Reviewerly/exHarmony huggingface dataset:
A survey on common criteria (CC) evaluating schemes for security assessment of IT products

Over the last few years, private and public organizations have suffered an increasing number of cyber-attacks owing to excessive exploitation of technological vulnerabilities. The major objective of these attacks is to gain illegal profits by extorting organizations which adversely impact their normal operations and reputation. To mitigate the proliferation of attacks, it is significant for manufacturers to evaluate their IT products through a set of security-related functional and assurance requirements. Common Criteria (CC) is a well-recognized international standard, focusing on ensuring security functionalities of an IT product along with the special emphasis on IS design and life-cycle. Apart from this, it provides a list of assurance classes, families, component, and elements based on which security EALs can be assigned to IT products. In this survey, we have provided a quick overview of the CC followed by the analysis of country-specific implementation of CC schemes to develop an understanding of critical factors. These factors play a significant role by providing assistance in IT products evaluation in accordance with CC. To serve this purpose, a comprehensive comparative analysis of four schemes belonging to countries including US, UK, Netherlands, and Singapore has been conducted. This comparison has aided to propose best practices for realizing an efficient and new CC scheme for the countries which have not designed it yet and for improving the existing CC schemes. Finally, we conclude the paper by providing some future directions regarding automation of the CC evaluation process.
Hyperspectral Image Classification With Adversarial Attack

The performance of a neural network is highly dependent on the labeled samples. However, the labeled samples are primarily clean, which prevents the network from capturing the features of the samples near the decision boundary. For hyperspectral images (HSIs), high spectral dimensions and same-spectra foreign matter lead to more boundary samples in the data. In this letter, we investigate an adversarial attack algorithm against these problems for HSIs. A modified DeepFool algorithm is implemented to generate boundary adversarial samples with minimal disturbance, and the generated boundary adversarial samples are simply added to the training set to improve the accuracy of the boundary samples in the data. Furthermore, we iteratively complete network training and boundary adversarial sample generation so that the decision boundary can be adjusted according to the real-time classification situation. Extensive experiments are carried out on the two HSI datasets, and the results demonstrate that the modified DeepFool algorithm can improve the accuracy of the decision boundary. Our findings also show that adversarial attacks are sensitive to high-dimensional and multiple-category data and are worthy of further study.
DILBERT: Customized Pre-Training for Domain Adaptation with Category Shift, with an Application to Aspect Extraction

The rise of pre-trained language models has yielded substantial progress in the vast majority of Natural Language Processing (NLP) tasks.However, a generic approach towards the pre-training procedure can naturally be sub-optimal in some cases.Particularly, finetuning a pre-trained language model on a source domain and then applying it to a different target domain, results in a sharp performance decline of the eventual classifier for many source-target domain pairs.Moreover, in some NLP tasks, the output categories substantially differ between domains, making adaptation even more challenging.This, for example, happens in the task of aspect extraction, where the aspects of interest of reviews of, e.g., restaurants or electronic devices may be very different.This paper presents a new fine-tuning scheme for BERT, which aims to address the above challenges.We name this scheme DILBERT: Domain Invariant Learning with BERT, and customize it for aspect extraction in the unsupervised domain adaptation setting.DILBERT harnesses the categorical information of both the source and the target domains to guide the pre-training process towards a more domain and category invariant representation, thus closing the gap between the domains.We show that DILBERT yields substantial improvements over state-ofthe-art baselines while using a fraction of the unlabeled data, particularly in more challenging domain adaptation setups. 1
SmartGift: Learning to Generate Practical Inputs for Testing Smart Contracts

With the boom of Initial Coin Offerings (ICO) in the financial markets, smart contracts have gained rapid popularity among consumers. Smart contract vulnerabilities however made them a prime target to malicious attacks that are leading to huge losses. The research community is thus applying various software engineering technologies to smart contracts to address them. In general, to detect vulnerabilities in smart contracts, mutation and fuzz based testing approaches have been widely studied and indeed achieved promising performance on benchmark datasets. Generating test inputs with mutation approaches essentially relies on the available test cases in a smart contract program. In our preliminary study, however, we observed that 56.4% of 218 identified open-source smart contract project repositories do not provide any test case for validation. Fuzzing test inputs leads to random values and lacks practical usefulness. Our work addresses this problem: we propose an approach, Smartgift, which generates practical inputs for testing smart contracts by learning from the transaction records of real-world smart contracts. Leveraging a collected set of over 60 thousand transaction records, Smartgift is able to generate relevant test inputs for ~77% smart contract functions, largely outperforming the traditional fuzzing approach (successful for only 60% functions). We further demonstrate the practicality of the test inputs by using them to replace the test inputs of the ContractFuzzer state of the art smart contract vulnerability detector: with inputs by Smartgift, ContractFuzzer can now detect 131 of the 154 vulnerabilities in its benchmark.
Color crosstalk correction for synchronous measurement of full-field temperature and deformation

