Reviewerly/exHarmony huggingface dataset:
A Vision-Based Monitoring Method for the Looseness of High-Strength Bolt

Bolts are widely applied to the connections of structural components. Under the actions of alternative forces induced by the external loads, the bolts are commonly inevitable to be loosening in engineering, which will be a threat to structural safety. To avoid the reduction of the pretightening force, it is vital to examine the status of bolts periodically. So far, manual inspection with a torque wrench is the most frequently used approach, even though it is time-consuming, labor-intensive, and low frequency of data acquisitions for structural maintenance. Due to the looseness of bolts which commonly follows with relative rotation between bolts and nuts, in this study, a vision-based bolt monitoring system with an Internet of Things (IoT) device is proposed. Specifically, to observe the relative rotation between bolts and nuts, a novel barcode marker (termed PAC-code) is introduced at first. As followed, a corresponding smart device embedded with PAC-code decoding algorithm is also described for the further long-term monitoring of bolt looseness. Finally, a laboratory-based experiment was carried out to validate the reliability and precision of our system. The results indicate that the proposed system is sensitive in the identification of angular changes and can be used to monitor the looseness of bolts as precise as 0.1°. This technique is not only easy to be deployed with high economic efficiency in engineering but also meaningful for the data acquisition in bolts looseness-proof research.
Are All Users Treated Fairly in Federated Learning Systems?

Federated Learning (FL) systems target distributed model training on decentralized and private local training data belonging to users. Most of the existing methods aggregate models prioritizing among them proportionally to the frequency of local samples. However, this leads to un-fair aggregation with respect to users. Indeed, users with few local samples are considered less during aggregation and struggle to offer a real contribution to federated optimization of the models. In real-world settings, statistical heterogeneity (e.g., highly imbalanced and non-i.i.d. data) is diffused and can seriously harm model training.To this end, we empirically analyze the relationship be-tween fairness of aggregation of user models, accuracy of aggregated models and convergence rate of FL methods. We compare a standard federated model aggregation and optimization method, FedAvg, against a fair (uniform) aggregation scheme, i.e., FairAvg on benchmark datasets. Experimental analyses show that fair model aggregation can be beneficial in terms of accuracy and convergence rate, whilst reducing at the same time fluctuations of accuracy of the aggregate model when clients observe non-i.i.d. data.
SQuARM-SGD: Communication-Efficient Momentum SGD for Decentralized Optimization

In this paper, we propose and analyze SQuARM-SGD, a communication-efficient algorithm for decentralized training of large-scale machine learning models over a network. In SQuARM-SGD, each node performs a fixed number of local SGD steps using Nesterov's momentum and then sends sparsified and quantized updates to its neighbors regulated by a locally computable triggering criterion. We provide convergence guarantees of our algorithm for general (non-convex) and convex smooth objectives, which, to the best of our knowledge, is the first theoretical analysis for compressed decentralized SGD with momentum updates. We show that the convergence rate of SQuARM-SGD matches that of vanilla SGD. We empirically show that including momentum updates in SQuARM-SGD can lead to better test performance than the current state-of-the-art which does not consider momentum updates.
Towards automating code review at scale

As neural methods are increasingly used to support and automate software development tasks, code review is a natural next target. Yet, training models to imitate developers based on past code reviews is far from straightforward: reviews found in open-source projects vary greatly in quality, phrasing, and depth depending on the reviewer. In addition, changesets are often large, stretching the capacity of current neural models. Recent work reported modest success at predicting review resolutions, but largely side-stepped the above issues by focusing on small inputs where comments were already known to occur. This work examines the vision and challenges of automating code review at realistic scale. We collect hundreds of thousands of changesets across hundreds of projects that routinely conduct code review, many of which change thousands of tokens. We focus on predicting just the locations of comments, which are quite rare. By analyzing model performance and dataset statistics, we show that even this task is already challenging, in no small part because of tremendous variation and (apparent) randomness in code reviews. Our findings give rise to a research agenda for realistically and impactfully automating code review.
A joint object detection and semantic segmentation model with cross-attention and inner-attention mechanisms

