Reviewerly/exHarmony huggingface dataset:
An Efficient Network Resource Management in SDN for Cloud Services

With the recent advancements in cloud computing technology, the number of cloud-based services has been gradually increasing. Symmetrically, users are asking for quality of experience (QoE) to be maintained or improved. To do this, it has become necessary to manage network resources more efficiently inside the cloud. Many theoretical studies for improving the users’ QoE have been proposed. However, there are few practical solutions due to the lack of symmetry between implementation and theoretical researches. Hence, in this study, we propose a ranking table-based network resource allocation method that dynamically allocates network resources per service flow based on flow information periodically collected from a software defined network (SDN). It dynamically identifies the size of the data transmission for each service flow on the SDN and differentially allocates network resources to each service flow based on this size. As a result, it maintains the maximum QoE for the user by increasing the network utilization. The experimental results show that the proposed method achieves 29.4% higher network efficiency than the general Open Shortest Path First (OSPF) method on average.
Four-party quantum operation sharing with composite quantum channel in Bell and Yeo–Chua product state

A four-party single-qubit operation sharing scheme is put forward by utilizing the Bell and Yeo–Chua product state in an entanglement structure as the composite quantum channel. Four features of the scheme are discussed and confirmed, including its determinacy, symmetry, and security as well as the scheme experimental feasibility. Moreover, some concrete comparisons between our present scheme and a previous scheme [H. Xing et al., Quantum Inf. Process. 13 (2014) 1553] are made from the aspects of quantum and classical resource consumption, necessary operation complexity, and intrinsic efficiency. It is found that our present scheme is more superior than that one. In addition, the essential reason why the employed state in the entanglement structure is applicable for sharing an arbitrary single-qubit operation among four parties is revealed via deep analyses. With respect to the essential reason, the capacity of the product state in quantum operation sharing (QOS) is consequently shown by simple presenting the corresponding schemes with the state in other entanglement structures.
Cloud-Edge-End Simulation System Architecture Study

Cloud based simulation system architecture concentrates a lot of simulation resources in the Datacenter for unified management, which has the advantages of efficiency and cost, but also faces problems such as collaborative interaction. A novel simulation system architecture called is proposed. In this architecture, edge computing is introduced. It employed resource virtualization and service technical to get through the integration and interaction of various simulation resources of cloud, edge, and end. It is the further development of cloud architecture-based simulation system architecture, which is conducive to the transfer of simulation ability to application edge frontier.
Cost-Effective and Smart Text Sensing and Spotting in Blurry Scene Images Using Deep Networks

An intelligent transportation system facilitates smart services and applications that can revolutionize the traffic and travel experience. Driver assistance system is a crucial part of such a system that helps to improve the safety and security of passengers by mitigating on-road collisions and potential hazards. The precise sensing (localization) and spotting of scene texts and traffic signs are important for achieving higher performance in real-time. It is however affected by motion blur and camera shake noise, which makes the process of spotting complex. In this paper, we propose a robust text spotter, denoted by Blurred TextSpotter, for efficient and cost-effective spotting in blurry scene images. We address different noises, like a motion blur, Gaussian blur, camera shake noise, and inter-class interference. We apply a multi-scale contextual information enriched encoder-decoder based backbone network followed by a spatial and channel-wise attentions. We predict text masks and accurately classify words using a hardware-efficient recognition module. The experimental results on five publicly available benchmark datasets show the efficiency of the proposed text spotter in terms of detection, recognition, and spotting of curve text instances in scene images.
Semantic similarity between short paragraphs using Deep Learning

Textual semantic similarity plays an increasingly important role in tasks such as information retrieval, text mining and text-based searches. Multiple approaches have been presented to enhance methods for information retrieval by understanding the underlying meaning of sentences. However, most of these focus on single line sentences. In this paper, we try to evaluate the effectiveness of these approaches to understand the semantic meaning of short paragraphs. We use an existing recurrent neural network architecture and train it using document embedding vectors to try and infer the meaning of small paragraphs consisting of one, two or three sentences. We use three different methods - Manhattan distance, Euclidean distance and cosine distance - to evaluate the performance and effectiveness of measuring the semantic similarity. The conclusion compares the performance of all three methods.
A privacy protection solution based on NLPCA for blockchain supply chain financial system

