Reviewerly/exHarmony huggingface dataset:
Design and Development of Applications on Smartphone of Connection to Social Media Via 3D

The results display through application of Smartphone by the android operating system, the data will link to the site's owner to serve as a channel to communicate with each other or for the purpose of advertising and public relations. It also has three-dimensional (3D) to add more interest and attract of application. As mention in the above project design and development application of smart phone to link to social media through 3D model to accessories attract to fulfill human needs of quick communication. And achieving the derma This project designs and develops an application on a Smartphone for connecting to social media via 3D. It uses Augmented Reality as a part of communication. The marker is printed on things that we use every day. When the marker is projected on the application, 3D animation will be show, making the connection even more convenient. After the development of application, we have done an evaluation survey with experts and a satisfactory survey 30 samples for our application. We used average and S.D. for the analysis of data. We found that the satisfactory survey scored 4.19 for overall, 4.30 for application features, 4.39 for user interaction and 3.9 for robustness. So, we can conclude that suitable and meet the requirements of users.
МЕТОД УЛУЧШЕНИЯ ПАМЯТИ И ВОСПРИЯТИЯ С ПОМОЩЬЮ МОДЕЛИ, ПОСТРОЕННОЙ НА ОСНОВЕ КОГНИТИВНЫХ СТРАТЕГИЙ ЛЕОНАРДО ДА ВИНЧИ

&#x0D; &#x0D; &#x0D; The subject of research is the method of implementation a cognitive model for active perception and memory development, and its effectiveness.The model based on Leonardo da Vinci’s cognitive strategies was previously constructed, and the method has been worked out for its application.The relevance of the method was confirmed: the participants increased the speed and depth of their perception, improved short-term, operational and long-term memory. The final measurements showed high dynamic of general intellectual processes and soft skills development, most important for the concept of education in the 21st century.The study detected the main parameters that increase the perception accuracy and durability of durability of memorization. The methodology, based on model of Leonardo's strategies, helps to develop cognitive and sensory mechanisms, the general dynamics of psychological processes, critical and creative thinking skills, and systematization of information. The application of the method engages the neurophysiological mechanisms of perception.&#x0D; &#x0D; &#x0D;
Convolutional Gated Recurrent Units for Obstacle Segmentation in Bird-Eye-View

Obstacle detection is a fundamental problem in autonomous driving. The most common solutions share the idea of modeling the free-space and marking as obstacles all the points that lie outside this model according to a threshold. Manually setting this threshold and adapting the model to the various scenarios is not ideal, whereas a machine learning approach is more suitable for this kind of task. In this work we present an application of Convolutional Neural Networks (CNNs) for the detection of obstacles in front of a vehicle. Our goal is to train a CNN to understand which patterns in this area are connected to the presence of obstacles. Our method does not require any manual annotation, since the training relies on a classification that comes from a LiDAR. During inference, our network requires as input a 3D point cloud generated from stereoscopic images. Moreover, we make use of recurrent units in our network, since they are able to exploit temporal information to provide more accurate results in case of occlusion. We compare different input configurations and show that our final selection is able to correctly predict the position of obstacles and to generalize well in unseen environments.
An Improved Content Delivery Technology Based on Genetic Algorithm in Cloud Storage

Abstract Compared with the traditional content delivery network (CDN), content delivery based on cloud storage has the advantage of reducing storage costs while providing superior data security over common storage solutions. There has been a lot of research on content delivery technology, but existing research on content delivery technology is rarely used in cloud storage based CDN. In this paper, we analyze the charging and content delivery mechanism of cloud storage, then propose a reasonable network topology and cost model. Based on the analysis of existing heuristic algorithms such as user greedy and server greedy, an improved content delivery technology based on genetic algorithm in cloud storage is proposed, which effectively reduces the content delivery cost.
Improving Robustness Against Adversarial Example Attacks Using Non-Parametric Models on MNIST

Deep learning research has been actively conducted, and neural networks including CNN have outstanding performance in computer vision. However, parametric models such as neural networks are known to be vulnerable to adversarial example attacks, making them inappropriate to employ when security becomes significant. Hence, non-parametric models are considered but there is a problem of having low accuracy. To solve the issue, we proposed a scheme where images are segmented into patch units for non-parametric models. Experimental results display that the proposed scheme improves both accuracy as well as robustness against adversarial example attacks.
Lightweight IoT Mutual Authentication Scheme Based on Transient Identities and Transactions History

