Reviewerly/exHarmony huggingface dataset:
Secure Data Carrying Methodology Through Digital Protection Wall Using Higher LSB Technique over Wireless Network

The secrecy and security of information has always been important to the people whenever it come to confidential data. Today there is advancement in internet technology and hence there has been increase in use of text and multimedia data transfer over internet. Thus this motivates us to add another layer of security to secrete data,which hides the confidential information within carrier. The carrier we used is the transfer of images that can hide secret information within the region of image. The paper proposes half toning visual cryptography technique with digital protection wall. Technique improves the imperceptibility as well as capacity of carrier by considering higher LSB layer for hiding secret information and replacing RGB component bit with secret information. Thus transfer of carrier through wireless media and recovering the secret data at receiving side. The data is extracted simply by using reverse algorithm. And the capacity, imperceptibility is compared by using various factors like cross correlation, MS error, peak to signal noise ratio. The executed protection wall is flexible so that carrier can hide the data within the protected area and shall protect from unauthorized user which will be known to receiver only.
Indefinite Kernels in One-Class Support Vector Machine and its Application on Virtual Screening

Imbalanced dataset is a common issue in many applications. The one-class Support Vector Machine (SVM) is found to be an effective algorithm to construct classification models over the underlying imbalanced dataset. In some cases, feature extraction is hard and one would prefer using pre-defined kernels to train the model. In traditional practice, a valid kernel has to satisfy the Mercer's condition, which may restrict the design of kernel functions or matrices. In this paper, an indefinite kernel extension is applied to the one-class SVM model in order to relieve such limitation. To illustrate its performance, the algorithm is applied to perform virtual screening of drugs.
POV in XR

This chapter will introduce and explain the applications of a taxonomy for discussing point of view (POV) in XR. The simple designations of first, second, and third person that are used to categorize books, movies, and video games don't cover all the options and combinations available in immersive media. Accordingly, XR requires a new taxonomy that will allow for clear communication about content and experiences. This chapter will do three things: (1) present the four main POV tiers: narrative, visual, effectual, and experiential; (2) address less common tiers and how they might be incorporated and acknowledged in future XR experiences; and (3) show the taxonomy in action by using it to describe contemporary XR content.
HMApriori–An Improved Association Analysis Algorithm and Its Application Research

In order to solve the problem of too many candidate sets and redundancy of the classical Apriori algorithm, an improved algorithm based on Hash function and Mark transaction compression HMApriori (Apriori algorithm based on Hash function and Mark transaction compression) was proposed. The HMApriori algorithm uses the hash function to construct the hash table H2 to reduce the number of C2 candidate sets, and reduces the number of comparisons of conflicts and candidate sets by marking transaction compression, thus improving the efficiency of the algorithm. A performance analysis experiment system was constructed for the field of colleges and universities. On the more than 15,000 pieces of true desensitization data derived from the university educational administration system, the HMApriori algorithm was used to analyze the students' multi- disciplinary results, and the experimental results were compared and analyzed. The results show that the algorithm has a significant improvement in reducing the number of C2 candidate sets and time performance.
BulkScreen

Digital images appearing on displays in everyday activities (e.g., photos on a smartphone) are automatically and instantly rendered without manual intervention such that we can seamlessly appreciate them. In contrast, shape displays require manual designs of outputs upon actuation of input images to render 3D shapes. In this work, we aim to achieve automatic and on-the-spot actuation of digital images so that we can seamlessly see 3D physical images. To this end, we developed BulkScreen, an image projection system that can automatically render 3D shapes of input images on a vertical pin-array screen. Our approach is based on a deep-neural-network saliency estimation coupled with our post-processing algorithm. We believe this spontaneous actuation mechanism facilitates applications with shape displays such as real-time picture browsing and display advertisement, building on the benefit of representing physical shapes; tangibility.
Predicting the Legal Risk of "Section 337 Investigations" by Elastic Time Predictor

In recent years, more and more patent lawsuits have been filed by Chinese enterprises, represented by the "Section 337 investigations" of the United States. In order to help Chinese enterprises cope with the challenges of patent litigation, a matrix factorization based recommendation system are used to predict the legal risk of 337 investigation. However, the results predicted by the model are prone to over-fitting. In order to solve this problem, this paper proposes a new recommendation framework, namely elastic time predictor. The model is a hybrid model combining matrix factorization and truncation function. We encode the information of the prosecution case of major companies and decompose it into two sub-matrices, and then combine the decomposed matrix with the segmentation of the truncation function to maintain the entire recommended frame flexible. In the recommended approach, we consider the risk of litigation that a company may experience when entering a new market, for example the risk that a potential competitor will file a lawsuit against a new entrant. We use actual data to conduct experiments, and the experimental results show that the proposed method is superior to the baseline method and has significant advantages.
Robot Autonomous Navigation Based on Program Learning in Dynamic Environment

