Reviewerly/exHarmony huggingface dataset:
Design Mode of Stage Performing Arts Based on 3D Modeling and Moving Edge Computing Technology

In recent years, the virtual stage background has become increasingly popular in the stage design of large-scale art evenings as well as in dance performances. It deftly blends the form of dance beauty with the content of dance performance, creating an atmosphere that not only sets the scene but also expands the stage space and enhances the artistic expression of dance. This paper focuses on the design of stage performance art using 3D modeling and MEC (mobile edge computing) technology. The data fusion method of multi-Kinect joints is researched and realized using 3DMAX to complete the modeling and virtual design of a 3D virtual stage scene. The performance scheme is created by analyzing music features with a computer, designing lighting actions, and matching music and lighting actions. Finally, it shows how DDLS (distributed deep learning shunting) configuration affects convergence performance as well as how model setting affects the diversion scheme.
Detecting needs of people in a crisis using Transformer-based question answering techniques

The usage of social media in everyday life has become a need for keeping up with the news, as well as for making queries and requesting assistance. Following a significant event like a disaster, online media correspondence is a significant part of the emergency reaction. Regardless of the sort of disaster, whether a storm, an earthquake, or a man-made disaster like a riot or a terrorist attack. Social media platforms such as Facebook, Twitter, and others have proven to be efficient communication and coordination tools for disaster victims and other groups. This gathered data can be utilized to react to individuals with the necessary requirements relying upon the kind of help or request mentioned. In this paper, we suggest an approach for extracting the needs of affected people during a crisis and providing the appropriate response using question answering techniques based on natural language processing techniques and neural networks. The approach was tested using Twitter data from various types of emergencies with different question answering techniques such as BERT (Bidirectional Encoder Representations from Transformers), DistilBERT, T5 (Text-to-Text Transfer Transformer Model). The results showed that the BERT transformer the proposed approach has a precision of 0.81, a recall of 0.76, and an f-score of 0.78 for providing appropriate guidelines.
A multi-objective optimization approach for palm vein feature selection based on the discrete PSO

palm vein biometrics has received huge interest from the biometric research community because of its uncountable advantages. However, the feature extracted from the palm vein images may contain noisy, irrelevant, and redundant features which imply the decrease in performance of the palm vein biometrics system and also lead to the dimension problem. To overcome these problems, we present in this paper a multi-objective optimization approach for palm vein feature selection based on the discrete Particle Swarm Optimization (PSO). The proposed multi-objective function takes into consideration both the accuracy of the biometrics system and the size of the selected feature vector aiming at maximizing the accuracy of the system and minimizing the size of the feature vector. The proposed approach has been tested on the CASIA palm vein database and compared to the full feature-based verification system and the discrete PSO-based feature selection with the traditional objective function. The experimental results show that the proposed multi-objective function outperforms the state-of-the-art.
Quantized Reservoir Computing for Spectrum Sensing With Knowledge Distillation

Quantization has been widely used to compress machine learning models for deployments on the field-programmable gate array (FPGA). However, quantization often degrades the accuracy of a model. In this work, we introduce a quantization approach to reduce the computation/storage resource consumption of a model without losing much accuracy. Spectrum sensing is a technique to identify the idle/busy bandwidths in cognitive radio. The spectrum occupancy of each bandwidth maintains a temporal correlation with previous and future time slots. A recurrent neural network (RNN) is very suitable for spectrum sensing. Reservoir computing (RC) is a computation framework derived from the theory of RNNs. It is a better choice than RNN for spectrum sensing on FPGA because it is easier to train and requires fewer computation resources. We apply our quantization approach to the RC to reduce the resource consumption on FPGA. A knowledge distillation (KD) called teacher-student mutual learning (TSML) is proposed for the quantized RC to minimize quantization errors. The TSML resolves the mismatched capacity issue of conventional KD and enables KD on small data sets. On the spectrum-sensing data set, the quantized RC trained with the TSML achieves comparable accuracy and reduces the resource utilization of digital signal processing (DSP) blocks, flip-flop (FF), and lookup table (LUT) by 53%, 40%, and 35%, respectively, compared to the RNN. The inference speed of the quantized RC is 2.4 times faster. The TSML improves the accuracy of the quantized RC by 2.39%, which is better than the conventional KD.
Novel Metaheuristic: Spy Algorithm

