Reviewerly/exHarmony huggingface dataset:
Window Navigation with Adaptive Probing for Executing BlockMax WAND

BlockMax WAND (BMW) and its variants can effectively prune low-scoring documents for fast top-k disjunctive query processing. This paper studies a boosting approach that further accelerates document retrieval by executing BMW, or one of its variants, on a sequence of posting windows with an order prioritized to tighten the threshold bound earlier. This optimization could add benefits to safely eliminate more operations involved in posting block visitation and document score evaluation. This paper evaluates such index navigation for BMW and two of its variants.
A Proposal of a Classification Scheme to a Survey of Augmented Reality for Education and Training

Augmented Reality (AR) is a field of knowledge that emerged in the middle of the last century, and its use has been spreading because of its usefulness, but also because of mobile platforms, accessible to most users. AR characteristics are valued in several fields of human activity, and also in the field of Education and Training, being AR pointed out as useful to the learning process. In this paper we search and analyse surveys and reviews of AR. We present a AR’s definition, and we create a classification scheme of two dimensions for AR: the dimension of the fields of application of AR, and the dimension of the technologies of AR.
Expertise and Experience in VR-supported learning: Achieving a deep non-verbal comprehension of four-dimensional space

Immersive Virtual Reality (VR) has shown to be effective in general training and learning applications, but whether it has potential in understanding and learning complex topics is not well researched. In addition, the role of users’ expertise in VR-based learning is not really understood. Here we present findings from our purpose-designed immersive VR system and investigation on whether experts with theoretical knowledge in a certain domain can develop deep non-verbal comprehension beyond pure understanding. Mathematically educated experts as well as non-experts are asked to interact with four-dimensional cubes projected into three-dimensional VR space (hypercubes)—an intentional task not found in real life. In the first of two studies, we validate the feasibility of the principle subject matter, apparatus, and proposed measurements with 22 participants. This study is based on a seminal study proposal from the 1970’s. We use the results of this first study to inform a second study based on a philosophical thought experiment known as Mary’s Room. With 70 participants we investigate experience, interaction, and prior knowledge, in an immersive VR learning environment. We can show that both experts and non-experts benefit from the immersive interaction with hypercubes. Both groups develop a non-verbal comprehension of this theoretical construct. Surprisingly though, experts, with prior theoretical knowledge, benefit stronger. Our findings have implications for immersive VR learning environments and open a future research space on the importance of and relationships between immersive VR, interaction, understanding, comprehension, and constructivist learning.
An Efficient Way to Recognize Faces Using Mean Embeddings

Face Recognition is one of the challenging tasks in computer vision. Recognizing various features correctly, which differs from one face to another face, is very difficult. This task becomes more difficult with the change of illumination, pose, expression, beard, age, and the quality of the picture. Before the rise of deep learning in computer vision, hand engineering feature extractor was used to recognize faces. However, they suffered mostly on a variety of illumination, pose, expression, and age. After deep learning evolution, these problems can be overcome easily by feeding a massive amount of data. Facenet is one of the deep learning architectures to recognize faces. It produces a point in a 128-dimensional vector space where the distance between two faces of the same person is small, and the distance between two faces of different persons is large. If we have a large amount of face data to compare it will be computationally expensive, and thus real-time face recognition becomes slow. Support Vector Machine or Logistic Regression can solve this problem. However, SVM or Logistic Regression cannot be able to recognize faces that not in the database as 'Unknown'. For solving these problems, we proposed a method called 'Mean Embeddings'. By using 'Mean Embeddings,' we achieved 6.5 times faster and 1.6% better performance on average with the ability to recognize unknown faces.
Graph Computing System and Application Based on Large-Scale Information Network

Graph computing is more and more widely used in various fields such as spatial information network and social network. However, the existing graph computing systems have some problems like complex programming and steep learning curve. This paper introduces GRAPE, a distributed large-scale GRAPh Engine, which has the unique features of solid theoretical guarantee, ease of use, auto-parallelization and high performance. The paper also introduces several typical scenarios of graph computing, including entity resolution, link prediction, community detection and graph mining of spatial information network. In these scenarios, various problems have been encountered in the existing systems, such as failure to compute over large-scale data due to the high computation complexity, loss of accuracy due to the cropping of original data and too long execution time. In the face of these challenges, GRAPE is easy to support these computing scenarios with a series of technical improvements. With the deployment of GRAPE in Alibaba, both effectiveness and efficiency of graph computing have been greatly improved.
Detecting Change between Urban Road Environments along a Route Based on Static Road Object Occurrences

