Reviewerly/exHarmony huggingface dataset:
Face Mask Detection using MediaPipe Facemesh

Recently, face masks have received increasing attention due to the COVID-19 pandemic, as their correct use can reduce and prevent the spread of outbreaks. Thus, several research studies focused on developing new strategies for identifying if individuals are wearing a face mask before they can be admitted into public spaces, buildings, and transportation systems. In this paper, we present an alternative approach to face mask detection pipeline for automatically detecting whether an individual is equipped with a face mask. Our proposed solution utilizes MediaPipe, a popular image segmentation and object detection machine learning model designed especially for cross-platform operation, with specific regard to mobile devices. We present the architecture of our pipeline, detail its operation, and report the results of an evaluation study in which we analyzed the performance of our model in real-world scenarios.
Anomaly detection of industrial state quantity time-Series data based on correlation and long short-term memory

Anomaly detection of multi-dimensional time-series data is a key research area, and the analysis of control, switching, and other state signals (i.e., industrial state quantity time series) is of particular importance to the operational sciences. When only the limited values of industrial state quantities are taken in the discrete set, there is no continuous change trend, making it difficult to achieve good results when applying analogue anomaly detection methods directly. In this study, assuming a correlation between the time series of state and analogue quantities in industrial systems, a model for anomaly detection in state quantity time-series data is built through a correlation supported by long short-term memory, and the model is verified using real physical process data. These results demonstrate that the proposed method is superior to extant industrial time-series models. Thus far, no studies focusing on the anomaly detection of two-state quantity time-series outliers have been performed. We believe that the research problem addressed herein and the proposed method contribute an interesting design methodology for the anomaly detection of time-series data in IIoT.
Self-guided information for few-shot classification

Few-shot classification aims to identify novel categories using only a few labeled samples. Generally, the metric-based few-shot classification methods compare the feature embedding of Query samples (unlabeled samples) with Support samples (labeled samples) in a metric algorithm to predict which category the Query sample belongs to. Obtaining a good feature embedding for each sample in the feature extraction stage can improve the classification accuracy in the metric stage. Based on this, we design the Self-Guided Information Convolution (SGI-Conv), an improved convolution structure, which utilizes the high-level features to guide the network to extract the required discriminative features. To effectively utilize the feature embeddings of samples, we divide the metric network into multiple blocks and build a multi-layer graph convolutional network by sharing adjacent matrices. The multi-layer structure enhances the aggregation ability of graph convolution. Extensive experiments on multiple benchmark datasets demonstrate that our method has achieved competitive results on the few-shot classification tasks.
Animal pose estimation: A closer look at the state-of-the-art, existing gaps and opportunities

Over the past few years, research on animal pose estimation in computer vision field has grown in many aspects such as 2D and 3D pose estimation, 3D mesh reconstruction, and behavior prediction. Promoted by deep learning, more and more animal pose estimation tools and animal pose datasets have also been made publicly available. However, compared to human pose estimation, which already has high accuracy and high applicability for complex scenes, animal pose estimation is still at a preliminary stage. The huge domain shift between each species, the scarce datasets, and uncooperative research subjects all pose intractable challenges to the development of robust and accurate animal pose estimation algorithms. In this review paper, we summarize the recent (from 2013 to 2021) work in animal pose estimation from computer vision perspective in order to present the state-of-the-art approaches and highlight the challenges they face in this field. We first categorize the various methods of animal pose estimation and present them according to several keywords. Also, we sort and introduce the released annotated image, video, and 3D models of animal poses as well as a promising substitute for real dataset. We also report the performances of the existing algorithms and visualize their results. Finally, we provide an in-depth analysis of the persisting obstacles in this field based on existing work, and offer potential solutions.
Patient's data privacy protection in medical healthcare transmission services using back propagation learning

• It introduces Healthcare Data Privacy to improve privacy maintenance. • It identifies the need for encrypting and decrypting the accumulated healthcare data. • It operates in two levels for verifying the security measures to prevent data losses. Medical healthcare services rely on communication technologies for exchanging digital records of patients. The new digitalizing of health records brings a specific change in healthcare services. The Electronic Health Records (EHRs) contains cumulative information about patients, such as medical history, observations, diagnostics, specimens, and reports. EHRs are sensitive information readily available for patient's and healthcare providers’ access while maintaining privacy. Therefore, preserving security and privacy is of utmost importance for healthcare systems since it reduces the impact of adversaries on EHR exchange and transmissions through wearable antennas. This research introduces Healthcare Data Privacy (HDP) through Backpropagation Learning (BL) to improve privacy maintenance in medical healthcare transmission services using wearable devices. The proposed method identifies the need for encrypting and decrypting the accumulated healthcare data based on data integrity verifications . It operates on two levels for verifying the security measures to prevent data losses in successive transmissions of wearable devices. Healthcare Data Privacy (HDP) using the Backpropagation Learning (BL) algorithm is utilized in the proposed method to promote privacy protection in medical healthcare transmission facilities. As a result of data integrity checks, the proposed method states that encrypted and decrypted medical records must be kept separate. Using appropriate data resources, the suggested method's performance is analysed and found superior in terms of types of experiments and accidental deletion.
Improved generative adversarial network for retinal image super-resolution