We proposed a population-based metaheuristic called the spy algorithm for solving optimization problems and evaluated its performance. The design of our spy algorithm ensures the benefit of exploration and exploitation as well as cooperative and non-cooperative searches in each iteration. We compared the spy algorithm with genetic algorithm, improved harmony search, and particle swarm optimization on a set of non-convex functions that focus on accuracy, the ability of detecting many global optimum points, and computation time. From statistical analysis results, the spy algorithm outperformed the other algorithms. The spy algorithm had the best accuracy and detected more global optimum points within less computation time, indicating that our spy algorithm is more robust and faster then these other algorithms.
Helstrom Bound for Squeezed Coherent States in Binary Communication

In quantum information processing, using a receiver device to differentiate between two non-orthogonal states leads to a quantum error probability. The minimum possible error is known as the Helstrom bound. In this work, we study the conditions for state discrimination using an alphabet of squeezed coherent states and compare them with conditions using the Glauber-Sudarshan, i.e., standard, coherent states.
Mobile Libraries and Digital Inclusion: A Study from Aotearoa New Zealand

ABSTRACTABSTRACTMobile libraries serve communities in a range of ways throughout Aotearoa New Zealand and internationally but their contribution to supporting digital inclusion in non-urban areas has not been explored in depth. This paper presents data from an exploratory research project focused on how mobile libraries in Aotearoa New Zealand extend digital services to those who cannot access public library buildings services conveniently. The findings show that the mobile librarians provide a range of digital inclusion services, predominantly access to technologies and skills development and, by so doing, contribute positively to digital inclusion in non-urban areas of Aotearoa New Zealand.KEYWORDS: Public librariesmobile librariesdigital inclusiondigital skillsdigital access Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsRachel BellRachel Bell is a recent graduate of the Master of Information Studies at Victoria University of Wellington in Aotearoa New Zealand, specializing in archives and records management. She works as an archivist, and has a particular interest in rural and agricultural issues and history.Anne GouldingAnne Goulding is Professor of Library and Information Management in the School of Information Management, Victoria University of Wellington in Aotearoa New Zealand. Her main research interest is the management of library and information services, with a specific focus on public libraries and on how GLAMR organisations demonstrate their impact and value. She also has a research focus on community-based approaches to digital inclusion.
Digraph Filter Design Based on Directed Laplacian Matrix and Least Squares Method

In this paper, the digraph filter design based on directed Laplacian matrix (DLM) and least squares method is presented. First, the eigen-decomposition of DLM is used to define the digraph Fourier transform (DGFT). Then, the spectral properties of DGFT are studied and applied to specify the ideal spectral response of the digraph filter. Next, the coefficients of polynomial digraph filter are determined by the least squares method which minimizes the integral absolute squared errors between ideal response and actual spectral response of filter. The matrix inversion can be used to compute the optimal solution. Finally, the numerical example of noise reduction application is demonstrated to show the effectiveness of the designed digraph filter.
A reversible information hiding algorithm in AMBTC domain based on human vision system

Abstract Compression is a common processing method of digital image, and the compression of carrier image usually affects the performance of hidden information. Therefore, it is necessary to research on the preprocessing method to improve some performance of hidden information in the compressed domain. Aiming at the problem of tampering attack on compressed domain image and the security of secret information preprocessing in the previous information hiding algorithms, we propose a reversible information hiding algorithm in AMBTC domain based on human vision system. Firstly, RGB color image is converted to YCbCr image, AMBTC coding is used to generate high and low average value sequence in brightness component. Secondly, the secret information is compressed by run length coding and scrambled. Thirdly, the histogram shift technique is used to embed the information into the mean sequence and generate the reserved information at the same time. Fourth, the color component is divided into normal block and absolute smooth fast, the specific position of the bit plane of the normal block is deleted, and the reserved information is embedded. The experimental results show that, compared with other algorithms, the proposed algorithm has better invisibility and robustness, and after a certain attack, the extracted secret information still has high recognizability.
Trail: An Architecture for Compact UTXO-Based Blockchain and Smart Contract

In Bitcoin and Ethereum, nodes require a large storage capacity to maintain all of the blockchain data such as transactions. As of September 2021, the storage size of the Bitcoin blockchain has expanded to 355 GB, and it has increased by approximately 50 GB every year over the last five years. This storage requirement is a major hurdle to becoming a block proposer or validator. We propose an architecture called Trail that allows nodes to hold all blocks in a small storage and to generate and validate blocks and transactions. A node in Trail holds all blocks without transactions, UTXOs or account balances. The block size is approximately 8 kB, which is 100 times smaller than that of Bitcoin. On the other hand, a client who issues transactions needs to hold proof of its assets. Thus, compared to traditional blockchains, clients must store additional data. We show that proper data archiving can keep the account device storage size small. Then, we propose a method of executing smart contracts in Trail using a threshold signature. Trail allows more users to be block proposers and validators and improves the decentralization and security of the blockchain.
Comparative Study on Job Scheduling Using Priority Rule and Machine Learning

