Reviewerly/exHarmony huggingface dataset:
Are AI-Assisted Works Copyrightable?

The article discusses important questions posed to Copyright law by the use of AI in creative activity. The first part offers a brief comparative analysis of the requirements laid down under international and national copyright law for a creative material to qualify as “work”. The second part tries to find out whether an AI-assisted material satisfies these requirements and is copyrightable. The third part focuses on the question of who owns the copyright in the cases of AI-assisted materials.
Efficient Max Pooling Architecture with Zero-Padding for Convolutional Neural Networks

Zero-padding is a widely used technique in convolutional and pooling layers of convolutional neural networks (CNNs) to maintain the consistent dimensions of input and output feature maps or to adjust the input size to fit the kernel size. This helps to prevent information loss at the boundaries of an image; however, it can also increase processing time and storage overhead. In this paper, an efficient hardware design for max pooling hardware with automated zero-padding is proposed for use in CNNs. For processing an image of n × n, the proposed max pooling design with zero-padding can reduce the processing time required from roughly 2n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> clock cycles to n <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> clock cycles, while avoiding increased memory usage and computational time.
Exploring the Role of Chat Bots in the Modern World: Architecture, Types, and Areas of Application

Digitalization is an inevitable part of everyday life and is increasingly entering every aspect of human life. The development of new technologies and especially their application in the creation of chatbots, artificial intelligence and the Internet of Things has led to the introduction of the term smart home. This paper provides a literature review of one of these technologies, chat bots.
An Exploration Algorithm Suitable for Indoor Mapping Based on RRT-Exploration

Many methods have been proposed for exploration algorithms for mobile robots. In this work, we focused on an exploration algorithm called RRT-Exploration. The advantage of this method is that it is less computational cost than other methods and is more efficient in large scale environments. However, unexplored areas remain on the exploration result map. Also, the robot moves in a roundabout way. To solve this problem, we incorporated an algorithm that makes it easier to find small unexplored area and an algorithm that preferentially explores for nearby unexplored area. Experiments in the simulation space demonstrate the effectiveness of the proposed system.
Development of a model for predicting timing of back-channel in a real-time spoken dialog system

In this study, we propose a model for predicting the timing of the back-channel that boasts high accuracy even with a few features, to realize a departmental dialog system that enables rhythmical and natural back-channel in real time.The proposed model is constructed using a neural network, LSTM, suitable for handling long-term time-series data. Since previous studies have shown that "pitch", "power", and "elapsed time from the start of speech" are effective in timing prediction, these features are used in this study as well. We manually labeled the speech in an existing dialog corpus after clarifying the definition of back-channels.As an evaluation experiment, we calculated the model’s prediction accuracy for each threshold. At a threshold value of 0.50, the recall was 90.5 percent, the precision was 96.2 percent, and the F value was 0.933. The optimal threshold for the proposed model was found to be 0.50.These results indicate that the higher-than-expected accuracy was partly due to a problem in the preparation method of the end-of-speech back-channels in the training data.
Cancellable Biometric Authentication System by Image Style Transfer

Recently, we have more and more opportunities to identify ourselves through information devices. In this paper, we propose a cancellable biometric authentication system, where biometrics is translated by using image style transfer. First, users provide their face image and choose a key image. Users must remember their own key image, since providing a different key image will be judged as an imposter at authentication phase. Moreover, if some template is compromised, the user can cancel his template by changing his key image. Experiments show that the proposed system can identify individuals with high accuracy.
An Implementation of Self-Organizing Multi-Stage Selection Procedure

This research article presents an implementation of a self-organizing multi-stage selection procedure, a method that has its roots in the work of Alan Turing, Alexey Ivakhnenko, and Angel Marchev. The multi-stage selection procedure is applied to simultaneous structural and parametric identification of complex systems. The current implementation employs linear regression, a non-linear cross function, and is adaptable for various regression and classification problems. A MATLAB software implementation is demonstrated, and the method’s advantages are compared with other approaches. A computational example is provided to illustrate the procedure’s effectiveness.
Designating a Leader Browser Tab to Perform Refreshing of Access Token in OAuth 2.0

Seamless user experience is crucial to any web application that is used daily by users. Especially for authenticated users, that access protected resources. Today the OAuth 2.0 protocol is widely used for authorizing authenticated users to access a protected resource for a period of time. Prolonging that period of time without the need for the user to enter their credentials increases the positive user experience. For many businesses it’s required that a user can use multiple tabs in the browser while authenticated and rarely be asked to enter their credentials after the initial authentication.This paper proposes a unique approach for prolonging the period of time for authenticated users to access protected resources while using multiple browser tabs.
Audio-visual mutual learning for Weakly Supervised Violence Detection

