Reviewerly/exHarmony huggingface dataset:
Improved Visual Background Extractor Based on Motion Saliency

Motion detection has been widely applied as the fundamental step of video analysis. In this paper, we present a technique for extracting the moving objects from video sequences, which is based on the off-the-shelf visual background extractor (ViBe) method. In order to improve the performance of ViBe, a series of modifications are introduced. First, inspired by the saliency detection model, the differences between the pixels of the coming frame and all its corresponding background samples are accumulated and the motion saliency map is generated, by which it can suppress the inaccuracy of the background model in that it takes all the samples into consideration instead of parts of them. Second, based on the above saliency maps, the modified fuzzy growing method is applied to extract the foreground mask. Experiments on a variety of visual surveillance databases validate the improvements of the proposed modifications. Especially in a scenario where the moving objects stop for a short while, our method can greatly suppress the diffusion of false negatives.
Adaptive Probabilistic Neural Network With Fuzzy Inference And Its Online Learning

The adaptive probabilistic neural networks with fuzzy interference is proposed. This network is designed to solve data classification task when data are fed sequentially in the online mode, and forming classes are mutually overlapped - the fuzzy case. The distinct feature of the network is that the learning process of the pattern layer uses the sliding window. This allows us to keep the constant number of neurons in this layer. Another point of the learning process is the tuning ability of activation functions' widespread parameters in online mode. The described advantage allows us to raise the classification quality. Last but not least is the ability to compute both the probability and membership levels of each observation to each of forming classes. The proposed adaptive probabilistic neural network with fuzzy interference is simple in numerical implementation and has high learning speed, confirmed by the computer experiment results.
The Five Games of Mr. Edgar Allan Poe: Strategic rationality in 'The Purloined Letter'

This paper investigates strategic thinking in the fictional world of Edgar Allan Poe’s “The Purloined Letter.” This short story has been rightly celebrated for its explicit analysis of strategic reasoning in which players attempt to outwit one another, which involves accounting for how they are all attempting to outwit one another. I differ from previous analyses by examining how the actors can often be wrong in their explicit analysis and consider the strategic actions they take rather than those they claim to take. Using elementary game theory, I describe the five games (and suggest a sixth) that make up the strategic heart of the story. These include games of signalling, screening, negotiation, revenge and a unique game called the “pincer.” I consider how literary sources like “The Purloined Letter” can provide insights into the applicability of strategic analysis in the “real world.”
The Design of Inductive Modeling Tools Using Ontologies

The architecture of the GMDH-based inductive modeling tools is considered. A feature is the use of the knowledge base in the form of an ontology of the subject area of inductive modeling. The application of the ontological approach to the design of the knowledge base makes it possible to automatically acquire new knowledge, efficiently process information in the modeling of complex objects of different nature according to statistical data, generate queries and obtain logical inferences. Fragments of the GMDH-based inductive modeling ontology are given as an example of creating a formal description of the subject area. The Protege onto editor was used to construct ontologies.
Analysis of design points of deep learning supported by maker space

Abstract The creator zone was a combination of practical meaning and ideas of reform and innovations. It pursued a cultural pattern of sharing, development, innovations, improvement. In the current stage of the education reform, the creator zone always supported students' deep learning and lifelong learning concept training. It constantly broadened the new territory in the students' main consciousness, and combined the classroom resources with the network resources, so that students could understand the knowledge from different angles and layers. Compared with the traditional teaching method, the creator zone education had an undeniable advantage. It could show the status of the student as the main body, stimulate the student's interest in learning, and make the student turn from a passive accepting to an active inquiring person. For this reason, based on the experience of the creator zone education, this article talked about the relationship between the creator zone and the deep learning, and provided a new teaching idea for other teachers.
Not All that Glitters Is Gold: Political Stability and Trade in Sub-Saharan Africa

This study examines linkages between political stability and trade openness dynamics in a panel of 44 countries in SSA from 1996 to 2016. The empirical evidence is based on the generalized method of moments. From the findings, the negative relationship between political stability and merchandise trade is not significant while the negative relationship between political stability and trade openness (exports plus imports) is significant. Hence, the findings do not validate the tested hypothesis that political stability/no violence increases trade in the sub-region. The perspective that some forms of political stability can slow down and prevent international trade is consistent with Oslon in Rise and Decline of Nations (RADON) and recent contributions to the economic development literature which have shown that not all forms of political stability are development friendly because much depends on the extent to which stability translates into, inter alia, good governance. The principal policy implication is that standards of political governance need to be boosted in order to improve the anticipated effects of political stability on trade, especially in the light of the ambitious African Continental Free Trade Area (AfCFTA). Other policy implications are discussed.
Dynamism in Financial Market Regulation: Harnessing Regulatory and Supervisory Technologies

