Reviewerly/exHarmony huggingface dataset:
End-to-End Quantum Machine Learning with Quantum Control Systems

Toward quantum machine learning deployed on imperfect near-term intermediate-scale quantum (NISQ) processors, the entire physical implementation of should include as less as possible hand-designed modules with only a few ad-hoc parameters to be determined. This work presents such a hardware-friendly end-to-end quantum machine learning scheme that can be implemented with imperfect near-term intermediate-scale quantum (NISQ) processors. The proposal transforms the machine learning task to the optimization of controlled quantum dynamics, in which the learning model is parameterized by experimentally tunable control variables. Our design also enables automated feature selection by encoding the raw input to quantum states through agent control variables. Comparing with the gate-based parameterized quantum circuits, the proposed end-to-end quantum learning model is easy to implement as there are only few ad-hoc parameters to be determined. Numerical simulations on the benchmarking MNIST dataset demonstrate that the model can achieve high performance using only 3-5 qubits without downsizing the dataset, which shows great potential for accomplishing large-scale real-world learning tasks on NISQ processors.arning models. The scheme is promising for efficiently performing large-scale real-world learning tasks using intermediate-scale quantum processors.
Video Colorization Methods: a Survey

Nowadays, there are a huge number of video colorization methods. This is because in the gray scale image one value (gray) must be converted into three corresponding values (RGB). In this paper, some of these methods have been presented and discussed. Then, different comparisons have been established between these methods and the results demonstrate the efficiency of each method.
Quantum speed-up process of atom in dissipative cavity

In this work, we obtain an analytical representation of the density operator of an atom in dissipative cavity when the reservoir is at zero temperature and the total number of excitation is N=1.We also investigated the quantum speed limit time(QSLT) of the atom and the non-Markovianity in the dynamics process.The results show that the QSLT and the non-Markovianity can be effectively manipulated by the atom-cavity coupling and the reservoir parameters.Both of the atom-cavity coupling and the detuning can induce a sudden transition from Markovian to non-Markovian dynamics and this transition is the main physical reason of the quantum speed-up process.The critical value of sudden transition from no speed-up to speed-up depends the atom-cavity coupling and the reservoir parameters.The corresponding physical explanation is also provided for our results.
Band Steering Technology Based on QoE-Oriented Optimization in Wireless Network

Abstract In mobile crowd-sensing (MCS) network, it is necessary to reduce the cost of sensing data transformation from mobile devices to the MCS platform in the core of the network. With the widespread deployment of access point (AP), explosive data traffic of the mobile devices is offloaded from cellular base stations to Wi-Fi networks. The congestion of the 2.4 GHz band in dual-band wireless area network makes the lower quality of experience (QoE) of users. To ease the load of 2.4 GHz band, the band-steering technology has been widely used by equipment manufacturers. However, the fixed threshold configuration strategy in band steering technology makes it easy to be exploited by setting up a great number of connection requirements in a short time, which may prevent a normal connection requirement of a user, making it difficult to satisfy the dynamic resource requirements of different services. Moreover, if the threshold is configured too high, it may result in QoE imbalance in dual bands. Besides, when the band (such as 2.4 GHz) resources cannot meet the terminal requirement, the terminal cannot autonomously switch to another band (such as 5 GHz), and this may result in a very poor QoE of users. In this paper, a secure QoE-oriented wireless network optimization framework is proposed to improve the QoE of users in dual-band network. Firstly, to ensure the overall QoE of network and balancing the QoE between 2.4 GHz and 5 GHz bands, the framework dynamically adjusts the load threshold of the frequency band based on QoE information during the association of a terminal. Secondly, to meet the basic QoE requirement of each user, the framework preferentially switches the terminals with poor QoE to the idle band after users join the network. The experimental results show that the proposed framework can improve the QoE both before and after the terminal accesses the network. Additionally, it enables the better QoE balance in dual bands.
THE DIGITAL GENERATION: A STUDY ON HOW UNDERGRADUATE STUDENTS FROM ROMANIA ARE CONSUMING DIGITAL MEDIA

R.A. Neghină1, D.G. Ilie2, V.A. Mănescu1, M.R. Ganciu3, G. Militaru4 1Webecom S.R.L. (ROMANIA) 2University of Bucharest (ROMANIA) 3Energomontaj S.A. (ROMANIA) 4University Politehnica of Bucharest (ROMANIA)
Special Issue on Deep Learning for Robotic Vision

