matching process and performance were studied. Experimental results show that for the semantic-based hierarchical matching algorithm proposed in this paper, the threshold value of service semantic correlation degree is 0.84, the threshold value of service basic concept matching degree is 0.89, the threshold value of service comprehensive similarity degree is 0.66, and the threshold value of service quality matching degree is 0.78. Statistics through the experiment, the above three methods of recall, respectively, 33%, 62%, 85%, the precision is respectively: 29%, 57%, 88%, and illustrate the hierarchical matching algorithm based on semantic is feasible in practical application, compared with the traditional service based on keyword matching algorithm and Massimo Paolucci elastic matching algorithm on the recall and precision are improved significantly.
BDSP in the cloud: Scheduling and Load Balancing utlizing SDN and CEP

With many applications generating a large streaming data set, there is a critical need for scalable computing frameworks for processing it with low latency. Although there are various big data stream processing frameworks, they lack adequate virtualization and optimization mechanisms aimed at enhancing the process of big data stream processing. To address this problem, we propose a cloud-based framework that couples virtual machines and software-defined networks to enhance Cloud resource allocations to the applications with streaming data processing requirements. A novel Cloud resource allocation algorithm based on the proposed framework is also proposed. We validated the proposed resource allocation algorithm using CloudSIM and compared the proposed algorithm with baseline algorithms to determine its effectiveness. The results indicate that by using a virtual machine and an SDN controller, the virtual machine is able to handle 2000 requests at a maximum of 136 seconds.
Performance Analysis of Distributed and Scalable Deep Learning

With renewed global interest for Artificial Intelligence (AI) methods, the past decade has seen a myriad of new programming models and tools that enable better and faster Machine Learning (ML). More recently, a subset of ML known as Deep Learning (DL) raised an increased interest due to its inherent ability to tackle efficiently novel cognitive computing applications. DL allows computational models that are composed of multiple processing layers to learn in an automated way representations of data with multiple levels of abstraction, and can deliver higher predictive accuracy when trained on larger data sets. Based on Artificial Neural Networks (ANN), DL is now at the core of state of the art voice recognition systems (which enable easy control over e.g. Internet-of- Things (IoT) smart home appliances for instance), self-driving car engine, online recommendation systems. The ecosystem of DL frameworks is fast evolving, as well as the DL architectures that are shown to perform well on specialized tasks and to exploit GPU accelerators. For this reason, the frequent performance evaluation of the DL ecosystem is required, especially since the advent of novel distributed training frameworks such as Horovod allowing for scalable training across multiple computing resources.In this paper, the scalability evaluation of the reference DL frameworks (Tensorflow, Keras, MXNet, and PyTorch) is performed over up-to-date High Performance Computing (HPC) resources to compare the efficiency of different implementations across several hardware architectures (CPU and GPU). Experimental results demonstrate that the DistributedDataParallel features in the Pytorch library seem to be the most efficient framework for distributing the training process across many devices, allowing to reach a throughput speedup of 10.11 when using 12 NVidia Tesla V100 GPUs when training Resnet44 on the CIFAR10 dataset.
Reachability Analysis Using Message Passing over Tree Decompositions

In this paper, we study efficient approaches to reachability analysis for discrete-time nonlinear dynamical systems when the dependencies among the variables of the system have low treewidth. Reachability analysis over nonlinear dynamical systems asks if a given set of target states can be reached, starting from an initial set of states. This is solved by computing conservative over approximations of the reachable set using abstract domains to represent these approximations. However, most approaches must tradeoff the level of conservatism against the cost of performing analysis, especially when the number of system variables increases. This makes reachability analysis challenging for nonlinear systems with a large number of state variables. Our approach works by constructing a dependency graph among the variables of the system. The tree decomposition of this graph builds a tree wherein each node of the tree is labeled with subsets of the state variables of the system. Furthermore, the tree decomposition satisfies important structural properties. Using the tree decomposition, our approach abstracts a set of states of the high dimensional system into a tree of sets of lower dimensional projections of this state. We derive various properties of this abstract domain, including conditions under which the original high dimensional set can be fully recovered from its low dimensional projections. Next, we use ideas from message passing developed originally for belief propagation over Bayesian networks to perform reachability analysis over the full state space in an efficient manner. We illustrate our approach on some interesting nonlinear systems with low treewidth to demonstrate the advantages of our approach.
Communication-Efficient String Sorting

