Reviewerly/exHarmony huggingface dataset:
Method for training a compact discrete neural network descriptor

Local patch descriptors are used in many computer vision tasks. Over the past decades, many methods of descriptor extraction were proposed. In recent years researchers have started to train descriptors via Convolutional Neural Networks (CNNs) which have shown their advantages in many other computer vision fields. However, the resulting descriptors are usually represented as a long real-valued vector. That leads to high computational complexity and memory usage in real applications with a large amount of data being processed. To deal with that problem binary local descriptors were designed, but they still have a large size. In this paper, we propose a method of discrete low-dimensional local descriptor creation with lightweight CNN. We show that for small-sized descriptors the quality drops significantly during simple binarization compared to floating-point ones. The experiments on HPatches dataset [1] demonstrate that our descretization approach dramatically outperforms the naive binarization for the compact descriptors.
Research on the Evaluation Algorithm of English Viewing, Listening, and Speaking Teaching Effect Based on DA-BP Neural Network

Improving the teaching of viewing, listening, and speaking courses is the key to produce better workers for society. Improving teaching quality is therefore a key teaching objective. One way to improve the quality of English instruction is to conduct a scientific evaluation of videos, audios, and spoken language instructions. This paper investigates the neural network model and the evaluation of teaching effect in depth in order to solve the complex nonlinear problem of evaluating the impact of English viewing, listening, and speaking. The DA-BP neural network model is proposed as a result of the existing research’s shortcomings. The BP neural network is basically an algorithm which is multilayered feed-forward network that is trained actually to the error back propagation algorithm. The accuracy of the BP neural network’s evaluation is influenced by its parameter selection in this paper, and the dragonfly algorithm (DA) is used to optimize the BP model’s initial connection weight and threshold parameters. According to the research results, DA-BP improves the accuracy of evaluating the viewing, listening, and speaking classroom teaching quality in college English and offers a new approach for evaluating the audio-visual classroom teaching effect in college English.
A double feature fusion SGM algorithm

Stereo Vision is the key to 3D reconstruction, autonomous navigation, and non-contact ranging etc. semi-global stereo matching (SGM) is one of the stereo matching algorithms to obtain dense depth map, which is with both relatively high accuracy and efficiency. This paper describes a double feature fusion SGM algorithm, which is a fusion of the mean absolute difference (MAD) and Census transform as intensity to make cost calculation. A combination of MAD for the sensitivity to the texture and Census transform for the advantages of adaptive illumination change. This method is especially suitable for parallel computing processing platform and can be used in practical scenarios which have a higher demand of real-time performance.
Counting Vehicle by Axes with High-Precision in Brazilian Roads with Deep Learning Methods

Traffic surveys provide insight into vehicle flow generation and distribution, which is essential for forecasting the future needs for the circulation of goods. The National Department of Transport Infrastructure (DNIT) maintains the National Traffic Counting Plan (PNCT) in Brazil. The main objective of the PNCT is to assess the current traffic flow on federal highways to define public policies. However, DNIT still carries out quantitative classificatory surveys that are not automated or with invasive equipment. This work applied YOLOv3 for object detection and Deep SORT for multiple object tracking to perform the classificatory counting fully automated and non-invasive. Based on the results obtained in two hours of real videos used in traffic counting surveys, we obtained a positive predictive value above 89.85% and recall above 87.32% in the vehicle classification count in four scenarios using the DNIT table with 14 classes. So YOLOv3 and Deep SORT allow us to perform traffic analysis to automate classificatory traffic surveys, improving DNIT’s agility and generating savings for transport organizations.
Self-organizing gates and circuits

Abstract This Chapter introduces terminal- gates, namely gates that do not distinguish between the traditional input or traditional output terminals. Memory is the physical property that allows the realization of these self-organizing gates. It also discusses the mathematical properties of these gates when assembled into circuits, and their possible hardware realization. It gives some examples of these circuits for the solution of factorization.
User-Defined Keyword Spotting Utilizing Speech Synthesis for Low-Resource Wearable Devices

In this paper, we propose a novel keyword spotting (KWS) system for wearable devices that allows users to add user-defined keywords in quick and easy way. Adding keywords in KWS requires developing a new model to support them, where the model development takes a lot of work and time. To overcome this, we propose an approach that automates the entire development phase of a KWS model for low-resource devices. The proposed system is characterized by automating the data collection step and training step using synthetic speech data. Our implementation and experiments show that the proposed system can automatically develop a user-defined KWS model within a minute.
Image Garden

