Reviewerly/exHarmony huggingface dataset:
Fast quantum search driven by environmental engineering

Abstract Studies have demonstrated that a joined complete graph is a typical mathematical model that can support a fast quantum search. In this paper, we study the implementation of joined complete graphs in atomic systems and realize a quantum search of runtime <?CDATA $O(\sqrt{N})$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>O</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:msqrt> <mml:mrow> <mml:mi>N</mml:mi> </mml:mrow> </mml:msqrt> <mml:mo stretchy="false">)</mml:mo> </mml:math> based on this implementation with a success probability of 50%. Even though the practical systems inevitably interact with the surrounding environment, we reveal that a successful quantum search can be realized through delicately engineering the environment itself. We consider that our study will bring about a feasible way to realize quantum information processing including quantum algorithms in reality.
Study on MindSpore Deep Learning Framework

Deep learning is the key core technology that has driven artificial intelligence to achieve breakthroughs in recent years, and a deep learning framework is a pivotal tool for realizing machine learning. However, the existing deep learning framework is difficult to meet the requirements of easy development and efficient execution at the same time. In this paper, a new deep learning framework called MindSpore developed by Huawei Technologies Co., Ltd. is introduced. Mindspore's features such as automatic parallelism, automatic differentiation, and end-side cloud collaborative training greatly improve the training efficiency of deep learning models and have a positive effect on model coding and debugging. Based on this, this enables MindSpore to achieve the three major goals of easy development, efficient execution, and full-scene coverage. We shall implement a deep learning model for human pose estimation and tracking based on the MindSpore in the field of computer vision, and experimental results show that MindSpore has the advantages of easy programming, high accuracy as well as high efficiency.
Finite-State Text Processing

Weighted finite-state transducers (WFSTs) are commonly used by engineers and computational linguists for processing and generating speech and text. This book first provides a detailed introduction to
Multidimensional Mining of Massive Text Data

Unstructured text, as one of the most important data forms, plays a crucial role in data-driven decision making in domains ranging from social networking and information retrieval to scientific resear
Applying Convolution Neural Network to Facemask Detection from Varied Images

In this study, Convolution Neural Network (CNN)-based learning method, which is well-used in deep learning is applied to facemask detection from facial images of diverse persons wearing varied facemasks. During the covid-19 era, the wearing of facemasks has become important to curb the spread of the deadly virus and mandatory at times in some places. To ensure rules on wearing of facemasks are adhered to, face detection and recognition systems are being adopted to be able to detect masks worn on the faces of individuals. While good results have been obtained from some of such systems, the data set of images are not as diverse in terms of race and the types of facemasks detected are typically surgical masks. However, in real-world application, the society is made up of people of different races, age groups and wearing different kinds of facemasks. Therefore, there is room for improvement in the training, testing, and optimization of the detection systems by introducing a more diverse set of image inputs. The convolutional layer in this study is made up of numerous convolution kernels which are used to compute different feature maps for representations of the inputs. The model is evaluated by varying the image data and optimizing the hyperparameters for improved performance in facemask detection. Statistical Analysis performed to obtain Accuracy of 76.79% and area under the curve result of 0.8525 that demonstrates the capabilities of CNN.
Wheelchair Conversion &amp; Automation

Human mobility is a fundamental quality of life need. Persons with mobility impairment can range from full mobility to complete paralysis, which leads to reduced quality of life. Wheelchairs, dating back to before the sixteenth century, have been vital to providing a method of movement for limited mobility individuals while helping to reduce the dependency on external assistance. As a result, a large portion of the current population depends on wheelchair accessibility. This paper presents the conversion of manual electric wheelchairs to autonomously driven wheelchairs, developing a testbed for implementing unique and novel path planning and localization indoors. The results depict different implementations for autonomous driving.
BEIRUT: Repository Mining for Defect Prediction

