Reviewerly/exHarmony huggingface dataset:
Enabling machine learning‐based side‐chaining for improving <scp>QoS</scp> in blockchain‐powered <scp>IoT</scp> networks

Abstract Problem The Internet of Things (IoT) makes developers for integrating various collection of device, saving, and processing a huge amount of data, allowing new services for improving several personal and professional activities. But, the privacy problems occur with a large amount of data produced and therefore the solutions on the basis of blockchain with sidechain technology have been developed to overcome these problems. Aim Blockchain powered IoT networks have high security levels, and are resilient to most network attacks. In order to deploy blockchains, a large set of hashing, encryption, and linking algorithms are implemented in the network. One of the recent developments is the sidechains and these sidechains reduce computational complexity during block creation, and thereby reduce the effect of high delay and high‐power consumption in the IoT network. But maintaining these sidechains requires an additional computational component, which adds to the computational complexity of the underlying system. To reduce the complexity of this maintenance while keeping the inherent advantages of side chaining networks, this article proposes a sidechain creation, updation, merging, and scanning processes. Methods In this article, the sidechain creation is done by modified two‐way peg protocol and updated by hybrid delegated practical byzantine fault tolerance‐delegated proof of stake (DPBFT‐DPOS), merging via continuous network information analysis and scanning via graph‐based searching mechanism to improve the Quality of Service (QoS) network and security. Results The proposed methodology achieves an accuracy and ‐score of about 98.6% and 99.5%, reduces end‐to‐end communication delay by 10% while increasing the energy efficiency by 15%, and improving the throughput by 15% when compared to other existing methods. Conclusion The IoT provides the possibility of creating real time data and the combination of the IoT with blockchain moves beyond authorization and financial recording. Then the utilization of sidechain provide better communication among devices and better sophistication of the data processing.
Does humanization or machinization make the IoT persuasive? The effects of source orientation and social presence

The advent of Internet of Things (IoT) technology has revolutionized both the roles and functions of everyday objects and how users interact with them. Using artificial intelligence (AI) and an advanced capacity for communication, smart objects can now function as communication sources and deliver persuasive messages. This study investigates how different types of agency and source cues shape the persuasiveness of a smart object via social presence. When users interacted with a smart object that exerted its own agency, they sensed greater social presence when the object used machine cues rather than human cues. Conversely, when users interacted with a smart object that allowed the user to exercise their own agency, human cues, rather than machine cues, produced greater feelings of social presence, which enhanced the persuasiveness of the messages conveyed by the object. However, the persuasive effects of social presence were reversed when the interaction prompted AI anxiety in the user.
Exact fisher information of generalized Dirichlet multinomial distribution for count data modeling

Despite the multiple benefits of the Fisher information matrix, it is generally disregarded and substituted by the identity matrix or an approximation format. However, when dealing with complicated real-world applications, ignoring the correlation between data features may compromise the modeling capability. To address this problem we present the exact calculation of the Fisher information matrix (EFIM) for the generalized Dirichlet multinomial (GDM) mixture that has proven its efficiency when modeling count data. We present a parametrization of GDM mixture model that allows the determination of the Fisher matrix’s elements by means of the Beta-binomial probability function. We also propose a novel count data modeling approach with the benefit of EFIM. In particular, we tackle the problem of mixture model estimation and selection using the Fisher scoring algorithm and minimum message length within the Deterministic Annealing Expectation-Maximization learning framework. Experiments on detecting depression in tweets, dialogue-based emotion recognition, and image-based sentiment analysis confirm the capability of the proposed approach and the merits of using the EFIM as compared with existing state-of-the-art methods and techniques that ignore the full determination of the Fisher information matrix’s elements.
Dynamic load balancing method based on optimal complete matching of weighted bipartite graph for simulation tasks in multi-energy system digital twin applications

As demands of renewable energy and more energy storages in power systems promote, the digital twin technology for multi-energy system draws attention from the researchers in power system and energy area. The load balancing algorithm of the digital twin server cluster significantly impacts the computing performance of the computing nodes. Optimal complete matching of a weighted bipartite graph is proposed to allocate computing task blocks to each computing node evenly. In the proposed algorithm, the computation times of simulation computing task blocks are estimated and the computation time of one single step in the task blocks are used as dynamic load indicators of the computing nodes. The indices are fed back to the scheduling server in real time. With the total steps of the simulation computing task blocks and the computation time of each single step for the task blocks, a fully weighted bipartite graph of computing nodes and simulation computing task blocks is constructed, greedy expansion of the Hungarian algorithm is used to optimize the complete matching of this weighted bipartite graph. Based on the optimization results, the task blocks are allocated to the computing nodes correspondingly in real time. Simulation-driven model parameter correction function, the representative simulation computing applications of digital twin for multi-energy system was used to test the proposed algorithm and the results show a significant improvement of the load balancing effect at the computing nodes.
High dynamic range 3D laser scanning with the single-shot raw image of a color camera

