Reviewerly/exHarmony huggingface dataset:
Design of Gesture-Controlled Interface for Mechatronic Systems: A Computational Approach

The paper focuses on designing and developing an interface for interacting with a mechatronic system through gestures. Using machine visionMachine vision” techniques, a methodology is developed that aims to record and locate the gesture through an image capture system. Once the gesture is isolated and gesture recognition is possible, the appropriate control commands of the system are determined. This makes it possible to control the mechatronicMechatronics” system through a dictionary of movements, called gesture vocabulary. The results of the methodology were applied to a real mechatronic system and showed significant results. The efficiency and effectiveness of the interface emerged from a satisfactory sample of users. It appeared quite easy to use, but also presented some basic difficulties in its application. The conclusion of the research is that controlling mechatronic systems through gestures can be a very important interaction interface.
Automatic multi-documents text summarization by a large-scale sparse multi-objective optimization algorithm

Abstract Due to the exponential overflow of textual information in various fields of knowledge and on the internet, it is very challenging to extract important information or to generate a summary from some multi-document collection in a specific field. With such a gigantic amount of textual content, human text summarization becomes impractical since it is expensive and consumes a lot of time and effort. So, developing automatic text summarization (ATS) systems is becoming increasingly essential . ATS approaches are either extractive or abstractive. The extractive approach is simpler and faster than the abstractive approach. This work proposes an extractive ATS system that aims to extract a small subset of sentences from a large multi-document text. First, the whole text is preprocessed by applying some natural language processing techniques such as sentences segmentation, words tokenization, removal of stop-words, and stemming to provide a structured representation of the original document collection. Based on this structured representation, the ATS problem is formulated as a multi-objective optimization (MOO) problem that optimizes the extracted summary to maintain the coverage of the main text content while avoiding redundant information. Secondly, an evolutionary sparse multi-objective algorithm is developed to solve the formulated large-scale MOO. The output of this algorithm is a set of non-dominated summaries (Pareto front). A novel criterion is proposed to select the target summary from the Pareto front. The proposed ATS system has been examined using (DUC) datasets, and the output summaries have been evaluated using (ROUGE) metrics and compared with the literature.
STDFormer: transformer-based network for arbitrary-shaped text detection in natural scenes

Natural scene text detection refers to locating and representing the text in natural scene images. The existing methods of natural scene text detection are based on convolutional neural network (CNN), but it is vulnerable to useless background noise in the process of extracting the features of curved text instances because the convolution kernel of CNN is fixed in size and rectangular in shape. In order to solve this problem, this paper proposes a novel Transformer-based Feature Fusion Module (TFFM) by integrating the transformer structure into feature pyramid network to reduce the influence of background noise in the process of feature fusion. On this basis, combined with the backbone and detection head of transformer structure, a network of natural scene text detection with full transformer structure is constructed. The method proposed in this paper achieves the state-of-the-art result on CTW1500 and Total-Text datasets, and the Transformer-based Feature Fusion Module (TFFM) proposed in this paper can be easily applied to other target detection frameworks in theory.
A Variation-Aware Quantum Circuit Mapping Approach Based on Multi-Agent Cooperation

Quantum circuit mapping is an essential process required by executing quantum circuits using a noisy intermediate-scale quantum (NISQ) device. Since qubits and quantum gates of a NISQ device are error-prone and variable in quality, it is crucial to choose qubits or quantum gates in a variation-aware manner to maximize the success rate of executing circuits. To this end, this article proposes a variation-aware method for quantum circuit mapping through the cooperation of multiple agents. Each agent in the proposed method can gradually construct a physical circuit that respects the device's connectivity constraints by inserting a SWAP gate at each step. Moreover, at each step, the circuit information of each agent is shared within the agent population through a communication mechanism that combines global and local information exchange, so that agents with poor fitness can get an opportunity to improve their physical circuits. The experimental results on extensive benchmark circuits confirm that the proposed method can effectively and consistently improve the overall circuit fidelity compared with the state-of-the-art methods.
Gender Differences in Multi-modal Interface System for 3D Object Modelling

