Reviewerly/exHarmony huggingface dataset:
Why should and how can quantum technologies be leveraged at national levels?

Quantum technologies (QT) promise to change the landscape of technologies disruptively in diverse industries. For this reason, many nations around the globe are investing to emerge within the global quantum ecosystem through initiating national programs and international partnerships. Nonetheless, some other countries are still running behind and yet their governments need to take series actions to help their private and public sectors adapt to the looming changes, considering the new regulations required and the huge influence that QT will present in the near future. In this opinion piece, we provide, for the best of our knowledge, the first generally applicable, yet comprehensive and brief, framework for leveraging the emerging quantum technologies to facilitate the establishment of national initiatives properly. The insights presented in this article were driven based on investigating various approaches, initiatives, and roadmaps adopted globally and meeting with local and regional leaders, professionals, and governmental officials. Furthermore, taken into account socioeconomic and institutional dimensions of the Libyan society, we project the framework for the Libyan nation. This opinion piece is intended to inspire researchers, technical industrial experts, stakeholders, and governmental bodies to find roles they need to play to bring QT forward.
Efficient Person Search: An Anchor-Free Approach

Person search aims to simultaneously localize and identify a query person from uncropped images. To achieve this goal, state-of-the-art models typically add a re-id branch upon two-stage detectors like Faster R-CNN. Owing to the ROI-Align operation, this pipeline yields promising accuracy as re-id features are explicitly aligned with the corresponding object regions, but in the meantime, it introduces high computational overhead due to dense object anchors. In this work, we present an anchor-free approach to efficiently tackling this challenging task, by introducing the following dedicated designs. First, we select an anchor-free detector (i.e., FCOS) as the prototype of our framework. Due to the lack of dense object anchors, it exhibits significantly higher efficiency compared with existing person search models. Second, when directly accommodating this anchor-free detector for person search, there exist several misalignment issues in different levels (i.e., scale, region, and task). To address these issues, we propose an aligned feature aggregation module to generate more discriminative and robust feature embeddings. Accordingly, we name our framework as Feature-Aligned Person Search Network (AlignPS). Third, by investigating the advantages of both anchor-based and anchor-free models, we further augment AlignPS with an ROI-Align head, which significantly improves the robustness of re-id features while still keeping our model highly efficient. Our framework not only achieves state-of-the-art or competitive performance on two challenging person search benchmarks, but can be also extended to other challenging searching tasks such as animal and object search. All the source codes, data, and trained models are available at: https://github.com/daodaofr/alignps .
A Novel Heteromorphic Ensemble Algorithm for Hand Pose Recognition

Imagining recognition of behaviors from video sequences for a machine is full of challenges but meaningful. This work aims to predict students’ behavior in an experimental class, which relies on the symmetry idea from reality to annotated reality centered on the feature space. A heteromorphic ensemble algorithm is proposed to make the obtained features more aggregated and reduce the computational burden. Namely, the deep learning models are improved to obtain feature vectors representing gestures from video frames and the classification algorithm is optimized for behavior recognition. So, the symmetric idea is realized by decomposing the task into three schemas including hand detection and cropping, hand joints feature extraction, and gesture classification. Firstly, a new detector method named YOLOv4-specific tiny detection (STD) is proposed by reconstituting the YOLOv4-tiny model, which could produce two outputs with some attention mechanism leveraging context information. Secondly, the efficient pyramid squeeze attention (EPSA) net is integrated into EvoNorm-S0 and the spatial pyramid pool (SPP) layer to obtain the hand joint position information. Lastly, the D–S theory is used to fuse two classifiers, support vector machine (SVM) and random forest (RF), to produce a mixed classifier named S–R. Eventually, the synergetic effects of our algorithm are shown by experiments on self-created datasets with a high average recognition accuracy of 89.6%.
Adaptation to the Context in M-Learning Application Using Mobile Design Patterns

The fast progress of mobile devices and the popularity of mobile applications in the market requires complex software to be run on mobile devices without issues. Mobile devices are known as handhelds and they have different characteristics such as size, resolution, battery life and processing power. These are limitations to consider when developing an M-learning application, which can make it difficult to design and develop mobile applications for different mobile devices. Design Patterns, a set of tried and tested solutions for common problems were originally designed for desktop computers, so application of these patterns in M-learning applications might not meet its requirements such as adaptation of the content to the context of the device mobile used by learners. However, mobile design patterns that have been adapted or designed for such an environment can be used to overcome the challenges and limitations of M-learning application development. In this paper we try to propose an approach to engineering design patterns allowing the development of M-learning applications to provide an adapted content taking into account the context of device mobile used by learners. We named these patterns: “Mobile design patterns”.
Types of Foreign Language Anxiety (FLA) Through Belief About Language Learning Inventory (BALLI): A Thematic Analysis

