Reviewerly/exHarmony huggingface dataset:
VR2ML: A Universal Recording and Machine Learning System for Improving Virtual Reality Experiences

We developed software for recording and reusing user actions and associated effects in various VR applications. In particular, we implemented two plugin systems that allow 3D motion data recorded in VR to be transferred to a machine learning module without programming. One is a system that runs on Unity and records actions and events in VR, and it is called “VRec.” The other is a system for using data recorded with VRec for machine learning, called “VR2ML.”
Performance Evaluation of Ensembles Algorithms in Prediction of Breast Cancer

Breast Cancer is the most dominant cause of mortality in women. Early diagnosis and treatment of the disease can stop the spreading of cancer in the breast. Due to this nature of the problem, accurate prediction is the most important measure of the predictive model. This paper proposes the comparison of ensemble learning techniques in predicting breast cancer. This approach is widely used for performance improvement of the predictive task. The ensembles algorithms used in this research study are AdaBoost, Random Forest, and XGBoost. The result indicates that the random forest is the best predictive model for this dataset. The model has the following performance measure, accuracy 97%, sensitivity 96%, and specificity 96%. The experiment is executed using scikit-learn machine learning library. With this high level of accuracy offered by the model, the model can help the doctor to identify whether the patient has malignant or benign tumor cancer cells with high precision.
Precise String Domain for Analyzing JavaScript Arrays and Objects

JavaScript is a scripting language that is used for creating web pages. It is widely used and a top contender in realworld usage. JavaScript has many dynamic features that makes it challenging to static analysis. Arrays and objects are one aspect that needs more attention. Array elements are inherently sparse where elements can be added at noncontiguous locations. Object properties can be dynamically accessed and they can also store values of different types. Existing JavaScript static analyzers use constant propagation domains that lose huge amount of precision when analyzing arrays and objects. In this paper, we propose a string abstract domain that is capable of capturing precise information about arrays and objects. The domain we propose provides useful information for the detection of some errors such as the attempt to access a nonexistent element or property. We also define the abstract semantics of some crucial operations over this domain.
IAS’2019 Cryptographic Protocol Allowing to Protect the Key in the Open Communication Channel

The proposed work considers the development of the cryptographic protocol IAS2019, which allows you to protect the public key in an open communication channel. The advantage of this protocol is that it can be integrated with other cryptographic protocols, thereby increasing their security properties, and also has a higher level of security compared to foreign counterparts.
Privacy Preservation Data Mining and Security

The paper is a research report on data mining - a subject which has been studied widely and whose application spans a wide range of field including the Internet of Things (IoT) and business developments. Data mining techniques come along with serious challenges due to the disclosure of private information and violation of privacy. Privacy preservation data mining and security (PPDMS) is a vital branch in the science of data mining and an essential topic in privacy preservation. The subject has gained significant attention in the recent past. PPDMS protects sensitive and private data from being disclosed without permission from the providers or data owners. The paper gives a research report on PPDMS techniques. It outlines security measures employed in preserving data and compares the techniques' advantages and disadvantages. The paper also suggests better ways to improve the methods.
The Application of Web Crawler in City Image Research

Grasping the city image accurately is always a challenge for city manager. The main work of our study is to capture the web pages of a specific city using web crawler technology, process and analyze the captured data to get keywords representing the city. We propose a distributed crawler architecture and collect city-related data based on the architecture. Subsequently, several example specific cities are crawled successfully. The crawled data are analyzed by using data preprocessing, data analysis, and other methods. The results show that the presented approach will be very useful for city manager.
CEUS-Net: Lesion Segmentation in Dynamic Contrast-Enhanced Ultrasound with Feature-Reweighted Attention Mechanism

Contrast-enhanced ultrasound (CEUS) has been a popular clinical imaging technique for the dynamic visualization of the tumor microvasculature. Due to the heterogeneous intra-tumor vessel distribution and ambiguous lesion boundary, automatic tumor segmentation in the CEUS sequence is challenging. To overcome these difficulties, we propose a novel network, CEUS-Net, which is a novel U-net network infused with our designed feature-reweighted dense blocks. Specifically, CEUS-Net incorporates the dynamic channel-wise feature re-weighting into the Dense block for adapting the importance of learned lesion-relevant features. Besides, in order to efficiently utilize dynamic characteristics of CEUS modality, our model attempts to learn spatial-temporal features encoded in diverse enhancement patterns using a multichannel convolutional module. The CEUS-Net has been tested on tumor segmentation tasks of CEUS images from breast and thyroid lesions. It results in the dice index of 0.84, and 0.78 for CEUS segmentation of breast and thyroid respectively.
Super‐resolution image reconstruction using molecular docking