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Online-Codistillation Meets LARS, Going beyond the Limit of Data Parallelism in Deep Learning

Data parallel training is a powerful family of methods for the efficient training of deep neural networks on big data. Unfortunately, however, recent studies have shown that the merit of increased batch size in terms of both speed and model-performance diminishes rapidly beyond some point. This seem to apply to even LARS, the state-of-the-art large batch stochastic optimization method. In this paper, we combine LARS with online-codistillation, a recently developed, efficient deep learning algorithm built on a whole different philosophy of stabilizing the training procedure using a collaborative ensemble of models. We show that the combination of large-batch training and online-codistillation is much more efficient than either one alone. We also present a novel way of implementing the online-codistillation that can further speed up the computation. We will demonstrate the efficacy of our approach on various benchmark datasets.
Research on Consistency of IoT Data Communication Based on Eigenvector Vector

Abstract Aiming at the large deviation of the traditional Internet of things (IoT) communication consistency algorithm, this paper designs the IoT information communication consistency algorithm. According to the obtained eigenvectors, the communication consensus protocol is generated, and the truncation factor is introduced to solve the problem that the cooperative variable deviation between the new node or restart node and the eigenvector is too large in the process of consistency, so as to realize the information communication consistency of the Internet of things. The experimental results show that the deviation value of this method is lower than that of the traditional algorithm, and is close to the standard value. This design ensures the consistency of the information communication of the Internet of things, and has certain application significance.
Adaptive graph convolutional neural network and its biomedical applications

Graph convolutional neural networks (GCN) are generalizations of classical CNNs to better work with graph-structured data that include biochemical molecular graph, 3D point cloud and social networks. Current convolutional kernels in GCNs were built upon fixed and shared graph structure. However, for most real-world data, graph structure varies in terms of both scale and topology. A generalizable convolutional operator on graph is supposed to be compatible with different graph topologies. In the article, authors introduced a generalized and flexible GCN framework along with a new spectral graph convolutional layer parameterizing distance metric and feature transform. Besides original graph structure, a residual graph is constructed and learned throughout training. Therefore, the introduced Adaptive graph convolutional network (AGCN) is adaptive to graphs of arbitrary topological structure and scale and is also adaptive to various learning tasks easily. With graph attention network, we enabled AGCN to learn graph representation from a bag of patches randomly sampled from large medical images such as Whole-slide-image for sophisticated understanding tasks, for example survival prediction. The DeepGraphSurv is an end-to-end framework that directly predict survival probability from patients’ WSI on tissues. Empowered by graph attention, an intuitive annotation of tumor cells is also learned and generated by the model. Extensive experiments of AGCN and state-of-the-arts on drug property benchmark datasets have demonstrated the superior performance improvement on both convergence speed and predictive accuracy for multi-tasks classification of graph (drug). We also executed survival time prediction experiment over 3 large WSI datasets of lung cancer patient, from which DeepGraphSurv indicated significant accuracy lift brought by graph representation learning over patches.
Supporting Privacy Protection in Personalized Web Search

In web personalization and recommender systems, the main role is played by the Web Usage Mining. This paper has proposed an efficient recommender system depending upon the Web Usage Mining and ontology. The very first stage of this proposed method is mining the features from the web documents as well as creating the relevant idea. Then, the site builds an ontology, utilize the substantial terms as well as concepts removed from the documents. As stated by the semantic resemblance of the web documents to group them in diverse semantic themes, various preferences are implied by the various themes. This method assimilates the semantic knowledge within the personalization processes and Web Usage Mining.
An FCR Approach Towards Detection of Outliers for Medical Data

Regression analysis has been brought into practical functionality in this paper to intensify the magnitude of adequacy for outliers' detection in medical data. Previously, linear regression model for the outlier detection in medical data has been used. The use of linear model to detect outliers leaves a certain amount of gap in increasing the efficiency of the expected values. Linear regression basically represents linear relationship between data and sometimes loosely fits the data. The proposed FCR technique on contrary contributes towards increasing the efficiency of expected data and improved effectiveness towards detecting outliers. The proposed method is adept to best fit curve to the data. It further verifies to be a constructive asset that increases the model adequacy on medical data. The proposed approach in this paper has demonstrated to deliver enhanced adequacy paralleled to linear regression.
α β-Relations and the Actual Meaning of α-Renaming

