Reviewerly/exHarmony huggingface dataset:
Adaptive cardinality balanced multi-target multi-Bernoulli filter based on cubature Kalman

The sequential Monte Carlo cardinality balanced multi-Bernoulli (SMC-CBMeMBer) filter provides a good framework to cope with the multi-target tracking problem. However, the standard SMC-CBMeMBer filter suffers from the particles’ degradation problem seriously. Using the measurements to construct the proposal density in the step of predict can effectively solve the above problem, but this kind of approach brings an amount of computation and causes the overestimation of the target number. To examine the quality of each predicted particle adaptively and use the cubature Kalman filter (CKF) to refine the poor-quality particles with the aid of the current measurements is proposed in this study. This method manages to alleviate the particles degradation problem without increasing the computational complexity seriously since only a part of the particles is refined by the CKF. Also, the proposed method can avoid cardinality overestimation caused by abuse of measurements. A range of simulations is performed to test the performance of the proposed method. The results confirm the effectiveness and robustness of the novel method.
On the Use of Neural Text Generation for the Task of Optical Character Recognition

Optical Character Recognition (OCR), is extraction of textual data from scanned text documents to facilitate their indexing, searching, editing and to reduce storage space. Although OCR systems have improved significantly in recent years, they still suffer in situations where the OCR output does not match the text in the original document. Deep learning models have contributed positively to many problems but their full potential to many other problems are yet to be explored. In this paper we propose a post-processing approach based on the application deep learning to improve the accuracy of OCR system (minimizing the error rate). We report on the use of neural network language models to accomplish the task of correcting incorrectly predicted characters/words by OCR systems. We applied our approach to the IAM handwriting database. Our proposed approach delivers significant accuracy improvement of 20.41% in F-score, 10.86% in character level comparison using Levenshtein distance and 20.69% in document level comparison over previously reported context based OCR empirical results of IAM handwriting database.
Research on monitoring method for thinning spinning process of the ultra-thin-walled cylinder based on the drum shape

In order to perceive the state of the process of reverse thinning spinning of the ultra-thin wall tube, a monitoring method based on drum shape is proposed. The method uses the non-uniform rational basis spline curve fitting method based on the moving least squares method to reconstruct the outer contour data of the spinning zone collected by the line laser displacement sensor and extracts the outer contour curve of the drum zone according to the spinning characteristics. Then, the dynamic time warping algorithm is used to analyze the similarity between the current and previous drum curves in order to judge whether the current spinning state is abrupt or not. At the same time, the current spinning state is judged by combining the curvature comb of the drum curve, the number of convex areas of the drum curve, the drum ratio, and the drum change trend. Finally, the drum shape detection and the spinning condition monitoring experiments are carried out. The experimental results show that this method can obtain the outer contour shape of the drum section at one time, and the accuracy is 0.05 mm with respect to the measured value of the three coordinates. The method can realize the visual monitoring of the abrupt change of the spinning process by judging the similarity of the drum curve at the adjacent time under different spinning states. In the state of instability, the curvature comb of the drum curve is discontinuous, the curve is G0 continuous, the drum ratio exceeds the critical drum ratio 2.0, and there is more than one convex region, which realizes the monitoring of spinning state more accurately. The experimental results verify the correctness of the proposed method and prove that the method can be used as a new method to judge the stability of the ultra-thin-walled cylinder spinning process.
Robust Granular Feedback Linearization

This paper addresses a novel granular feedback linearization approach to robustly and efficiently control imprecise nonlinear systems. This approach employs an evolving participatory learning algorithm to estimate the nonlinearities and to cancel their effects in the control loop. Simulation experiments with a surge tank are used to evaluate and to compare the performance of the robust granular feedback linearization with an adaptive controller based on a bacterial foraging technique, and an alternative granular evolving fuzzy robust feedback linearization mechanism. The results suggest that the robust granular feedback linearization performs better than the other approaches from the point of view the performance indexes commonly adopted in the process control practice like integral of the absolute error, the integral of the time-weighted absolute error, and the integral of the variability of the error.
Multiple States Fall Detection System for Senior Citizens

Since the situation of aging society and injury in elderly have become big problems in our daily life, an experiment which uses several models to detect people's action and selects the appropriate one in fall detection is presented in this paper. This study carries out a series of meticulous classification of user actions. In ideal environment of the experiment, the RealSense D435 depth camera developed by Intel is used to obtain the user's actions videos, and the datasets of images are created according to the action of objects. After binarization, normalization, and recovery, the images are saved as sample database, including training data and testing data for the models. Then the performance of the models are tested after training the models of machine learning (HOG + SVM) and deep learning (AlexNet and GoogLeNet). In these models, a pretreatment layer is constructed to process the images to improve the recognition rate. Finally we analyze the result of each experiment to select the appropriate method, which can perform with the best efficiency and recognition rate.
A Study on Lightweight Anonymous CP-ABE Access Control for Secure Data Protection in Cloud Environment

