Reviewerly/exHarmony huggingface dataset:
Modelling the boundary temperature regime in a regenerative heat recovery unit using differential equations and artificial neurons

The mathematical model of boundary temperature regime for accumulation and recovery of heat in a stationary switching regenerative heat recovery unit (SRHRU) is constructed. Modelling of the boundary regime in SRHRU is based on experimental data, and it is required to develop algorithms for calculating thermal constants and the general temperature regime. The boundary temperature regime is modelled by a system of nonlinear ordinary differential equations of the first order, which take into account the results of experimental observations and measurements. To determine the unknown parameters of the system of differential equations, artificial neurons are used: a two-layer perceptron is developed with three neurons on the first layer and with five neurons on the second layer.
Multi-label Learning Linearity in Ensemble of Pruned Set

In Multi-label learning or Multi-label classification, each instance is associated with a set of classes. Current MLL studies show dataset characterization have an effect on the performance of certain MLL algorithm. Dataset understanding by exploratory data analysis is needed to identify MLL algorithms to be used. The study starts with the hypothesis that dataset linearity characteristic affects algorithm performance. The study identifies four commonly used MLL dataset with linear and non-linear characteristics. EPS with different base classifier was modified to test the hypothesis. Results shows that MLL linearly separated dataset are with better performance in EPS with linear base classifier. Also, EPS with non-linear base classifier works better in MLL dataset with non-linear in character.
Real-time Head Detection for Automated Passenger Counting in Embedded Systems

Head detection is a key problem for automated passenger counting systems. In recent decades, considerable effort has been expended to develop an accurate and reliable head detector. However, head detection is still a challenging task because of problems caused by variations in pose and occlusions. Recently, general object detection algorithms based on convolutional neural networks (CNNs), such as Faster R-CNN, SSD and YOLO, have been successful. However, these algorithms require the use of a Graphics Processing Unit (GPU) for real-time performance. In this study, we focused on developing real-time head detection in an embedded system. Starting with the Tiny-YOLOv3 network, we applied the following strategies to achieve real-time performance in a non-GPU environment. First, we reduced the input image size to 224x224. Second, we added an extra yolo layer to detect smaller heads. Third, we removed batch normalization. Finally, we conducted depthwise separable convolution rather than traditional convolution. Three public datasets, HollywoodHeads, SCUT_HEAD, and CrowdHuman, were exploited to train and test the proposed network, and Average Precision (AP) at Intersection over Unit (IoU) = 0.5 were used to evaluate the tests. Experimental results showed that the proposed network perform better and faster than Tiny-YOLOv3.
XSStudent: Proposal to Avoid Cross-Site Scripting (XSS) Attacks in Universities

QR codes are the means to offer more direct and instant access to information. However, QR codes have shown their deficiency, being a very powerful attack vector, for example, to execute phishing attacks. In this study, we have proposed a solution that allows controlling access to the information offered by QR codes. Through a scanner designed in APP Inventor which has been called XSStudent, a system has been built that analyzes the URLs obtained and compares them with a previously trained system. This study was executed by means of a controlled attack to the users of the university who through a flyer with a QR code and a fictional link accessed an infected page with JavaScript code that allowed a successful cross-site scripting attack. The results indicate that 100% of the users are vulnerable to this type of attacks, so also, with our proposal, an attack executed in the universities using the Beef software would be totally blocked.
Image Restoration using Blind Deconvolution

The process of recovering a picture from a part of blur and noise is called the restoration of the picture. In current years, image restoration algorithms are using different patch based processing approaches. In image processing, image restoration plays a rudimentary role. The actual world images experience various types of degradation during different stages such as image capture, acquisition, storage, transmission or reproduction. Different features such as blur, contrast, etc result in image degradation. The duty of an automated image quality examination system is to make a steadfast decision on image quality in near real time with minimum human involvement. Universally, the image is classified as subjective or objective techniques with respect to quality measures. It is an expensive procedure for self-determining and then evaluating it. This process is time consuming which results in a lengthy process. Also it is dependent on the viewing angle. Compared to subjective metrics, objective image quality metrics are faster also yield instantaneous results without much human involvement. Here image sharpening is applied to the pre-blurred images. Here resize of image occurs followed by the RGB channel separation. Analysis of motion blur and also Gaussian blur is done in this paper. Iterative image restoration technique is being used. MATLAB is used for testing which allows real image characterisation. This ideology is highly effiective which yields advanced results in denoising, deblurring, segmentation.
Estimation of Groundwater Vulnerability to Pollution Based on DRASTIC and SI Methods

