Reviewerly/exHarmony huggingface dataset:
Gesture recognition based on transfer learning

At present, the general methods of gesture recognition are machine learning method and deep learning method. However, machine learning method has the disadvantage of extracting image features manually, while deep learning method requires high hardware requirements and is easy to over-fitting in small samples. To solve above problems, this paper proposes gesture recognition based on transfer learning. Firstly, transfer the pre-trained model and convolutional layer parameters on the Image-Net dataset to the gesture dataset with small samples. And then frozen the convolution layer as the feature extractor and fine-tune the output layer of the network to fit the target data. Finally, initialize the 3 fully connection layer of the network with improved normal distribution initialization method and retrain the net again. The simulation results show that the proposed method has a good recognition rate in the small sample gesture image dataset and avoid the over-fitting, which has a great significance for gesture recognition.
Solving Combinatorial Puzzles with Parallel Evolutionary Algorithms

Rubik’s cube is the most popular combinatorial puzzle. It is well known that solutions of the combinatorial problems are generally hard to find. If 90 $$^\circ $$ clockwise rotations of the cube’s sides are taken as operations it will give a minimal cube’s grammar. By building formal grammar sentences with the usage of the six operations ([L]eft, [R]ight, [T]op, [D]own, [F]ront, [B]ack) all cube’s permutations can be achieved. In an evolutionary algorithms (like genetic algorithms for example) set of formal grammar sentences can be represented as population individuals. Single cut point crossover can be efficiently applied when population individuals are strings. Changing randomly selected operation with another randomly selected operation can be used as efficient mutation operator. The most important part of such global optimization is the fitness function. For better individuals fitness value evaluation a combination between Euclidean and Hausdorff distances is proposed in this research. The experiments in this research are done as parallel program written in C++ and Open MPI.
Study Centre Sanitary Conditions and Its Impact on Health and Academic Outcomes of Distance Learners at University of Cape Coast.

This study examined the impact of Study Centre Sanitary and hygiene conditions and its impact on health and academic outcomes of Distance Learners at the University of Cape Coast (UCC). The major objective of the study was to find out how sanitary conditions at the host institutions used by the College of Distance Education (CoDE), as study centers for distance education (DE) delivery, impact on the health and academic status of distance education learners. Data was gathered using mainly self-developed and administered questionnaire. The simple random technique was used to select 300 DE learners from 10 study centers in the Ashanti Region of Ghana. Data was analyzed using descriptive and inferential statistics. Approval by the Institutional Research and Ethics Committee of the University of Cape Coast and informed consent from study participants were sought. The results of the study indicated that the state of sanitary conditions and general hygiene practices at CoDE study centres was poor, unmaintained, and inadequate in almost all study centres. This demonstrated that investment in school infrastructure was not given the due priority. The negative impact on the health status and academic outcomes of learners were due to inadequate sanitary facilities despite learners’ knowledge on good personal hygiene and sanitation practices. The study concluded that physical sanitary facilities at CoDE study centres within the study area were in a deplorable state and inadequate for the learner population in the host institutions. Gaps were identified in school management of resources and enforcement of school health, hygiene and sanitation practices. It was recommended that (CoDE-UCC) collaborates with host institutions to improve sanitation and hygiene practices at the study centres for the benefit of learners.
An Inter User Image Re-Ranking System

Nowadays, a number of images has been shared across the social network platform comprising of both relevant and irrelevant images. To find the relevant images,are too hard for a normal user. The IUIR(Inter User Image Re-ranking) system aimed to find those relevant images. The inter user image re-ranking system helps to detect accurate pictures and the contribution of each social users data. IUIR is applicable to find more accurate and quality images and for query related image retrieval with the consideration of images relevance and diversity. Re ranking image informations consist of visually, semantically and click count of images. The ranking of the user with highest contribution is retrieved first, then the similar images are removed by the feature extraction and the resulting dissimilar image set are ranked by click count and the resulting set is the final retrieved result.
Using Gradient Based Multikernel Gaussian Process and Meta-Acquisition Function to Accelerate SMBO

Automatic machine learning (automl) is a crucial technology in machine learning. Sequential model-based optimisation algorithms (SMBO) (e.g., SMAC, TPE) are state-of-the-art hyperparameter optimisation methods in automl. However, SMBO does not consider known information, like the best hyperparameters high possibility range and gradients. In this paper, we accelerate the traditional SMBO method and name our method as accSMBO. In accSMBO, we build a gradient-based multikernel Gaussian process with a good generalisation ability and we design meta-acquisition function which encourages that SMBO puts more attention on the best hyperparameters high possibility range. In L2 norm regularised logistic loss function experiments, our method exhibited state-of-the-art performance.
A CNN-RNN Hybrid Model with 2D Wavelet Transform Layer for Image Classification