With the advancement in technology, the multi-modal interface system (MMIS) is now becoming a vital part of almost every intelligent system. The multi-modal interface system uses humanlike input to convey information to a computing device instead of typical mouse/keyboard inputs. However, the acceptance of an MMIS by individuals depends upon various factors related to both the users and the system. This study focuses on investigating the gender-related factors affecting the performance of an MMIS in 3D object modelling applications. The multi-modal inputs used in this study are speech and gestures. The participants model a 3D object in AutoCAD using both traditional keyboard/mouse inputs and multimodal speech/gesture inputs. The data has been collected through questionnaires and audio/video log files. The initial finding confirms that there exists a gender-based difference. The results indicate female participants are more comfortable using speech and gesture inputs compared to male participants. The findings also suggest that the performance of the MMIS is not just task-specific but also gender-specific.
Industries Application of Type-2 Fuzzy Logic

For type-2 fuzzy structures were already used within a selection of domains, including intelligent control, pattern matching, and categorization. The values with the greatest accuracy have been computed using standard computational mathematics approaches. Regarding terms, type-1 fuzzy arrangements were employed. Inside perspectives in and that’s the amount at inaccuracy it rather strong, type-2 fuzzy series have been used. This analysis focuses on T2FL implementations’ previous, current, including upcoming tendencies. The examination of type-2 fuzzy structures in commercial presentations around various fields permitted us to outline the available research domains, which will be valuable in prioritizing upcoming research issues. Since the 1990s, T2FL has demonstrated substantial accomplishments within a spectrum of uses and this has been widely employed to handle uncertainty in actual difficult issues. I feel this study would provide a useful beginning moment further in support of studies in this area for those who are motivated.
The Malaysian Higher Education Students’ Perceptions and Acceptance Toward Mobile Learning During the COVID-19 Pandemic

In the era of pandemic, almost all educational institutions had to undergo education via the Internet instead of face-to-face teaching approach. Thus, the use of mobile devices application is becoming more significant and prevalent among students at higher education institutions (HEIs) during the pandemic. Furthermore, many universities currently use their smartphones and download the mobile applications for their studies. Therefore, the survey study aimed to investigate the university students’ perceptions and acceptance toward mobile learning (M-learning) during the COVID-19 pandemic. This study employed a quantitative method, and an online survey was conducted to the university students in the higher educational institutions in Malaysia. For this study, an online questionnaire was distributed to the respondents. A total of 276 university students from selected public and private universities were take part and responded to the online questionnaires. The study concluded that Malaysian university students have positive perceptions and acceptance toward mobile learning. Additionally, the results also indicates that mobile devices aided university students in their learning process, primarily at home, during the pandemic. In conclusion, M-learning has the possibility to be a very effective instructional tool in a virtual learning environment.
Extraction of Association Rules from Data Stream Using Genetic Algorithm

In this work, we are interested in the extraction of association rules on the data stream. We propose three strategies to deal with this problem: resolution by local vision, resolution by local and independent global vision, resolution by global vision. Since this task is very time consuming, the genetic algorithm for extraction of association rules GAARM is used to speed up the extraction process. The strategies have been tested on two datasets of different size and have given promising results.
Application Interface of CH Rooted File Browser for Web Servers

CH rooted is a concept used for sharing file and transferring data from one pc to another pc without any third-party software or application. When we to have to share files of big size, securely and we have to give the limited access or some part of our computer memory, the we use this concept. This concept is used in Linux, Unix operating system, but we are making this project so that it can work in windows operating system also i.e., it should be platform independent and easy to use and is secure. The project basically focuses on the following objectives – 1) Create an OS independent file serving API with CH-root feature using java. 2) Create a full-fledged file browsing website using the API mentioned above.
Automated Training Set Size Reduction for Detection of Small and Thin Objects

Convolutional neural network-based object detectors such as traffic sign detectors can achieve high accuracy, but are computationally expensive to train. This becomes an acute problem as the size of training sets increase. Thus we need methods to eliminate (1) redundant data in the training sets to reduce the training time and (2) mislabelled data to increase accuracy. Recent works have shown that it is possible to decrease training time, but struggle in improving accuracy. Therefore, we propose three methods for training set size reduction with the aim to improve the detector’s accuracy measured in intersection over union. The first method uses label information to remove images from the training set: we discard images without objects of interest. In contrast, the second method uses feature data of the input images: we discard images that are too similar. Finally, the third method combines the two above-mentioned methods.
A Semantic Interoperability Approach for Heterogeneous Meteorology Big IoT Data

