Reviewerly/exHarmony huggingface dataset:
A Road Surface Distinction Method Based on Integral of Reflected Wave Using Ultrasonic Sensors

In this paper, a new road surface distinction method using ultrasonic sensors is proposed. The ultrasonic sensor introduced for constructing the measurement device, combining with microcomputer and Bluetooth module. The measurement device, which can send ultrasonic waves to road surfaces and receive reflected wave, is designed to be fixed on a wheel chair for road surface distinction. The integration of the reflected waveform in time is called voltage-time integral. In previous study, it was confirmed that the strength of reflected wave from road surfaces depends on the distance between the measurement device and road surface, the incidence of the ultrasonic wave and the roughness of the road surfaces. In this paper, four types of road surfaces were distinguished: floor, mat, artificial turf, and gravel. A classifier was built with support vector machine (SVM) using the average and standard deviation of the voltage-time integral as features. The sample size was varied and the accuracy score for each was tested. Hence, the effectiveness of the road surface distinguish method using the average and standard deviation of the voltage-time integral as parameters was confirmed.
Subset-based stereo calibration method optimizing triangulation accuracy

Calibration of vision systems is essential for performing measurement in real world coordinates. For stereo vision, one performs stereo calibration, the results of which are used for 3D reconstruction of points imaged in the two cameras. A common and flexible technique for such calibration is based on collection and processing pairs of images of a planar chessboard calibration pattern. The inherent weakness of this approach lies in its reliance on the random nature of data collection, which might lead to better or worse calibration results, depending on the collected set of image pairs. In this paper, a subset-based approach to camera and stereo calibration, along with its implementation based on OpenCV, is presented. It utilizes a series of calibration runs based on randomly chosen subsets from the global set of image pairs, with subsequent evaluation of metrics based on triangulating the features in each image pair. The proposed method is evaluated on a collected set of chessboard image pairs obtained with two identical industrial cameras. To highlight the capabilities of the method to select the best-performing calibration parameters, a principal component analysis and clustering of the transformed data was performed, based on the set of metric measurements per each calibration run.
Peer Grading the Peer Reviews: A Dual-Role Approach for Lightening the Scholarly Paper Review Process

Scientific peer review is pivotal to maintain quality standards for academic publication. The effectiveness of the reviewing process is currently being challenged by the rapid increase of paper submissions in various conferences. Those venues need to recruit a large number of reviewers of different levels of expertise and background. The submitted reviews often do not meet the conformity standards of the conferences. Such a situation poses an ever-bigger burden on the meta-reviewers when trying to reach a final decision.
Generalising Discrete Action Spaces with Conditional Action Trees

There are relatively few conventions followed in reinforcement learning (RL) environments to structure the action spaces. As a consequence the application of RL algorithms to tasks with large action spaces with multiple components require additional effort to adjust to different formats. In this paper we introduce Conditional Action Trees with two main objectives: (1) as a method of structuring action spaces in RL to generalise across several action space specifications, and (2) to formalise a process to significantly reduce the action space by decomposing it into multiple sub-spaces, favoring a multi-staged decision making approach. We show several proof-of-concept experiments validating our scheme, ranging from environments with basic discrete action spaces to those with large combinatorial action spaces commonly found in Real Time Strategy (RTS) style games.
Forecasting of Key Indicators of the Manufacturing System in Changing External Environment

The management of a manufacturing system faces with the problem of long-term and medium-term forecasting of the system-forming factors of the stable functioning of the system(raw material prices, expected demand, the volume of orders, cost of products), changing due to dynamics of the markets for raw materials and end-products, high competition, as well as other political, economic and global factors. The expected improvement of the forecasting quality has not been achieved even with a large amount of qualitative and quantitative information about the system. The events accompanied by cumulative influences from many factors often lead to rapid changes in prices (structural shifts, changes in trends and price relationships) that are reflected in the quantitative data with a delay. Analysis of the strength and direction of cumulative influences of many factors on prices improves the quality of forecasting. This paper proposes the general scheme of the forecasting algorithm based on a system of models that includes a situation cognitive map and a set of the quantitative forecasting models of changes in the key indicators of a manufacturing system.
A Dynamic Fuzzy Histogram Equalization for High Dynamic Range Images by Using Multi-Scale Retinex Algorithm

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Research and application of sports health system based on Block Chain Technology