Convolutional neural networks (CNNs) have recently achieved impressive performances in image processing tasks such as image classification and object recognition. However, CNNs only process images in the spatial domain whereas spectral analysis operates in the frequency domain. In this paper, we propose the 2D wavelet transform layer. The learned features from images are viewed as two-directional sequential data, and we use two LSTM layers that sweep both horizontally and vertically across the image to compress feature matrices. Based on this, the 2D wavelet transform decomposes the above feature matrices as a learned mixing of different harmonic functions, and therefore integrating the spectral analysis into CNNs. We also select 3 × 3 convolutional mixing style with Gaussian+LSM filter to mix the output of the 2D wavelet transform to generate new output features. Finally, we combine the sequential and spectral features to build our CNN-RNN architecture with skip layers and apply it to image classification. Our proposed network is evaluated on three widely-used benchmark datasets: CIFAR-10, CIFAR-100 and Tiny ImageNet. Experiments show that our CNN-RNN hybrid model achieves better accuracy in image classification tasks.
Advanced Signal Processing for Personal Image Encryption on Consumer Electronics

In this paper, an advanced signal encryption processing for personal image is developed, which provide a more secure way to protect the privacy of images transmitted through consumer electronics. A selected moving window is decided before operating the two main encryption stages, which divides the plain image into several sub-images. Shuffling pixels in binary is obtained in the permutation stage, and multi-level ciphering process, comprising cipher-data picking up as well as information hiding, asymmetric translation, is implemented in the diffusion stage. Simulation results show that the proposed advanced signal processing for image encryption is effective to protect the privacy of personal images.
Web-based management and visualization of myocardial ischemia data

Data visualization provides a way to receive information from the massive disordered medical data intuitively. In this paper, a web-based visualization system for myocardial ischemia information is established. On one hand, we use the ECharts to express different types of medical information, including medical records, and medical graphs. Based on this, the medical information can be represented in a novel way. On the other hand, we advance the existing health figures (hFigures). With the improved hFigures, an overview of a patient’s health evolution can be visualized. The proposed web-based visualization system can be helpful to achieve the collaborative work between the medical data management and the medical data visualization.
Study of Dynamic Group Evolution for Health Prediction of Mangrove Ecosystem

Many researchers have applied neural networks to mangrove ecosystem health research. However, it is challenging to find efficient training algorithms for neural networks, so the prediction results are often difficult to meet the needs. The dynamic group evolution (DGE) algorithm is a recently proposed metaheuristic algorithm, which exhibits a rapid convergence rate and good performance in searching and avoiding local optima. In the present study, we use DGE and back propagation (BP) for training feed-forward neural network and build a DGBPNN algorithm model, which takes into account both global exploration and local exploitation. In this paper, we use the model for man-grove ecosystem health prediction, and the performance of the proposed algorithm is determined by comparing its performance with those of other optimization training algorithms. The experimental results show that our proposed training algorithm exhibits promising performance in training neural networks and is expected to apply to more real-world problems.
Robust Higher Desensitized Cubature Kalman Filter for Uncertain Parameter

A robust fifth-degree desensitized cubature Kalman filter (5thDCKF) is proposed to estimates nonlinear state with uncertain parameters. State error sensitivities of the states and covariance about uncertain parameters are propagated by using the fifth-degree cubature points with weights. Then, a novel cost-function is modified by penalizing cost-function, which only includes trace of the posteriori covariance, with state error sensitivities and their weights. A closed-form gain matrix is calculated by minimizing the novel designed cost-function, and this 5thDCKF algorithm is derived. Numerical simulation with a falling body model demonstrates the good performance of the presented 5thDCKF.
An Exploration on the Effect of Augmented Reality Learning System on Situational Interest in Historical Building Guide

Starting with situational interest, this study explores how students can generate temporary situational interest in the current teaching situation, which may then affect their learning effectiveness, interest changes and learning behavior. As one of the elements for improvement of situational interest is to stimulate sensory perception of the environment, this study proposes a multimedia learning system based on augmented reality technology, observes and evaluates whether application of this system in guide can improve students' situational interest and the effectiveness of improvement in learning motivation and efficiency. In this study, 31 college students were arranged to use the system for guide in six historical sites such as Oxford College in Taiwan. The experimental results showed that the system presented excellent performance in terms of learning effectiveness and enhancing situational interest.
Research on Time Concealed Channel Technology of Cloud Computing Platform Based on Shared Memory

