Reviewerly/exHarmony huggingface dataset:
CodeBERT‐Attack: Adversarial attack against source code deep learning models via pre‐trained model

Abstract Over the past few years, the software engineering (SE) community has widely employed deep learning (DL) techniques in many source code processing tasks. Similar to other domains like computer vision and natural language processing (NLP), the state‐of‐the‐art DL techniques for source code processing can still suffer from adversarial vulnerability, where minor code perturbations can mislead a DL model's inference. Efficiently detecting such vulnerability to expose the risks at an early stage is an essential step and of great importance for further enhancement. This paper proposes a novel black‐box effective and high‐quality adversarial attack method, namely CodeBERT‐Attack (CBA), based on the powerful large pre‐trained model (i.e., CodeBERT) for DL models of source code processing. CBA locates the vulnerable positions through masking and leverages the power of CodeBERT to generate textual preserving perturbations. We turn CodeBERT against DL models and further fine‐tuned CodeBERT models for specific downstream tasks, and successfully mislead these victim models to erroneous outputs. In addition, taking the power of CodeBERT, CBA is capable of effectively generating adversarial examples that are less perceptible to programmers. Our in‐depth evaluation on two typical source code classification tasks (i.e., functionality classification and code clone detection) against the most widely adopted LSTM and the powerful fine‐tuned CodeBERT models demonstrate the advantages of our proposed technique in terms of both effectiveness and efficiency. Furthermore, our results also show (1) that pre‐training may help CodeBERT gain resilience against perturbations further, and (2) certain pre‐training tasks may be beneficial for adversarial robustness.
Joint learning of text alignment and abstractive summarization for long documents via unbalanced optimal transport

Abstract Recently, neural abstractive text summarization (NATS) models based on sequence-to-sequence architecture have drawn a lot of attention. Real-world texts that need to be summarized range from short news with dozens of words to long reports with thousands of words. However, most existing NATS models are not good at summarizing long documents, due to the inherent limitations of their underlying neural architectures. In this paper, we focus on the task of long document summarization (LDS). Based on the inherent section structures of source documents, we divide an abstractive LDS problem into several smaller-sized problems. In this circumstance, how to provide a less-biased target summary as the supervision for each section is vital for the model’s performance. As a preliminary, we formally describe the section-to-summary-sentence (S2SS) alignment for LDS. Based on this, we propose a novel NATS framework for the LDS task. Our framework is built based on the theory of unbalanced optimal transport (UOT), and it is named as UOTSumm. It jointly learns three targets in a unified training objective, including the optimal S2SS alignment, a section-level NATS summarizer, and the number of aligned summary sentences for each section. In this way, UOTSumm directly learns the text alignment from summarization data, without resorting to any biased tool such as ROUGE. UOTSumm can be easily adapted to most existing NATS models. And we implement two versions of UOTSumm, with and without the pretrain-finetune technique. We evaluate UOTSumm on three publicly available LDS benchmarks: PubMed , arXiv , and GovReport . UOTSumm obviously outperforms its counterparts that use ROUGE for the text alignment. When combined with UOTSumm, the performance of two vanilla NATS models improves by a large margin. Besides, UOTSumm achieves better or comparable performance when compared with some recent strong baselines.
Leader–follower control of multi-unmanned aerial vehicle based on supervisory control theory for a broad tributary area mapping scenario

This study proposes a discrete event system-based control strategy for autonomous tributary mapping using multi-unmanned aerial vehicle. When considering the unmanned aerial vehicles as discrete event systems, supervisory control theory is used to model and control individual unmanned aerial vehicle behavior in the system. In tributary mapping, the situation changes each time depending on environmental factors (e.g. weather) and the work must be performed in an unstructured environment. Therefore, this article proposes a multi-unmanned aerial vehicle-based supervisory control system to solve real-field problems. Unlike the control systems of recent studies, which mainly deal with continuous-time dynamics, we modeled a multi-unmanned aerial vehicle system based on a discrete event system in which the dynamic states are transitioned by asynchronous events. The proposed multi-unmanned aerial vehicle-based supervisory control system was validated in dynamic simulators and demonstrated that multi-unmanned aerial vehicle satisfies the behavior specifications. The supervisor proposed in this study was validated using a physics-based simulator.
Determining sentiment views of verbal multiword expressions using linguistic features

