Reviewerly/exHarmony huggingface dataset:
Detecting Adversarial Perturbations in Multi-Task Perception

While deep neural networks (DNNs) achieve impressive performance on environment perception tasks, their sensitivity to adversarial perturbations limits their use in practical applications. In this paper, we (i) propose a novel adversarial perturbation detection scheme based on multi-task perception of complex vision tasks (i.e., depth estimation and semantic segmentation). Specifically, adversarial perturbations are detected by inconsistencies between extracted edges of the input image, the depth output, and the segmentation output. To further improve this technique, we (ii) develop a novel edge consistency loss between all three modalities, thereby improving their initial consistency which in turn supports our detection scheme. We verify our detection scheme's effectiveness by employing various known attacks and image noises. In addition, we (iii) develop a multi-task adversarial attack, aiming at fooling both tasks as well as our detection scheme. Experimental evaluation on the Cityscapes and KITTI datasets shows that under an assumption of a 5% false positive rate up to 100% of images are correctly detected as adversarially perturbed, depending on the strength of the perturbation. Code is available at https://github.com/ifnspaml/AdvAttackDet. A short video at https://youtu.be/KKa6gOyWmH4 provides qualitative results.
Technology-Enhanced Instruction into Extensive L2 Academic Reading for Specific Purposes Within the ESP/ESAP Course for Postgraduate Language Pedagogy Students

Teaching and learning to read domain specific texts and research articles within the course of academic English for specific/academic purposes (ESAP) is often underestimated and outsourced to students’ independent study with minimal instructors’ intervention. Students often demonstrate inability to gain knowledge about basic conventions established in the international academic communities such as research article (RA) structure, style, rules of citation and referencing etc. while reading RAs. The study undertaken by researchers of Saint Petersburg State University (SPbSU) and Peter the Great Saint Petersburg Polytechnic University (SPbPU) showed that a specially designed extensive academic reading module (EARM) can help solve this problem. In the first part, the paper describes the underlying rationale for the development of the EARM, its syllabus, and its experimental approbation in the spring term of 2021–22 academic year with two academic groups of postgraduate language pedagogy students taking their master program at SPbSU and SPbPU. The second part of the paper focuses on the analysis of the data collected, namely two questionnaires completed by the experiment participants, their reports about extensive reading of RAs selected, and analysis of some structural and linguistic features of 36 RAs selected by the students which was conducted by the researchers. The role of technology in teaching the EARM is stressed. The positive attitude of students to the EARM and improvement of some their micro academic skills, e.g. pre-reading self-questioning, during the half-term experiment allow the authors to recommend including teaching academic reading module in ESAP course for postgraduate students on a regular basis. Further research steps are outlined.
Performance Comparison of Machine Learning Algorithms for Albanian News articles

This paper presented a methodology to classify news articles in Albanian languages with machine learning algorithms and NLP. In this proposed work Multinomial Naïve Bayes, Logistic Regression, k-nearest neighbors, Bernoulli, Centroid, SVM, SGD, Perceptron, Passive Aggressive, Decision Tree, and Random Forest machine learning Classification algorithms are trained to classify Albanian news articles and compared in term of accuracy, training time and testing time. We have used 70% of the dataset for training and 30% for tests. The execution time of training and testing algorithms is recorded and presented for different input sizes of data. For experimental we used different numbers of inputs, starting from 8000 articles, and continuing in increasing order by 8000 until the level of 80000 in 8 categories. Experimental results indicate that the Passive Aggressive algorithm shows the best performance in terms of accuracy for Albanian news articles. While the classifier with the lowest accuracy was Random Forest. Logistic Regression, SVM, and Decision Tree have a high delay in training data. SVM also has the longest testing time compared to other classifiers.
A Private Blockchain Platform to Manage Data Exchange Between Supply Chain Partners