Abstract Image Garden is an AI-based toolbox for curating collections and designing smart exhibitions that was developed by ART+COM Studios [ART+COM Studios. 2021. ART+COM. https://artcom.de/en/ ] in the research project QURATOR [QURATOR Bündnis. 2021. Qurator. https://qurator.ai ]. This paper describes the application scenarios underlying the development of Image Garden. It also presents the technologies integrated in its processing pipeline, and sketches Show Cases that were implemented to demonstrate and evaluate functionality and usability.
Design of Combinational Logic Circuits using Simulated Annealing

Conventional methods to design combinational logic circuits (CLCs) is time consuming and needs expert knowledge. Evolutionary computing techniques have proved to be a competitive field for the evolution of CLCs. Simulated annealing is a metaheuristic which helps in finding a global optimum for a given function. The proposed work aims to design CLCs using simulated annealing (SA). Various circuits proposed in the literature are realized and experiments reveal that a maximum of 33.33&#x0025; of resources are saved and 2.0x speed enhancement is achieved over the circuits reported in literature. The proposed work acquires the design requirements from the designer/user to yield scripts for FPGA implementation.
The effect of automation trust tendency, system reliability and feedback on users’ phishing detection

As a new intrusion method in the security field, phishing poses an enormous threat to network security and personal privacy. Thus, improving the level of network security and preventing phishing are a matter of great concern to both the state and researchers. A 2 (automation trust tendency) *2 (system reliability level) *2 (feedback) between-subjects design was adopted to study the impact of individual characteristics and system features on phishing detection. Three hundred ninety-eight participants completed a phishing email task to identify whether 40 emails were legitimate or fraudulent. The results showed that systems with feedback and high reliability improve users' performance in email identification. Users with a high tendency towards automation trust have a higher risk of phishing. However, feedback from the system helps calibrate a high automation trust tendency. These research results can promote an understanding of phishing prevention mechanisms and provide support for the design of email defence systems.
An example of SMS service development at the University of Montenegro Information System

The paper presents an example of upgrade to the user account management system at the University of Montenegro (UoM) Information System. This upgrade involves the integration of SMS service that provides automatic sending of credentials to users via SMS messages. Usage statistics of the developed service from its launch until today is presented and recommendations for the protection of the service from unauthorized use are given. Also, description of possibilities for integration of SMS services into other UoM Information System&#x0027;s services is given.
A proposal of eye glance input interface using smartphone built‐in camera

Abstract The purpose of this study is to develop a line‐of‐sight input interface using the eye‐glance input method, which uses a glance eye movement and method of estimating the eye‐control movement by analyzing the image of the area near the eye‐ball. The motion was discriminated by taking an image using the built‐in camera of a smartphone to rapidly look at the four corners of the smartphone and calculate the moving distance of the eyeball, which was divided into horizontal and vertical components using the optical flow function in Open CV library. In the experiment, we obtained data by asking the subjects to look at the reciprocate of quick gaze movements at each of the four corners and the center of the screen. As a result of data analysis, characteristics of waveforms were obtained for the movements looking at the four corners, and we could determine which of the four corners the users were looking at. Furthermore, by distinguishing between camera shake and line‐of‐sight movement using the variance value, the discrimination rate was 90% or more even when held in the hand. It was shown that the Eye Glance movement can be measured with a smartphone as well as a PC.
A Novel Three-Stage Short-Term Photovoltaic Prediction Approach Based on Neighborhood Component Analysis and ANN Optimized with PSO (NCA-PSO-ANN)

