Reviewerly/exHarmony huggingface dataset:
Experimenting with smart contracts for access control and delegation in IoT

In a Smart City scenario, the authors envisioned an IoT-Cloud framework for the management of boards and resources scattered over a geographic area. It can also become a tool to let device owners contribute freely to the infrastructure. In this paper, we present an authorization and delegation model for the IoT-Cloud based on blockchain technology. We focus on smart city’s design and simulation of smart contracts to address the specific characteristics of smart environments and analyze access control and delegation mechanism in IoT. In particular, the scheme is implemented for the Ethereum platform in the form of smart contracts. We present the theoretical analysis of the proposed solution. We perform experiments on local testnet Ganache and public testnet Rinkeby to evaluate the performance of the presented models. Finally, we discuss the results and limitations of the system and possible solutions to issues.
Investigative Protocol Design of Layer Optimized Image Compression in Telemedicine Environment

Telemedicine in India is expanding and thus it is essential for application to reduce storage and transmission bandwidth. The prime objective in this paper is to demonstrate an investigative protocol for medical image compression in telemedicine. The method is based on Integer Wavelet Transform (IWT) and Huffman's Coding for image compression with an added novelty of machine learning terminology for extracting the region of image based on pixel density and optimized layering for neighboring pixel ratio analysis. The experimental results are evaluated and demonstrated on open medical datasets and has acquired minimal predictive error, improvised Signal to Noise Ratio (SNR) with respect to Compression Ratio (CR) ratio and optimized bandwidth under transmission.
Multilevel Identification and Classification Analysis of Tor on Mobile and PC Platforms

In digitalized and automated systems, more and more intelligent devices have become an import part of industrial Internet of Things (IIOT). However, the lack of security in IIOT makes people facing unprecedented threats from the Dark web. Traffic classification is an important means to prevent anonymous attacks. However, the growing usage of smartphones in daily life is deeply changing the nature of network traffic, which makes traffic classification more challenging. In this article, we propose a Tor traffic identification and multilevel classification framework based on network flow features, which realizes the identification of anonymous traffic (L1), traffic types (L2) of anonymous traffic, and applications (L3) on a mobile and a PC platform, respectively. We further analyze differences between the mobile and the PC platform. We conclude that the impact of time-related features is higher than that of the nontime-related features on the mobile platform, while it is opposite on the PC platform. And it is more difficult to identify and classify Tor types (L2) and specific Tor applications (L3) on the mobile platform than on the PC platform, including using different number of features and early identification and classification.
Representation of <i>k</i>-Mer Sets Using Spectrum-Preserving String Sets

Given the popularity and elegance of k-mer-based tools, finding a space-efficient way to represent a set of k-mers is important for improving the scalability of bioinformatics analyses. One popular approach is to convert the set of k-mers into the more compact set of unitigs. We generalize this approach and formulate it as the problem of finding a smallest spectrum-preserving string set (SPSS) representation. We show that this problem is equivalent to finding a smallest path cover in a compacted de Bruijn graph. Using this reduction, we prove a lower bound on the size of the optimal SPSS and propose a greedy method called UST (Unitig-STitch) that results in a smaller representation than unitigs and is nearly optimal with respect to our lower bound. We demonstrate the usefulness of the SPSS formulation with two applications of UST. The first one is a compression algorithm, UST-Compress, which, we show, can store a set of k-mers by using an order-of-magnitude less disk space than other lossless compression tools. The second one is an exact static k-mer membership index, UST-FM, which, we show, improves index size by 10%-44% compared with other state-of-the-art low-memory indices.
CaptionNet: A Tailor-made Recurrent Neural Network for Generating Image Descriptions

Image captioning is a challenging task of visual understanding and has drawn more attention of researchers. In general, two inputs are required at each time step by the Long Short-Term Memory (LSTM) network used in popular attention based image captioning frameworks, including image features and previous generated words. However, error will be accumulated if the previous words are not accurate and the related semantic is not efficient enough. Facing these challenges, a novel model named CaptionNet is proposed in this work as an improved LSTM specially designed for image captioning. Concretely, only attended image features are allowed to be fed into the memory of CaptionNet through input gates. In this way, the dependency on the previous predicted words can be reduced, forcing model to focus on more visual clues of images at the current time step. Moreover, a memory initialization method called image feature encoding is designed to capture richer semantics of the target image. The evaluation on the benchmark MSCOCO and Flickr30K datasets demonstrates the effectiveness of the proposed CaptionNet model, and extensive ablation studies are performed to verify each of the proposed methods. The project page can be found in https://mic.tongji.edu.cn/3f/9c/c9778a147356/page.htm.
A Novel Learning Rate Schedule in Optimization for Neural Networks and It’s Convergence

