Reviewerly/exHarmony huggingface dataset:
Digital Archives Supporting Document Content Inference

Authoring documents is based on analysis of existing documents, collecting and processing their data, which is time consuming creative work. New documents may use parts of existing ones, e.g., header data, names of persons and titles of organizations, tabular data and their description, footers, etc. Semantic description of frequently used classes of documents allows simplifying the authoring process. The paper presents an approach to creation of open document catalogs that involves technologies of Linked Open Data, document templates, and logical inference for deducing parts of documents from data of the catalogs. The approach is being realized as open software toolset and services on the base of other open source systems. The software under development is used to construct information system components in various application areas, e.g., for synthesis educational processes regulations in departments of Irkutsk State University and organizations of joint accounting for source data collection.
Weighted SVMBoost based Hybrid Rule Extraction Methods for Software Defect Prediction

The software testing efforts and costs are mitigated by appropriate automatic defect prediction models. So far, many automatic software defect prediction (SDP) models were developed using machine learning methods. However, it is difficult for the end users to comprehend the knowledge extracted from these models. Further, the SDP data is of unbalanced in nature, which hampers the model performance. To address these problems, this paper presents a hybrid weighted SVMBoost-based rule extraction model such as WSVMBoost and Decision Tree, WSVMBoost and Ripper, and WSVMBoost and Bayesian Network for SDP problems. The extraction of the rules from the opaque SVMBoost is carried out in two phases: (i) knowledge extraction, (ii) rule extraction. The experimental results on four NASA MDP datasets have shown that the WSVMBoost and Decision tree hybrid yielded better performance than the other hybrids and WSVM.
Weakly Supervised Semantic Segmentation Based on Superpixel Sampling Clustering Networks

Semantic segmentation models based on deep neural networks depend highly on a large number of pixel-level annotations, while pixel-level annotations require a lot of manpower and resources. Therefore, we propose a weakly supervised image semantic segmentation algorithm based on deep convolutional neural network. This method only the quantity image-level annotations to perform image semantic segmentation. In this paper, the non-conductible superpixel sampling algorithm SLIC is transformed into derivable by relaxing restrictions, added to the deep convolutional neural network to achieve end-to-end training, and the center of the superpixel is dynamically obtained step by step. The saliency map with semantic information is used as the seed, and the seed is used as the cluster center. Then the superpixel block is used as the basic unit when seed grows, and the dynamically generated superpixel module is clustered using the K-Means algorithm. Set the number of categories for image segmentation to the number of clusters of the K-Means algorithm. Until a steady state is reached. Finally, the segmentation results optimized by CRF algorithm to generates fined graind contour. The algorithm is tested in the open challenge data set PASCAL VOC 2012, which is superior to the existing weakly supervised semantic segmentation algorithm, the convergence speed is significantly improved, and the segmentation result is more refined.
Digital Design and Research of Ink Art Based on Infrared Interactive Projection Technology - Taking the Work of “Listen to the Ink” as an Example

In the interactive desktop projection “Listening to Ink”, through the appreciation of the existing digital ink art, we try to use the infrared interactive projection technology to combine the ink art and the interactive device, to achieve a new interactive way, breaking through the traditional inertial cognition in ink art. The infrared interactive desktop projection system of “Listen to the Ink” works includes a projection module, an infrared transmitting module and a sensing module. Firstly, an infrared interactive desktop projection system consisting of three modules was built; the infrared light reflected by the finger touch or the laser pen was illuminated by the camera; then the information was transmitted to the image processing module, and multi-point positioning and analysis were performed; After judging the operation intention, the processing result was transmitted to the corresponding application in a standard form, and corresponding operations were performed. Finally, a digital ink art based on interactive projection was completely realized, which provided new ideas for exploring ink digital art.
Study on Automatic Generation of Teaching Video Subtitles Based on Cloud Computing

Teaching videos are being used in increasing numbers in teaching. As an important element of teaching video, video subtitle is time-consuming and laborious. Based on the speech recognition interface of Baidu Cloud Computing Open Platform, this paper designs and implements an automatic subtitle generation system for teaching video. The system consists of four steps, namely, audio extraction, audio segmentation, speech recognition, and subtitle generation. Finally, it generates standard SRT subtitle format of teaching video. Taking the teaching video of Minjiang University as an example, the experiment shows that the system has high accuracy of speech recognition, which can meet the requirements of daily subtitle production and avoid the time-consuming and laborious manual subtitle adding process.
Integration of movements in methodical activities of students of a pre-school teacher college