Abstract With the continuous development of block chain technology, people realize that this trusted underlying mechanism has important practical significance as the foundation of various activities. This paper combines block chain technology with sports health system, and improves it based on Ethereum system. In the traditional block chain system, the time to generate blocks is too slow and the centralization problem is caused by the large asset gap. We propose the concept of surplus advantage of account to determine which account has more power to generate the new block in the block chain. In this way, users of the system can maintain the system, which can further ensure the security of the whole system and the authenticity of data information, and greatly improve the efficiency of the whole system. And we write smart contracts to make sure that things always follow the agreed rules. This will also make the whole sports health system has better security and credibility. With this block chain technology as the guarantee of the underlying technology, the sports health system can be more accurate, the relevant departments for the implementation and supervision of sports health subsidies, but also ensure the authenticity and security of the data information.
KNN-DK: A Modified K-NN Classifier with Dynamic k Nearest Neighbors

K-nearest neighbor (k-nn) is a widely used classifier in machine learning and data mining, and is very simple to implement. The k-nn classifier predicts the class label of an unknown object based on the majority of the computed class labels of its k nearest neighbors. The prediction accuracy of the k-nn classifier depends on the user input value of k and the distance measure used to compute the nearest neighbors from the training objects. If we use a static value for k for a particular classification task, the prediction accuracy of a k-nn classifier may decrease due to class imbalance in a dataset. In this paper, we propose a modified k-nn classifier that considers class imbalance in a dataset, and computes an appropriate value for k. The proposed k-nn classifier has been validated on a large number of benchmark datasets from various domains. The method is compared with traditional k-nn, decision tree, random forest and SVM classifiers, and the method yields significantly better prediction accuracy than the traditional the k-nn classifier and other algorithms.
Cloud Computing Security Using Blockchain Technology

In 2006, cloud computing existed after Amazon’s deployment of the first of its category of cloud services. Cloud computing is now simply the topmost in every record of existing theme as a research topic, for computer science in view of the fact that of its across-the-board implications in various areas in computing and which has become present day’s most recent research area because of its capability to decrease the operational costs linked with computing. A rapid growth in cloud computing adaption has been observed but, still, the data security concerns have not been fully countered. Data security anxiety is still an obstacle to the expansion of cloud computing to some extent and needs to be determined. Earlier we have used techniques for cloud environment security in our research work like two-factor authentication (OTP), AES algorithm, RSA cryptography, elliptic curve cryptography (ECC), hyperelliptic curve cryptography (HECC), homomorphic encryption, steganography, usage control (UCON) collective with encryption and the digital watermarking technology. Apart from used techniques, blockchain has come into view as a key technology to ensure security particularly in aspects of authenticity confidentiality and integrity. Therefore, this time we have selected blockchain technology to avoid the security concerns of the cloud environment. This time we will review the various features of security in blockchain and further analyze the application of blockchain in cloud environment for computing security.
Transformation of PrivUML into XACML Using QVT

In the context of our work, we devote this paper to the derivation from the PIM level to the PSM level in accordance with the MDA approach. PrivUML, the meta-model that we proposed in our previous work to model the privacy protection requirements, will be transformed into XACML by relying on the QVT transformation language. This paper presents this derivation process as well as the changes made to make our transformation a success. It also discusses the concept of anonymization and our integration proposal to be able to guarantee dynamic data anonymization.
A Domain Classification-based Information Retrieval System

Information Retrieval (IR) is the process of retrieving relevant documents from a large collection of documents. The applications of these technologies are no longer restricted to only online search engines, instead, these are widely being used by organizations to facilitate different organizational information searches. Efficient Retrieval of documents from a large collection of organizational data is still a challenging task. In this paper, a domain categorization-based Information Retrieval system is presented for offline document repository. Several tests were conducted to evaluate the performance of the presented model. The performance of the model is also compared with existing retrieval techniques and the results obtained show the efficiency of the model in providing relevant documents quickly. The model achieves approximately 93% precision and 92% recall accuracy in the considered domains and data set.
Performance comparison of native host and hyper-based virtualization VirtualBox

This paper is related to the issue of comparing the performances of hypervisor-based virtualization, with Oracle VM Virtual Box as a representative, and a native host. The measurements are carried out on the same PC and under the equivalent conditions, using the Filebench software. The used operating system is Ubuntu 20.04.01. The goal of this paper is to show an impact of one and more VirtualBox virtual machines on the file system performances. The results have been illustrated in graphical form.
CryptoEyes: Privacy Preserving Classification over Encrypted Images