Guest editorial In this special issue we consider the topic of Robotic Vision in the context of deep learning techniques. Vision for robotics, while closely related to classical computer vision, and often using it for inspiration, focuses on specific vision tasks which are required for robot navigation and interaction...
Chargrid-OCR: End-to-end trainable Optical Character Recognition through Semantic Segmentation and Object Detection

We present an end-to-end trainable approach for Optical Character Recognition (OCR) on printed documents. Specifically, we propose a model that predicts a) a two-dimensional character grid (\emph{chargrid}) representation of a document image as a semantic segmentation task and b) character boxes for delineating character instances as an object detection task. For training the model, we build two large-scale datasets without resorting to any manual annotation - synthetic documents with clean labels and real documents with noisy labels. We demonstrate experimentally that our method, trained on the combination of these datasets, (i) outperforms previous state-of-the-art approaches in accuracy (ii) is easily parallelizable on GPU and is, therefore, significantly faster and (iii) is easy to train and adapt to a new domain.
Real-time Online Education of College English Based on Cloud Computing

Abstract The implementation of internationalization makes English education more and more important. In colleges and universities, English is an important basic course. With the popularization of cloud computing, more and more universities are applying cloud computing to English teaching. Cloud computing provides new ideas and methods for college English education. In view of this, this paper proposes a study of cloud-based college English real-time online education. This article first describes the impact of cloud computing on college English education, and conducts research through controlled experiments and questionnaires. The experimental results in this article show that after an experimental period, the students in the experimental group have made significant progress, especially in the listening level. The listening level of the students in the experimental group has increased by 57%. In contrast, the control group has improved, but Obviously lags behind the experimental group. In addition, students also have a good opinion of real-time online education. Although the advantages and disadvantages of real-time online education, the advantages outweigh the disadvantages in general, and most students say that this education method is more novel and attractive. The research results of this paper provide a new idea for the future teaching model of college English education and have good reference value.
Detecting Human Behavioral Pattern in Rock, Paper, Scissors Game Using Artificial Intelligence

As entertainment tools, computer games are important phenomena in the world, which are considered as a popular medium, an effective educational solution and a considerable economy resource. In this paper, Multi-Layer perceptron (MLP) neural network was used to detect human behavior pattern in rock, paper, scissors game. The similarity of artificial neural networks (ANNs) to the human brain is the main motivation of this study. MATLAB software was used to implement the network code. These codes consisted of two phases: 1) training the ANN to learn the human behavioral pattern considering forty games. 2) real play against a human by doing ten games. After the implementation of the network, its effectiveness in detecting human behavioral patterns was investigated. The network was tested on 40 people (20 women and 20 men). Each player played with the target network in three stages. The results of this study showed that the win percentage of computers with MLP neural network was 57.5% for men and 60.8% for women. While the percentage of the computer without neural networks and with random selections in 60 games was 52.5% for men and 42.5% for women
Multi-Sensor Information Fusion and Machine Learning for High Accuracy Rate of Mechanical Pedometer in Human Activity Recognition

Many Human Activity Recognition (HAR) research works have been engineered, however, only a few studies combine Kinect and the smartphone. Also, most findings are geared towards wearable sensors, which however may develop wearer discomfort, predominantly for nonstop action applications where one may perhaps have to wear for a lengthy period. Additionally, while the novelties of these researches are encouraging, accuracy is subject to fluctuation over time. To address these concerns, this paper reports the fusion of sensor data to determine a robust accuracy. The system uses Microsoft Kinect Sensor (MKS) to detect angle change in the hip-knee-ankle and a gravitational accelerometer of a smartphone for step tracking while engaging users. In our study, three methods are compared: 1) Manually counted steps, 2) raw data from our algorithm and 3) implementation of parameterized classification algorithm classifiers, k-Nearest Neighbors (kNN) to improve the step counts. Best-first search hierarchy containing the maximum distance of the kNN algorithm is engaged. Varying k, an accuracy rate of 96.44% is achieved with σ = 0.040.
Mining Publication Papers via Text Mining: A Case Study

The amount of data that produced is increased day after day especially data as a text, so with this massive production it would be difficult to analyze or extract information to discover the patterns from the unstructured text. Text mining is used for availing the massive amount of knowledge that is in the text and deriving high quality information from the text automatically. This Process would save effort and time. Text mining considered as a subset of data mining where data mining is more generic. This paper proposes a methodology of mining a text for a case study related to publication papers. Some of text mining approaches will be introduced for mining the publication papers using machine learning (ML) and natural language processing (NLP) techniques. Describing each phase as following: First phase is keywords extraction using natural language processing techniques, second phase named entity recognition and last phase is document classification. The last two phases are using the ML techniques. Then a case study is built to simulate the system phases, showing what is the input and the output in each phase.
Detecting Ghostwriters in High Schools.