Predmet istraživanja je povezivanje više oblasti vaspitno-
Research on Intelligent Campus Monitoring Management System Based on Deep Neural Network Algorithm

Abstract With the development of the economy and the advancement of science and technology, the informationization of education management has become an inevitable trend, and the construction of a intelligent campus cannot be delayed. In order to comply with the current social development, major universities have invested heavily in IoT equipment to improve the management level of the school. Based on the large number of video cameras installed in colleges and universities, this paper uses the monitoring image big data and the CNN algorithm to train a set of graphical features that are easy to manage, without adding additional hardware purchases. In addition, combined with the camera position and other information, an intelligent campus monitoring system is built to help the campus administrator to understand the people flow, traffic flow and physical condition in real time, which is convenient for enhancing the effective monitoring and management of campus crowds, environment and security.
iPads or computer labs? A technical communication classroom study

For some schools, using iPads instead of computer labs can be a cost- and space-saving endeavour. We decided to test students’ attitudes toward tablet functions and confidence in classroom objectives in three technical communication courses by administering a pre- and post-test. Two classes used iPads and one did not (the control). We analysed the data and triangulated it with classroom observations. We found that attitudes toward tablets did not change but confidences did, particularly in document design. We analyse the reasons for this and look at potential takeaways on a pedagogical level before addressing if these results can guide administrators contemplating purchasing tablets for the classroom. The study looks at current research in tablet pedagogy, discusses our classroom and study methodology, analyses the results, triangulates student responses with our observations and implicates conclusions while looking to future research angles.
PriDe: A Quantitative Measure of Privacy-Loss in Interactive Querying Settings

This paper presents, PriDe, a model to measure the deviation of an analyst's (user) querying behaviour from normal querying behaviour. The deviation is measured in terms of privacy, that is to say, how much of the privacy loss has incurred due to this shift in querying behaviour. The shift is represented in terms of a score - a privacy-loss score, the higher the score the more the loss in privacy. Querying behaviour of analysts are modelled using n-grams of SQL query and subsequently, behavioural profiles are constructed. Profiles are then compared in terms of privacy resulting in a quantified score indicating the privacy loss.
Research on a New Model Structure for Unknown Radar Emitter Signal Sorting

Aiming at the defects and shortcomings of the traditional radar emitter signal sorting model structure, this paper proposes a new sorting model structure for the unknown source signal in complex dense electromagnetic environment, comprehensively utilizing the inter-pulse parameters and intra-pulse of the signal. Modulation features are sorted. The paper studies the problem of radar emitter signal sorting with a new idea. The characteristics and innovation of the new sorting model structure are expounded, and the functions and functions of each component of the model structure are briefly described.
Auto-Scaling Cloud Resources using LSTM and Reinforcement Learning to Guarantee Service-Level Agreements and Reduce Resource Costs

Abstract Auto-Scaling cloud resources aim at responding to application demands by automatically scaling the compute resources at runtime to guarantee service-level agreements (SLAs) and reduce resource costs. Existing approaches often resort to predefined sets of rules to add/remove resources depending on the application usage. However, optimal adaptation rules are difficult to devise and generalize. A proactive approach is proposed to perform auto-scaling cloud resources in response to dynamic traffic changes. This paper applies Long Short-Term Memory (LSTM) to predicting the accurate number of requests in the next time and applies Reinforcement Learning (RL) to obtaining the optimal action to scale in or scale out virtual machines. To validate the proposal, experiments under two real-world workload traces are conducted, and the results show that the approach can ensure virtual machines to work steadily and can reduce SLA violations by up to 10%-30% compared with other approaches.
Simple Method for Detecting Visual Saliencies Based on Dictionary Learning and Sparse Coding

The human brain and the human vision system, in front of a given scene, focuses on regions with more information. Many pieces of research in the field of psychology, neuropsychology and cognitive neurosciences, have detailed the way in which the extraction of such information is carried out, even proposing models of the functioning of visual attention. From these models, computer scientists have proposed computational variants, which imitate human visual attention. This field of research is known as saliency detection on images. In this paper, a new and simple approach for detecting visual saliencies based on Dictionary Learning and Sparse Coding is presented. Our method first subdivides the image into full overlapped patches and runs a dictionary learning over them for obtaining its sparse representation. Then, by analyzing the sparse coding matrix, we compute how many image patches a dictionary atom affects in order to classify them as frequent or rare. Then, we calculate the saliency map of the image according to the composition of the image patches, i.e. an image patch is considered salient if it is mainly composed of rare atoms, an atom is rare whether it affects a few patches. Numerical results validate our method.
Selective pressure in constrained differential evolution