This research uncovers the types of Foreign Language Anxiety (FLA) proposed by Horwitz, Elaine K., Horwitz, Michael B., &amp; Joann Cope (1986) felt by students. This research uses instrument of Belief About Language Learning Inventory (BALLI) proposed by Horwitz, Elaine K. (1988) in the form of questionnaire using Guttman scale. It is narrative design of qualitative research. Furthermore, the analysis is completed by thematic analysis passing five steps to draw a conclusion correlated with the respondents’ respond to the questionnaire. The respondents of this research are the university students of Economic Department, Universitas Islam Sumatera Utara (UISU), Indonesia, Academic Year 2022-2023. The research result found that the highest percentage of people who are anxious about language learning is 64%. It focuses on test anxiety, specifically fear of making mistakes, followed by fear of negative evaluation, specifically fear of receiving a low grade (63%). Then it is on test anxiety, specifically feeling inferior (57%) followed by fear of negative evaluation, specifically fear of correction (49%). Finally, communication anxiety: lack of self-confidence ranks fifth (43%) followed by fear of speaking (35%). Because the percentage of test anxiety and fear of negative evaluation is higher than that of communication skill, the researchers conclude that what makes students anxious in learning foreign language (English) is the grammar or structure of the language.
MFSR: Light Field Images Spatial Super Resolution Model Integrated with Multiple Features

Light Field (LF) cameras can capture angular and spatial information simultaneously, making them suitable for a wide range of applications such as refocusing, disparity estimation, and virtual reality. However, the limited spatial resolution of the LF images hinders their applicability. In order to address this issue, we propose an end-to-end learning-based light field super-resolution (LFSR) model called MFSR, which integrates multiple features, including spatial, angular, epipolar plane images (EPI), and global features. These features are extracted separately from the LF image and then fused together to obtain a comprehensive feature using the Feature Extract Block (FE Block) iteratively. Gradient loss is added into the loss function to ensure that the MFSR has good performance for LF images with rich texture. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods, with a peak signal-to-noise ratio (PSNR) improvement of 0.208 dB and 0.274 dB on average for the 2× and 4× super-resolution tasks, and structural similarity (SSIM) of both improvements of 0.01 on average.
New Odoma Distribution with Application to Cancer Data

Objective: To find appropriate model more flexible than classical models for fitting the survival data in health research especially in cancer blood (Leukemia) studies by introducing some additional parameters to the basic models.Methods: Based on the Alpha Power transformation technique and using Two-Parameter Odoma distribution, the new distribution called the Alpha Power Two-Parameter Odoma distribution is introduced and studied.The maximum likelihood estimation procedure is employed to estimate the unknown parameters.A simulation study is carried out to evaluate the performance of the maximum likelihood estimators.Finding: Common statistical properties such as quantile function, rth moments, moment generating function, characteristic function, incomplete moments, entropy and order statistics are derived.The practical importance of the proposed model is illustrated by using goodness-of-fit criterias based on real data set.Novelty: The APTPO distribution is the most appropriate model for fitting survival data of cancer studies comparing with other competitive distributions.
Heterogeneous Graph Purification Network: Purifying Noisy Heterogeneity without Metapaths

Heterogeneous graph neural networks (HGNNs) deliver the powerful capability to model many complex systems in real-world scenarios by embedding rich structural and semantic information of a heterogeneous graph into low-dimensional representations. However, existing HGNNs encounter great difficulty in balancing the ability to avoid artificial metapaths with resisting structural and informational noise in a heterogeneous graph. In this paper, we propose a novel framework called Heterogeneous Graph Purification Network (HGPN) which aims to solve such dilemma by adaptively purifying the noisy heterogeneity. Specifically, instead of relying on artificial metapaths, HGPN models heterogeneity by subgraph decomposition and adopts inter-subgraph and intra-subgraph aggregation methods. HGPN can learn to purify noisy edges based on semantic information with a parallel heterogeneous structure purification mechanism. Besides, we design a neighborhood-related dynamic residual update method, a type-specific normalization module and cluster-aware loss to help all types of node achieve high-quality representations and maintain feature distribution while preventing feature over-mixing problems. Extensive experiments are conducted on four common heterogeneous graph datasets, and results show that our approach outperforms all existing methods and achieves state-of-the-art performances consistently among all the datasets.
Aspect Based Sentiment Analysis and Opinion Mining on Twitter Data Set Using Linguistic Rules

