Reviewerly/exHarmony huggingface dataset:
Bernstein Polynomial Multiwavelets Operational Matrix for Solution of Differential Equation

A new application of the Bernstein polynomials based multiwavelets approach for the numerical solution of differential equations is given. In the proposed method, Bernstein polynomial multiwavelets are obtained by using orthonormality of Bernstein polynomials. We present the operational matrix of integration of Bernstein polynomial based multiwavelets basis which diminishes the taken differential equation into the system of algebraic equations for less demanding calculations. High accuracy of these results even in the case of a small number of polynomials is observed. The convergence and exactness are described by comparing the ascertained approximated solution and the known analytical solution. The error estimates of the approximate solution are given and also some comparative examples with figures are given to confirm the reliability and accuracy of the proposed method. Some physical problems that lead to the differential equations are examined by the proposed method.
Attentional Bi-directional LSTM for Semantic Attribute Prediction

Attribute prediction is a basic task in CV field, and it belongs to a multi-label prediction problem in practical terms. Most studies using deep features to handle the problem while ignoring the potential dependencies that exist within attributes and images. Differ from previous work, we propose a novel deep architecture named ABLSTM, which not only taking advantage of CNN and Bi-LSTM, but also utilizing a multi-task-multi-loss design for attribute detection. Based on ABLSTM, we further construct a simple but extremely effective regression module to improve the accuracy of high-level abstract semantic attributes. Extensive experiments on the largest attribute prediction dataset of DeepFashion show the consistency superiority and efficiency of the proposed model.
Mining and monitoring human activity patterns in smart environment-based healthcare systems

Abstract Rapid evolution of sensing technology and increasing power computation has resulted in the emergence of smart environments with smart health services. Smart environments can generate hundreds of thousands of transactions per day and storage over the long term is a major issue. Therefore smart environment big data is utilized, which stores large volumes of datasets, both structured and unstructured. Healthcare services are recent and challenging aspects in analytics and sensor technology. People migrating from rural areas to urban areas affect healthcare services to a large extent. Due to migration and developing technology, cities around the world are investing in digital transformation, which aims to provide people with a healthier environment. New healthcare applications are based on activity recognition of people and can include wearable or ambient sensors to gather information related to health and human activity. Consumption of energy is analyzed along with the activity pattern of humans by determining the level of appliance usage that relates to human activity. The pattern determined is used to monitor elderly people living alone. The activity monitored can be learned just from appliances and their time usage. The proposed model detects appliance usage in particular with reliable accuracy. Mining is done purely based on absolute prediction along with clustering models.
Multi-observable uncertainty equality based on the sum of standard deviations in the qubit system

We construct a multi-observable uncertainty equality as well as an inequality based on the sum of standard deviations in the qubit system. Firstly, an uncertainty equality as well as an inequality is formulated, and we demonstrate that the new uncertainty inequality is tighter than other recent uncertainty relations. Then, a proof, that the triviality problem of the product form uncertainty relation can be completely fixed by the obtained uncertainty relation, is presented. Finally, we show that the uncertainty equality can be used as a measure of the mixedness of the system which usually is expensive in terms of resources involved.
Robust Basketball Player Tracking Based on a Hybrid Detection Grouping Framework for Overlapping Cameras

We propose a robust basketball player tracking framework for multi-cameras which have high portion of overlapping with each other and are set at human height. A novel detection grouping method is proposed to more correctly merge the projected detection results. Instead of using linear motion assumption to predict the human motion, we applied a regional consistency assumption to calculate the motion affinity. Further-more, we design a one-to-one clustering method to associate the most matching tracklets together using correlation values between tracklets and generate final trajectory results. Since there is no public labeled overlapping cross-cameras basketball dataset, we collected our own dataset, MISBasketball, and labeled the ground truth to evaluate the proposed tracking framework.
Causal Discovery of Cyber Attack Phases

Causal discovery algorithms are increasingly being used to discover valid, novel, and significant causal relationships from large amounts of observational data. Cyberattacks are hypothesized to evolve according to the Cyber Kill Chain® which consists of a causal model describing the phases of a cyberattack. This paper introduces causal discovery to cybersecurity research and provides evidence of the kill chain with an extensive empirical assessment of two databases of real cyberattacks.
Implementation of Quasi-Euclidean Distance-Based Similarity Model for Retrieving Information from OHSUMED Dataset

