Reviewerly/exHarmony huggingface dataset:
Omnidirectional Panoramic Video System With Frame-by-Frame Ultrafast Viewpoint Control

This study developsan omnidirectional panoramic video system that can simultaneously observe zoom-in images at dozens of different viewpoints in a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$360^{\circ }$</tex-math></inline-formula> field of view by synchronizing hundreds of frames per second video shooting, with a rotation of tens-of-rotation-per-second mirror-rotator. When video shooting a scene with a certain exposure utilizing a mirror-rotator, apparent motion in the captured images becomes too large to observe non-blurred images, owing to its high-speed rotation. To reduce motion blurs caused by the rotation of a mirror-rotator, our system introduces an ultrafast pan-tilt mirror mechanism that can cancel apparent motion in images by executing frame-by-frame viewpoint control, so that the apparent speed in images becomes zero when the camera shutter is open, and it returns to its home position when the shutter is closed. Our system can capture 40 omnidirectionally different view images at 10 fps in real time by synchronizing 400-fps video shooting with frame-by-frame ultrafast pan-tilt mirror control for motion blur reduction when a mirror-rotator rotates at 10 rps. Its performance is verified by experimental results for several omnidirectional real scenes, and our system can capture clear and bright panoramic zoom-in images without decreasing the camera exposure when omnidirectionally observing dozens-of-meters-distance outdoor scenes.
A Generic Machine Learning Approach for IoT Device Identification

The rapidly prevailing Internet of Things (IoT) devices in numerous sectors, may jeopardize a vast amount of confidential data, raising threats to network security. Thereby, it is crucial to verify the data source and device identity to ensure network security. Thus, the identification of IoT devices is a substantial step in securing the underlying network system. The models which are proposed in previous studies are trained and tested on the same dataset, which leads to overfitting. In this work, we propose a generic machine learning approach for IoT device identification and test the trained models on four publically available datasets. To better identify IoT devices in the network through machine learning models, we first extracted 85 features from packet capture (.pcap) files using NFStream. We then selected 20 features using the information gain method and trained six machine learning models in our experiments on two publicly available datasets, i.e., UNSW IoT Traces, and Your Things dataset, for binary classification. In the training phase, we obtained the highest 99% accuracy for IoT device identification using Random Forest and Na&#x00EF;ve Bayes classifiers over UNSW and Your Things dataset respectively. Further, we evaluated these models on two other publicly available datasets. Overall, the Na&#x00EF;ve Bayes classifier outperformed all other classifiers for detecting both IoT and non-IoT traffic, with 92% average accuracy.
Design and Development of an Assistive System Based on Eye Tracking

This research concerns the design and development of an assistive system based on eye tracking, which can be used to improve the quality of life of disabled patients. With the use of their eye movement, whose function is not affected by their illness, patients are capable of communicating with and sending notifications to caretakers, controlling various appliances, including wheelchairs. The designed system is divided into two subsystems: stationary and mobile assistive systems. Both systems provide a graphic user interface (GUI) that is used to link the eye tracker with the appliance control. There are six GUI pages for the stationary assistive system and seven for the mobile assistive system. GUI pages for the stationary assistive system include the home page, smart appliance page, eye-controlled television page, eye-controlled air conditional page, i-speak page and entertainment page. GUI pages for the mobile assistive system are similar to the GUI pages for the stationary assistive system, with the additional eye-controlled wheelchair page. To provide hand-free secure access, an authentication based on facial landmarks is developed. The operational test of the proposed assistive system provides successful and promising results.
Application of Knowledge Distillation Based on Transfer Learning of ERNIE Model in Intelligent Dialogue Intention Recognition

The 'intention' classification of a user question is an important element of a task-engine driven chatbot. The essence of a user question's intention understanding is the text classification. The transfer learning, such as BERT (Bidirectional Encoder Representations from Transformers) and ERNIE (Enhanced Representation through Knowledge Integration), has put the text classification task into a new level, but the BERT and ERNIE model are difficult to support high QPS (queries per second) intelligent dialogue systems due to computational performance issues. In reality, the simple classification model usually shows a high computational performance, but they are limited by low accuracy. In this paper, we use knowledge of the ERNIE model to distill the FastText model; the ERNIE model works as a teacher model to predict the massive online unlabeled data for data enhancement, and then guides the training of the student model of FastText with better computational efficiency. The FastText model is distilled by the ERNIE model in chatbot intention classification. This not only guarantees the superiority of its original computational performance, but also the intention classification accuracy has been significantly improved.
Nondeterministic Impact of CPU Multithreading on Training Deep Learning Systems

