Reviewerly/exHarmony huggingface dataset:
Ameliorated Particle Swarm Optimization Algorithm and Its Application in Robot Path Planning

Since particle swarm optimization (PSO) was put forward in 1995, it has been paid more and more attention in the research field because of its simple concept and easy implementation. This algorithm can simulate the mutual learning and cooperation behaviour of birds during foraging, and find the optimal solution of the population. However, there are some problems in the development of PSO, such as lack of theoretical basis, high computational cost, easy to fall into local optimums, and so on. This paper starts with the basic PSO algorithm and proposes an improved algorithm applied for robot path planning. In this algorithm, PSO and quantum computing are combined to simplify the search model. The weight factor's self-adjusting strategy is improved, and the convergence speed of the improved algorithm is better. The mutation operation is introduced to the global optimum, which increases the diversity of solutions in the search process. The improved algorithm is used in robot path planning simulation, and the results indicate that the planned path is more safe and reliable.
Multiclass Classification of Modulation Formats in the presence of Rayleigh and Rician Channel Noise using Deep Learning Methods

Wireless communication technologies have revolutionized the communication scenario completely. In the last four decades, the transition from 1G to 5G communication systems has been instrumental in opening up many new possibilities. An important step for wide-range applications in intelligent Communication network is automated modular format identification. It is a significant step for an exact detecting of the signal on the receiver side. Deep learning methods are used extensively in the fields of speech recognition, image recognition, object detection and reinforcement learning etc. In this research, 2D and 3D Convolution neural networks (CNNs) were compared and contrasted for the purpose of automatic recognition of modulation formats. We have used random cat images that are modulated with 16 and 64 Quadrature Amplitude Modulation (QAM) signals and are classified using 2D and 3D CNN architecture after passing them through Rayleigh and Rician noise channels. We tried 5 and 10 fold cross-validation procedures to train multiclass (4-classes) classifiers. We observed that, in terms of reported metrics, the performance of the 3D CNN architecture trained using 10-fold cross validation procedure was the best and that of 2D CNN architecture trained using 5-fold cross validation procedure was the worst. Overall, we have found the performances of 3D architectures to be better than their 2D counterparts.
From Coarse to Fine: A Two-Stage Network for Dense Haze removal

Dense haze degrades details and obscures color information, which increases the difficulty of extracting global features at deep layers of CNN. In this paper, we propose a two-stage progressive network for single image dehazing. Unlike previous typical learning-based approaches that use hazy and clean image pairs to train a mapping between the two directly, our method learns different levels of haze with different supervision in each stage. Hence the network is divided into rough and refinement as two steps and each step is modeled based on encoder-decoder architecture. To handle different size objects and uneven haze distribution, we utilize the special designed Pyramid Dilated Convolution Block (PDCB) and the latest Fusion Attention Module (FAM) which are embedded in the refinement network to form the final dehazed image with R, G and B color channels. Based on this model, we formulate a loss function that adopts contrast enhancement and color consistency constraint to recover distinct contrast and natural color. We perform extensive experiments on our new dense-haze dataset synthesized with benchmark RESIDE and real-world images, and show our network is effective to deal with previously challenging hazy images.
An Approach to Cloud Computing for Medical Image Analysis

Cloud computing has become popular among users in organizations and companies. Security and efficiency are the two major problems facing cloud service providers and their customers. Cloud data allocation facilities that allow groups of users to work together to access the shared data are the most standard and effective working styles in the enterprises. So, in spite of having advantages of scalability and flexibility, cloud storage service comes with confidential and security concerns. A direct method to defend the user data is to encrypt the data stored at the cloud. In this research work, a secure cloud model (SCM) that contains user authentication and data scheduling approach is scheduled. An innovative digital signature with chaotic secure hashing (DS-CS) is used for user authentication, followed by an enhanced work scheduling based on improved genetic algorithm to reduce the execution cost.
The Wireless IoT Device Identification based on Channel State Information Fingerprinting

Since the existing device identity authentication technology based on wireless signal feature extraction and recognition of the physical layer of the Internet of Things needs to make estimation assumptions on the channel model, there are significant limitations in the changing environment of the actual application scenario. In view of the above problems, this paper proposes an identity authentication mechanism for IoT devices based on channel state information (CSI) recognition. This mechanism combines a channel estimation algorithm with a channel state feature recognition algorithm. The channel-based CSI at the receiving end is used to distinguish the identity of the sender. The legal hypothesis test and the Euclidean distance between two consecutive CSIs are used as the test statistic, to realize the identity authentication of the device by judging the comparison of the statistic and the detection threshold. This mechanism can realize the identification of the access terminal without making special assumptions about the channel model. Test analysis shows that the method has low false alarm rate and missed detection rate.
High Throughput and Secure Authentication-Encryption on Asynchronous Multicore Processor for Edge Computing IoT Applications