Synchronous measurement of temperature and deformation at elevated temperatures based on the noncontact optical method can be achieved through the allocation of channels R, G, and B for a color camera. Channels R and G are used for the radiation-based temperature measurement, while channel B is adopted for the deformation measurement. However, the existence of color crosstalk, which is mainly caused by the color camera's spectral response and the use of the narrow-band filtering technique, has a significant influence on the accuracy of temperature and deformation calculations. In this work, the color crosstalk effect in high-temperature optical imaging is analyzed, and a crosstalk correction algorithm based on grayscale calibration is performed on the different color channels (channels R, G, and B) of the color image. By solving the calibration equations, three corrected color channels are generated for a high-precision temperature and deformation calculation. A flame heating experiment of a C/SiC material up to 1100 °C is carried out to validate the applicability accuracy of the proposed method. The results show that the proposed method can effectively eliminate the color crosstalk between different channels and provide a non-contact, full-field, and synchronous measurement of temperature and deformation with higher accuracy than the existing methods.
An Improved Cuckoo Search Algorithm Utilizing Nonlinear Inertia Weight and Differential Evolution for Function Optimization Problem

This paper proposes an improved cuckoo search (CS) algorithm combining nonlinear inertial weight and differential evolution algorithm (WCSDE) to overcome the shortcomings of the CS algorithm, such as low convergence accuracy, lack of information exchange within the population, and inadequate local search capabilities.Compared with other CS variants, two strategies are proposed in this paper to improve the properties of the WCSDE.On the one hand, a non-linearly decreasing inertia weight with the number of evolutionary iterations is employed in the WCSDE to improve the update method of the bird's nest position, enhance the balance between the exploration and development capabilities, and strengthen the local optimization capability.On the other hand, the mutation and cross-selection mechanisms of the differential evolution (DE) algorithm are introduced to make up for the lack of the mutual relationship between the populations, avoid the loss of practical information, and increase the convergence accuracy.In the experiment part, 13 classic benchmark functions are selected to execute the function optimization tasks among the standard CS, the WCSDE, and other four CS variants to verify the effectiveness of the proposed algorithm from two aspects.The results and corresponding statistical analysis reveal that the proposed algorithm has better global search ability and strengthener robustness.
Applicability of Collaborative Work in the COVID-19 Era: Use of Breakout Groups in Teaching L2 Translation

Social distancing became a must during the pandemic, which not only had implications for people’s social lives, but also for their learning. Collaborative work was almost impossible, especially in the classroom, despite a great need for this approach. For example, in their translation classes, the learners needed to collaborate with their peers, assisting each other in translating texts. Thus, the use of breakout groups is proposed in this study, although there is no guarantee that learners will accept this online approach. Consequently, the current research looks at learners’ acceptance of breakout groups on Blackboard in a translation class. To examine their acceptance, an existing scale was used, developed by Davis (1989) to measure two factors of technology acceptance: perceived usefulness and ease of use. A sample of 54 students on a Translation course at Al-Imam Mohammed Ibn Saud Islamic University in Riyadh, Saudi Arabia, participated in this study. The results show that the learners found breakout groups on Blackboard to be useful and easy to use.
Prediction of Merchandise Sales on E-Commerce Platforms Based on Data Mining and Deep Learning

Online business has grown exponentially during the last decade, and the industries are focusing on online business more than before. However, just setting up an online store and starting selling might not work. Different machine learning and data mining techniques are needed to know the users’ preferences and know what would be best for business. According to the decision-making needs of online product sales, combined with the influencing factors of online product sales in various industries and the advantages of deep learning algorithm, this paper constructs a sales prediction model suitable for online products and focuses on evaluating the adaptability of the model in different types of online products. In the research process, the full connection model is compared with the training results of CNN, which proves the accuracy and generalization ability of CNN model. By selecting the non-deep learning model as the comparison baseline, the performance advantages of CNN model under different categories of products are proved. In addition, the experiment concludes that the unsupervised pretrained CNN model is more effective and adaptable in sales forecasting.
A New World Of Scholarly Communication