Molecular-docking is an essential tool in the drug designing process, where a small molecule ligand (drug) binds with disease-causing protein molecule to prevent its further activity. Docking process helps in predicting the most appropriate configuration and the optimal interaction energy between the interacting molecules (ligand and protein) to form a stable complex. Based on this idea, a new learning-based single image super-resolution reconstruction (LSI-SRR) method is proposed here. Estimation of a high resolution (HR) patch is achieved by optimising the interaction energy between the input low resolution patch and its corresponding candidate patches appropriately chosen from the training image dataset via Genetic algorithm. Structured-spatiogram based measure; a new and competent similarity criterion is proposed to select potentially efficient training images which encompass better statistical and structural co-relation with the input image. The proposed method is tested on synthetic and real-time images at different magnification factors. Performance analysis of the proposed work is compared with some of the representative state-of-the-art LSI-SRR methods. Experimental results demonstrate that the proposed method produces HR images with enhanced image details, minimal artefacts and most importantly enables an efficient trade-off between the image qualities to speed than the competing methods.
Classification of Compressed Multichannel Images and Its Improvement

A task of classification of multichannel remote sensing images compressed in a lossy manner is considered. It is recalled that lossy compression usually leads to reduction of classification accuracy both in aggregate and for particular classes. Distortions due to compression are characterized by visual quality metric desired values of which can be provided at compression stage. Dependence of probability of correct classification on image quality and compression ratio is analyzed for several widely used classifiers using a test image composed of three component images of Landsat data in visible range. It is shown that different classifiers are sensitive to distortions introduced by lossy compression in sufficiently different degree. We also propose a way to combine classifiers' outputs to improve classification results.
Operation Framework of the Command Information System Based on Big Data Analysis

With the development of Internet and information technology, big data analysis brings new opportunities for all industries and fields in the world. This paper expounds the basic concept of big data and data analysis, builds a framework of the command information system based on big data analysis, puts forward the solution of real-time data analysis and data distribution for the command information system, provides theoretical foundation for our army command information system in period of big data.
Unsupervised Pretraining and Transfer Learning-Based Bangla Sign Language Recognition

For hearing-impaired peoples, Bangladeshi Sign Language (BdSL) is a common medium in Bangladesh that is used for their day-to-day conversation. In this work, we have developed a system for BdSL recognition. We have used transfer learning and unsupervised pretraining for recognition. A dataset of 2080 image was used for conducting the experiment. As the number of samples in the used dataset was very small, we have performed augmentation to increase the amount of data samples. This dataset consists of 46 Bangla character sign language. Among them, 10 are Bangla digits, 6 are vowels, and 36 are consonants. We have conducted two different experiments on the dataset. In one, we have used unsupervised pretraining. It has shown excellent performance in the field of image classification. We have acquired 94.86% accuracy using unsupervised pretraining. Our second experiment was done using transfer learning. Transfer learning is mostly used when the amount of data available is very limited. We have attained 96.57% state-of-the-art accuracy using transfer learning.
Cybernetics of Conflict Within Multi-Partner Technology and Software Engineering Programmes

Large technology and software engineering programmes, such as enterprise system programmes, are increasingly implemented through a mixture of customer and specialist third-party resources. These multi-partner working environments can be thought of as a complex social system, which oftentimes experience various forms of conflict. This can be due to competing objectives and priorities of the various organizations, along with incompatibilities of team members within the work-based social network of the implementation programme. If not brought under control, conflict can lead to complex emergent behaviours and dynamics within the wider social network, which can severely impact the likelihood of successful programme implementation of these software-intensive systems. Using social network analysis and thematic coding analysis within a case study, we show that the project management of complex software-intensive implementations requires considerable focus on control and communication across the programme-wide social network of team members, which we represent as a cybernetic system. A conceptual framework has been developed that extends extant literature around conflict in teams by framing the individual projects and the overall programme-wide implementation as cybernetic systems. The conceptual framework illustrates how a cybernetics approach to conflict within enterprise system implementations, can provide new insights into how conflict develops within project teams. Finally, we argue that the cybernetic approach allows us to develop project management interventions to mitigate the risk of conflict development, or control and regulate conflict once it has developed. We conclude by setting the agenda for future research on how conflict can be controlled within the implementation of software-intensive systems, such as enterprise systems.
Identification of Variables That Cause Agricultural Graduates Not to Return to the Rural Sector in Ecuador. Application of Fuzzy Cognitive Maps

