Reviewerly/exHarmony huggingface dataset:
An Image Splicing and Copy-Move Detection Method Based on Convolutional Neural Networks with Global Average Pooling

Splicing and copy-move are two well-known methods of image tampering, while detection of image splicing and copy-move forgery is an important research topic in image forensics. In this paper, a method based on convolutional neural network with global average pooling was proposed for splicing and copy-move tampering detection. To detect image tampering, the inconsistency between the authentic images and the tampered images should be captured regardless of the image contents. So, the existing strategy using high-pass filter in SRM as initialization of the first layer was improved to reduce the influence of image content and make the features more diverse on each channel at the same time. In order to reduce the number of parameters in the fully connected layers and avoid overfitting, global average pooling was utilized before fully connected layers in the proposed model. Experiments on three public image tampering datasets demonstrated that the proposed method outperformed some state-of-the-art methods.
Multi-face detection based on improved YCbCr color space

Abstract Face recognition has become a popular means of recognition. It is required in image retrieval, video monitoring, entrance and exit control of military bases or other sensitive areas and other occasions. Face recognition has three key steps: face detection, feature extraction and feature matching. Face detection plays an important role in face recognition. Skin color segmentation is an important part of face detection. The traditional skin color modeling rules of YCbCr color space still have some defects, so we made some improvements. We changed the parameters of the conversion matrix used in the conversion from RGB space to YCbCr color space, so that the chosen skin color region is closer to the face skin color region. By this way, the system can detect the face most accurately.
Image Enhancing in Poorly Illuminated Subterranean Environments for MAV Applications: A Comparison Study

This work focuses on a comprehensive study and evaluation of existing low-level vision techniques for low light image enhancement, targeting applications in subterranean environments. More specifically, an emerging effort is currently pursuing the deployment of Micro Aerial Vehicles in subterranean environments for search and rescue missions, infrastructure inspection and other tasks. A major part of the autonomy of these vehicles, as well as the feedback to the operator, has been based on the processing of the information provided from onboard visual sensors. Nevertheless, subterranean environments are characterized by a low natural illumination that directly affects the performance of the utilized visual algorithms. In this article, an novel extensive comparison study is presented among five State-of the-Art low light image enhancement algorithms for evaluating their performance and identifying further developments needed. The evaluation has been performed from datasets collected in real underground tunnel environments with challenging conditions from the onboard sensor of a MAV.
Internal State Recovery Attack on Stream Ciphers: Breaking BIVIUM

This paper proposes an attack on shift register based stream ciphers. The attack consists of recovering the internal state of the registers at a starting clock instant from which the output stream is available. For a given output stream the evolution of the output function at the clocking times is first computed in symbolic form as a sequence of Boolean functions from the symbolic state update map of the internal state dynamics. Then the Boolean equations are solved using a Boolean solver which returns all possible internal states which match the output stream. Once the internal state (or most of its assignments) are obtained this way the internal state is reversed sequentially to the initial condition. The resulting equations give solutions to key bits solved by comparing the IV bits with unknown variable equations. This discovers most of the key bits and reduces the unknown key bits to a very small number. Then by brute force search on the remaining key bits the output stream is regenerated to recognize the remaining key bits when the output stream exactly matches. In case when all the bits of the internal state are solved from the Boolean equations of the output stream, but there are more than one solutions to the internal state, the correct initial state is recognized when the IV bits match exactly with the initial condition obtained by reversing the internal state. The attack is practically useful when the all computations involved in steps such as solutions of the Boolean equations from the output stream, the symbolic equation generation from the output function for the length of output stream, symbolic reversing of the internal state with small number of unknown variables are feasible. This paper shows feasibility of this approach for the stream cipher BIVIUM with 80 bits of key and shows that a complete recovery of the key is feasible in practical time by parallel search requiring memory space which is feasible in modern day clusters.
Efficiently Predicting Frequent Patterns over Uncertain Data Streams

