Reviewerly/exHarmony huggingface dataset:
A Stereo Matching with Reconstruction Network for Low-light Stereo Vision

To solve the problem existing in the stereo matching of low-light images, this paper proposes a stereo matching with reconstruction network based on pyramid stereo matching network(PSMNet) and reconstruction module. In view of the characteristics of the low-light image with severe and complex noise, the image reconstruction module is added into the traditional stereo matching network for automatic denoising. In this process, the image reconstruction module assists the stereo matching module in model training, so as to reduce the influence of noise on stereo matching and obtain more accurate results. The proposed method has been evaluated and achieves good performance on the Middlebury dataset which is preprocessed. In addition, a low-light binocular platform is built to get the true low-light image and test our network in night environment, results show the disparity maps are more accurate compared with previous methods.
Recognition of Concordances for Indexing in Digital Libraries

We describe a system for the automatic transcription of books with concordances. Even if the recognition of printed text with OCR tools is nearly solved for high quality documents, the recognition of structured text, where dictionaries and other linguistic tools can be of little help, is still a difficult task. In this work, we propose to use several techniques for correcting the imperfect text recognized by the OCR software by taking into account both physical features of the documents and the redundancy of information implicit in concordances.
Adaptive Multi-Threshold Energy-Aware Virtual Machine Consolidation in Cloud Data Center

The ever-increasing energy consumption in cloud data centers not only translates to high operating costs, but also leads to negative impact on environment. Dynamic consolidation of virtual machine (VM) is proven to be an efficient way to improve resource utilization and reduce energy consumption in cloud data centers. In this paper, both the CPU utilization of system and SLA of users are taken into account to classify hosts and an adaptive multi-threshold energy-aware virtual machine consolidation algorithm is proposed to provide different consolidation mechanisms for different types of hosts. First, compound threshold is designed for overload hosts and will be adjusted dynamically to ensure both CPU utilization and SLA. Then a Q-Iearning based method is proposed to further divide underload hosts to save energy. Experiment results show that, our proposed algorithm can optimize resource utilization and reduce energy consumption of the data centers while minimizing the SLA violation rate and the number of migrations.
Improving the educational process in the development of mathematical disciplines based on the use of modern computer technologies

The article considers the issue of improving the educational process through the use of computer technology. Attention is paid to the optimization of the educational process, the most important components of the learning process are analyzed, such as independent work, methodological support and management of students’ independent work; quality control assimilation of the studied material. Various tasks can be solved using the Microsoft Excel program, while the authors propose to perform calculations using application packages of mathematical programs, which is more preferable. In this case, computation time is saved, a graphical solution is easily presented, and it is also possible to carry out calculations using various methods and with different calculation parameters, comparing them with each other. The authors emphasize the importance of organizing self-fulfilling assignments by students. It is proposed to use knowledge testing not only as an element of control, but also as a management of the learning process. Examples of the organization and results of testing are given, the positive impact of testing on the educational process, which allows for preliminary, current, thematic and final control of knowledge, is substantiated.
Miniature 3D Depth Camera for Real-time Reconstruction

This paper developed a miniature 3D vision system for dynamic depth recovery. Our system is constituted by a DOE projector and two cameras. A single pattern with pseudo-random speckles was projected onto the surface of the target object and the coupling pattern was imaged by the stereo cameras. An optimal camera calibration algorithm is adopted to guarantee the measurement accuracy. The speckle pattern projected through a diffractive optical element (DOE) of size 8mm×8mm. The baseline between the stereo cameras is as short as 50mm. The system has the advantages of simple structure, small size, and low cost. It can be easily integrated into mobile devices such as tablet computers and mobile phones. In comparison with several other mature products, the proposed 3D depth camera can achieve 3D reconstruction with higher accuracy.
Speaking ability media implementation in learning teaching on public high school

Abstract Javanese language is one of the local wisdom in Javanese society. It has uniqueness where varied language in the communication process. It is included the utterance level which has its own function and role It will influence the student in building character, self-motivation and so on. If the teaching-learning material can be combined with digital media, it will give the student acceleration to comprehend the lesson. The study is for finding out the students’ response to how intensive the implementation of media usage in speaking of a high school student in Purworejo. There are two points-in focus: response and feedback. The points have four indicators; relevancy 86.3%, attention 83%, satisfaction 82.4%, and self-confidence 83.6%. The questioner shows that the students gave positive feedback to the media usage in speaking.
Detecting Global Exam Events in Invigilation Videos Using 3D Convolutional Neural Network