With the wide deployment of deep learning (DL) systems, research in reliable and robust DL is not an option but a priority, especially for safety-critical applications. Unfortunately, DL systems are usually nondeterministic. Due to software-level (e.g., randomness) and hardware-level (e.g., GPUs or CPUs) factors, multiple training runs can generate inconsistent models and yield different evaluation results, even with identical settings and training data on the same implementation framework and hardware platform. Existing studies focus on analyzing software-level nondeterminism factors and the nondeterminism introduced by GPUs. However, the nondeterminism impact of CPU multi-threading on training DL systems has rarely been studied. To fill this knowledge gap, we present the first work of studying the variance and robustness of DL systems impacted by CPU multithreading. Our major contributions are fourfold: 1) An experimental framework based on VirtualBox for analyzing the impact of CPU multithreading on training DL systems; 2) Six findings obtained from our experiments and examination on GitHub DL projects; 3) Five implications to DL researchers and practitioners according to our findings; 4) Released the research data (https://github.com/DeterministicDeepLearning).
FPGA simulation of chaotic tent map‐based S‐Box design

Abstract The chaotic system has a characteristically random behavior by nature, and these systems have their own characteristics in a completely deterministic structure. This feature of a chaotic system makes it difficult to predict encryptions designed based on such a system. Thanks to this unpredictable and strong feature, maps produced from chaotic systems are an important alternative in the field of encryption. One of the structures obtained by employing chaotic maps is the substitution box. S‐Box, which provides the confusion principle used in block ciphers, is the main block that dynamically replaces unencrypted data with confidential data and makes a significant contribution to ensuring high security in the encryption system. Therefore, S‐Boxes hold a critical role in block ciphers. Speed and reliability are important parameters in the creation of this main block. Especially, applications performed on hardware are more reliable and high performance. Therefore, in this study, an S‐Box was designed using field‐programmable gate arrays (FPGA) simulation from a chaotic tent map to create a fast and reliable S‐Box because FPGAs offer solutions that may be important in this field considering their fast and customizable architecture. In the proposed method, the S‐Box was created in 0.16 s. In addition, the dynamic properties of the chaotic tent map were analyzed with Lyapunov exponents, and the NIST SP 800‐22 test was applied for the information encryption suitability of the proposed chaotic system. Also, to test the reliability of the produced S‐Box structures, SAC, non‐linearity, bit independence criteria, and input/output XOR distribution table metrics were implemented. The results showed that the proposed chaotic map was dynamic and passed the reliability tests successfully.
Culturally centring digital inclusion and marginality: A case study in Aotearoa New Zealand

The dominant approach to digital inclusion positions technology as a ‘fix’ to the challenges experienced by marginalized communities. Largely erased are the broader structures of marginalization, the role of technology in relationship to structures and the cultural contexts within which technologies are negotiated. In this essay, we culturally centre digital inclusion to offer insights into the ways in which technologies play out within the margins, drawing from ethnographic fieldwork, including 60 initial interviews, group meetings and 25 interviews carried out amidst the lockdown in response to COVID-19 in Aotearoa New Zealand. We argue that the pandemic offers a window into the relationship between inequalities and technologies, rendering structural contexts of these inequalities visible. We highlight how technology adoption produces marginality in service delivery, situating technology amidst the ecologies of everyday life and the interplays of culture, structure and agency.
A hybrid approach based on Legendre wavelet for numerical simulation of Helmholtz equation with complex solution

In this article, with the help of Legendre wavelets a hybrid approach is presented for the numerical solution of the Helmholtz equation which has a complex solution. The present hybrid approach is based on the Legendre Wavelet Collocation Method (LWCM). Initially, the Helmholtz equation is converted to the coupled equation with suitable transformation, later all the derivatives in the equations including boundary conditions are approximated with the help of Legendre Wavelets. Later, with the help of the Legendre wavelet operational matrix of integration, a system of linear algebraic equations are formed for obtaining the numerical solution of Helmholtz equation. The aforementioned proposed algorithm is very simple and can easily be executed in any computer-oriented language efficiently. To demonstrate the efficiency and performance of the newly established technique, the method is tested on various well-known examples from previous literature including other Legendre wavelet-based methods. The obtained results produce better accuracy and widespread use of the newly developed technique for a range of benchmark problems which have complex solutions.
Fast and Accurate Cardinality Estimation by Self-Morphing Bitmaps