In recent decades, frequent pattern mining has become popular in real time computing because it helps users extract meaningful business or technical trends over uncertain data streams. Generally, mainstream frequent pattern mining approaches can accurately explore frequent patterns whether the approaches are used in expected support or probabilistic frequentness mechanisms. However, whichever are implemented, they all have time issues when extracting frequent patterns if there exists a volume of data tuples in each time instance. To explore frequent patterns over uncertain data streams efficiently, we propose a predicted approach for forecasting frequent patterns with hidden Markov models. The model consists of a construction phase and a testing phase. In the former phase, the hidden Markov model is constructed by the history of past data, and in the latter phase, the results of evaluations and adjustments of transition probabilities occur. By constructing models of patterns, frequent patterns can be easily obtained. Experimental results show the proposed approach can save time cost when exploring frequent patterns and provide approximate 72% average accuracy in 50 sequential predictions; thereby, our approach can efficiently and accurately predict frequent patterns over uncertain data streams.
Seamless image stitching in the gradient domain

The algorithm of seamless image stitching in the gradient domain based on the variational derivative estimation method. The functionality of this algorithm is confirmed by computational experiments with real images of different types.
Is artificial intelligence the solution to all our problems? Exploring the applications of AI for automated driving

Deep Learning and AI has become the standard model for object detection and recognition such as situation understanding, prediction and planning. In this chapter we explore how AI can be used to improve parts of the classical ADAS algorithm chain. Based on this, we investigate the full Automated Driving pipeline. Firstly, we describe the building blocks of the pipeline composed of standard computer vision tasks. We provide an overview of use cases for automated driving based on the authors’ experience in commercial deployment, e.g. Sensor-Fusion, Perception, SLAM or End-2-End Driving. Finally, we discuss the opportunities of using AI and Deep Learning to improve upon state-of-the-art classical methods.
Massive-Scale Genre Communities Learning Using a Noise-Tolerant Deep Architecture

Accurately categorizing million-scale Internet users (e.g., Flickr or Google Picasa) into multiple communities based on their genre tastes is an indispensable techniquein machine learning and multimedia. It can facilitate a series of applications, such as fashion recommendation and 3D non-realistic photo rendering. Conventional methods cannot handle this task appropriately because of the inherent contaminated image labels, which are produced by auxiliary image label predictors. In this article, we propose a noise-tolerant deep architecture which optimally encodes stable templates, <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">11</sup> Stable template is our proposed new concept. It denotes the distribution of co-occurring semantic categories toward an image set. Both theoretical and empirical analysises have demonstrated that such distribution (i.e. stable templates) remains almost unchanged in the presence of contaminated image tags. discovered from a collection of images with contaminated semantic labels. Specifically, we first construct a semantic space by encoding image labels using manifold embedding. Afterward, we observe that in the semantic space, the distribution of superpixels from images with the same label remains stable, regardless of the noises from image labels. According to this observation, a probabilistic generative model (Hidden Stable Analysis) is proposed to learn the stable templates toward each image label. To globally represent the composition of a user's images, a deep aggregation network is developed which statistically concatenates the CNN features learned from all its generated stable templates. Subsequently, an affinity graph is built, in which the genre difference among users is determined by their deep features. Finally, we employ a dense subgraph discovery technique which effectively mines the communities toward various genre tastes. Experiments on a million-scale image set (>1.4 million) compiled from Flickr have demonstrated the effectiveness of our method. Additionally, empirical study on the 33 SIFT-flow categories have shown that the detected stable templates maintain almost unchanged under nearly 32% contaminated image labels.
Improving Clustering via a Fine-Grained Parallel Genetic Algorithm with Information Sharing

Clustering is a very common unsupervised machine learning task, used to organise datasets into groups that can provide useful insight. Genetic algorithms (GAs) are often applied to the task of clustering as they are effective at finding viable solutions to optimization problems. Parallel genetic algorithms (PGAs) are an existing approach that maximizes the effectiveness of GAs by making them run in parallel with multiple independent subpopulations. Each subpopulation can also communicate by exchanging information throughout the genetic process, enhancing their overall effectiveness. PGAs offer greater performance by mitigating some of the weaknesses of GAs. Firstly, having multiple subpopulations enable the algorithm to more widely explore the solution space. This can reduce the probability of converging to poor-quality local optima, while increasing the chance of finding high-quality local optima. Secondly, PGAs offer improved execution time, as each subpopulation is processed in parallel on separate threads. Our technique advances an existing GA-based method called GenClust++, by employing a PGA along with a novel information sharing technique. We also compare our technique with 2 alternative information sharing functions, as well with no information sharing. On 5 commonly researched datasets, our approach consistently yields improved cluster quality and a markedly reduced runtime compared to GenClust++.
Theory of reflexive-value support in professional training of future educators

