Reviewerly/exHarmony huggingface dataset:
Automatic Attendance Marking using Face Recognition and SMS Alert using IoT

The conventional method in the institutions are the faculty passes an attendance sheet or make roll calls to mark attendance of the student, which sometimes disturbs discipline of the class and the sheet further goes to the admin department, which is then updated to an excel sheet. This process is quite hectic and time consuming. The proposed solution for the current problem is through automation of attendance system using face recognition. This project describes the method of detection and recognition the face in real time.Here, the camera is fixed in the classroom and which will capture the image.The faces in the images are detected and then recognized with the database after which the attendance is marked.If the attendance is marked as absent, the message about the student's absence is sent to their parents through IoT.This project can be used to replace the manual method, which takes the time consuming, difficult to maintain and manual error.And also, for many other applications where face recognition can be used for authentication.
Learning the Set Graphs: Image-Set Classification Using Sparse Graph Convolutional Networks

Image-set classification has recently made great progress in computer vision. Compared with traditional image classification tasks, set-based classification exhibits great challenges due to huge intra-class variability and high inter-class ambiguity. In this paper, we propose to model the image set as a graph and formulate image set classification as the graph matching task. Without relying on the strong structure assumption, we build the first end-to-end graph convolutional network, the Deep SetNet, to learn the graph structure of an image set. Specifically, the SetNet consists of one convolutional network (CNN) to sufficiently extract the discriminative vertex, one graph convolutional Network (GCN) to faithfully learn the substructure in set graphs and graph pooling layers to aggregate the vertex features from the GCN. Moreover, we propose imposing the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,2</sub> -norm based sparsity constraint to select vertex features, which largely improves the model generalization capability. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art methods, showing its great effectiveness in set-based image classification.
Integration of Interpolation and Inference with Multi-antecedent Rules

The efficacious fuzzy rule based systems perform their tasks with either a dense rule base or a sparse rule base. The nature of the rule base decides on whether compositional rule of inference (CRI) or fuzzy rule interpolation (FRI) should be applied. Given a dense rule base where at least one rule exists for every observation, CRI can be effectively and sufficiently employed. For a sparse rule base where rules do not cover all possible observations, FRI is required. Nonetheless, certain observations may be matched partly or completely with any of the existing rules in the sparse rule-base. Such observations can be directly dealt with using CRI and the conclusion can be inferred via firing the matched rule, thereby avoiding extra overheads of interpolation. If no such matching can be found then correct rules should be selected to ensure the accuracy while performing FRI. This paper proposes a generalised approach for the integration of FRI and CRI. It utilises the notion of alpha-cut overlapping to determine the matching degree between rule antecedents and a given observation in order to determine if CRI is to be applied. In the event of no matching rules, the nearest rules will be chosen to derive conclusion using FRI based on the best suitable distance metric among possible alternatives such as the Centre of Gravity, Hausdorff Distance and Earth Mover’s Distance. Comparative results are presented to demonstrate the effectiveness of this integrated approach.
Selective Sampling and Optimal Filtering for Subpixel-Based Image Down-Sampling

Subpixel-based image down-sampling has been widely used to improve the apparent resolution of down-sampled images on display. However, previous subpixel rendering methods often introduce distortions, such as aliasing and color-fringing. This study proposes a novel subpixel rendering method that uses selective sampling and optimal filtering. We first generalize the previous frequency domain analysis results indicating the relationships between various down-sampling patterns and the aliasing artifact. Based on this generalized analysis, a subpixel-based down-sampling pattern for each image is selectively determined by utilizing the edge distribution of the image. Moreover, we investigate the origin of the color-fringing artifact in the frequency domain. Optimal spatial filters that can effectively remove distortions caused by the selected down-sampling pattern are designed via frequency domain analyses of aliasing and color-fringing. The experimental results show that the proposed method is not only robust to the aliasing and color-fringing artifacts but also outperforms the existing ones in terms of information preservation.
Raw Cardinality Information Discovery for Big Datasets

Real-time discovery of all different types of unique attributes within unstructured data is a challenging problem to solve when dealing with multiple petabytes of unstructured data volume everyday. Popular discovery solutions such as the creation of offline jobs to uniquely identify attributes or running aggregation queries on raw data sets limits real time discovery use-cases and often results into poor resource utilization. The discovery information must be treated as a parallel problem to just storing raw data sets efficiently onto back-end big data systems. Solving the discovery problem by creating a parallel discovery data store infrastructure has multiple benefits as it allows such to channel the actual search queries against the raw data set in much more funneled manner instead of being widespread across the entire data sets. Such focused search queries and data separation are far more performant and requires less compute and memory footprint.
Multiple Homography Estimation via Stereo Line Matching for Textureless Indoor Scenes