Software Defect Prediction is an important activity used in the Testing Phase of the software development life cycle. Within the research of new defect prediction approaches and the selection of training sets for the classification task, different benchmarks have been analyzed in the literature. They provide several features and defective information over specific software archives. Therefore, they are commonly used in research to evaluate new approaches. However, the current benchmarks contain several limitations, such as lack of project variability, outdated benchmarks, single-version projects, a small number of projects and metrics, unavailable resources, poor usability, and non-extensible tools. Therefore, we introduce a novel tool Bgu rEpository mlning foR bUg predicIion (BEIRUT) for benchmark generation for defect prediction, composed of three main features: Given an open-source repository from GitHub, BEIRUT mines the software repository by (1) selecting the best <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> versions, based on the defective rate of each version, (2) generating training sets and a testing set for defect prediction, composed of a large number of metrics and defective information extracted from each of the selected versions and (3) creating defect prediction models from those extracted metrics. In the end, BEIRUT extracts a diversified catalog of 644 metrics and the defective information from each component of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> versions, automatically selected based on the rate of defects in each version. They were collected from 512 different projects, starting from 2009. The tool is also supplemented with an easy-to-use web interface that provides a configurable selection of projects and metrics and an interface to manage the defect prediction tasks. Moreover, this tool is adapted to be extended with new projects and new extractors, introducing new metrics to the benchmark. The web service tool can be found at rps.ise.bgu.ac.il/beirut.
UDP-based Extremely Low Latency Streaming

Recently, with the rapid development of network and mobile device technologies, the number of services based on video streaming increased drastically. Most streaming applications such as YouTube, Netflix, and Twitch utilize large size buffers (over 1 s) to absorb sudden network dynamics. Before the buffer is depleted, lost packets are retransmitted, thus allowing users to enjoy watching video without recognizing network changes. However, some applications such as remote surgery and game streaming require extremely low end-to-end latency of less than 30 ms. We propose a streaming method suitable for such special streaming applications that need to transfer video with a very short latency. For this purpose, timeliness is paramount and we use UDP rather than TCP. To recover packet losses in a timely manner, we mainly utilize Forward Error Correction (FEC) rather than retransmissions that require time longer than round trip time. The proposed scheme consistently monitors real-time network conditions to decide the appropriate data rate and FEC parameters. Through extensive evaluations, we show that the proposed scheme efficiently achieves both timeliness and high robustness to packet losses.
Compliance of head-mounted personal protective equipment by using YOLOv5 object detector

Workplace safety is a scientific discipline that has been constantly evolving along with industrial development. Nowadays technological progress of tools and materials used in industry, in addition to all the positive impacts, increase the probability of injuries of the operators that use them. Consequently, there are industry standards and recommendations that specify appropriate personal protective equipment (PPE) for certain workplaces. Although every company is able to provide protective equipment for its employees, the major challenge is the compliance and control of their proper use. The aim of this study was to assess the possibility of applying artificial intelligence and deep learning techniques for automated PPE compliance, which could help in taking preventive action with the aim of reducing injuries caused due to non-use or misuse of prescribed PPEs. The obtained results showed that the YOLOv5 algorithm achieved high precision (average 0.857) for the detection of various types of head-mounted personal protective equipment. Accordingly, there is a high potential for future use of such tools in improving workplace safety and PPE compliance. Potential users of the application based on this recognition algorithm would be companies which regulations define the type of PPEs that have to be used at a certain working position.
Customer-satisfaction-aware and deadline-constrained profit maximization problem in cloud computing

As a new and modern service method that can meet the requirements of customers effectively, cloud computing is becoming more and more popular. For cloud service providers, they play an important role in constructing the cloud computing platform, which is convenient for customers to enjoy the services without paying attention to the execution process of the services. For cloud service providers and customers, they focus on profit and satisfaction respectively in service supply-demand relationship, and the pursuit of their objectives both have an influence on each other. On one hand, the configuration of the cloud computing platform is the main solution for cloud service providers to earn profit, which affects customer satisfaction based on specific constraints. On the other hand, customer satisfaction affects the arrival rate of service requests, which will affect the profit in turn. In this paper, we devote ourselves to analyzing a deadline constrained profit maximization problem in a cyclic cascade queuing system, in which the maximum tolerance of customers towards task waiting time is taken into consideration. On this basis, the definition of customer satisfaction is given. And then, a mathematical model is formulated in detail on how customer satisfaction affects the revenues of cloud service providers, and further affects their profits. However, the exact solutions of such model can hardly be calculated due to its complexity. Hence, we propose a heuristic algorithm to find the high-quality solution, then the optimal configuration of cloud computing platform can be obtained to achieve the maximization of the profit for cloud service providers on the basis of promoting customer satisfaction. At last, a series of numerical simulations are conducted to validate the performance of the proposed algorithm. The results show that the proposed algorithm can not only optimize profit and customer satisfaction simultaneously but also maintain them at a high level in the long run of the system.
Sentiments Analysis of Indonesian Tweet About Covid-19 Vaccine Using Support Vector Machine and Fasttext Embedding