Differential Evolution (DE) is a highly competitive numerical optimization method for constrained optimization problems. In this study, a new selective pressure technique is applied in DE, which considers both function values and constraint violations with respect to ϵ-constraint level. The new modification was tested against known variants of constrained DE, and it is shown that selective pressure allows significant improvement of algorithm performance in various application scenarios.
A Deep Recursive Neural Network Model for Fine-Grained Opinion Classification

In recent times, deep neural networks (DNN) have acquired greater significance in providing solutions to many deep learning tasks. Particularly, recursive neural networks (RNN) have been efficiently utilized in exploring semantic compositions for natural language content represented with structured formats (e.g. parse-trees). Despite the fact that RNN are deep in structure, yet they fail to exhibit hierarchical representations observed in traditional deep feed-forward networks (DFNN) and also in revolutionary deep recurrent neural networks (DRcNN). However, the notion of depth can be incorporated through stacking multiple recursive layers, which results in deep recursive neural networks (DRNN). On the other hand, enhanced word spaces offer added benefits in capturing fine-grained semantic regularities. In this paper, we address the problem of fine-grained opinion classification using DRNN and word embeddings. Furthermore, the efficiency of DRNN model is estimated through the conduction of a series of experiments over several opinion datasets. The results report that the proposed DRNN architecture achieves better prediction rate for fine-grained classification when compared with conventional shallow counterparts that employ similar parameters.
User Test Logger: An Open Source Browser Plugin for Logging and Reporting Local User Studies

Tooling user studies tasks is fundamental to reduce the burden of evaluators, practitioners, facilitators, and observers during user interface (UI) evaluations. However, available tools for collecting data beyond clicks are usually paid, count on limited vocabulary of UI events, have complex setup, or do not provide easy download of captured data. In this context, we present a general-purpose data logger for local user studies able to capture up to 40 different types of UI events. It provides detailed data logging, downloading, and reporting features. The evaluation of the tool was performed in a focus group with 4 Human-Computer Interaction (HCI) specialists. The focus group resulted in new features and changes/corrections in existing features. The redesigned tool is open source and freely available to the HCI community. The tool can be used by HCI specialists or data scientists as a core tool for empirical evaluations or an additional data source in mixed methods assessments, offering greater control on types of events captured and counting on multiple types of report.
Quantum Algorithm for Support Vector Machine with Exponentially Improved Dependence on Precision

Supervised machine learning is the classification of new data on the basis of already classified training data, from which support vector machine is one of the most significant model. In 2014, Rebentrost, Mohseni and Lloyd showed that for a suitable specified N × M data set, there is a quantum algorithm that constructs a support vector machine model in quantum settings. If the data set is well-defined, their algorithm runs in time poly(logMN, 1/ε), where ε is the desired precision of the output state. We in this paper present an improved quantum support vector machine model whose running time is polynomial in log(1/ε), exponentially improving the dependence on precision while keeping essentially the same dependence on other parameters.
A Pen-Grip Shaped Device for Estimating Writing Pressure and Altitude

A stylus, which is a pen-shaped instrument, is often used to input commands to touch-screen devices. Especially, the stylus is suitable for creation of digital illustrations and handwriting. Most styluses are capacitance-type, which are unable to detect writing pressure and altitude that are important in the expression of drawing and writing. Moreover, although digitizer styluses such as Apple pencil have a function for detecting writing pressure and altitude, it is limited by the dedicated touch panel due to the specifications. In this research, we propose a pen-grip shaped device for estimating writing pressure and altitude from the force of the fingertip. The proposed device consists of the pen-grip part and 3 axis force sensors embedded into the position that is related to the state of the stylus. In the estimations for writing pressure, the result showed that the proposed method enables accurate estimation in many cases, although it did not work well in the cases of a low or a drastically changed writing pressure. On the other hand, the result of the estimated altitude showed low accuracy.
Learning Discriminative and Complementary Patches for Face Recognition