We describe an Authentication based Matrix-transformation cum Parallel-encryption (AMP-MP) implemented on an Asynchronous Multicore Processor to achieve a high throughput and yet secure Advanced Encryption Standard based on Counter with Chaining Mode (AES-CCM). Our AMP-MP is realized on an 8-bit asynchronous 9-core processor fabricated based on 65nm CMOS process. The experimental results show that the throughput of the authentication is 13.54Gbps while the throughput for both authentication and encryption collectively is 8.32Gbps, which are 17× and 70× faster than the reported counterparty respectively. Based on power dissipation and EM SCA on our AMP-MP, the secret key is unrevealed at 5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">5</sup> traces, which is ~ 17× more secured than the standard ASIC AES-CCM implementation.
Mobile Tablet Integration Using Augmented Reality

The purpose of this chapter is to help educators successfully integrate mobile tablets in their classrooms. Research has shown that mobile tablet integration can increase learning of students. Integrating advanced technologies in classrooms will support learners and advance all students' learning experiences in ways that cannot be done without mobile tablets. This chapter provides educators with transformative tools to improve their in-person lessons and their lessons through augmented reality. This chapter also includes a set of resources educators can use to obtain augmented reality apps that will enhance their lessons, a list of ways to improve their curriculum, and how to engage students via virtual reality when home learning is necessary.
Spacecraft Health Monitoring Using a Weighted Sparse Decomposition

In space operations, spacecraft health monitoring and failure prevention are major issues. This important task can be handled by monitoring housekeeping telemetry time series using anomaly detection (AD) techniques. The success of machine learning methods makes them attractive for AD in telemetry via a semi-supervised learning. Semi-supervised learning consists of learning a reference model from past telemetry acquired without anomalies in the so-called learning step. In a second step referred to as test step, most recent telemetry time-series are compared to this reference model in order to detect potential anomalies. This paper presents an extension of an existing AD method based on a sparse decomposition of test signals on a dictionary of normal patterns. The proposed method has the advantage of accounting for possible relationships between different telemetry parameters and can integrate external information via appropriate weights that allow detection performance to be improved. After recalling the main steps of an existing AD method based on a sparse decomposition Pilastre et al (Sign Proc, 2019 [1]) for multivariate telemetry data, we investigate a weighted version of this method referred to as W-ADDICT that allows external information to be included in the detection step. Some representative results obtained using an anomaly dataset composed of actual anomalies that occurred on several satellites show the interest of the proposed weighting strategy using external information obtained from the correlation coefficient between the tested data and its decomposition on the dictionary.
Euler-Milstein and relevant approaches for high-precision stochastic simulation of quantum trajectories

Abstract For quantum diffusive measurements, the system’s dynamics can be described by the Itô stochastic master equation, which works well for an infinitesimal time resolution. However, in practical quantum experiments, one cannot make a time step to be infinitesimal, as it can introduce correlation of noise in time, making the Markovian assumption invalid. On the other hand, increasing a time step can cause errors from non-commuting operations describing the system’s dynamics. We therefore consider implementing the Euler-Milstein and relevant approaches, namely the Itô map, the high-order completely positive map, and the quantum Bayesian, to simulate quajntum trajectory for a quantum system under diffusive continuous measurements. In particular, we numerically simulate trajectories for a qubit measurement in z basis. We show the comparison of individual trajectories and their averaged trajectories among these approaches. We find that the high-order completely positive map approach yields the most accurate averaged quantum trajectory. Furthermore, we also investigate the trace distance from true stochastic quantum trajectories, comparing the four approaches using the numerical simulation. We show that, for a realistic time resolution (as in a superconducting qubit, experiment), the high-order map does give the most accurate estimate of the qubit trajectories.
Towards the Integration of Internet of Things Devices to Monitor Older Adults Activities in a Platform of Services

The research study reported by the present paper aimed to integrate in a platform of services several Internet of Things (IoT) devices to monitor older adults’ activities together with analytical procedures to infer relevant events. To guarantee the interoperability of the processed information, as well as its privacy, integrity, and confidentiality, the study applied several services provided by the platform, namely interoperability and security (i.e., authentication, authorization, logging and auditing) services. The results show the viability of the approach and it is foreseen the development and evaluation of additional algorithms for the detection of significant events that might be related to health conditions of older adults.
ANALYZING DIABETES MELLITUS CASES AT GWERU PROVINCIAL HOSPITAL