Cloud computing is a potential technique for running resource-intensive applications on a wide scale. Implementation of a suitable scheduling algorithm is critical in order to properly use cloud resources. Shortest Job First (SJF) and Longest Job First (LJF) are two well-known corporate schedulers that are now used to manage Cloud tasks. Although such algorithms are basic and straightforward to develop, they are limited in their ability to deal with the dynamic nature of the Cloud. In our research, we have demonstrated a comparison in our investigations between the priority algorithm performance matrices and the machine learning algorithm. In cloudsim and Google Colab, we finished our experiment. CPU time, turnaround time, wall clock time, waiting time, and execution start time are all included in this research. For time and space sharing mode, the cloudlet is assigned to the CPU. VM is allocated in space-sharing mode all the time. We’ve achieved better for SJF and a decent machine learning algorithm outcome as well.
A Graph-Based Approach to Boundary Estimation With Mobile Sensors

We consider the problem of adaptive sampling for boundary estimation, where the goal is to identify the two-dimensional spatial extent of a phenomenon of interest. Motivated by applications in estimating the spread of wildfires with a mobile sensor, we present a novel graph-based algorithm that is efficient in both the number of samples taken and the distance traveled. The key idea behind our approach is that by sampling locations close to known cut edges (edges whose vertices lie on opposite sides of the boundary), we can reliably find additional cut edges. Our approach repeats this process of using the newly discovered cut edges to find additional cut edges, eventually identifying all vertices lying adjacent to the boundary. We show that our method achieves both a sample complexity and a distance traveled that are within a constant factor of the optimal values. Moreover, the computational complexity of determining sample locations and paths is <inline-formula><tex-math notation="LaTeX">$O(1)$</tex-math></inline-formula>, making its deployment on mobile sensors highly realistic. Experimental results on both synthetic and historical wildfire data show that our proposed algorithm outperforms existing methods in terms of sample complexity, distance traveled, and computation time.
A Person Re-Identification Network Based upon Channel Attention and Self-Attention

With the development of deep learning, person ReIdentification (Re-ID) technology has made great achievements. However, there are still some problems, such as pedestrian occlusion, different imaging conditions of posture changes, etc, which can lead to large changes in appearance, and it is difficult to obtain sufficient distinguishable features. Therefore, this paper proposes a network based on the fusion of channel attention mechanism and self-attention mechanism. The network learns more discriminative global features and local features from the spatial dimension and channel dimension. ResNet-50 is utilized as the backbone network. The channel attention module can capture the dependence of channel dimensions, obtain the weight of the importance of feature channels, and improve useful local feature to suppress useless feature. The self-attention module captures the context information from the spatial dimension to obtain the weight of each feature, and further obtains the global feature. Experiments on two datasets reveal the proposed model improves the accuracy compared with the state-of-the-art methods.
Dynamic face authentication systems: Deep learning verification for camera close-Up and head rotation paradigms

The popularity of face-authentication systems has also generated interest in the study of malicious authentication attempts, such as face spoofing attacks. In this study we investigate two dynamic face-authentication challenges: the camera close-up, and head-rotation paradigms. For each paradigm we developed an ML-based face-authentication system that performs the tasks of liveness detection and face verification. In order to generate structured data-representations from the videos collected in the wild, we designed feature representations that extract three-dimensional and spatial characteristics of a face, while also capturing the particular liveness cues of the requested challenge-based movements. Furthermore, a set of Neural-Network models that employ Convolutional Neural Networks and Siamese Neural Network architectures were proposed. To train and test our models we collected a dataset of 177 live videos recorded by 41 different subjects and a set of 243 attack attempts in uncontrolled scenarios. The resulting NN models yield good performance against multiple types of media-based attacks (printed-attacks, screen-attacks, 2D-masks, videos acquired from public social media, deep fakes). The camera close-up system presented an overall liveness detection accuracy of 97.7% and a face verification accuracy of 97.6%. On the other hand, the evaluation of the head-rotation system resulted in a liveness detection accuracy of 92.4% and face verification accuracy of 98.1%. Face authentication methods based on dynamic user-challenges constitute a scalable approach that does not require specialized hardware to increase the security of face authentication systems in realistic usage scenarios. The proposed methods not only make it harder for attackers to generate spoofing attacks, but they also constitute a practical complement to static biometric authentication systems.
Analysis of Wrist Hand Motion for Monitoring of Basic Welder Training using Wearable Sensors