Security issues severely restrict the development and popularization of cloud computing. As a way of data leakage, covert channel greatly threatens the security of cloud platform. This paper introduces the types and research status of covert channels, and discusses the classical detection and interference methods of time-covert channels on cloud platforms for shared memory time covert channels.
Balanced Circulant Binary Convolutional Networks

Binary convolutional neural networks (BCNNs) are widely used to improve memory and computation efficiency of deep convolutional neural networks (DCNNs) for mobile and AI chips based applications. However, current BCNNs are not able to fully explore their corresponding full-precision models, causing a significant performance gap between them. In this paper, we propose balanced circulant binary convolutional networks (BCBCNs), towards optimized BCNNs, by balancing the distribution of feature maps while enhancing the orientation ability of kernels. In particular, we adjust the architecture by introducing more batch normalization (BN) layers and circulant convolutional layers in an end-to-end framework, which significantly improve the performance of BCNNs. This combination can be easily exploited into existing DCNNs such as LeNet and ResNet. Extensive experiments demonstrate the superior performance of the proposed BCBCNs over most state-of-the-art BCNNs.
Quantitative evaluation of the scope of application of hand motion recognition based on SVM

Pattern recognition based on surface electromyography (sEMG) has become a hot topic in the field of rehabilitation medicine. In this paper, the algorithm model of time-domain feature and SVM was used to classify 7 hand movements, and the applicable scope of this model was quantitatively evaluated through a series of experiments on 12 able-bodied subjects (including 5 children). In this paper, MYO armband and BioRadio are taken as examples to evaluate the influence of different sensors on their classification performance from three aspects of channel number, channel positions and sampling frequency of different devices. Finally, it is proved that when the number of channels is greater than or equal to 4, the classification performance of this algorithm will not change significantly (P=0.531) as long as the appropriate electrode placement position is selected. It is also proved that different sampling frequencies of different devices will not significantly affect their classification performance (P=0.092). At the same time, we found that age did not significantly affect the quality of sEMG signals by comparing the classification results of children and adults.
NIST Statistical Test for Random Sequence Generated by Möbius Function

Random sequences play important roles in many security applications. Several security protocols have been developed based on random sequences. Hence, their generation is one of the topic of interests among many cryptographic researchers. Previously, the authors proposed a pseudo random sequence over odd characteristic field which is generated by applying primitive polynomial, trace function and Mobius function. Some important properties such as period, autocorrelation and cross-correlation have been presented in previous work. Randomness is generally measured by statistical tests and NIST Statistical Test Suite (NIST STS) is one of the most popular tools for randomness analysis. In this work, the randomness of the generated sequence is investigated by NIST STS.
Attention-guided Face Alignment Based on Inverted Residuals

In this paper, we build an attention-guided network based on inverted residuals for face alignment. This network consists of a multi-resolution branches structure module and an attention-guided feature fusion module. These two modules employs inverted residuals to construct a light weight network. The multi-resolution branches structure consists of two branches, high-resolution branch and low-resolution branch. The high-resolution branch extracts low-level features from the original faces; The low-resolution branch generates the attention maps by using low-level features from the trunk. The attention-guided feature fusion module is designed to merge features from two branches based on attention mechanism. Extensive experiments are conducted on two public benchmark datasets, 300-W and AFLW. We compare our method with the state-of-the-art methods to indicate the superiority of our method. We achieve 3.49%mean error on 300-W full set, and 1.47%mean error on AFLW-Full datasets. Meanwhile, our method achieves the real-time detection (10ms) on 300-W with 68 landmarks.
Terrain Traversability Classification Based on SVDD

Terrain travel classification is one of the most important content for robot autonomous navigation, especially in terms of following robots. For this reason, a novel travel ability classification method which is suitable for following robots are studied in this paper. First, slope, roughness, and step height of elevation map are used to describe terrain features. Then, robots will acquire these parameters by means of leader track during following behind, and mark these parameters as terrain travel positive samples. By this, the terrain travel ability classification turns into one-class classification only with positive samples, because the robot may not get the full negative samples and it is dangerous for robots to acquire these samples. At last, the SVDD algorithm is used to train terrain classification model, which enables the robot to have the ability of terrain recognition and learning. The experiment results illustrate the feasibility and effectiveness of the proposed algorithms.
The Trojan Message Attack on the Pay-to-Public-Key-Hash Protocol of Bitcoin