In Ecuador, the factors that encourage agricultural graduates to return to the rural production sector are unknown. Therefore, this study seeks to identify the variables that influence why agricultural graduates do not return to the rural sector. Fuzzy cognitive maps and hierarchical clustering were used to achieve this. Interviews were carried out with academic experts from the rural development field and agricultural planning managers who work across 10 of Ecuador’s provinces. Eighteen categorized variables were identified in the study, of which five were strategies for agricultural development that involve different actors. These were: (1) State policies for rural development, (2) quality academic training, (3) innovation and entrepreneurship training, (4) national research and university extension programs, and (5) associative cooperativism. The identification of these variables supports state authorities and universities in designing strategies that promote the involvement of agricultural graduates in the rural production sector.
An improved remote sensing image blind deblurring algorithm

The remote sensing image acquisition may become blurred and the texture is unclear due to the remote sensor's jitter, defocusing and other factors. Therefore, deblurring remote sensing images is a major problem in remote sensing research. In order to improve the quality of remote sensing images, an improved blind deblurring algorithm for remote sensing images based on dark channel prior is proposed. First, the blur kernel estimation based on the dark channel prior is performed, and then the image deconvolution is performed using the estimated blur kernel to achieve clear image restoration. A joint bilateral filter is introduced to maintain the image detail and enhance edges for the image ringing effect produced during the original clear image restoration process. The experimental results show that the peak signal-to-noise ratio (PSNR) of the objective evaluation index of this method is improved by about 1.5dB, and the structural similarity (SSIM) is improved by about 0.04. Compared with the existing image deblurring algorithm, this method can estimate the blur information more accurately, so that the restored result image can achieve the effect of maintaining the edge to remove the ringing.
PUB-SalNet: A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation

Salient segmentation is a critical step in biomedical image analysis, aiming to cut out regions that are most interesting to humans. Recently, supervised methods have achieved promising results in biomedical areas, but they depend on annotated training data sets, which requires labor and proficiency in related background knowledge. In contrast, unsupervised learning makes data-driven decisions by obtaining insights directly from the data themselves. In this paper, we propose a completely unsupervised self-aware network based on pre-training and attentional backpropagation for biomedical salient segmentation, named as PUB-SalNet. Firstly, we aggregate a new biomedical data set from several simulated Cellular Electron Cryo-Tomography (CECT) data sets featuring rich salient objects, different SNR settings and various resolutions, which is called SalSeg-CECT. Based on the SalSeg-CECT data set, we then pre-train a model specially designed for biomedical tasks as a backbone module to initialize network parameters. Next, we present a U-SalNet network to learn to selectively attend to salient objects. It includes two types of attention modules to facilitate learning saliency through global contrast and local similarity. Lastly, we jointly refine the salient regions together with feature representations from U-SalNet, with the parameters updated by self-aware attentional backpropagation. We apply PUB-SalNet for analysis of 2D simulated and real images and achieve state-of-the-art performance on simulated biomedical data sets. Furthermore, our proposed PUB-SalNet can be easily extended to 3D images. The experimental results on the 2d and 3d data sets also demonstrate the generalization ability and robustness of our method.
Noisy polymedia in urban Ghana: Strategies for choosing and switching between media under unstable infrastructures

This ethnographic article examines how Ghanaian urbanites choose between new media platforms under inadequate infrastructures. By combining participant observation with digital ethnography, the case studies described here show how informants change their media choosing and switching habits due to unstable electricity and dysfunctional inter-city roads. Thus, moral and social choices face an infrastructural ‘noise’ that diverts people’s digital practices. These findings contribute to Madianou and Miller’s theory of polymedia by suggesting that constraints of economy, access, literacy, or infrastructure cannot be exempt from the analysis of media choice, especially in Africa. It suggests the term ‘noisy polymedia’ as an expansive version that acknowledges how people use digital tools under unstable infrastructural conditions. Furthermore, this article demonstrates how smartphones became essential tools for ‘leapfrogging’ inadequate infrastructure in Africa as informants choose strategically between devices, payment plans, applications, and usage patterns.
Research on Data Analysis for Time Deposit of Bank Customers Based on Ensemble Learning

Nowadays, time deposit is considered to be the main business of banks but due to the rapid development of the Internet which has a great impact on the financial service products of banks. More and more people choose to invest time deposits in Internet financial products, which also mean to lose bank's financial client holders. This paper is based on data analysis of 21 influencing factors of whether bank customers are subscribed to time deposits or not and to find out the important features of customer's subscription to time deposits by constructing an ensemble learning model and finally evaluating the model to obtain a better prediction model.
Stitching Methodology for Whole Slide Low-Cost Robotic Microscope Based on a Smartphone

