Reviewerly/exHarmony huggingface dataset:
Mirror Descent View for Neural Network Quantization

Quantizing large Neural Networks (NN) while maintaining the performance is highly desirable for resource-limited devices due to reduced memory and time complexity. It is usually formulated as a constrained optimization problem and optimized via a modified version of gradient descent. In this work, by interpreting the continuous parameters (unconstrained) as the dual of the quantized ones, we introduce a Mirror Descent (MD) framework for NN quantization. Specifically, we provide conditions on the projections (i.e., mapping from continuous to quantized ones) which would enable us to derive valid mirror maps and in turn the respective MD updates. Furthermore, we present a numerically stable implementation of MD that requires storing an additional set of auxiliary variables (unconstrained), and show that it is strikingly analogous to the Straight Through Estimator (STE) based method which is typically viewed as a trick to avoid vanishing gradients issue. Our experiments on CIFAR-10/100, TinyImageNet, and ImageNet classification datasets with VGG-16, ResNet-18, and MobileNetV2 architectures show that our MD variants obtain quantized networks with state-of-the-art performance. Code is available at this https URL.
A novel measure to evaluate generative adversarial networks based on direct analysis of generated images

The generative adversarial network (GAN) is a state-of-the-art technique in the field of deep learning. A number of recent papers address the theory and applications of GANs in various fields of image processing. Fewer studies, however, have directly evaluated GAN outputs. Those that have been conducted focused on using classification performance, e.g., inception score (IS) and statistical metrics, e.g., Fréchet inception distance (FID). Here, we consider a fundamental way to evaluate GANs by directly analyzing the images they generate, instead of using them as inputs to other classifiers. We characterize the performance of a GAN as an image generator according to three aspects: 1) creativity: non-duplication of the real images; 2) inheritance: generated images should have the same style, which retains key features of the real images; and 3) diversity: generated images are different from each other. A GAN should not generate a few different images repeatedly. Based on the three aspects of ideal GANs, we have designed the likeness score (LS) to evaluate GAN performance and have applied it to evaluate several typical GANs. We compared our proposed measure with two commonly used GAN evaluation methods: IS and FID, and four additional measures. Furthermore, we discuss how these evaluations could help us deepen our understanding of GANs and improve their performance. The code can be found in author's website linked up with the ORCID: https://orcid.org/0000-0002-3779-9368 .
Mixed Analysis of the Flipped Classroom in the Concrete and Steel Structures Subject in the Context of COVID-19 Crisis Outbreak. A Pilot Study

A sudden lockdown was declared on 14 March 2020 due to COVID-19 crisis, leading to an immediate change from face-to-face to online learning in all universities within Spanish jurisdiction. At La Salle School of Architecture, the Concrete and Steel Structures subject started online classes immediately after the lockdown law was published, using a methodology based on the flipped classroom approach and adapting the monitoring of the student to the virtual environment. This article presents a pilot study to analyze the adaptation of the model to the online format using a mixed approach in which qualitative and quantitative surveys were conducted at the end of the course with 48 participants. Responses from both surveys were organized according to six categories (teachers, assessment, methods, class development, students and documents) and 14 subcategories, as developed in an undergoing research project involving the subject since the academic year 2017/2018. Thus, the open responses of the students have been analyzed alongside with the quantitative data. The results demonstrate a proper adaptation of the model, as well as the negative perception of the students of the online format due to the loss of face-to-face benefits of the flipped classroom.
Hand Gesture Recognition Using FMCW Radar in Multi-Person Scenario

Remote hand gesture recognition using radio frequency (RF) waves is an emerging approach for the interaction between users and smart devices. In this paper, the feasibility of using frequency-modulated continuous-wave (FMCW) radar to recognize hand gestures when multiple closely spaced subjects are illuminated by the same sensor is investigated. Four hand gestures are suggested. Existing radar-based hand gesture recognition mainly relied on the Doppler effect caused by one motion in the sensor's field of view. Besides, they did not consider the random body motion of targets situated at the same range, which is not practical in real-life scenarios. Leveraging the range discrimination capability of FMCW radars and characteristic signal extraction, interference rejection is achieved in this paper. Experimental results confirm the effectiveness of the FMCW radar based remote control approach in the presence of multiple interferences.
The barriers and enablers to downloading the COVIDSafe app – a topic modelling analysis

