Reviewerly/exHarmony huggingface dataset:
An adaptive switchover hybrid particle swarm optimization algorithm with local search strategy for constrained optimization problems

Practical engineering optimization problems are almost constrained optimization problems and difficult to be solved effectively, therefore, how to handle these problems has attracted more and more attention. Particle swarm optimization (PSO) is one of the most popular algorithms in solving the complicated optimization problems due to its relatively strong global optimization capability and low requirement for computing resources. However, PSO is easy to converge prematurely like other swarm intelligence algorithms due to the loss of diversity among particles. This article proposes an adaptive switchover hybrid PSO framework with local search process (ASHPSO), which adaptively switches the optimization searching process between the standard PSO and the differential evolution (DE) modified by a full dimension crossover strategy to avoid the premature convergence problem. Moreover, a local search strategy is employed to improve the boundary search capability of PSO in consideration of the engineering problems characteristics. Experiments on 28 well-known benchmark functions, 5 engineering problems and a full vehicle multi-disciplinary optimization problem demonstrate the effectiveness of the proposed algorithm compared with other hybrid variants.
Speckle patterns for DIC in challenging scenarios: rapid application and impact endurance

Abstract Digital image correlation (DIC) is a widely used technique in experimental mechanics for full field measurement of displacements and strains. The subset matching based DIC requires surfaces containing a random pattern. Even though there are several techniques to create random speckle patterns, their applicability is still limited. For instance, traditional methods such as airbrush painting are not suitable in the following challenging scenarios: (i) when time available to produce the speckle pattern is limited and (ii) when dynamic loading conditions trigger peeling of the pattern. The development and application of some novel techniques to address these situations is presented in this paper. The developed techniques make use of commercially available materials such as temporary tattoo paper, adhesives and stamp kits. The presented techniques are shown to be quick, repeatable, consistent and stable even under impact loads and large deformations. Additionally, they offer the possibility to optimise and customise the speckle pattern. The speckling techniques presented in the paper are also versatile and can be quickly applied in a variety of materials.
Hand Gesture Recognition using Deep Feature Fusion Network based on Wearable Sensors

Hand gesture recognition is an important way for human machine interaction, and it is widely used in many areas, such as health care, smart home, virtual reality as well as other areas. While many valuable efforts have been made, it still lacks efficient ways to capture fine grain hand gesture as well as track data of long distance dependency in complex gesture. In this paper, we firstly design a novel data glove with two arm rings and a specially integrated three-dimensional flex sensor to capture fine grain motion from full arm and all knuckles. Secondly, an improved deep feature fusion network is proposed to detect long distance dependency in complex hand gestures. In order to track detailed motion features, a convolutional neural network based feature fusion strategy is given to fuse data from multi-sensors by extracting both shallow and deep features. Moreover, a residual module is introduced to avoid over fitting and gradient vanishing during deepening the neural network. Thirdly, a long short term memory (LSTM) model with fused feature vector as input is introduced to classify complex hand motions into corresponding categories. Results of comprehensive experiments demonstrate that our work performs better than related algorithms, especially in America sign language (with the precise of 99.93%) and Chinese sign language (with the precise of 96.1%).
Immersive Learning Predicted: Presence, Prior Knowledge, and School Performance Influence Learning Outcomes in Immersive Educational Virtual Environments

Media Learning is an internal process nested within a complex combination of learner-specific and external factors. The Educational Framework for Immersive Learning hypothesizes learners' presence, motivational traits, emotional states, cognitive capabilities, and previous knowledge as predictors of learning outcomes in immersive educational virtual environments. This article proposes a research model for investigating relations between these variables that seem to be crucial for explaining Immersive Learning processes. Three Virtual Realities for learning three topics of Computer Science Education (components of a computer, asymmetric cryptography, and finite state machines), each provided on three distinct levels of technological immersion, were used to carry out a study with 78 participants. Path analysis was used to test the hypotheses deriving from the research model, showing that presence, prior knowledge about the content, and school performance influence learning outcomes. Presence was predicted by the users' academic emotional states prior to the study and the provided level of immersion. The emotional states were influenced by the students' school performance. Prior knowledge and school performance of the students were affected by the motivational variables. This study contributes to existing research as it adds factors that are crucial for learning processes to the discussion on Immersive Learning.
Is code law? Current legal and technical adoption issues and remedies for blockchain-enabled smart contracts