The aims of this study are the vulnerability assessment of an aquifer in a semi-arid environment, perimeter irrigated of Tadla plain. The vulnerability determined by two different methods: the DRASTIC and the SI methods. In the Drastic method, seven parameters of the model used to calculate the intrinsic vulnerability index and the vulnerability specific. The DRASTIC method gives two classes of vulnerability to pollution; the class of "low vulnerability", which is the most dominant as it covers a large area of 78.5%, and the "moderate" vulnerability with 21.5% of occupation. The SI method which relates to human activities, gives three vulnerability classes: the low vulnerability class, the moderate vulnerability class, which is the most dominant one as it covers almost the entire study area, and the high vulnerability class, with 11.5%, 85.3%, and 3.2% respectively. For the validation of the results obtained by the two methods, we use the concentrations of nitrate. These coincidence rates are satisfactory for both methods used; however, the specific method SI better assesses the vulnerability by nitrates.
Petrology, geochemistry and mineral chemistry of Shahrak intrusive body (East of Takab, Northwest of Iran)

Petrology, geochemistry and mineral chemistry of Shahrak intrusive body (East of Takab, Northwest of Iran)
BRUKID Sort - A New Sorting Approach Using Multi Dimensional Array

Out of diverse problems in the field of computer sciences, sorting is a primary operation in most applications. Sorting means organizing the data either in increasing order or decreasing order. There are plentiful algorithms that are developed to sort the data. Each algorithm has its own approach and also uses its own mechanism. The main objective of this paper is to reduce the time taken to sort the elements using the multi dimensional array. Here in this paper, a new algorithm is presented which reduces the time complexity by sorting elements based on their size. It is clearly shown in this paper that by arranging the given data based on its size in a multi dimensional array and then applying the sorting over it reduces the sorting time.
In-Time 3D Reconstruction and Instance Segmentation from Monocular Sensor Data

In most implementations of 3D reconstruction, depth information is provided by RGB-D sensors recording RGB and depth for each pixel. However, these sensors are still considerably expensive, characterized by high consumption of resources on the hardware itself, and have failed to reach mass markets of everyday mobile devices. With this paper, we aim to demonstrate the possibility of leveraging devices lacking 3D sensory data, in combination with external processing, to enrich the AR experience. We propose a client-server architecture that, in combination with a mobile client, allows for scanning of indoor environments as 3D semantically labeled sceneries while providing the user with instant feedback about the scanning results. At its core, the system is composed of the following expendable and exchangeable modules: (1) Depth Prediction from 2D images (2) Semantic Instance Segmentation of 2D images and (3) 3D Projection and Reconstruction. The result is a continuously updated mesh of the scenery with instance level segmentations in 3D. In comparison to the state-of-the-art, we are not only independent of the RGB-D input, but offer an architecture that complements and enhances the current AR client frameworks while demanding little extra computation from the mobile device.
Towards Proxy-Attendance Detection using a Digital Attendance System and Machine Learning

A digital solution to the attendance problem comprising of user interfaces, server technologies and a persistent database is presented. The system combines features of all the aforementioned technologies to maintain a record of students attending lectures scheduled in the respective university. The proposed system also incorporates an anomaly detection algorithm, backed by Machine Learning to ensure that the records entered accurately represent the students present in the lecture hall which therefore fortifies the system against unprofessionalism and malpractices. The system primarily consists of three main modules, namely student module, teacher module and server module. To explain further the anomaly detection process of the server module, plot graphs are provided which represent the position of students in the lecture hall documented by the system. This system overcomes the drawbacks of the canonical paper-driven attendance system, thus making the process swift and integral.
Analyzing the Challenges of Security Threats and Personal information in Mobile Cloud Computing Infrastructure

The rise of cloud computing is considered nowadays as an inspiring technology to the computing sector. The term “software as a services” (SAAS) is representing a model of software distribution which led third-party provider to hosts an application and arrange it to be available for a client through a mean of Internet. The latest scheme of mobile cloud computing is reliant directly on cloud computing to deliver to mobile devices a numerous sets of applications. However, with all the advantages delivered by such scheme there are many challenges that we need to address; the maximum bandwidth capacity available, as well as the battery life, and main storage capacity of mobile devices. Cloud computing has two important factors that must be considered in terms of the services that are provided by this technology and these are mainly privacy and security. In this research article we will investigate the challenges in mobile cloud computing, the architecture involved and modeling, as well as surveying mobile cloud computing challenges.
Application of an Ontological Approach to Process of Development and Implementation of Electronic Flight Bag Systems