The similarity is the critical concern area in the field of information retrieval and data mining. The squared Euclidean distance is the most simple and extensively used measure with its high sensitivity toward noise, whereas Quasi-Euclidean-based similarity measure is significantly faster for the large and sparse corpus. BM25 is a robust field weight method for scoring documents while TF-IDF is a term scoring method. BM25 achieves better execution contrasted with TF-IDF; however, the main disadvantage of BM25 is its heuristics in optimizing the parameters in the information retrieval measure. The present paper proposes a Quasi-Euclidean-based similarity measure for information retrieval. The Quasi-Euclidean is used as a similarity measure which synergized together BM25 scoring method and citation references to compute the Quasi-Euclidean distance score among the documents and queries. The proposed Quasi-Euclidean-based information retrieval model is implemented using whoosh library in python. The experiment has been carried out for TREC OHSUMED-9 medical dataset, and it is identified from performance results that the proposed Quasi-Euclidean-based similarity measure is shown to perform well on precision and recall measures for the state of the art information retrieval methods.
Machine learning based Detection of Zinc Mineralization North India using Hyperspectral Image Processing

This paper presents the use of hyperspectral image processing as an alternative to traditional mineral exploration techniques for identification of regions in the study area rich in zinc mineral. Zincian Dolomite is a known ore of zinc found in the study area. The Hyperion sensor dataset of a study area in Rajasthan, India is obtained from the USGS (United States Geological Survey) Earth explorer and pre-processed using ENVI (Environment for Visualizing Images) software to remove noise. The pre-processed data is later exported to a dedicated python program which uses machine learning algorithms to identify the regions rich in Zincian Dolomite. The proposed technique form mineral exploration is faster and cheaper than the traditional techniques and is also found to be very accurate in identification of the minerals.
Self-supervised Image Classification based on the Distances of Deep Feature Space

Deep neural networks have demonstrated their effectiveness in computer vision, especially for image classification and detection. Mixup is recently proposed as a data augmentation scheme, which applies linear combination of two random training examples and corresponding targets. However, the linear assumption is inappropriate for training a non-linear model. In this paper, we propose a self-supervised method which requires the consistency of original and mixed images on feature space. Our work is motived by the semantic information is related to the relative position of features. To implement this idea effectively and efficiently, we perform two-stage of training procedure, i.e., running estimation of class centers of original data in feature space, and training deep neural networks with modified loss term of Mixup. Besides, the proposed approach is also compatible with other variants of Mixup. We validate our approach on two popular image classification datasets, CIFAR10 and CIFAR100 by a variety of advanced deep neural networks, and demonstrate consistent generalization improvements, sometimes significantly. We also conduct analytical experiments to evaluate the robustness of our method to hyperparameters.
Environment Generation from Real Map to Investigate Path Planning and Obstacle Avoidance Algorithm for Electric Vehicle

The use of autonomous vehicles is expected to become common in various applications. Tremendous research studies are being made on automated movement, which requires path planning and obstacle avoidance algorithms to plan trajectories. However, an appropriate algorithm must be carefully selected before the implementation of a real system. Therefore, this paper presents the developed software that generates a virtual environment to investigate path planning and obstacle avoidance algorithms. This software consists of the simulation model of a motor-driven electric vehicle, in which vehicle and sensor parameters and algorithms for path generation and obstacle avoidance can be redefined. For practicability and visualization, the software generates a 3D virtual testing environment using real maps from Google map and the Unity 3D engine. The test is performed on a small four-wheeled electric vehicle in the environment with and without an obstacle and A* algorithm is used.
Triplet-Based Regularized Diffusion Process for Improving Visual Retrieval

As the object retrieval problem cannot be well solved by pairwise distances, many algorithms have been developed for improving visual retrieval result. In recent studies, it is demonstrated that the contextual (dis-)similarities based on diffusion process can be obtained directly by solving an optimization problem with a general regularization framework, which contains a smoothness constraint and a fitting constraint. To improve the effectiveness of visual retrieval, this paper introduces a novel smoothness constraint named triplet-based smoothness constraint into the regularization framework of diffusion process. According to the random walk model, the proposed model can be used to simultaneously regularize three elements, and provides a quite different form of high-order information. Then we proposed the triplet-based regularized diffusion process by introducing the hybrid fitting constraint into our method. The experimental results on different shape databases demonstrate that retrieval results can be effectively improved by using the proposed methods.
Remote Sensing and Time Series Data Fused Multimodal Prediction Model Based on Interaction Analysis

