Reviewerly/exHarmony huggingface dataset:
Asymmetry Recognition of Mammogram Images Based on Convolutional Neural Network

Mammography is the first process to acquire breast imaging data. Approved radiologists screen the mammogram images for detecting suspicious lesions, e.g., mass, calcification, asymmetry. However, image screening is time-consuming and is a subjective decision-making process. In this paper, we propose an asymmetry recognition scheme with various convolutional neural network (CNN) architectures. The proposed scheme not only determines whether a pair of mammogram images is asymmetry, but it indicates that which image asymmetry occurs in.
Computed tomography dataset analysis for stereotaxic neurosurgery navigation

Experience of design methods of image-guided navigation for stereotaxis neurosurgery is described. The above approach allows obtaining high accuracy to identify spatial the position of the reference stereotactic landmarks. Visualization of the contours of the third ventricle gives the ability to calculate the center of intracerebral stereotactic coordinates system. Use of the proposed techniques in the clinic allows significantly (up to 20%) to improve the effectiveness of stereotactic operations in parkinsonism. Methods for computer tomography image processing are proposed and analyzed. Further, increase accuracy stereotactic targeting functional surgery is associated with the development of machine analysis algorithms tomographic data with a view maximum recognition automation intracerebral landmarks taking into account their individual variability and allow to improve systems for surgical planning by using virtual and full-scale simulation modeling of the main stages a surgical intervention.
Towards Explaining the ReLU Feed-Forward Network

Recently, machine learning algorithms have increasing become popular tools for economic and financial forecasting. While there are several machine learning algo- rithms for doing so, a powerful and efficient algorithm for forecasting purposes is the multi-layer, multi-node neural network with rectified linear unit (ReLU) activa- tion function â€“ deep neural network (DNN). Studies have demonstrated the empir- ical applications of DNN but have devoted less research to investigate its statistical properties which is mainly due to its severe nonlinearity and heavy parametrization. By borrowing tools from a non-parametric regression framework, sieve estimator, we first show that there exists such a sieve estimator for a DNN. We next establish three asymptotic properties of the ReLU network: consistency, sieve-based convergence rate, and asymptotic normality, and then validate our theoretical results using Monte Carlo analysis.
Tokenizing and Managing the Copyrights of Digital Content on Blockchains

This paper proposes a method for managing digital content on blockchains in which content copyrights are distributed in the form of tokens. Our implementation of the proposed method resulted in fast retrieval of the history on the blockchain without relying on an external database.
Asymmetric Single Magnitude Four Error Correcting Codes

Limited magnitude asymmetric error model is well suited for flash memory. In this paper, we consider the construction of asymmetric codes correcting single error over Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2kr</sub> which is based on so called B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> r) set. In fact, we reduce the construction of a maximal size B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sup> r) set for k ≥ 3 to the construction of a maximal size B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k-3</sup> r) set. Finally, we give an explicit formula of a maximal size B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](4r) set and some lower bounds of a maximal size B <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> [4](2r) set. By computer searching up to q ≤ 106, we conjecture that those lower bounds are tight.
Knowledge Graph Representation Learning Based Drug Informatics

Drug Discovery and assessment is increasingly a complex albeit an acute process. Current approaches to the study of drugs involving clinical evaluation and post-marketing surveillance is very time consuming and costly. Though the machine-learning based systems have been proposed in the literature, but those systems are task-dependent. This work presents a rather data-intensive approach to the study of drugs using the large-scale DrugBank dataset, a Linked Open Dataset to further the process of drug discovery. We used Representation Learning approach on the large-scale drug dataset which not only provides automated framework for feature learning for machine learning task but also helps in prediction of new facts in the knowledge base through Link-Prediction. The proposed representation learning system is promising to be used general approach to solve different problems and we experiment on Drug-Drug Interaction prediction and Drug-Target Prediction with comparable with respect to other work in the former task.
Parking Space Detection using Complemented-ULBP Background Subtraction

Local Binary Pattern (LBP), a popular and effective textural pattern, is considered as a standard local descriptor in most of the vehicle detection systems. It is also used for various pattern recognition systems in different areas. In this paper, Complemented-Uniform Local Binary Pattern (Complemented-LBP) which finds the whole value of the image before ULBP and simple ULBP are used for the space occupancy detection of a parking lot monitoring system. Uniform-LBP (ULBP) applies on the result binary complemented image and also on the background image. After that Mean Square Error (MSE) value is calculated between background and input images. The parking space's status occupied or not is determined by the MSE threshold value.
Code Readability Metric for PHP