Blockchain technology has enabled so-called smart contracts between different parties on a decentralized network. These self-enforceable and self-executable computerized contracts could initiate a fundamental paradigm shift in the understanding and functioning of our legal practices. Opportunities for their application are increasingly understood, and numerous tests of feasibility have been completed. However, only very few use cases have yet been implemented at scale. This article—as the first of its kind—comprehensively analyzes the underlying challenges and locates a key reason for the slow adoption in the discrepancy between legal requirements and IT capabilities. Our work combines a wide range of academic sources and interviews with 30 domain experts from IT, the legal domain and private industry. First, we establish that smart contracts still fall within the boundaries of the general legal framework. We then systematically dissect current shortcomings of smart contracts on three distinct levels, namely, (1) how smart contracts are likely to cause conflicts with existing laws, (2) how smart contracts are intrinsically limited on an individual contract level and (3) how they are impeded by their current technical design. Across those levels, we dissect 20 distinct issues concerning the current implementation of smart contracts for which we derive potential remedies. We further outline implications for policy-makers as well as IT management, and examine how information systems research can play an important role in advancing smart contracts. Finally, we show how managerial and organizational issues might represent an ongoing challenge for the widespread adoption of smart contracts.
K-Means and K-Medoids: Cluster Analysis on Birth Data Collected in City Muzaffarabad, Kashmir

In the field of medical, each and every analysis is decisive as the study links to life of the subject under observation. One of the most vital area in the field of medical is the healthcare of expecting women in low income countries. High mortality rate due to increased number of caesarean section is evident because of poor medical infrastructure in the region, misunderstood religious teachings, low education and lack of proper decision making at the right time. The root cause analysis of situations demanding caesarean section is a tough job, however in the presence of historical data, one may extract useful information that will help supporting a medical decision by predicting the outcome. It is obvious that regional disparities have a huge impact on the residents of that region. A study performed on any region cannot be all applicable to the residents of some other distant region. This motive has established grounds to conduct a local study upon the data collected from expecting women in city Muzaffarabad, Kashmir. It is believed that the findings of this study will be significant for women that share more or less similar physical, social and maternal traits. Keeping this in mind, study presents an analysis of two clustering techniques for the investigation of appropriate algorithm that groups data into relevant clusters robustly. Firstly, we analyzed K-means and K-medoids algorithms' capability to cluster the data using different distance metrics. Secondly, data transformation techniques including scale, range and Yeo-Johnson are applied. Finally, transformed data are used in K-means and K-medoids algorithms' to generate cluster accuracy. It is observed that the results produced from transformed data are better than using raw data. Yeo-Johnson transformation method is found best for k-means (Hartigan & Wang), K-medoids (SEV distance function) and Rank k-medoids (SEV distance function) with mean accuracy 67.58%, 69.58% and 72.64% respectively.
Density peaks clustering with gap-based automatic center detection

Clustering is a task used to group data from variegated sources, including Big Data, the Internet of Things, and social media. Density peaks clustering (DPC) has become a popular clustering technique for its simplicity and quality. However, DPC requires a proper subset of input data points to be selected as centers using a plot called “decision graph”. This manual specification adds subjectivity and instability, besides breaking the continuous flow of the algorithm. Automatic center detection approaches struggle with obtaining good results while avoiding to add parameters and complexity to the algorithm. We propose an approach to automatically determine cluster centers by detecting gaps between data points in a one-dimensional version of the decision graph; we detect these gaps heuristically by comparing the distance (difference) between pairs of consecutive points in terms of their gamma score. We tested our approach on synthetic and UCI data sets. Results show that the number of clusters is accurately predicted in comparison to other state-of-the-art methods using F-score and Adjusted Rand Index.
Limited-projection volumetric tomography for time-resolved turbulent combustion diagnostics via deep learning

Time-resolved volumetric tomography (VT) has been applied extensively for turbulent flow/combustion diagnostics, due to its great capacity in reconstructing three dimensional scalar/vector fields. However, it usually suffers from high computational costs of conventional iterative methods in processing thousands of tomographic frames, and also the requirement of multiple high-speed camera/intensifiers to ensure sufficient spatial sampling, resulting in high experimental costs. In this work, we aim to take the full advantage of the recent progress in deep learning algorithms and develop an inversion method which not only reduces the processing time of a single frame down to the milliseconds level but also the number of projections required without sacrificing the imaging quality. Two distinct frameworks of convolutional neural network were designed and tested for VT reconstructions of turbulent flames for the first time. The results from proof-of-concept experiments implementing computed tomography of chemiluminescence (CTC) confirmed the feasibility of our method. Our data-driven approach can expedite the reconstruction process by a factor of ∼ 105 compared with conventional iterative methods (e.g., algebraic reconstruction technique). This work is expected to be valuable for all tomographic modalities which are seeking expedited reconstruction and reduced costs.
Unsupervised Learning of Optical Flow With CNN-Based Non-Local Filtering