The retina is the only organ in the body that can use visible light for non-invasive observation. By analyzing retinal images, we can achieve early screening, diagnosis and prevention of many ophthalmological and systemic diseases, helping patients avoid the risk of blindness. Due to the powerful feature extraction capabilities, many deep learning super-resolution reconstruction networks have been applied to retinal image analysis and achieved excellent results.Given the lack of high-frequency information and poor visual perception in the current reconstruction results of super-resolution reconstruction networks under large-scale factors, we present an improved generative adversarial network (IGAN) algorithm for retinal image super-resolution reconstruction. Firstly, we construct a novel residual attention block, improving the reconstruction results lacking high-frequency information and texture details under large-scale factors. Secondly, we remove the Batch Normalization layer that affects the quality of image generation in the residual network. Finally, we use the more robust Charbonnier loss function instead of the mean square error loss function and the TV regular term to smooth the training results.Experimental results show that our proposed method significantly improves objective evaluation indicators such as peak signal-to-noise ratio and structural similarity. The obtained image has rich texture details and a better visual experience than the state-of-the-art image super-resolution methods.Our proposed method can better learn the mapping relationship between low-resolution and high-resolution retinal images. This method can be effectively and stably applied to the analysis of retinal images, providing an effective basis for early clinical treatment.
Analysis of Deep Learning Libraries: Keras, PyTorch, and MXnet

As many artificial neural libraries are developing the deep learning algorithm and implementing it became accessible to anyone. This study points out the disparity of performance in deep learning models such as convolutional neural networks (CNN) when implemented with different artificial neural libraries. Libraries such as Keras, Pytorch, and MXnet was utilized for each three CNN model then binary image classification was done based on the Dogs vs. Cats dataset from Kaggle. With using 75% of the dataset as the training set and the rest of 25% as a testing set, and as a result, each CNN model gave a different F1 score value and accuracy.
Hierarchical Representation and Deep Learning–Based Method for Automatically Transforming Textual Building Codes into Semantic Computable Requirements

Most of the existing automated compliance checking (ACC) systems are unable to fully automatically convert building-code requirements, especially requirements that have hierarchically complex semantic and syntactic structures, into computer-processable forms. The state-of-the-art rule-based ACC methods that are able to deal with complex requirements are based on information extraction and transformation rules, which are inflexible when applied to different types of regulatory documents. More research is thus needed to develop a flexible method to automatically process and understand requirements to support the downstream tasks in ACC systems, such as information matching and compliance reasoning. To address this need, this paper proposes (1) a new representation of requirements, the requirement hierarchy, and (2) a deep learning-based method to automatically extract semantic relations between words from building-code sentences, which are used to transform the sentences into such hierarchies. The proposed method was evaluated using a corpus of sentences from multiple regulatory documents. It achieved high semantic relation and requirement hierarchy extraction performance.
Sine–Cosine-Barnacles Algorithm Optimizer with disruption operator for global optimization and automatic data clustering

In this paper, an improved Barnacles Mating Optimizer (BMO) is proposed to deal with optimization problems and develop a new automatic clustering approach. BMO is a well-established optimization technique inspired by the mating behavior of barnacles in real-life. The exploratory trends of BMO are influential and can maintain the right balance among exploration and exploitation. However, this population-based method can be improved further to reduce the probability of potential drawbacks for any optimization technique. As such, we revised the core searching phased of BMO based on a sine–cosine algorithm (SCA) and disruption operators (DO). The proposed method is named BMSCD, which updates the current solution by switching between the mechanisms of the BMO and SCA based on a probability calculated using the fitness value of the current solution. The experiments results on various benchmark cases for global optimizations demonstrate the improved performance of the proposed BMSCD in terms of quality of solutions, the balance of the exploration–exploitation, and convergence rates. Besides, the proposed BMSCD is evaluated by nine measures in solving different clustering problems. The results show that the BMSCD can effectively and powerfully address the tested problems and provide excellent performance compared to the state-of-the-art methods.
Nudging towards news diversity: A theoretical framework for facilitating diverse news consumption through recommender design