With the concern of privacy, a user usually encrypts the images before they were uploaded to the cloud service providers. Classification over encrypted images is essential for the service providers to collect coarse-grained statistical information about the images, therefore offering better services without sacrificing users' privacy. In this paper, we propose CryptoEyes to address the challenges of privacy-preserving classification over encrypted images. We present a two-stream convolutional network architecture for classification over encrypted images to capture the contour of encrypted images, therefore significantly boosting the classification accuracy. By sharing a secret sequence between the service provider and the image owner, CryptoEyes allows the service provider to obtain category information of encrypted images while preventing the unauthorized users from learning it. We implemented and evaluated CryptoEyes on popular datasets and the experimental results demonstrate the superiority of CryptoEyes over existing state of the arts in terms of classification accuracy over encrypted images and better privacy preservation performance.
Hierarchical Clustering of Natural Disaster Images Using Visual Features

This paper presents a hierarchical clustering method for natural disaster images. By displaying natural disaster images with similar categories, users are able to intuitively understand their surrounding environment and potential risks such as flood and sediment damage. Because there are unstructured natural disaster images without textual descriptions, a method that does not use textual features is necessary. The contribution of this study is to newly develop a method to hierarchically cluster natural disaster images using only visual features. Experimental results for images that are available from the research database show the superiority of our hierarchical clustering compared with flat clustering.
Pet cat behavior recognition based on YOLO model

With the progress of the times and the rapid development of science and technology, machine learning and artificial intelligence are increasingly used in transportation, logistics, and homes. In terms of pets, pet monitoring has also become very popular in recent years. Therefore, this study for the real-time monitoring of home pets, using the raspberry pie as a monitoring system, proposed a raspberry pi-based YOLOv3-Tiny identification system YOLOv3-Tiny method with rapid detection and better boundary frame prediction. One thousand one hundred twenty-eight pictures of cats' movements were collected in the room for marking and training. Finally, according to the input image categories, the results of six cat action categories were output. They were sleeping, eating, sitting down, walking, going to the toilet, and search on a trash can. The average accuracy was 98%. Through image recognition, the images were sent to the user's mobile phone app and computer. When the cat goes to the toilet for too long or flips through the trash can, the system will instantly send a message to the owner's mobile phone to achieve an instant preventive remote pet monitoring system.
A new multi-scale backbone network for object detection based on asymmetric convolutions

Real-time object detection on mobile platforms is a crucial but challenging computer vision task. However, it is widely recognized that although the lightweight object detectors have a high detection speed, the detection accuracy is relatively low. In order to improve detecting accuracy, it is beneficial to extract complete multi-scale image features in visual cognitive tasks. Asymmetric convolutions have a useful quality, that is, they have different aspect ratios, which can be used to exact image features of objects, especially objects with multi-scale characteristics. In this paper, we exploit three different asymmetric convolutions in parallel and propose a new multi-scale asymmetric convolution unit, namely MAC block to enhance multi-scale representation ability of CNNs. In addition, MAC block can adaptively merge the features with different scales by allocating learnable weighted parameters to three different asymmetric convolution branches. The proposed MAC blocks can be inserted into the state-of-the-art backbone such as ResNet-50 to form a new multi-scale backbone network of object detectors. To evaluate the performance of MAC block, we conduct experiments on CIFAR-100, PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO 2014 datasets. Experimental results show that the detection precision can be greatly improved while a fast detection speed is guaranteed as well.
Models and Methods Flexible Documents Matching Based on the Recognized Words

The chapter discusses methods for business documents’ recognition with a flexible structure. The subject of the research is algorithms for matching areas meant for the extraction of text data. This task is applicable, for instance, for scanned paper documents recognition and retrieval of attributes for further processing. A mock-up for hierarchical matching using recognized words is described. Classifying text objects methods (zones, lines, fields) for speculation of an object’s boundaries are considered. A modification of the Levenshtein distance is proposed to compare a set of recognized words with a limited vocabulary. An algorithm for speculation of the fields’ boundaries in recognized text line using text feature points is proposed. The proposed methods have been time-tested on our private data array. The proposed method showed high recognition quality for documents of Agreement and Consistent type.
High pollutant levels of produced water around Al-Ahdab oil field in Wasit governorate (Iraq)