Vaccination is one of the efforts to overcome the COVID-19 pandemic in many countries, including Indonesia. Various responses through social media have also come from diverse levels of Indonesian society regarding the COVID-19 vaccine. Sentiment analysis about vaccines on social media is one way to investigates public responses to these efforts. This study proposes to develop a model that can analyze these sentiments by classifying the public responses on Twitter into positive, negative, and neutral sentiment classes. One of the success factors in sentiment analysis is the selection of the appropriate feature extraction. In general, tweets contain a lot of non-standard words. The fastText is a feature extraction that can handle non-standard word representations so that vectors can be presented as other standard words. Therefore, the proposed tweet sentiment analysis model consists of the fastText as a feature extractor and SVM as a classifier. This study utilizes 832 Indonesian tweets for experiment purposes. Furthermore, another feature extractor, WORD2VEC, and another classifier, MNB, are also used for comparison. The experimental results show that the fastText-SVM model has outperformed others in terms of accuracy, i.e., 88.10%.
IAMnet: Presentation of Parallelization Approach for Repetitive Mining on Network Topologies with an Improved Apriori Method

Recently, the discovery of association rules and the consequent mining frequent patterns have attracted the attention of many researchers to discover unknown relationships in big data, especially in networking and distributed environments. In this research, a parallelization-based approach is proposed to improve the performance of the Apriori algorithm in repetitive mining patterns on network topologies. The proposed approach includes two main features: (1) combining centrality criteria of the node and the Apriori algorithm to identify repetitive patterns and (2) using the mapping/reduction method to create parallel processing and achieve optimal values in the shortest time. This approach also pursues three main objectives: reducing the temporal and spatial complexity of the Apriori algorithm, improving the association rules mining process and identifying repetitive patterns, and comparing the proposed approach’s performance on different network topologies to determine the advantages and disadvantages of each topology. Comparing our proposed method and the basic Apriori algorithm, it is concluded that our approach provides acceptable efficiency in terms of evaluation criteria such as energy consumption, network lifetime, and runtime compared to other methods. Experimental results also show that when using our proposed method compared to the basic Apriori algorithm, network life is increased by 7.1%, the runtime is reduced by 43.2%, and the energy consumption is saved by about 41.2%.
A Two-Stage Congestion-Aware Routing Method for Automated Guided Vehicles in Warehouses

With multi-AGV (automated guided vehicle) systems gradually employed in modern warehouses, the research on how to coordinate AGVs in an efficient way has attracted more scholars than ever. As the size of AGVs grows over time, the possibility of traffic congestion is increasing rapidly, especially in a limited space. Therefore, the congestion-aware routing method is one of the most vital and fundamental topics in multi-AGV control research. In this paper, a two-stage congestion-minimizing routing method is presented. The proposed method not only plans paths that avoid congested areas, but also changes paths in real time to cope with the volatile traffic situations. An experiment is carried out to test the effectiveness and efficiency of the proposed method, and the results are highly promising in terms of alleviating traffic congestion.
Underwater Image Saliency Detection Via Attention-based Mechanism

Abstract Due to complex lighting circumstances and other factors, visual saliency recognition of underwater images is not as good as visual saliency detection models that function well on land when applied to underwater environments. The dearth of large-scale annotated underwater picture datasets has further hindered the development of visual saliency detection in underwater images. we propose an attention-based approach for underwater image saliency detection to address this issue. The backbone network is pre-trained using both land and underwater image datasets, which effectively solves the problem of fewer datasets. After that, a residual refinement module incorporating a channel-space attention mechanism is merged, the model as a whole is trained end-to-end using underwater image datasets, and the model accuracy is experimentally tested. The experimental results in three underwater image datasets show that our method is better than several other methods in the mainstream evaluation indicators of the saliency detection.
An Adaptive Learning Image Denoising Algorithm Based on Eigenvalue Extraction and the GAN Model

This paper proposes a self-adjusting generative confrontation network image denoising algorithm. The algorithm combines noise reduction and the adaptive learning GAN model. First, the algorithm uses image features to preprocess the image and extract the effective information of the image. Then, the edge signal is classified according to the threshold value to suppress the problem of "excessive strangulation," and then the edge signal of the image is extracted to enhance the effective signal in the high-frequency signal. Finally, the algorithm uses an adaptive learning GAN model to further train the image. Each iteration of the generator network is composed of three stages. And then, we get the best value. Through experiments, it can be seen from the data that the article algorithm is compared with the traditional algorithm and the literature algorithm. Under the same conditions, the algorithm can ensure the operating efficiency while having better fidelity, and it can still denoise at the same time. The edge signal of the image is preserved and has a better visual effect.
Embedded AR Storytelling Supports Active Indexing at Historical Places