Students hiring ghostwriters to write their assignments is an increasing problem in educational institutions all over the world, with companies selling these services as a product. In this work, we develop automatic techniques with special focus on detecting such ghostwriting in high school assignments. This is done by training deep neural networks on an unprecedented large amount of data supplied by the Danish company MaCom, which covers 90% of Danish high schools. We achieve an accuracy of 0.875 and a AUC score of 0.947 on an evenly split data set.
Integrating Blockchain and Broad Learning for Smart Energy Innovation: Design and Experiment

With the rapid development in recent years, the architecture and data sources of energy internet (EI) becomes increasingly complicated.It is a good opportunity and trend to combine energy equipment and information based on blockchain technology.However, high computational complexity inherently due to the blockchain must be addressed to achieve rapidly user electricity prediction.In this paper, an algorithm model of a blockchain technology combined with a broad learning data fusion algorithm is proposed.A novel and detailed combination model is introduced, mainly including CNN and LSTM model.Combination model considers energy data and blockchain data as parameter, and defines selection logic to guarantee user electricity forecast accuracy.Our proposed solution takes the advantage of CNN model in prediction accuracy and computation complexity, and advantage of LSTM model in long term memory.Further experiments illustrate that our solution achieves higher prediction accuracy of 94.1% (without considering the influence of real-time data), with the best result achieved ever.The experimental prediction results provide underlying insights to guide the direction of system resource planning.
Quantum Alice and silent Bob: Qubit-based Quantum Key Recycling with almost no classical communication

We answer an open question about Quantum Key Recycling (QKR): Is it possible to put the message entirely in the qubits without increasing the number of qubits compared to existing QKR schemes? We show that this is indeed possible. We introduce a prepare-and-measure QKR protocol where the communication from Alice to Bob consists entirely of qubits. As usual, Bob responds with an authenticated one-bit accept/reject classical message. Compared to Quantum Key Distribution (QKD), QKR has reduced round complexity. Compared to previous qubit-based QKR protocols, our scheme has far less classical communication. We provide a security proof in the universal composability framework and find that the communication rate is asymptotically the same as for QKD with one-way postprocessing.
A Stable and Reliable Self-tuning Pointer Type Meter Reading Recognition Based on Gamma Correction

Existing reading recognition methods of the pointer type meter have problems of inaccurate target segmentation, high manual dependence of dial detection and low accuracy. This paper presents a self-tuning parameter pointer type meter reading recognition based Gamma correction. In detail, the proposed approach firstly processes the image by Gamma correction with the contrast to output the high contrast image. Then the presented method makes the circle Hough transform by automatically adjusting the parameter to segment the dial. Next, the algorithm further processes image by OTSU, refinement and Hough line transform to segment image and extract pointer. Finally, it calculates the slope of the detected line, and combines the lookup method with the formula method to interpret the indicator. Experimental results show that the proposed method can realize the automatic detection of the dial and accurate segmentation of the pointer, and the obtained reading is more close to the actual reading. The algorithm is more stable and reliable.
Beyond Sentiment Classification: A Novel Approach for Utilizing Social Media Data for Business Intelligence

Extracting people's opinions from social media has attracted a large number of studies over the years.This is as a result of the growing popularity of social media.People share their sentiments and opinions via these social media platforms.Therefore, extracting and analyzing these sentiments is beneficial in many ways, for example, business intelligence.However, despite a large number of studies on extracting and analyzing social media data, only a fraction of these studies focuses on its practical application.In this study, we focus on the use of product reviews for identifying whether the reviews signify the intention of purchase or not.Therefore, we propose a novel lexicon-based approach for the classification of product reviews into those that signify the intention of purchase and those that do not signify the intention of purchase.We evaluated our proposed approach using a benchmark dataset based on accuracy, precision, and recall.The experimental results obtained prove the efficiency of our proposed approach to purchase intention identification.
Public-Key Certificate Management and Use Cases

This chapter explains certificate management and public-key infrastructure (PKI), what they provide, technical mechanisms and architectures, and challenges. Two major certificate use cases are also considered here as examples: TLS as used in HTTPS for secure browser-server communications, and end-to-end encrypted email. Additional applications include SSH and IPsec (Chapter 10), DNSSEC (Chapter 11), and trusted computing.
Mutation-Based Chaotic Gravitational Search Algorithm