We report a survey in regional Queensland to understand the reasons for suboptimal uptake of the COVIDSafe app.A short five-minute electronic survey disseminated to healthcare professionals, mining groups and school communities in the Central Queensland region. Free text responses and their topics were modelled using natural language processing and a latent Dirichlet model.We received a total of 723 responses; of these, 69% had downloaded the app and 31% had not. The respondents' reasons for not downloading the app were grouped under four topics: lack of perceived risk of COVID-19/lack of perceived need and privacy issues; phone-related issues; tracking and misuse of data; and trust, security and credibility. Among the 472 people who downloaded the app and provided text amenable to text mining, the two topics most commonly listed were: to assist with contact tracing; and to return to normal.This survey of a regional population found that lack of perceived need, concerns around privacy and technical difficulties were the major barriers to users downloading the application. Implications for public health: Health promotion campaigns aimed at increasing the uptake of the COVIDSafe app should focus on promoting how the app will assist with contact tracing to help return to 'normal'. Additionally, health promotors should address the app's impacts on privacy, people's lack of perceived need for the app and technical barriers.
Enhancement of damaged-image prediction through Cahn–Hilliard image inpainting

We assess the benefit of including an image inpainting filter before passing damaged images into a classification neural network. We employ an appropriately modified Cahn–Hilliard equation as an image inpainting filter which is solved numerically with a finite-volume scheme exhibiting reduced computational cost and the properties of energy stability and boundedness. The benchmark dataset employed is Modified National Institute of Standards and Technology (MNIST) dataset, which consists of binary images of handwritten digits and is a standard dataset to validate image-processing methodologies. We train a neural network based on dense layers with MNIST, and subsequently we contaminate the test set with damages of different types and intensities. We then compare the prediction accuracy of the neural network with and without applying the Cahn–Hilliard filter to the damaged images test. Our results quantify the significant improvement of damaged-image prediction by applying the Cahn–Hilliard filter, which for specific damages can increase up to 50% and is advantageous for low to moderate damage.
Efficient and Secure Data Transmission Approach in Cloud-MANET-IoT Integrated Framework

The Internet of Things (IoT) devices have capabilities to interact and communicate in 5G heterogeneous networks. The IoT devices also have capabilities to form a network with neighborhood devices without a centralized approach. This network is called the mobile ad hoc network (MANET). Through an infrastructure-less system of the Internet of Things environment, the MANET enables IoT nodes to interact with one another. Those IoT nodes could interactively connect, communicate as well as share knowledge between several nodes. The role of cloud throughout this structure would be to store as well as interpret information through IoT nodes. The communication security has also been introduced to be one of the techniques in which trying to solve the data transmission security issue that could result in the performance increase in cloud consumption and ubiquity. Our purpose in this research would be to establish a communication system among IoT nodes in such an embedded Cloud and MANET structure. The main goal of this research is to create an efficient and secure approach for communication in Cloud-MANET-IoT integrated framework. This approach is implemented and tested.
Efficient and Privacy-Preserving Massive Data Processing for Smart Grids

Analysis and utilization of massive meter data can help decision-makers provide reasonable decisions. Therefore, multi-functional meter data processing has received considerable attention in recent years. Nevertheless, it might compromise users' privacy, such as releasing users' lifestyles and habits. In this paper, we propose an efficient and privacy-preserving massive data process for smart grids. The presented protocol utilizes the Paillier homomorphic encryption and Horner's Rule to achieve a privacy-preserving two-level random permutation method, making large-scale meter data permuted randomly and sufficiently in a privacy-preserving way. As a result, the analysis center can simultaneously implement various data processing functions (such as variance, comparing, linear regression analysis), and it does not know the source of data. The security analysis shows that our protocol can realize data confidentiality and data source anonymity. The detailed analyses demonstrate that our protocol is efficient in terms of computational and communication costs. Furthermore, it can support fault tolerance of entity failures and has flexible system scalability.
Denoising of magnetic resonance images using discriminative learning-based deep convolutional neural network