Robust authentication of Internet of Things (IoT) is a difficult problem due to the fact that quite often IoT nodes are resource-constrained and lack the storage and compute capability required by conventional security mechanisms. We propose, in the current paper, a new lightweight multi-factor authentication scheme for IoT nodes that combines temporary identities and challenge/response based on transactions history. The approach allows IoT nodes to anonymously and mutually authenticate in an unlinkable manner. We evaluate the efficiency of the proposed scheme, and establish the security of our protocol through informal security analysis and formally by using the automated validation of Internet security protocols and applications (AVISPA) toolkit.
DeepCompareJ: Comparing Image Classification Models

Image classification is a computer vision task that has several applications in diverse fields like security, biology or medicine; and, currently, deep learning techniques have become the state-of-the-art to create image classification models. This growing use of deep learning techniques is due to the large amount of data, the fast increase of the computer processing capacity, and the openness of deep learning tools. However, whenever deep learning techniques are used to solve a classification problem, we can find several deep learning frameworks with their own peculiarities, and different models in each framework; hence, it is natural to wonder which option fits better our problem. In this paper, we present DeepCompareJ, an open-source tool that has been designed to compare, with respect to a given dataset, the quality of deep models created using different frameworks.
Semisupervised learning in pattern recognition with concept drift

We describe the use of semisupervised learning to adapt decision rule in the pattern recognition problem with concept drift. There has been generated a new classifier using additional dataset that becomes available from the changing environment. The classifier is a combined cluster structure with a modified weighted Bayesian decision rule, where the weights are dynamically updated using the classifier's current decision. The probability density functions are identified in each cluster and the decision rule is defined as a distribution mixture. The adaptation mechanism allows the algorithm to track the environment changes by weighting the most recent and relevant cluster higher. The adaptive algorithm is described, and its performance is compared to the static one by using specific model problem.
Effective thinking: think by D. Kahneman introducing infographic models

Two modes of thinking are known in the scientific community, these are fast and slow thinking. The article discusses the infographic model of thinking according to Kahneman, showing the differences between fast and slow thinking. A person thinks in images (models), and not words, when switching from fast thinking to slow, he eventually gets the correct conclusion. Modeling requires new cybernetic modeling approaches. The article discusses the three main tasks of visual modeling. One of the approaches in modeling is standardization, which covers a huge number of categories and gives us reason to immediately identify possible anomalies. Two models of the diversity of the space of norms and a model for the emergence of «new» information from which the information base of the human brain is composed are presented. The manifestation of intuition is nothing but recognition.
Hand gesture keyboard for blind using CNN

Human Computer Interaction(HCI) is becoming popular in this modern world. Their widespread use suggests that the ability to handle computers is perhaps equally essential for visually impaired as well as for sighted persons. Even though a large amount of work has been performed in the gesture based human computer interface, blind users still feel it is tough to interact with computers. The major obstacle is the lack of knowledge about blind users preferences toward hand gestures. Mouse and keyboard are the basic input devices to interact with a computer. People who are sightless find it difficult to interact with these means of HCI. Though Braille systems are being used by blind people but this system has a disadvantage also. A Braille device has only 64 keys whereas a computer keyboard consists of 104 keys. In many applications the capability of deep learning techniques has been confirmed to outperform classic approaches. Accordingly, we use convolutional neural network to classify the hand gestures. The proposed system has four main phases: Data set collection, pre processing, feature extraction and classification. A hand gesture captured by the camera will be recognised and classified or mapped to corresponding symbol(alphabets, digits etc.). The matched output is saved in the file as well as audio feedback is given to the blind user. A real time application where this proposed system can be used is in competitive examination for blind people. The experiment results show that the prediction accuracy of hand gesture recognition goes upto 90% with samples around 332.
Drawing Three-Dimensional Objects Based on Virtual Viewpoint Synthesis

In order to obtain all-round information of an object or scene, 3D reconstruction technology used to reconstruct it in the past decades. This technology needs to reconstruct the 3D virtual model of the surface of the object in the computer, so it is necessary to carry out complex modeling processing on the object. Aiming at the complexity of traditional modeling, drawing three-dimensional objects based on virtual viewpoint synthesis is proposed. IBR technology with implicit geometric information mainly used in Depth-Image-Based Rendering (DIBR). The reconstruction of virtual objects is realized by bidirectional mapping, and finally the reproduction of the entire three-dimensional object can be completed by using a small amount of image data.
A Survey on Hand-based Behavioral Activity Recognition