Many supply chain finance problems can be fundamentally solved by using blockchain. The current technical challenge of applying blockchain in supply chain finance is the lack of systematic privacy protection architecture. Based on the architecture of Julongchain, through the nonlinear principal component analysis in private data feature extraction, this paper proposed a new type of privacy protection architecture based on specific supply chain financial system. This privacy protection solution split privacy data by decision machine, then mixed data using mixed mechanism, finally determined the characteristic weight of privacy data by applying the decentralized decision institution.
Synthesizing Realistic Brain MR Images with Noise Control

Image synthesis in magnetic resonance (MR) imaging has been an active area of research for more than ten years. MR image synthesis can be used to create images that were not acquired or replace images that are corrupted by artifacts, which can be of great benefit in automatic image analysis. Although synthetic images have been used with success in many applications, it is quite often true that they do not look like real images. In practice, an expert can usually distinguish synthetic images from real ones. Generative adversarial networks (GANs) have significantly improved the realism of synthetic images. However, we argue that further improvements can be made through the introduction of noise in the synthesis process, which better models the actual imaging process. Accordingly, we propose a novel approach that incorporates randomness into the model in order to better approximate the distribution of real MR images. Results show that the proposed method has comparable accuracy with the state-of-the-art approaches as measured by multiple similarity measurements while also being able to control the noise level in synthetic images. To further demonstrate the superiority of this model, we present results from a human observer study on synthetic images, which shows that our results capture the essential features of real MR images.
Noninvasive MapReduce Performance Tuning Using Multiple Tuning Methods on Hadoop

There are more than 190 configuration parameters affecting the performance of MapReduce jobs on Hadoop. It is time-consuming and tedious for general users who have no deep knowledge of Hadoop configuring to tune the parameters of a MapReduce job for optimal performance. Therefore, a self-tuning system to improve MapReduce performance in an automated and efficient manner in a complicated Hadoop environment is needed. This article explores multiple tuning methods to improve tuning efficiency for MapReduce performance on Hadoop. The proposed Catla system employs succinct templates and proper schemes of MapReduce algorithms, which can be incorporated in facilitating the tuning and optimization of MapReduce performance. A comprehensive evaluation of the Catla system, with the support of multiple tuning approaches, is discussed in this article. Direct search-based and derivative-free optimization-based tuning techniques for improved efficiency and usability are evaluated using a series of tuning experiments. The experimental results reveal that our work can identify optimal Hadoop parameters for deployed MapReduce jobs in a noninvasive, flexible, automated, and comprehensive manner.
A College Major Recommendation System

College students are required to select a major but are often provided with only a modest amount of support in making this important decision. A poor decision is detrimental to the student, since it may result in the student later switching to a different major with a delay in graduation—or even result in the student leaving the university. This also impacts the university since time to graduation and retention rate are used to evaluate the quality of a university. There is a general lack of research on recommender systems for college majors, with the most relevant systems focusing on course-level recommendations. This study describes and evaluates a recommender system for selecting an undergraduate major, utilizing nine years of historical student data from a large university. The system bases its recommendations on the courses that the student takes in the first few years of college, and how well they performed in these courses. The system is designed to recommend majors that the student is likely to be interested in and will perform well in. Recommendations are evaluated based on the likelihood that the student's actual major was in the top five recommended majors, and whether the student performed above average in that major. The recommendation system dramatically outperforms the baseline strategy of randomly selecting a major, and when the recommendation is followed the student is 12% more likely to perform above average in the major.
Mixing realities: reflections on presence and embodiment in intermedial performance design of<i>Blue Hour VR</i>

Created for the 2019 Prague Quadrennial’s 36Q°, Blue Hour VR was a site-responsive mixed reality performative installation that placed the spectator, as experiencer, within a hybrid landscape of real-time three-dimensional computer graphics and 360-degree video. This article describes the design process, staging and experience of Blue Hour VR from the vantage point of its creators. Using a phenomenological perspective, the article discusses how Blue Hour VR staged presence and embodiment within an intermedial haptic experience. Blue Hour VR demonstrates how virtual reality technology can be harnessed by a mixed reality performance design, which includes both the material and virtual environment, creating a complex stratigraphy of intermedial textures and visual dramaturgies that co-exist inside, outside and in between perceptual realities. In doing so, the article aims to contribute to the limited body of work on mixed and virtual reality in the context of theatre and performance design.
Isolated Word Automatic Speech Recognition System

The paper is devoted to an isolated word automatic speech recognition. The first part deals with a theoretical description of methods for speech signal processing and algorithms which can be used for automatic speech recognition such as a dynamic time warping, hidden Markov models and deep neural networks. The practical part is focused on the description of the proposal which is based on convolutional neural networks (CNN). The system was designed and implemented in Python using Keras and TensorFlow frameworks. An open audio dataset of spoken words was used for training and testing. A contribution of the paper lies in the specific proposal using CNN for automatic speech recognition and its validation. The presented results show that the proposed approach is able to achieve 94% accuracy.
Autoencoders