Parameters of solar radiation are unstable, which affect the prediction accuracy of photovoltaic (PV) power. Many hybrid models have been applied recently to improve the prediction accuracy of the models. In this chapter, a novel three-stage hybrid model of Neighborhood Component Analysis (NCA), and Artificial Neuronal Network (ANN) optimized with Particle Swarm Optimization (PSO) is introduced. First, the NCA technique is applied as a feature extraction technique to determine the relevant features that have substantial influence on photovoltaic power. The study now applies the chosen feature components as inputs into the optimized ANN with PSO to create the PV prediction model. The prediction strength of the proposed NCA-PSO-ANN model is compared with other variant hybrid models such as Principal Component Analysis (PCA), PSO and ANN (PCA-PSO-ANN), PSO-ANN, and PCA-ANN. The proposed NCA-PSO-ANN model constitutes a very reliable computational tool as it performed better in the selected performance indexes than the compared models.
Data driven based fuzzy logic inference algorithm for quality of experience modeling for video streaming in LTE network: Case of Addis Ababa City LTE network

Nowadays, video streaming has become one of the most dominant services due to increasing interest in watching online television programs and video-on-demand activities. Providing this service requires high quality, high speed and high-capacity network infrastructures. In this work, we propose a data-driven-based fuzzy logic inference algorithm for quality of experience (QoE) modeling of video streaming services in Addis Ababa City LTE network. The proposed fuzzy logic inference system model is used to measure the user perception from the key quality of indicator (KQI) parameters. The model is essential to replace conventional subjective measurement techniques that are costly and inefficient. In addition, the proposed fuzzy logic inference system model is helpful for business decision making, network planning and optimization activities. To analyze the performance of the proposed model, we consider main KQI parameters such as video streaming start success rate, video streaming start delay, video streaming play disconnection rate, video streaming stall frequency and video streaming stalled time rate. We perform numerical simulation to analyze the proposed model and to validate the impacts of main KQI parameters on the quality of experience in LTE video streaming services. The simulation results show that video streaming stall frequency and video streaming start delay rate play a major impact on user perception by 33% and 25%, respectively. Besides, validation of the results shows that the proposed fuzzy logic inference system model is accurate, consistent and linear compared to currently existing models.
Purification in entanglement distribution with deep quantum neural network

Entanglement distribution is important in quantum communication. Since there is no information with value in this process, purification is a good choice to solve channel noise. In this paper, we simulate the purification circuit under true environment on Cirq, which is a noisy intermediate-scale quantum (NISQ) platform. Besides, we apply quantum neural network (QNN) to the state after purification. We find that combining purification and quantum neural network has good robustness towards quantum noise. After general purification, quantum neural network can improve fidelity significantly without consuming extra states. It also helps to obtain the advantage of entangled states with higher dimension under amplitude damping noise. Thus, the combination can bring further benefits to purification in entanglement distribution.
Detection Method of Compound Chemical Material Hole Based on Deep Learning Image

As the degree of industrialization is getting higher and higher, the requirements for the accuracy of materials are getting higher and higher. Among them, the detection of round holes in materials is particularly important. Round hole inspection is one of the important methods for material forming and precision inspection. This paper studies the round hole detection method of composite chemical materials and aims at using deep learning image technology to provide an efficient and convenient detection method for round hole detection. This paper proposes a fast circular hole detection algorithm based on contour extraction and validity judgment. The algorithm can extract the circular holes on the material sufficiently and quickly, and the image recognition technology based on deep learning can effectively improve the accuracy and efficiency of circular hole detection. Whether it is in circular contour extraction, validity analysis, or parameter calculation, the improved algorithm has shown good results. The experimental results show that the improved algorithm is significantly better than the canny algorithm for the extraction of circular hole contours. In terms of effectiveness, the calculation time of the improved algorithm is lower than the original algorithm in different data sets, and the highest is 1.14 seconds lower than the original algorithm. The error in parameter calculation is also the lowest, and the error of a set of data is as low as 0.1%.
Grateful Med: Direct access to MEDLINE for health professionals with personal computers

Donald A.B. Lindberg M.D. arrived as Director, U.S. National Library of Medicine (NLM) in late 1984 with the intention of implementing a physician-friendly interface to MEDLINE, a prime example of his interest in making NLM information services more directly useful in medical care. By early 1986, NLM's Grateful Med, an inexpensive PC search interface to MEDLINE useful for health professionals, had joined the group of end-user systems for searching MEDLINE that emerged in the 1980s. This chapter recounts Grateful Med's rapid iterative development and the subsequent campaign to bring it to attention of health professionals. It emphasizes Lindberg's role, the challenges faced by those introducing and using the interface in a pre-Internet world, and some longer-term effects of the effort to expand health professionals' use of MEDLINE during the decade from 1986 to 1996.
Inclusive Convolutional Neural Network Design Enabling Partially Sighted People to Expand Viewing-Experience on Smart Screens