Object detection and semantic segmentation are two fundamental techniques for Intelligent Vehicles (IV) and Advanced Driving Assistance System (ADAS). Motivated by recent studies demonstrating that object detection and semantic segmentation are two highly-correlated tasks, this paper handles the problem of joint object detection and semantic segmentation in traffic scenes. Existing methods perform the joint object detection and semantic segmentation by sharing the same backbone network, but always ignore the interactive connection between the subdividing detection branch and segmentation branch, leading to the insufficient interaction between the two branches. Considering this situation, this paper proposes a joint object detection and semantic segmentation model with the cross-attention and inner-attention mechanisms. The cross-attention mechanism enables to build up the essential interaction between the subdividing detection branch and segmentation branch to fully make use of their correlation. In addition, the inner-attention contributes to strengthening the representations of feature maps in the model. Given an image, an encoder-decoder network is firstly used to extract initial feature maps. Then, the inner-attention mechanism is applied to strengthen the initial feature maps to obtain segmentation feature maps. Subsequently, the cross-attention mechanism utilizes the segmentation feature maps to guide the generation of object detection feature maps. Finally, the semantic segmentation is performed on the segmentation feature maps and object detection is performed on the detection feature maps. In the experiments, two well-known public traffic datasets are used to evaluate our model. Our model achieves the highest performance in comparison with several recently-proposed methods. In addition, some ablation studies are conducted to evaluate the proposed inner-attention and cross-attention mechanisms, and experiment results validate their effectiveness.
Empirical analysis of practitioners' perceptions of test flakiness factors

Summary Identifying the root causes of test flakiness is one of the challenges faced by practitioners during software testing. In other words, the testing of the software is hampered by test flakiness. Since the research about test flakiness in large‐scale software engineering is scarce, the need for an empirical case‐study where we can build a common and grounded understanding of the problem as well as relevant remedies that can later be evaluated in a large‐scale context is a necessity. This study reports the findings from a multiple‐case study. The authors conducted an online survey to investigate and catalogue the root causes of test flakiness and mitigation strategies. We attempted to understand how practitioners perceive test flakiness in closed‐source development, such as how they define test flakiness and what practitioners perceive can affect test flakiness. The perceptions of practitioners were compared with the available literature. We investigated whether practitioners' perceptions are reflected in the test artefacts such as what is the relationship between the perceived factors and properties of test artefacts. This study reported 19 factors that are perceived by professionals to affect test flakiness. These perceived factors are categorized as test code , system under test , CI/test infrastructure , and organization‐related . The authors concluded that some of the perceived factors in test flakiness in closed‐source development are directly related to non‐determinism, whereas other perceived factors concern different aspects, for example, lack of good properties of a test case, deviations from the established processes, and ad hoc decisions. Given a data set from investigated cases, the authors concluded that two of the perceived factors (i.e., test case size and test case simplicity) have a strong effect on test flakiness.
Ratiometric Raman nanotags enable intraoperative detection of metastatic sentinel lymph node

Sentinel lymph node (SLN) imaging and biopsy has been advocated as an important technique to evaluate the metastatic status of regional lymph nodes and determine subsequent surgical procedure for many cancers, yet there is no reliable means to provide accurate and rapid diagnosis of metastatic SLN during surgery. Here we develop a new approach, named “Ratiometric Raman dual-nanotag strategy”, that using folic acid functionalized targeted and nontargeted gap-enhanced Raman tags (FA-GERTs and Nt-GERTs) to detect metastatic SLN based on Raman imaging combined with classical least square data processing methods. By using this strategy, with built-in self-calibration for signal correction, rather than absolute intensity-dependent signal readout, we realize the visualization and prompt intraoperative diagnosis of metastatic SLN with a high accuracy of 87.5 % when the cut-off value of ratio (FA-GERTs/Nt-GERTs) set at 1.255. This approach may outperform the existing histopathological assessment in diagnosing SLN metastasis and is promising for guiding surgical procedure in the future.
Modeling Habit Patterns Using Conditional Reflexes in Agency

For decision-making and behavior dynamics in humans, the principal focus is on cognition. Cognition can be described using cognitive behavior, which has multiple states. This cognitive behavior can be incorporated with one of... | Find, read and cite all the research you need on Tech Science Press
PremiUm-CNN: Propagating Uncertainty Towards Robust Convolutional Neural Networks