There has been surprisingly little work on algorithms for sorting strings on distributed-memory parallel machines. We develop efficient algorithms for this problem based on the multi-way merging principle. These algorithms inspect only characters that are needed to determine the sorting order. Moreover, communication volume is reduced by also communicating (roughly) only those characters and by communicating repetitions of the same prefixes only once. Experiments on up to 1280 cores reveal that these algorithm are often more than five times faster than previous algorithms.
Usability Testing of e-Government Online Services Using Different Methods – a Case Study

The article presents the methodology used to analyse the usability of the e-government online service intended for a wide audience. These recipients are to use the service without initial training, solving problems with the help of the call centre service. In the study, three research groups were used, implementing scenarios which will be most often realised in the service. A whole range of methods and research techniques of software interfaces have been used in the research, such as: user scenario, survey (own, but also in accordance with SUS methodology), eye-tracking technique, phono-video recording, concurrent think aloud protocol and individual in-depth interview. The data obtained as a result of the study were processed by usability experts in order to develop recommendations for improvement of the service interface. The usability report was used to increase the usability of the e-government online service during its development.
Influence of organizational culture in the adoption of agile

This research investigates the influence of organizational culture in the adoption of agile. Agile methodologies are increasingly being adopted to leverage its benefits. However, to do so, the organizational culture needs to be conducive to realize the benefits associated with agile methodologies. This is a result of organizational culture affecting the adoption and use of agile methodologies. As a result, an organization that has a culture that is not aligned with agile principles and values could result in such an organization not realizing the full benefits of agile methodologies. The study compares the culture from the area under investigation to the cultures that have been determined to be conducive to realizing the full benefits from agile methodologies. It was found that the culture present in the area of the investigation had a limited positive influence on the use and adoption of agile methodologies. This research paper allows organizations to make an informed decision on the use of agile methodologies based on their culture. This will help to determine if a culture change is required to realize the benefits of agile methodologies.
Current Challenges of Modern-Day Domestic Abuse

This chapter centres on the emergence of technology in cases of Domestic Abuse using two adjunct parts; (1) how digital coercive control using smart home devices is now an attack vector for abusers and (2) it sets out to highlight the use of a governance model incorporating a risk-based approach following the principles of the Cyber Security Maturity Model (CSMM). Recent reports in the media have identified cases of Domestic Abuse where attackers are using smart home devices to exert coercive control over their partners or former partners. Research importantly highlights a lack of awareness of technology facilitated Domestic Abuse by victims, support workers and law enforcement. This has resulted in the development of a new proposed framework titled SHADA—a Smart Home Anti Domestic Abuse framework, practical challenges and areas of e-policing to better engage stakeholders. The chapter concludes with additional areas of further research and provides an outlook on the use of newer technologies such as AI and considerations for the privacy of citizens in smart societies.
Educational Stream Data Analysis: A Case Study

Virtual Learning Environments (VLEs) are Web-based platforms where educational contents, together with study support tools, are provided. Logs recording the interactions between students and VLEs are collected on a daily basis, thus automatic techniques are needed to manage and analyze such huge quantities of data. Students, teachers, managers, and in general all stakeholders involved in the VLEs' learning activities, can take advantage of the insights coming from educational data and useful information can be extracted by using machine learning techniques. Traditionally, educational data have been studied as stationary data by using conventional machine learning methods. However, educational data are non-stationary by nature and they can be better treated as data streams. In this paper, we show the results of a classification study where the random forest algorithm, applied both in batch and adaptive mode, is used to develop a model for predicting the failure/success of students' exams. Moreover, a feature importance analysis is carried out to detect the most discriminant attributes for the predictive task. Experiments were performed on the Open University Learning Analytics Dataset (OULAD) showing the reliability of adaptive random forest in creating accurate classification models from evolving educational data.
Size biased sampling from the Dickman subordinator