Abstract We examine the binary classification of sentiment views for verbal multiword expressions (MWEs). Sentiment views denote the perspective of the holder of some opinion. We distinguish between MWEs conveying the view of the speaker of the utterance (e.g., in “ The company reinvented the wheel ” the holder is the implicit speaker who criticizes the company for creating something already existing) and MWEs conveying the view of explicit entities participating in an opinion event (e.g., in “ Peter threw in the towel ” the holder is Peter having given up something). The task has so far been examined on unigram opinion words. Since many features found effective for unigrams are not usable for MWEs, we propose novel ones taking into account the internal structure of MWEs, a unigram sentiment-view lexicon and various information from Wiktionary. We also examine distributional methods and show that the corpus on which a representation is induced has a notable impact on the classification. We perform an extrinsic evaluation in the task of opinion holder extraction and show that the learnt knowledge also improves a state-of-the-art classifier trained on BERT. Sentiment-view classification is typically framed as a task in which only little labeled training data are available. As in the case of unigrams, we show that for MWEs a feature-based approach beats state-of-the-art generic methods.
Convergence Towards Blockchain-Based Patient Health Record and Sharing System: Emerging Issues and Challenges

The traditional technologies and digital systems of managing and maintaining data are inherently prone to manipulation at various levels. Ensuring the anonymity of the patient's identity, the safety of the medical records, and preventing the patient data from accidental and intended manipulations have been the industry's biggest challenges for decades. Failing to control the integrity of the Patient Health Records (PHRs) and Medical Health Records (MHRs) in the Healthcare Data Management System (HDMS)/ Healthcare Information System (HIS) may create challenges in identifying, diagnosing, and treating the disease and puts the patient at a greater risk. The frequency of healthcare data breaches, the magnitude of compromised records, and the financial impact rapidly increase with time. This chapter systematically and critically reviews the issues and challenges faced by various healthcare stakeholders in PHRs/MHRs-based HDMS/HIS systems. Blockchain powered patient health record and sharing schemes can be used to ensure the integrity and safety of healthcare data and share data among various healthcare ecosystem stakeholders using smart contracts to promote transparency, tamper-proofing, and consented access to data in distributed multi-stakeholder environment. This chapter highlights the need for post-quantum cryptography and recommendations for future improvements in blockchain-based patient health records and sharing system.&lt;br&gt;
Accelerating Deformable Convolution Networks with Dynamic and Irregular Memory Accesses

Deformable convolution networks (DCNs) proposed to address image recognition with geometric or photometric variations typically involve deformable convolution that convolves on arbitrary locations of input features. The locations change with different inputs and induce considerable dynamic and irregular memory accesses that cannot be handled by classic neural network accelerators (NNAs). Moreover, bilinear interpolation (BLI) operation, which is required to obtain deformed features in DCNs, also cannot be deployed on existing NNAs directly. Although a general purposed processor (GPP) seated along with classic NNAs can process the deformable convolution, the processing on GPP can be extremely slow due to the limited parallel computing capability and massive additional data movement. To address the problem, we develop a DCN accelerator on existing NNAs to support both the standard convolution and deformable convolution. Specifically, for the dynamic and irregular accesses in DCNs, we have both the input and output features divided into tiles and build a tile dependency table (TDT) to track the irregular tile dependency at runtime. With the TDT, we further develop an on-chip tile scheduler to handle the dynamic and irregular accesses efficiently. In addition, we propose a novel mapping strategy to enable parallel BLI processing on NNAs and apply layer fusion techniques for more energy-efficient DCN processing. According to our experiments, the proposed accelerator achieves orders of magnitude higher performance and energy efficiency compared to the typical computing architectures including ARM, ARM+TPU, and GPU with 6.6% chip area penalty to a classic NNA.
Building Augmented Reality Integrated Automated Supervisory Flush Valve Testing System

The plastic manufacturing industry, like many other manufacturing industries, is going through great and rapid changes since the 4th industrial revolution. In addition to more conventional innovations such as factory automation, this industry has started to experience more advanced technologies like Internet of Things, augmented reality,... This article presents a case study on the applicability of automation and several technologies from the 4th industrial revolution to the plastic manufacturing industry. To be more specific, the article will demonstrate the process of building a semi-automated flush valve testing system to replace the manual testing process and integrating monitoring, data acquisition, and augmented reality technologies into the above system. The results compared with the manual testing process at a sanitary accessory manufacturing in Vietnam show that the application of those technologies is possible and can solve many issues commonly found in the manual testing process.
Optimizing healthcare system by amalgamation of text processing and deep learning: a systematic review