For robot navigation applications, indoor scenes usually contain texture-less planar surfaces, which renders traditional point-based matching approaches deficient. As an emerging alternative, line-based methods have attracted growing interest. In our paper, we develop a unified approach for line detection and stereo matching that can produce appealing results in un-textured scenes. In addition, we propose a co-planar line classification approach, based on which we are able to enforce the epipolar line constraints to multiple homography compatibility validation. Experimental results demonstrate the efficiency of our method as well as its improved capability on multi-homography estimation over traditional methods.
VERBAL MODEL OF MANAGEMENT OF A VULNERABLE PROCESS OF DEDICATING THE ACCESS OF USERS TO THE SOFTWARE OF THE ELECTRONIC DOCUMENT SYSTEM

Objectives Traditional methods for solving the problem of protecting the information resource of electronic document management systems based on protective basic functions (identification, authentication and verification of users, etc.) of available tools and information security subsystems have a significant drawback - inefficiency when an attacker masters the password of regular users. In order to eliminate it, it is necessary to develop new methods related to managing the weakly vulnerable process of delimiting user access not only to confidential information, but also to the software of the electronic document management system. Method One way to solve this problem is to use the modified “soft administration” method, which consists in automatically generating lists of allowed source files in the authorization matrix by reducing the lists and comparing file integrity functions with a reference list for an existing software package. This allows you to automate the process of developing access control rules and provide management of the weakly vulnerable process of access control to software tools of the electronic document management system. Result A verbal model is developed for automated control of a weakly vulnerable process of differentiating access to software tools of an electronic document management system, and a system of performance indicators for the functioning of a promising subsystem for protecting confidential information characterizing it as a control object is proposed. Conclusion The presented verbal model provides for the possibility of distributed control, in which the security administrator directly controls the specified process using a remote software tool. The formation (change) of the authority establishment matrix for managing a weakly vulnerable process of access control using a promising subsystem for protecting confidential information and the development of a system of indicators to comprehensively evaluate the effectiveness of its functioning is one of the most important tasks of protecting information from unauthorized access, guaranteeing the implementation of a security policy throughout the entire operation period secure electronic document management system.
Deformable Mesh Evolved by Similarity of Image Patches

We propose a deformable model for manually initialized segmentation of images, which may contain both textured and non-textured regions. Image segments and segment boundaries are represented using a deformable triangle mesh, providing all advantages of an explicit geometry representation, but allowing for adaptive topology. Deformation forces are computed using a probabilistic model of local self-similarity, based on clustering of image patches. Both our curve representation and our similarity model naturally support multi-label segmentation. We demonstrate the properties of our approach on a number of natural color images as well as composed textured images.
Vulnerability Assessment of Authorization System for USB-Based Storage Devices

USB-based storage devices such as flash drives and external hard disks are very popular for data transfer because of their high speed and portability. But they also give rise to information security threats like data loss and spread of malware. In this paper, we present an authorization system to allow only authorized devices to be accessed on a computer for reducing the chances of data loss via USB-based removable storage media. The unique identification of devices is based on device parameters which are part of the USB standard. These parameters have been used for authorizing USB-based storage devices connected to a computer. A vulnerability assessment of the authorization system as well as device authorization parameters has been carried out.
Geochemical Modelling of Groundwater Using Multivariate Normal Distribution (MND) Theory