PHP is one of the most popular languages that millions of websites and web applications are developed every month using PHP. In this paper, software metric related to code readability is developed and suitable set of attributes are identified for the given system to establish a software quality observatory for PHP language. Correlation criterion was used to empirically validate the developed metric and to find out the nature of relationship between the metric and other well-known metric. Moreover, the other validation, such as validation by using PHP Mess Detector (PHPMD) tool was also evaluated in the system. According to these analysis results, the proposed metric is applicable and usable to the field of software engineering metrics by providing a practical summary of the metric validation criteria found in the academic literature.
Aesthetic Style Transfer through Text-to-image Synthesis and Image-to-image Translation

This paper presents a style transfer method combining generative adversarial networks and style transfer networks. In the previous style transfer methods, transformation from one image to another has been proposed. On the other hand, our method enables style transfer from a text to an image. This will be helpful when there are no images that represent the desired style. Experimental results show the effectiveness of our method.
Preservation Authentication and Authorization on Blockchain

Block chain technology is a relatively new technology that has recently emerged in the technology world. Attracting the attention of many organizations, institutions, and governments. This technique has many characteristics of decentralization, reliability, trust, and stability. Blockchain is a peer to peer network. However, as a result of increasing the volume of data on a daily basis in all areas and fields, the problems of how to prove that the person is the correct sender. Authentication based on traditional methods like biometric fingerprinting authentication and authorization approaches suffers from some problems in terms of big data size and time required to verifying from them, which are the most important things that cannot be ignored and waiting to be solving. In this paper, the simulation system is introduced on a blockchain technique which provides some possible solutions to proof the user authentication and authorization in a blockchain network. This is done by using the hash function (SHA 256 algorithm) and how to authenticate the user after recording the basic data by applying the hash function and signed them to the data regardless of how large and obtained the digest of data. Accordingly, the license will be demonstrated at the user's request. In this system, the results in terms of time are measured for four blocks, where the average time obtained through the execution of the same scenario for both the authentication and authorization phases are 5 ms and 1.75 ms respectively.
TIRR: A Code Reviewer Recommendation Algorithm with Topic Model and Reviewer Influence

Code review is an important way to improve software quality and ensure project security. Pull Request (PR), as an important method of collaborative code modification in GitHub open source software community platform, is very important to find a suitable code reviewer to improve code modification efficiency for Pull Request submitted by code modifiers. In order to solve this problem, we have proposed a review recommendation algorithm based on Pull Request topic model and reviewer's influence. This algorithm has not only extracted the topic information of PR through Latent Dirichlet Allocation (LDA) method, but also analyzed the professional knowledge influence of reviewers through influence network. Whatâ€™s more, it has combined the topic information of reviewers to find the appropriate PR reviewers. The experimental results based on GitHub show that the algorithm is more efficient, which can effectively reduce the time of code review and improve the recommendation accuracy.
A Scaffolding Empathic Methodology in the Robotics Teacher Formation Using Log Book and the BNCC References

The adoption of active teaching methodologies in schools aims to provide students with a greater role in all classroom activities, and seeks the development of important skills for the 21st century. In this scenario, Robotics Teacher Formation is a challenge to this interdisciplinary and very attractive theme. This paper presents an improvement of Formative Assessment methodology applied to Robotics Teacher Education in public schools with scaffolding strategies. Our proposal focus on a greater care in the use of cooperative Log-Book records and empathic approach, as well as in the digital planning of interdisciplinary activities using the skills proposed in the Curricular Common National Base BNCC).
Privacy Aware Recommendation: Reinforcement Learning Based User Profile Perturbation

User profile release in recommendation systems can apply the user profile perturbation technique to protect user privacy, in which each user sends a perturbed user profile such as the a list of clicked items to receive a recommendation service from a server. The perturbation policy such as the privacy budget determines the recommendation quality and the privacy level, while its optimization usually depends on the known attack model, which is rarely known by the users. In this paper, we propose a reinforcement learning based user profile perturbation scheme that applies differential privacy to protect user privacy for recommendation systems. According to reinforcement learning, the privacy budget to perturb the released user profile depends on the features of the actual user profiles and the released user profiles, and the estimated user privacy level. This scheme enables a user to optimize his or her perturbation policy in terms of both the user privacy level and the received recommendation quality without being aware of the attack model. We evaluate the computational complexity of this scheme and analyze a case study, a privacy aware movie recommendation system. Simulation results show that this scheme improves user privacy protection for a given level of recommendation quality compared with a benchmark profile perturbation scheme.
A Fuzzy Logic Controller for Electric Powered Wheelchair based on Lagrange Model