The explosion of clinical textual data has drawn the attention of researchers. Owing to the abundance of clinical data, it is becoming difficult for healthcare professionals to take real-time measures. The tools and methods are lacking when compared to the amount of clinical data generated every day. This review aims to survey the text processing pipeline with deep learning methods such as CNN, RNN, LSTM, and GRU in the healthcare domain and discuss various applications such as clinical concept detection and extraction, medically aware dialogue systems, sentiment analysis of drug reviews shared online, clinical trial matching, and pharmacovigilance. In addition, we highlighted the major challenges in deploying text processing with deep learning to clinical textual data and identified the scope of research in this domain. Furthermore, we have discussed various resources that can be used in the future to optimize the healthcare domain by amalgamating text processing and deep learning.
Research on multi-context aware recommendation methods based on tensor factorization

Compared to the traditional recommender systems, context-aware recommender systems are more in line with actual application contexts. However, the existing researches are mostly focused on single context-aware recommendation, such as time-aware recommendation or location-aware recommendation, and lack of in-depth research on multi-context-aware recommendation. Therefore, we proposed a recommendation method of high-order tensor factorization based on multi-context-aware. First, on the basis of analyzing the influence of context on users’ interest preferences, the sensitivity of users to multiple contexts was detected using statistical methods. For context-sensitive users, four-dimensional tensors and feature matrices used to solve data sparsity were constructed based on rating matrix and situational information. And then the stochastic gradient descent algorithm was used for iterative calculation to fill in missing data values and carry out parameter optimization. For context-insensitive users, we used matrix factorization to predict users’ interest preferences. Finally, we tested and validated our method on a multi-context-aware movie dataset, and the experimental results show that the proposed method could effectively reduce the prediction error and improve the recommendation quality.
An efficient hybrid fuzzy image encryption models for the secured cloud accessing in portable robotics devices

Secure sharing of image data through different communication channels is a challenging research issue in digital networks. In such cases, cryptography algorithms are widely used to encrypt and decrypt the information reliably at both ends of source and target destinations. Various state-of-the-art image encryption algorithms are surveyed and investigated to assess the best image encryption. In this paper, image encryption is described with various fuzzy operation modes. It ensures secure image transmission over to the digital channels. Most reliable image encryption is the aim of the work, and it is achieved through developing a hybrid fuzzy-based framework that composites the techniques of image encryption methods and fuzzy operation modes. Proposed hybrid fuzzy-based models are highly recommended to maintain secure information on portable robotics devices, communication channels, and routing devices. These hybrid encrypted models provide the users' privacy to access the image data with reliable security mechanisms.
Online video course design of elliptic partial differential equation based on image high-resolution processing

At present, the quality of online video courses in China is mixed. There are several reasons for the quality of online video courses. 1. The advantages and disadvantages of the front-end video capture equipment itself; 2. The distance of online video transmission; 3. The medium through which the video is transmitted; 4. Watch whether there is relevant interference information in the signal where the video is located and whether the video is compressed during transmission. These reasons lead to that although there is much to learn in the video, the resolution is too low to see from the video. With the development of the current social environment, most of the courses need online teaching. Therefore, in order to improve some problems in video playing caused by the increase of online teaching amount caused by the current environment, this paper provides higher resolution video for online courses by using high-resolution image processing technology based on the elliptic partial differential equation online video course. The high resolution processing technology used in this paper is centered on filtering algorithm. On the basis of the existing online video course of elliptic partial differential equations, the use of high-resolution technology can overcome the resolution limit of the hardware itself and further improve the video quality of online video teaching.
General ordering theorem

The problem of ordering operators has afflicted quantum mechanics since its foundation. Several orderings have been devised, but a systematic procedure to move from one ordering to another is still missing. The importance of establishing relations among different orderings is demonstrated by Wick's theorem (which relates time ordering to normal ordering), which played a crucial role in the development of quantum field theory. We prove the General Ordering Theorem (GOT), which establishes a relation among any pair of orderings, that act on operators satisfying generic (i.e. operatorial) commutation relations. We expose the working principles of the GOT by simple examples, and we demonstrate its potential by recovering two famous algebraic theorems as special instances: the Magnus expansion and the Baker-Campbell-Hausdorff formula. Remarkably, the GOT establishes a formal relation between these two theorems, and it provides compact expressions for them, unlike the notoriously complicated ones currently known.
ALSketch: An adaptive learning-based sketch for accurate network measurement under dynamic traffic distribution