The novelty of reflexive-value support in professional training of future educators is caused by social and economic changes, which made it necessary to review the principals of teacher training. The analysis of concepts and notions highlighted the key terms: support, pedagogical support, reflection, values, value reflection. Theoretical studies revealed the major trends in the process of pedagogical support and feature its main peculiarities. The author presents her own definition of «reflexive-value support in professional training of future educators». As the result of the content analysis there was defined the structure of reflexive-value support in professional training of future educators. The structure manifests the essence and the dynamics of the process, its valuable, reflexive, creative, communicative, cognitive components. The functional analysis showed the main functional components: motivation, values, cognition, communication, regulation, reflection, evaluation, self-development and strategies and soft skills for implementing these functional aspects.
Water Pump Management Using Photovoltaic Plates in Santa Fe - Amazonas Community

Electricity in today's world is one of the biggest concerns. Through clean sources, photovoltaic systems emerge as an alternative to this kind of problem.This work proposes the development of a photovoltaic system that will be applied to water pumping using a pump. Through the presented data, it will be possible to analyze if the photovoltaic energy is reliable and to propose solutions to the problem of residential water supply in the communities that do not have access to the electricity grid.
Psychological and pedagogical aspects of interaction between students and retro-students in the educational process at the Institute of the third age

The article deals with the features of intergenerational interaction of students of different ages within a single educational environment and an ambiguous attitude to the social and psychological experience of generations. Empirical research has shown that within the framework of a gerontological unit all age groups of students have an opportunity to receive a qualitatively new social experience on a permanent basis in professional, cultural, leisure and volunteer fields. It should be noted that the younger generation has a positive attitude to retro students; they want to and can become retro students’ mentors, helping the elderly to master modern competencies. Retro students, despite the difficulties of interpersonal relations, are ready as mentors to share their knowledge, social and life experiences with young people. The results presented in the article have shown that the most promising forms of cooperation between students and retro-students are master classes in various areas, and creation of gerontological faculties at universities is the strategic direction of gerontology education.
Interactive Genetic Algorithms in Product-Service Clustering Design: Early Results and Roadmap

Service bundling is a well-known practice for companies to gain a competitive edge over others, by offering various differentiated products and services in a package. Unfortunately, currently each of them are typically delivered individually in splendid isolation, *either* as a product *or* as a service. We argue the need to investigate interactive genetic algorithms to shape product-service clusters wherefore recommendation as well as design, development, operation, and verification techniques from both fields can be joined together for cross-fertilization and mutual benefits. This article illustrates the aforementioned vision and evaluates it over early results from preliminary experimentation in the context of a real industrial case-study. Results show promise but also highlight plenty of avenues for further research.
Complex system risk assessment based on the fuzzy probabilistic Bayesian inference

The paper proposes a method for complex system risk assessment based on the fuzzy probabilistic Bayesian inference, which enables us to solve the problem of result uncertainty accumulation, which arises from the multiple account of the degree of fuzziness of the same fuzzy variables in massive fuzzy computations. The effect is achieved due to the implementation of modal interaction among the fuzzy variables of fuzzy Bayesian models.
Search Algorithm Using Prior Knowledge of Similar Task

This study introduces a concept of transfer learning to search tasks such as function approximation and optimization, and aims to achieve effective search using fewer number of samples. We propose a search procedure transfer (SPT) algorithm which extracts knowledge of efficient search procedure from well-known task and uses it to search similar unknown tasks. Experiments reveal that the closer the target task becomes to the source task, the higher the performance of the SPT algorithm becomes. In addition, experiments also demonstrate the SPT algorithm shows higher performance than the existing method using the Bayesian optimization algorithm (BOA) when the difference between a source task and a target task is small. Applying the idea of domain randomization, the SPT algorithm can utilize even human ambiguous heuristic knowledge.
Convolution Acceleration: Query Based Filter Pruning