Computer networks are vulnerable to cyber attacks that can affect the confidentiality, integrity and availability of mission critical data. Intrusion detection methods can be employed to detect these attacks in real-time. Anomaly detection offers the advantage of detecting unknown attacks in a semi-supervised fashion. This paper aims to answer the question if autoencoders, a type of semi-supervised feedforward neural network, can provide a low cost anomaly detector method for computer network data streams. Autoencoder methods were evaluated online with the KDD'99 and UNSW-NB15 data sets, demonstrating that running time and labeling cost are significantly reduced compared to traditional online classification techniques for similar detection performance. Further research would consider the trade-off between single vs stacked networks, multi-label classification, concept drift detection and active learning.
A Quantum Entanglement-Based Approach for Computing Sentence Similarity

It is important to learn directly from original texts in natural language processing (NLP).Many deep learning (DP) models needing a large number of manually annotated data are not effective in deriving much information from corpora with few annotated labels.Existing methods using unlabeled language information to provide valuable messages consume considerable time and cost.Our provided sentence representation based on quantum computation (called Model I) needs no prior knowledge except word2vec.To reduce some semantic noise caused by the tensor product on the entangled words vector, two improved models (called Model II and Model III) are proposed to reduce the dimensions of the sentence embedding stimulated by Model I.The provided models are evaluated in the STS tasks of 2012, 2014, 2015 and 2016, for a total of 21 corpora.Experimental results show that using quantum entanglement and dimensionality reduction in sentence embedding yields state-of-the-art performances on semantic relations and syntactic structures.Compared to the Pearson correlation coefficient (Pcc) and mean squared error (MSE), the results of 16 out of 16 corpora are better than the results of the comparative methods.
A semi-hierarchical clustering method for constructing knowledge trees from stackoverflow

To help students learn how to programme, we have to give them a clear knowledge map and sufficient materials. Question-based websites, such as stackoverflow, are excellent information sources for this goal. However, for beginners, the process can be a little tricky since they may not know how to ask correct questions if they do not have sufficient background knowledge, and a knowledge tree is usually considered more helpful in such a scenario. In this research, a method to infer a knowledge tree automatically from the type of websites and to group documents based on the resulting knowledge tree is proposed. The proposed method mainly addresses two issues: first, the quality of tags cannot be guaranteed, and second, clustering-based methods usually generate the flat schema. The occurrence count and the co-occurrence ratio were used together to identify important tags. Then, an algorithm was developed to infer the hierarchical relationship between tags. Using these tags as centres, the clustering performance is better than applying k-means alone.
Autonomous Flight Vehicle Incorporating Artificial Intelligence

The integration of Artificial Intelligence with the aircraft or an Unmanned Ariel Vehicles that could automate the task of manual flight and navigation would be considered a milestone in the field of Aeronautics. The idea of an autonomous flight vehicle isn't novel. It has been cited into various papers decades ago. In the subject of prior art, though, there have been a very few advancement in the development of such technology. The major challenge encountered by the individuals involved in the development of such a system was the integration of multidisciplinary domains specifically with the backgrounds of Computer Science and Aeronautics. The research work will contribute to the advancement in the incorporation of Artificial Intelligence to achieve a simulated autonomous flight vehicle.
PUNER-Parsi ULMFiT for Named-Entity Recognition in Persian Texts

Named Entity Recognition (NER) is an information extraction technique for the automatic recognition of named entities and their classification in a natural language text. Applications of NER include extracting named entities from texts such as academic, news and medical documents, content classification for news providers, improving the search algorithms, etc. Most of the NER research works explored is for high resource languages such as English, German, and Spanish. Very less NER related work is done in low-resource languages such as Persian, Indian, and Vietnamese due to lack of annotated corpora for these languages. Among the mentioned languages very few works have been reported for the Persian language NER till now. Hence, this paper presents PUNER—a Persian NER system using Transfer Learning (TL) model that makes use of Universal Language Model Fine-tuning (UMLFiT) for NER in Persian language. This is accomplished by training a Language Model on Persian wiki text and using that model to develop a system for identifying and extracting named entities from the given Persian texts. Performance of the proposed model is compared with the Deep Learning (DL) models using BiLSTM by applying five word embedding models namely, Fasttext, HPCA, Skipgram, Glove, and COBOW and conventional Machine Learning (ML) model. All the models are evaluated on two Persian NER datasets and the results illustrate that TL model performs better than ML and DL models.
Privacy-preserving Crowd-sensed Trust Aggregation in the User-centeric Internet of People Networks