Wrist-worn devices such as smart-watches can be a perfect way to collect personalized user data, since these devices are embedded with various sensors such as the accelerometer and gyroscope which can sense user's hand movement, as well as help in identifying the activity performed by the user. Additionally, smart-watches are light and unobtrusive unlike the wristbands used in past studies which require the user to be connected by several wires and bulky sensors. In this study, we examined the different neural network adopted for the recognition of hand-based behavioral activity to recognize six complex hand motion activities such as smoking, drinking, eating, typing giving a talk and writing with a pen in a non-controlled environment, using accelerometer and gyroscope sensor signals acquired from smart-watch. Unlike other machine learning methods which use hand crafted features, CNN can learn features automatically from the data as well as capture local dependency.
Implementing Arabic Handwritten Recognition Approach using Cloud Computing and Google APIs on a mobile application

Cloud computing application for automatic recognition of Arabic handwritten is needed nowadays and of great importance especially when implemented on a mobile application. It is a type of applications that includes many challenging aspects. This paper introduces for the first time a cloud based mobile app that applies Arabic handwritten recognition for translation purposes and finding locations on Google maps. Proposing such a service for non-Arabic speakers is of great importance especially while visiting Arabic speaking countries. Our approach is the first to build a mobile app based on cloud computing that proposes a multi-phase hybrid classifier for Arabic Handwritten text recognition. Google Maps and Google Translate APIs are applied on the recognized text as part of the introduced cloud computing application. The recognition part of the proposed approach is a multi-stage classifier introduced to cope with big database and high computation complexities. The experiment applied on our approach shows better results of our Arabic handwritten recognition when compared with similar approaches.
SQA Models Comparative Analysis

The high competition among various companies in software development enforces strict metrics to measure the quality of software produced and enhance its performance while evaluating the cost carried out to apply those metrics. This comes along with critical demands such as safety, accuracy, availability, scalability and durability. One of the key principles to ensure such metrics is to consider integrating software quality assurance (SQA) models as they add special assessment features into the software development process. In this paper, we present a comprehensive review for the state-of-the-art of various SQA models applied in the realm of software development. In addition, our study encompasses a comparative analysis between the different SQA models. Our work covers SQA Models, SPI, SPA, ISO standards, CMM, CMMI and SPICE.
Human Motion Recognition Based On Inertial Sensor

Abstract Human activity recognition (HAR) has attracted considerable research attentions from all walks of life due to its application in human-computer interaction, smart medical treatment and smart home health care. There are various ways of using different motion capture methods for HAR. Among which, HAR based on inertial sensor signals has recently emerged as a challenging and hot research topic. In this paper, we presented a inertial sensor-based method for HAR. Firstly, the variance, mean, information entropy, peak, etc. were extracted from the inertial data as robust features of the actions. Then, the robust features were further processed by Principal Component Analysis (PCA) to reduce the dimensions of features. Finally, the features with different actions label were used to train the Extreme Learning Machine (ELM) and Support Vector Machine (SVM) model. The result can achieve an mean accuracy of 83.49%, it also verified the effectiveness of our method.
In-the-wild No-Reference Image Quality Assessment using Deep Convolutional Neural Networks

With the ever-growing portion of internet traffic associated with multimedia data and the existence of multiple copies of the same content in various forms, it has become vital to measure the quality of the image and video files. In most cases, without access to the original file, the quality shall be assessed solely based on the available file. Specifically, the challenge of no-reference image quality assessment (NR-IQA) is to predict a quality measure for given images in a consistent manner with human perception of quality. Conventional NR-IQA methods try to fit certain distortion models to a given image and quantify the quality. In practice, however, an image is affected by a combination of multiple distortion types. In this paper, we propose a deep convolutional neural network (CNN) for in-the-wild (no specific distortion model) NR-IQA challenge, which learns the quality measure without classifying the distortion type. Unlike most methods which extract features, the proposed CNN structure receives the full image as the input and estimates the distribution of human opinion score for the quality.
The Role and Importance of Digital Practical Training of Personnel Management Students

Nowadays, it is possible to see digitization in almost every aspect of social life. Digital technologies and people become mutually dependent, constituting each other; they do not only mediate existing forms, but are an integral part of the practice. Experts are recognized by their specialized knowledge. They have made a deep personal commitment to develop and improve their skills, and where appropriate make the conditions which serve as the basis of this knowledge. This paper discusses how digital technology affects almost all aspects of modern life from individuals to societies, from economies to cultures, and changes the world. The rapidly changing technological, economic, and thought meanings of professions are forced to change according to the world order while professional organizations are looking for new approach models. In today’s world of labor, digitization has become necessity rather than choice. As in other professions, the profession of personnel management is also changing and developing as a result of digitization and technological development. Thanks to computer systems, the workload of personnel managers has been reduced, once complicated and difficult administrative transactions are now performed easily and quickly. The future of personnel managers requires digitization and transformation. Traditional methods and equipment used by personnel managers (such as paper, certificates, registrations, declarations, notifications, etc.) will eventually disappear, and all will be done through Internet information systems. This course deals with the aspects of practical training for engineering students aiming to familiarize students with the concepts of entrepreneurial planning through knowledge, understanding, and practice.
The Status Quo and Effects of Undergraduate Students’ Cybersecurity Judgment: A study in China