Robot navigation under dynamic environment is considered as one of the key indications of machine's intelligence. This paper proposed a program learning algorithm for robot navigation, so as to efficiently increase the intelligence of the machine. The Tolman mouse maze experiment was expanded to help the robot to develop intuitive intelligence, that it can lean the regularity the environment change. Firstly, the efficient environment map is constructed by SOM based on the cloud point generated randomly according to the environment. Then the robot will automatically choose how to reach the goal destination according to the shortest path generated by A star algorithm formerly. And corresponding reactions of the robot are determined by cycling time, which is the program we help the robot realize which path it is supposed to select. The validity of the proposed algorithm is proved by MATLAB and gazebo of ROS indigo, besides, we also did some physical experiments which have well shown the robots' highly developed intelligence of finding the shortest path rapidly.
Global and Local Signed Pressure Force Functions Active Contour Model Based on Entropy

Image segmentation is considered as a challenge in MRI images, synthetic and real images, because of intensity inhomogeneity which alters the final result of segmentation. Paper presents a novel method which deals with intensity inhomogeneous images. The proposed method introduces global and local fitting image energy function, where global fitting image is based on entropy and, moreover, presents global and local signed pressure function for stabling the gradient descent flow that is solving the energy function. Local signed pressure function has been established by multiplying entropy with local image difference and global signed pressure has been calculated by global image difference. Entropy used for estimating the bias field, global fitting term focused on homogeneous regions as well as provided the robustness to initialization of contour and local fitting term useful to detect objects with intensity inhomogeneity. Experimental result shows that in the presence of entropy, this method gives the superior performance concern to accuracy, time, and robust to initialization.
Academicopter: Metasearch with PDF Automatic Summary of Scientific Articles

Existing academic search engines provide information on the search results as snippets which obtained from abstracts. Users have to read the PDF document to get more detailed information. Sometimes, users need more rich snippets, which also provide data from the entire PDF document. Therefore, an academic metasearch engine (Academicopter) was developed, which is focused on giving those rich snippets. Academicopter automatically summarizes PDF content of scientific journals to provide more information to users. The summarization is implemented by using a modified graph-based summarization. The summarization is also added metadata formula features for title, keywords and abstract. The results of experimental studies using data sources obtained from Google Scholar and Microsoft Academic show that Academicopter succeeded in combining search results from both data sources into a single list of search results. The ranking is the combination of both sources. Academicopter also removes duplicate data between the two data sources by displaying only one data.
Research on hybrid collaborative filtering recommender system based on spark

Based on the study of the recommender engine and big data process platform, a hybrid collaborative filtering recommender method is proposed to solve the drawbacks of the current recommender algorithm in practical engineering application. Dimensionality reduction and clustering are adopted to overcome the limitations of collaborative filtering such as sparsity of data. Apache Spark is used to realize an efficient parallel implementation. With the increase of data, the scalability of recommender scheme can be solved by expanding Spark cluster nodes. The advantages of the proposed method have been proven in the practical application of the teaching information resource service platform of a university.
Improved image registration scheme based on multiscale nonlinear Harris and Gaussian–Krawtchouk moment

Image registration is an indispensable operation in image processing. To solve the problem of nonlinear distortion which easily causes low registration property, a registration scheme including a nonlinear scale space, a new discrete orthogonal Gaussian–Krawtchouk (GK) descriptor vector, and a dual similarity measurement of distance-cosine is proposed. Nonlinear scale space is constructed to preserve a lot of important structures for increasing the quantity of the salient keypoints. Meanwhile, considering both blur stability and simple computation, multiscale Harris algorithm on the basis of nonlinear scale space is applied to extract the keypoints. Moreover, according to different scales, a descriptor vector based on multiscale GK invariant moments is presented and applied to the following image matching. Finally, a dual similarity measurement of the initial Euclidean distance and the fine space cosine is proposed for the purpose of enhancing the matching accuracy. The effectiveness of the proposed algorithm has been tested on the standard images with different types of variations. The experimental results show that the performance of feature extraction and the correct matching rate of the proposed scheme are superior to those of other classical methods. The proposed scheme has a better robustness and a higher registration accuracy.
Problems on track runners