Abstract During the training of a welder, either novice or professional, most activities are focused on the acquisition of wrist-hand motion skills. In the basic welding training, trainees initially required hand-on practices to acquire the skills of wrist hand motion to maintain the distance of electrode tip to a base metal such that the welding arc was continuously flaming. Secondly, trainees were practices of manipulating hand motion to follow seam tracking for joining two metals within defined speed &amp; torch height. These practices were then continued for various types of weld joints. The result of acquiring this skill level was then assessed by inspecting the visual appearance of the weldment. In this study, an effort was undertaken to monitor and assess the progress of acquiring wrist-hand motion skills using wearable sensors: accelerometer, gyroscope, and magnetometer. Then, the record of those sensors was plotted as a time series signal compared with those performed by the training instructor. Their achievement of skills grade was analyzed using the Supervised Vector Machine (SVM) Learning Method. The result has indicated that this proposed method can assist in assessing welder trainees’ efforts to improve their skills.
Research on Software Defect Prediction and Analysis Based on Machine Learning

Abstract The defects of machine learning prediction technology can be more comprehensive and automatic learning model to find the defects in software has become the main method of defect prediction, selection and study of algorithm is the key to improve the accuracy and efficiency of machine learning. Comparing different machine learning defect prediction methods reveals that the algorithms have different advantages in different evaluation indicators, the use of these advantages and combining the stacking ensemble learning method in machine learning is put forward different prediction algorithm of prediction results. As software metrics and again the prediction model of software defect prediction combined machine learning algorithm is based on the experiment with the model of Eclipse, the data sets show the effectiveness of the model.
Custom distribution for sampling-based motion planning

Sampling-based motion planning algorithms are widely used in robotics because they are very effective in high-dimensional spaces. However, the success rate and quality of the solutions are determined by an adequate selection of their parameters such as the distance between states, the local planner, and the sampling distribution. For robots with large configuration spaces or dynamic restrictions, selecting these parameters is a challenging task. This paper proposes a method for improving the performance to a set of the most popular sampling-based algorithms, the Rapidly-exploring Random Trees (RRTs) by adjusting the sampling method. The idea is to replace the uniform probability density function (U-PDF) with a custom distribution (C-PDF) learned from previously successful queries in similar tasks. With a few samples, our method builds a custom distribution that allows the RRT to grow to promising states that will lead to a solution. We tested our method in several autonomous driving tasks such as parking maneuvers, obstacle clearance and under narrow passages scenarios. The results show that the proposed method outperforms the original RRT and several improved versions in terms of success rate, tree density and computation time. In addition, the proposed method requires a relatively small set of examples, unlike current deep learning techniques that require a vast amount of examples.
Age Gender and Sentiment Analysis to Select Relevant Advertisements for a User Using CNN

With the expanding growth in areas of machine perception, there is an encouraging potential of providing personalized content recommendations to users. This project targets extracting information from images and videos of people. This comprises basic human emotions through the interpretation of facial expressions and personal information like age and gender. These prototypical facial expressions are angry, disgust, fear, happiness, sadness, surprise, and neutral. In our case study, the real-time dataset of various age groups is considered, and using a face detection algorithm, facial features along with personal details of an individual are determined. This project focuses on developing a model that can help detect different elements like age, gender, and moods from real-time inputs using videos, webcam, or images. Later we would train the model based on a convolutional neural network followed by predicting the accuracy of the FER-2013 dataset for emotion and gender classification. After feature selection from images or videos we will recommend different brand products using emotions to drive connection, audience notice, share and buy. This model can be implemented in various shopping malls, mobile, multiplex, and various public places. Also, it can be used for various market researches, competitor analysis, and customer responses.
Optimized Hardware Vision System for Vehicle Detection based on FPGA and Combining Machine Learning and PSO

Vision-based hardware driver assistance systems are the most important systems in the world because of their low cost and ability to provide information on driving environments. Improving safety and reducing accidents are the two main objectives of these systems. For this, in this paper a new Vision-based Hardware Advanced Driver Assistance System (VH-ADAS) based machine learning incorporating the hybridization of Support Vector Machine (SVM)-Histogram of Oriented Gradient (HOG) classifier and Particle Swarm Optimization (PSO) technique is proposed for traffic scenes from both video and captured images. First, the proposed system uses a feature extraction method based on the HOG. Then, the Particle Swarm Optimization technique is used for selection and so to optimize the features. The SVM method is applied to obtain fast detection and high accuracy. Finally, a hardware synthesizable architecture of the complete system was developed and then co-simulation validity was succeeded using the Matlab-Vivado System Generator (VSG) and a Field Programmable Gate Array (FPGA). The results show that the proposed new system supports real-time detection in both images and video. Also, they show that the proposed vehicle detection method is competitive in terms of parallel run time with only 1.483 ms and in terms of accuracy rate with only 97.84%.