This paper designs a structure of 3D convolutional neural network to detect the global exam events in invigilation videos. Exam events in invigilation videos are defined according to the human activity performed at a certain phase in the entire exam process. Unlike general event detection which involves different scenes, global event detection focuses on differentiating different collective activities in the exam room ambiance. The challenges lie in the great intra-class variations within the same type of events due to various camera angles and different exam room ambiances, as well as inter-class similarities which are challengeable. This paper adopts the 3D convolutional neural network based on its ability in extracting spatio-temporal features and its effectiveness in detecting video events. Experiment results show the designed 3D convolutional neural network achieves an accuracy of its capability of 93.94% in detecting the global exam events, which demonstrates the effectiveness of our model.
Q-Learning Classifier

Machine learningMachine learning (ML) is aimed at autonomous extraction of knowledge from raw real-world data or exemplar instances. Machine learningMachine learning (Barreno et al. in Proceedings of the 2006 ACM symposium on information, computer and communications security, pp 16–25, 2006 [1]) matches the learned patternPatterns with the objects and predicts the outcome.
Semantic Round-Tripping in Conceptual Modelling Using Restricted Natural Language

Conceptual modelling plays an important role in information system design and is one of its key activities. The modelling process usually involves domain experts and knowledge engineers who work together to bring out the required knowledge for building the information system. The most popular modelling approaches to develop these models include entity relationship modelling, object role modelling, and object-oriented modelling. These conceptual models are usually constructed graphically but are often difficult to understand by domain experts. In this paper we show how a restricted natural language can be used for writing a precise and consistent specification that is automatically translated into a description logic representation from which a conceptual model can be derived. This conceptual model can be rendered graphically and then verbalised again in the same restricted natural language as the specification. This process can be achieved with the help of a bi-directorial grammar that allows for semantic round-tripping between the representations.
Towards Understanding Lifelong Learning for Dialogue Systems

Lifelong learning is the ability of a software system to adapt to new situations during its lifetime. We explore how this paradigm can be applied to dialogue systems, how it might be implemented, and how we can evaluate the lifelong learning progress.
ТРАНСМЕДІЙНІ НАВИЧКИ У ПРОФЕСІЙНІЙ МИСТЕЦЬКІЙ ОСВІТІ: АНАЛІЗ ЗАРУБІЖНОГО ДОСВІДУ

and the language of indigenous peoples.It is revealed that the current tendency in educational policy is transition from assimilation policy to integration policy.It is shown that modern education of indigenous peoples in the USA and Canada is based on the principles of multiculturalism, democracy, humanism, nationalist approach, taking into account educational needs and labor market needs.
A New Method for Constructing Squeezed States for the Isotropic 2D Harmonic Oscillator

We introduce a new method for constructing squeezed states for the 2D isotropic harmonic oscillator. Based on the construction of coherent states in [1], we define a new set of ladder operators for the 2D system as a linear combination of the x and y ladder operators and construct the SU(2) coherent states. The new ladder operators are used for generalizing the squeezing operator to 2D and the SU(2) coherent states play the role of the Fock states in the expansion of the 2D squeezed states. We discuss some properties of the 2D squeezed states.
Predicting Digging Success for Unmanned Aircraft System Sensor Emplacement

We have developed an autonomous, digging, Unmanned Aircraft System (UAS) for sensor emplacement. A key challenge is quickly determining whether or not a particular digging activity will lead to successful emplacement, thereby allowing the system to potentially try another location. We have designed a first-of-its-kind decision-making algorithm using a Markov Decision Process to autonomously monitor the activity of a digging UAS activity to quickly decide if success is likely. Further, we demonstrate through many experimental trials that our method outperforms other decision-making methods with an overall success rate of 82.5%.
KNN-LOF Algorithm Based on Skew Detection and Correction for Myanmar Handwritten Documents

Skew detection and correction play key roles in helping character recognition processes to achieve more accurate recognition results. In this paper, we bring the idea of detection of outlier into detection skewness of Myanmar handwritten characters in printed documents. For this purpose, we present a novel detection and correction algorithm based on the k-nearest neighbor-local outlier factors algorithm. The aim is to detect local skewness that quite happens in the human's handwritten process which is in the form of fluctuation in written lines of text. Based on local skewness detection, we detect and further correct global skewness of input character image. In order to reveal the efficiency and performance of the proposed algorithm, we first prepare the datasets composed of different handwritten styles with various fonts and skew angles. We afterward perform experiments to witness how this proposed algorithm can perform well in local and global skewness detection and correction in Myanmar handwritten documents. The results show that we achieve better results in different experimental settings.
Overcoming spatio-angular trade-off in light field acquisition using compressive sensing

