Reviewerly/exHarmony huggingface dataset:
Logical Analysis of Data for outliers detection

We propose a method of logical analysis of data (LAD) to detect noise imprecision and inaccuracies (outliers) for machine learning (ML) tasks. The method proposed can be applied for objects with multidimensional heterogeneous attributes. It includes the following steps: constructing an activation function of the classifier which is supposed to search all classes possible within the original dataset; study of the objects that were not assigned to any of the constructed classes by several or one characteristics; and results testing for outlier presence in data.
Recognition of face photos on the basis of sketch using deep CNN and transfer learning

Catching the criminal on the basis of eyewitness description sketches which are generated by any software or hand drawn this technique becomes useful when there is deficiency of evidence. Recognising the sketches with face photos to find the results. A facial recognition is a technology capable of verifying a person by using deep learning is the part of data science and this creating more importance in law enforcement agencies. Deep learning generally used for recognising application like voice recognition, face recognition, audio and video recognition. In this paper, we are working larger dataset because deep learning not useful for small amount of data. There is one colorFERET database is available, on that database we are going to apply the data augmentations technique to extend that data so it is easy to apply the deep learning methods on that database.
Don't forget about synchronization! Guidelines for using locks on graphics processing units

Summary Heterogeneous devices are becoming necessary components of high performance computing infrastructures, and the graphics processing unit (GPU) plays an important role in this landscape. Given a problem, the established approach for exploiting the GPU is to design solutions that are parallel, without data dependencies. These solutions are then offloaded to the GPU's massively parallel capability. This design principle often leads to developing applications that cannot maximize GPU hardware utilization. The goal of this article is to challenge this common belief by empirically showing that allowing even simple forms of synchronization enables programmers to design solutions that admit conflicts and achieve better performance. Our experience shows that lock‐based solutions to the k‐means clustering problem, implemented using two well‐known locking strategies, outperform the well‐engineered and parallel KMCUDA on both synthetic and real datasets; with an average 8× faster runtimes across all locking algorithms on a synthetic dataset and 1.7× faster on a real world dataset across all locking algorithms (and max speedups of 71.3× and 2.75×, respectively). We validate these results using a more sophisticated clustering algorithm, namely fuzzy c‐means and summarize our findings by identifying three guidelines to help make concurrency effective when programming GPU applications.
The Development of Teaching Materials Based on Quantum Teaching Model Using Lectora Inspire

This study aims to find out how to design teaching materials based on quantum teaching models using lectora inspire interactive media. The research method used is the Borg & Gall Research and Development Method with the steps of preliminary study, planning, product design, product design validation, product testing and final product results. The research was conducted in six face-to-face meetings, based on the analysis of the needs of grade V elementary school students in Kuningan. Validation of teaching material product design was carried out by four expert teams as validators which produced a score of 86.45% with very good assessment criteria. Readings from the results of book tests by students obtained a score of 88. The results of product trials obtained very good criteria, namely teacher practicality response 91.67% and practicality of student responses 86.57%. In conclusion, quantum teaching model-based teaching materials use interactive media lectora inspire valid and practical to use.
Augmented Reality for Tissue Converting Maintenance

Tissue converting lines represent one of the key plants in the paper production field: thanks to them, paper tissue is converted into its final form for domestic and sanitary usage. One of the critical points of the tissue converting lines is the productivity and the possibility to follow the conversion process at a relatively low cost. Although the actual lines have high productivity yet, the study of state of the art has shown that choke-points still exist, caused by inadequate automation. In this paper, we present the preliminary results of a project which aims at removing such obstacles towards complete automation, by introducing a set of innovations based on ICT solutions applied to advanced automation. In detail, advanced computer vision and video analytics methods will be applied to monitor converting lines pervasively and to extract automatically process information to self-regulate either specific machine and global parameters. Augmented reality interfaces are being designed and developed to support converting line monitoring and maintenance, both ordinary and extraordinary. An Artificial Intelligence module provides suggestions and instructions to the operators in order to guarantee the production level even in the case of unskilled staff. The automation of such processes will improve factory safety, decrease manual interventions, and, thus, will increase production line up-time and efficiency.
HA-UVC: Hybrid approach for unmanned vehicles cooperation