The paper considers application of an ontological approach to development and implementation of Electronic Flight Bag information systems (EFB). An ontology of EFB systems’ subject area with description of a conceptual model of EFB software application for take-off and landing characteristics calculations of aircraft has been developed. The ontology was formalized using Protégé software.
Automatic Evaluation of Iconic Image Retrieval based on Colour, Shape, and Texture

Product image search is required to deal with large target image datasets which are frequently updated, and therefore it is not always practical to maintain exhaustive and up-to-date relevance assessments for tuning and evaluating the search engine. Moreover, in similar product image search where the query is also an image, it is difficult to identify the possible search intents behind it and thereby verbalise the relevance criteria for the assessors, especially if graded relevance assessments are required. In this study, we focus on similar product image search within a given product category (e.g., shoes), wherein each image is iconic (i.e., the image clearly shows what the product looks like and basically nothing else), and propose an initial approach to evaluating the task without relying on manual relevance assessments. More specifically, we build a simple probabilistic model that assumes that an image is generated from latent intents representing shape, texture, and colour, which enables us to estimate the relevance score of each image and thereby compute graded relevance measures for any image search engine result page. Through large-scale crowdsourcing experiments, we demonstrate that our proposed measures, InDCG (which is based on per-intent binary relevance) and D-InDCG (which is based on per-intent graded relevance), align reasonably well with human SERP preferences and with human image preferences. Hence, our automatic measures may be useful at least for rough tuning and evaluation of similar product image search.
Clustering in Hypergraphs to Minimize Average Edge Service Time

We study the problem of clustering the vertices of a weighted hypergraph such that on average the vertices of each edge can be covered by a small number of clusters. This problem has many applications, such as for designing medical tests, clustering files on disk servers, and placing network services on servers. The edges of the hypergraph model groups of items that are likely to be needed together, and the optimization criteria that we use can be interpreted as the average delay (or cost) to serve the items of a typical edge. We describe and analyze algorithms for this problem for the case in which the clusters have to be disjoint and for the case where clusters can overlap. The analysis is often subtle and reveals interesting structure and invariants that one can utilize.
Denoising and Segmentation of Epigraphical Estampages by Multi Scale Template Matching and Connected Component Analysis

Epigraphy refers to the study of Epigraphs, or inscriptions. Estampage or stamping, is a term that is commonly used in epigraphy in order to get an exact copy of an inscription. An estampage is typically derived by applying wet paper onto the rock face, over which any ink material is wiped. This process also results in the appearance of unwanted noise which arises due to the texture of the rock, human errors or a variety of different factors. In this paper, we propose CCD: Connected Component Denoising, a novel approach for denoising and character segmentation via connected component analysis and multi scale template matching. A dynamic threshold is obtained via histogram density projections to effectively remove noisy areas. A comparative study is then performed between the different approaches (MSTM, CC, Hybrid). Inspection of the results of the hybrid approach by the HVS(Human Visual System) shows that it outperforms all the previous techniques.
Basic Techniques for Data Security

In this chapter, we introduce basic theorems, cryptographic primitives, and techniques for protection of data security. These theorems, cryptographic primitives, and techniques serve as fundamental building blocks in subsequent chapters and have widely used in different application scenarios to ensure data security.
Enhancing the Efficiency of the Interval-Valued Fuzzy Rule-Based Classifier with Tuning and Rule Selection

Interval-Valued fuzzy rule-based classifier with TUning and Rule Selection, IVTURS, is a state-of-the-art fuzzy classifier. One of the key point of this method is the usage of interval-valued restricted equivalence functions because their parametrization allows one to tune them to each problem, which leads to obtaining accurate results. However, they require the application of the exponentiation several times to obtain a result, which is a time demanding operation implying an extra charge to the computational burden of the method. In this contribution, we propose to reduce the number of exponentiation operations executed by the system, so that the efficiency of the method is enhanced with no alteration of the obtained results. Moreover, the new approach also allows for a reduction on the search space of the evolutionary method carried out in IVTURS. Consequently, we also propose four different approaches to take advantage of this reduction on the search space to study if it can imply an enhancement of the accuracy of the classifier. The experimental results prove: 1) the enhancement of the efficiency of IVTURS and 2) the accuracy of IVTURS is competitive versus that of the approaches using the reduced search space.
SCGN: novel generative model using the convergence of latent space by training

Generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) have been recently applied to various fields. However, the VAE and GAN models have blur and mode collapse problems, respectively. Here, the authors propose a novel generative model, self-converging generative network (SCGN), to address the issues. Self-converging means the convergence of latent vectors into themselves through being trained in pairs with training data, by which the SCGN can reconstruct all training data. In the authors’ model, the latent vectors and weights of the generator are alternately trained. Specifically, the latent vectors are trained to follow a normal distribution, using a loss function derived from the Kullback–Leibler divergence and a pixel-wise loss. The weights of the generator are adjusted for the generator to produce training data by means of a pixel-wise loss. As a result, their SCGN did not fall into the mode collapse, which occurs in GANs, and made clearer images than VAEs thanks to no use of sampling. Moreover, the SCGN successfully learned the manifold of the dataset in the extensive experiments with CelebA.
Occlusion‐handling tracker based on discriminative correlation filters

Visual object tracking (VOT) based on discriminative correlation filters (DCF) has received great attention due to its higher computational efficiency and better robustness. However, DCF-based methods suffer from the problem of model contamination. The tracker will drift into the background due to the uncertainties brought by shifting among peaks, which will further lead to the issues of model degradation. To deal with occlusions, a novel Occlusion-Handling Tracker Based on Discriminative Correlation Filters (OHDCF) framework is proposed for online visual object tracking, where an occlusion-handling strategy is integrated into the spatial–temporal regularized correlation filters (STRCF). The occlusion-handling tracker follows a hybrid approach to handle partial occlusion and complete occlusion. Specifically, we first present a function to determine whether occlusion occurs. Then, the proposed filter uses block-based and feature-matching methods to determine whether an object is partially occluded or completely occluded. Following this, we use different methods to track the target. Extensive experiments have performed on OTB-100, Temple-Color-128, VOT-2016 and VOT-2018 datasets, the results show that OHDCF achieves promising performance compared to other state-of-the-art trackers. On VOT-2018, OHDCF significantly outperforms STRCF from the challenge with a relative gain of 4.8 in EAO and a gain of 4.6 in Accuracy.
Automatic Detection of Dynamic and Static Activities of the Older Adults Using a Wearable Sensor and Support Vector Machines

Although Support Vector Machines (SVM) are widely used for classifying human motion patterns, their application in the automatic recognition of dynamic and static activities of daily life in the healthy older adults is limited. Using a body mounted wireless inertial measurement unit (IMU), this paper explores the use of an SVM approach for classifying dynamic (walking) and static (sitting, standing and lying) activities of the older adults. Specifically, data formatting and feature extraction methods associated with IMU signals are discussed. To evaluate the performance of the SVM algorithm, the effects of two parameters involved in SVM algorithm—the soft margin constant C and the kernel function parameter γ —are investigated. The changes associated with adding white-noise and pink-noise on these two parameters along with adding different sources of movement variations (i.e., localized muscle fatigue and mixed activities) are further discussed. The results indicate that the SVM algorithm is capable of keeping high overall accuracy by adjusting the two parameters for dynamic as well as static activities, and may be applied as a tool for automatically identifying dynamic and static activities of daily life in the older adults.
Geographic information system design of tourism in Bengkayang regency

Abstract Bengkayang Regency is a place that has a uniqueness that is rarely found in another area because Bengkayang has a coastline, islands, and forests that are still sustainable and protected. Bengkayang also has many unique cultures and traditions. Although it has a uniqueness, which is rare in Bengkayang Regency, only a few people understand about the various potentials that exist in Bengkayang Regency. That is because the regional government still has problems in developing Bengkayang Regency tourism. After all, there are no media that can be a means of information as well as promotion for tourists. Therefore, the authors build a web-based geographic information system using the PHP programming language and MySQL database called PARAYANG. This system is equipped with a Travel Destination Map feature to provide tourists with an overview of the distribution of the location points of each tourist destination in Bengkayang Regency. PARAYANG is intended for tourists because it can convey complete and interactive information about attractions and available events around Bengkayang Regency. The output from the development of this system is that the application can facilitate tourists in getting information about tourism and events in Bengkayang Regency. Besides, the existing tourist map feature in the method can provide information about the distribution of tourist destination locations in Bengkayang Regency so that it can assist tourists in determining the desired tourist destination.
Automatic Detection of Pointer Automobile Instrument Based on Machine Vision

Abstract Automotive instrument detection is usually performed manually. Factors such as human subjectivity and external environment have a great influence and are prone to errors. This paper proposes a method for automatic detection of pointer automobile instruments based on machine vision. Firstly, the common part of multiple instrument images with different pointer angles is extracted as the background image; secondly, based on the frame difference method, binarization, morphological operation and other techniques the instrument pointer is identified, and then the intersections of the pointer extension lines are used to determine the center point; thirdly, we extract the image of the scale position and calculate the scale template array; finally, the pointer value of each input instrument image is calculated based on the scale template array. Experiments show that this method can effectively solve the shortcomings of poor applicability and poor intelligence of existing algorithms, and can identify the value of the pointer accurately.