In this study, the ANN approach was employed in forecasting Diabetes Mellitus (DM) cases attended to at Gweru Provincial Hospital (GPH). For this study, DM cases have been classified into two basic pathophysiological categories, that is, type 1 DM (DM1, reffered to as variable Y) and type 2 DM (DM2, reffered to as variable X). The employed data covers the period January 2010 to December 2019, for each variable under consideration. The forecast evaluation criteria of the applied models show that the models are suitable for forecasting DM cases recorded at GPH. The results of the study show that DM2 incidence will remain more prevalent than DM1 incidence in GPH catchment areas. The study offers a 2-fold policy prescription: [i] GPH should, through continuous health education programs, continue to train workers in effective detection and management of DM; [2] GPH should, through educational campaigns, educate patients on DM, especially on the concepts of self-care and good care-seeking attitude. In this regard, there is need also to disseminate information on healthy diets and regular exercise.
Improved Adversarial Attack against Black-box Machine Learning Models

The existence of adversarial samples makes the security of machine learning models in practical application questioned, especially the black-box adversarial attack, which is very close to the actual application scenario. Efficient search for black-box attack samples is helpful to train more robust models. We discuss the situation that the attacker can get nothing except the final predict label. As for this problem, the current state-of-the-art method is Boundary Attack(BA) and its variants, such as Biased Boundary Attack(BBA), however it still requires large number of queries and kills a lot of time. In this paper, we propose a novel method to solve these shortcomings. First, we improved the algorithm for generating initial adversarial samples with smaller L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> distance. Second, we innovatively combine a swarm intelligence algorithm - Particle Swarm Optimization(PSO) with Biased Boundary Attack and propose PSO-BBA method. Finally, we experiment on ImageNet dataset, and compared our algorithm with the baseline algorithm. The results show that:(1)our improved initial point selection algorithm effectively reduces the number of queries;(2)compared with the most advanced methods, our PSO-BBA method improves the convergence speed while ensuring the attack accuracy;(3)our method has a good effect on both targeted attack and untargeted attack.
Application of Meta Learning in Face Recognition

Abstract Now is the information age, artificial intelligence rises under the background of this era, but in the field of artificial intelligence, deep learning stands out in machine learning with its excellent learning ability and is rapidly applied to face recognition engineering. However, the success of mainstream deep learning often depends on a lot of training data and training time. This paper proposes to use meta-learning technology to realize face recognition. metalearning is to use the prior knowledge and experience to guide the learning of new tasks and has the ability to learn so that it can avoid the phenomenon of overfitting in the case of only a small number of samples. We regard the data set as a task, that is, meta-train task and meta-test task, using the MAML algorithm [1] fine-tuning gradient model based on prior knowledge to obtain optimization parameters to realize face recognition. Experiments show that deep learning neural networks using meta-learning technology can achieve higher accuracy and rate than ordinary face recognition neural networks. In the experiment, the recognition rate of the face can reach more than 92.6%. It has more intelligence and has made a contribution to the development of face recognition technology.
Research on the design and Creation of contemporary Powder enamel based on NURBS modeling method

as an important discovery of ceramic design, contemporary ceramic design must be able to keep pace with the development of the times, learn to actively introduce new elements into the creation. This paper attempts to carry out the research of contemporary ceramic design and creation based on NURBS modeling, which is to integrate digital technology into the traditional ceramic design, and use NURBS modeling to innovate the decoration design of contemporary Famile-rose porcelain, so that the pastel porcelain works have a stronger sense of the times.
Speech Language Classification Algorithm Based on Multiple-instance Learning with Negative Sample Cleaning

Speech language classification is an important direction in the field of machine learning. In order to further improve the effect of speech classification, this paper propose a new speech language classification algorithm based on multiple-instance learning with negative instance cleaning, namely NUMI-Biased SVM algorithm. Firstly, extract MFCC features from speech files, regarding each file as an instance bag, and every speech frame as an instance. Secondly, adopt PU learning algorithm to clean negative instances in positive bags to reduce their impact on classification. Finally, multiple-instance learning algorithm is used to classify the speech files. Experiments show that, compared with the SVM algorithm and multiple-instance SVM algorithm, NUMI-Biased-SVM has achieved significant improvement in classification performance.
Finding Patterns in Adversarial Training