Recently, great progresses in controlling Electric Powered Wheelchair (EPW) were made for people whom walking are difficult or impossible, due to illness or disability. This paper deals with the nonlinear control of an electric wheelchair. In this study, the unicycle robot is powered by two Permanent Magnet Synchronous Motors (PMSM) that are characterized by high efficiency, high torque, low noise and robustness. This research aims is to present the dynamic modelling of both EPW motors with Lagrangien method in the first step, and Fuzzy Logic Control (FLC) with the generating trajectory in the second. Indeed, fuzzy logic has been introduced to approach human reasoning with the help of an adequate representation of knowledge. Also, the success of the fuzzy control largely originates in its capacity to translate a control strategy of a skilled operator into a set of linguistic rules. The numerical simulations show the effectiveness of the proposed controller.
Performance Analysis on the Basis of Learning Rate

In this research paper, a detailed analysis is done on effect of learning rate on machine. Subtle changes in learning rate can bring great impact and can simplify complex research methodologies to a great extent. Our main focus will be on trade-off between learning rate and convergence rate toward an optimal solution. In range of this research, it will be made sure that local optimal solution will not be skipped and the most optimal global solution will be achieved. Keeping all other factors like number of iterations, dataset and algorithm static, we would be verifying our results using experimental analysis done with the help of graphical and statistical observations.
Self-Adaptive Layer: An Application of Function Approximation Theory to Enhance Convergence Efficiency in Neural Networks

Neural networks provide a general architecture to model complex nonlinear systems, but the source data are often mixed with a lot of noise and interference information. One way to offer a smoother alternative for addressing this issue in training is to increase the neural or layer size. In this paper, a new self-adaptive layer is developed to overcome the problems of neural networks so as to achieve faster convergence and avoid local minimum. We incorporate function approximation theory into the layer element arrangement, so that the training process and the network approximation properties can be investigated via linear algebra, where the precision of adaptation can be controlled by the order of polynomials being used. Experimental results show that our proposed layer leads to significantly faster performance in convergence. As a result, this new layer greatly enhances the training accuracy. Moreover, the design and implementation can be easily deployed in most current systems.
Robust Tracking for Small Target Groups With Dynamic Outlines

Low altitude small target group has the special motion characteristics of orderly as a whole and flexibly as individuals. The performances of traditional target tracking algorithms (such as centroid tracking, geometric centre tracking, etc.) decrease obviously when the group outline changes. Hence in this paper, a robust tracking algorithm for target group with dynamic outline is studied. Aiming at the two key issues of group division and equivalent tracking point estimation, a robust group division algorithm, which can maintain the stability of group division results under any reference point selection, is firstly introduced. Then, on this basis, a new optimal tracking point estimation method is proposed by introducing the group structure into the likelihood function. The simulation results show that this proposed method can effectively improve the robustness of low altitude small target group tracking in the scene of group merging and group splitting.
Human Activity Recognition Based on Loss-Net Fusion Domain Convolutional Neural Networks

Human activity recognition is now a hot issue in artificial intelligence research. The purpose of activity recognition is to analyze behaviors in an unknown video or image sequence by computer. Unlike previous static recognition, the challenge of behavior recognition is how to capture motions between still frames. For reports of Convolutional Neural Network(CNN) architecture in the past, we used a method of implicitly capturing motion information between adjacent frames to improve the CNN architecture, taking the original video frames as input and predicting the action class without explicit optical action class calculation directly. Our architecture was trained and tested using videos from the UCF-101 human behavior database and achieved very ideal results.
MLFV: Network-Aware Orchestration for Placing Chains of Virtualized Machine Learning Functions