The ensemble of convolutional neural networks (CNNs) has widely been used in many computer vision tasks including face recognition. Many existing ensembles of face recognition CNNs apply a two-stage pipeline to target performance improvement [10], [20], [22], [23], [29]: (1) it trains multiple CNNs separately with many face patches covering different facial areas; (2) the features derived from different models are aggregated off-line by different fusion methods. The well-known face recognition work, DeepID2 [20] trains 200 networks based on 200 arbitrarily chosen facial areas and chooses the best 25 ones to achieve impressive performance. However, it is very time-consuming to train so many networks. In addition, a brute-force like way of choosing facial patches is used without knowing which face patches are complementary and discriminative. It might be lack of generalization capability for cross-database applications. To solve that, we propose a novel end-to-end CNN ensemble architecture which automatically learns the complementary and discriminative patches for face recognition. Specifically, we propose a novel Patch Generation Engine (PGE) with Patch Search Spatial Transformer Network (PS-STN) and ROI shrunk loss to perform the patch selection process. ROI shrunk loss enlarges the distance of learned features in spatial space and feature space and learn complementary features. In order to get final aggregated feature, we use a supervised fusion module named Two Stage Discriminative Fusion Module (TSDFM) which effective to capture the global and local information and further guide the PGE to learn better patches. Extensive experiments conducted on LFW and YTF datasets show the effectiveness of our novel end-to-end ensemble method.
Lung Cancer Detection with FPCM and Watershed Segmentation Algorithms

Lung malignant growth drives the causes among disease related passing around the world. WHO information showed 1.69 million passing away in 2015. An early disease investigation can enhance the viability of therapy and also upgrades victim's possibility of exist. The Precision of disease investigation, Rate and Computerization levels decides the achievement of CAD frameworks. In this paper, we worked with the few effectively existing frameworks and discovered the best methodology for recognition of tumors. This article talks about the division with FPCM and Watershed Transform calculations. Computer-aided design includes six stages – a. Image Acquisition, b. Image Pre-processing, c. Lung Region Extraction, d. Segmentation, e. Feature Extraction and f. Classification. Firstly, RGB picture is transfers to dark scale thus the picture clamour is a greater distanced from original picture. Next essential job is division which is performed by utilizing Watershed Transform display. Watershed method characterizes the dark scale picture. After division, highlight extraction is dissected by mean of the fragmented lung region lastly delineation of lung lumps classifies with the help of SVM method. By using this method we accomplished precision: 99% and the time are less than 2 s. The proposed frameworks were executed in MATLAB programming.
Monitoring microclimate-induced deformations on hygroscopic materials using conoscopic holography sensors

Changes of temperature and relative humidity of the environment where the artefact is stored can cause deformations of the surface that can harm the object or affect the repeatability of the measurement. Currently, there are very few techniques and approaches that allow studying non-invasively the surface deformations of objects of different sizes maintaining accuracy of the order of micrometres. To address this problem, three different systems based on a conoscopic holography sensors have been tested on hygroscopic samples. Moreover, we implemented a low-cost apparatus for controlling relative humidity. Eventually, we investigated procedures that use reference standards, insensitive to thermal and hygrometric variations with the scope to assess short and long-term drifts of the instrumental set-up. We tested the approach on samples of paper and lambskin parchment and on a wooden icon.
A Flexible Assessment Platform for Middle School Supported on Students Goals

Rooted in many education system, assessment by written exams in a live only time evaluation is a traditional way to judge the student learnings. This live only time evaluation method leads to dysfunctionalities such as high levels of stress and the assumption, by the students and by some teachers, that exams reflect the educational goals. This paper presents the Assessment Centers in Schools (ACiSs) project which aims to mitigate some of the identified problems. The final goal of the ACiSs system is a web platform which allows students to have an out of class assessment system, reducing factor like stress by granting the possibility to complete more than one examination and consequent approval per module of the studied subjects. The ACiSs system is comprised by a dataset of questions, properly categorized, which allows the automatic generation of exams wordings for summative and formative assessment. In the latter case, exams can be generated in order to mitigate individual weaknesses in the subject’s curricular goals.
Improved Non-local Means Algorithm for Image Denosing

Abstract In order to solve the problem that Non-local means method (NLM) results in a weaker retention and noise immunity, an improved non-local means (INLM) is proposed. This paper introduces bilateral structure tensor combined with the spatial distance and the gradient distance to weight the structure tensor matrix. It has stronger noise immunity and can extract the texture structure more accurately. Mahalanobis distance is applied as a measure of the similarity of image pixels to suppress noise interference. Analyses and simulation results prove the validity of the proposed algorithm.