Groundwater has become scarce especially in arid and semi-arid regions but is essential for drinking, domestic purposes and for survival of human beings. It must be conserved and not used for irrigation and industrial purposes especially in areas where no surface water is available. Groundwater is rarely polluted by natural processes but often by excess use of chemical fertilizers in agriculture and/or by disposal of untreated domestic/industrial wastes. Groundwater quality can be assessed using both physical (pH, EC, TDS etc.) and chemical characteristics (volume/weight concentrations of cations and anions measured on a sufficient water sample). Before use, groundwater must be tested for its required quality especially for drinking/domestic use. Quality of groundwater is assessed using linear statistical technology (multivariate normal distribution; MND) for measured continuous random variables on each sample. Prerequisites for applying MND theory are: (i) samples are independent and belong to one homogeneous population, and (ii) all input random variables possess Gaussian density. Since chemical constituents add to a constant sum (1.0 or 100%, 1000 (per mil), 1 million, 1 billion etc.) they are NOT INDEPENDENT as desired, but possess spurious negative correlations, as well as the random variables are not Gaussian but approximately log-normal with high positive skewness. Both these defects are simultaneously eliminated by log(c/(1 − c)) pre-transformation, where c (0 < c< 1) is the fractional concentration of any constituent. As chemical constituents (cations or anions) are found in trace quantities in groundwater, the log(c/(1 − c)) transform reduces to a simpler log (c) transform as inputs to MND model. The main MND methods are PCA and FA, Multiple Regression (Correlation) for a Single Populations and MANOVA, Linear Discriminant Functions (LDFs), MANCOVA for Multiple Populations. Geochemical models provide us with expected outcomes/correlations which can be compared/contrasted with observed outcomes/correlations to take appropriate and optimal decisions. Time series/geostatistical modeling requires very large number of samples along each line of investigation, hence these methods should not be used for routine groundwater studies.
The Responsibility of the Social Media in the UAE towards Terrorism from the Perspective of the Emirati Youth: A Field Study on the Emirati Audience

The one, who scrutinizes the phenomenon of terrorism and extremism, finds that it gained great importance through the peoples and governments all over the world due to their critical impacts on the state security and stability upon discovering that we deal with an organized criminal phenomenon, which aims at creating a general atmosphere of fear, terror and threat by practicing violence against individuals and properties; i.e. this critical phenomenon aims at hindering the stabilities of communities, impacting their political statuses and destroying their national economy.
From Body Tracking Interaction in Floor Projection Displays to Elderly Cardiorespiratory Training Through Exergaming

The opportunity to develop new natural user interfaces has come forward due to the recent development of inexpensive full body tracking sensors, which has made this technology accessible to millions of users. In this paper, we present a comparative study between two natural user interfaces, and a cardiorespiratory training exergame developed based on the study results. The focus was on studying interfaces that could easily be used by an elderly population for interaction with floor projection displays. One interface uses both feet position to control a cursor and feet distance to trigger activation. In the alternative interface, the cursor is controlled by forearm ray casting into the projection floor and interaction is activated by hand pose. These modes of interaction were tested with 19 elderly participants in a point-and-click and a drag-and-drop task using a between-subjects experimental design. The usability, perceived workload and performance indicators were measured for each interface. Results show a clear preference towards the feet-controlled interface and a marginally better performance for this method. The results from the study served as a guide to the design of a cardiorespiratory fitness exergame for the elderly. The game “Grape Stomping” uses ground projection and mapping to display real-size winery elements. These virtual elements are used to simulate, in a playful way, the process of grape maceration through repeated stomping. A playtest session with nine elderly users was completed and its insights are presented in addition to the description of the game.
Research on Path Automatic Planning Algorithm for Embedded Cable of Unmanned Aerial Vehicle (UAV) Shell

The shell members that bury the stereo cable network into the fuselage can effectively save the assembly space of cable network. So it is necessary to have a comprehensive path optimization algorithm which can be suitable for this kind of cabling environment and overall situations. Firstly, considering the shell plate wiring environment of the embedded cable of UAV shell, this paper puts forward the method of taking control of the path point by adding the factor of adhesion in the algorithm to control the normal distance between the cable path point and the wiring space base surface, which can reduce the interference between the cables and so on. Secondly, aiming at the problem that the path point of the basic ant colony algorithm is easy to diverge when the path search is undergoing, the pheromone update rule and the optimization iteration method of the ant colony algorithm are optimized, and the pheromone evaporation mode based on the improved ant circumference model is proposed, which reduces the iteration times of the path optimization process. Finally, based on the simplified model, the path optimization model is established for simulation experiments to verify the feasibility and effectiveness of this method.
Macroscopic Reconstruction for Histopathology Images: A Survey