In this work we provide an alternative, and equivalent, formulation of the concept of λ-theory without introducing the notion of substitution and the sets of all, free and bound variables occurring in a term. We call α β-relations our alternative versions of λ-theories. We also clarify the actual role of α-renaming in the lambda calculus: it expresses a property of extensionality for a certain class of terms. To motivate the necessity of α-renaming, we construct an unusual denotational model of the lambda calculus that validates all structural and beta conditions but not α-renaming. The article also has a survey character.
Inter-device Language Translation Application for Smartphones

With the recent trends of globalization and diversification in various social environments becoming the norm, along with the advantages of having multiple perspectives, there are some issues that can get introduced in the cliques as well. One such issue is linguistic disparity, which proves to be a bona fide issue while communicating. Due to this hindrance, crucial transfer and exchange of information are affected, and the overall efficacy of the same is altered. The proposed smartphone application is based on the principle of connecting two distinct smartphone devices, having a language that they understand set on their respective devices in an effort to bridge this linguistic communication gap.
Design and Application of Intelligent Course Selection Algorithm for School

Abstract Course supermarkets are emerging in many schools in China. Therefore, choosing the right courses has become a practical problem for many students. One of the primary concerns of students is to earn enough credits in a particular time limit. In this paper, we study the problem of credits maximization and evaluate the accuracy and efficiency of three different algorithms: greedy method, search method, and dynamic programming. Finally, we propose a practical implementation of the course selection algorithm that would provide new ideas for students to choose courses and suggestions for schools to set up online curriculums and course selection models.
Real Time Object Tracking Using Color and Shape Features

Object tracking is one of the most advanced area of research in artificial intelligence and computer science. This Paper describes the design and implementation of object tracking system based on color and shape of the object. The proposed system consists of three stages: preprocessing stage, image processing stage, and tracking stage. in preprocessing stage, we transform the camera output from RBG to HSV. In image processing stage we subtract the object from the background and remove noise plops by erosion. For identifying the shape of the object, the number of points of its contour is counted and connected to a boundary line. After that we used the output to control robot moving to track the object. The proposed algorithm written in python 3.7.3 and run in a PC. The results show that the proposed system is simple, fast and can track the objects in Realtime.
Tackle the Smart Contract Vulnerabilities

A smart contract is a computerized transaction protocol that executes the terms of a contract in a trusted decentralized manner, meaning the execution of this contract is no more dependent on one single party or a node and the execution is immutable. This key core idea also leads to many vulnerabilities, which in turn results in huge financial loss to the consumers. The main objective of this article is to cover the various common vulnerabilities stages from development to implementation and proposed some of the best-recommended practices to tackle the above mentioned potential security risks.
A Survey on Blockchain Technology with Uuse-cases in Governance

Blockchain is a distributed ledger technology which provides a way to afford more transparency to our everyday systems by exposing all the interactions of the system as a bunch of transactions. Efficient and Effective Governance – this is the need of the day for any country or organization. This not only solves the current problems but also sets a clear path towards progress and development. Unfortunately, this might not be the case always. Progressive countries are always on the hunt to find technological solutions to their everyday problems. This paper discusses how Blockchain Technology might be able to tackle these problems and push the systems in the right direction. We discuss four problem use cases – intergovernmental trade, land registration, voting and Dubai smart city, and present how blockchain is working towards the problems surrounding them. Finally we look at some problems from the Indian perspective that could be addressed using the Distributed Ledger technology.
Solving Approximate Nearest Neighbor by Hash Aggregate and Clustering in High Dimensional Euclidean Space

Abstract Approximate Nearest Neighbor search has attracted great attention in the field of information search. The method based on Locality-Sensitive Hashing has good performance with probability guarantee, which needs m hash functions for collision detection. In the hash table corresponding to each hash function, collision counts are performed respectively during querying, which will result in additional collision counts and longer response time. To avoid the shortcoming, we aggregate the m hash values as the representation of the original data object. Then the aggregate values of all data objects in the dataset are clustered. In the process of querying, it is needed to find the class of the aggregate value of the query for searching. Experiments show the effectiveness of our method.
Research Challenges in Off-Line Ancient Handwriting Recognition – A Deep Learning Approach

