Reviewerly/exHarmony huggingface dataset:
Identifying Statistical Bias in Dataset Replication.

Dataset replication is a useful tool for assessing whether improvements in test accuracy on a specific benchmark correspond to improvements in models' ability to generalize reliably. In this work, we present unintuitive yet significant ways in which standard approaches to dataset replication introduce statistical bias, skewing the resulting observations. We study ImageNet-v2, a replication of the ImageNet dataset on which models exhibit a significant (11-14%) drop in accuracy, even after controlling for a standard human-in-the-loop measure of data quality. We show that after correcting for the identified statistical bias, only an estimated $3.6\% \pm 1.5\%$ of the original $11.7\% \pm 1.0\%$ accuracy drop remains unaccounted for. We conclude with concrete recommendations for recognizing and avoiding bias in dataset replication. Code for our study is publicly available at this http URL .
CBL: A Clothing Brand Logo Dataset and a New Method for Clothing Brand Recognition

In this work, we presented a novel clothing brand logo prediction method which is rooted on a dense-block based deep convolutional neural network for brand logo detection and recognition. To learn convolutional neural networks deeper and more accurately, we adopted dense blocks into deep convolutional networks to make connections between layers shorter. In our work, we propose several dense-block structure designs to improve detection and recognition accuracy on clothing brand logos. We also built a new large-scale clothing brand logo (CBL) dataset with the brand attribute and logo information to facilitate this task. To reduce complexity for the proposed framework, two pixel search steps for the bounding movement are implemented in the training procedure. In the experiment, we demonstrate our search reduced model can outperform some state-of-the-art methods and achieve very good results.
RF-Based UAV Surveillance System: A Sequential Convolution Neural Networks Approach

In recent years, popularity of commercial unmanned air vehicles (UAVs) or drones enormously increased due to their ductility and availability in various applications domains. This also results in some security threats to sensitive area, that urgently needs proper investigation and surveillance system to protect the security sensitive institutions. In this paper, we propose a drone detection system which can detect drones and identify different types of drone respectively. The proposed network structure is constituted based on sequential convolution neural network (CNN) with several one-dimensional layer to successively learn the different scales feature map of radio frequency signals, collected from drone. To train the proposed CNN model, we use challenging DroneRF dataset, a free accessible database containing background noise and three different drone's radio frequency signals. The empirical results verify that the proposed model can detects all UAVs correctly and outperforms the existing RF based CNN model with average classification rate of 92.5% along with 93.5% F1 score.
Hybrid Blockchain for IoT—Energy Analysis and Reward Plan

Blockchain technology has brought significant advantages for security and trustworthiness, in particular for Internet of Things (IoT) applications where there are multiple organisations that need to verify data and ensure security of shared smart contracts. Blockchain technology offers security features by means of consensus mechanisms; two key consensus mechanisms are, Proof of Work (PoW) and Practical Byzantine Fault Tolerance (PBFT). While the PoW based mechanism is computationally intensive, due to the puzzle solving, the PBFT consensus mechanism is communication intensive due to the all-to-all messages; thereby, both may result in high energy consumption and, hence, there is a trade-off between the computation and the communication energy costs. In this paper, we propose a hybrid-blockchain (H-chain) framework appropriate for scenarios where multiple organizations exist and where the framework enables private transaction verification and public transaction sharing and audit, according to application needs. In particular, we study the energy consumption of the hybrid consensus mechanisms in H-chain. Moreover, this paper proposes a reward plan to incentivize the blockchain agents so that they make contributions to the H-chain while also considering the energy consumption. While the work is generally applicable to IoT applications, the paper illustrates the framework in a scenario which secures an IoT application connected using a software defined network (SDN). The evaluation results first provide a method to balance the public and private parts of the H-chain deployment according to network conditions, computation capability, verification complexity, among other parameters. The simulation results demonstrate that the reward plan can incentivize the blockchain agents to contribute to the H-chain considering the energy consumption of the hybrid consensus mechanism, this enables the proposed H-chain to achieve optimal social welfare.
A simple low-latency real-time certifiable quantum random number generator