With the rapid development of the times, human's life is becoming more and more modern, helping people experience surrounding environment better. People can see attractive scenery, hear marvelous voice, smell fragrant flavor, touch soft objects and taste delicious food. All these feelings can be generalized by 'Modality'. As there are heterogeneous modalities, the way to learning from multiple such modalities become an emerging research topic. Multimodal machine learning has a wide range of applications while it still has many challenges. Challenges can be included in five categories: Representation, Translation, Alignment, Fusion, Co-learning. In this paper, we focus on the representation and fusion problem of multimodal and solve a practical problem of urban functional area classification. In this paper, we propose a scalable interaction model based on Squeeze-and-Excitation block to fuse image modality from remote sensing images and temporal modality from user visit sequence. Crucially, our model produces improvements over typical multimodal methods.
SSA-GAN: End-to-End Time-Lapse Video Generation with Spatial Self-Attention

We usually predict how objects will move in the near future in our daily lives. However, how do we predict? In this paper, to address this problem, we propose a GAN-based network to predict the near future for fluid object domains such as cloud and beach scenes. Our model takes one frame and predict future frames. Inspired by the self-attention mechanism [25], we propose introducing the spatial self-attention mechanism into the model. The self-attention mechanism calculates the reaction at a certain position as a weighted sum of the features at all positions, which enables us to learn the model efficiently in one-stage learning. In the experiment, we show that our model is comparable compared with the state-of-the-art method which performs two-stage learning.
CyberSure: A Framework for Liability Based Trust

CyberSure is a programme of collaborations and exchanges between researchers aimed at developing a framework for creating and managing cyber insurance policy for cyber systems. Creating such policies will enhance the trustworthiness of cyber systems and provide a sound basis for liability in cases of security and privacy breaches in them. The framework is supported by a platform of tools enabling an integrated risk cyber system security risk analysis, certification and cyber insurance, based on the analysis of objective evidence during the operation of such systems. CyberSure develops its cyber insurance platform by building upon and integrating state of the art tools, methods and techniques. The development of the CyberSure platform is driven by certification, risk analysis and cyber insurance scenarios for cyber system pilots providing cloud and e-health services. Through these, CyberSure addresses the conditions required for offering effective cyber insurance for interoperable service chains cutting across application domains and jurisdictions. CyberSure platform aims to tackle the challenges of offering cyber insurance for interoperable service chains cutting across application domains and jurisdictions.
Towards a Universal Appearance for Domain Generalization via Adversarial Learning

Domain generalization aims to learn a generalized feature space across multiple source domains so as to adapt the representation to an unseen target domain. In this paper, we focus on two main issues of domain generalization for image classification. First, instead of focusing on aligning data distributions among source domains, we aim to leverage the generalization capability by explicitly referring to image content and style from different source domains. We propose a novel appearance generalizer to learn a universal appearance, which captures domain-invariant content but exhibits various styles. Second, to tackle the class-discriminative issue, we resort to the prominent adversary learning and impose an additional constraint on the discriminator to boost the class-discriminative capability. Experimental results on several benchmark datasets show that the proposed method outperforms existing methods significantly.
Design of a Forensic Enabled Secure Cloud Logging

Adoption of cloud computing services greatly reduce the cost of managing businesses and increase the productivity. But, due to complex network configurations of cloud, it is a vector for various malicious attacks. Logs are the most valuable element which can be helpful in revealing the insights of any activity happened in cloud. Experienced attackers and malicious users always targets to destroy logging service first, after their attacks to remain untraceable. The existing logging techniques, which consider logger as a trusted stakeholder cannot be applied in cloud as there is possibility of collusion in between logger of cloud i.e. cloud service provider and fraud cloud service consumer or cloud forensic investigators to falsify the logs.
Strategies of Information Service Based on Intelligent Library

With the development of science and technology and the change of users’ demand, the traditional library ushered in a great change. On the basis of re-examining library function and information service, intelligent library has worked out a set of brand-new user information service scheme. This paper analyzed the concept of intelligent library and the characteristics of information service, summarized the implementation strategy of information service of intelligent library, and finally discussed how to construct the mode of information service of intelligent library, so that to better promote the intelligent construction of the library.
Automatic CNN Compression System for Autonomous Driving

A system has been developed to realize autonomous driving (AD) with Convolutional Neural Networks (CNNs). To apply CNNs to AD, the amount of calculations must be compressed with little decline in accuracy because it is too large to implement in the in-vehicle processors. However, in the conventional methods, an optimal configuration for CNN compression takes a long time to explore. In this paper, we propose a technique to explore an optimal configuration within a short time by combining both training time reduction per configuration and the priority search only around the optimal solution. The results shown that the proposed system can reduce processing time and improve mean Average Precision (mAP) by 70.0% and 2.19 pt. compared with conventional methods, respectively.
Building Classifier Models for on-off Javanese Character Recognition

