Reviewerly/exHarmony huggingface dataset:
On the Significance of Category Prediction for Code-Comment Synchronization

Software comments sometimes are not promptly updated in sync when the associated code is changed. The inconsistency between code and comments may mislead the developers and result in future bugs. Thus, studies concerning code-comment synchronization have become highly important, which aims to automatically synchronize comments with code changes. Existing code-comment synchronization approaches mainly contain two types, i.e., (1) deep learning-based (e.g., CUP), and (2) heuristic-based (e.g., HebCUP). The former constructs a neural machine translation-structured semantic model, which has a more generalized capability on synchronizing comments with software evolution and growth. However, the latter designs a series of rules for performing token-level replacements on old comments, which can generate the completely correct comments for the samples fully covered by their fine-designed heuristic rules. In this article, we propose a composite approach named CBS (i.e., Classifying Before Synchronizing ) to further improve the code-comment synchronization performance, which combines the advantages of CUP and HebCUP with the assistance of inferred categories of Code-Comment Inconsistent (CCI) samples. Specifically, we firstly define two categories (i.e., heuristic-prone and non-heuristic-prone) for CCI samples and propose five features to assist category prediction. The samples whose comments can be correctly synchronized by HebCUP are heuristic-prone, while others are non-heuristic-prone. Then, CBS employs our proposed Multi-Subsets Ensemble Learning (MSEL) classification algorithm to alleviate the class imbalance problem and construct the category prediction model. Next, CBS uses the trained MSEL to predict the category of the new sample. If the predicted category is heuristic-prone, CBS employs HebCUP to conduct the code-comment synchronization for the sample, otherwise, CBS allocates CUP to handle it. Our extensive experiments demonstrate that CBS statistically significantly outperforms CUP and HebCUP, and obtains an average improvement of 23.47%, 22.84%, 3.04%, 3.04%, 1.64%, and 19.39% in terms of Accuracy, Recall@5, Average Edit Distance (AED) , Relative Edit Distance (RED) , BLEU-4, and Effective Synchronized Sample (ESS) ratio, respectively, which highlights that category prediction for CCI samples can boost the code-comment synchronization performance.
SoK

Side-channel attacks that leak sensitive information through a computing device's interaction with its physical environment have proven to be a severe threat to devices' security, particularly when adversaries have unfettered physical access to the device. Traditional approaches for leakage detection measure the physical properties of the device. Hence, they cannot be used during the design process and fail to provide root cause analysis. An alternative approach that is gaining traction is to automate leakage detection by modeling the device. The demand to understand the scope, benefits, and limitations of the proposed tools intensifies with the increase in the number of proposals. In this SoK, we classify approaches to automated leakage detection based on the model's source of truth. We classify the existing tools on two main parameters: whether the model includes measurements from a concrete device and the abstraction level of the device specification used for constructing the model. We survey the proposed tools to determine the current knowledge level across the domain and identify open problems. In particular, we highlight the absence of evaluation methodologies and metrics that would compare proposals' effectiveness from across the domain. We believe that our results help practitioners who want to use automated leakage detection and researchers interested in advancing the knowledge and improving automated leakage detection.
Low-Light Image Enhancement Method Based on Retinex Theory by Improving Illumination Map

Recently, low-light image enhancement has attracted much attention. However, some problems still exist. For instance, sometimes dark regions are not fully improved, but bright regions near the light source or auxiliary light source are overexposed. To address these problems, a retinex based method that strengthens the illumination map is proposed, which utilizes a brightness enhancement function (BEF) that is a weighted sum of the Sigmoid function cascading by Gamma correction (GC) and Sine function, and an improved adaptive contrast enhancement (IACE) to enhance the estimated illumination map through multi-scale fusion. Specifically, firstly, the illumination map is obtained according to retinex theory via the weighted sum method, which considers neighborhood information. Then, the Gaussian Laplacian pyramid is used to fuse two input images that are derived by BEF and IACE, so that it can improve brightness and contrast of the illuminance component acquired above. Finally, the adjusted illuminance map is multiplied by the reflection map to obtain the enhanced image according to the retinex theory. Extensive experiments show that our method has better results in subjective vision and quantitative index evaluation compared with other state-of-the-art methods.
Blockchain-enabled multi-authorization and multi-cloud attribute-based keyword search over encrypted data in the cloud