Abstract Quantum random numbers distinguish themselves from others by their intrinsic unpredictability arising from the principles of quantum mechanics. As such they are extremely useful in many scientific and real-world applications with considerable efforts going into their realizations. Most demonstrations focus on high asymptotic generation rates. For this goal, a large number of repeated trials are required to accumulate a significant store of certifiable randomness, resulting in a high latency between the initial request and the delivery of the requested random bits. Here we demonstrate low-latency real-time certifiable randomness generation from measurements on photonic time-bin states. For this, we develop methods to certify randomness taking into account adversarial imperfections in both the state preparation and the measurement apparatus. Every 0.12 s we generate a block of 8192 random bits which are certifiable against all quantum adversaries with an error bounded by 2 −64 . Our quantum random number generator is thus well suited for realizing a continuously-operating, high-security and high-speed quantum randomness beacon.
Multi-objective optimization for VM placement in homogeneous and heterogeneous cloud service provider data centers

We address the virtual machine placement problem that arises in Cloud Service Providers data centers. We purpose, a Multi-Objective Integer Linear Programming model which aims at optimizing simultaneously the number of hosted virtual machines, the resource wastage and the number of active physical machines (PM) in order to minimize power consumption. This new combination of objectives enables to maximize the client satisfaction rate with minimizing the data center (DC) operational costs. We modelize this problem with a multi-objective integer linear program and solve it through two different methods. The first method computes a unique solution for a given preference order over the objectives whereas the second computes a set of non-dominated solutions. Both methods are compared through extensive simulation scenarios. We consider two DC architectures: homogeneous DCs (i.e., a DC with PMs having the same amount of resources) and heterogeneous DCs. We study the impact of each DC configuration on the performances of the solutions. We show that the second method leads to solutions with a reduction of up to 30% over the number of used PMs and that the heterogeneous DCs outperforms the homogeneous one across all objectives.
Cognitive Load Implications for Augmented Reality Supported Chemistry Learning

This paper presents a study about augmented-reality-based chemistry learning in a university lecture. Organic chemistry is often perceived as particularly difficult by students because spatial information must be processed in order to understand subject specific concepts and key ideas. To understand typical chemistry-related representations in books or literature, sophisticated mental rotation- and other spatial abilities are needed. Providing an augmented reality (AR) based learning support in the learning setting together with text and pictures is consistent with the idea of multiple external representations and the cognitive theory of multimedia learning. Using multiple external representations has proven to be beneficial for learning success, because different types of representations are processed separately in working memory. Nevertheless, the integration of a new learning medium involves the risk to hinder learning, in case of being not suitable for the learning topic or learning purpose. Therefore, this study investigates how the AR-use affects students’ cognitive load during learning in three different topics of organic chemistry. For this purpose also the usability of AR learning support is considered and the possible reduction of the influence of the mental rotation on learning success will be investigated.
Unsupervised extractive multi-document summarization method based on transfer learning from BERT multi-task fine-tuning

Text representation is a fundamental cornerstone that impacts the effectiveness of several text summarization methods. Transfer learning using pre-trained word embedding models has shown promising results. However, most of these representations do not consider the order and the semantic relationships between words in a sentence, and thus they do not carry the meaning of a full sentence. To overcome this issue, the current study proposes an unsupervised method for extractive multi-document summarization based on transfer learning from BERT sentence embedding model. Moreover, to improve sentence representation learning, we fine-tune BERT model on supervised intermediate tasks from GLUE benchmark datasets using single-task and multi-task fine-tuning methods. Experiments are performed on the standard DUC’2002–2004 datasets. The obtained results show that our method has significantly outperformed several baseline methods and achieves a comparable and sometimes better performance than the recent state-of-the-art deep learning–based methods. Furthermore, the results show that fine-tuning BERT using multi-task learning has considerably improved the performance.
Single-cell conventional pap smear image classification using pre-trained deep neural network architectures

Abstract Background Automating cytology-based cervical cancer screening could alleviate the shortage of skilled pathologists in developing countries. Up until now, computer vision experts have attempted numerous semi and fully automated approaches to address the need. Yet, these days, leveraging the astonishing accuracy and reproducibility of deep neural networks has become common among computer vision experts. In this regard, the purpose of this study is to classify single-cell Pap smear (cytology) images using pre-trained deep convolutional neural network (DCNN) image classifiers. We have fine-tuned the top ten pre-trained DCNN image classifiers and evaluated them using five class single-cell Pap smear images from SIPaKMeD dataset. The pre-trained DCNN image classifiers were selected from Keras Applications based on their top 1% accuracy. Results Our experimental result demonstrated that from the selected top-ten pre-trained DCNN image classifiers DenseNet169 outperformed with an average accuracy, precision, recall, and F1-score of 0.990, 0.974, 0.974, and 0.974, respectively. Moreover, it dashed the benchmark accuracy proposed by the creators of the dataset with 3.70%. Conclusions Even though the size of DenseNet169 is small compared to the experimented pre-trained DCNN image classifiers, yet, it is not suitable for mobile or edge devices. Further experimentation with mobile or small-size DCNN image classifiers is required to extend the applicability of the models in real-world demands. In addition, since all experiments used the SIPaKMeD dataset, additional experiments will be needed using new datasets to enhance the generalizability of the models.
A YOLO-based Neural Network with VAE for Intelligent Garbage Detection and Classification