The Dickman function and associated distribution play a prominent role in probabilistic number theory and in the theory of Poisson–Dirichlet distributions. These distributions in turn are closely related to properties of ordered jumps of subordinators. In the present paper we derive some remarkable invariance properties and other identities for the Dickman subordinator, related to the large jumps of the process. The implications of this for size-biased sampling from the Dickman distribution are drawn out.
Automated repair of fragmented tracks with 1D CNNs

Multiple object tracking is an important but challenging computer vision problem. The complex motion of objects makes tracking difficult during long periods of object occlusion, and as a result occlusions frequently cause fragmented tracks with gaps. Previous works use linear interpolation to fill in such gaps, a technique which is only able to model simple motion. As a result, tracked bounding box locations can be quite poor in these situations. In this paper, we propose a 1D CNN based solution to filling gaps which models complex motion in a data-driven way. Our proposed solution uses only bounding box coordinates as input, and as such does not incur the computational cost of processing image features directly. We show that our model significantly outperforms linear interpolation on dynamic sports datasets in terms of mean intersection over union between predicted and ground truth bounding boxes.
NameSpec asks

We present NameSpec, a voice interface that specifies Mandarin Chinese personal names with the user through dialog. Chinese personal names can contain thousands of different, sometimes rare, characters, many of which are homophones that require further disambiguation. Integrated in larger CUI ecosystems as a bot skill, Namespec is activated endogenously when the need arises to input personal names as part of e.g. ticket booking systems or financial services. The results of a preliminary user experience evaluation trail (n=100) show that NameSpec currently completes around 82% of user queries successfully, but challenges remain related to unseen user input, tone recognition and Pinyin-character mapping.
Realization of Different Techniques for Anomaly Detection in Astronomical Databases

In this work we address the problem of anomaly detection in large astronomical databases by machine learning methods. The importance of such study is justified by the existence of a large amount of astronomical data that can not be processed only by human resource. We evaluated five anomaly detection algorithms to find anomalies in the light curve data of the Open Supernova Catalog. Comparison of the algorithms revealed that expert supervised active anomaly detection method shows the best performance, while among purely unsupervised techniques Gaussian mixture model and one-class support vector machine methods outperform isolation forest and local outlier factor methods.
Design of Employee Comment Sentiment Analysis Platform Based on AE-SVM Algorithm

Abstract In order to solve the high-dimensional data set of employees’ sentiment analysis on enterprise comment, in this paper, we presented three model algorithms: SVM algorithm, PCA-SVM algorithm, and AE-SVM algorithm. And we made a comparative analysis of three index parameters such as the precision, recall and F1 in three different algorithm model scenarios. According to the experimental results, the AE-SVM algorithm is superior to other two algorithms in this kind of Chinese text sentiment analysis. Based on the AE-SVM Algorithm, we also design a solution of sentiment analysis platform of employee comment. This solution has the characteristics of high availability and high concurrency. It can also collect and analyse the sentiment text information of employees’ comment in time.
The Communication Performance of BCDC Data Center Network

The BCDC network is a new server-centric data center network based on crossed cube. Its decentralized and recursively defined features solve the bandwidth bottleneck problem of the upper switch in the tree structure and make it more scalable. In addition, the degree of BCDC server is constant, which reduces connection cost and technical difficulty relative to DCell and BCube. In this paper, we study and analyze the data communication, fault tolerance, and node-disjoint paths of BCDC through practical experiments. The results show that BCDC is superior to DCell and Fat-Tree in data communication. Moreover, in terms of fault-tolerant routing and node-disjoint paths, the performance of BCDC is no worse than that of DCell and FatTree. We also propose a one-to-one communication algorithm in BCDC under 1-restricted connectivity, analyze time complexity of algorithm, and give an upper bound of the conditional faulty diameter of BCDC under 1-restricted connectivity. The work in this paper provides an important basis for the design and application of the new data center network.
Perceptual Metric Guided Deep Attention Network for Single Image Super-Resolution

