Reviewerly/exHarmony huggingface dataset:
University Library as Knowledge Management Centre

The article discusses the transition to a new stage of civilization, the knowledge society. In the modern world, knowledge is one of the major socio-economic resources for both the individual and the state. Accordingly, the educational strategy of any higher educational institution should be refocused to ensure smooth transition from the traditional model to innovative in order to prepare the modern student for a life in the society where innovation and intellectual resources play the major role. The modern library can act as a control centre of knowledge, integrate with the world information resources and mediate for the user in global information space using the tools and mechanisms of the electronic library. In this regard, the e-library becomes a knowledge management centre focused on the development and maximum use of intellectual potential of library for the purpose of preservation, reproduction, building up and broadcasting of knowledge. The article analyses the opportunities which are given by electronic library for the University. The authors constructed the scheme of interrelation of knowledge and information resources. The article considers the concept of proactive electronic library of the University as a system of information service of readers, the main purpose of which is to provide resources in any form and on any carrier using the classical and network forms of services based on the advanced inquiry services. The authors constructed the model of proactive University library, which is a complex system, incorporating elements of various properties and complexity. The proactive electronic library itself searches for information corresponding to the information needs of the user, without waiting for his request; that can influence the development of his information needs. Thus, the library transforms from a passive storage of information into an interaction partner. Information is automatically selected for the user, relevant to his educational and scientific interests, hobbies and preferred leisure.
Complementary Features with Reasonable Receptive Field for Road Scene 3D Object Detection

Accurate and efficient 3D object detection is of great importance for autonomous driving and robot perception. There are two problems in lidar based 3D object detection networks. (1) Semantic information (e.g., class label of each point) and spatial details are not fully explored for feature extraction. (2) The variance of object sizes represented by point cloud is much smaller than those represented by 2D images. But existing methods do not make use of this property and the receptive field sizes generally mismatch the physical sizes of road scene objects. Based on these two aspects, we propose Complementary Features with Reasonable receptive field networks (CFRNet). CFRNet first exploits Complementary Feature Extractor to learn semantic and positional features, then utilizes a RPN (Region Proposal Networks) with reasonable receptive field to collect correlated context in road scene. Experimental results on KITTI benchmark show the effectiveness of our method. Moreover, our method achieves state-of-the-art performance at a high inference speed.
Efficient implementation of Smalltalk activation records in language implementation frameworks

Language implementation frameworks such as RPython or Truffle help to build runtimes for dynamic languages. For this, they make certain design decisions and trade-offs upfront to make common language concepts easy to implement. Because of this, however, some language-specific concepts may be rather tedious to support, especially the modification of activation records. For example, Smalltalk provides reification of activations through context objects. Since they are used to implement other mechanisms such as exception handling on the language level, contexts need to be entirely supported by the underlying runtime. We present an approach for efficiently implementing Smalltalk context objects in frameworks that do not support unrestricted modification of activation records.
Comparison of Lightning Optical Observation of Shenzhen Tower on the Guangdong-Hong Kong-Macao Lightning Location System

The paper Introduced the validating results of the Guangdong-Hong Kong-Macao lightning location system based on 3 High-speed camera recorded flashes striking the Shenzhen Meteorological Gradient Tower. The GD-HK-MC-LLS is one of the based source of the lightning warning system in Shenzhen. Based on the analysis of both data in these 3 flashes, it is shown that the the lightning return stroke detection efficiency of this LLS is about 92.9% and the lightning return stroke loaction error is better than 300 meters. The cloud ground flash identification accuracy is 84.6%, however, we found that the first RS on HSC data was always not recorded as the first RS in the LLS database, indicated a pointed improvement of the LLS is needed.
Between Clones and Snow-Flakes: Personalization in Intelligent Tutoring Systems

This work improves intelligent tutoring systems by combining the benefits of online personalization of contents with methods that have strong non-personalized long-term optimized policies. Our hypothesis is that students are very diverse but they are not all completely different from each other. We will generalize previous algorithms by creating a new approach that (1) creates profiles of students based on historical data, (2) in real time is able to recognize the type of student that is being encountered, (3) personalizes their experience taking into account the information of similar students. We perform several simulations to study the impact on teaching of the amount of data, the diversity of students, and errors in the estimation of parameters.
Solving Motion and Action Planning for a Cooperative Agent Problem Using Geometry Friends