The noise in magnetic resonance (MR) images causes severe issues for medical diagnosis purposes.In this paper, we propose a discriminative learning based convolutional neural network denoiser to denoise the MR image data contaminated with noise.The proposed method incorporates the use of depthwise separable convolution along with local response normalization with modified hyperparameters and internal skip connections to denoise the contaminated MR images. Moreover, the addition of parametric RELU instead of normal conventional RELU in our proposed architecture gives more stable and fine results. The denoised images were further segmented to test the appropriateness of the results. The network is trained on one dataset and tested on other dataset produces remarkably good results.Our proposed network was used to denoise the images of different noise levels, and it yields better performance as compared with various networks. The SSIM and PSNR showed an average improvement of (7.2 ± 0.002) % and (8.5 ± 0.25) % respectively when tested on different datasets without retaining the network. An improvement of 5% and 6% was achieved in the values of mean intersection over union (mIoU) and BF score when the denoised images were segmented for testing the relevancy in biomedical imaging applications. The statistical test suggests that the obtained results are statistically significant as p< 0.05.The denoised images obtained are more clinically suitable for medical image diagnosis purposes, as depicted by the evaluation parameters. Further, external clinical validation was performed by an experienced radiologist for testing the validation of the resulting images.
Metrological assessment of multi‐sensor camera technology for spatially‐resolved ultra‐high‐speed imaging of transient high strain‐rate deformation processes

Abstract The present work proposes a metrological route for capturing spatially‐resolved ultra‐high‐speed kinematic full‐field data from high strain‐rate experiments and multi‐sensor camera technology. This paper focuses, from an application point of view, on highly resolved rotating mirror cameras, such as the Cordin‐580. This camera allows 78 frames of 8 megapixels to be recorded at up to 4 million frames per second (fps). The optical apparatus induces distortions that need to be taken into consideration. Distortions are modelled with Zernike polynomials and recovered using Digital Image Correlation (DIC) with a tailored synthetic speckle pattern. Effective displacements can then be quantitatively obtained with subpixel precision. After an assessment of the calibrated camera performance, this methodology is used to record, at 480,000 fps, the fracture of a pre‐notched sample subjected to an inertial impact test. The kinematic fields obtained quantitatively captured the events occurring during the test, such as the compression wave and the induced Poisson effect, the Mode‐I crack initiation and the shear strain concentration at the notch tip. The achievement of a DIC displacement and strain random error of, respectively, 5 μm (0.15 pixels) and 2 mm m −1 , combined with a high spatio‐temporal sampling, provides a promising way for quantitatively analysing very fast transient and heterogeneous phenomena.
Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.
Personalized Federated Learning using Hypernetworks

Personalized federated learning is tasked with training machine learning models for multiple clients, each with its own data distribution. The goal is to train personalized models in a collaborative way while accounting for data disparities across clients and reducing communication costs. We propose a novel approach to this problem using hypernetworks, termed pFedHN for personalized Federated HyperNetworks. In this approach, a central hypernetwork model is trained to generate a set of models, one model for each client. This architecture provides effective parameter sharing across clients, while maintaining the capacity to generate unique and diverse personal models. Furthermore, since hypernetwork parameters are never transmitted, this approach decouples the communication cost from the trainable model size. We test pFedHN empirically in several personalized federated learning challenges and find that it outperforms previous methods. Finally, since hypernetworks share information across clients we show that pFedHN can generalize better to new clients whose distributions differ from any client observed during training.
COVID-19 and Biocybersecurity's Increasing Role on Defending Forward

The evolving nature of warfare has been changing with cybersecurity and the use of advanced biotechnology in each aspect of the society is expanding and overlapping with the cyberworld. This intersection, which has been described as “biocybersecurity” (BCS), can become a major front of the 21st-century conflicts. There are three lines of BCS which make it a critical component of overall cybersecurity: (1) cyber operations within the area of BCS have life threatening consequences to a greater extent than other cyber operations, (2) the breach in health-related personal data is a significant tool for fatal attacks, and (3) health-related misinformation campaigns as a component of BCS can cause significant damage compared to other misinformation campaigns. Based on the observation that rather than initiating the necessary cooperation COVID-19 helped exacerbate the existing conflicts, the authors suggest that BCS needs to be considered as an essential component of the cyber doctrine, within the Defending Forward framework. The findings are expected to help future cyber policy developments.
WA Notify: the planning and implementation of a Bluetooth exposure notification tool for COVID-19 pandemic response in Washington State