Growing concern about the democratic impact of automatically curated news platforms urges us to reconsider how such platforms should be designed. We propose a theoretical framework for personalised diversity nudges that can stimulate diverse news consumption on the individual level. To examine potential benefits and limitations of existing diversity nudges, we conduct an interdisciplinary literature review that synthesises theoretical work on news selection mechanisms with hands-on tools and implementations from the fields of computer science and recommender systems. Based thereupon, we propose five diversity nudges that researchers and practitioners can build on. We provide a theoretical motivation of why, when and for whom such nudges could be effective, critically reflect on their potential backfire effects and the need for algorithmic transparency, and sketch out a research agenda for diversity-aware news recommender design. Thereby, we develop concrete, theoretically grounded avenues towards facilitating diverse news consumption on algorithmically curated platforms.
An empirical analysis of high school students' practices of modelling with unstructured data

Abstract To date, many AI initiatives (eg, AI4K12, CS for All) developed standards and frameworks as guidance for educators to create accessible and engaging Artificial Intelligence (AI) learning experiences for K‐12 students. These efforts revealed a significant need to prepare youth to gain a fundamental understanding of how intelligence is created, applied, and its potential to perpetuate bias and unfairness. This study contributes to the growing interest in K‐12 AI education by examining student learning of modelling real‐world text data. Four students from an Advanced Placement computer science classroom at a public high school participated in this study. Our qualitative analysis reveals that the students developed nuanced and in‐depth understandings of how text classification models—a type of AI application—are trained. Specifically, we found that in modelling texts, students: (1) drew on their social experiences and cultural knowledge to create predictive features, (2) engineered predictive features to address model errors, (3) described model learning patterns from training data and (4) reasoned about noisy features when comparing models. This study contributes to an initial understanding of student learning of modelling unstructured data and offers implications for scaffolding in‐depth reasoning about model decision making. Practitioner notes What is already known about this topic Scholarly attention has turned to examining Artificial Intelligence (AI) literacy in K‐12 to help students understand the working mechanism of AI technologies and critically evaluate automated decisions made by computer models. While efforts have been made to engage students in understanding AI through building machine learning models with data, few of them go in‐depth into teaching and learning of feature engineering, a critical concept in modelling data. There is a need for research to examine students' data modelling processes, particularly in the little‐researched realm of unstructured data. What this paper adds Results show that students developed nuanced understandings of models learning patterns in data for automated decision making. Results demonstrate that students drew on prior experience and knowledge in creating features from unstructured data in the learning task of building text classification models. Students needed support in performing feature engineering practices, reasoning about noisy features and exploring features in rich social contexts that the data set is situated in. Implications for practice and/or policy It is important for schools to provide hands‐on model building experiences for students to understand and evaluate automated decisions from AI technologies. Students should be empowered to draw on their cultural and social backgrounds as they create models and evaluate data sources. To extend this work, educators should consider opportunities to integrate AI learning in other disciplinary subjects (ie, outside of computer science classes).
Explainable Artificial Intelligence (XAI): Connecting Artificial Decision-Making and Human Trust in Autonomous Vehicles

Automated navigation technology has established itself as an integral facet of intelligent transportation and smart city systems. Several international technological organizations have realized the immense potential of autonomous vehicular systems and are currently working towards their complete development for mainstream application. From deep learning algorithms for road object detection to intrusion detection systems for CAN bus monitoring, the functioning of a self-driving vehicle is powered by the simultaneous working of multiple inner vehicle module systems that perform proper vehicle navigation while ensuring the physical safety and digital privacy of the user. Transparency of the vehicle’s thought processes can assure the user of its credibility and reliability. This paper introduces explainable artificial intelligence, which aims to converge the decision-making processes of Autonomous Vehicle Systems (AVS). Here, the domain of Explainable AI (XAI) provides clear insights into the role of explainable AI in autonomous vehicles and increase human trust for AI based solutions in the same sector. This paper exhibits the trajectories of transportation advancements and the current scenario of the industry. A comparative quantitative and qualitative analysis is performed to compare the simulations of XAI and vehicular smart systems to showcase the significant developments achieved. Visual explanatory methods and an intrusion detection classifier were created as part of this research and achieved significant results over extant works.
Hybrid superconducting photonic-phononic chip for quantum information processing