The process of machine learning is to find parameters that minimize the cost function constructed by learning the data. This is called optimization and the parameters at that time are called the optimal parameters in neural networks. In the process of finding the optimization, there were attempts to solve the symmetric optimization or initialize the parameters symmetrically. Furthermore, in order to obtain the optimal parameters, the existing methods have used methods in which the learning rate is decreased over the iteration time or is changed according to a certain ratio. These methods are a monotonically decreasing method at a constant rate according to the iteration time. Our idea is to make the learning rate changeable unlike the monotonically decreasing method. We introduce a method to find the optimal parameters which adaptively changes the learning rate according to the value of the cost function. Therefore, when the cost function is optimized, the learning is complete and the optimal parameters are obtained. This paper proves that the method ensures convergence to the optimal parameters. This means that our method achieves a minimum of the cost function (or effective learning). Numerical experiments demonstrate that learning is good effective when using the proposed learning rate schedule in various situations.
Rehabilitation Exercise Recognition and Evaluation Based on Smart Sensors With Deep Learning Framework

Exercise therapy is seen as one of the major treatments for the rehabilitation for patients, particularly using modern technologies, such as virtual reality or augmented reality.Computer-assisted physical rehabilitation training involves measuring performance by analyzing the movement data collected with a sensory system during prescribed rehabilitation exercises.Human activity recognition is a challenging topic for machine learning in the present area of research.Since the sensor-based activity recognition seeks deep knowledge from various low-level sensor readings concerning human activities.In this paper, the Smart Sensor-based Rehabilitation Exercise Recognition (SSRER) system has been proposed using a deep learning framework.For the recognition of rehabilitation exercise with sensor information, a convolutional neural network (CNN) has been used on dynamic platform(D-CNN) where it has sensory data for physical rehabilitation exercise body movement by Gaussian mixture models (GMM).The input signals and GMMs are in various segments contains shapes for many CNN routes.To retrieve the state transition likelihood of hidden states, the Sensor (S-CNN) utilizes the algorithm of improved lossless information compression as discriminant features of various movements.Therefore, the hybridized CNN of the Sensor (S-CNN) and D-CNN are combined with a deep learning classifier to assess every rehabilitation class exercise at different levels.The categorized deep learning methods show improved performance with best-learned features for any rehabilitation exercise.The difference between the best attribute and the test score analyzed mathematically with our collected data and a variety of activity recognition datasets has been illustrated in this article with test results.
Transfer Learning for Photovoltaic Power Forecasting with Long Short-Term Memory Neural Network

Data-driven modeling is one of the research hotspots of photovoltaic (PV) power prediction. However, for some newly built PV power plants, there are not enough historical data to train an accurate model. Therefore, constructing a forecasting model for the PV plants lacking historical data is an urgent problem to be solved. In this paper, we propose a method to transfer the knowledge obtained from historical solar irradiance data to the output prediction. Firstly, based on the historical solar irradiance data, the hyperparameters of the long short-term memory neural network (LSTM) are optimized and the weights in the neurons are pre-trained, then fine-tuning the deep transfer model with PV output data. In this way, knowledge can be transferred from solar irradiance data to PV output data. The experimental results show that the proposed method can significantly reduce the prediction error.
Establishment of college English teachers’ teaching ability evaluation based on Clementine data mining

Teacher evaluation, an important part of teaching quality management, has an important impact on the cultivation of talents. As scientific and objective teaching evaluation can objectively measure the effectiveness of teachers’ teaching means, it is an important criterion for teaching authorities t o evaluate teachers’ teaching methods as well as effective means and important ways to improve teaching methods. The evaluation method of traditional English teachers’ teaching ability is subject to subjective factors, which leads to unreliable evaluation results. This paper describes the use of data mining software Clementine to introduce data mining technology into teaching management, extracts useful information hidden from teaching evaluation data, and explores key factors that influence the quality of teaching. Meanwhile, this paper anonymizes and encodes the data based on data mining technology, discretizes the teaching characteristic variables of English teachers with a large number of eigenvalues, processes teaching data by rough set reduction method, processes English teachers’ teaching database to be mined by rough set method, determines the decision table, sets minimum support and minimum confidence, obtains association rules through association rule mining method to realize English teachers’ teaching data mining, obtains the evaluation indicators through principal component analysis, calculates the English teacher’s teaching ability index, and evaluates English teachers’ teaching ability according to the index value at last. The experimental results show that the evaluation results of the proposed method are reliable and can reflect the true ability of teachers, thus providing a decision-making reference for the management of teaching authorities.
Development of Electronic Information and Educational Environment of the University 4.0 and Prospects of Integration of Engineering Education and Humanities