In this paper we discuss the development of agents for the Geometry Friends game, which poses simultaneously problems of planning and motion control in an physics-based puzzle and platform 2D world. The game is used in a competition, held yearly, that challenges participants to solve single player and cooperative levels. Our work addresses the two. The approach followed uses Rapidly-Exploring Random Trees with strategies to accelerate the search. When comparing with other agents on the competition, our results show that our agents can solve the single player challenges without overspecialization and are also promising for the cooperative levels with either agent-agent and human-agent players.
Adaptive Algorithms for Filtering Ionograms and Measuring Channel Parameters to Estimate its Availability

Paper is addressed to the problem of increasing efficiency of data transmission across a HF radio channel. The algorithms for estimating the parameters of each radio channel from the useful frequency band by processing recorded ionograms were developed. Additionally, we developed the algorithms for estimating HF radio channel availability with the use of the sounding data to support digital HF communication systems.
Complexity Reduction of Multi-Level DP Quantization Through Inter-Level Redundancy Elimination

Designing an optimum quantizer can be treated as the optimization problem of finding the quantization indices that minimize the quantization error. One solution to the optimization problem, DP quantization, is based on dynamic programming. Some applications, such as bit-depth scalable codec and tone mapping, require the construction of multiple quantizers with different quantization levels, for example, from 12bit/channel to 10bit/channel and 8bit/channel. Unfortunately, conventional DP quantization optimizes the quantizer for just one quantization level. That is, it is unable to simultaneously optimize multiple quantizers. Therefore, when DP quantization is used to design multiple quantizers, there are many redundant computations in the optimization process. This paper proposes an extended DP quantization with a complexity reduction algorithm for the optimal design of multiple quantizers. Experiments show that the proposed algorithm reduces complexity by 20.3%, on average, compared to conventional DP quantization.
A Study on School Members’ Perceptions about the Operation of Innovational Education District: Focusing on the Saha Innovational Education District in Busan Area

The purpose of this study is to analyze the perceptions of the school members about the operation of the Saha Innovational Education District in the Busan Innovational Education District. In order to achieve the purpose of the research, a questionnaire survey was conducted on a total of 3,552 members, 547 teachers, 1,861 students, and 1,144 parents.BR The results of the study are as follows. First, teachers
Memorizing All for Implicit Discourse Relation Recognition

Implicit discourse relation recognition is a challenging task due to the absence of the necessary informative clues from explicit connectives. An implicit discourse relation recognizer has to carefully tackle the semantic similarity of sentence pairs and the severe data sparsity issue. In this article, we learn token embeddings to encode the structure of a sentence from a dependency point of view in their representations and use them to initialize a baseline model to make it really strong. Then, we propose a novel memory component to tackle the data sparsity issue by allowing the model to master the entire training set, which helps in achieving further performance improvement. The memory mechanism adequately memorizes information by pairing representations and discourse relations of all training instances, thus filling the slot of the data-hungry issue in the current implicit discourse relation recognizer. The proposed memory component, if attached with any suitable baseline, can help in performance enhancement. The experiments show that our full model with memorizing the entire training data provides excellent results on PDTB and CDTB datasets, outperforming the baselines by a fair margin.
Multi-Stream Single Shot Spatial-Temporal Action Detection

We present a 3D Convolutional Neural Networks (CNNs) based single shot detector for spatial-temporal action detection tasks. Our model includes: (i) two short-term appearance and motion streams, with single RGB and optical flow image input separately, in order to capture the spatial and temporal information for the current frame; (ii) two long-term 3D ConvNet based stream, working on sequences of continuous RGB and optical flow images to capture the context from past frames. Our model achieves strong performance for action detection in video and can be easily integrated into any current two-stream action detection methods. We report a frame-mAP of 71.30% on the challenging UCF101-24 [1] actions dataset, achieving the state-of-the-art result of the one-stage methods. To the best of our knowledge, our work is the first system that combined 3D CNN and SSD in action detection tasks.
Blockchain and Its Integration as a Disruptive Technology

Blockchain is the process of development in bitcoin. It's a digitized, decentralized, distributed ledger of cryptocurrency transactions. The central authorities secure that transaction with other users to validate transactions and record data, data is encrypted and immutable format with secured manner. The cryptography systems make use for securing the process of recording transactions in private and public key pair with ensuring secrecy and authenticity. Ensuring bitcoin transaction, to be processed in network, and ensuring transaction used for elliptic curve digital signature algorithm, all transactions are valid and in chronological order. The blockchain systems potential to transform financial and model of governance. In Blockchain, databases hold their information in an encrypted state, that only the private keys must be kept, so these AI algorithms are expected to increasingly be used, whether financial transactions are fraudulent, and should be blocked or investigated.
Randomized routing of virtual machines in IaaS data centers