As a typical technique of optical three-dimensional (3D) shape measurement, laser scanning can provide good measurement accuracy by using simple and low-cost optical configuration. The performance of 3D laser scanning greatly depends on the center detection of the laser stripe. In general, laser stripe detection algorithm expects the intensity of the laser stripe remaining moderate and stable. To deal with the negative impact of dramatic change in the intensity of the laser stripe, a high dynamic range (HDR) laser scanning technique with concise algorithm and simple hardware configuration is proposed in this paper. The Bayer filter in the sensor chip of a color camera is exploited to provide different intensity responses to the laser. Then the sub-images of the laser stripe, which correspond to different color channels and have different intensity levels, can be decomposed from the raw image captured by the color camera. A dedicated algorithm is proposed to achieve HDR laser stripe detection, which collects coordinates with the best quality from different sub-images. Finally, 3D surface of improved quality can be reconstructed with the detected laser stripe. The proposed HDR laser scanning technique can be achieved from single-shot raw image by trading pixel resolution for time efficiency. The validity of the proposed method is demonstrated in comparative experiments.
ScienceVR: A Virtual Reality Framework for STEM Education, Simulation and Assessment

This paper addresses the use of Virtual Reality (VR) in Science, Technology, Engineering, and Math (STEM) education. There are limited studies investigating the proper design and effectiveness of VR in STEM education, and current VR frameworks and applications lack explicit links to the established learning theories and assessment mechanisms to evaluate learning outcomes. We present ScienceVR, an educational virtual reality design framework, illustrated through a science laboratory prototype, to bridge some of the gaps identified in the design and development of a VR environment for learning. We established design guidelines and implemented an in-app data collection system to measure users’ learning, performance, and task completion rate. Our evaluation using ANOVA and other non-parametric methods with 36 participants in three groups: immersive VR (IVR), desktop VR(DVR), and 2D indicated improved usability and learning outcomes for the IVR group. Task completion rate in the IVR group was higher (68% compared to DVR with 50%). For memorability, the IVR condition performed better than DVR while for learnability, IVR&DVR performed significantly better than 2D. IVR group has performed better and faster with more accuracy compared to the DVR group in completing the tasks.
3D Deep Attentive U-Net with Transformer for Breast Tumor Segmentation from Automated Breast Volume Scanner

Breast cancer has become the primary factor threatening women's health. Automated breast volume scanner (ABVS) is applied for automatic scanning which is meaningful for the rapid and accurate detection of breast tumor. However, accurate segmentation of tumor regions is a huge challenge for clinicians from the ABVS images since it has the large image size and low data quality. Therefore, we propose a novel 3D deep convolutional neural network for automatic breast tumor segmentation from ABVS data. The structure based on 3D U-Net is designed with attention mechanism and transformer layers to optimize the extracted image features. In addition, we integrate the atrous spatial pyramid pooling block and the deep supervision for further performance improvement. The experimental results demonstrate that our model has achieved dice coefficient of 76.36% for 3D segmentation of breast tumor via our self-collected data.
PRHAN: Automated Pull Request Description Generation Based on Hybrid Attention Network

Descriptions of pull requests (PRs) are posted by developers for describing the modifications that they have made and the corresponding reasons in these PRs. Although PRs help developers improve the development efficiency, some developers usually ignore writing the descriptions for PRs. To alleviate the above problem, researchers generally utilize text summarization model to automatically generate descriptions for PRs. However, current RNN-based models still face the challenges such as low efficiency and out-of-vocabulary (OOV), which may influence the further performance improvement to their models. To break this bottleneck, we propose a novel model aiming at the above challenges, named PRHAN (Pull Requests Description Generation Based on Hybrid Attention Network). Specifically, the core of PRHAN is the hybrid attention network, which has faster execution efficiency than RNN-based model. Moreover, we address the OOV problem by the utilizing byte-pair encoding algorithm to build a vocabulary at the sub-word level. Such a vocabulary can represent the OOV words by combining sub-word units. To reduce the sensitivity of the model, we take a simple but effective method into the cross-entropy loss function, named label smoothing. We choose three baseline models, including LeadCM, Transformer and the state-of-the-art model built by Liu et al. and evaluate all the models on the open-source dataset through ROUGE, BLEU, and human evaluation. The experimental results demonstrate that PRHAN is more effective than baselines. Moreover, PRHAN can execute faster than the state-of-the-art model proposed by Liu et al.
Instance-level Context Attention Network for instance segmentation