Consider the circle C of length 1 and a circular arc A of length ℓ∈(0,1). It is shown that there exists k=k(ℓ)∈N, and a schedule for k runners along the circle with k constant but distinct positive speeds so that at any time t≥0, at least one of the k runners is not in A. On the other hand, we show the following. Assume that k runners 1,2,…,k, with constant rationally independent (thus distinct) speeds ξ1,ξ2,…,ξk, run clockwise along a circle of length 1, starting from arbitrary points. For every circular arc A⊂C and for every T>0, there exists t>T such that all runners are in A at time t. Several other problems of a similar nature are investigated.
Characterization and Uncertainty Analysis of the Interlaminar Inelastic Properties of Unidirectional Fibre-reinforced Composites

Abstract The compression failure mode has been achieved through changing the SBS (Short Beam Shear) experiment parameters, and the nonlinear constitutive relationship along thickness direction for the typical specimens loaded in 1-3 principal material plane has been computed iterative by FEMU with the reconstruction strain field and the numerical simulated stress data of FEM model. The uncertainly of the constitutive parameters is induced by the measurement system noise in the DIC technique and the approximation error in the displacements and strains smoothing algorithm. The covariance matrix of the extracted material constitutive parameters has been given explicitly. Three material constitutive parameters were identified from a customized short-shear experiment simultaneously using an estimated optimal reconstruction mesh size as an illustration. Sensitivity of measurement noise and reconstruction parameter on extracted material properties has been investigated. The effects of region of interest (ROI) and DIC image number on uncertainties of extracted material properties have been addressed. It is suggested that there exist an appropriate ROI and the number of images, from which reliable material parameters can be identified, but much more data used in identification process always lead to smaller standard deviation and COV. It is observed that the material constants used to characterize the interlaminar stress-strain behavior show strong robustness to the measurement noise. Another key finding is that the reconstruction parameter in the global finite-element based approximation approach is critical for reliable material properties identification. Its value has to stay close to optimum for guaranteeing reliable identification of material properties.
Towards Beauty: Robot Following Aesthetics Gradients

Increasing numbers of devices are equipped with cameras generating large amounts of images. State of the art technologies allow to automatically identify relevant and aesthetically pleasing images after they were stored. Here, we demonstrate a robot that estimates the gradient of image aesthetics in its environment and actively navigates towards the maximum. Aesthetics navigation is integrated into a modified robotic lawnmower, switching online between tasks based on estimated aesthetics scores. This behavior generates higher aesthetics scores than offline selection of images captured during standard behavior. The proposed system extends robotic behavior from the purely functional towards a cooperative and empathic level.
A Simple Arithmetic Calculator to Solve Single Sentence Mathematical Word Problems

In the recent past, solving mathematical word problems of varying complexities has gained popularity among researchers, for which Natural Language Processing (NLP) and Understanding (NLU) techniques, Machine Learning, Deep learning and several artificial intelligence (AI) based approaches are widely used. The paper presents a work aiming to solve single-sentence mathematical or arithmetic word problems or queries given in natural language (English). The work involves identification of problem types, simplification of the given word problem to extract the operators and the operands, mapping the operators to appropriate operands to create the mathematical expression and solve the query to generate the final result. We developed a dataset consisting of 430 diverse arithmetic word problems of different complexity levels. Our natural language calculator was evaluated on this dataset and it resulted in 86.28% accuracy.
Comparison of Modified Hierarchical Clustering Based on Density Peaks Using Kernel Function with Support Vector Machines in the Classification of Sinusitis

There are two classes of sinusitis, acute and chronic. This research compares the modified hierarchical clustering based on density peaks (HCDP), using kernel function with support vector machines, utilizing dataset taken from Cipto Mangunkusumo Hospital, Indonesia. This collected detail consists of 102 acute and 98 chronic sinusitis samples. The results of this research therefore conclude that HCDP, using Gaussian radial basis kernel function, with the parameter $$ \sigma = 0.01 $$ possess an accuracy of 72.69% and has a better running time. However, the average accuracy of support vector machines is 98.53%.
Implementation of Password-based Key Derivation Function for Authentication Scheme in Patrolling System

Outsourced security guards are often used by building owners or companies to help them maintain security. Problems arise from complaints about how companies can monitor surveillance activities in real-time and reliably. Supervisors must be able to know with certainty who is the security guard on duty. Therefore, an authentication process to oversee the work of security officers is needed in the patrolling system. The authentication system required the security guards to come to the location to read the QR code on the display device. The QR code was dynamically and only a valid account in an android application could verify the codes. QR code information was generated using key derivation function based on shift schedule information and security guard account number. The authentication process used PBKDF2. The resulting system consisted of a QR code viewer device and an Android mobile application. The QR code displayed the information that was sent by the server. This information was been unreadable by humans and it changed periodically according to the shift schedule and who is in charge of the inspection location. Based on the active account, the Android application could verify whether the QR code is valid or not for the account. If valid, the system recorded that the location has been monitored by a valid security guard.
Proposal and Model for Designing a Cybrary - With Reference to Fr. Moseslibrary, Rajagiri College of Social Sciences, Kerala