Objectives: To propose the most effective machine learning technique for aspect based sentiment analysis of consumer feedback reviews by correlating people's emotions of different aspects of a product or service with word importance, linguistic rules, tagging, dependency relations and filtering to enhance the classification accuracy and to explore the different machine learning approaches in order to determine their significance and to spark interest in this field of study.Methods: The dataset has been taken from the most popular networking site twitter using twitter API.A total of 33,036 tweets were retrieved for this study from16 July 2022 to 16 Sept. 2022, but were only able to include 7570 tweets after data cleaning and then proposes a model named SVMS (SVM using Spacy) for sentiment Analysis.To measure the performance of a particular model, numerous analyses are conducted on the dataset and then the effectiveness of proposed model is compared with 4 basic classifiers LR, NB, RF and SVM.The parameters used in this study are accuracy, precision, recall, and F1 score.Findings: It is found that the Proposed SVMS model shows better results than all other algorithms by having 96% accuracy which is 1% faster than LR, 2% faster than NB, 5% faster than RF, and 4% faster than SVM, 96% precision, 98% recall and 97% F1 score.SVMS is recommended in this research context for the classification of the text and has a great significance for future researchers in sentences and text classification.Novelty: In this paper the performance of sentiment analysis classifiers is evaluated.In addition to general opinions, the work examines specific aspects such as product, quality, service, price, and ease of use.Comparing the proposed model with the existing models, it has been found that it outperformed the other models in most of the aspects.
Combining Modern Communication Methods With Heritage Education

Moving from fully online to hybrid requires extra hard work to furnish teaching-tasks as easier to adopt and adapt by students in this transitioning era. In cultivating local heritage, the IDLE teacher should mix the tasks with the topic of ‘revealing local heritage' to insert on hybrid teaching. Therefore, this paper aims to explore teachers' experience in implementing IDLE on hybrid teaching at Indonesia's secondary level. By using narrative research design, the authors will set up bundles of participants' life stories into a holistic image; and analyze the chapter by using language appraisal and transitivity. The authors recruited six teachers to narrate the story behind the implementation of IDLE on hybrid teaching. The authors sharpen the data collection by doing a semi-structured interview to emerge fruitful data transcription to be analyzed. The study will contribute to a practical recommendation to implement IDLE in hybrid teaching.
Ontological Approach to the Development of Unified Knowledge Base of Multimodal Transportations

Purpose: In the frames of ongoing digital transformation of transport complex in a whole and railway transport in particular, to give brief analytical characterization of being developed digital platforms and services. To consider promising technologies that are either at launch stage or on the way to the peak of overpriced expectations as to technological hype cycle of Gartner Research Company. To give an example of unified knowledge base as a basis of digital platform for multimodal transportation. Methods: Methods for visual structuring of information were used, in particular, mental maps (intellect-maps). To represent subject area ontology, a semantic network was used as a method for knowledge representation. Results: The pursued analysis of being developed and existing digital platforms and services has shown that besides data, there should lie at their basis more complex informational units — an active knowledge. This requires the method of data integration from disparate sources, their integration and circulation as within one industry as well as between various industries in order of knowledge generation and spread. In view of work specifics of transport various kinds, participating in multimodal transportations, the subject area, uniting all kinds of transport, should be chosen for this. The application of "cargo transportation" subject area has been justified by virtue of which, it is necessary to create management system of cargo flows on railway transport. Ontology fragment, describing "cargo transportation" domain for railway transport, has been worked out. Practical significance: The necessity of applying active knowledge to create autonomous intellectual productions on transport is shown. The use of knowledge bases, established on ontologies, will allow to raise the level of interaction of transport various kinds in "narrow" places during cargo transshipment as well as to expand the range of services, provided to customers.
Time-resolved determination of velocity fields on the example of a simple water channel flow, using a real-time capable FPGA-based camera platform

Abstract Field Programmable Gate Arrays (FPGAs) have become a widely available computational resource for real-time image processing and can be used in time-critical optical process measurements. In this study, we investigate the applicability of the spatial filtering technique for time-resolved velocity field determination, on the example of a simple water tunnel flow. For this, the spatial filtering technique was implemented for a custom FPGA platform and analyzed regarding the effect of spatial and temporal resolution on the accuracy of the results. It was possible to prove the algorithm as suitable for velocity field estimation and identify algorithm parameters, which allow for increasing the accuracy of the results. Graphical abstract
Attribute preserving recommendation system based on graph attention mechanism