Abstract Internet users’ cybersecurity psychology and cognition play an important role in their cybersecurity behavior. Taking 347 Chinese undergraduate students for example, this study assessed internet users’ cybersecurity judgment and its effect on cybersecurity behavior in the Eastern countries. Results showed that the average percentage correct of cybersecurity judgment was 63.62%, cybersecurity judgment had positive impact on cybersecurity behavior. Furthermore, gender had a moderating effect on the relationship between cybersecurity judgment and cybersecurity behavior, compared with female, cybersecurity judgment had more positive influence on male’ cybersecurity behavior.
Research on the Application of Double Conditional General Advantage Nets (DCGAN) to generate Simple Background Image with Specified Hue

Abstract Since the General Advanced Nets (GAN) proposed by Ian Goodfellow in 2014, the research on GAN has become a hot spot of deep learning. People can use GAN to generate images, but there are many problems in GAN, such as the inability to generate images of specified categories, poor model stability, and high training difficulty. Wasserstein GAN improves the disadvantages of the original GAN. Meanwhile, Conditional General Advanced Nets proposes to add condition vectors on the basis of the original GAN, so that the GAN can generate specified pictures, making the original GAN more and more easy to use. Most of the research work of GAN focuses on the real face and real scene generation, but the generation of the commonly used background image in the design is less. In this paper, we use two conditional GAN (DCGAN) and WGAN model framework to generate a simple background image of specified hue and random graphics, which can provide designers with fast and available background images in some simple projects.
ZU1: An Efficient Early Zrange Test Unit

GPU has been applied to more and more visions which demand the hardware with higher performance, faster speed and lower computational cost. We propose a novel early z architecture named ZU1 who locates prior to the pixel shader stage for z range test. The module treats 8x8 pixel patch called tile as an input entry to calculate the tile's z range, meanwhile acquiring cache zrange from z cache to which the tile zrange compares against according to the compare mode, so as to fastly evaluate z test for accepting or rejecting the tile. We benchmark our design on 3DMark set demonstrate substantial gains with 40% reject ratio and thousands of workloads per frame saving.
As Smart as a Human? Leveraging Models of Human Intelligence to Assess the Intelligence of Systems

ABSTRACT As we evolve artificial and machine intelligence concepts, and consider their extension into intelligent systems, it becomes important to be able to assess the system's intelligence level. This assessment serves several important needs in systems engineering. First, it enables the tradeoff of system design alternatives based on a system's intelligence, along with other factors including cost and performance. Second, it influences system verification and validation methods. Lastly, it will help stakeholders specify the required system intelligence. Assessing or specifying the system's intelligence presents many challenges and difficulties similar to those faced by psychologists and neurologists in measuring the intelligence of human beings and animals. This article explores some human and animal intelligence assessment concepts and shows their application to assessing a system's intelligence.
Was the Wild Frontiersman a Prolific Penman? A Stylometric Investigation into the Works of Davy Crockett

The American icon and folk hero David (Davy) Crockett (1785–1836) is known as the legendary bear-fighting “King of the Wild Frontier,” but it is not as widely known that Crockett was also a member ...
Co-viewpoint Multi-camera Combined Calibration for Optical Circle-imaging System

In this paper, we propose a multi-camera combined calibration method for co-viewpoint circle-imaging system to obtain panoramic images. In recent years, high-resolution images are applied in wide fields of artificial intelligence to satisfy various scenes, such as tele-observation, 3D environment reconstruction and road detection. But most common methods are based on complicated matching algorithm processing. The result has high precision but also costs much time to deal with images captured by cameras. However, the optical circle-imaging system in this paper only applies simple cropping and splicing to image matrix instead of feature points matching among images captured by different cameras or at different angles. And in order to carry out the calibration and adjust positions of cameras in this system, we design an improved template consisting of three identical chessboards with settled relationship between every two boards. Once the system is constructed, it can give the panorama in a short time with high resolution and accuracy.