Instance segmentation has made great progress in recent years. However, current mainstream detection-based methods ignore the process of distinguishing different instances in the same detected region, making it hard to segment the correct instance when a detected region contains multiple instances. To address this problem, we propose an Instance-level Context Attention Network (ICANet) to generate more discriminative features for different instances based on the context from their respective instance scopes, called instance-level context. Specifically, given a detected region, we first propose an instance attention module to obtain the specific attention maps that focus on the relationships between pixel pairs from the same instance by learning an embedding space. With this type of attention map, the features from the same instance achieve mutual gains, while the features from different instances become more discriminative. Then, we propose a spatial attention module to incorporate spatial information and enhance feature representations based on both feature and spatial relations. Moreover, to obtain clearer attention maps, we further propose a weight clipping strategy to filter out the noise by cutting off the lower weight. We perform extensive experiments to verify the effectiveness of the proposed method. As reported in the results, our method steadily outperforms the baseline by over 1.5% on the COCO dataset using different backbones and 3.7% on the Cityscapes dataset, which demonstrates the effectiveness of our method.
Robust Human Activity Recognition by Integrating Image and Accelerometer Sensor Data Using Deep Fusion Network

Studies on deep-learning-based behavioral pattern recognition have recently received considerable attention. However, if there are insufficient data and the activity to be identified is changed, a robust deep learning model cannot be created. This work contributes a generalized deep learning model that is robust to noise not dependent on input signals by extracting features through a deep learning model for each heterogeneous input signal that can maintain performance while minimizing preprocessing of the input signal. We propose a hybrid deep learning model that takes heterogeneous sensor data, an acceleration sensor, and an image as inputs. For accelerometer data, we use a convolutional neural network (CNN) and convolutional block attention module models (CBAM), and apply bidirectional long short-term memory and a residual neural network. The overall accuracy was 94.8% with a skeleton image and accelerometer data, and 93.1% with a skeleton image, coordinates, and accelerometer data after evaluating nine behaviors using the Berkeley Multimodal Human Action Database (MHAD). Furthermore, the accuracy of the investigation was revealed to be 93.4% with inverted images and 93.2% with white noise added to the accelerometer data. Testing with data that included inversion and noise data indicated that the suggested model was robust, with a performance deterioration of approximately 1%.
Cryptocurrency Price Prediction by Using Hybrid Machine Learning and Beetle Antennae Search Approach

Cryptocurrencies are defined as digital mediums of exchange, that use strong cryptography for securing the transactions and verifying the ownership of the coins. Blockchain operates in the background to guarantee the security, transparency and traceability of the transactions. Consequently, cryptocurrencies became more and more popular and established their considerable presence in financial sector. However, one of the major drawbacks in the cryptocurrency market is the unreliability and unpredictability of their values, that poses a major risk for any kind of investment. Predicting the price of cryptocurrencies is therefore a hot research domain today. This paper proposes a novel method to predict the prices, that is based on a hybrid machine learning and swarm intelligence approach. The results of the conducted experiments suggest that the proposed model obtains higher accuracy than other recent similar approaches, and that it can be successfully applied for this important task.
DMPP: Differentiable multi-pruner and predictor for neural network pruning

Neural network pruning can trim the over-parameterized neural networks effectively by removing a number of network parameters. However, the traditional rule-based approaches always depend on manual experience. Existing heuristic search methods in discrete search spaces are usually time consuming and sub-optimal. In this paper, we develop a differentiable multi-pruner and predictor (DMPP) to prune neural networks automatically. The pruner composed of learnable parameters generates the pruning ratios of all convolutional layers as the continuous representation of the network. The neural network-based predictor is employed to predict the performance of different structures, which can accelerate the search process. Pruner and predictor enable us to directly employ gradient-based optimization to find a better structure. In addition, multi-pruner is presented to improve the efficiency of search, and knowledge distillation is leveraged to improve the performance of the pruned network. To evaluate the effectiveness of the proposed method, extensive experiments are performed on CIFAR-10, CIFAR-100, and ImageNet datasets with VGGNet and ResNet. Results show that the present DMPP can achieve a better performance than many previous state-of-the-art methods.
Environmentally smart contracts for artists using non-fungible tokens