Learning-based sketch improves accuracy and reduces the space overhead of traditional sketches by using machine learning to recognize input data patterns. However, most learning-based sketches adopts offline learning to train a learning model, which shows poor accuracy in measuring dynamic network traffic. To accommodate unpredictable changes in network traffic, it is urgent to study a learning-based sketch that not only supports timely updating recognition rules but also keeps accurate and efficient network traffic measurement. We propose a novel adaptive learning-based sketch (ALSketch) to accurately and efficiently measure dynamic network traffic with online machine learning. ALSketch incrementally constructs a learner to identify hot and cold flows and store them separately. Such a strategy reduces hash conflicts and improves estimated accuracy under limited memory usage. We perform extensive comparative experiments to verify the performance of ALSketch. Experimental results reveal that the average relative error of ALSketch is approximately 1.86∼5.25 times lower than that of traditional sketches under the same memory conditions.
Robust attention ranking architecture with frequency-domain transform to defend against adversarial samples

Deep neural networks have powerful capabilities, but they are vulnerable to adversarial attacks. For image classification tasks, if a small disturbance is introduced into the input image, the model is likely to be misled and causes misclassification. In this paper, we develop a robust attention ranking architecture with frequency-domain transform to defend against adversarial samples, which is called RARFTA. We import the discrete cosine transform as the activation layer after the first convolutional layer, which effectively suppresses the attack based on the gradient method. To eliminate the interference of residual adversarial noise, we dynamically select key points from the feature map for classification with the attention mechanism to reduce the impact of other attacked pixels. Experimental results on different datasets show that our method is superior to the existing defense methods in both black-box and white-box attacks and significantly improves the robustness of the deep neural network model. The code for our work is available at https://github.com/lixiaowenaaa/RARTFA/tree/master.
Don't turn a blind eye to cyber security

There's often a gap between security threats as understood by the C-suite and the day-to-day threats faced by security professionals. This can condemn the latter to working with outdated and inadequate solutions. Executives need to understand that cyber security is an area crying out for investment.
Consumer trust and perspectives on cyber security

When your networks are breached, you may lose more than your data. Customers are increasingly wary of doing business with organisations that have been hacked. The reputational damage can last a long time – perhaps longer than the business itself.
A high-performance xylene-free tissue-clearing agent for routine histology: Preliminary histomorphological results

After the discovery of the hazardous effects of xylene, less toxic substitutes were proposed for routine histology in the last years. However, the introduction of new xylene-free substitutes in histological processes requires a careful evaluation of their performance in terms of morphological and microscopic details to permit a solid diagnosis as well as good quality immunohistochemical and biomolecular analyses. In this study, we analyzed the performance of a new commercially available xylene-free Tissue-Tek® Tissue-Clear® agent in comparison with another routine xylene-free solvent yet available and employed in routine histological process. Serial histological tissue samples (n = 300) were selected and processed with the two clearing agents. Comparison and evaluation were also performed on slides obtained 6 months after paraffin embedding and archive storage. Blinded semiquantitative analysis of technical performance and morphological details, including tissue architecture and nuclear and cytoplasmic details, was performed on Haematoxylin-Eosin stained sections by two technicians and two pathologists, respectively. Evaluation of tissue slides documented a good overall histological performance in slides obtained after processing with the two different clearing agents. Slides obtained with Tissue-Tek® Tissue-Clear® displayed a higher score in some quality parameters, further supporting its use as a valid alternative to the other commercial routine xylene-free solvents.
FBN: Federated Bert Network with client-server architecture for cross-lingual signature verification

Online signature verification has a great challenge due to the poor performance of deep learning techniques on cross-lingual datasets under privacy constraints. In this paper, we propose a novel Federated Bert Network (FBN) by embedding the Bidirectional Encoder Representations from Transformers (Bert) into a Federated Learning (FL) framework with client-server architecture. A new Length Alignment Algorithm is employed to unify the signature pairs’ sequence length, and the input representations are fed into the different clients to complete the independent learning of local-models. In addition, the server (coordinator) uses the improved Federated Average Algorithm with Reward-Punishment Mechanism (FedAvgRP) to aggregate these local-models and further generate a global-model. After multiple iterations, the optimal model can be obtained and cross-tested on four datasets (SVC 2004, MCYT-330, BioecurID, and Ours) with skilled forged (random forged) EERs of 7.65% (4.76%), 10.73% (8.46%), 10.09% (7.13%), and 8.28% (5.74%), respectively, far higher than that of the independent learning of state-of-the-art methods. Compared with the domain adaptation and improved FL models, our FBN model performs best in random and skilled forgery scenarios. Moreover, the FedAvgRP algorithm helps our model maintain high performance in the face of data attacks.