This work is framed within the general objective of helping to reduce the cost of telepathology in developing countries and rural areas with no access to automated whole slide imaging (WSI) scanners. We present an automated software pipeline to the problem of mosaicing images acquired with a smartphone, attached to a portable, low-cost, robotic microscopic scanner fabricated using 3D printing technology. To achieve this goal, we propose a robust and automatic workflow, which solves all necessary steps to obtain a stitched image, covering the area of interest, from a set of initial 2D grid of overlapping images, including vignetting correction, lens distortion correction, registration and blending. Optimized solutions, like Voronoi cells and Laplacian blending strategies, are adapted to the low-cost optics and scanner device, and solve imperfections caused using smartphone camera optics. The presented solution can obtain histopathological virtual slides with diagnostic value using a low-cost portable device.
Random CFI (RCFI): Efficient Fine-Grained Control-Flow Integrity Through Random Verification

In theory, Control-Flow Integrity (CFI) is considered a principled solution against control-data attacks. However, most fine-grained CFI schemes that ensure such high security suffer from significant performance overhead. Existing practical implementations have been proposed to overcome this performance overhead problem, but they have proven unable to guarantee high security because development of these implementations has focused on only improving performance, at the expense of the security guarantee. Even though it is important for CFI schemes to provide both high security and low performance overhead, existing research on CFI is limited either by way of performance or security guarantee. We propose a new approach of verification method in fine-grained CFI to achieve these two goals. Our scheme performs selective and random verifications for certain branches rather than all branches, and thus, can reduce performance overhead. We show improved performance by evaluating our proof-of-concept implementation on SPEC CPU 2017. In addition, we also show that our scheme does not significantly sacrifice the security guarantee of fine-grained CFI by analyzing the structure of existing control-data attack exploits, which were collected from real-world exploits DB and related literature.
New Approach to Face Recognition Using Co-occurrence Matrix and Bayesian Neural Networks

Faces represent complex multidimensional significant visual stimuli and developing a computational model for face recognition is difficult. In this paper we present a new approach to the face recognition problem by combining Cooccurrence Matrix and Bayesian Neural Networks.Firstly, we use Co-occurrence Matrix to extract the relevant information in a face image, which are important for identification. Using this we can represent face pictures with several coefficients instead of having to use the whole picture.Then, Bayesian Neural networks are used to recognize the face through learning correct classification of the coefficients calculated by the Co-occurrence Matrix. The experimental results on the ORL database illustrate that the proposed approach has better performance in term of accuracy compared to old approaches.
Research on imbalanced data : based on SMOTE-AdaBoost algorithm

There are a lot of imbalanced data in many fields such as finance, information security and industrial system. How to extract valuable information from imbalanced data is a research hotspot and focus. In this paper, the imbalanced consumption data of credit card in UCI database are used. Oversampling SMOTE method, AdaBoost algorithm and cost-sensitive algorithm are used to process the imbalanced data. Empirical results show that SMOTE-AdaBoost method is better than traditional AdaBoost method, and the cost-sensitive algorithm that increases the weight of the minority class samples is also higher than that of traditional AdaBoost method. Finally, this paper describes the challenges and future research directions of imbalanced data classification.
Multiple Instance Models Regression for Robust Visual Tracking

In comparison to single-model based trackers, the model-ensembled tracking strategy has shown a substantial adaptivity in handling various tracking challenges. As the performance of the tracker has been improved by combining different model outputs linearly, the insufficient consideration of each ensemble member’s contribution still limits the tracking performance to be further enhanced. As the performance of the tracker has been improved by combining different model outputs linearly, the insufficient consideration of each ensemble member’s contribution still limits the tracking performance to be further enhanced. In this paper, a tracking strategy based on multiple instance models regression (MIMRT) is proposed with a unified ensembling scheme. By formulating the tracking initialization with an instance model, the encoding process for an object’s specific detail is performed corresponding to the samples in each frame. The advantage of this operation is to guarantee the model frame-wise discrimination of short-term training, as well as to evaluate the reliability of each instance model by utilizing the long-lifetime samples obtained throughout the whole tracking procedure. To finalize the proposed tracking, all the independent instance models attached to the learned regression coefficients are ensembled with respect to the long-lifetime samples. This also effectively bridges the instance model as a latent variable to investigate a semantic association between the tracking model and the overall samples. A comprehensive experiment has shown that the proposed tracker is able to achieve superior performances compared to the state-of-art tracking approaches on both short-term datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , OTB2013, OTB100, VOT2016, UAV123) and long-term dataset (UAV20L).