Today we are relying on Internet technologies for numerous services, for example, personal communication, online businesses, recruitment, and entertainment. Over these networks, people usually create content, a skillful worker profile, and provide services that are normally watched and used by other users, thus developing a social network among people termed as the Internet of People. Malicious users could also utilize such platforms for spreading unwanted content that could bring catastrophic consequences to a social network provider and the society, if not identified on time. The use of trust management over these networks plays a vital role in the success of these services. Crowd-sensing people or network users for their views about certain content or content creators could be a potential solution to assess the trustworthiness of content creators and their content. However, the human involvement in crowd-sensing would have challenges of privacy preservation and preventing intentional assignment of the fake high score given to certain user/content. To address these challenges, in this article, we propose a novel trust model that evaluates the aggregate trustworthiness of the content creator and the content without compromising the privacy of the participating people in a crowdsource group. The proposed system has inherent properties of privacy protection of participants, performs operations in the decentralized setup, and considers the trust weights of participants in a private and secure way. The system ensures privacy of participants under the malicious and honest-but-curious adversarial models. We evaluated the performance of the system by developing a prototype and applying it to different real data from different online social networks.
Exploring the Impact of Handwriting Recognition on the Automated Scoring of Handwritten Student Answers

Handwriting is still the preferred writing mode in the classroom, but it makes introducing intelligent tutoring systems much harder. A major building block within these systems are scoring algorithms that assess whether the content of free-text student answers is correct or not. We collect a dataset of handwritten student answers which we make publicly available to foster future research in the field. We use a state-of-the-art system for handwriting recognition containing a line and word segmentation module and a neural recognition model. We find that scoring performance on handwritten answers is significantly worse compared to typed answers due to recognition errors. Using a simple postprocessing step based on a unigram language model is already sufficient to achieve a performance very close to that on typed input.
Burden of Persuasion in Argumentation

This paper provides a formal model for the burden of persuasion in dialogues, and in particular, in legal proceedings. The model shows how an allocation of the burden of persuasion may induce single outcomes in dialectical contexts in which, without such an allocation, the status of conflicting arguments would remain undecided. Our approach is based on a two-stage labelling. The first-stage labelling determines what arguments are accepted, rejected or undecided, regardless of the allocation of burden. The second-stage labelling revises the dialectical status of first-stage undecided arguments, according to burdens of persuasion. The labelling is finally extended in such a way as to obtain a complete labelling. Our model combines two ideas that have emerged in the debate on the burden of persuasion: the idea that the burden of persuasion determines the solution of conflicts between arguments, and the idea that its satisfaction depends on the dialectical status of the arguments concerned. Our approach also addresses inversions of the burden of persuasion, namely, cases in which the burden of persuasion over an argument does not extend to its subarguments.
Simulation and Analysis of Clustering Routing Protocol Based on Improved LEACH

In view of the problem that the random selection of LEACH Routing Protocol cluster head in WSN network will lead to the short lifetime of nodes and network, an e-leach protocol based on energy improvement is proposed in this paper, which optimizes the cluster head selection stage by considering both energy and distance factors, and introduces the average value of node residual energy, and the distance between nodes and base station as weight parameters to the threshold in the probability. A new threshold judgment formula is constructed, and the cluster head node is selected according to it, so that the node which is close to the base station, has more residual energy and less energy change is more likely to become the cluster head. The simulation results show that e-leach protocol can prolong the lifetime of network and nodes, and has better performance.
Research of Face Detection Based on Skin Color Feature

Abstract Face image contains many pattern features and this paper studies a face detection algorithm based on skin color features. Through the analysis of the face detection method based on skin color features, the calculation process and principle are understood, and the algorithm and detection techniques such as color space, preprocessing face image detection technology. Building a corresponding skin color model based on face characteristics are analyzed in depth. Because the skin color only occupies a specific range in the primary color of color space, the probability likelihood graph of skin color is obtained by calculating skin color similarity. Then the binary image of the possible face region is obtained by smooth filtering, threshold segmentation, and other operations. Then the area center and height ratio are used to calculate the center of mass and deflection angle, and the correlation coefficient is matched with the face module. Finally, the face area is detected and marked. Test results show that the algorithm can achieve face detection well.