Machine Learning as a Service (MLaaS) platforms enables access to Machine Learning (ML) processing with scalable infrastructure, from anywhere, and at any time, but requires sending large amounts of data to the cloud. ML on the edge is emerging as an option to reduce latency and bandwidth usage, maintaining data privacy. However, the existing edge approaches are not aware of the current network state for orchestrating the tasks. Network- aware orchestration services are supported by the Network Function Virtualization (NFV) architecture, making it a promising approach to manage and place ML tasks. In this paper, we propose Machine Learning Function Virtualization (MLFV), a fully network-aware framework that explores the NFV environment to virtualize ML tasks as virtual network functions. We describe a novel model for placing chains of ML tasks, considering constraints on CPU, memory, the existence of ML libraries, and the network overload, aiming to reduce the overall execution time of a chain. The results showed that MLFV outperformed existing cloud and edge approaches, particularly when network connections present instabilities. MLFV was able to identify the irregularities, allocating the ML tasks on hosts with normal connections, and thus, reducing the time for classifying single and multiple concurrent requests.
Named Entity Recognition Method for Fault Knowledge based on Deep Learning

Aiming at the problem that fault text is difficult to be directly parsed and utilized, a fault knowledge extraction method is proposed based on deep learning method. Firstly, the characteristics of fault knowledge are analyzed, and a fault knowledge extraction model is established based on multi-layer neural network. Finally, the presented model is discussed comprehensively from the extraction accuracy, recall rate and F1 value, which proves the feasibility of the method. The unstructured text data is used to provide reference for fault diagnosis and prediction.
Performance Evaluation of Edge Computing Assisted Adaptive Streaming Algorithms

Multi-Access Edge Computing (MEC) adaptive streaming presents an opportunity to jointly optimize the quality of experience in cellular networks by moving the adaptation intelligence from the client to the edge cloud. In this paper, we investigate the performance of MEC-assisted algorithms and compare their performance with the client based adaptation logic. We conduct extensive experiments and quantify benefits and drawbacks of edge computing-assisted adaptation algorithms. The results from our experiments reveal that MEC-assisted algorithms outperforms the purely client-based heuristics in most of the video quality metrics. However, the results also show that the MEC-assisted algorithms are not able to protect the playback buffer from drying up under different network settings.
Autonomous Exploration for Automated Valet Parking Based on Road Structure

Automated valet parking technology allows the vehicle to automatically drive into the parking lot and park itself without a human in the vehicle, relieving humans from parking completely. However, the existing automated valet parking system relies on infrastructural intelligence or pre-acquisition of parking lot maps. Given the disadvantages of these researches, this paper proposes a general method for automatic valet parking system based on autonomous exploration. This method depends on no prior knowledge of the parking lot. Our method extracts road structure from perception result using the Voronoi diagram. A multi-factor exploration strategy we proposed is used to generate exploration candidates for autonomous exploration from the road structure. Also, a motion planning method based on the lateral priority of the road guides the vehicle to the candidates while obeys the rules as far as possible. The autonomous exploration will continue until it finds free parking slots or all the spaces in the parking lot are explored. Related experiments have verified the effectiveness of the method presented in this paper.
Non-monotonous behavior of the number variance, Mandel factor, invariant uncertainty product and purity for the quantum damped harmonic oscillator

We consider the relaxation of the quantum harmonic oscillator in the framework of the standard master equation. Analyzing the known solutions, we have found some new interesting effects. Namely, we show that the evolution of some parameters, such as the energy fluctuations, the Mandel factor, the invariant uncertainty product and quantum purity, can exhibit non-monotonous behavior with big deviations from the initial and final values. Also, we show that highly excited initial Fock states maintain their non-classical nature longer than low excited ones. During the evolution, the quantum purity of these states stays at almost constant low level during a long period, after the fast decrease initially and before the fast return to the final value at the end.
Detection of Fake and Cloned Profiles in Online Social Networks

Online Social Network (OSN) is a platform to build social relations with others who share similar personal or career interests or real-life connections. OSNs allow users to share their views, likes, comments, opinions, photos, videos etc with other users in the network. As the popularity of OSNs are increasing day by day, the threats related to them are also increasing. Fake and Cloned profiles have become a severe security issue in social networks. Profile Cloning is an act of identity theft of existing user's profile credentials to create duplicate profiles. This cloned profile is misused for defaming legitimate profile owner. They even launch phishing attacks, harvest sensitive user information, stalking or spread viruses to other users. Fake profiles are created to carry out malicious activities and online social crimes. So, a detection method has been proposed which can effectively detect cloned and fake profiles in Online Social Networks.