Reviewerly/exHarmony huggingface dataset:
Shadow Detection in Satellite Images by Computing Its Characteristics

The shadows in satellite images have many interesting features. It is possible to define common properties of shadows of buildings. The shadows angle, relation umbra and penumbra, time of image capture for every region include a lot of information for shadow correction and scene reconstruction. In this paper, we propose the algorithm to detect shadows on the base of definition of its structure and features on urban or built-up satellite images by using neural networks.
Internal Organizational Security

The triad of CIA, i.e., confidentiality, integrity and availability, are the key principles that are essential for any security system. It deals with confidentiality, which makes sure that no unauthorized person can access the system, integrity, which keeps the track of data such that it does not change in any manner and that its authenticity is maintained, and availability, such that it is readily accessible to those it is intended to. The most endangered areas of a given system are the location of the server and the places from where it can be accessed, the virtually secured region of an organization like the workplace where all the data available to its employees are kept and the last is the mind of the employees which, however limited, can be crucial. It may happen that an employee having limited data at his/her disposal becomes a major security threat. The tally and threat of the attacks of this nature are increasing with every new security measure being placed into action. The attackers are deploying new methods to break into the institutional structure. Therefore, it is necessary to look into all the possible threats so that the attacker has very limited to no access to the internal physical infrastructure. The aim of the following paper is to help new and existing organizations in developing a physical security system which will look into the internal security from all angles.
Learning in the Absence of Training Data—A Galactic Application

There are multiple real-world problems in which trainingSpire, Cedric data is unavailable, and still, the ambition is to learn values of the system parameters, at which test data onChakrabarty, Dalia an observable is realised, subsequent to the learning of the functional relationship between these variables. We present a novel Bayesian method to deal with such a problem, in which we learn the system function of a stationary dynamical system, for which only test data on a vector-valued observable is available, though the distribution of this observable is unknown. Thus, we are motivated to learn the state space probability density function (pdf), where the state space vector is wholly or partially observed. As there is no training data available for either this pdf or the system function, we cannot learn their respective correlation structures. Instead, we perform inference (using Metropolis-within-Gibbs), on the discretised forms of the sought functions, where the pdf is constructed such that the unknown system parameters are embedded within its support. The likelihood of the unknowns given the available data is defined in terms of such a pdf. We make an application of this methodology, to learn the density of all gravitating matter in a real galaxy.
Regular methods for coding of raster images of remote sensing of Earth

The work is devoted to the consideration of the issues of eliminating information redundancy of raster data of remote sensing of the Earth (RDRSE), including the latest hyperspectral data (HSD). The characteristic properties of raster hyperspectral images (RHSI) are listed, a brief description of the existing methods of RDRSE compression is given. The possibility of using local, homogeneous "well-adapted" basic functions (LHWABF) to eliminate information redundancy and adaptive compression of RDRSE is considered. An algorithm for constructing a LHWABF system for the RHSI based on the Chebyshev approximation is proposed. The results of computational experiments are given. The effectiveness of the proposed method of adaptive compression RHSI is shown.
A HOL Theory of the Differential for Matrix Functions

The differential of matrix functions(DMF) plays an important role in mathematics and engineering. Common applications of it are found in optimization analysis, computer vision, robotics, etc. In this paper, a formal method based on HOL is used to construct the DMF based on Fréchet differential in matrix space. In order to illustrate the practical effectiveness of our work, we use our formalization to verify a property of matrix exponential.
Single Fingertip Detection Using Simple Geometric Properties of the Hand Image: A Case Study for Augmented Reality in an Educational App

We propose a fingertip detection method suitable for portable devices’ applications where the user can interact with objects or interface elements located in the augmented space. The method has been experimentally implemented in the context of an application that uses a board containing ArUco markers [1]. The user can press virtual buttons laid on the board or drag items along predetermined paths on the board level by extending the index finger (or thumb) and placing its edge on the object of interest, while the other hand holds the device so that both the object and the hand are visible. We present brief but indicative results of our technique.
A Guide to the NeurIPS 2018 Competitions

Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility. In this volume, we present the details of eight competitions in the area of AI which took place between February to December 2018 and were presented at the Neural Information Processing Systems conference in Montreal, Canada on December 8, 2018. The competitions ranged from challenges in Robotics, Computer Vision, Natural Language Processing, Games, Health, Systems to Physics.
Nudges Driven Networks: Towards More Acceptable Recommendations for Inducing Targeted Social Communities