With the development of smart surveillance systems, multimodal violence video data provides enough data source for smart surveillance systems. At present, intelligent violence detection faces two main challenges. One of the important challenges to apply weakly supervised learning for violence detection is to accurately identify normal segments of abnormal videos, and another challenge is how to make full use of visual and audio features of videos. Therefore, we explored methods for fusing visual and audio information together with temporal information. We propose a novel neural network containing three parts: 1) co-attention module fusing audio and video features with LSTM to extract temporal information, 2) mutual learning branches with threshold to generate high-quality pseudo labels, and 3) a simple and effective post-processing method to ensure continuity of forecast results. Our experiment results show that the proposed model exceeds the existing state-of-art models on the XD-Violence dataset by 1.92% in AP.
Artificial intelligence based trust management for vehicular networks using blockchain

Vehicular network is a network that vehicles and roadside units (RSUs) communicate with each other, in order to improve traffic safety and efficiency, with a blockchain-based trust management system inside calculating the trust value for each vehicle and saving them in a public blockchain to ensure the credibility of messages transferred inside. However, in the original vehicular network model, the vehicle predicts the events only depending on the distance between the message sender and the reported event, and the only usage of the trust value is to show the credibility of a vehicle. This paper is planning to implement a machine-learning algorithm to support the model. In addition, since the trust value can be considered as an aggregation of the history of a vehicle, this paper is also planning to take the trust value into consideration. In this paper, the modification will be made during the event prediction process. A machine-learning algorithm with several features will be applied here to improve the accuracy and the trust value will be considered as a positively related factor. This will increase the cost of malicious behaviors and against spoofing attacks and badmouthing attacks. What’s more, A double-chain structure will be introduced to fight against the compromised RSUs attack.
A three-circle triangle model of Bearing-Only Passive Locating of the UAVs

This paper proposes a three-circle triangle positioning adjustment model for bearing-only passive locating in circular formation of UAV. Considering the realistic factor of transmitter position bias, the model stipulates the use of three signal transmitters and analyzes the relative position relationship of the transmitter and the target UAV to determine the trajectory circles. Using the triangle formed by the intersection points of the circles, the model utilizes the inner center to determine the most probable position of the target UAV. The UAV formation adjustment scheme is then designed based on the principle of maximizing accuracy and corrected priority. The model presented in in this paper has high positioning accuracy, wide applicability and strong generalization.
A Finding Place Method Considering distance between Robots

Autonomous mobile robots make paths when moving, but if the made path overlaps with the paths of other robots, there is a possibility of collision, so it is necessary to remake non-collision paths. At that time, it wants to pass a path that is a certain distance away from the other robot. Therefore, it is necessary to find a place where the robots can pass the other robot and there is no collision, and to avoid collision. When the robot moves in the direction of the other robot at a constant speed, both collided at the midpoint. Therefore, in this paper, we propose a method to find a place with the necessary distance to pass at the midpoint. Next, we evaluate the performance of the proposed method by observing the relative distances and paths with the other robot in a dynamic environment on the Gazebo simulator.
Effects of Data Hiding on Vision Transformer Classification for Encryption-then-Compression Images

A privacy-preserving image classification method using a vision transformer (ViT) model was proposed so that the encryption processes for a model and test images never affects the classification accuracy. In this paper, we assume that arbitrary information is further embedded to encrypted images and evaluate the effects of the data hiding process on the classification accuracy. In view of compression of encrypted images, an encryption-then-compression (EtC) system is employed for encryption in our method. Simulation results show that the classification accuracy could be preserved without severe degradation on both the classification accuracy and compression performance.
Vehicle Speed Estimation &amp; Traffic Light Classification using Deep-Learning Approaches

The videos taken from a dashboard camera mounted in a car could be used to determine if the corresponding driver is law-abiding or not, as a result, an insurance company can determine to increase the insurance rate if the driver is found to be aggressive or lower the rate if otherwise. In this paper we perform vehicle speed estimation and traffic light classification as the foundation for judging a driver’s behavior. Optical flow is used, together with histogram equalization to enhance image quality, and the frame’s GPS information is also incorporated to estimate the speed of a vehicle. Furthermore, traffic light classification, i.e., distinguishing whether the light is red, yellow or green, is achieved using YOLOv5 and YOLOv6.
Age group identification using gaze-guided feature extraction