This paper addresses the problem of ecological ocean pollution by using semi-autonomous unmanned vehicles. A hybrid approach for unmanned vehicles cooperation (HA-UVC) is presented for unmanned aerial vehicles (UAV) to monitor ocean regions and clean up their dirty areas with a swarm of unmanned su rface vehicles (USV). Thus, the proposed HA-UVC addresses the problem of trajectory planning, where unmanned vehicles must find a trajectory from the starting position to the goal position while avoiding static obstacles. Consequently, two solutions are proposed in order to manage the trajectory planning problem for the semi-autonomous USV Swarm. The first solution is performed by a modified genetic algorithm (GA). However, the second one is achieved by a proposed Cartesian coordinate algorithm (CCA). In order to optimize the applicable performance, the proposed solutions make it possible to detect and reduce the level of pollution in ocean regions, while avoiding obstacles and failures of unmanned vehicles.
Anti-Shake HDR Imaging Using RAW Image Data

Camera shaking and object movement can cause the output images to suffer from blurring, noise, and other artifacts, leading to poor image quality and low dynamic range. Raw images contain minimally processed data from the image sensor compared with JPEG images. In this paper, an anti-shake high-dynamic-range imaging method is presented. This method is more robust to camera motion than previous techniques. An algorithm based on information entropy is employed to choose a reference image from the raw image sequence. To further improve the robustness of the proposed method, the Oriented FAST and Rotated BRIEF (ORB) algorithm is adopted to register the inputs, and a simple Laplacian pyramid fusion method is implanted to generate the high-dynamic-range image. Additionally, a large dataset with 435 various exposure image sequences is collected, which includes the corresponding JPEG image sequences to test the effectiveness of the proposed method. The experimental results illustrate that the proposed method achieves better performance in terms of anti-shake ability and preserves more details for real scene images than traditional algorithms. Furthermore, the proposed method is suitable for extreme-exposure image pairs, which can be applied to binocular vision systems to acquire high-quality real scene images, and has a lower algorithm complexity than deep learning-based fusion methods.
Facial Expression Recognition Based on Edge Computing

Facial action unit (AU) detection recognizes facial expressions by analyzing cues about the movement of certain atomic muscles in the local facial area. According to the detection of facial feature points, we can calculate the value of AU, and then classification algorithms are performed on these AU values for emotion detection in realtime. When this advanced system is in the actual production process, large network bandwidth is usually required to transmit a large number of frames from cameras to the backend servers. To overcome this issue, we propose to offload the computing to edge devices in which the raw image data from each camera is directly processed with our optimized and customized algorithms, and then the detected emotions are transmitted to the end-user more easily compared with less data.
Image Quality Assessment for Image Filtering Algorithm: Qualitative and Quantitative Analyses

This paper will introduce the image quality assessment for image filtering algorithm based on qualitative and quantitative analyses. Image with noise becomes a problem for gaining information. The process begins by improving the image quality by applying various filters that can enhance the image to be subjectively better. The image result can be seen after the image has gone through the enhancement process (image filter) where more detail can be seen, unwanted flickering removed and the image contrast increasingly better. The objective is to find an image filtering algorithm that gives an excellent result based on the image quality assessment. The image quality assessment is the analyses based on the parameter of means square error and peak- signal noise ratio in decibels by using MATLAB software. The image filtering algorithm then uses the median filter, gaussian filter, mean filter and wiener filter. Different noises such as gaussian and salt & pepper are applied with different noise percentage added to the image to determine which image filter has better performance. Last but not least, this paper image quality assessment for the image filtering algorithm through the qualitative and quantitative analyses will discuss the most efficient image filter to remove unwanted signal and noise on the image.
A new challenge on video recommendation by content

When it comes to searching online, massive information is available, it is really hard to provide relevant information to users based on their interest. Although while searching for data based on user inputs, they need to search the entire database, which is also very frustrating and time-consuming. Video consumption becoming essential in most users' life. On the most video platforms, users get their recommended videos based on some algorithms, calculations, implicit feed-backs, watch, search behaviors and search history. New videos suffer from cold-start which happens to freshly uploaded videos in which no data or reviews are available. Therefore, it is not easy to recommend these videos to some users. Another real problem that users face every day is that finding the desired content depends on the video being labeled or has multiple views. The search engine will find the videos based on keywords or tags, not on the content inside the video. One of the solutions for this problem is recommending videos based on content. This paper presents a new challenge on proposing a video recommendation system based on content using objects and features with the ability to search or block specific scenes.
A novel technique for image encryption using transform based scrambling and DNA based multi chaotic encoding scheme