Gravitational search algorithm, a stochastic, nature-inspired algorithm, is designed on the basis of gravitational kinematics of physics. To promote an appropriate stability between exploration and exploitation and to diminish the weakness that was affecting the search to reach global optima, a new variant of GSA named as mutation-based chaotic gravitational search algorithm (MCGSA) is introduced in this work. This new variant of GSA consists of five mutation strategies. These five mutations are applied to the best solution in a successive manner. To justify the overall functioning of the proposed method, it has been evaluated on CEC2005 benchmark suit and has been compared with four other modified versions derived from original GSA, namely standard GSA (SGSA), chaotic-based GSA (CGSA), crossover-based GSA (CROGSA), and self-adaptive GSA (GGSA). The comprehensive result of MCGSA has outperformed other GSA variants.
Multi-objective clustering algorithm using particle swarm optimization with crowding distance (MCPSO-CD)

Clustering, an unsupervised method of grouping sets of data, is used as a solution technique in various ﬁelds to divide and restructure data to become more signiﬁcant and transform them into more useful information. Generally, clustering is difﬁcult and complex phenomenon, where the appropriate numbers of clusters are always unknown, comes with a large number of potential solutions, and as well the datasets are unsupervised. These problems can be addressed by the Multi-Objective Particle Swarm Optimization (MOPSO) approach, which is commonly used in addressing optimization problems. However, MOPSO algorithm produces a group of non-dominated solutions which make the selection of an “appropriate” Pareto optimal or non-dominated solution more difficult. According to the literature, crowding distance is one of the most efficient algorithms that was developed based on density measures to treat the problem of selection mechanism for archive updates. In an attempt to address this problem, the clustering-based method that utilizes crowding distance (CD) technique to balance the optimality of the objectives in Pareto optimal solution search is proposed. The approach is based on the dominance concept and crowding distances mechanism to guarantee survival of the best solution. Furthermore, we used the Pareto dominance concept after calculating the value of crowding degree for each solution. The proposed method was evaluated against five clustering approaches that have succeeded in optimization that comprises of K-means Clustering, MCPSO, IMCPSO, Spectral clustering, Birch, and average-link algorithms. The results of the evaluation show that the proposed approach exemplified the state-of-the-art method with significant differences in most of the datasets tested.
Handwriting Numerals Recognition Using Convolutional Neural Network Implemented on NVIDIA’s Jetson Nano

An efficient handwriting numerals recognition structure based on Convolutional Neural Network (CNN) with RMSProp optimizer algorithm and Adam optimizer algorithm is presented in this paper. The experiment is implemented on NIVIDIA’s Jetson Nano platform, where we compare the performance of CNN models with two different optimizer algorithms. Experimental results show that the training accuracy of the model using the Adam optimization algorithm is better than that of the model with the RMSProp optimization algorithm. The training accuracy is 98.25%. Adam algorithm has fast convergence speed and RMSProp algorithm.
The Application of Immersive Experience in Museum Teaching

In recent years, museums have shifted from “things as the core to people as the core”. The people-oriented navigation model has increasingly become the center of the museum’s youth education. However, the inherent shortcomings of traditional museum teaching no longer meet the needs of today’s society. Therefore, from the perspective of immersive teaching, the author innovates museum teaching. The design of a new museum teaching mode through the integration of the Dick &amp; Carey model and the “learning” -based teaching model is important for the application of immersive teaching in museum education.
Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.
Trapdoor Digital Shredder: A New Technique for Improved Data Security without Cryptographic Encryption

Along with the increase of the importance of information used in practice, adversaries tried to take valuable information in diverse ways.The simple and fundamental solution is to encrypt the whole data.Since the cost of encryption is increasing along with the size of data, the cost for securing the data is a burden to a system where the size of the data is not small.For the reason, in some applications where huge data are used for service, service providers do not use any encryption scheme for higher security, which could be a source of trouble.In this work, we introduce a new type of data securing technique named Trapdoor Digital Shredder(TDS) which disintegrates a data to multiple pieces to make it hard to re-construct the original data except the owner of the file who holds some secret keys.The main contribution of the technique is to increase the difficulty in obtaining private information even if an adversary obtains some shredded pieces.To prove the security of our scheme, we first introduce a new security model so called IND-CDA to examine the indistinguishability of shredded pieces.Then, we show that our scheme is secure under IND-CDA model, which implies that an adversary cannot distinguish a subset of shreds of a file from a set of random shreds.