In contrast to conventional cameras which capture a 2D projection of a 3D scene by integrating the angular domain, light field cameras preserve the angular information of individual light rays by capturing a 4D light field of a scene. On the one hand, light field photography enables powerful post-capture capabilities such as refocusing, virtual aperture, depth sensing and perspective shift. On the other hand, it has several drawbacks, namely, high-dimensionality of the captured light fields and a fundamental trade-off between spatial and angular resolution in the camera design. In this paper, we propose a compressive sensing approach to light field acquisition from a sub-Nyquist number of samples. Using an off-the-shelf measurement setup consisting of a digital projector and a Lytro Illum light field camera, we demonstrate the efficiency of the compressive sensing approach by improving the spatial resolution of the acquired light field. This paper presents a proof of concept with a simplified 3D scene as the scene of interest. Results obtained by the proposed method show significant improvement in the spatial resolution of the light field as well as preserved post-capture capabilities.
Dynamic Look-up Table Method for Optimizing the Training of Deep Neural Networks on Many-Core Architecture

Due to the excessive training parameters and computation of Deep Neural Network (DNN) models, we have witnessed the training time increases with the continued increase of the scale of DNN models. Convolution computation is the key step of feature extraction in DNN models and occupies about 90% of the computation operations in DNN models. It is therefore of great necessity to accelerate the speed of convolution calculation in order to improve the training efficiency of system. Currently, the traditional method is to transform convolution computation into matrix multiplication and execute on many-core architecture such as Graphic Processing Unit (GPU). However, due to matrix conversion and high computational complexity of matrix multiplication, these methods based on matrix multiplication such as Caffe, consume a lot of time in accessing memory and need to copy redundant data in process of matrix conversion. The low capacity of GPU memory further leads to the low training efficiency of DNN models. Therefore, we orchestrate a dynamic look-up table method instead of matrix multiplication to realize convolution calculation in order to optimize the training of DNN on many-core architecture. We further improve the parallelism of the look-up table method in a more fine-grained parallelism by paralleling the building convolution table and look-up table operation based on GPU. In our experiment, we tested and trained MINIST, CIFAR-10, CIFAR-100 and ImageNet data sets respectively. Experiments show that, compared to the original Caffe, the proposed dynamic look-up table method referred as LTP-Caffe can achieve up to 31% speed-up ratio in the training process of DNN models. Experiments further show that LTP-Caffe and Caffe are comparable in accuracy, but the iteration speed of LTP-Caffe is more quickly than the Caffe.
Digital image processing and recognition technology for classification and recognition of hydrothorax cancer cells

Pathological diagnosis is the most common and reliable method of cancer diagnosis, but the technology of pathological diagnosis is relatively backward. It is an urgent problem to identify and classify the pathological pictures of cancer cells. Based on this, the digital image processing and recogni tion technology are analyzed for the classification and recognition of hydrothorax cancer cells. There is a big difference in the morphology of pleural effusion cancer cells, and uncertainty, so the edge detection algorithm is improved, with the simulated edge detection method used to extract information. After image segmentation, feature extraction is of vital importance for cell image classification. A method of block statistics based on Gabor coefficient is proposed. Firstly, the cell image is filtered by multi-scale and multi-directional filtering, then the average and variance are calculated, and the image is divided into several blocks to solve the problem of large amount of data. Finally, BP neural network is established to input the morphological characteristics of hydrothorax cells, and the results are classified directly. After the experiment, the proposed classification method can improve the classification effectiveness; the design model can accurately identify the breast water cancer cells, and can be effectively applied to the early diagnosis of breast water cancer cells.
UTILIZATION OF VIRTUAL REALITY FOR ENGINEERING DISCIPLINE SELECTION

Fully immersive Virtual Reality (VR) has the ability to take students to remote locations or allow them to interact with a simulated environment without having to leave campus. This study investigated whether VR is an effective tool to help students increase their knowledge and understanding of various engineering programs and as a result increase their confidence when selecting an engineering discipline. Two purpose-built VR applications were developed; one for Mining Engineering and one for Engineering Chemistry at Queen’s University. The intention of the VR applications was to introduce students to the program of study by bringing them directly into the classroom or lab environment as well industrial sites where engineers in that discipline work. Each VR application took 5-7 minutes to complete and was intended to be interactive. To determine if the VR applications increased the student knowledge, understanding and confidence when selecting an engineering discipline, a series of questions were added to the annual first year engineering survey at Queen’s. Results from the study showed that the VR applications strongly or moderately increased the knowledge and understand of numerous students who attend the Mining Engineering and Engineering Chemistry discipline selection nights. Furthermore there were a number of students whose confidence when making their discipline selection was enhanced by the VR applications.
Vision Based Robust Autonomous Landing of a Quadrotor on a Moving Target