Cloud computing technology has been a game changer in recent years. Cloud computing providers promise cost-effective and on-demand resource computing for their users. Cloud computing providers are running the workloads of users as virtual machines (VMs) in a large-scale data center consisting a few thousands physical servers. Cloud data centers face highly dynamic workloads varying over time and many short tasks that demand quick resource management decisions. These data centers are large scale and the behavior of workload is unpredictable. The incoming VM must be assigned onto the proper physical machine (PM) in order to keep a balance between power consumption and quality of service. The scale and agility of cloud computing data centers are unprecedented so the previous approaches are fruitless. We suggest an analytical model for cloud computing data centers when the number of PMs in the data center is large. In particular, we focus on the assignment of VM onto PMs regardless of their current load. For exponential VM arrival with general distribution sojourn time, the mean power consumption is calculated. Then, we show the minimum power consumption under quality of service constraint will be achieved with randomize assignment of incoming VMs onto PMs. Extensive simulation supports the validity of our analytical model.
Optimal Design of Monocular Stereo Vision System

To compensate for the deficiencies of current monocular and binocular stereo vision systems, a novel monocular stereo vision system configuration that simulates the human vision system but surpasses its functions is designed based on Stewart platform. For the sake of improving the searching range and ranging accuracy, throughout analyzing the kinematic and dynamic models, nondominated sorting genetic algorithm II (NSGA-II) is adopted to conduct the multi-objective optimization of the performance indexes including the kinematic performance index, the maximum inclination of the upper platform as well as the dynamic performance index under mechanical and dimensional constraints. The prototype was built according to the optimized structural parameters. Actual measurement data show that the monocular stereo vision system designed can meet the requirements.
Improving feature location accuracy via paragraph vector tuning

Feature location techniques are still not highly accurate despite advances in the field. This paper aims at investigating the impact of applying different tunings to paragraph vector to the feature location problem. It evaluates the influence of different artificial neural network (ANN) configurations for learning rate and negative sampling loss function in paragraph vectors training. The suggested weight configuration relies on the search for an adequate ANN learning rate and an adequate calibration of negative sampling skip-gram mode of the Doc2vec (DV) algorithm. A dataset with 633 feature descriptions, extracted from six open-source Java projects, organized within method granularity, is used for the empirical assessment. Our results suggest that feature location techniques benefit from the use of paragraph vector with systematic tuning. We show that an adequate update policy for ANN weights can increase feature location accuracy. An adequate calibration for negative sampling also improved accuracy. We got it with no default values of negative sampling pointed by literature. Moreover, an ensemble of learning rate policies and the use of a tuned DV negative sampling option had overcome state-of-the-art approaches. We show evidence of a relationship between hyper-parameter settings and accuracy gain. Modern paragraph vector approaches require adequate calibration to produce better results, and we have improved the accuracy of feature location process with proper tuning.
Determination of Water-Soluble Fraction in Soils

Method of allocation and analysis of mobile forms of chemical elements in soil are considered. Chemical characteristics of the extracted liquid phase of the soil reflect its water-soluble part properties. Two reference soil profiles on glaciolacustrine clays (soddy-eluvial-metamorphic soil) and sands (soddy podzol) were investigated on the Russian Plain. The comparison of the data obtained with the results of analysis for the whole soil allows assessment of a share of highly mobile and less mobile forms of chemical compounds. The behavior of macro- and microelements was studied in soilsSoils and their liquid phase. Mobile forms of chemical elements play the important role as the basis for the development of a model describing their migrationMigration on soil profile, used in prospecting geochemistryGeochemistry and in environmental geochemistryGeochemistry.
VISE

We present VISE, or Vehicle Image Search Engine, to support the fast search of similar vehicles from low-resolution traffic camera images. VISE can be used to trace and locate vehicles for applications such as police investigations when high-resolution footage is not available. Our system consists of three components: an interactive user-interface for querying and browsing identified vehicles; a scalable search engine for fast similarity search on millions of visual objects; and an image processing pipeline that extracts feature vectors of objects from video frames. We use transfer learning technique to integrate state-of-the-art Convolutional Neural Networks with two different refinement methods to achieve high retrieval accuracy. We also use an efficient high-dimensional nearest neighbor search index to enable fast retrieval speed. In the demo, our system will offer users an interactive experience exploring a large database of traffic camera images that is growing in real time at 200K frames per day.
Har Enhanced Weakly-Supervised Semantic Segmentation Coupled with Adversarial Learning