For over a decade, urban road environment detection has been a target of intensive research. The topic is relevant for the design and implementation of advanced driver assistance systems. Typically, embedded systems are deployed in these for the operation. The environments can be categorized into road environment-types. Abrupt transitions between these pose a traffic safety risk. Road environment-type transitions along a route manifest themselves also in changes in the distribution of traffic signs and other road objects. Can the placement and the detection of traffic signs be modelled jointly with an easy-to-handle stochastic point process, e.g., an inhomogeneous marked Poisson process? Does this model lend itself for real-time application, e.g., via analysis of a log generated by a traffic sign detection and recognition system? How can the chosen change detector help in mitigating the traffic safety risk? A change detection method frequently used for Poisson processes is the cumulative sum (CUSUM) method. Herein, this method is tailored to the specific stochastic model and tested on realistic logs. The use of several change detectors is also considered. Results indicate that a traffic sign-based road environment-type change detection is feasible, though it is not suitable for an immediate intervention.
A Deep Learning Co-training Framework for e-book Classification

Automatic e-book classification is an important research issue since more and more people read and acquire information on their mobile devices (i.e., smartphones). Many writers digitize their works for users to acquire data on their mobile devices and result in the number of e-books has grown significantly. An e-book is a digital or electronic book that is formatted into a file that can be read on a mobile device. Some features of books are used in e-books. However, the important difference is that an e-book has a lot of images to describe the contents or knowledge of writers. Many researches proposed their methods of e-book classification to make users easily to find out and then read an e-book on their mobile devices. In these methods, an e-book can be categorized by several criteria. One of it is based on its type, i.e., novel, reference, and encyclopedia. Another is based on its topic, i.e., economy, religion, and technical. The classification systems based on the topics usually use well-known methodology such as Dewey Decimal Classification, in which, every category reflected by a decimal. These researches use Naïve Bayes Classifier and focus on automatic thesis classification. As deep learning proves its usefulness in an ever greater number of applications, there is a rise in demand for faster computational resources to train ever complex learning-based models. Based on the concept of deep learning, some researches proposed their methods to automatic e-book classification. W. A. Wiegand proposed a convolutional-neural-network (CNN) book label recognition algorithm to find out the misplaced books. On the other hand, four steps are illustrated in X. Yang et al. First, the keywords are extracted from the description data of e-books. Then, the description data is modeled as vectors of keywords. Third, the statistical categorization rules are obtained from meta-information of e-books. Finally, the vectors and the statistical categorization rules are combined to obtain an classification model. In this paper, a deep learning co-training framework (namely DLC) is proposed for improving the accuracy of automatic e-book classification. DLC combines the features of texts and images in e-books to co-training an e-book classifier. In order to increase the variety of feature sets, the texts in e-books are represented as vectors by Word2Vec. The images in e-books are translated and then combined into the vectors. Furthermore, DLC adopts the softmax regression function to co-train the combined features (i.e., vectors and images) in CNN to improve the accuracy of e-book classification. The experimental results demonstrate that our DLC has higher accuracy than other e-book classifiers.
Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud

Big data is gaining overwhelming attention since the last decade. Almost all the fields of science and technology have experienced a considerable impact from it. The cloud computing paradigm has been targeted for big data processing and mining in a more efficient manner using the plethora of resources available from computing nodes to efficient storage. Cloud data mining introduces the concept of performing data mining and analytics of huge data in the cloud availing the cloud resources. But can we do better? Yes, of course! The main contribution of this chapter is the identification of four game-changing technologies for the acceleration of computing and analysis of data mining tasks in the cloud. Graphics Processing Units can be used to further accelerate the mining or analytic process, which is called GPU accelerated analytics. Further, Approximate Computing can also be introduced in big data analytics for bringing efficacy in the process by reducing time and energy and hence facilitating greenness in the entire computing process. Quantum Computing is a paradigm that is gaining pace in recent times which can also facilitate efficient and fast big data analytics in very little time. We have surveyed these three technologies and established their importance in big data mining with a holistic architecture by combining these three game-changers with the perspective of big data. We have also talked about another future technology, i.e., Neural Processing Units or Neural accelerators for researchers to explore the possibilities. A brief explanation of big data and cloud data mining concepts are also presented here. Quantum Computing is a paradigm that is gaining pace in recent times which can also facilitate efficient and fast big data analytics in very little time. We have surveyed these three technologies and established their importance in big data mining with a holistic architecture by combining these three game-changers with the perspective of big data. We have also talked about another future technology, i.e., Neural Processing Units or Neural accelerators for researchers to explore the possibilities. A brief explanation of big data and cloud data mining concepts are also presented here.
A Novel Approach to Scene Graph Vectorization