We propose Environmentally Smart Contracts, a new kind of smart contract for non-fungible tokens to solve the prudential-moral dilemma facing digital artists. The current proof-of-work-dominated non-fungible token environment requires artists to trade off the prudential benefits and the environmental costs of selling their art on blockchains. By fully correcting for environmental externalities, Environmentally Smart Contracts will allow artists to reap the sales benefits of non-fungible tokens without contributing to environmental degradation. Concrete steps to encourage the development of Environmentally Smart Contracts are discussed.
A new lightweight convolutional neural network for radiation-induced liver disease classification

Histopathological image analysis is used in the diagnosis of many diseases such as cancer, brain tumor, fatty liver and congenital heart diseases. In clinical practice, the time-consuming diagnostic process requires an expertise in the field, and disagreements can arise among pathologists at the decision stage. Machine learning methods can be used in diagnostic stage to overcome these issues. Especially, the use of deep learning systems in the analysis of histopathological images can reduce the workload of pathologists and can provide more objective results. Over traditional machine learning methods using handcrafted features, deep learning approaches that can automatically learn features stand out with their performance in the analysis of histopathology images. In this paper, we propose a patch-based lightweight convolution neural network for histopathological image classification for detecting radiation-induced liver damage. The proposed model was trained to classify the radiation-induced liver disease dataset comprising 555 histopathological images. An accuracy of 100% for binary classification and an accuracy of 87.57% for multi-class classification were achieved on the unseen test set. Our model outperforms state-of-the-art models, including ResNet-50, AlexNet, Vgg16, and GoogleNet. This paper is the first to classify radiation-induced liver disease data with deep learning, and it can be a promising tool for diagnosis purpose of pathologists. Since it is vital to detect a damage that leads to deteriorating liver function, this study is valuable in the field of medicine.
A Novel Hybrid Deep Learning Model for Human Activity Recognition Based on Transitional Activities

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision.
Deep Learning for Coexistence Radar-Communication Waveform Recognition

This paper presents a deep learning-based method to automatically recognize the waveform types of wireless signals in coexistence radar-communication systems. In this method, we leverage smooth pseudo Wigner-Ville distribution (SPWVD) to analyze signals in time and frequency domains concurrently, and then design an efficient deep convolutional neural network (CNN) with several processing blocks which integrate residual connection and attention connection to improve feature learning efficiency. Based on the simulations on a synthetic dataset of eight common radar and communication waveforms, the proposed method achieves the average recognition accuracy of 97.59% at 30 dB SNR and outperforms a Fuzzy support vector machine approach by approximately 17.36%.
Teachers’ Key Competencies for Innovative Teaching

The educational process is by its nature and essence very dialectical, so it necessarily requires teachers to have a developed whole set of various competencies. The aim of the research was to examine teachers on self-assessment of competencies that were crucial for innovating the teaching process. The sample of respondents included 1300 elementary and secondary school teachers in the territory of Southern Serbia. The instrument used was a five-point TCS scale of attitudes, with a total of 48 items (individual competencies). The results of the analysis showed that teachers, based on their self-assessments, highly value all diverse groups of competencies, which fully confirms the general hypothesis. The results also showed the existence of statistically significant differences in the degree of expression of attitudes about competency groups with regard to independent research variables, within special hypotheses, based on which the first was rejected, the second partially and the third and fourth special hypothesis fully confirmed. The issue of key competencies of teachers is certainly one of the fundamental issues when it comes to quality teaching, its innovation, generally successful and efficient dealing with the educational process. Based on these postulates, there is hope that the obtained research results will represent a good starting point and incentive for further research work in this field in the future.
Prediction of Breast Cancer using Machine Learning Algorithms

Breast cancer growth is an exceptionally normal issue in ladies now a days. It is the reason for part of passings in ladies among various kinds of diseases. There is a need to do more research work so that breast cancer can be controlled. In this paper AI calculations are applied to foresee the breast cancer disease in ladies. The exhibition of AI calculations are assessed based on their expectation accuracy. The four AI calculations are applied on a standard informational collection. After the execution in python programming language it is seen that the Support vector Machine algorithm is best for breast cancer disease predcition.