The rising ubiquity of Convolutional Neural Networks (CNN) for learning tasks has led to their use on a variety of devices. CNNs can be used on small devices, such as phones or embedded systems; however, compute time is a critical enabling factor. On these devices, trading high accuracy for improved performance may be worthwhile. This has led to active research in high-level convolution optimizations. One successful class of optimizations is filter pruning, in which filters that are determined to have a small effect on the network's final output are deleted. In this work, we present a self-pruning convolution that is intended to accelerate convolutions for use on small devices. We call it an ALSH Convolution because it uses Asymmetric Locality Sensitive Hashing to generate a subset of the convolution's filters that are likely to produce large outputs for a given input. Our methodology is accessible: it generalizes well to many architectures and is easy to use, essentially functioning as a regular layer. Experiments show that a network modified to use ALSH Convolutions can stay within 5% accuracy on CIFAR-10 and 10% on CIFAR-100. Further, on small devices, a network built with our implementation can be 2× faster than the same network composed of PyTorch's convolution.
Semi-supervised Multi-category Classification with Generative Adversarial Networks

For training robust deep neural architectures to generate complex samples across varied domains, Generative Adversarial Networks (GANs) have shown promising performance in recent years. In previous works, the effectiveness of GANs in transforming images from the laseled source domain to the unlabeled target domain has shown high potential. In this paper, we outline a generalized semi-supervised learning framework where proposed 'Semi-supervised Multi-category Classification with Generative Adversarial Networks (SMC-GAN)' model, first, maps the data in the source domain to target domain to generate target-like source images and, then, learns to discriminate the target domain data using semi-supervised classifier. Extensive experimental evaluations on standard cross-domain datasets show that the proposed model is an efficient classifier and allows faster convergence than a conventional GAN approach for digit classification tasks.
UIC-KORAIL TRAINING SESSION 2018 on Railways Maintenance and Operation on Digital Era – UIC - International union of railways |Events

Context Context Objectives Comprehending overview of railway operation and maintenance on Digital era Understanding IT Trend of international railway Sharing the knowledge and best practices of…
Personal Peculiarities of Development of Styles of Educational and Cognitive Activity of Students in Technological Park

The article deals with the development of the leading ways of educational and cognitive activity of students enrolled in programs of specialized education in the technological Park, which provides their pre-professional training for education in specialized universities. The author, based on stylistic approach to the development of educational-cognitive activity reveals personality traits of natural science, physics, mathematics and Humanities students determines the learning technology to ensure the linkages of learning and teaching in the technological Park. Quantum are presented, which ensure the development of educational research, educational search, educational design and educational project activities of students within the framework of functioning of the technological Park.
Development of Good Procurement Information System Web Based at PT. XYZ

PT. XYZ is one of the companies engaged in the production of motorcycle spare parts. In carrying out its operational activities, PT. XYZ has a procurement process. Generally in applying of good procurement, employees must submit a document submission of goods in the printed form and must meet their supervisor directly to ask for approval in a signature. This condition requires quite a long time until the submission document reaches the Director because they have to wait for the signature of each related section. That is quite an obstacle for the employee who submits because the longer the approval process, the longer the procurement process. To overcome this obstacle, a good procurement information system web based was built using phased development methodology. The information system uses SQL Server as database and ASP.Net as programming language. Good procurement information system web-based can accelerate the procurement process.
Digital learning model based on intelligent tutoring system in 3DMUVLE

Abstract Information and Communication Technology (ICT) has been used in broad education both for pre-learning, in-learning, and post-learning. But its use often generalizes learning material for all students even though each student has different characters and abilities. Intelligent Tutoring System (ITS) is able to provide learning materials according to students’ models. This study aims to model ITS-based digital learning. This model consists of four parts, namely: domain knowledge, pedagogy, students’ model, and interface. Domain knowledge produces learning materials that are suitable for each student’ model and pedagogy while virtual learning environment is a learning interface for students. This interface integrates MOODLE, SLOODLE and OPENSIM. The student’s model used is Myer Briggs Type Indicator (MBTI). The results of this study are 16 types of students and 16 types of learning material. The implication of this research is a comprehensive model that integrates ITS and VLE.
Investigation of Electromagnetic VLF-range Signals Applying Sparse Approximation Schemes

The article is devoted to the application of the sparse approximation method for the study of electromagnetic signals of the VLF range registered in Kamchatka. The authors propose a model of the electromagnetic emission pulse signals and an algorithm for its identification, developed on the basis of the Matching Pursuit method. The choice of the basic functions most suitable for the description of electromagnetic pulses is described. The results of computational experiments on the analysis of real electromagnetic signals are presented.