In recent times due to the advancement in perceptual applications, focus in computer vision has been inclined towards tasks that require a significant level of semantic understanding of scenes. Combination of textual and visual information has resulted in a great improvement in performance on tasks like retrieval, captioning and visual q/a etc. In this regard, scene graphs have become a popular form of structural knowledge. But unlike Word embedding, general-purpose scene graph embedding has not been explored significantly. In this work, we propose a general-purpose scene graph embedding model that combines linguistic and graph processing techniques through a reconstruction based learning network to learn a low-dimensional data-driven vectorized embedding of scene graphs. Visualization of embedding of COCO dataset has shown to possess semantic separability and distance-based abstraction of scenes. When applied to a retrieval task and evaluated using Med-r and recall metric on COCO-stuff and VRD dataset, our model showed promising results.
The Accurate Repair of Image Contour of Human Motion Tracking Based on Improved Snake Model

With the rapid development of sports science, human motion recognition technology, as a new biometric recognition technology, has many advantages, such as noncontact target, long recognition distance, secret recognition process, and so on. Traditional human motion recognition technology is affected by environmental factors such as motion background, which is prone to rough edges of the recognized objects and loss of motion tracking information, thus further reducing the recognition accuracy. In this paper, the traditional snake model will be improved and optimized to improve the defect of human motion model contour extraction, so as to realize the accurate repair of image contour; in terms of algorithm running time, this paper innovatively improves the construction process of the snake model, further improves the running time of model evaluation, and solves the concave contour problem of corresponding moving objects in the snake model. In order to solve the problem of accurate convergence, this paper improves the snake model of the average moving algorithm and sets the corresponding weight coefficient to distinguish the corresponding moving target background, so as to achieve the convergence of the differential concave contour. In order to verify the superiority of the improved optimized snake model, experiments are carried out in the corresponding database. The experimental results show that the contour of the moving object extracted by the improved snake model algorithm is complete and the segmentation effect is obvious. At the same time, the running speed of the whole algorithm has been significantly improved.
A Research on Image Captioning by Different Encoder Networks

Many current research issues of image captioning focus on modifying the CNN (Convolutional Neural Network) or RNN (Recurrent Neural Network), while supplementing the attention mechanism to enhance the long-term memory ability of the RNN. However, the relationship with input data and CNN model could be another important point. This paper defines the image complexity to enhance model's accuracy. After analyzing the data set, some criteria of the image complexity are defined according to the image grayscale entropy and the two-dimensional entropy for image Captioning. In this paper, a new model is setup to compare with the other model. Although the result is better than the other model by a revised bilingual evaluation understudy (R-BLEU) evaluation index which is a new calculation formula to evaluate image captioning performance.
Hybrid probabilistic information matrix fusion

In autonomous driving systems, distributed sensor fusion is widely used where each sensor has its tracking system and only the local tracks (LT) are transmitted to the fusion center. We consider the fusion of LTs taking into account all the Fusion Center (FC) track-to-LT association hypotheses via probabilities in the proposed Hybrid Probabilistic Information Matrix Fusion (HPIMF) algorithm. In HPIMF, the track association and fusion are carried out with probabilistic weightings rather than using a single track association only. Different from track-to- track fusion (T2TF), which is one of the most commonly used approaches for distributed tracking systems, the associations considered in HPIMF are between the predicted FC state and the LTs from local sensors. At each time for an association event, up to one of the tracks within the track list of a local sensor can be associated with the FC state. In real world scenarios there can be large uncertainties and missed tracks due to sensor imperfections and sensor-target geometry. Consequently, the association might be unreliable and fusion based on only a single association hypothesis could fail. It is shown in the simulations of a realistic autonomous driving system that HPIMF can successfully track a target of interest and is superior to T2TF which relies on hard association decisions.
An Encryption Method of Power Cloud Data Based on n-RSA