Over the past decade, due to a dramatic increase in diseases, histopathology has become a vital part of medical science. Histopathology involves examining of tissue under a microscope to examine the manifestation of diseases. The term histology deals with the study of entire cells of the sample specimen, whereas histopathology is a technique, which deals with analyzing for microscopic changes or abnormalities in tissues that are caused as a result of diseases. With the computational advancement, tissue histopathology slides can be digitized with the help of whole slide digital scanners. Histopathology is a powerful diagnosis method to analyze the tissue structure. However, the microscopic images generally produce two-dimensional views. With the advent of new computer algorithms, 3D reconstruction from 2D histopathology images is a routine technique. However, the already developed algorithms exhibit distorted image and recur information loss, which is unacceptable for the pathologists. An accurate high-resolution 3D reconstruction for histopathology sections will lead to a better diagnosis. This paper describes the 3D visualization techniques and its applicability in histopathology.
Simulation on Effects of Various Heat Load Layouts in Air Temperature Distribution of Container Data Center

Nowadays, with the rapid development of information technology, various computer devices must work with high performance, resulting in greater heat generated by computer devices. So that the heat generated can be disposed of as much as possible, then the computer room or data center requires an air conditioner. However, data center air conditioners work with constant cooling capacity, consequently there is energy waste in the data center air conditioning system. The heat generated by each point of the data center room can vary according to its workload, so it requires different cooling capacity of CRAC (computer room air conditioning). If the CRAC works at high cooling capacity, electrical energy inefficiency will occur, while if CRAC works at low cooling capacity, the computer device will overheat by lack of cooling system. Therefore, study about the temperature in the area around a computer device is needed so that the cooling capacity of the air conditioner can be adjusted according to its needs. The purpose of this study is to simulate the distribution of temperature in various cases by heat loads layout in the data center without disturbing the performance of various computer devices. Some schemes of cases of heat loading are made based on the position of the heat load, then the cases are simulated using the CFD software. The results of this study found that the flow of hot air at a loading of 20-80% would be better if the heat load was placed near the CRAH (computer room air handler).
A Method to Evaluate Air Distribution in Data Centers

This paper mainly studies a method to evaluate air distribution in a straight-ventilated wind wall data centers. Firstly, put forward an evaluation index of air distribution and divide it into four grades according to the results. Then, the data center of fan wall is analyzed by the single factor, using the response surface method to design the experiment according to the influencing factors. An empirical formula between the evaluation parameters and influencing factors can be derived. Finally, based on the empirical formula, the reference suggestions for the layout design of the direct-ventilated wind wall data center are proposed.
Automated Verbal-Pattern Extraction from Political News Articles using CAMEO Event Coding Ontology

Structured metadata extraction from raw-text in different domains is gaining strong attention from the research communities and offering good applicable scenarios. In Political Science, these metadata play a significant role in studying and predicting intra-and inter-state relationships and often follows a certain structural representation. Our work exploits CAMEO (Conflict and Mediation Event Observations) ontology to extract events from political news articles. This event will be represented by metadata such as who-did-what-to-whom format. We face a number of challenges in this extraction process due to the incompleteness of CAMEO dictionaries. In particular, for some verb patterns appeared in articles we may not find a appropriate match in CAMEO verb dictionary (i.e "what"). Our goal is to recommend new verb patterns by mining a large number of news articles to enhance the existing CAMEO verb dictionaries. For this, first we apply Association Rule Mining to find frequent verb patterns. Next, to assign categories of these verb patterns, we develop a novel algorithm based on Word-to-Vec model. Finally, we develop a prototype system for automatically recommending a number of useful verb patterns.
Improving Energy Efficiency of Hadoop Clusters using Approximate Computing

There is an ongoing search for finding energy-efficient solutions in multi-core computing platforms. Approximate computing is one such solution leveraging the forgiving nature of applications to improve the energy efficiency at different layers of the computing platform ranging from applications to hardware. We are interested in understanding the benefits of approximate computing in the realm of Apache Hadoop and its applications. A few mechanisms for introducing approximation in programming models include sampling input data, skipping selective computations, relaxing synchronization, and user-defined quality-levels. We believe that it is straightforward to apply the aforementioned mechanisms to conserve energy in Hadoop clusters as well. The emerging trend of approximate computing motivates us to systematically investigate thermal profiling of approximate computing strategies in this research. In particular, we design a thermal-aware approximate computing framework called tHadoop2, which is an extension of tHadoop proposed by Chavan et al. We investigated the thermal behavior of a MapReduce application called Pi running on Hadoop clusters by varying two input parameters - number of maps and number of sampling points per map. Our profiling results show that Pi exhibits inherent resilience in terms of the number of precision digits present in its value.