Garbage recycling is becoming an urgent need for the people as the rapid development of human society is producing colossal amount of waste every year. However, current machine learning models for intelligent garbage detection and classification are highly constrained by their limited processing speeds and large model sizes, which make them difficult to be deployed on portable, real-time, and energy-efficient edge-computing devices. Therefore, in this paper, we introduce a novel YOLO-based neural network model with Variational Autoencoder (VAE) to increase the accuracy of automatic garbage recycling, accelerate the speed of calculation, and reduce the model size to make it feasible in the real-world garbage recycling scenario. The model is consisted of a convolutional feature extractor, a convolutional predictor, and a decoder. After the training process, this model achieves a correct rate of 69.70% with a total number of 32.1 million parameters and a speed of processing 60 Frames Per Second (FPS), surpassing the performance of other existing models such as YOLO v1 and Fast R-CNN.
Robustness of Adaptive Neural Network Optimization Under Training Noise

Adaptive gradient methods such as adaptive moment estimation (Adam), RMSProp, and adaptive gradient (AdaGrad) use the temporal history of the gradient updates to improve the speed of convergence and reduce reliance on manual learning rate tuning, making them a popular choice for off-the-shelf Deep Neural Network (DNN) optimizers. In this article, we study the robustness of neural network optimizers in the presence of training perturbations. We show that popular adaptive optimization methods exhibit poor generalization while learning from noisy training data, compared to vanilla Stochastic Gradient Descent (SGD) and its variants, which manifest better implicit regularization properties. We construct an illustrative example of a family of two-class linearly separable toy-data such that models trained under noise using adaptive optimizers show only 52% test accuracy (random classifier), whereas SGD-based methods can achieve 100% test accuracy. We strengthen our hypothesis by empirical analysis using Convolutional Neural Networks (CNNs) on publicly available image datasets. For this purpose, our method trains neural network models with various optimizers on noisy training data, and we compute test accuracy on clean test data. Our results further highlight the robustness of SGD optimization against such noisy training data compared to its adaptive counterparts. Based on the results, our paper suggests a reconsideration of the extensive use of adaptive gradient methods for neural network optimization, especially when the training data is noisy.
PETRA: A generalised real-time event detection platform at JET for disruption prediction, avoidance and mitigation

Plasma Event TRiggering for Alarms (PETRA) is a flexible new system at JET for detecting plasma events to trigger exception handling responses. It provides a platform for all existing and new techniques which involve processing JET real-time data to identify when to change the trajectory of a plasma shot, and reduces the technical cost and risk in demonstrating new techniques. At the same time as providing a protection function to the machine, it still supports additions and modifications to the event detector library during the course of an experimental campaign. It has successfully demonstrated novel techniques, and has computational capacity to continue supporting research into further event detection triggers.
Perception matters: Exploring imperceptible and transferable anti-forensics for GAN-generated fake face imagery detection

Recently, generative adversarial networks (GANs) can generate photo-realistic fake facial images which are perceptually indistinguishable from real face photos, promoting research on fake face detection. Though fake face forensics can achieve high detection accuracy, their anti-forensic counterparts are less investigated. Here we explore more imperceptible and transferable anti-forensics for fake face imagery detection based on adversarial attacks. Since facial and background regions are often smooth, even small perturbation could cause noticeable perceptual impairment in fake face images. Therefore it makes existing transfer-based adversarial attacks ineffective as an anti-forensic method. Our perturbation analysis reveals the intuitive reason of the perceptual degradation issue when directly applying such existing attacks. We then propose a novel adversarial attack method, better suitable for image anti-forensics, in the transformed color domain by considering visual perception. Conceptually simple yet effective, the proposed method can fool both deep learning and non-deep learning based forensic detectors, achieving higher adversarial transferability and significantly improved visual quality. Specially, when adversaries consider imperceptibility as a constraint, the proposed anti-forensic method achieves the state-of-the-art attacking performances in the transfer-based black-box setting (i.e. around 30% higher attack transferability than baseline attacks). More imperceptible and more transferable, the proposed method raises new security concerns to fake face imagery detection. We have released our code for public use, and hopefully the proposed method can be further explored in related forensic applications as an anti-forensic benchmark.
Analyzing the Essential Attributes of Nationally Issued COVID-19 Contact Tracing Apps: Open-Source Intelligence Approach and Content Analysis