This article analyzes the importance and potential of e-learning and online learning in the integration of engineering education and humanities. The authors analyzed the views of both domestic and foreign experts on the role of strengthening the relationship between technical and humanitarian principles of engineering education in the renovation of a higher technical school and training of specialists with a set of competencies corresponding to challenges of the 4th industrial revolution. The key trends in the changed humanitarian component of training of engineering personnel, its relationship with the complex of technical and natural disciplines in the context of implementation of the new University 4.0 model were considered. It is concluded that effective interconnection between technical and humanitarian knowledge is a necessary component of renovation of professional training of technical specialists and the University 4.0 model. In this situation, the prospects for integration of engineering education and humanities are justifiably linked with further development and improvement of the electronic and online learning system. This option is the most optimal, cost-effective and painless (not requiring a fundamental change in the curriculum), and it coincides with the basic directions of renovation of technical universities and optimization of the educational process. Further modernization of higher professional education with regard to the trends in the knowledge economy, which determined the new University 4.0 model, is inextricably linked with the improvement of the e-learning system.
A Coarse-to-Fine Facial Landmark Detection Method Based on Self-attention Mechanism

Facial landmark detection in the wild remains a challenging problem in computer vision. Deep learning-based methods currently play a leading role in solving this. However, these approaches generally focus on local feature learning and ignore global relationships. Therefore, in this study, a self-attention mechanism is introduced into facial landmark detection. Specifically, a coarse-to-fine facial landmark detection method is proposed that uses two stacked hourglasses as the backbone, with a new landmark-guided self-attention (LGSA) block inserted between them. The LGSA block learns the global relationships between different positions on the feature map and allows feature learning to focus on the locations of landmarks with the help of a landmark-specific attention map, which is generated in the first-stage hourglass model. A novel attentional consistency loss is also proposed to ensure the generation of an accurate landmark-specific attention map. A new channel transformation block is used as the building block of the hourglass model to improve the model's capacity. The coarse-to-fine strategy is adopted during and between phases to reduce complexity. Extensive experimental results on public datasets demonstrate the superiority of our proposed method against state-of-the-art models.
Dynamic gesture recognition based on feature fusion network and variant ConvLSTM

Gesture is a natural form of human communication, and it is of great significance in human–computer interaction. In the dynamic gesture recognition method based on deep learning, the key is to obtain comprehensive gesture feature information. Aiming at the problem of inadequate extraction of spatiotemporal features or loss of feature information in current dynamic gesture recognition, a new gesture recognition architecture is proposed, which combines feature fusion network with variant convolutional long short-term memory (ConvLSTM). The architecture extracts spatiotemporal feature information from local, global and deep aspects, and combines feature fusion to alleviate the loss of feature information. Firstly, local spatiotemporal feature information is extracted from video sequence by 3D residual network based on channel feature fusion. Then the authors use the variant ConvLSTM to learn the global spatiotemporal information of dynamic gesture, and introduce the attention mechanism to change the gate structure of ConvLSTM. Finally, a multi-feature fusion depthwise separable network is used to learn higher-level features including depth feature information. The proposed approach obtains very competitive performance on the Jester dataset with the classification accuracies of 95.59%, achieving state-of-the-art performance with 99.65% accuracy on the SKIG (Sheffifield Kinect Gesture) dataset.
A generic framework for sentiment analysis: Leveraging opinion-bearing data to inform decision making