Bitcoin is the first and seemingly the most successful cryptocurrency based in a peer-to-peer network that uses blockchain technology. Given Bitcoin’s growing real-life deployment and popularity, its security has aroused more and more attention in both financial and information industries. As a body containing a variety of cryptosystems, Bitcoin may also suffer from cryptanalysis attacks. This paper focuses on one of such attacks: the Trojan message attack, and presents in detail how to conduct the attack according to the structure and workflow of the Pay-to-Public-Key-Hash protocol of Bitcoin. The attack aims at forging an upcoming transaction record and results from the fact that all users’ candidate input transactions are open to the attacker. The construction of the attack employs a combination of the Bitcoin transaction structure with standard Merkle–Damgard extension vulnerabilities. The conclusion of the attack shows that both the mathematical structure of the hash function itself and the public information in the blockchain are important to the security of Bitcoin. These factors should be considered in the future for the design of other cryptocurrency and blockchain systems.
Note about Bias in Bayesian Genetic Algorithms for Discrete Missing Values Imputation

A Genetic Algorithm is a sophisticated searching technique that finds the best possible solution in the solution space. The search process is driven by a fitness function which measures the fitness level of a candidate solution. The chosen fitness function varies according to the problem being solved. However, any fitness function should satisfy some conditions. In the Bayesian genetic algorithm for missing values imputation we noted that the used fitness function performs poorly when be applied with datasets that contain missing values, since such datasets hold a level of bias which would adversely affect the efficiency of the entire searching process. In this paper we mentioned that some assumptions should hold in order to apply the technique of Bayesian genetic algorithm efficiently.
Fraudulence Detection and Recommendation of Trusted Websites

We live in the world of science where everything could be done possibly through online facilities. As the online services increase, the fraudulent sites also increase, however. Therefore, it enhances the difficulty of people to identify a fake or fraudulent or scam website. Fraudsters are highly intelligent in making or persuading fake products which seem exactly same as the original. Some scam websites use low prices to attract people. Most of the fraudulent sites use domain names that reference a popular brand or product name and design with mere change. Some websites will get your card details before you intend to buy a product. Fraudsters try to attract people’s attention by advertising the fake products in many social websites such as Facebook, Instagram and Twitter, and when people wishes to view about the product and its details, they are forced to install an app of that site. So, here we have a solution to find those fraudulent products and recommend some trusted products for people to buy with good quality and at a fair price. We use the reviews or feedback or comments and the rating from the users who already purchased the product and shared their experience for identifying fraudulent products and giving a caution to the remaining people. This paper concentrates on analyzing the reviews and extracting the useful information to guide the customers using text mining. As a result of this, people will get a better idea of making a proper decision in buying the product through online.
Deep Reinforcement Learning for the Fighter Theater

The Fighter Theater is an AI game in which heroes of the two sides compete against the opposite side by finding energy stones or fighting against enemy heroes based on state-machine algorithm. We propose new strategies to heroes from each side and modify the state-machine algorithm to increase the confrontations of the game environment. Besides, inspired by deep reinforcement learning, we use convolutional neural network to model Q-function and implement Deep Q-Networks with experience replay to train one side of the game. Experimental result demonstrates despite the high dimensionality of raw pixel input, the side with Deep Q-learning can boost the performance of heroes by finding an optimal policy and is capable of outperforming the opposite side that is controlled by the state-machine algorithm.
Information Disclosure, Project Popularity and Crowdfunding Performance

Starting from the information disclosure and project popularity of crowdfunding projects, this paper explores the influence and difference of crowdfunding projects on the completion rate of crowdfunding and social support. 955 valid data of the end of the online crowdfunding project were collected, and multiple linear regression was used for hypothesis testing. It was found that it has a significant difference in the impact on the social support of crowdfunding and the achievement rate of crowdfunding, in which the experience of crowdfunding has a significant positive impact on the achievement rate of crowdfunding, and the project attention has the highest and significant impact on the social support of crowdfunding. The research on the results of crowd-funding has been enriched by reasonably considering the social support degree into the research on the results of crowd-funding.
Blind Panoramic Image Quality Assessment Based on Project-Weighted Local Binary Pattern

The majority of existing objective panoramic image quality assessment algorithms are based on peak signal to noise ratio (PSNR) or structural similarity (SSIM). However, they are not highly consistent with human perception. In this paper, a new blind panoramic image quality assessment metric is proposed based on project-weighted gradient local binary pattern histogram (PWGLBP), which explores the structure degradation in sphere by combining with the nonlinear transformation relationship between the projected plane and sphere. Finally, support vector regression (SVR) is adopted to learn a quality predictor from feature space to quality score space. The experimental results demonstrate the superiority of our proposed metric compared with state-of-the-art objective PIQA methods.