Embedded interfaces support active indexing, the process of making sense of the experienced. While this supports the creation of meaningful relationships, findings are limited on how it could support sustaining the significance of historical places. We selected two historical places of daily use and developed two AR apps for each. The apps aimed at fostering participants’ indexing and meaningful connection to the places by presenting their history. One app offered passive, informative content. The other displayed an interactive, moral dilemma emphasizing the places’ social values. We tested the apps in two between-subjects studies with N=42 participants in total. We explored how participants ’ historical understanding can be improved by embedding our interfaces on five levels (spatially, physically, contextually, historically, and morally). We found that the morally embedded AR stories supported active indexing stronger than the passive ones and argued that they could foster meaningful relationships between users and historical places.
Out-of-Distribution Detection through Relative Activation-Deactivation Abstractions

A deep learning model always misclassifies an out-of-distribution input, which is not of any category that the deep learning model is trained for. Hence, out-of-distribution detection is practically an important task for ensuring the safety and reliability of a deep learning based system. We present in this paper the notion of relative activation and deactivation to interpret the inference behavior of the deep learning model. Then, we propose a relative activation-deactivation abstraction approach to characterize the decision logic of the deep learning model. The relative activation-deactivation abstractions enjoy close intra-class aggregation for each category under training, as well as diverse inter-class separation between various categories under training. We further propose an out-of-distribution detection algorithm based on the relative activation-deactivation abstraction approach, following the underlying principle that the relative activation-deactivation abstraction of a deep learning model under an out-of-distribution input is far away from the one for the predicted category the deep learning model outputs. Our detection algorithm does not require any designed perturbation to the input data, nor any hyperparameter tuning to the deep learning model with out-of-distribution data. We evaluate the detection algorithm with 8 typical benchmark datasets in literature. The experimental results show that our detection algorithm can achieve better and more stable performance than the state-of-the-art white-box abstraction based detection algorithms, with significantly more true positive and less false positive alerts for out-of-distribution detection.
Comparison Analysis of Different Face Detecting Techniques

This paper gives a comparative analysis of face detection techniques. An overview of general information regarding various face detection techniques is given. These techniques are preferred based on the application. The techniques used are the Haar-based technique, Multi-Task Cascaded Convolutional Neural Networks (MTCNN)-based technique and Histogram of Oriented Gradients (HOG) &#x002B; Support Vector Machine (SVM). These techniques are compared based on occlusion, normal condition and low lighting. As face detection is an important step towards automatic face recognition, the main purpose of this paper is to elucidate the approach that is a good candidate for face detection.
The Construction of a Smart Education System in Colleges and Universities Based on Cloud-Side Collaborative Computing Task Scheduling Algorithm

In order to improve the resource scheduling efficiency of the smart education system in colleges and universities and improve the stability and security of educational resource processing, this paper combines cloud-side collaborative computing technology to improve the cloud computing task scheduling algorithm and uses the improved algorithm for task scheduling. Moreover, this paper combines the global optimization and local optimization processes to make the final optimal solution performance better and further reduce the task scheduling execution time in the cluster, thereby improving the computing efficiency of cloud computing. In addition, this paper combines the improved algorithm to construct the system structure framework and verifies the system performance through experiments. The experimental research results show that the smart education system in colleges and universities based on the cloud-side collaborative computing task scheduling algorithm constructed in this paper is very good in all aspects and has a greater advantage in processing educational resources.
Train in Austria, Race in Montecarlo: Generalized RL for Cross-Track F1<sup>tenth</sup> LIDAR-Based Races

Autonomous vehicles have received great attention in the last years, promising to impact a market worth billions. Nevertheless, the dream of fully autonomous cars has been delayed with current self-driving systems relying on complex processes coupled with supervised learning techniques. The deep reinforcement learning approach gives us newer possibilities to solve complex control tasks like the ones required by autonomous vehicles. It let the agent learn by interacting with the environment and from its mistakes. Unfortunately, RL is mainly applied in simulated environments, and transferring learning from simulations to the real world is a hard problem. In this paper, we use LIDAR data as input of a Deep Q-Network on a realistic 1/10 scale car prototype capable of performing training in real-time. The robot-driver learns how to run in race tracks by exploiting the experience gained through a mechanism of rewards that allow the agent to learn without human supervision. We provide a comparison of neural networks to find the best one for LIDAR data processing, two approaches to address the sim2real problem, and a detail of the performances of DQN in time-lap tasks for racing robots.