A New World of Scholarly Communication November 2003 Higher-education leaders invariably have long lists of difﬁcult issues to confront. These days, high on my list is the future of our university libraries. Although libraries form the basic infra- structure of the academic endeavor, I have come face to face with an unhappy fact: University librarians are now being forced to work with faculty members to choose more of the publications they can do without. The ballooning costs of academic publi- cations are preventing faculty members and researchers from gaining access to the world’s scholarship and knowledge. Even in the best of economic times, university libraries cannot hope to keep pace with the 6 to 12 percent annual inﬂation rate in the price of scholarly journals. And the ﬁscal environment today is particularly difﬁcult—states are facing unprecedented budget crises just as expanding faculties and student bodies are increasing the demand for scholarly information. Neither
It's a Scheduling Affair: GROMACS in the Cloud with the KubeFlux Scheduler

In this work, we address the problem of running HPC workloads efficiently on Kubernetes clusters. To do so, we compare the Kubernetes&#x0027; default scheduler with KubeFlux, a Kubernetes plug-in scheduler built on the Flux graph-based scheduler, on a 34- node Red Hat OpenShift cluster on IBM Cloud. We detail how scheduling can affect the performance of GROMACS, a well-known HPC application, and we show that KubeFlux can improve its performance through better pod scheduling. In our tests, KubeFlux demonstrates the tendency to limit the number of subnets spanned by a job and the maximum number of pods per node, translating to a &#x003E; 2x speedup over the Kubernetes default scheduler in several cases.
Authorship Attribution of Social Media and Literary Russian-Language Texts Using Machine Learning Methods and Feature Selection

Authorship attribution is one of the important fields of natural language processing (NLP). Its popularity is due to the relevance of implementing solutions for information security, as well as copyright protection, various linguistic studies, in particular, researches of social networks. The article is a continuation of the series of studies aimed at the identification of the Russian-language text’s author and reducing the required text volume. The focus of the study was aimed at the attribution of textual data created as a product of human online activity. The effectiveness of the models was evaluated on the two Russian-language datasets: literary texts and short comments from users of social networks. Classical machine learning (ML) algorithms, popular neural networks (NN) architectures, and their hybrids, including convolutional neural network (CNN), networks with long short-term memory (LSTM), Bidirectional Encoder Representations from Transformers (BERT), and fastText, that have not been used in previous studies, were applied to solve the problem. A particular experiment was devoted to the selection of informative features using genetic algorithms (GA) and evaluation of the classifier trained on the optimal feature space. Using fastText or a combination of support vector machine (SVM) with GA reduced the time costs by half in comparison with deep NNs with comparable accuracy. The average accuracy for literary texts was 80.4% using SVM combined with GA, 82.3% using deep NNs, and 82.1% using fastText. For social media comments, results were 66.3%, 73.2%, and 68.1%, respectively.
MagNet: Detecting Digital Presentation Attacks on Face Recognition

Presentation attacks on face recognition systems are classified into two categories: physical and digital. While much research has focused on physical attacks such as photo, replay, and mask attacks, digital attacks such as morphing have received limited attention. With the advancements in deep learning and computer vision algorithms, several easy-to-use applications are available where with few taps/clicks, an image can be easily and seamlessly altered. Moreover, generation of synthetic images or modifying images/videos (e.g. creating deepfakes) is relatively easy and highly effective due to the tremendous improvement in generative machine learning models. Many of these techniques can be used to attack the face recognition systems. To address this potential security risk, in this research, we present a novel algorithm for digital presentation attack detection, termed as MagNet, using a “Weighted Local Magnitude Pattern” (WLMP) feature descriptor. We also present a database, termed as ID Age nder , which consists of three different subsets of swapping/morphing and neural face transformation. In contrast to existing research, which utilizes sophisticated machine learning networks for attack generation, the databases in this research are prepared using social media platforms that are readily available to everyone with and without any malicious intent. Experiments on the proposed database, FaceForensic database, GAN generated images, and real-world images/videos show the stimulating performance of the proposed algorithm. Through the extensive experiments, it is observed that the proposed algorithm not only yields lower error rates, but also provides computational efficiency.
Potential quality pitfalls of digitalized whole slide image of breast pathology in routine practice