Deep learning has been widely applied to image super-resolution (SR) tasks and has achieved superior performance over traditional methods due to its excellent feature learning capabilities. However, most of these deep learning-based methods require training image sets to pre-train SR network parameters. In this paper, we propose a new single image SR network without the need of any pre-training. The proposed network is optimized to achieve the SR reconstruction only from a low resolution observation rather than training image sets, and it focuses on improving the visual quality of reconstructed images. Specifically, we designed an attention-based decoder-encoder network for predicting the SR reconstruction, in which a residual spatial attention (RSA) unit is deployed in each layer of decoder to capture key information. Moreover, we adopt the perceptual metric consisting of L1 metric and multi-scale structural similarity (MSSSIM) metric to learn the network parameters. Different than the conventional MSE (mean squared error) metric, the perceptual metric coincides well with perceptual characteristics of the human visual system. Under the guidance of the perceptual metric, the RSA units are capable of predicting the visually sensitive areas at different scales. The proposed network can thus pay more attention to these areas for preserving visual informative structures at multiple scales. The experimental results on the Set5 and Set14 image set demonstrate that the combination of Perceptual metric and RSA units can significantly improve the reconstruction quality. In terms of PSNR and structural similarity (SSIM) values, the proposed method achieves better reconstruction results than the related works, and it is even comparable to some pre-trained networks.
The prosody of other-repetition in Italian: A system of tunes

Abstract As part of the project reported on in this special issue, the present study provides an overview of the types of action accomplished by other-repetition in Italian, with particular reference to the variety of the language spoken in the northeastern province of Trento. The analysis surveys actions within the domain of initiating repair, actions that extend beyond initiating repair, and actions that are alternative to initiating repair. Pitch contour emerges as a central design feature of other-repetition in Italian, with six nuclear contours associated with distinct types of action, sequential trajectories, and response patterns. The study also documents the interplay of pitch contour with other prosodic features (pitch span and register) and visible behavior (head nods, eyebrow movements). (Repetition, conversation, prosody, intonation, action, Italian)*
Use of stochastic nature-inspired population-based algorithms within an online adaptive controller for mechatronic devices

Stochastic nature-inspired population-based algorithms are very powerful tools for solving stationary and deterministic, NP-hard optimization problems. These algorithms have rarely been applied to real-world dynamic and uncertain optimization due to their complexity. In this paper, this kind of algorithms were ported onto real hardware (i.e., the velocity controller of a one degree of freedom robot mechanism), where they were used to control the behavior of a non-linear system online. This means that the feedback response from the system must be less than 5 ms. Due to the complexity of the fitness function evaluation, a surrogate linear model was used, implemented as a single-layer artificial neural network, consisting of two phases: learning and simulation. In the first phase, the model of the nonlinear plant is learned during online operation, while in the second, the value of the fitness function needed by the optimization algorithms is predicted. Six algorithms were compared with the PI-controller in our experimental work. This were: classical evolution strategies, contemporary evolution strategies, differential evolution, self-adaptive differential evolution, particle swarm optimization, and the bat algorithm. The results showed that the algorithms outperformed PI-controller in the sense of stability, flexibility and adaptability.
Experimental witnessing of the quantum channel capacity in the presence of correlated noise

The authors demonstrate an experimental approach to detect lower bounds to the quantum capacity of two-qubit communication channels with correlated noise realized with the polarization degrees of freedom of two photons. The major advantage of this approach is that it does not need a complete experimental reconstruction of the channel.
Theoretical aspects of prototyping

Abstract The evolution of programming began at a time when most programs were developed by a single developer. As the complexity of the programs increased, the complexity of their development, debugging and the number of participants in the process also increased, which led to the development of automation systems for programming and organization of teamwork, and later to the development of tools and methods for software design (CASE-tools).