A recommendation System (RS) is an emerging technology to figure out the user’s interests and intentions. As the amount of data increases exponentially, it is hard to analyze the user intentions and trigger the recommendation accordingly. In this research work, a novel recommendation system called the Deep Knowledge Graph based Attribute Preserving Recommendation (DKG-APR) is presented to analyze massive data and provide personalized recommendations to users. The Deep Knowledge Graph for Recommendation System (DKG-RS) uses Deep Convolutional Neural Network (DCNN) and attention mechanism to explicitly model high-order connections in knowledge graphs. According to empirical findings, Knowledge Graph Attention Network (KGAT) performs better than other state-of-the-art recommendation techniques like RippleNet and Neural FM. Additional research demonstrates the effectiveness of embedding propagation for high-order relation modeling and the advantages of the attention mechanism for interpretability.The results also show that user information is crucial in the recommendation system, as seen from the optimal node-drop-out ratio of 0.2, which led to the best recall value of 0.2 for all datasets.
Addressing Class Imbalances in Software Defect Detection

Software defect detection (SDD) is concerned with detecting the existence of defects in software modules. There has been a growing interest in applying machine/deep learning to SDD. However, SDD is a binary classification problem, which involves class imbalances causing a bias in learning and there is little work addressing this problem. In this work, we apply four different class balancing techniques—SMOTE, ADASYN, SMOTE-Tomek, and SMOTE-ENN to the SDD problem using both deep learning and machine learning. We use MLP, CNN, and LSTM for deep learning; and decision tree, random forest, logistic regression, and XGB for machine learning. We evaluate the effect of class balancing techniques on those models and conduct a comparative analysis. The study found that class balancing techniques are positive on MLP, but negative on CNN and LSTM, while being positive in all the machine learning techniques. Overall, they are more compatible with machine learning.
Study on Blockchain-based data sharing security

Within the context of the big data age, data sharing is gradually rising with the embodiment of data value. Data value can increase through sharing, but there are security problems during the period of data sharing such as centralized deployment, malicious theft, and tampering, which greatly affect the security of data. Aiming at the common privacy leakage problem during data sharing, this research builds a data sharing platform on the chain based on blockchain technology, and combines the function encryption technology and zero-knowledge proof technology to realize the sharing of verifiable computing results, and proposes a sharing model. The purpose of this model is to enable the data owner to control the data sharing, so as to guarantee the security and privacy of the data while sharing, ensure that the original data is not leaked, and realize the availability and invisibility of the data. In addition, it is necessary to ensure the reliability of data processing results, eliminate the risk of unreliable data processing caused by original data encryption, and protect the legal right of data users.
Analysis of the investment value of Motional based on SWOT analysis

With the continuous rise of investment diversification, more and more people invest in enterprises, hence it is more important to analyze the investment value of enterprises. Many enterprises have no investment value analysis. Therefore, the theme of this paper is to analyze the investment value of emotional based on the SWOT model. The research method of this paper is as follows: first, search the information of listed companies which is similar to emotional, and analyze the data. The researchers find Hyundai motor group is a company that is similar to motional's financial and non-financial situation recently. Secondly, collect the financial and non-financial data of Hyundai Motor Group through its annual report and official website, such as balance sheet, cash flow statement, income statement, management team information, and customer group information; Finally, analysis of the existing data of Hyundai Motor Group through the swot model, draw a conclusion about its advantages, disadvantages, trade environment and industry competition. Hence it can get a conclusion about whether Hyundai Motor Group has investment value by summarizing the above aspects. Because of the high similarity in the industry and development between emotional and Hyundai Motor Group, it can draw an analogy whether emotional has investment value. The study finds that Hyundai Motor Group has many advantages; few disadvantages; an excellent trade environment and weak industry competition. Based on the above contents, the research concludes that Hyundai Motor Group has a high investment value. Hence emotion has a high investment value.
LiNLNet: Gauging required nonlinearity in deep neural networks

Feedforward deep neural networks (DNNs) commonly involve layer-wise linear operations and subsequent nonlinear operations, which are repeated through all layers. The nonlinear operations by nonlinear activations in each layer remarkably enhance the expressiveness of DNNs, resulting in the great success in a variety of application domains. Although the necessity of layer-wise nonlinear operations is agreed, the optimal nonlinearity for each layer in a given DNN is not clear. In this regard, we propose an easy-to-use method to layer-wise measure the optimal nonlinearity for a given DNN using its replica termed a linear-nonlinear network (LiNLNet). The key to the LiNLNet is the use of linear-nonlinear units (LiNLUs) whose degree of nonlinearity is parameterized by a trainable parameter p. The parameter p is shared among all LiNLUs in a given layer, thus indicating the layer-wise optimal nonlinearity. This method allows layer-level pruning such that the layers that do not require nonlinearity are merged into the subsequent layers, reducing computational complexity. For proofs of concept, we applied the proposed method to a MLP, AlexNet, VGG16, and ResNet18 on CIFAR-10 and ImageNet. The results commonly indicate the last hidden layer as a linear layer that may be merged into the output layer, reducing memory usage by 27% while maintaining the accuracy for LiNL-AlexNet on ImageNet.