Deep neural networks (DNNs) have surpassed human-level accuracy in various learning tasks.However, unlike humans who have a natural cognitive intuition for probabilities, DNNs cannot express their uncertainty in the output decisions.This limits the deployment of DNNs in missioncritical domains, such as warfighter decision-making or medical diagnosis.Bayesian inference provides a principled approach to reason about model's uncertainty by estimating the posterior distribution of the unknown parameters.The challenge in DNNs remains the multi-layer stages of non-linearities, which make the propagation of high-dimensional distributions mathematically intractable.This paper establishes the theoretical and algorithmic foundations of uncertainty or belief propagation by developing new deep learning models named PremiUm-CNNs (Propagating Uncertainty in Convolutional Neural Networks).We introduce a tensor normal distribution as a prior over convolutional kernels and estimate the variational posterior by maximizing the evidence lower bound (ELBO).We start by deriving the first-order mean-covariance propagation framework.Later, we develop a framework based on the unscented transformation (correct at least up to the second-order) that propagates sigma points of the variational distribution through layers of a CNN.The propagated covariance of the predictive distribution captures uncertainty in the output decision.Comprehensive experiments conducted on diverse benchmark datasets demonstrate: 1) superior robustness against noise and adversarial attacks, 2) self-assessment through predictive uncertainty that increases quickly with increasing levels of noise or attacks, and 3) an ability to detect a targeted attack from ambient noise.Index Terms-Density propagation, first-order approximation, sigma points, convolutional neural network (CNN), evidence lower bound (ELBO), tensor normal distribution (TND). I. INTRODUCTIOND EEP neural networks (DNNs) have achieved state-of-the- art performance in a wide assortment of tasks, including computer vision and pattern recognition [1], [2].However, DNNs being inherently deterministic, are unable to provide calibrated confidence or a measure of uncertainty in their predictions [3]- [7].The soft-max output is often misinterpreted as a measure of confidence because the soft-max function transforms its domain values from (-∞, ∞) to a range of
Memento: A Prototype Lifelog Search Engine for LSC'21

In this paper, we introduce a new lifelog retrieval system called Memento that leverages semantic representations of images and textual queries projected into a common latent space to facilitate effective retrieval. It bridges the semantic gap between complex visual scenes/events and user information needs expressed as textual and faceted queries. The system, developed for the 2021 Lifelog Search Challenge, also has a minimalist user interface that includes primary search, temporal search, and visual data filtering components.
Detection and Location of Safety Protective Wear in Power Substation Operation Using Wear-Enhanced YOLOv3 Algorithm

Wearing personal safety protective equipment (PSPE) plays a key role in reducing electrical injuries to electrical workers.However, due to the lack of safety awareness, operators often do not wear PSPE when carrying out inspection or maintenance projects in substations, which is the main reason for personal injury accidents.Therefore, it is necessary to detect the wearing of PSPE in real-time through a video surveillance system.In this paper, a wear-enhanced YOLOv3 method for real-time detection of PSPE wear of substation operators is proposed.In order to improve the detection accuracy, the gamma correction is applied as the preprocessing method to highlight the details of the operators.Besides, K-means++ algorithm is introduced to get the most suitable prior bounding box size to improve the detection speed.Based on the proposed method, it can quickly and effectively detect whether the substation operators are wearing safety helmets and insulating gloves and boots correctly.Finally, extensive experiments are carried out using a dataset of real substation monitoring images to illustrate the effectiveness of the proposed method for realtime PSPE wear detection.
The impact of hardware specifications on reaching quantum advantage in the fault tolerant regime

We investigate how hardware specifications can impact the final run time and the required number of physical qubits to achieve a quantum advantage in the fault tolerant regime. Within a particular time frame, both the code cycle time and the number of achievable physical qubits may vary by orders of magnitude between different quantum hardware designs. We start with logical resource requirements corresponding to a quantum advantage for a particular chemistry application, simulating the FeMo-co molecule, and explore to what extent slower code cycle times can be mitigated by using additional qubits. We show that in certain situations, architectures with considerably slower code cycle times will still be able to reach desirable run times, provided enough physical qubits are available. We utilize various space and time optimization strategies that have been previously considered within the field of error-correcting surface codes. In particular, we compare two distinct methods of parallelization: Game of Surface Code's Units and AutoCCZ factories. Finally, we calculate the number of physical qubits required to break the 256-bit elliptic curve encryption of keys in the Bitcoin network within the small available time frame in which it would actually pose a threat to do so. It would require 317 × 106 physical qubits to break the encryption within one hour using the surface code, a code cycle time of 1 μs, a reaction time of 10 μs, and a physical gate error of 10−3. To instead break the encryption within one day, it would require 13 × 106 physical qubits.
Successful Gamification of Cybersecurity Training

The behavioral aspect of cybersecurity has gained more attention in recent years. By their actions, people can improve the security of their devices and organizations, but also hinder the successful implementation of security in these areas. As awareness campaigns where information is merely distributed are not effective, we designed a cybersecurity serious game applicable for cybersecurity training. The effectiveness of this game was experimentally tested against a noncybersecurity game that did or did not contain cybersecurity information, through measures of the theory of planned behavior. Results showed that the cybersecurity game resulted in higher self-reported scores on attitudes, perceived behavioral control, intentions, and behavior compared with both noncybersecurity games. For subjective norms, we only found an effect in the comparison between the cybersecurity game and the noncybersecurity game without additional information.
Breast Cancer Prediction and Diagnosis through a New Approach based on Majority Voting Ensemble Classifier