This encryption technique involves two major steps such as scrambling the image using a transform based scheme and encoding the image using a DNA based chaotic map encoding scheme. For scrambling the image, a transform based method namely Arnold transform is used. This new algorithm improves the image security effectively to avoid deciphering, and it also can restore the image as same as the original image almost, which reaches to the purpose of image safe and reliable transmission. Later the scrambled image will undergo an image encoding using a DNA based multi chaotic mapping. Simulation results show that the algorithm has good encryption effect and security.
A Unified Architecture for AES/PRESENT Ciphers and its Usage in an SoC Environment

Electronic data security is of vital concern for secure communication applications of cyber-physical system (CPS) that relies on Internet-of-things (IoT) based technologies. To achieve multi-level data security, a combination of long-term secure cipher, advanced encryption standard (AES), and short-term secure cipher, PRESENT are deployed together for forming a common cipher chip. The core is used for secure audio application in an open source system-on-chip (FPGA-SoC) environment. An integrated implementation of the cores is done on FPGA-SoC and ASIC. FPGA implementation of the architecture on Xilinx xc5vlx110t-1-ff1136 FPGA device consumes 14% slices. Further, the design is implemented in SCL 180 nm CMOS ASIC technology, it takes 2×2 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> die size containing 0.867 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> standard cell area. At 100 MHz clock frequency, total power consumption of the chip is 11.9 mW.
Fully homomorphic encryption scheme on a nonCommutative ring R

This article is an introduction to a well known problem on the ring F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [e] where e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> =e <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> : Fully homomorphic encryption scheme. In this paper, we introduce a new diagram of encryption based on the conjugate problem on F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [e] , (ESR(F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> [e])).
A Teaching Evaluation System Based On Visual Recognition Technology

Abstract This paper designs and implements a student-centered teaching evaluation system based on face recognition and pose estimation technology. Our work firstly combines classroom attendance and behavior analysis in an evaluation system. For checking attendance, we select student faces as the identification object, employing a multi-task cascaded convolutional networks (MTCNN) as a face detector and a deep learning network FaceNet to extract face features. Then the head pose information is analyzed using Ensemble of Regression Trees (ERT) algorithm, which is able to detect 68 key feature points of faces. At last, we design and implement the whole system, including designs of functional modules, service software, database and telecommunication of various parts. This system can check attendance and collect student behavior information automatically, enhancing the intelligent level of the learning and teaching system.
Structured light self-calibration algorithm based on random speckle

High-precision and high-resolution depth perception based on structured light is widely used. This measurement system consists of projector and camera equipment. Depth sensing based on the structured light method, the calibration process of obtaining the intrinsic and extrinsic parameters of the measurement system is a necessary step. However, the traditional calibration method has certain limitations. In this paper, we propose a self-calibration method. We overcome the problems in existing calibration techniques, and can quickly obtain the intrinsic and extrinsic parameters of the random speckle structured light system in motion scene. We designed a random speckle matching algorithm and proposed a non-coplanar point acquisition scheme. An effective method for finding the correspondence points between dense point pattern views is introduced. Next, the self-calibration process is described, using non-coplanar points matching to calculate the parameters required by the random speckle structured light system in real time. The results obtained in this paper are shown in the experimental section and analyzed from both quantitative and qualitative aspects.
A Novel approach for Hand Gesture Recognition using Kirsch edge with Bit Plane

Over the last few decades, a number of researchers have proposed a variety of hand gesture recognition techniques. The paper presents a new approach for hand recognition. The preprocessing is done by applying Gaussian scale space and to enhance the feature average gray intensity has been found. The kirsch's convolution mask is computed for edge map and has collaborated with result obtained by combining different Bit Planes. The aim of the proposed descriptor is to illuminate the noise and unwanted data and make it invariant to scale rotation and illumination changes. The local edge computation gives the result which is invariant to scale rotation but it does not tend to eliminate noise. Bit plane encoding used the binary form of image and reduce the data used to describe a series of sample values. The bit plane slicing is done through lossless binary compression technique and the bits are recombined to build the feature vector. The aim is to make the hand recognition result under rotation, illumination and roughness effective. In this paper, we summarized the proposed descriptor and compares it with other methods found in literature for recognition of the same data.
Development of Hybrid Learning-Based Animation Media to Improve the Learning Outcomes of Multimedia Learning