When an observer identifies the age group of a subject in an image, the observer’s gaze focuses on a region containing an informative feature. Our aim is to improve the accuracy of age group identification by extracting features from the regions where an observer’s gaze converges. Existing studies have analysed observer gaze on facial images, but not on images containing the subject’s whole body. Here, we analysed which regions of the whole-body image an observer’s gaze focused on while the observer performed this task. The experimental results revealed that an observer’s gaze is drawn to the head of the subject regardless of the subject’s age group. They also revealed that the gaze-guided feature extraction in deep learning and machine learning improved the accuracy of age group identification.
Experiencing Music Technology - Experiencing Music Technology (Fourth Edition) by David Brian Williams and Peter Richard Webster, Oxford University Press, 2022. Pbk, 576pp. £82.45. ISBN: 978-0190635794.

Experiencing Music Technology - Experiencing Music Technology (Fourth Edition) by David Brian Williamsand Peter Richard Webster, Oxford University Press, 2022. Pbk, 576pp. £82.45. ISBN: 978-0190635794. - Volume 40 Issue 3
Blockchain Based Trusted Distributed Machine Learning for Credit Scoring

Artificial intelligence and blockchain are the rising stars in the current technology market. The cooperation of machine learning and blockchain technologies is twofold: either machine learning is used to support or improve blockchain or machine learning unlocks new possibilities on the top of blockchain. The paper discusses the possibilities to utilize blockchain for trusted distributed machine learning applied for the problem of credit scoring. A federated learning architecture for credit scoring model is suggested based on smart contracts.
Development of an Interactive Speech Bot Using Dialogflow

The COVID-19 pandemic has introduced the new normal of introducing contactless transactions and interactions. This made voice assistive systems useful to business process requiring company to client communications. A generative-based interactive speech artificial intelligence for Asia Pacific College's (APC) Admissions Office is discussed in this paper. It collects data from user's voice input and generates a response that answers questions and inquiries concerning the services of the office. Software tests revealed an average accuracy in getting the right answers to questions of 88%. The system was able to provide the correct response in the presence of background noise at 91.5%. The average response time of the system was 2.36 seconds.
Cryptography and Fermat’s Last Theorem

A non-trivial one-time pad (OTP) key generation scheme is proposed, allowing exchange or pre-negotiation of only very short messages. This scheme will eventually enable the wider use of one-time pad in modern cybersecurity systems.
YOLO Deep-Learning Based Driver Behaviors Detection and Effective Gaze Estimation by Head Poses for Driver Monitor System

This work develops a non-contact driver behavior monitoring system based on intelligent visual sensing to improve the driving safety. By the deep learning technology with YOLO, a car-specification near-infrared (NIR) camera is installed to detect the driver’s behaviors and gaze directions. The YOLO-based head pose inference method is developed, and the driver’s gaze directions are predicted with the simplified calibration. In experiments, the input size of YOLOv4-tiny based model is set to 416x416 pixels. After functional tests, the proposed method performs average precision (AP) to be 86.58% for detecting eleven classes including driver’s objects and behaviors. Besides, the proposed gaze estimation technology by driver’s head poses performs average detection accuracy up to 83% to estimate twelve driver’s gaze directions.
Sound Image Generation in 3D Virtual Space Considering Relationship Between Bounding Box by Object Detection in 2D Image and Sound Pressure Level

A system for automatic generation of sound image from a moving or still image has been proposed in the previous studies, and a design principle and issues were discussed. The system detects objects in a moving or still image, a direction and distance for each object are estimated, and it generates a sound image based on the direction and distance. In this paper, a method for sound pressure level controlling is proposed to generate a sound image of an object based on coordinates, width and height of the bounding box obtained by object detection. The proposed method is discussed and demonstrated.
Deblurred Image Quality Improvement in Learning-based Deblurring Datasets Utilizing Frame Interpolation

The performance of machine learning in deblurring depends on the quality of the dataset used for training. In this paper, we propose a method to make the blurred images used for training closer to the real ones. Experimental results show that the model can achieve high performance in restoring blurred images if training is performed on blurred images created from videos of 480 fps or higher frame rates.
Edge Detection Algorithm for Color Noise-Laden Images in A Composite Color Space