A deep neural network-based picture enhancement technique that enables partially sighted people to expand their viewing-experience on smart large TV screens is proposed. Reflecting insights from our previous studies on preferred picture enhancement features for low vision people, a convolutional neural network architecture that can generate visibility-enhanced images on screen is presented. The neural network which has very large scales of convolutioinal layers is trained to output super-resolved and salient feature-improved images for helping the visually impaired to see more clearly images on screens. Our experiment result proves that synthesized images by the proposed neural network are expected to give more vivid visual experiences when people with low vision are watching screens. To the best of our knowledge, inclusive neural network design in terms of the picture quality is the first approach which can help the visually impaired to see directly any content itself on screen.
Software Defect Data Collection Framework for Github

Software has become part of every sphere of life. This increasing dependence on software has put tremendous pressure on software development teams to deliver software applications as early as possible at the cost of compromised software quality and reliability. Software quality requires extensive testing and validation of software, which is not possible with limited human resources, time and budget, so researchers moved to a new paradigm of software quality assurance i.e., Software Defect Prediction (SDP). SDP aims to build automated Machine Learning (ML) models to aid development teams in prioritizing the key aspects of software testing while maintaining the short software development life cycle. SDP requires huge amount of data to train and test ML models, traditionally PROMISE and NASA defect datasets are most prominently used by researchers, but with changes in programming languages, programming styles and limited size of datasets has made them infeasible for SDP in current scenarios. In this paper, we have developed a software defect dataset collection framework, which mines commit level defect data from GitHub. The efficiency of data mining, accuracy of data and validity of data is verified by SDP models. Results shows that proposed method is feasible as well as efficient to execute even on regular computer systems.
Solution counts and sums of roots of unity

In this work, we count the number of ways to represent an element in a prime finite field as a sum of elements from different multiples of a small subset. More generally, we study the problem of solution counting of certain linear equations over subsets of finite fields. We establish the basic properties about the number of solutions, and connect the number with lower bounds of complex norms of sums of roots of unity.
Adversarially robust subspace learning in the spiked covariance model

Abstract We study the problem of robust subspace learning when there is an adversary who can attack the data to increase the projection error. By deriving the adversarial projection risk when data follows the multivariate Gaussian distribution with the spiked covariance, or so‐called the Spiked Covariance model, we propose to use the empirical risk minimization method to obtain the optimal robust subspace. We then find a non‐asymptotic upper bound of the adversarial excess risk, which implies the empirical risk minimization estimator is close to the optimal robust adversarial subspace. The optimization problem can be solved easily by the projected gradient descent algorithm for the rank‐one spiked covariance model. However, in general, it is computationally intractable to solve the empirical risk minimization problem. Thus, we propose to minimize an upper bound of the empirical risk to find the robust subspace for the general spiked covariance model. Finally, we conduct numerical experiments to show the robustness of our proposed algorithms.
An incentive-driven edge intelligence based on Stackelberg game

Compared with cloud computing, edge computing shows real-time, security and other advantages when dealing with the explosive growth of the Internet of Things. In the process of data processing and mining data value, edge intelligence combines the advantages of edge computing and artificial intelligence to become a research hotspot. Federated learning is one of the most popular machine learning frameworks for edge intelligence. It provides stronger privacy protection for the client through the aggregation of the server-side client-side model. However, the latter often do not participate in the federal learning system due to resource constraints and economic considerations. Therefore, incentive mechanisms are necessary to attract higher quality clients. To this end, we use the Stackelberg game model to model the server and the client, and establish an incentive-driven federated learning algorithm FEDID. Finally, we verified the effectiveness of the incentive mechanism through experiments.
Robustness verification of ReLU networks via quadratic programming

Abstract Neural networks are known to be sensitive to adversarial perturbations. To investigate this undesired behavior we consider the problem of computing the distance to the decision boundary (DtDB) from a given sample for a deep neural net classifier. In this work we present a procedure where we solve a convex quadratic programming (QP) task to obtain a lower bound on the DtDB. This bound is used as a robustness certificate of the classifier around a given sample. We show that our approach provides better or competitive results in comparison with a wide range of existing techniques.