The Internet of Things (IoT) devices generate a huge, heterogeneous volume of data that only Big Data can absorb. This data is based on a domain-specific format; this is the first challenge to be addressed by interoperability. The second challenge is using of semantic web technologies to enable the semantic interoperability of heterogeneous Big IoT Data. The objective of this paper is to: (1) Enable data interoperability (2) Facilitate knowledge integration of the common ontology created with other domain ontologies, and sharing them (3) Allow advanced analytics on collected data using Linked Open Data (LOD) initiatives, and define a common communication strategy for the representation, exchange and visualization of knowledge. Farther the paper discusses about our proposed approach and the related research works which includes creating a semantic model and a common ontology that focuses on supporting cross-domain. Moreover, we apply the proposed approach through a case study meteorological domain.
Palynological Study and Age Dating of the Early Miocene Dukku Claystone Type Section of Gwandu Formation, Sokoto Basin, Northwestern Nigeria

Outcrop samples were collected from Dukku area for the purpose of subjecting them to palynological analysis in order to determine the relative age of Gwandu Formation and palaeoenvironment of deposition of the deposit. Field and laboratory methods were carried out in order to obtain lithological sequence description of the outcrop. The samples obtained went through processes of acid digestion with hydrochloric acid, hydrofluoric acid and separation of macerals by floatation with the use of zinc bromide. The prepared slides were observed under microscope for palynomorph contents. Diagnostic palynomorphs that are stratigraphically important for palynozonation were retrieved such as Belskipollis eligans, Verrutricolporites laevigatus. Striamonocolpites sp., Retibrevitricolporites sp., Retibrevitricolporites obodoensis, Retistephanocolpites sp., Cingulatisporites ornatus, Longapertites marginatus and Proteacidites sp. The marker fossils belong to Magnatariatites horwardii zone; Zone P600 and Subzone P630 and C1 Zone of Nigerian Niger Delta sedimentary basin zonal schemes, dated Early Miocene age. Dinoflagellate forms recovered include Batiacasphaera sp., Senegalinium sp., Andalusiella polymorpha Selenophemphix nephroides, Andalusiella sp., Andalusiella laevigata, Histrichosphaera turocona and undifferentiated dinoflagellate cysts of peridinacean forms that belong to marginal marine setting.
Inducing semantic hierarchy structure in empirical risk minimization with optimal transport measures

The cross-entropy (CE) loss is arguably the most important empirical risk minimization objective for deep discriminative models for classification, and has achieved notable success in numerous applications. Though the CE loss is widely adopted, it essentially ignores the correlation between categories. For example, predicting a shepherd dog to husky is more acceptable than a tiger for the subsequent decision processes, while these two misclassifications result in the same CE loss. Therefore, the usually used CE loss does not incorporate the risk of misclassification of different categories, which can be measured by the distance between the predicted category and ground-truth category in a semantic hierarchical tree (SHT). In this work, to explicitly take the SHT-defined risk-aware inter-categorical correlation into consideration, by proposing a discrete optimal transport (DOT) training framework via configuring its ground distance matrix. We are able to predefine ground distance matrix in optimal transport measurement following a priori of hierarchical semantic risk. Specifically, the tree-induced error (TIE) on SHT is adopted as our ground distance matrix. Furthermore, it can be extended to its increasing function from the optimization perspective. In addition, we can also adaptively learn the matrix following an alternative optimization scheme. The semantic similarity in each level of a tree is integrated with the information gain. We demonstrated the effectiveness of our framework in several benchmarks of large-scale image classification with the semantic tree structure, and showed superior performance in a plug-and-play manner.1
Optimization of NumPy Transcendental Functions for Arm SVE

The high-performance computing instruction set for the Arm processors, Scalable Vector Extension (SVE), is adopted in "Fujitsu A64FX" of the Fugaku supercomputer, and in "Graviton3" of AWS. NumPy, the primary Python numerical computing package, does not support SVE. Large-scale machine learning and other applications are often implemented in Python, which is easy to implement. NumPy is often used for Python numerical operations. Therefore, even if such an application is executed on an SVE-capable processor, it is unable to benefit from SVE. For this study, we assessed the NumPy performance before and after implementing SVE on A64FX. Results show that, by Single Instruction Multiple Data, inlining, and loop-unrolling number optimization, we achieved about six times speeding up of transcendental function processing for array data.
Generating synthetic multispectral images using neural style transfer: A study with application in channel alignment