Autonomous landing is the most difficult maneuver in a quadrotor mission because of challenges such as external disturbances and localization error, both of which cause deviation from the desired trajectory and may result in a crash. In this paper, we present a guidance framework that allows a multirotor to land on a moving target accurately. The framework utilizes onboard vision to detect and estimate the landing target parameters. We then analyze the effects of environmental disturbances, and abrupt changes in the motion of the landing target to allow us to investigate them from a computer vision perspective. This will aid in development of a robust autonomous landing strategy for a moving target. We present robustness results through outdoor hardware experiments.
An Ideology for the Prediction of Critical Haplotype Blocks of Variants in Genes (Cyp2c9 And Vkorc1) for Warfarin (Anticoagulant) Drug Dosage to Treat Heart Patients Efficiently by Using Ml (Machine Learning) and Data Stream Mining Techniques

Now a day's on time treatment of heart diseases is a very critical part of medical diagnoses. So far there are total 50 SNP (Single Nucleotide Polymorphism) diagnosed that are responsible for the heart problems. But it is very hard to study all of the SNP together because of their different base pairs' locations or changes in base pairs positions (variations in genetic code A C G T). These all 50 SNP are present in all individuals with different variations, it is a tough job to calculate all the changes in this SNP set as there are total of (50^50) positions to calculate which is making it a huge data set. For acquiring a data set of all these positions, we will need some good Data Stream Mining (data mining techniques) to find out all the possible locations of all the variants responsible for the heart problems. In this research paper, we are giving a short analysis and introduction to the problem of heart patients drug dosage associated with anticoagulant (Warfarin) and its risks, solution for the challenge of calculating all variants of two genes (CYP2C9 and VKORC1) and advantages of the proposed solution in the future.
Probability as a modal operator: the possibilities of its combination with other modalities

Abstract In this work we examine some of the possibilities of combining a simple probability operator with other modal operators, in particular with a belief operator. We will examine the semantics of two possible situations for expressing probabilistic belief or the lack of it, a simple subjective probability operator (SPO) versus the composition of a belief operator, plus an objective modal operator (BOP). We will study their interpretations in two probabilistic semantics: a relational Kripkean one and a variation of neighbourhood semantics, showing that the latter is able to represent the lack of probabilistic belief more directly, just with the SPO, whereas relational semantics needs the combination of BOP probability to represent lack of belief.
Performance Evaluation of Methods for Mining Frequent Itemsets on Temporal Data

Data mining is a method, used to extract usable and valuable information from the bulk of the data. Frequent data mining is an interesting task that is used to find frequent patterns from the database. In this paper, we used to perform frequent item set mining on temporal data. Temporal data contains data that primarily ranges over time. The idea of the time hierarchy is introduced to generate rules from temporal data. In this paper, we try to solve the problems using three popular data mining algorithms such as FP growth, Eclat and Apriori algorithm. The main focus of this study is to generate efficient algorithms that consume very less runtime and present the more frequent item set from the dataset. We evaluate our algorithms through experiments.
Vertex Degree Aware Two-Stage Graph Partitioning for Distributed Data-Flow Computing of Deep Learning

To solve the data-flow graph partitioning problem of deep learning in distributed architecture, this paper presents a vertex degree aware two-stage graph partitioning method. At first stage, a vertex degree aware method is designed to preliminarily partition data-flow graph; at second stage, a fuzzy clustering based edge re-distributing algorithm is formulated for load balancing across computing nodes. Combining two stages, the framework of the two-stage method is constructed. Theoretical analysis demonstrates that the proposed method can achieve smaller upper bound of communication cost than comparison methods. Experimental results validate that the proposed method outperforms comparison methods in terms of replication factor, edge imbalance and execution time of data-flow graph computing.
Provision and Visualization of Solar Radiation Data for Energy Management System

A data interface system was constructed to enable the effective use of quasi-real-time solar radiation and solar power generation estimates based on data from the Himawari satellite in the energy management system. As one of the efforts introduced to apply meteorological data to the management of energy systems with renewable energy, we provided weather information to our solar car race team. We constructed a data interface system that provided data via web forms on the Azure cloud and on-premises servers. The implementation of a JSON format WebAPI enabled seamless data provision.