Recently, with the development of cloud computing technology, people can use the cloud to store their own data and share it with others. However, since the cloud is networked, there are various security threats in the network environment. To solve this problem, the CP-ABE access control scheme, which is an attribute-based encryption scheme, is used. However, a third party can deduce the attribute of the user who wants to access the data through the access policy specified in the cipher text, which can infringe the user's privacy. An anonymous CP-ABE scheme that protects user's privacy by giving anonymity to the access policy is being studied. However, since the size of the ciphertext increases as the number of attributes increases in the previously proposed anonymous CP-ABE scheme, the efficiency of the cloud storage space can be wasted. Also, the amount of computation of the user to decode is inefficient in proportion to the number of attributes. In this paper, we increase the efficiency of storage space by fixing the size of ciphertext regardless of the number of attributes. And we propose a anonymous CP-ABE access control technique that can increase the efficiency of the user's computation amount by supporting outsourcing techniques.
GPU Accelerated Fuzzy C-Means (FCM) Color Image Segmentation

In this paper, computational acceleration of color image segmentation using fuzzy c-means (FCM) algorithm has been presented. The color image is first converted from the Red Green Blue (RGB) color space to the YUV color space. Then, the luma (Y) information values are grouped according to the desired number of clusters using the FCM algorithm. The FCM algorithm is implemented on a Graphical Processing Unit (GPU) using the Compute Unified Device Library (CUDA) library which is developed by NVidia to speed up the computing time. Images used in this research are red blood cell images, geometry images and leaf images. The results of segmented images processed using GPU were seen identic to the results of segmented images processed using the Central Processing Unit (CPU). The computational time of the FCM algorithm can be accelerated by speed-up to 5,628 times faster and the average speed-up of all simulations done is 5,517 times faster.
Improved object tracking technique using hybrid approach

Object tracking is need of activity log applications used in security surveillance videos. The object tracking using CAMShift method uses histogram estimation of seed point pixels of ROI object in each frame. The object is considered on the basis of its histogram estimation and then score is estimated using accuracy ratio based tracking window locations and ground truth locations. The occlusion occurrences in tracking is main challenge where, after occlusion updated window possesses the features that are not exact as needed and hence tracking window deviates from required position. To overcome this issue, we have proposed method with inclusion of speeded up robust features (SURF) based feature matching approach to estimate tracking window correctly even after occlusion. The results are estimated using standard dataset videos for tracking and compared with original tracking by estimating precision and tracking window location error.
A personalized clustering-based approach using open linked data for search space reduction in recommender systems

Recommender systems use information about the users' preferences to define relatedness scores towards items. Regardless of the method, a noticeable problem is that the system is required to compute scores for a large amount of unknown items in the database, even though these items may not be related to a determined user. In this manuscript, we propose a technique called search space reduction for recommender systems (SSR4Rec) that reduces the number of unknown pairs the recommender must process. As a pre-processing step, we cluster related items and assign only the closest group to each user, producing a reduced set of unknown pairs. The distance between items, and between clusters and users, is computed by comparing item representations and user profiles built based on attributes extracted from the Linked Open Data cloud. We assess the quality of SSR4Rec by applying it into two well-known RS and comparing the results against the same recommenders without our pre-processing step, as well as against other related baselines. Results show a significant improvement in both ranking accuracy and computational time.
New Residue Signed-Digit Addition Algorithm

In this paper, we propose a new residue signed-digit (SD) addition algorithm, which can be used in RSA public-key cryptosystem with a large modulus m. We use $$ \upmu = {\text{m}} - 2^{n} $$ and $$ - 2^{n - 1} + 1\, \le \,\mu \, < \,0 $$ to calculate the residue n-digit SD number additions. Thus, the residue addition is implemented by (1) adding two n-digit SD numbers and (2) reducing the (n+1)-digit SD number obtained by the SD addition to an n-digit SD number using $$ \mu $$ . Thus, the circuit of residue SD adder is constructed with two SD adders and some multiplexers, and no carry propagations arise during the residue addition. We have designed the circuits with VHDL for the encryption processor using the proposed residue SD adders. By comparing the performance of the encryption processor with that of the binary architectures, the proposed encryption processor is superior to the binary ones in computing time and low power.
A Novel Cooperative Pursuit Strategy in Multiple Underwater robots