Adversarial training has become an universally accepted robust method to train networks defending against adversarial attacks. However, feeding networks with adversarial examples is a cumbersome process, and there is no clear explanation for the differences between networks with the same architecture that are learned by adversarial training and learned by natural training method. So in this paper, we focus on the patterns of network learned by adversarial training. For comparison, we visualize the weights and feature maps of networks learned by these two training methods on MNIST, and find some patterns of adversarially trained model that are important for defending against adversarial examples and that are unique to naturally trained model. First, we find that adversarially trained network denoises the noisy data caused by adversarial images and enhances the outlines of semantically informative content in feature maps. Second, we find that adversarial training performs model compression, making some of the channels of a convolutional layer have no activation on input images. Further observation shows that different from naturally trained model, which have different most activation channel corresponding to different input, adversarially trained model often has the same most activation channel regardless of input. This phenomenon also leads to a sparse network reducing 50% number of parameters and 87% computation costs, and the pruned network performs as good as original network after pruning the least activation channels without retraining. But the naturally trained counterpart loses more than 40% accuracy using the same pruning strategy.
Microblog Popularity Prediction Based on Multimodal Feature Fusion

The change of public opinion popularity shows complex non-linear characteristics under the joint action of many influencing factors. Taking microblog as the research object, considering the real-time and complexity of microblog, this paper analyzes the trend of microblog popularity from the aspects of microblog content, user characteristics and microblog event timeliness, and proposes a multi-modal feature fusion microblog popularity prediction algorithm. Firstly, the original features of microblog are analyzed, key features are extracted from them, and GDBT algorithm is used for feature screening. Secondly, the popularity of microblog is measured according to the information gain method. Finally, combined with XGBoost algorithm, the popularity of short-term events is predicted according to the text content, image content, video content and user influence of microblog, thus predicting the popularity value of users whose microblog events have been published, and then using LSTM algorithm to predict the overall event trend. Experiments show that. The accuracy of XGBoost algorithm in predicting the popularity value of microblog can reach 80%, while LSTM algorithm has a small RMS for predicting the overall event trend, which well predicts the overall development trend of events.
GMDH-based Models for Mid-term Forecast of Cryptocurrencies (on example of Waves)

Cryptocurrencies became one of the main trends in modern economy. However by the moment the forecast of cryptocurrencies values is an open problem, which is almost non-reflected in publications related to finance market. Reasons consist in its novelty, large volatility and its strong dependence on subjective factors. In this experimental research we show possibilities of GMDH-technology to give weekly and monthly forecast for values of cryptocurrency 'Waves' (waves/euro rate). The source information is week data covering the period 2017-2019. We tests 4 algorithms from the GMDH Shell platform on the whole period and on the crisis period 4-th quarter 2017 – 2nd quarter 2018. Baseline is provided by the popular statistical method of double exponential smoothing. The results of Pilot study can be considered as the very promising ones having in view the large variability of data.
A genetic algorithm approach for image representation learning through color quantization

Over the last decades, hand-crafted feature extractors have been used to encode image visual properties into feature vectors. Recently, data-driven feature learning approaches have been successfully explored as alternatives for producing more representative visual features. In this work, we combine both research venues, focusing on the color quantization problem. We propose two data-driven approaches to learn image representations through the search for optimized quantization schemes, which lead to more effective feature extraction algorithms and compact representations. Our strategy employs Genetic Algorithm, a soft-computing apparatus successfully utilized in Information-retrieval-related optimization problems. We hypothesize that changing the quantization affects the quality of image description approaches, leading to effective and efficient representations. We evaluate our approaches in content-based image retrieval tasks, considering eight well-known datasets with different visual properties. Results indicate that the approach focused on representation effectiveness outperformed baselines in all tested scenarios. The other approach, which also considers the size of created representations, produced competitive results keeping or even reducing the dimensionality of feature vectors up to 25%.
Elementary Attestation of Cryptographically Useful Composite Moduli

This paper describes a non-interactive process allowing a prover to convince a verifier that a modulus n is the product of two primes (p, q) of about the same size. A further heuristic argument conjectures that $$p-1$$ and $$q-1$$ have sufficiently large prime factors for cryptographic applications. The new protocol relies upon elementary number-theoretic properties and can be implemented efficiently using very few operations. This contrasts with state-of-the-art zero-knowledge protocols for RSA modulus proper generation assessment. The heuristic argument at the end of our construction calls for further cryptanalysis by the community and is, as such, an interesting research question in its own right.