The integration of qubits with long coherence times and functional quantum devices on a single chip, and thus the realization of an all-solid-state quantum computing chip, is an important goal in current experimental research on quantum information processing. Among various quantum platforms, a series of significant progresses have been made in photonic quantum chips and superconducting quantum chips, while both the number of qubits and the complexity of quantum circuits have been increasing. Although these two chip platforms have respective unique advantages and potentials, their shortcomings have been gradually revealed and need to be solved. By introducing phonon-integrated devices, it is possible to combine all unsuspended phononic, photonic, and superconducting quantum devices organically on the same chip to achieve coherent coupling among them. Here, we provide a prospect and a short review on the integrated photonic, superconducting, and hybrid quantum chips for quantum information processing.
Application of Truffle Suite in a Blockchain Environment

Advancement is a term that never stops with something so is technology. Blockchain technology is an old term but with updation, it has become a key-buzz term in the technological market. The advancements in blockchain led to the formation of distributed and decentralized applications. This advancement is only possible in the applications phase of blockchain. A simplified ecosystem in which decentralized apps (DApps) can be built is truffle suite. Majorly, three constituents that are completing the truffle suite are truffle, drizzle and ganache. DApps are those that operate among the users and are not monitored by a central authority. In DApps, the ecosystem of a peer-to-peer network works as a complete operating system. This paper focuses on these DApps using truffle suite and its different scenarios. A quick depiction of blockchain with its connection to the truffle suite is also highlighted in this paper. Moreover, this paper also illustrates a popular use case of a DApp with its real-time issues and concerns.
The cybersecurity behavioral research: A tertiary study

Cybersecurity has seen a rise in behavioral investigation during the past decades. This includes studies being carried out at the primary as well as secondary levels. There are a number of reviews on cybersecurity behavioral research that are reported in different publication venues. To get a holistic view of the cybersecurity behavioral research, a synthesis of this literature as a tertiary study is needed. This paper aims to investigate the demographic, specific and quality trends regarding reviews by synthesizing secondary literature in cybersecurity behavioral research. We use a systematic literature review protocol to carry out tertiary study till February 2022. A total of 107 secondary studies including regular as well as systematic reviews are included in our tertiary study. The results reveal a growing trend in secondary studies. The quality of the secondary studies lacks quality assessment of primary study and adoption of a synthesis method as per Database of Abstract of Reviews of Effect (DARE) quality assessment criteria. Another finding is the nascent area of cybersecurity behavioral research that is still at the theory development stage due to latest endeavors to identify theoretical building blocks and inconsistent findings in empirical results. Gaps exist in the conceptualization of constructs as taxonomies need to be constructed for non-organizational settings. The replication of studies is lacking as almost half of the secondary studies lack systematic approach to carry out reviews. The dominant epistemological and ontological beliefs are positivist in nature with paucity of research that employs other paradigms to study the human aspect of cybersecurity.
A study on length measurement method of hot large forgings based on binocular vision system

A length measurement method of hot large forgings is proposed in this paper. Firstly, the images of hot forgings are acquired by two monochrome Charge Coupled Device (CCD) cameras, and the light strips are detected according to the characteristics that the value of the pixels in the vertical direction of the light strips is approximate the Gaussian distribution. Secondly, the sub-pixel centers of each light strips in the left and right images are calculated and used for matching and three-dimensional (3D). Finally, the curve represented by quintic polynomial is employed to fit the two-dimensional (2D) points which are projected by 3D points on the fitted plane, and the edge of the forging is calculated by the curvature of the fitted curve. The experimental results in laboratory show that the measurement accuracy can reach 0.547%. The length measurement time of hot forging is 3.7 s.
QPlayer: Lightweight, scalable, and fast quantum simulator

Abstract With the rapid evolution of quantum computing, digital quantum simulations are essential for quantum algorithm verification, quantum error analysis, and new quantum applications. However, the exponential increase in memory overhead and operation time is challenging issues that have not been solved for years. We propose a novel approach that provides more qubits and faster quantum operations with smaller memory than before. Our method selectively tracks realized quantum states using a reduced quantum state representation scheme instead of loading the entire quantum states into memory. This method dramatically reduces memory space ensuring fast quantum computations without compromising the global quantum states. Furthermore, our empirical evaluation reveals that our proposed idea outperforms traditional methods for various algorithms. We verified that the Grover algorithm supports up to 55 qubits and the surface code algorithm supports up to 85 qubits in 512 GB memory on a single computational node, which is against the previous studies that support only between 35 qubits and 49 qubits.