A novel cooperative pursuit strategy for multiple underwater robots is proposed in this paper. In this pursuit strategy, the pursuit-evasion game is studied in two stages. In the first stages, when the target keeps the static state, the rapidly-exploring random tree (RRT) search algorithm is used to realize the real-time path replanning for multiple underwater robots, so as to avoid the target detection range and reach the desired pre-capture position. Therefore, the concealed problem of encircling the static target is solved. In the second stages, when multiple underwater robots enter into the target detection range, the target will start to escape. In order to solve the problem of capturing a moving target, the cooperative control law for multiple underwater robots is proposed. A circular formation is formed to pursue the target using the cooperative control law so that the capture error can meet a certain performance indexes. Finally, the simulation results show the effectiveness and feasibility of the proposed cooperative pursuit strategy.
Apollo APA

In Brazil, there are more than 9.3 million People with Hearing Impairment (PWHI) and deaf who face daily accessibility difficulties. On the other hand, there is the growth of the use of mobile devices and the application of the Internet of Things (IoT). The motivation for the development of this work lies in the absence of user interface systems based on sign language and customizable according to the profile that is applied to the prevention of risks external to the health of the deaf. This paper proposes the Apollo APA (Auditive Personal Assistant) model, which promotes accessibility for PWHI and deaf people in a smart home environment, especially regarding security. The scientific contribution of Apollo APA lies in the detection of ambient risks using neural networks via machine learning with supervised training. Besides this, the model APA also considers the user profile, mainly the degree of deafness, to generate accessible notifications represented in Brazilian Sign Language (LIBRAS in Portuguese). The notifications of risk sent to the smartphone of PWHI or deaf also can vibrate or turn on the light of the device. We implemented a prototype of a smart home that collects environmental sounds and notifies the deaf user. The scenario-based assessment included 3 activities of daily living (ADL) events of a deaf user: a kettle boiling over the stove, a dog barking and one person knocking on the door. The results indicate the means of the f-score of 0.73. Usability and acceptance evaluations were performed by 2 deaf users in a real setting. The results indicate the approval of 87% in the perceived ease of use and 100% in the perceived utility.
A Geometrical Approach to Human Saccade Simulation

Modeling and simulation of human eye movement have a wide range of applications in many domains. Various attempts have been made to model and simulate eye movements in a physically accurate manner. All the existing models show limitations and problems in simulating secondary and tertiary eye movements. Recent investigation of pulley models (passive and active hypotheses) in representing human eye motion has recognized mathematical complexity in modeling eye behavior. Sophisticated techniques of modeling are required to investigate eye movements. This paper presents a procedure for eye movement simulation through geometrical modeling (an OpenSim script with its recent MATLAB binding) for binocular vision. First order neural dynamics with Millard's muscle model are used to actuate six Extra Ocular Muscles (EOMs). Pulse-step inputs are used to generate the muscle forces around the eye globe. The implemented model is successful in simulating horizontal and vertical movements of the human eye with respect to the prescribed activation. The developed technique is evaluated using responses from lumped parameter models and EOG recordings.
Achieving Efficient and Verifiable Assured Deletion for Outsourced Data Based on Access Right Revocation

With the growing use of cloud storage facilities, outsourced data security becomes a major concern. However, assured deletion for outsourced data, as an important issue for users, but received less attention in academia and industry. Most of traditional deletion solutions require specific data organization forms or storage media, and are not applicable for outsourced data. Moreover, existing access control schemes for cloud which used ciphertext-policy attribute-based encryption (CPABE), focused on fine-grained access control, and completely ignored data deletion. In this paper, we aim to design an effective data deletion scheme that can be applied to any CPABE built on linear secret sharing-scheme. However, the challenge is how to maintain the traits of traditional CPABE while implementing a universal deletion method. To address this challenge, we propose a policy graph to describe relationships among users, policies, attributes, and files and introduce a new deletion concept for CPABE: when all users are unauthorized for a file, we say that the file is deleted. Then, we extend an efficient and verifiable deletion scheme on a CPABE. Specifically, we give an effective method to select key attributes and update the relevant parts of ciphertext so that all users become unauthorized. Furthermore, we verify the cipher update performed by third-party server through merkle trees. We also demonstrate its universality and prove the security under q-BDHE assumption. Finally, the performance evaluation and simulation results reveal that our solution achieves better performance compared with other schemes.
Residual Attention Generative Adversarial Networks for Nuclei Detection on Routine Colon Cancer Histology Images