Huge volumes of antient handwriting documents which has a wealth of information and knowledge, in forms of books, manuscripts or scanned images have been existing in various libraries, offices, museums and different archives all over the world. However, in order to further usage of these raw materials, they need to be transformed into a digital form that would allow the users to read, index, brows and query or even to understand further easily. It is a challenge to maintain and understand the paper-based documents. In this paper, some deep learning based approaches are presented to deal with this sort of documents. This paper also proposed a framework of such a system and future directions for the upcoming researchers in the field.
Development of GUI Using Visual Studio to Monitor Sensor Signals

This paper deals with how to develop a ‘graphical user interface’, using Visual Studio and how to interface the Arduino with the CPU using GUI to track the real-time data from Arduino. The real-time data will be appearing as a graph, and an Excel sheet can also be generated to store the required data values from Arduino.
Institutionalized Algorithmic Enforcement – The Pros and Cons of the EU Approach to UGC Platform Liability

Algorithmic copyright enforcement – the use of automated filtering tools to detect infringing content before it appears on the internet – has a deep impact on the freedom of users to upload and share information. Instead of presuming that user-generated content ("UGC") does not amount to infringement unless copyright owners take action and provide proof, the default position of automated filtering systems is that every upload is suspicious and that copyright owners are entitled to ex ante control over the sharing of information online. If platform providers voluntarily introduce algorithmic enforcement measures, this may be seen as a private decision following from the freedom of companies to run their business as they wish. If, however, copyright legislation institutionalizes algorithmic enforcement and imposes a legal obligation on platform providers to employ automated filtering tools, the law itself transforms copyright into a censorship and filtering instrument. Nonetheless, the new EU Directive on Copyright in the Digital Single Market (“DSM Directive”) follows this path and requires the employment of automated filtering tools to ensure that unauthorized protected content does not populate UGC platforms. The new EU rules on UGC licensing and screening will inevitably lead to the adoption of algorithmic enforcement measures in practice. Without automated content control, UGC platforms will be unable to escape liability for infringing user uploads. To provide a complete picture, however, it is important to also shed light on counterbalances which may distinguish this new, institutionalized form of algorithmic enforcement from known content filtering tools that have evolved as voluntary measures in the private sector. The DSM Directive underlines the necessity to safeguard user freedoms that support transformative, creative remixes and mash-ups of pre-existing content. This feature of the new legislation may offer important incentives to develop algorithmic tools that go beyond the mere identification of unauthorized takings from protected works. It has the potential to encourage content assessment mechanisms that factor the degree of transformative effort and user creativity into the equation. As a result, more balanced content filtering tools may emerge in the EU. Against this background, the analysis shows that the new EU legislation not only escalates the use of algorithmic enforcement measures that already commenced in the private sector years ago. If rightly implemented, it may also add an important nuance to existing content identification tools and alleviate the problems arising from reliance on automated filtering mechanisms.
Recommender System to Enhancing Efficacy of E-Learning System

Combination of identified learning styles with cognitive parameters including motivational and knowledge ability factors give a unique combination of learning style preferences. Personalization provides according to the preferences. This is an automatic detection system in which such combination changes dynamically and hence adaptively provided accordingly. Combining Neural Networks and Fuzzy Logic generate the adaptive abilities of Neural Networks with the reasoning capabilities of Fuzzy Logic. It is better known as a Neuro-Fuzzy system. Adaptive Neuro Fuzzy Inference System (ANFIS) is used in numerous practical applications in the areas of control, prediction, and inference. It is commonly used for nonlinear function modelling and chaotic time series prediction. Implementation of ANFIS model for adaptation and personalization can be applied. In this chapter you get the model which has also shown the efficiency of an ANFIS with hybrid system. The outcomes of ANFIS can be analysed by standard error measurement techniques which shows that optimization is mandatory for forecasting in a better way.
Text layout methods overlaid on images considering readability and balance

In the visual design of magazines, advertisements, videos, and so forth, it is necessary to insert linefeeds and pagefeeds on text at appropriate positions due to the limited area that can be displayed. Both comfortable reading of the text and the balance of the entire design should be considered. In this paper, we propose a text layout method that estimates the line/page feed positions for the text element overlaid on images. We formulate a combinatorial optimization problem that considers readability and balance by providing constraints on both the number of characters in a line and the number of lines. Our proposed method has several advantages: (1) it is independent of annotation data; (2) the constraints on the number of characters and lines can be changed; and (3) it works in real time. The proposed method reduces the production time and dynamic design generation in visual design. Moreover, it can be applied not only to visual design, but also to a wide range of domains such as web pages and the text windows of chat applications.