Abstract Exploration activities of the oil and gas industry generate loads of formation water called produced water (PW) up to thousands of tons each day. Depending on the geographic area, formation depth, oil production techniques, and age of oil supply wells, PW from different oil fields contain different chemical compositions. Currently, PW is also known as industrial waste water containing heavy metals that are toxic to humans and the environment, requiring special processing so that they can be disposed of in the environment. To determine the heavy metals content in PW from the Al-Ahdab oil field (AOF), the Ministry of Science and Technology/Agricultural Research Department determined some parameters including the concentrations of Cd, Co, Cr, Pd, and Ni using instrument inductively couple plasma (ICP-OES). Results of this study showed high concentrations of Cd (0.51-2.05, Cr (0.06-1.81), Co (0.11-0.72), Ni (0.12-0.22) and Pb (5.52-20.6) in the AOF compared to concentrations in water bodies about 16 km outside the field; Cd (0.01-0.32), Cr (0.01-0.11), Co (0.03-0.18), Ni (0.02-0.11) and Pb (0.04-1.73). These findings indicate there are increased levels of pollutants in the PW within the AOF of the Main Outfall Drain (MOD). The PW could not be as a source of drinking water and other daily activities, including fisheries and crop planting, unless advanced treatment, to remove the heavy metal content.
A Statistical-Based Light-Weight Anomaly Detection Framework for Wireless Body Area Networks

Abstract In healthcare scenario, the major challenge in anomaly detection for remote patient monitoring is to classify true medical conditions and false alarms. This paper proposes a light-weight anomaly detection (LWAD) framework for detecting anomalies in remote patient monitoring based on wireless body area networks. The proposed framework uses distance correlation for finding correlated (both linear and non-linear) physiological parameters. It also uses a statistical-based improvised dynamic sliding window algorithm for efficient short-range prediction of physiological parameters. Finally, the proposed LWAD framework detects anomalies using anomaly detection framework based on robust statistical techniques. The validation of LWAD framework is performed using three real time datasets with various statistical measures. The proposed LWAD framework outperforms existing methods.
Analysis of Fault Characteristics Based on Clock Glitch Injection

Fault injection attack can create instantaneous electrical pulses or thermal noise in the actual operating cryptographic circuit, thereby destroying the original time constraints on the cryptographic circuit, triggering the inversion of the intermediate state of the cryptographic algorithm and reducing the security strength of the cryptographic algorithm. Currently, clock glitch presents one of the most commonly used fault injection methods, which pose serious threat to the security of password applications. Focusing on the implementation of block cipher FPGA applications and based on the built-in clock glitch fault injection test platform, we study the fault characteristics caused by clock glitch in this paper. The relevant conclusions can be extended to other fault injection mechanisms with fault caused by time constraint violation of the cipher circuit.
Understanding Driving Distractions: A Multimodal Analysis on Distraction Characterization

Distracted driving is a leading cause of accidents worldwide. The tasks of distraction detection and recognition have been traditionally addressed as computer vision problems. However, distracted behaviors are not always expressed in a visually observable way. In this work, we introduce a novel multimodal dataset of distracted driver behaviors, consisting of data collected using twelve information channels coming from visual, acoustic, near-infrared, thermal, physiological and linguistic modalities. The data were collected from 45 subjects while being exposed to four different distractions (three cognitive and one physical). For the purposes of this paper, we experiment with visual and physiological information and explore the potential of multimodal modeling for distraction recognition. In addition, we analyze the value of different modalities by identifying specific visual and physiological groups of features that contribute the most to distraction characterization. Our results highlight the advantage of multimodal representations and reveal valuable insights for the role played by the two modalities on identifying different types of driving distractions.
PSR: Unified Framework of Parameter-Learning-Based MR Image Superresolution

Magnetic resonance imaging has significant applications for disease diagnosis. Due to the particularity of its imaging mechanism, hardware imaging suffers from resolution and reaches its limit, and higher radiation intensity and longer radiation time will cause damage to the human body. The problem is expected to be solved by a superresolution algorithm, especially the image superresolution based on sparse reconstruction has good performance. Dictionary generation is a key issue that affects the performance of superresolution algorithms, and dictionary performance is affected by dictionary construction parameters: balance parameters, dictionary size, overlapping block size, and a number of training sample blocks. In response to this problem, we propose an optimal dictionary construction parameter search method through the experiment to find the optimal dictionary construction parameters on the MR image and compare them with the dictionary obtained by multiple sets of random dictionary construction parameters. The dictionary we searched for the optimal parameters of the dictionary construction training has more powerful feature expressions, which can improve the superresolution effect of MR images.