Mind Cognitive Impairment is one of the most common clinical manifestations affecting the elderly. In this paper, we report the work in progress (in the frame of our SENIOR project) to provide elderly with new Nudge theory driven advices for influencing their interest to a conscious and functional participation to “targeted” social communities where suggestions on the overall wellness can be shared, recognized as usefull by users and supported by health care providers.
Learning Classifiers for Multimodal Image Detection

Learning Classifiers are essential analysis tools in todays era due to the rapid expansion in data-intensive applications. Every type of the classifier has own distinct architectural properties which allow to apply them in various application-specific requirements. Here, an experimental work is performed to analyse the performance and accuracy of most commonly used learning classifiers applied within the scope of multimodal image classification problem space. The purpose of this study is to investigate the usefulness of the learning-classifiers with the multimodal datasets which are in many cases have the curse of dimensionality and may contain a very high volume of noise. Validation is conducted over multimodal datasets with the proposed classification algorithms operating in a parallel manner. Finally, results are discussed to explore possible ways to apply every learning classifier in multi-modal image classification applications.
Is it real?

The concept of Mixed Reality has existed in research for decades but has experienced rapid growth in recent years, mainly due to technological advances and peripherals such as the Microsoft HoloLens reaching the market. Despite this, certain design aspects of Mixed Reality experiences, such as the different nuances of real and virtual elements, remain largely unexplored.
Analysis of philosophy and humanization art relationship in the first decade of the XX century

When writing the article, the authors pursued one goal: to understand why extreme subjectivity in art, the abandonment for centuries of setting traditions, laws and techniques in art led to the loss of aesthetic criteria by the modernism art, and what philosophical trends influenced this process. The methodological basis of the article is the principles of integrity, objectivity and historicism. The following methods were applied: historical and philosophical analysis, focused on the objective completeness of the study; cultural and comparative approaches aimed at establishing spiritual ties between artists of different historical eras. The article analyzes the works of philosophers and artists, such as: Bergson, Schopenhauer Nietzsche, Chirico, Apollinaire, Lenin, Bru, Kandinsky. Various directions of bourgeois art are considered. The authors believe that abstract art, fully antisocial and dehumanized, fully meets the aesthetic ideals of the modernism art, which opposed itself to human from its very birth. The authors argue that if you take the artists’ position of that time, you can make an unambiguous conclusion, the dehumanization of art could not be avoided. According to the authors, abstract art, fully antisocial and extremely dehumanized, is fully consistent with the goals of bourgeois ideologists and fully meets the aesthetic ideals of the art of modernism, which has opposed itself to man since its inception.
Analysis of agriculture land pollution in Pangkal Baru village, Sintang district using Sugeno method

In Indonesia, the condition of the land of each region is different. It depends on the type of soil and the geographical location of a place or area so that it affects the quality of the soil. For farmers, especially in the countryside, to understand the knowledge of good soil quality is certainly difficult and requires a relatively long time to determine the right type of vegetation, which fits the soil condition, especially for farmers. The purpose of this paper is to help the process of disseminating information and knowledge through calculations using fuzzy logic. In this study, the processed data are the aspect data of the soil as a benchmark in determining whether or not the soil is polluted including pH or acidity of the soil, soil minerals, soil structures and soil nutrient elements. At this time the analysis of fuzzy calculations is performed using the Sugeno method. This paper show that the results obtained by the Sugeno method are not in accordance with the actual data. This is because among the six data, only five data match the actual data, then the percentage of errors with the Sugeno method is 16%.
Virtually Constrained Dancing

This paper presents the development of the TED (Tap Encoding Decoding) program with results and reflections on its usage. TED is a program to be used in partnership with a tap dancer for decoding tap dancing audio. To perform with TED, a tap dancer must execute their dance with steps that encode Morse code. This paper will elaborate on the processes by which TED was developed, including methodologies such as audio signal peak detection for tap dancing and audio decoding analysis. We also explore the relationship developed between the dancer and TED during experimentation and live performance. We draw upon notions of extended and embodied cognition to explain observations regarding the dancer's feedback driven adaptations to TED's outputs during their performance. The programmatic constraints introduced during partnership with TED result in novel choreographic challenges and impose atypical structure in improvisation, leading to unusual performance characteristics.
Multi-dimensional Feature Fusion Modulation Classification System Based on Self-training Network