Estimating optical flow from successive video frames is one of the fundamental problems in computer vision and image processing. In the era of deep learning, many methods have been proposed to use convolutional neural networks (CNNs) for optical flow estimation in an unsupervised manner. However, the performance of unsupervised optical flow approaches is still unsatisfactory and often lagging far behind their supervised counterparts, primarily due to over-smoothing across motion boundaries and occlusion. To address these issues, in this paper, we propose a novel method with a new post-processing term and an effective loss function to estimate optical flow in an unsupervised, end-to-end learning manner. Specifically, we first exploit a CNN-based non-local term to refine the estimated optical flow by removing noise and decreasing blur around motion boundaries. This is implemented via automatically learning weights of dependencies over a large spatial neighborhood. Because of its learning ability, the method is effective for various complicated image sequences. Secondly, to reduce the influence of occlusion, a symmetrical energy formulation is introduced to detect the occlusion map from refined bi-directional optical flows. Then the occlusion map is integrated to the loss function. Extensive experiments are conducted on challenging datasets, i.e. FlyingChairs, MPI-Sintel and KITTI to evaluate the performance of the proposed method. The state-of-the-art results demonstrate the effectiveness of our proposed method.
Breast cancer histopathology image classification using kernelized weighted extreme learning machine

Abstract Histopathology is considered as the gold standard for diagnosing breast cancer. Traditional machine learning (ML) algorithm provides a promising performance for cancer diagnosis if the training dataset is balanced. Nevertheless, if the training dataset is imbalanced the performance of the ML model is skewed toward the majority class. It may pose a problem for the pathologist because if the benign sample is misclassified as malignant, then a pathologist could make a misjudgment about the diagnosis. A limited investigation has been done in literature for solving the class imbalance problem in computer‐aided diagnosis (CAD) of breast cancer using histopathology. This work proposes a hybrid ML model to solve the class imbalance problem. The proposed model employs pretrained ResNet50 and the kernelized weighted extreme learning machine for CAD of breast cancer using histopathology. The breast cancer histopathological images are obtained from publicly available BreakHis and BisQue datasets. The proposed method achieved a reasonable performance for the classification of the minority as well as the majority class instances. In comparison, the proposed approach outperforms the state‐of‐the‐art ML models implemented in previous studies using the same training‐testing folds of the publicly accessible BreakHis dataset.
A New Fuzzy-DNA Image Encryption and Steganography Technique

Image encryption and steganography techniques are receiving a lot of interest and investigations due to their high importance in multimedia communication systems. A novel highly efficient image encryption and steganography technique are presented in this paper. For the first time, the proposed technique uses hybrid DNA encoding and Choquet's Fuzzy Integral sequences. At first, a confused version of the image, using a simple chaotic map, is encoded using DNA's bases. Four coded images are generated using the four DNA bases, namely AT, CG, GC, and TA. Parallel to that, a Choquet's fuzzy Integral sequence is generated and DNA encoded similarly to obtain four pseudo-random sequences. Secondly, the resulting four fuzzy/DNA sequences are used to diffuse the four DNA encoded images using the complementary DNA XOR rule, according to certain control code. Finally, the wavelet fusion algorithm is then used to fuse the resulting four fuzzy-DNA encoded images, to get the encrypted image. For added security, a new steganography approach is used. In particular, the encrypted image is divided into four sub-images, each of which is hidden in a different carrier image selected from a known group of carrier images according to a given key. The simulation results and security analysis confirmed the efficiency of the proposed image encryption algorithm as well as the steganography approach used for enhanced security. Ten different images with a size of 256 x 256 are used to test the proposed method. The results show that the proposed algorithm has a higher key sensitivity. The pixel correlation coefficient values are very small (between 5.3220e-04 and 0.0011 horizontally, between 8.7670e-04 and 0.0022 vertically and between 0.0002 and 0.0045 diagonally). Furthermore, the measured information entropy of the encrypted images is between 7.9970 and 7.9979 which are very close to the ideal value of 8. Additionally, the measured unified average changing intensity and number of pixels change rate values take the values between 33.46 and 33.39 and between 99.61 and 99.64, respectively, which are again closed to the ideal values. The steganography test shows that the hidden encrypted images are almost invisible at high values of SNR and are characterized by good NCC values under different types of attacks. The performance of the new proposed algorithm is proved to overcomes many other previously published image encryption techniques.
Event prediction based on evolutionary event ontology knowledge