With the arrival of the 5G era, fast and secure data sharing is attracting increased attention. Among of the presented solutions, keyword search plays an important role since it can identify users and search ciphertext without decryption. However, the existing searchable algorithms have many limitations, such as powerful central authority, match processes being only carried out on the cloud server, and center corruption and single point of failure being easy to cause. Presently, the fairness of the keyword search is still a challenge. To solve these problems, we present a multi-authority and multi-cloud keyword search scheme based on the consortium blockchain and attribute-based encryption. Under this framework, multi-cloud block storage technique is used to address the unreliability of cloud servers, where at least two ciphertext files are stored in blocks on multiple cloud servers. It can resist malicious leaks, ciphertext tampering, and deletion of ciphertext by cloud servers. Blockchain allows the proposed scheme to achieve the integrity and correctness of the encrypted data, which also solves the fairness of search results. Furthermore, the scheme realizes privacy protection by hiding the keyword and the access policy in the trapdoor matching process. Finally, the security analyses and performance comparisons show the advantages of the proposed scheme over the existing work.
Model Extraction Attacks on Graph Neural Networks

Machine learning models are shown to face a severe threat from Model Extraction Attacks, where a well-trained private model owned by a service provider can be stolen by an attacker pretending as a client. Unfortunately, prior works focus on the models trained over the Euclidean space, e.g., images and texts, while how to extract a GNN model that contains a graph structure and node features is yet to be explored. In this paper, for the first time, we comprehensively investigate and develop model extraction attacks against GNN models. We first systematically formalise the threat modelling in the context of GNN model extraction and classify the adversarial threats into seven categories by considering different background knowledge of the attacker, e.g., attributes and/or neighbour connections of the nodes obtained by the attacker. Then we present detailed methods which utilise the accessible knowledge in each threat to implement the attacks. By evaluating over three real-world datasets, our attacks are shown to extract duplicated models effectively, i.e., 84% - 89% of the inputs in the target domain have the same output predictions as the victim model.
Decentralized Federated Learning for Internet of Things Anomaly Detection

With the improvement of computing power and the development of network technology, Internet of Things (IoT) devices are widely used in many industries. But it also faces various security threats. Anomaly detection is a commonly used method, but traditional methods face shortcomings such as low accuracy. Therefore, in this paper, we introduce a decentralized federated learning method for anomaly detection, using neural networks to improve accuracy and take advantage of the characteristics of federated learning to protect local data security. The decentralized algorithm avoids the drawbacks of traditional federated learning such as the single point of failure. Finally, we conduct simulation experiments on the IoT23 dataset, which verify the performance of our system.
MaterIA: Single Image High‐Resolution Material Capture in the Wild

Abstract We propose a hybrid method to reconstruct a physically‐based spatially varying BRDF from a single high resolution picture of an outdoor surface captured under natural lighting conditions with any kind of camera device. Relying on both deep learning and explicit processing, our PBR material acquisition handles the removal of shades, projected shadows and specular highlights present when capturing a highly irregular surface and enables to properly retrieve the underlying geometry. To achieve this, we train two cascaded U‐Nets on physically‐based materials, rendered under various lighting conditions, to infer the spatially‐varying albedo and normal maps. Our network processes relatively small image tiles ( 512 × 512 pixels) and we propose a solution to handle larger image resolutions by solving a Poisson system across these tiles. We complete this pipeline with analytical solutions to reconstruct height, roughness and ambient occlusion.
Augmented assistive technology: the importance of tailoring technology solutions for people living with dementia at home

This research investigates the benefits of personalised augmented assistive technology to support people living with dementia in their daily activities in the home. With the aim to achieve a tailored and person-centred technology solution for each participant, we discuss the challenges and potential benefits of Augmented Reality (AR) for people living with dementia. We highlight the need to take a human-centred design approach for this user group and demonstrate how AR technology should not drive the research focus in this challenging field but should instead be responsive to the needs and goals of stakeholders. The qualitative approach demonstrates the benefits of using modular technology for the shifting needs of individuals with dementia. The breadth of tailored solutions is described via two detailed participant stories. We discuss the multi-sensory dimensions of AR and how this is important to maintain and increase independence for the user group. We investigate the challenges and benefits of using technologies including Microsoft HoloLens, digital projectors, smart mirrors, Amazon Alexa, and aroma diffusers for assistive purposes.
Membrane fouling diagnosis of membrane components based on multi-feature information fusion