Background Contact tracing apps are potentially useful tools for supporting national COVID-19 containment strategies. Various national apps with different technical design features have been commissioned and issued by governments worldwide. Objective Our goal was to develop and propose an item set that was suitable for describing and monitoring nationally issued COVID-19 contact tracing apps. This item set could provide a framework for describing the key technical features of such apps and monitoring their use based on widely available information. Methods We used an open-source intelligence approach (OSINT) to access a multitude of publicly available sources and collect data and information regarding the development and use of contact tracing apps in different countries over several months (from June 2020 to January 2021). The collected documents were then iteratively analyzed via content analysis methods. During this process, an initial set of subject areas were refined into categories for evaluation (ie, coherent topics), which were then examined for individual features. These features were paraphrased as items in the form of questions and applied to information materials from a sample of countries (ie, Brazil, China, Finland, France, Germany, Italy, Singapore, South Korea, Spain, and the United Kingdom [England and Wales]). This sample was purposefully selected; our intention was to include the apps of different countries from around the world and to propose a valid item set that can be relatively easily applied by using an OSINT approach. Results Our OSINT approach and subsequent analysis of the collected documents resulted in the definition of the following five main categories and associated subcategories: (1) background information (open-source code, public information, and collaborators); (2) purpose and workflow (secondary data use and warning process design); (3) technical information (protocol, tracing technology, exposure notification system, and interoperability); (4) privacy protection (the entity of trust and anonymity); and (5) availability and use (release date and the number of downloads). Based on this structure, a set of items that constituted the evaluation framework were specified. The application of these items to the 10 selected countries revealed differences, especially with regard to the centralization of the entity of trust and the overall transparency of the apps’ technical makeup. Conclusions We provide a set of criteria for monitoring and evaluating COVID-19 tracing apps that can be easily applied to publicly issued information. The application of these criteria might help governments to identify design features that promote the successful, widespread adoption of COVID-19 tracing apps among target populations and across national boundaries.
TriATNE: Tripartite Adversarial Training for Network Embeddings

Existing network embedding algorithms based on generative adversarial networks (GANs) improve the robustness of node embeddings by selecting high-quality negative samples with the generator to play against the discriminator. Since most of the negative samples can be easily discriminated from positive samples in graphs, their poor competitiveness weakens the function of the generator. Inspired by the sales skills in the market, in this article, we present tripartite adversarial training for network embeddings (TriATNE), a novel adversarial learning framework for learning stable and robust node embeddings. TriATNE consists of three players: 1) producer; 2) seller; and 3) customer. The producer strives to learn the representation of each sample (node pair), making it easy for the customer to differentiate between the positive and the negative, while the seller tries to confuse the customer by selecting realistic-looking samples. The customer, a biased evaluation metric, provides feedback for training the producer and the seller. To further enhance the robustness of node embedding, we model the customer as a two-layer neural network, where each unit in the hidden layer can be regarded as a customer with different preferences. TriATNE also plays against the producer by adjusting the weight of each customer. We test the performance of TriATNE on two common tasks: classification as well as link prediction. The experimental results on various publicly available datasets show that TriATNE can exploit the network structure well.
Recommending API Function Calls and Code Snippets to Support Software Development

Software development activity has reached a high degree of complexity, guided by the heterogeneity of the components, data sources, and tasks. The proliferation of open-source software (OSS) repositories has stressed the need to reuse available software artifacts efficiently. To this aim, it is necessary to explore approaches to mine data from software repositories and leverage it to produce helpful recommendations. We designed and implemented FOCUS as a novel approach to provide developers with API calls and source code while they are programming. The system works on the basis of a context-aware collaborative filtering technique to extract API usages from OSS projects. In this work, we show the suitability of FOCUS for Android programming by evaluating it on a dataset of 2,600 mobile apps. The empirical evaluation results show that our approach outperforms two state-of-the-art API recommenders, UP-Miner and PAM, in terms of prediction accuracy. We also point out that there is no significant relationship between the categories for apps defined in Google Play and their API usages. Finally, we show that participants of a user study positively perceive the API and source code recommended by FOCUS as relevant to the current development context.