The evolution and development of breaking news events usually present regular patterns, leading to the happening of sequential events. Therefore, the analysis of such evolutionary patterns among events and prediction to breaking news events from free text is a valuable capability for decision support systems. Traditional systems tend to focus on contents distribution information but ignore the inherent regularity of evolutionary events. We introduce evolutionary event ontology knowledge (EEOK) structuring the evolutionary patterns in five different event domains, namely Explosion, Conflagration, Geological Hazard, Traffic Accident, Personal Injury. Based on EEOK which provides a representing general-purpose ontology knowledge, we also explore a framework with a pipeline semantic analysis procedure of event extraction, evolutionary event recognition, and event prediction. Since the evolutionary event under each event domain has different evolution patterns, our proposed event prediction model combines the event types to capture the inherent regulation of evolutionary events. Comparative analyses are presented to show the effectiveness of the proposed prediction model compared to other alternative methods.
Developing a Reliable Service System of Charity Donation During the Covid-19 Outbreak

Drawing upon the functional characteristics of blockchain technology, this article envisages the feasibility and reliability of developing a charity donation service system loaded onto blockchain in response to the complex service demands encountered by charity operators due to the Covid-19 epidemic. With blockchain technology's support as the underlying data book, this article focuses on the practical issues of charity donation fund and material allocation, as well as information release and sharing, charity donation organization, and organization self-management. The paper thereby discusses the key technologies in terms of overall structure design, specific service sector, and functional design of the donation service system and further summarizes the operational mechanism of the system as combined with the needs of help-seeking, receiving, and management users. It is argued that all the above proposals have the potential to alleviate the trust crisis of charity services in China in view of low transparency. The paper expects to provide a useful reference for charity business innovation propelled by blockchain technology.
Hyperledger Fabric Performance Characterization and Optimization Using GoLevelDB Benchmark

Hyperledger Fabric is an implementation that enables permissioned blockchains, which provide a general blockchain framework with identifiable participants for a variety of business applications. Although many performance issues of Hyperledger Fabric have been alleviated to some extent, its performance is still limited - e.g. 2.2k transactions per second in our experiment that executes two reads and two writes in a transaction. A major performance bottleneck is incurred by accesses to the databases that store the latest key-value pairs in the ledger data, indexes to transactions, and the update history. In this paper, we characterize the performance of database systems used in Hyperledger Fabric to identify optimization opportunities by running a Hyperledger Fabric GoLevelDB (HLF-GLDB) benchmark. We developed HLF-GLDB as a standalone benchmark to simulate database accesses in Hyperledger Fabric. Results of the performance characterization revealed that: (1) the data compression of GoLevelDB is a major performance bottleneck in Hyperledger Fabric, and disabling the compression improved the performance by 54%; (2) the size of a database affects the performance significantly. For example, when the size increased by four times, the performance degraded by 25%; (3) To reduce the database access overhead in chaincode, it is better to combine small values so that they can be represented by a single key.
A Novel Teaching Strategy Through Adaptive Learning Activities for Computer Programming

Contribution: This article presents the instruction of computer programming using adaptive learning activities considering students' cognitive skills based on the learning theory of the Revised Bloom Taxonomy (RBT). To achieve this, the system converts students' knowledge level to fuzzy weights, and using rule-based decision making, delivers adequate learning activities regarding their kind and complexity degree. Background: Tutoring through adaptive learning activities can be a powerful tool to support learners in practical courses, like computer programming. However, published results from pertinent literature are not oriented to the adaptivity of the domain knowledge in terms of the volume, kind, and complexity of the learning activities delivered to students. There is scope for a lot of improvement to this direction. Intended Outcomes: Combining learning theories with adaptive tutoring enhances student-centered learning, promotes student engagement, and improves knowledge acquisition. Application Design: An adaptive tutoring system was developed for supporting undergraduate students in the C# programming language course for an academic semester. The system delivers adaptive learning activities to students' cognitive skills using the technology of fuzzy weights in a rule-based decision-making module and the learning theory of a RBT for designing the learning material. Findings: At the end of the academic semester, students' data have been collected and a detailed evaluation was conducted. The results showed that the presented approach outperforms others which lack adaptivity in domain knowledge and learning theories, improving significantly the students' learning outcomes.