In this paper, we demostrated the building process of four classifier models as a part of an on-off character recognition system for Javanese characters. As Javanese character is no longer used in everyday writing and books, the dataset were collected by scanning the historical manuscripts and a reading lesson book. The rough dataset comprises 15.414 annotated characters and 633 classes. However, only 162 classes have sufficient data samples to be the training and testing one. Using this dataset, we measured the performance of four classifiers, namely k-NN, LDA, SVM, and Gaussian NB on the accuracy, micro-averaged precision, micro-averaged sensitivity and weighted-averaged precision and sensitivity metrices. The experiment shows that k-NN outperforms any other classifiers almost in most metrices, while SVM suffers the poorest performance. The research byproduct worth mentioning here is that it has identified 633 classes of distinct Javanese characters which comprise both common characters and compound characters found in modern Javanese writing as well as the archaic characters found in the literary works only.
Automated Mapping of Service Level Agreements to Application Programming Interfaces of Different Cloud Service Providers

Most of the web (mobile) applications are using cloud computing technology. Developers who want to develop an application can make use of the provisions and services provided by a cloud service provider. There are a number cloud service providers (CSPs). Each of these cloud service providers offer a number of services. Going through all the services of all the CSPs and finding out what service matches the requirements in the SLA document, manually is time consuming, error prone and tedious. And opting for all the services will add extra charges for the owner. Hence it is important to have a system that can match the requirements in the SLA (Service Level Agreement) document with the APIs (Application Programming Interfaces) of the cloud service providers. This paper proposes a method to match the requirements of a businessman with the APIs of the CSPs. The requirements are written in a text document and the description of the APIs of CSPs are written in another text document. Then using text similarity in natural language processing, the APIs that satisfy the requirements in the text document are identified.
An Efficient Cloud-Based Framework for Digital Media Knowledge Extraction

Most of the oil industries have a substantial volume of physical subsurface data generated as part of the exploration study. This data is collected over many decades and exists in various formats such as tapes, cartridges, CDs, DVDs, paper media comprising of maps, technical well reports, and seismic logs. These items are usually stored in large offsite repositories across the globe and is maintained by third-party vendors. Access to this historical data is crucial for oil companies as it helps to find potential prospects for oil extraction which otherwise require an exploratory study by geologists using satellite imagery, surface rocks, terrain, and seismology. Storing large volumes of technical data in offsite repositories also posts many key challenges such as high storage cost, high retrieval time and inaccessibility of information. To address the above challenges, companies are digitizing the physical data and complementing with rich metadata extraction by Optical Character Recognition(OCR). This introduces some more technical challenges while dealing with lower Dots Per Inch (DPI) scans, poor quality scans, and huge file size. Several frameworks are developed which store the data in local repositories but these frameworks have limitations with respect to the number of documents processed, huge file size and storage scalability. To deal with above-mentioned problems, we present a high-performance computing cloud-based framework by storing the digitized data in the cloud, metadata enrichment through OCR along with image enhancement by a series of Image Processing (IP) techniques and provide high data availability to users using cloud-based search. We have tested this framework with big oil and gas company's data on a huge scale and the results are encouraging. Although this paper addresses oil industries domain problem, the proposed framework can be applied to other domains that have huge physical data.
Big Data Performance Analysis on a Hadoop Distributed File System Based on Geometric Data Perturbation Technique

In this paper, we proposed a Big Data Security Analysis based on a modified perturbation technique in a Hadoop Distributed File System which is commonly used in various real time applications. This work have utilized an improved K-means clustering algorithm, which selects the initial clustering centres based on the density parameters. The poor assurance of initial centres and in order to improve the precision and packing effect of the K-means clustering computation is to be need a new method. In order to group the data of same cluster based on their similarities clustering algorithm was used. In the various clustering methods, this paper is utilizing the geometric data perturbation technique which is clustered, data are perturbed whose values are challenging to be recognized. The performance was evaluated on a sample Health Care database and the metrics under studies are memory usage, precision, recall, accuracy, F-measure, clustering time and execution time, Perturbation time, De-perturbation time. In this paper, we focused only on Perturbation Time, De-Perturbation Time and the Accuracy. The experimental result shows that the proposed approach reduces the complexity and shown to less computation time and better accuracy than that of the existing techniques.