The automatic detection of nuclei in pathological images plays an important role in diagnosis and prognosis of cancers. Most nuclei detection algorithms are based on the assumption that the nuclei center should have larger responses than their surroundings in the probability map of the pathological image, which in turn transforms the detection or localization problem into finding the local maxima on the probability map. However, all the existing studies used regression algorithms to determine the probability map, which neglect to take the spatial contiguity within the probability map into consideration. In order to capture the higher-order consistency within the generated probability map, we propose an approach called Residual Attention Generative Adversarial Network (i.e., RAGAN) for nuclei detection. Specifically, the objective function of the RAGAN model combines a detection term with an adversarial term. The adversarial term adopts a generator called Residual Attention U-Net (i.e., RAU-Net) to produce the probability maps that cannot be distinguished by the ground-truth. Based on the adversarial model, we can simultaneously estimate the probabilities of many pixels with high-order consistency, by which we can derive a more accurate probability map. We evaluate our method on a public colorectal adenocarcinoma images dataset with 29756 nuclei. Experimental results show that our method can achieve the F1 Score of 0.847 (with a Precision of 0.859 and a Recall of 0.836) for nuclei detection, which is superior to the conventional methods.
Raising the Quality of Bug Reports by Predicting Software Defect Indicators

The descriptive quality of bug reports is one of the essential parts of defect management. Sometimes they provide inadequate or incorrect data about software problems, which can lead to incomplete defect fixing or omission of serious defects. Therefore, it is vital to evaluate and improve the quality of bug reports. This paper proposes an approach that helps to resolve this problem by predicting various indicators. The values of these indicators allow QA engineers to evaluate the quality of defect description and correct it in a suitable way. This paper also introduces Nostradamus, a new open source tool built to implement the approach. The tool uses machine learning techniques to analyze the data stored in software defect repositories and evaluates the interdependence of defect attributes, including such a crucial element as a defect description. This paper describes the approach, the tool that is based on it and the typical use cases.
Modelling curves of mean monthly minute-by-minute and hourly relative humidity, air temperature and solar radiation of Ota, Nigeria

Abstract Agricultural, biological, environmental and physical processes require one or a combination of temperature, relative humidity and solar radiation. One-minute-data of these parameters for Ota, Nigeria, for September - December 2012 were obtained from meteorological centre of the Department of Physics Covenant University. Using MATLAB, diurnal patterns of mean monthly minute-by-minute and mean monthly hourly were fitted. Fournier, Gaussian, Sum of Sine Functions and Exponential models were also fitted, with the accuracy of the models determined from the Coefficient of Determination (R 2 ), Sum of Square Error (SSE) and Root Mean Square Error (RMSE). Best-fit-model was Fourier, the worst being Exponential. Best models obtained in case of minute-by-minute basis occurred when R 2 = 0.9998, RMSE = 0.02948 and SSE = 1.237 for air temperature; for relative humidity, R 2 = 0.9998, RMSE = 0.1007 and SSE = 14.43 while for solar radiation, R 2 = 0.9997, RMSE = 9.301 and SSE = 1.23e+005.In case of hourly basis R 2 = 0.9991, RMSE = 0.09226 and SSE = 0.8512 for air temperature; for relative humidity, R 2 = 0.9984, RMSE = 0.4976 and SSE = 1.981 while for solar radiation, R 2 = 0.9995, RMSE = 6.631 and SSE = 351.7. The results can produce input for weather pattern information planning in Ota, Nigeria.
Blockchain-Based Digital Identity Management Scheme for Field Connected IoT Devices

Abstract We propose a design for securing machine-to-machine (M2M) communications for field connected IoT devices by using the blockchain technology for digital identity management. Our approach considers a fundamental issue with public key cryptography, which is certificate issuance, revocation, and distribution. The blockchain design eliminates the need for a single authority for identity management and provides security by design for digital identity management and distribution.
Extraction of Cognitive Operations from Scientific Texts

Rhetorical structure theory defines the relations between predicates and larger discourse units, but it does not consider the extralinguistic nature of text-writing at all. However, the text-writing process is totally related to the particular targeted activity. This paper presents a new approach that does not model a text as a result of a researcher’s cognitive activity embodied in it, but it models cognitive activity reflected in the scientific text. We also propose and evaluate a framework for detection of text fragments, which is related to cognitive operations in scientific texts. The obtained results confirm the usefulness of the suggested set of cognitive operations for the analysis of scientific texts. Moreover, these results justify the applicability of the proposed framework to cognitive operation extraction from scientific texts in Russian.