Bluetooth exposure notification tools for mobile phones have emerged as one way to support public health contact tracing and mitigate the spread of COVID-19. Many states have launched their own versions of these tools. Washington State's exposure notification tool, WA Notify, became available on November 30, 2020, following a one-month Seattle campus pilot at the University of Washington. By the end of April 2021, 25% of the state's population had activated WA Notify, one of the highest adoption rates in the country. Washington State's formation of an Exposure Notification Advisory Committee, early pilot testing, and use of the EN Express system framework were all important factors in its adoption. Continuous monitoring and willingness to make early adjustments such as switching to automated texting of verification codes have also been important for improving the tool’s value. Evaluation work is ongoing to determine and quantify WA Notify’s effectiveness, timeliness, and accessibility.
Blockchain Technology Development as Tool for Enhancing Security in Management and Protection of Intellectual Property Rights in Additive Manufacturing

The purpose of the study is to analyze the capabilities of blockchain technology as a tool for enhancing the security in management and protection of intellectual property rights in additive manufacturing. The study is research-oriented and is an introduction to the use of blockchain technology for intellectual property protection. The following methods are used in the study: the analysis of documents on the use of blockchain technology in industrial asset management and the protection of content creators' rights, as well as the expert survey to assess the reliability of the selected sources. The authors introduce the term blockchain and discuss the capabilities of this technology for intellectual property protection in the field of design and production. A digital platform is proposed to enable the file exchange. Moreover, the authors describe the three main steps for developing a digital platform based on blockchain technology: the development of specialized tokens to reward users, the definition of conditions that should be included in smart contracts and the size of awards and incentives for each interested party that gains access to the platform.
How Robust are Fact Checking Systems on Colloquial Claims?

Knowledge is now starting to power neural dialogue agents.At the same time, the risk of misinformation and disinformation from dialogue agents also rises.Verifying the veracity of information from formal sources are widely studied in computational fact checking.In this work, we ask: How robust are fact checking systems on claims in colloquial style?We aim to open up new discussions in the intersection of fact verification and dialogue safety.In order to investigate how fact checking systems behave on colloquial claims, we transfer the styles of claims from FEVER (Thorne et al., 2018) into colloquialism.We find that existing fact checking systems that perform well on claims in formal style significantly degenerate on colloquial claims with the same semantics.Especially, we show that document retrieval is the weakest spot in the system even vulnerable to filler words, such as "yeah" and "you know".The document recall of WikiAPI retriever (Hanselowski et al., 2018) which is 90.0% on FEVER, drops to 72.2% on the colloquial claims.We compare the characteristics of colloquial claims to those of claims in formal style, and demonstrate the challenging issues in them.
Ultra-short-term Photovoltaic Power Prediction Based on VMD-LSTM-RVM Model

Abstract Aiming at the randomness and obvious fluctuation of photovoltaic power, this paper proposes a method that combines Variational Modal Decomposition (VMD), Long Short-Term Memory (LSTM) network and Relevance Vector Machine (RVM) to achieve ultra-short-term photovoltaic power prediction. Firstly, the VMD decomposition technology is used to decompose the historical photovoltaic power sequence into different modes to reduce the non-stationarity of the data; then an LSTM prediction model is established for each mode, and the modal prediction values are reconstructed to obtain the power prediction value; in order to further improve the prediction accuracy of the model, the error sequence is modeled and predicted by RVM; finally, the prediction power value and the prediction error value are superimposed to obtain the final prediction result. Simulation results show that this method effectively improves the accuracy of photovoltaic power prediction.
Impact of Parameter Tuning for Optimizing Deep Neural Network Models for Predicting Software Faults

Deep neural network models built by the appropriate design decisions are crucial to obtain the desired classifier performance. This is especially desired when predicting fault proneness of software modules. When correctly identified, this could help in reducing the testing cost by directing the efforts more towards the modules identified to be fault prone. To be able to build an efficient deep neural network model, it is important that the parameters such as number of hidden layers, number of nodes in each layer, and training details such as learning rate and regularization methods be investigated in detail. The objective of this paper is to show the importance of hyperparameter tuning in developing efficient deep neural network models for predicting fault proneness of software modules and to compare the results with other machine learning algorithms. It is shown that the proposed model outperforms the other algorithms in most cases.