CBAM-MUL-CNN (convolutional block attention module - multiple - convolutional neural networks) model based on attention mechanism is proposed to solve the problem that the membrane fouling feature extraction capability of membrane bioreactor membrane component is insufficient, which resulted in the complex structure of the membrane fouling data, so that the efficient localization and classification of membrane fouling in membrane bioreactor could not be achieved. First, the time domain and frequency domain information about the fault data is used as the input of CNN (convolutional neural networks), and the features are extracted by convolution layer. Then, the input classifier is classified by splicing the time domain and frequency domain features using the full connection layer. BN (batch normalization) layer in the model can effectively prevent the disappearance of gradients, ReLU (rectified linear uint) layer can improve the non-linear model expression ability, CBAM (convolutional block attention module) can simplify the model complexity, improve the network features expression ability, and pooling layer can improve the model fault tolerance. The comparison results show that the model has excellent comprehensive performance in the membrane fouling diagnosis experiments of series tubular membrane devices and parallel hollow fiber membrane devices, and can effectively classify and locate all membrane fouling, making the treatment of water by membrane process improve the quality of effluent while reducing energy consumption, which provides a theoretical basis for actual production.
An improved Harris Hawks Optimization algorithm for continuous and discrete optimization problems

Harris Hawks Optimization (HHO) is a population-based meta-heuristic optimization algorithm that has been used for the solution of test functions and real-world problems by many researchers. However, HHO has a premature convergence problem. The main motive of the novel approach in this paper is that the performance of an MHA could be improved by simplification and by modifying the way random parameters are determined. The proposed algorithm aims to solve both continuous and discrete optimization problems. HHO is improved in three stages. First, the method to determine the random parameters is modified. Second, the strategy of HHO to produce a new solution is updated. Third, the six-step decision mechanism of HHO is shortened to four. The proposed algorithm is compared to five recently published competitor algorithms by applying to the CEC2019 test functions and a three-dimensional bin packing problem (3D-BPP) dataset with 320 samples. All the algorithms are run on the same computer and the results of 30 independent studies are saved. Minimum, average, and standard deviation values and solution times of CEC2019 functions are used as comparison parameters. For the 3D-BPP, the number of bins and the solution time are used as comparison parameters for in the Wilcoxon test. The proposed algorithm performs better than the selected competitors in terms of its %5 significance level. Moreover, the algorithm proposed in the 3D-BPP data set is the most successful algorithm with its 9745 bins. Besides, the proposed algorithm is also compared to the four most popular algorithms in the literature. The results obtained confirm the validity of the proposed algorithm.
YOLO-Anti: YOLO-based counterattack model for unseen congested object detection

Object detection is advancing rapidly with the development of deep learning solutions and big data dimensions. This paper takes the challenging recognition task as the core work and proposes a novel and efficient network framework dedicated to unseen congestion detection. To guarantee the accuracy as well as the speed of inference, the detector utilizes the advanced You Only Look Once v4 (YOLOv4) as the backbone and agglutinates the four proposed strategies, called YOLO-Anti. Our model mainly consists of three modules: First, an adaptive context module similar to valve control is proposed to obtain contextual information that balances foreground and background features. Second, to solve the problem that the imbalance between feature levels weakens the detection performance, a balanced prediction layer method is developed. Finally, we propose an anti-congestion network to selectively expand the local domain to achieve finer-grained detection. Besides, in the training procedure, a designed heterogeneous cross-entropy loss is utilized to strengthen the detector's discrimination of similar targets in different categories. Extensive experiments were conducted on the PASCAL VOC, COCO, and UA-DETRAC data sets. The state-of-the-art results were achieved on UA-DETRAC and the leading performance on PASCAL VOC and COCO. Also, compared with baseline YOLOv4, the proposed method brings significant accuracy improvement and negligible time consumption.
Sentiment Classification in Bengali News Comments using a hybrid approach with Glove

Sentiment analysis (SA) is a greater part of Natural Language Processing (NLP) in the research field. Previously a lot of work was done by SA in various languages. Most of the work was done in the English language. A little work was done in the Bengali language and it's increasing day by day. Nowadays, Bangla News comments are very demanding for research work in the Bangla language. The procedures of text categorization, classifying, and different techniques for extracting features in textual information were discussed in this paper. Some Bangla newspaper dataset is currently available in online platforms. This research study has analyzed Bangla news comments sentiment using a hybrid approach and a pre-trained deep learning classifier. The proposed hybrid model utilizes an optimizer function “Adam” along with a word embedding “Glove”. The dataset utilized in the proposed model is collected from online platform Kaggle, the largest data science forum on the world. This dataset contains 13802 data. Using five different classes to our dataset. For building a model, this research study has applied preprocessing techniques, which makes a significant contribution in cleaning our dataset. This leads to get a well formed dataset. The proposed hybrid model has been combined with two familiar deep learning methods, namely BiLSTM and CNN. By comparing these two methods, the proposed hybrid model has gained better accuracy than the FastText model. The accuracy of hybrid model is about 89.89%.
Tailored text augmentation for sentiment analysis