Using digitalized whole slide images (WSI) in routine histopathology practice is a revolutionary technology. This study aims to assess the clinical impacts of WSI quality and representation of the corresponding glass slides. 40,160 breast WSIs were examined and compared with their corresponding glass slides. The presence, frequency, location, tissue type, and the clinical impacts of missing tissue were assessed. Scanning time, type of the specimens, time to WSIs implementation, and quality control (QC) measures were also considered. The frequency of missing tissue ranged from 2% to 19%. The area size of the missed tissue ranged from 1-70%. In most cases (>75%), the missing tissue area size was <10% and peripherally located. In all cases the missed tissue was fat with or without small entrapped normal breast parenchyma. No missing tissue was identified in WSIs of the core biopsy specimens. QC measures improved images quality and reduced WSI failure rates by seven-fold. A negative linear correlation between the frequency of missing tissue and both the scanning time and the image file size was observed (p < 0.05). None of the WSI with missing tissues resulted in a change in the final diagnosis. Missing tissue on breast WSI is observed but with variable frequency and little diagnostic consequence. Balancing between WSI quality and scanning time/image file size should be considered and pathology laboratories should undertake their own assessments of risk and provide the relevant mitigations with the appropriate level of caution.
Fast writer adaptation with style extractor network for handwritten text recognition

Writing style is an abstract attribute in handwritten text. It plays an important role in recognition systems and is not easy to define explicitly. Considering the effect of writing style, a writer adaptation method is proposed to transform a writer-independent recognizer toward a particular writer. This transformation has the potential to significantly increase accuracy. In this paper, under the deep learning framework, we propose a general fast writer adaptation solution. Specifically, without depending on other complex skills, a well designed style extractor network (SEN) trained by identification loss (IDL) is introduced to explicitly extract personalized writer information. The architecture of SEN consists of a stack of convolutional layers followed by a recurrent neural network with gated recurrent units to remove semantic context and retain writer information. Then, the outputs of the GRU are further integrated into a one-dimensional vector that is adopted to represent writing style. Finally, the extracted style information is fed into the writer-independent recognizer to achieve adaptation. Validated on offline handwritten text recognition tasks, the proposed fast sentence-level adaptation achieves remarkable improvements in Chinese and English text recognition tasks. Specifically, in the HETR task, a multi-information fusion network that is equipped with a hybrid attention mechanism and that integrates visual features, context features and writing style is proposed. In addition, under the same condition (only one writer-specific text line used as adaptation data), the proposed solution, without consuming extra time, can significantly outperform the previous multiple-pass decoding method. The code is available at https://github.com/Wukong90/Handwritten-Text-Recognition.
Efficient Online Object Tracking Scheme for Challenging Scenarios

Visual object tracking (VOT) is a vital part of various domains of computer vision applications such as surveillance, unmanned aerial vehicles (UAV), and medical diagnostics. In recent years, substantial improvement has been made to solve various challenges of VOT techniques such as change of scale, occlusions, motion blur, and illumination variations. This paper proposes a tracking algorithm in a spatiotemporal context (STC) framework. To overcome the limitations of STC based on scale variation, a max-pooling-based scale scheme is incorporated by maximizing over posterior probability. To avert target model from drift, an efficient mechanism is proposed for occlusion handling. Occlusion is detected from average peak to correlation energy (APCE)-based mechanism of response map between consecutive frames. On successful occlusion detection, a fractional-gain Kalman filter is incorporated for handling the occlusion. An additional extension to the model includes APCE criteria to adapt the target model in motion blur and other factors. Extensive evaluation indicates that the proposed algorithm achieves significant results against various tracking methods.
JLAN: medical code prediction via joint learning attention networks and denoising mechanism

Abstract Background Clinical notes are documents that contain detailed information about the health status of patients. Medical codes generally accompany them. However, the manual diagnosis is costly and error-prone. Moreover, large datasets in clinical diagnosis are susceptible to noise labels because of erroneous manual annotation. Therefore, machine learning has been utilized to perform automatic diagnoses. Previous state-of-the-art (SOTA) models used convolutional neural networks to build document representations for predicting medical codes. However, the clinical notes are usually long-tailed. Moreover, most models fail to deal with the noise during code allocation. Therefore, denoising mechanism and long-tailed classification are the keys to automated coding at scale. Results In this paper, a new joint learning model is proposed to extend our attention model for predicting medical codes from clinical notes. On the MIMIC-III-50 dataset, our model outperforms all the baselines and SOTA models in all quantitative metrics. On the MIMIC-III-full dataset, our model outperforms in the macro-F1, micro-F1, macro-AUC, and precision at eight compared to the most advanced models. In addition, after introducing the denoising mechanism, the convergence speed of the model becomes faster, and the loss of the model is reduced overall. Conclusions The innovations of our model are threefold: firstly, the code-specific representation can be identified by adopted the self-attention mechanism and the label attention mechanism. Secondly, the performance of the long-tailed distributions can be boosted by introducing the joint learning mechanism. Thirdly, the denoising mechanism is suitable for reducing the noise effects in medical code prediction. Finally, we evaluate the effectiveness of our model on the widely-used MIMIC-III datasets and achieve new SOTA results.