Subject gateways are becoming one of the most commonly used media for effective and efficient delivery of information as it not only offers information about library resources and services, but also provides all the open access resources such as electronic books, electronic journals, online databases, online dictionary, encyclopedia, bibliography, software, etc. The Cybrary – Open Access Subject gateway proposal brings together all open access e-resources at one place so that the students, research scholars and faculty members may make optimum use of them. The building of a strong subject gateway for Library is needed in the present scenario as a tool which is useful for all users.
Coordination Control Strategy of DSSC Converter Based on Multi-objective Optimal Immune Algorithms

Based on the analysis of the interaction between multi-converters of distributed static synchronous series compensator (DDSC), a coordinated control strategy based on multi-objective optimization immune algorithm is proposed, which converts the interaction between DSSC into multi-objective optimization of controller. Firstly, the objective function of coordinated control of DSSC is established, and the immune algorithm is used to screen the antibody fitness, and the Pareto optimal solution set of controller parameters is obtained. Then the simulation model is established and analyzed. Finally, the effectiveness of the coordination strategy is verified by comparing the power flow parameters before and after optimization.
Salient Object Detection with Detail-Preserving Pooling and Feature Channel Refinement

This paper proposes a novel end-to-end approach for salient object detection task to enhance the performances. The traditional downscaling method such as max pooling layer is replaced by detail preserving pooling layer to capture more effective features. Moreover, the squeeze and excitation block is adopted to extract image features with the channel wise importance. Finally, densely connected architecture is introduced to maximizes feature reuse and reduce the computational cost in the process of generate saliency maps. The proposed method, on several public benchmarks acquires competitive or better performances than other similar approaches.
Application of nonconventional mineral exploration techniques: case studies

This chapter presents case studies on the application of nonconventional exploration techniques from Africa and Asia. The first case study is on the Kabanga Ni–Cu sulfide deposit using mainly the work of Macheyeki. In this case study, procedures on how to obtain lithogeochemical vectors are given along with their limitations. The second case study elucidates the use of Mineral Prospectivity modeling on ore field scale and at Prospect scale, in the Yangtze Metallogenic Belt, China by Li et al. and Yuan et al. In this study, workflows of 3D geological and mineral prospectivity modeling are presented. The third case study is on the use of multifractal modeling for identification of Cu and Au geochemical anomalies in northern Xinjiang Province, China. It is the belief of the authors that such case studies could be extended to other parts of the world having similar geological settings.
Applying External Guidance Commands to Deep Reinforcement Learning for Autonomous Driving

End-to-end deep reinforcement learning [1] algorithms used in autonomous car field and trained on lane-keeping task achieve good results in roads that don’t require decision making but cannot deal with situations where getting driving direction is mandatory like choosing to turn left or right in an upcoming crossroads, deciding when to leave a traffic circle or toward which path/destination to go. In this paper we introduce a new Deep Reinforcement Learning model that enable to integrate guidance commands at test time as a complementary input that indicate the right direction, that we call Deep Reinforcement Learning with guidance (DRLG), we apply the DRLG architecture on two algorithms, the asynchronous advantage actor-critic A3C and the Deep Deterministic Policy Gradient algorithm DDPG. For the training and experimentations of the new model, we adopt the CARLA virtual environment, a High-fidelity realistic driving simulator as a testbed since leading driving tests in the real world turns out to be neither safe nor affordable in term of materials and requirements. The results of testing show that DDPG and A3C with Guidance (DDPGG and A3CG) models succeed on their driving task through roads/roundabouts, by being appropriately responsive to the external commands, which allow to the autonomous car to follow the indicated route and take the right turns.
A Coarse-to-fine Classification for Motion Blur Kernel Size Estimation with Cascaded Neural Networks

Blur kernel size estimation significantly affects the quality of image deblurring. However, existing methods have a large limitation in estimation accuracy as well as in the adaptability to large kernel sizes. In this paper, we propose a novel blur kernel size estimation method using CNNs. We first convert the 2-D kernel size estimation to a 1-D classification problem. A cascaded network model using homogeneous networks is then designed to cut the entire estimation space into 38 small intervals to have a more accurate kernel size estimation in a coarse-to-fine manner. Experiments show that the proposed method has reached a satisfying performance of kernel size estimation with a largely improved adaptability to various blur kernels.