In synonym replacement-based data augmentation techniques for natural language processing tasks, words in a sentence are often sampled randomly with equal probability. In this paper, we propose a novel data augmentation technique named Tailored Text Argumentation (TTA) for sentiment analysis. It has two main operations. The first operation is the probabilistic word sampling for synonym replacement based on the discriminative power and relevance of the word to sentiment. The second operation is the identification of words irrelevant to sentiment but discriminative for the training data, and application of zero masking or contextual replacement to these words. The first operation expands the coverage of discriminative words, while the second operation alleviates the problem of misfitting. Both operations tend to improve the model's generalization capability. Extensive experiments on simulated low-data regimes demonstrate that TTA yields notable improvements over six strong baselines. Finally, TTA is applied to public sentiment analysis on measures against Covid-19, which again proves the effectiveness of the new data augmentation algorithm.
A Multifaceted Evaluation of a COVID-19 Contact Tracing Program in King County, Washington

Despite the massive scale of COVID-19 case investigation and contact tracing (CI/CT) programs operating worldwide, the evidence supporting the intervention's public health impact is limited.To evaluate the Public Health-Seattle & King County (PHSKC) CI/CT program, including its reach, timeliness, effect on isolation and quarantine (I&Q) adherence, and potential to mitigate pandemic-related hardships.This program evaluation used descriptive statistics to analyze surveillance records, case and contact interviews, referral records, and survey data provided by a sample of cases who had recently ended isolation.The PHSKC is one of the largest governmental local health departments in the United States. It serves more than 2.2 million people who reside in Seattle and 38 other municipalities.King County residents who were diagnosed with COVID-19 between July 2020 and June 2021.The PHSKC integrated COVID-19 CI/CT with prevention education and service provision.The PHSKC CI/CT team interviewed 42 900 cases (82% of cases eligible for CI/CT), a mean of 6.1 days after symptom onset and 3.4 days after SARS-CoV-2 testing. Cases disclosed the names and addresses of 10 817 unique worksites (mean = 0.8/interview) and 11 432 other recently visited locations (mean = 0.5/interview) and provided contact information for 62 987 household members (mean = 2.7/interview) and 14 398 nonhousehold contacts (mean = 0.3/interview). The CI/CT team helped arrange COVID-19 testing for 5650 contacts, facilitated grocery delivery for 7253 households, and referred 9127 households for financial assistance. End of I&Q Survey participants (n = 304, 54% of sampled) reported self-notifying an average of 4 nonhousehold contacts and 69% agreed that the information and referrals provided by the CI/CT team helped them stay in isolation.In the 12-month evaluation period, CI/CT reached 42 611 households and identified thousands of exposure venues. The timing of CI/CT relative to infectiousness and difficulty eliciting nonhousehold contacts may have attenuated the intervention's effect. Through promotion of I&Q guidance and services, CI/CT can help mitigate pandemic-related hardships.
Hippocampus-heuristic character recognition network for zero-shot learning in Chinese character recognition

The recognition of Chinese characters has always been a challenging task due to their huge variety and complex structures. The current radical-based methods fail to recognize Chinese characters without learning all of their radicals in the training stage. To this end, we propose a novel Hippocampus-heuristic Character Recognition Network (HCRN), which can recognize unseen Chinese characters only by training part of radicals. More specifically, the network architecture of HCRN is a new pseudo-siamese network designed by us, which can learn features from pairs of input samples and use them to predict unseen characters. The experimental results on the recognition of printed and handwritten characters show that HCRN is robust and effective on zero/few-shot learning tasks. For the printed characters, the mean accuracy of HCRN outperforms the state-of-the-art approach by 23.93% on recognizing unseen characters. For the handwritten characters, HCRN improves the mean accuracy by 11.25% on recognizing unseen characters.
Proving as fast as computing: succinct arguments with constant prover overhead

Succinct arguments are proof systems that allow a powerful, but untrusted, prover to convince a weak verifier that an input x belongs to a language L ∈ NP, with communication that is much shorter than the NP witness. Such arguments, which grew out of the theory literature, are now drawing immense interest also in practice, where a key bottleneck that has arisen is the high computational cost of proving correctness.