We can obtain information about food plantations by imaging technology such as satellite and unmanned aerial vehicles which enables rapid decision-making. Unmanned aerial vehicles provide fast and precise high-resolution multispectral imaging of plantation areas. However, unmanned aerial vehicle images are subject to some misalignment among their bands mainly due to the sensor’s displacement, vehicle movements, and others. To quantitatively evaluate channel alignment algorithms, it is necessary to have fully aligned images. For this purpose, we propose a method for generating fully aligned synthetic multispectral images based on real misaligned unmanned aerial vehicle multispectral images using Neural Style Transferring algorithms. We applied our methods over a real multispectral images dataset and we generated a new synthetic aligned image for each original misaligned multispectral image. Our experiments demonstrate that images generated by our method have fully aligned channels, and the new channels resemble the original ones. We also designed two case studies that demonstrate the applicability of our approach.
Privacy-Preserving Federated Graph Neural Network Learning on Non-IID Graph Data

Since the concept of federated learning (FL) was proposed by Google in 2017, many applications have been combined with FL technology due to its outstanding performance in data integration, computing performance, privacy protection, etc. However, most traditional federated learning-based applications focus on image processing and natural language processing with few achievements in graph neural networks due to the graph’s nonindependent identically distributed (IID) nature. Representation learning on graph-structured data generates graph embedding, which helps machines understand graphs effectively. Meanwhile, privacy protection plays a more meaningful role in analyzing graph-structured data such as social networks. Hence, this paper proposes PPFL-GNN, a novel privacy-preserving federated graph neural network framework for node representation learning, which is a pioneer work for graph neural network-based federated learning. In PPFL-GNN, clients utilize a local graph dataset to generate graph embeddings and integrate information from other collaborative clients to utilize federated learning to produce more accurate representation results. More importantly, by integrating embedding alignment techniques in PPFL-GNN, we overcome the obstacles of federated learning on non-IID graph data and can further reduce privacy exposure by sharing preferred information.
A Smart Contract Vulnerability Detection Model Based on Syntactic and Semantic Fusion Learning

As a trusted decentralized application, smart contracts manage a large number of digital assets on the blockchain. Vulnerability detection of smart contracts is an important part of ensuring the security of digital assets. At present, many researchers extract features of smart contract source code for vulnerability detection based on deep learning methods. However, the current research mainly focuses on the single representation form of the source code, which cannot fully obtain the rich semantic and structural information contained in the source code, so it is not conducive to the detection of various and complex smart contract vulnerabilities. Aiming at this problem, this paper proposes a vulnerability detection model based on the fusion of syntax and semantic features. The syntactic and semantic representation of the source code is obtained from the abstract syntax tree and control flow graph of the smart contract through TextCNN and Graph Neural Network. The syntactic and semantic features are fused, and the fused features are used to detect vulnerabilities. Experiments show that the detection accuracy and recall rate of this model have been improved on the detection tasks of five types of vulnerabilities, with an average precision of 96% and a recall rate of 90%, which can effectively identify smart contract vulnerabilities.
A Strategic Approach to IoT Security by Working Towards a Secure IoT Future

The internet of things' (IoT) fast-growing development and adoption are undeniable. Although the IoT development is moving at a fast rate, the security of these technologies is not keeping up. Lack of security infrastructure on the internet of things is due to such security measures being excluded from the average company business plan and the lack of security configurations established. The increasing need for robust security architecture is needed to address said vulnerabilities. How do the internet of things companies create a dynamic security approach to defend and combat threats while also being flexible to accommodate future technological advancements? This paper will answer this question by addressing the strategic approach to developing a strong IoT security infrastructure that promotes confidentiality, integrity, and availability with dynamic elements that allow for future developments to address new security concerns. This research question will be answered through analysis of extensive thematic literature review.
A Systematic Literature Review of Issue-Based Requirement Traceability

Issue reports are software artifacts that often specify the changed requirements of software systems. As software systems evolve according to these changed requirements, issue reports have become the essential artifacts that should be covered by requirement traceability. While researchers have developed automatic approaches for establishing the traceability links of issue reports, no papers have surveyed these approaches. In this paper, we conduct a systematic literature review of issue-based requirement traceability. We searched for articles published in renowned conferences and journals in the software engineering field from 2011 to 2022. From 1,347 initial articles, we identified 40 relevant articles. We investigated four aspects of issue-based traceability: problems, artifact pairs, techniques, and evaluation targets. Our findings are as follows. First, the challenges of issue-based requirement traceability are relevant to accuracy, effort, support, information, and trustworthiness. Second, issue reports are linked to commits, source code, user reviews, and test cases. Third, the studies mainly adopted machine learning and information retrieval techniques to generate and recover trace links. Finally, the main evaluation targets were open-source projects, but open datasets were also provided.