Researchers have extensively used machine learning techniques and data mining methods to build prediction models and classify data in various domains such as aviation, computer science, education, finance, marketing and particularly in medical field where those methods are applied as support systems for diagnosis and analysis in order to make better decisions. On this subject, our research paper attempts to assess the performance of Individual and Ensemble machine learning techniques based on the effectiveness and the efficiently, in terms of accuracy, specificity, sensitivity and precision to choose the most effective. The main object of our research paper is to define the best and effective machine learning approach for the Breast Cancer diagnosis and prediction. To achieve our objective, we applied individual based level machine learning algorithms Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Decision tree (C4.5), Simple Logistic and well known ensembles methods like Majority Voting and Random Forest with 10 cross field technique on the Breast Cancer Diagnosis Dataset obtained from UCI Repository. The experimental results show that the Majority Voting Ensemble technique based on 3 top classifiers SVM, K-NN, Simple Logistic gives the highest accuracy 98.1% with the lowest error rate 0.01% and outperformed all other individual classifiers. This study demonstrates that our proposal approach based on Majority Voting Ensemble technique was the best classification machine learning model with the highest level of accuracy for breast cancer prediction and diagnosis. All experiments are effectuated within a simulation environment and realized in Weka data mining tool.
ALMNet: Adjacent Layer Driven Multiscale Features for Salient Object Detection

Nowadays, the usage of deep learning-based approaches for salient object detection (SOD) is increasing exponentially to detect and localize visually distinct regions in static images. However, the variability in scales of salient objects requires further attention given the abstract nature of the multi-layer feature hierarchy of convolution neural networks (CNNs). Firstly, feature maps of different layers in CNNs embed abstract information about objects that changes with the object’s scale. Secondly, the progressive feature fusion in models such as the feature pyramid network loses its effectiveness in detecting sharp boundaries due to the late fusion of detailed features. This work proposes two modules namely, adjacent layer attention block and partial encoder-decoder block to handle the aforementioned issues. The proposed adjacent layer attention block facilitates communication among the layers of closest abstraction to mine abundant scale features at the current resolution. The resultant integrated feature at a resolution contains detailed as well as semantic information from interaction among the adjacent layers useful to extract scale information of complex objects. A partial encoder-decoder module utilizes the resolution-specific integrated features from the adjacent layer attention block of its corresponding encoder to generate multi-scale features, and fuse them in an top-down manner. This level-specific distribution of aggregated features within a partial encoder-decoder helps coarser-layers of the network to acquire boundary information. Experimental results on five broadly used salient object detection datasets are compared with recent 20 state-of-the-art SOD models. The proposed method performs favorably against its competitors without any pre-processing or post-processing.
Quantum Computing

Have you ever tried to retrieve that forgotten key code for your suitcase? After one year without traveling, many of us found themselves having forgotten the combination and manually trying all permutations. The same situation, but more complex, would be to systematically try identifying that forgotten access code for an online app that you had not used for a while. Cyberattackers are doing exactly this, of course, at high speed and with increasing computing performance. The recommended security key length is thus getting longer by the year. Yet, the stepwise process to achieve this is tedious or consumes lots of computing power. Now imagine that all of these possible states could be tried in a single step. This would be good for your own number lock, but frightening for our security infrastructure.
Playing Against Deep-Neural-Network-Based Object Detectors: A Novel Bidirectional Adversarial Attack Approach

In the fields of deep learning and computer vision, the security of object detection models has received extensive attention. Revealing the security vulnerabilities resulting from adversarial attacks has become one of the most important research directions. Existing studies show that object detection models can also be threatened by adversarial examples, just like other deep-neural-network-based models, e.g., those for classification. In this article, we propose a bidirectional adversarial attack method. First, the added perturbation pushes the prediction results given by the object detectors far away from the ground-truth class while getting close to the background class. Second, a confidence loss function is designed for the region proposal network to reduce the foreground scores. Third, the adversarial examples are generated by a pretrained autoencoder, and the model is trained using an adversarial approach, which can enhance the similarity between the adversarial examples and the original image and speed up algorithm convergence. The proposed method was verified on the most popular two-stage detection framework (Faster R-CNN), and 55.1% drop in the mean average precision (mAP-drop) was obtained. In addition, the adversarial examples have superior transferability, migrating which to the common one-stage detection framework (YOLOv3) gets a 39.5% mAP-drop.