Estimating the cardinality of a data stream is a fundamental problem underlying numerous applications such as traffic monitoring in a network or a datacenter and query optimization of Internet-scale P2P data networks. Existing solutions suffer from high processing/query overhead or memory in-efficiency, which prevents them from operating online for data streams with very high arrival rates. This paper takes a new solution path different from the prior art and proposes a self-morphing bitmap, which combines operational simplicity with structural dynamics, allowing the bitmap to be morphed in a series of steps with an evolving sampling probability that automatically adapts to different stream sizes. We further generalize the design of self-morphing bitmap. We evaluate the self-morphing bitmap theoretically and experimentally. The results demonstrate that it significantly outperforms the prior art.
Edge Intelligence for Smart EL Images Defects Detection of PV Plants in the IoT-Based Inspection System

Given the huge installed capacity of photovoltaic (PV) worldwide, the traditional defect detection system for PV plants is infeasible, especially for large-scale plants. In this article, unmanned aerial vehicles (UAVs) mounted with several sensors and a computer in the cloud are used cooperatively to establish an Internet of Things-based cloud-edge computing infrastructure, which can automatically detect defects with low latency, low cost, and high accuracy. The pretrained models trained in the cloud server are embedded into the processor in UAVs to implement online detection. Specifically, given the characteristic of defects in electroluminescence images, a two-stage algorithm is proposed to identify the defects with high performance. In the first stage, cells, the basic unit in the PV module, are extracted using an encoder–decoder network. Then, a vision-based incremental defect classification algorithm is proposed for defect detection that integrates deep learning with prior knowledge to maximize computing efficiency. The performance of the proposed system is evaluated through extensive experiments.
iFace: A Deepfake Resilient Digital Identification Framework for Smart Cities

Digital ID is the gateway of &#x201C;Smart City&#x201D; for &#x201C;Smart Citizens&#x201D;. It gives citizen access to all other stakeholders of smart cities like smart healthcare, smart transport, smart finance, smart energy, etc. effectively and easily. In this paper, we propose a biometric based digital ID which is implemented in IoT environment. It is a secured and robust system against deepfake attacks. A convolutional neural network (CNN) based feature extraction method has been employed to defeat deepfake attacks. The dlib face detector has been used in detecting face landmark points and in calculating distances in the iris and nose region to obtain unique facial features. A bio-key is generated from the combination of features from facial landmarks and various facial distances along with the username. An encoded key is stored in a cloud database during the registration process of the user. For accessing any facilities in a smart city, the user needs to be authenticated. The authentication process is performed at the edge. Small changes in an image due to unconstrained settings are corrected using the Reed Solomon algorithm. Once authenticated at a particular smart facility, the user is now eligible to use that facility.
Self-supervised learning for medieval handwriting identification: A case study from the Vatican Apostolic Library

In this paper, we consider the task of automatically identifying whether different parts of medieval and modern manuscripts can be traced back to the same copyist/scribe, a problem of significant interest in paleography.Currently, the application of deep learning techniques in the context of scribe recognition has been hindered by the lack of a sufficiently large, labeled dataset, since the labeling process is incredibly complex and time-consuming.Here, we propose the first successful application of the recent framework of self-supervised learning to the field of digital paleography, wherein we pretrain a convolutional neural network by leveraging large amounts of unlabeled manuscripts.To this end, we build a novel dataset consisting of both labeled and unlabeled manuscripts for copyist identification extracted from the Vatican Apostolic Library.We show that fine-tuning this model to the task of interest significantly outperforms other baselines, including the common setup of initializing the network
A study of visually linked keywords to support exploratory browsing in academic search

Abstract While the search interfaces used by common academic digital libraries provide easy access to a wealth of peer‐reviewed literature, their interfaces provide little support for exploratory browsing. When faced with a complex search task (such as one that requires knowledge discovery), exploratory browsing is an important first step in an exploratory search process. To more effectively support exploratory browsing, we have designed and implemented a novel academic digital library search interface (KLink Search) with two new features: visually linked keywords and an interactive workspace. To study the potential value of these features, we have conducted a controlled laboratory study with 32 participants, comparing KLink Search to a baseline digital library search interface modeled after that used by IEEE Xplore. Based on subjective opinions, objective performance, and behavioral data, we show the value of adding lightweight visual and interactive features to academic digital library search interfaces to support exploratory browsing.
Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks

Every year approximately 1.24 million people are diagnosed with blood cancer. While the rate increases each year, the availability of data for each kind of blood cancer remains scarce. It is essential to produce enough data for each blood cell type obtained from bone marrow aspirate smears to diagnose rare types of cancer. Generating data would help easy and quick diagnosis, which are the most critical factors in cancer. Generative adversarial networks (GAN) are the latest emerging framework for generating synthetic images and time-series data. This paper takes microscopic cell images, preprocesses them, and uses a hybrid GAN architecture to generate synthetic images of the cell types containing fewer data. We prepared a single dataset with expert intervention by combining images from three different sources. The final dataset consists of 12 cell types and has 33,177 microscopic cell images. We use the discriminator architecture of auxiliary classifier GAN (AC-GAN) and combine it with the Wasserstein GAN with gradient penalty model (WGAN-GP). We name our model as WGAN-GP-AC. The discriminator in our proposed model works to identify real and generated images and classify every image with a cell type. We provide experimental results demonstrating that our proposed model performs better than existing individual and hybrid GAN models in generating microscopic cell images. We use the generated synthetic data with classification models, and the results prove that the classification rate increases significantly. Classification models achieved 0.95 precision and 0.96 recall value for synthetic data, which is higher than the original, augmented, or combined datasets.
A Delphi study to recognize and assess systems of systems vulnerabilities

System of Systems (SoS) is an emerging paradigm by which independent systems collaborate by sharing resources and processes to achieve objectives that they could not achieve on their own. In this context, a number of emergent behaviors may arise that can undermine the security of the constituent systems. We apply the Delphi method with the aims to improve our understanding of SoS security and related problems, and to investigate their possible causes and remedies. Experts on SoS expressed their opinions and reached consensus in a series of rounds by following a structured questionnaire. The results show that the experts found more consensus in disagreement than in agreement about some SoS characteristics, and on how SoS vulnerabilities could be identified and prevented. From this study we learn that more work is needed to reach a shared understanding of SoS vulnerabilities, and we leverage expert feedback to outline some future research directions.
Comparative analysis of CycleGAN and AttentionGAN on face aging application

Recently, there is incredible progress in the arena of machine learning with generative adversarial network (GAN) methods. These methods tend to synthesize new data from input images that are highly realistic at the output. One of its applications in the image-to-image transformation way is the face aging task. In the face aging process, new face images are synthesized with the help of the input images and desired target images. Face aging can be beneficial in several domains such as in biometric systems for face recognition with age progression, in forensics for helping to find the missing children, in entertainment, and many more. Nowadays, several GANs are available for face aging applications and this paper focuses on the insight comparison among the frequently used image-to-image translation GANs which are CycleGAN (Cycle-Consistent Adversarial Network) and AttentionGAN (Attention-Guided Generative Adversarial Network). The first model (CycleGAN) comprises two generators, two discriminators, and converting an image from one domain to another without the need for paired images dataset. The second is AttentionGAN, which consists of attention masks and content masks multiplied with the generated output in one domain to generate a highly realistic image in another domain. For comparison, these two are trained on two dataset which is CelebA-HQ (CelebFaces Attributes high-quality dataset) and FFHQ (Flickr Faces HQ). Efficacy is evaluated quantitatively with identity preservation, five image quality assessment metrics, and qualitatively with a perceptual study on synthesized images, face aging signs, and robustness. It has been concluded that overall CycleGAN has better performance than AttentionGAN. In the future, a more critical comparison can be performed on the number of GANs for face aging applications.
Deep Phenotyping and Prediction of Long-term Cardiovascular Disease: Optimized by Machine Learning

Prediction of cardiovascular disease (CVD) is important in clinical practice. Machine learning (ML) may offer an improved alternative to current CVD risk stratification in individual patients. We aim to identify important predictors and compare ML models with traditional models according to their prediction performance in a large long-term follow-up cohort.The Atherosclerosis Risk in Communities (ARIC) study was designed to study the progression of subclinical disease to cardiovascular events over a 25-year follow-up period. All phenotypic variables at visit 1 were obtained. All-cause death, CVD, and coronary heart disease were the outcomes for analysis. The ML framework involved variable selection using the random survival forest (RSF) method, model building, and 5-fold cross-validation. Model performance was evaluated by discrimination using the Harrell concordance index (C-index), accuracy using the Brier score (BS), and interpretability using the number of variables in the model.Of the 14,842 participants in ARIC, the average age was 54.2 years, with 45.2% male and 26.2% Black participants. Thirty-eight unique variables were selected in the RSF top 20 importance ranking of all 6 outcomes. Aging, hypertension, glucose metabolism, renal function, coagulation, adiposity, and sodium retention dominated the predictions of all outcomes. The ML models outperformed the regression models and established risk scores with a higher C-index, lower BS, and varied interpretability.The ML framework is useful for identifying important predictors of CVD and for developing models with robust performance compared with existing risk models.