Abstract This research aims to determine student learning outcomes and produce animated learning based on hybrid learning. In this case, the type of research is research and development (R&amp;D) with 4D models with stages namely potential or problems, data collection, product design, design validation, design revisions, product testing, product revision, practical utility testing and revision of the final product. The sample data were recorded in the Faculty of Economics, Universitas Negeri Medan. The result of the development of learning media based on interactive multimedia indicated that valid criteria with the percentage of the post-test 73.33%, did not pass 26.66%. The learning with Hybrid Learning-based animation media in Multimedia Learning subjects is better. The hypothesis testing is obtained tcount &amp;lt; ttable namely -3.74 &amp;lt; 2.00 which means that. there is a difference in learning outcomes between pre-test and post-test time.
Hourglass Network for Hand Pose Estimation with RGB Images

Recent studies have shown effectiveness in using RGB images to estimate 3D gestures. However, due to hand flexibility and occlusion problems, it is very difficult to achieve the best results. In the current research, to estimate 3D gestures from RGB images, it is necessary to locate the 2D coordinate position of each key point firstly, and then derive the 3D posture. Therefore, it is very important for the final pose estimation accurately to locate the 2D coordinate positions of gestures. In this paper, we propose to use Hourglass Network to locate the 2D coordinates of each key point. The Hourglass Network locates the key points from the whole to the local, which has a good effect on grasping the relationship between the key points. The experimental results show that the method effectively improves the accuracy of 2D coordinate estimation, and thus makes the best effect in the estimation of 3D pose, and evaluates it on the sign language identification dataset.
Study of Influence of Dimension Reduction of High Dimensional Datasets in Classification Problem

We often develop many applications based on datasets. In case of high dimensional dataset, we face some problems while building any data mining model. When there are too many attributes in the dataset, then there may be dependency between attributes. There may be some irrelevant attributes too. Because of the influence of the dependent and irrelevant attributes, we get less accuracy in the data mining process. To overcome this problem, we need to reduce the dimensions of the dataset. In this work, we experimentally tested influence of dimension reduction on classification problems. For this purpose, we used four different datasets. We used backward elimination method to reduce the dimension of the dataset down to seven dimensions. We have experimented with Multi-layer Perceptron, Naïve Bayes, Decision Tree, Random Forest, K-Nearest Neighbor, and Support Vector Machine classification methods. We used 10-fold validation to train and test our dataset. Experimental results show that when the dimension is reduced, then the accuracy is improved for some classification algorithms like Multi-layer Perceptron, Naïve Bayes, and Random Forest. Our general finding is that if we exclude the less significant and irrelevant attributes, then the classification model gives better accuracy than it does without dimension reduction.
Document Management System and Reminder using SMS Gateway

Abstract This research is an observational research using descriptive analysis on rural areas that have little technological exposure to support social welfare, especially for baby. The lack of good documentation in social facility services for baby, especially on immunization has cost failure in meeting local government program’s standard. This has led to suboptimal health care for baby. Another impact is that repeat immunization will not be effective for the social services themselves as there are no sufficient &amp; reliable records. The information regarding to the implementation of health care for baby has failed to reach sizeable part of the society. As a result, many people are still not yet well-informed with the service schedule or types of the immunization suitable for their baby. This research Is done in ABC village through data collection in forms of observation, interviews and documentation. With the existence of document management system and reminder using SMS as to leverage health care for baby, it is expected that government program in terms of social health care especially for baby can be implemented optimally.
Dangerous or Desirable: Utilizing Augmented Content for Field Policing

Location-based systems (LBS) can be of benefit to obtain information when and where it is needed. What is not captured with LBS are points of interest (dynamic hotspots) that are not fixed in one location, such as people. Furthermore, using 2D interface elements makes it difficult to communicate precise spatial information to users. The current study aims to assess the viability of dynamic hotspots and augmented content in a mobile hotspot policing application. Mock cases were constructed to mimic real field policing work. The system, comprised of a smartphone and smartwatch, gave static and dynamic hotspot information, which was enriched with the use of augmented elements viewed through the smartphone-camera. Interviews, observations and usability measures were used to gain insight for augmented reality (AR) design considerations in the domain of field-policing. The detection of dynamic hotspots was seen as desirable. While static and dynamic hotspot information was seen as valuable, AR information, as it was utilized in the current study, has too many drawbacks to be used in the field. Alternative modalities, insights into possible future iterations and technical limitations of AR for the emergency response domain are discussed.