To solve the problem that the single feature extraction method cannot fully express the radar signal at low SNR and the large-scale deep learning network cannot deal with small sample size of radar signal, this paper proposes a multi-dimensional feature fusion modulation classification system, which can classify radar signals including CW, BPSK, LFM, COSTAS, FRANK, T1, T2, T3 and T4. The machine could extract time-frequency feature of radar signal automatically through small self-training network. Combined with the idea of multi-dimensional feature fusion, the time-frequency entropy feature, the higher-order statistics feature and network self-extraction feature are normalized and fused by non-negative matrix factorization (NMF), which improves the classification performance of the proposed system at low SNR. The simulation results show that the recognition rate of the proposed system is 78% at −3 dB. Compared with the traditional method, the recognition rate of proposed system has a significant improvement.
Multi-scale Feature and Spatial Relation Inference for Object Detection

Real scene images contain rich contextual visual cues. How to use contextual information is a fundamental problem in object recognition task. Contextual information mainly includes visual relevance and spatial relations. In this work we propose a context model that utilize both kinds of context. Our model is based on common detection framework Faster R-CNN. A multi-scale feature module extract features from different scales, and use a gated bi-directional structure to control message pass. This module collects contextual information from relevant region and different scales. A spatial relation inference module builds object spatial relations through a GRU structure, which can learn effective object spatial state. By special designing to adapt to CNN structure, both modules work well in the integrated detection model. And the whole model can be trained end-to-end. Experiments prove the effectiveness of our context model. Our method achieves 76.82% mAP on PASCAL VOC 2007 dataset, which outperforms Faster R-CNN by 3.5%.
Multi-scale Attention Aided Multi-Resolution Network for Human Pose Estimation

In this paper, we propose attention maps at various scales on multi-resolution feature extractor baseline network for human pose estimation. The baseline network captures information across various scales with the help of repeated bottom-up and top-down approach using successive pooling and up-sampling. We propose a network named Refinement Net for regressing the predicted heatmaps to 2D joint locations to remove ambiguities in predicted position. We experiment with three levels of attention schemes - global, heatmap and multi-resolution. Attention masks helps in generating basin of attraction that helps the network on deciding where to "look". The proposed network performance is at par with the state-of-the-art two dimensional pose estimation methods on MPII dataset.
SliceNet: Mask Guided Efficient Feature Augmentation for Attention-Aware Person Re-Identification

Person re-identification (re-ID) is a challenging task since the same person captured by different cameras can appear very differently, due to the uncontrolled factors such as occlusion, illumination, viewpoint and pose variation etc. Attention-based person re-ID methods have been extensively studied to focus on discriminative regions of the last convolutional layer, which, however, ignore the low-level fine-grained information. In this paper, we propose a novel SliceNet with efficient feature augmentation modules for open-world person re-identification. Specifically, with the philosophy of divide and conquer, we divide the baseline network into three sub-networks from low, middle and high levels, which are called slice networks, followed by a Self-Alignment Attention Module respectively to learn multi-level discriminative parts. In contrast with existing works that uniformly partition the images into multiple patches, our attention module aims to learn self-alignment masks for discovering and exploiting the align-attention regions. Further, SliceNet is combined with the attention free baseline network to characterize global features. Extensive experiments on the benchmark datasets including Market-1501, CUHK03, and DukeMTMC-reID show that our proposed SliceNet achieves favorable performance compared with the state-of-the art methods.
A Big Data and Visual Analytics System for Port Risk Warning

Big data and visual analytics are critical for a product tracking and risk warning system to provide friendly GUIs, with which users can find the associated facts fast and precisely. In this work, the authors developed a big data and visual analytics tool for an underdeveloped product tracking and risk warning system for the smart port. The goal is to provide information statistics and further risk mining to reveal possible hidden facts. The proposed tool is to improve the data comprehensiveness, deep insight, and relevance. The Knowledge Graph is applied to organize and describe risk-related data, while data mining algorithms such as clustering and association analysis are used to tap the in-depth value of information. Based on Knowledge Graphs, the system solves the problem of searching related risk products with the same attributes, and analyzes the potential relationship between products of the same category through association analysis. The tool has been developed to support visual navigation in multi-dimensional, multi-faceted, multi-attribute, including association rules, risk warning and dynamic target.
Q2 YouTube: Quantitative and Qualitative Information Analysis based Influencer-aware YouTube Channel Ranking Scheme