The increased exposure of the average citizen and customer to polarised content from various sources has been of significant consequence for companies and governmental organisations. Such content has, for example, served as a catalyst for violent uprisings and shifts in stock market prices. The collection and study of opinion have therefore become a necessity in many industries. Due to the vast nature of such data, manual approaches to this problem are no longer feasible. Several computational approaches have been proposed within the field of sentiment analysis, which successfully address many aspects of this problem, such as the classification of data into one of several sentiment categories. The research in the field is lacking, however, with respect to the integration and application of these techniques in practice, as well as their incorporation into the decision-making process of affected entities. In this paper, a generic framework for sentiment analysis is proposed, with a focus on facilitating the model development process for a user in a manner such that good performance may be achieved irrespective of the problem domain, as well as facilitating a flexible, exploratory analysis of model results in combination with existing structured attributes in order to gain actionable insights. The objective of the framework is to aid organisations in successfully leveraging unstructured, opinion-bearing data in combination with structured data sources to inform decision making.
Blockchain Applications in Human Resources Management

Blockchain is perceived as a revolutionary technology offering considerable impact on a vast magnitude of sectors including healthcare, supply chain, Internet of Things (IoT), finance, voting systems, property and real-estate management, e-government, higher education, etc. This paper examines the results of an empirical study that conducted ten in-depth, one-to-one semi-structured interviews with Human Resources Management (HRM) experts in order to identify the potential opportunities related to blockchain technology utilization in the HRM domain as well as the anticipated adoption challenges that may hinder its utilization. The study is of significance to both blockchain and HRM researchers who can utilize the proposed opportunities as a future research agenda and work towards mitigating the anticipated adoption challenges.
Application of multi-dimensional wavelet transform to fluid mechanics

This paper first reviews the application research works of wavelet transform on the fluid mechanics. Then the theories of continuous wavelet transform and multi-dimensional orthogonal (discrete) wavelet transform, including wavelet multiresolution analysis, are introduced. At last the applications of wavelet transform on 2D and 3D turbulent wakes and turbulent boundary layer flows are described based on the hot-wire, 2D particle image velocimetry (PIV) and 3D tomographic PIV.
What 5 million job advertisements tell us about testing

Software testing is a crucial part of business success to ensure final product quality. However, little concrete data exists on technical demands about it in the industry, mostly collected through personal opinion surveys on a restricted sample of professionals.
Voltages prediction algorithm based on LSTM recurrent neural network

Because of the characteristic of delay of adaptive optics system, the performance of adaptive optics system can be improved effectively by predicting the deformation mirror voltages. This paper presents a nonlinear voltages prediction algorithm based on LSTM recurrent neural network. It is found that the predicted voltages by LSTM neural network are consistent with the actual voltages. By comparing the prediction results of the proposed algorithm with those of other prediction algorithms, the prediction algorithm based on LSTM has higher prediction accuracy. This algorithm has strong applicability and higher accuracy in adaptive optics voltages prediction.
Exam paper generation based on performance prediction of student group

Exam paper generation is an indispensable part of teaching. Existing methods focus on the use of question extraction algorithms with labels for each question provided. Obviously, manual labeling is inefficient and cannot avoid label bias. Furthermore, the quality of the exam papers generated by the existing methods is not guaranteed. To address these problems, we propose a novel approach to generating exam papers based on prediction of exam performance. As such, we update the quality of the initially generated questions one by using dynamic programming, as well as in batches by using genetic algorithms. We performed the prediction task by using Deep Knowledge Tracing. Our approach considered the skill weight, difficulty, and distribution of exam scores. By comparisons, experimental results indicate that our approach performed better than the two baselines. Furthermore, it can generate exam papers with adaptive difficulties closely to the expected levels, and the related student exam scores will be guaranteed to be relatively reasonable distribution. In addition, our approach was evaluated in a real learning scenarios and shows advantages.
Cumulative belief peaks evidential K-nearest neighbor clustering

This paper introduces a new evidential clustering algorithm based on finding the "cumulative belief peaks" and evidential K-nearest neighbor rule. The basic assumption of this algorithm is that a cluster center has the highest cumulative possibility of becoming a cluster center among its neighborhood and size of its neighborhood is relatively large. To measure such cumulative possibility, a new notion of cumulative belief is proposed in the framework of belief functions. By maximizing an objective function, an appropriate size of the relatively large neighborhood is determined. Then, the objects with highest cumulative belief among their own neighborhood of this size are automatically detected as cluster centers. Finally, a credal partition is derived by evidential K-nearest neighbor rule with the fixed cluster center. Experimental results show that the proposed evidential clustering algorithm can automatically detect cluster centers and well reveal the data structure in form of a credal partition in tolerable time, when tackling datasets with small number of data objects and dimensions. As the sizes of datasets increase, running time of such new clustering algorithm increases sharply and this reduces the practicability of it. Simulations on synthetic and real-world datasets validate our conclusions.