Aiming at the non-uniform and unintuitive RGB color space, the HSI color space which is more in line with human color visual characteristics is used for edge detection. This paper proposes an improved multi-scale multi-structure mathematical morphology for edge detection of color noisy images. Firstly, the R, G, and B components of the color-containing noise image are extracted, and the R, G, and B components are separately subjected to morphological denoising preprocessing using structural elements of different scales. Then, the multi-scale top-hat transform is used to denoise. The image is contrast-enhanced; finally, the processed R, G, and B components are converted into H, S, and I components, and the H, S, and I components are edge-detected using the improved morphology, and the weight values are defined according to the image information entropy. Weighted fusion is performed to obtain the final color edge image. The experimental data shows that the edge detected by the method is more in line with human color visual characteristics, the denoising effect is good, and the contour and detail information of the original color image can be more retained.
An Expert Collaborative Filtering Algorithm Integrating Multimodal Information

To solve the problem of data sparsity in recommendation system, this paper proposes an expert collaborative filtering algorithm integrating multimodal information. Firstly, the algorithm uses a multimodal feature extractor to capture the text features formed by expert review text and item text and the visual features extracted from item images. The item's multimodal information is effectively integrated using bilinear pooling method, and the multimodal representation of the item is obtained. Then, the multimodal representation is input into the neural network model together with user vectors and item vectors, and the user preference is represented by the Euclidean distance between user vectors and item vectors. At the same time, to avoid the phenomenon of "default positive comments" that may exist among users and the ambiguity of text sentiment, the paper extracts the sentiment score of text and user facial features and calculates their similarity to correct the rating matrix, making the rating matrix closer to the user's real preferences. The effectiveness of the model is verified on the MovieLens and Amazon Book multimodal datasets.
Potential Field-Based Path Planning with Interactive Speed Optimization for Autonomous Vehicles

Path planning is critical for autonomous vehicles (AVs) to determine the optimal route while considering constraints and objectives. The potential field (PF) approach has become prevalent in path planning due to its simple structure and computational efficiency. However, current PF methods used in AVs focus solely on the path generation of the ego vehicle while assuming that the surrounding obstacle vehicles drive at a preset behavior without the PF-based path planner, which ignores the fact that the ego vehicle's PF could also impact the path generation of the obstacle vehicles. To tackle this problem, we propose a PF-based path planning approach where local paths are shared among ego and obstacle vehicles via vehicle-to-vehicle (V2V) communication. Then by integrating this shared local path into an objective function, a new optimization function called interactive speed optimization (ISO) is designed to allow driving safety and comfort for both ego and obstacle vehicles. The proposed method is evaluated using MATLAB/Simulink in the urgent merging scenarios by comparing it with conventional methods. The simulation results indicate that the proposed method can mitigate the impact of other AVs' PFs by slowing down in advance, effectively reducing the oscillations for both ego and obstacle AVs.
Application research of English vocabulary Game system for art students based on Big data technology

At present, there are few researches on college English vocabulary games for art students, and the mining of English word test results is not deep enough. The uncertainty problem can be effectively solved by taking advantage of quantum probability and parallel computing. The neural network has the intelligent characteristics of self-learning and self-adaptation, which is suitable for nonlinear problem processing. In this paper, a QSOFM neural network model is constructed by combining the quantum neuron model with the self-organizing feature mapping network model, and it is applied to the calculation of grade risk to obtain the classification results of assessment samples. Finally, the effectiveness and reliability of the proposed method are verified by experiments, and the comparison experiment with the self-organizing feature mapping network shows that the QSOFM neural network has certain advantages in the evaluation of sample classification accuracy and operation time.
Intelligent Thresholding Method for Surface Mount Devices Based on Q-Learning

Threshold selection for thresholding segmentation methods is a critical issue in vision-based detection of surface mount devices (SMDs). The thresholds in actual industrial equipments rely on manual adjustment in SMDs applications, which depends on the experience and ability of an operator. We propose a novel method based Q-Learning to automatically learn the threshold using a segmentation metric instead of manual adjustment, making the threshold adjustment process more intelligent. This framework regards 256 thresholds as state sets. A threshold list with a reasonable step is selected as actions set simulating an operator's threshold adjustment process. The metric difference of SMDs before and after threshold adjustment forms the reward/punishment of Q-Learning framework. Finally, a new strategy that determines the search direction according to previous reward is proposed to improve search efficiency. We conducted experiments on different chips and lights, and the results show that the proposed Q-Learning method performed better on search efficiency and segmentation than the traditional methods.
Design and Implementation of Web Crawler System Based on Python

Web crawlers are programs to download resources from internet, one of basic componments of search engines. Using web crawler can collect data in batch es, which greatly improves the speed an d efficiency of data analysis. Python language is simple and efficient, known as "glue language", with rich third-party library, which is very convenient to handle web pages and links. This paper introduces the types of web crawler, working principle and Python crawler technology, and constructs a crawler system for crawling web images.