With the development of big data, artificial intelligence and deep learning, various social information networks are becoming exponentially intelligent.Of all the various social networks, YouTube is so popular that it is called the YouTube era.Not only video viewers, but also actual video producers, influencer youtubers, are increasing, allowing individuals as well as operators to use and market contents on various topics.As the number of users and contents increases, the choice of information increases, but it is more difficult for individual and business owners to select information that meets their needs.Therefore, this study aims to analyze the channel of the subject that the user needs from various angles and provide the ranking of the channel to individuals and businesses.We crawl the channels and measure the average awareness and influencer youtuber of channels and channel videos by analyzing the qualitative data of quantitative data and comments which are statistical information.As a final case study, we recommend the English learning channel to individual users based on numerical data statistics and emotional analysis results to show the maximum of flipped learning effect regardless of time and space.Plus, we prove the maximum of the effectiveness of marketing by influencer channel which is influential to operators in the medium and long term.
Live Video Streaming Application with Geo- Tagging to alert authorities about an on-going crime

An increase in crime rate around the globe has left people agitated for their safety. A victim is usually unable to report to the police during a crime occurrence. Due to which help could not get to them on time. This could result in the loss of the victim's life or property. Moreover, after a crime has happened the authorities spend limited resources and time on gathering evidence to present in court. So, it was imperative to solve this problem. Thus, Live Video Streaming Application (LVSA) was proposed, where a victim could not only transmit live video of crime occurrence to authorities but could also send their current GPS location through video. Moreover, this stream could be stored on a server which could be used as a piece of evidence in court. For this, the Android operating system was used. If this app is used by Police authorities then it would not only make people safe and secure but would also help the authorities in catching criminals.
Keywords Recommender for Scientific Papers Using Semantic Relatedness and Associative Neural Network

The purpose of this paper is to extract keywords from scientific papers. Keyword extraction is used to assist in suggesting keywords for indexers or to produce a summary in a form of keywords for scientific writings. Scientific writings are also categorized as structured document because the scientific papers have parts such as title, abstract, content, and conclusion, so we are going to find important information on those parts. So the method that suitable was Associative Neural Network Algorithm and using trained Associative Neural network with chosen keywords data sets we are able to find keywords features in scientific documents. So the automated keywords extraction and pre-included keywords from the writer will be compared to choose either the most relevant or complementary and providing recommendation or perhaps the extracted automated keywords and the keywords from the writer are exactly the same.
Pop-up digital tabletop: seamless integration of 2D and 3D visualizations in a tabletop environment

We propose a pop-up digital tabletop system that seamlessly integrates two-dimensional (2D) and three-dimensional (3D) representations of contents in a digital tabletop environment. By combining a digital tabletop display of 2D contents with a light-field display, we can visualize a part of the 2D contents in 3D. Users of our system can overview the contents in their 2D representation, then observe a detail of the contents in the 3D visualization. The feasibility of our system is demonstrated on two applications, one for browsing cityscapes, the other for viewing insect specimens.
Implementation of Steganography on Voice Over Internet Protocol (VOIP)

Steganography is the science of hiding messages into a container without changes detected by the human senses. Cryptography is the science to keeping the message still safe. Combine between Steganography and cryptography can be used, but if there is exchange information still use separately, worry about there is a change in file size, which can result in damage. VoIP is a technology that allows to communicate with use communication lines on a network. VoIP refers more to voice communication. I am utilizing VoIP as a voice communication channel with voice as a medium for inserting secret messages. The research results that steganography techniques can be used with VoIP. By inserting a text message that is first encrypted and then entered into sound by the Least Significant Bit method. The test results, the Alpha testing, and Beta testing, resulted in a percentage above 90%.
Interactive Analysis of the Discussion from a Virtual Community on Neuroscience

Social media on the Internet has been promoting disruptive transformations in the society, enabling new possibilities for knowledge construction. Nowadays, researchers can fastly gather a large number of discussions from a virtual community of interest for analysis. This paper presents the study of a relevant community on neuroscience, with more than 43,000 registered members. The research method employs a process based on Grounded Theory and Knowledge Discovery in Databases (KDD), using a tool crafted to support interactively and iteratively use of data mining algorithms such as topic modeling and sentiment analysis. From the analysis of 2,927 posts and 19,227 comments, the results reveal the most prominent subject regards Alzheimer’s disease, followed by general acknowledgments and requests for help whether concerning symptom assessment, examination results analysis, and medical advice. Most of the identified topics have a positive polarity, indicating that interactions are predominantly friendly. Negative feelings emerged from controversial topics, being mostly non-technical or speculative subjects such as mind control techniques, help with MRI results, answers to medical advice, and theories about consciousness. The findings reinforce the feasibility of such studies and show useful insights regarding the community interest in neuroscience.