With the development and progress of the Internet, the traditional grids are unable to meet the needs of the power industry. The development of the power industry requires the support of cloud computing technology. However, the storage and calculation of data are performed in the cloud, which threatens the privacy issues of power users and institutions. In this issue, this paper proposes a multi-level security model of the n-RSA encryption algorithm to solve the privacy problem of sensitive data transmitting in the cloud. Firstly, the power cloud security center conducts a security assessment of the data and determines the security level. Then, select the appropriate prime number for the n-RSA algorithm according to the security level of the data. The detailed steps of the proposed method for n-RSA encryption are given, and the advantages and disadvantages of this method for power cloud are analyzed. Experimental results show that the proposed method can effectively improve the security and flexibility of the secure transmission of power cloud data.
GENES ISP: code analysis platform

In this paper we present a novel code analysis platform referred as “GENESISP”. Its aim is to collect vast database of open source software and apply several integrated analyses. This analysis allows to understand relations within source and binary code, as well as detect existing defects. All the analyses are compatible with each other and can be combined, which provides more robust possibilities. In the first stage the framework tries to collect, process and store software related data into database. Various open source resources are used for that purpose. For open source software, we collect and store: source and binary code, debug information, build dependencies, linker commands, confirmed CVE and their possible fixes, etc. For operating systems distributions, we additionally store the list of available software packages. In the second stage the tool performs various analysis: source code clone detection, binary code clone detection, source/binary fragments search, statically linked libraries identification in binaries, unfixed CVE search, source and binary code matching, patch analysis, etc. We provide web interface for convenient use of the provided analyses. Beside it, there is an interactive terminal which enables users to query the database and combine the results of different analysis. We were able to find number of confirmed CVE in mainstream versions of different open source software, which proves the effectiveness of proposed platform.
Natural language query handling using extended knowledge provider system

Extraction of knowledge data from knowledge database using natural language query is a difficult task. Different types of natural language processing (NLP) techniques have been developed to handle this knowledge data extraction task. This paper proposes an automated query-response model termed Extended Automated Knowledge Provider System (EAKPS) that can manage various types of natural language queries from user. The EAKPS uses combination based technique and it can handle assertive, interrogative, imperative, compound and complex type query sentences. The algorithm of EAKPS generates structure query language (SQL) for each natural language query to extract knowledge data from the knowledge database resident within the EAKPS. Extraction of noun or noun phrases is another issue in natural language query processing. Most of the times, determiner, preposition and conjunction are prefixed to a noun or noun phrase and it is difficult to identify the noun/noun phrase with prefix during query processing. The proposed system is able to identify these prefixes and extract exact noun or noun phrases from natural language queries without any manual intervention.
Robust Data Compression Algorithm utilizing LZW Framework based on Huffman Technique

In this work we propose a case of Hybrid compression i.e., Huffman based LZW. The performance of this algorithm is better than the traditional algorithm and it is observed to offer better compression ratio than in single compression or when compared with the individual two algorithms. If we tend to compress the information first by Huffman encoding technique and then LZW will be performed, better compression ratio is achieved for this hybrid technique. Hence it is named as “Data compression algorithm utilizing LZW Framework based on Huffman Technique”. By this approach we get an average compression ratio and saving percentage of 5 and 70%. This would provide us inexpensive, credible and cost-effective lossless encoding systems for electronic communication systems.
Comparative Survey on Big data Security Applications, A Blink on Interactive Security Mechanism in Apache Ozone

Nowadays, companies are transitioning towards security-based technology to secure data at the maximum level and there are several solutions provided to solve the big data security issues. The existing solutions are highly multidisciplinary security embedded, but still, there are openings to solve the upcoming Hadoop technology like Apache Ozone which is scalable, redundant, and more importantly a distributed object storage for Hadoop. With the growing information and technology, the volume of data traffic is transmitted over storage that requires extensive security measures and privacy in the environment of big data. In this paper, we describe our efforts towards the traditional way of securing the data and compare different methods to secure the data in Hadoop technology and suggested an optimum solution.