Semantic segmentation is a challenging computer visual task which needs enormous pixel-level annotation data. But collecting a large amount of pixel-level annotation data is labor intensive. To address this issue, our work focuses on weakly-supervised learning approach which combines the adversarial learning and localization ability of classification model together, in this way, data with different annotations can be fully utilized. Specifically, the adversarial learning encourages the high order spatial consistences thus offers a relatively reliable initial confidence map. And we find that the hybrid atrous rate (HAR) can improve the localization ability of the classification model, thus indicate more precise object-related regions, which serves as strong supervision information. We conduct experiments with different settings to demonstrate the effectiveness of this weakly-supervised learning approach. The results show that our approach can improve the performance of baseline adversarial learning from 73.2 to 75.1 (mIOU), which is pretty effective.
TextCube

Today's society is immersed in a wealth of text data, ranging from news articles, to social media, research literature, medical records, and corporate reports. A grand challenge of data science and engineering is to develop effective and scalable methods to extract structures and knowledge from massive text data to satisfy diverse applications, without extensive, corpus-specific human annotations. In this tutorial, we show that TextCube provides a critical information organization structure that will satisfy such an information need. We overview a set of recently developed data-driven methods that facilitate automated construction of TextCubes from massive, domain-specific text corpora, and show that TextCubes so constructed will enhance text exploration and analysis for various applications. We focus on new TextCube construction methods that are scalable, weakly-supervised, domain-independent, language-agnostic, and effective (i.e., generating quality TextCubes from large corpora of various domains). We will demonstrate with real datasets (including news articles, scientific publications, and product reviews) on how TextCubes can be constructed to assist multidimensional analysis of massive text corpora.
Neural Network based Online Flow Classifier Implemented by FPGA in Optical DCN

We implemented the online flow classifier based on neural network by FPGA. Experimental results show the classification accuracy reaches 99.8% and the training time of our scheme is 1719 times faster than CPU implementation.
Robust Covert Channels Based on DRAM Power Consumption

To improve the energy efficiency of computing systems, modern CPUs provide registers that give estimates on the power consumption. However, the ability to read the power consumption introduces one class of security concerns called covert channels, which are communication channels that enable one process to transmit a message to another one in a system where these processes were meant to be isolated. Our contribution consists in the first covert channel in which messages are transmitted by modulating the DRAM power consumption. The channel implementation outperforms similar proposals, achieving 1800 bps with 10% error, and 2400 bps with 15% error, when running on a notebook and on a desktop platforms, respectively, To test its robustness against application interference, we considered the channel’s performance when running concurrently with different benchmarks: MRBench, Terasort and LINPACK. When running on the notebook, the channel is fairly robust, achieving between 300 and 600 bps with around 10% error depending on the workload considered.
Millipixel image correlation for sub nm measurement of MEMS motion

A 1D digital image correlation algorithm capable of measuring the motion of microelectromechanical systems (MEMS) devices with sub nm resolution is presented. Successive micro photographic images are recorded, then subsets of the images are column averaged and the resulting 1D profiles are cross correlated. Factors that limit the correlation resolution are examined and methods for achieving millipixel level repeatability are discussed. Key to high resolution correlation of MEMS motion were low image noise and high sum of square of subset intensity gradients (SSSIG), a measure of image contrast. Using this correlation algorithm, the motion of MEMS thermal actuators was measured. Sub nm resolution was achieved using a wide range of image subsets, with measured standard deviations as low as 0.10 nm (0.6 millipixels).
Automatic Detection of Play and Break Segments in Basketball Videos Based on the Analysis of the Slope of the Basketball Court Boundary

Content-based video analysis is still a very intensively developed area of research in computer science. One of the most frequent purpose of content-based analysis is a video summarization. A basketball game coverage usually lasts for around two hours whereas the game itself is less than one hour. The basketball video can be modeled as a sequence of plays being defined as the segments when an important action occurs interleaved with breaks which can be ignored in video summarizing or highlight detection automatic processes. The paper proposes a method a basketball game segmentation into plays and breaks. The proposed method is based on the analysis of the slope of the basketball top court boundary. The tests performed in the AVI Indexer showed that the analysis of the slope of the playing field leads to the correct detection of more than 85% of play and break segments.
Moving Target Detection based on Multi-feature Adaptive Background Model

For the problems of shadow and the influence of dynamic background, a moving object detection method based on multi-feature adaptive background model is presented in this paper. Firstly, establish the chromaticity model and texture model respectively to represent each pixel. Then, adjust the model adaptively according to the background complexity and extract the moving object. Finally, find the inner contour of extracted object to fill, which makes the results more complete. At the same time, update the background model. The test results show that the presented method can eliminate the influence of shadow and dynamic background well. Compared with several other methods, it has a better overall performance.