In a complex and changing market environment, enterprises recognize the benefit of collaborating with their suppliers and B2B customers to minimize risks and increase the agility and efficiency of the overall supply chain. To ensure this collaboration, they are asked to share real-time data related to their activities (stock, capacity, etc.) and to collaborate with their partners to optimize operation management, to reduce costs and reach optimal service level. Therefore, they require robust and reliable IT platforms allowing partners to exchange sensitive information and guarantee transparency but also security. That explains the increasing interest among supply chain practitioners and academicians toward the blockchain technology thanks to what it promises in terms of security, transparency and automation. In this paper, we present initial steps of a study aiming to develop, test and deploy a supply chain collaboration platform using the blockchain technology. The purpose of our study is to present answers to many unsolved questions related to the ability of the blockchain technology to manage real scale supply chain data and maintain the promised advantages.
Understanding Security Risks of Ad-based URL Shortening Services Caused by Users' Behaviors

URL shortening services enable us to shorten or simplify URLs. Ad-based URL shortening services display advertisements to users who access short URLs and reward short URL creators. However, ad-based URL shortening services have specific security risks that URL shortening services without ads do not, such as displaying malicious advertisements to users. In this study, we reveal previously unknown security risks of these services caused by users' behaviors. We conducted a comprehensive measurement of ad-based URL shortening services. First, we accessed short URLs of these services, clicked buttons on the web pages, and reached the final destinations of the short URLs. Then, we reveal the security risks posed to users by monitoring and analyzing traffic logs when such short URLs are accessed. We found that all services generated an average of 86.5 web requests to malicious domain names per short URL. We then showed the security risk of unintentionally communicating malicious domain names even when users click only on buttons that correctly move users to their desired destinations. Finally, we discuss countermeasures to mitigate these risks from the perspective of each stakeholder in ad-based URL shortening services.
Feature Importance Measures as Explanation for Classification Applied to Hospital Readmission Prediction

Many machine learning (ML) applications in healthcare that are capable of generating very good performance are still not integrated in the clinical workflow primarily because the end-users, the physicians, could not understand the logic used by these models. For them, these are black boxes incapable of providing reasons for arriving at the diagnosis thus creating trust issues and acceptability. In the end, it is still the physician who will make the final decision and so for the tool to be considered a worthy clinical decision support tool, it should be able to communicate very well how it arrived at the result. Only then can it gain the physician's trust and acceptance. Thus, Explainable AI (XAI) should be feature in any ML application in order to gain acceptability and integration in the workplace. In this study we presented a tool that is capable of predicting patients who will more likely be readmitted and its reasons for arriving at such a conclusion. We used Random Forest (RF), AdaBoost, and K-Nearest Neighbors (K-NN) to build the models, performed hyperparameter tuning in order to improve performance, calculated the feature importance to understand which features are deemed important to each model, and then added a visual explainer using Local Interpretable Model-Agnostic Explanation (LIME) to help the physician understand the logic employed by each model in making the classification.
Application of Variational Bayesian-based Noise Remodeling in AUV Navigation

The precise sensor fusion algorithm is the key part to achieve Autonomous Underwater Vehicle (AUV) navigation, but it’s difficult to estimate the time-varying noise of the sensors on board the vehicle. Existing data fusion algorithms usually set the noise of these sensors as a fixed white Gaussian noise, which is difficult to meet the needs of accurate AUV navigation. In this paper, we model the measurement noise as Inverse-Gamma distribution and Student-t distribution, and use Variational Bayesian method to estimate the noise distribution parameters to resist the time-varying noise and improve the adaptive capability of the algorithms. Two most commonly used nonlinear Kalman filters are improved to verify the effectiveness of the proposed algorithms in AUV navigation. The AUV sea trial data show that the proposed algorithms can effectively improve the navigation capability.
Cross-modal Contrastive Distillation for Instructional Activity Anticipation

In this study, we aim to predict the plausible future action steps given an observation of the past and study the task of instructional activity anticipation. Unlike previous anticipation tasks that aim at action label prediction, our work targets at generating natural language outputs that provide interpretable and accurate descriptions of future action steps. It is a challenging task due to the lack of semantic information extracted from the instructional videos. To overcome this challenge, we propose a novel knowledge distillation framework to exploit the related external textual knowledge to assist the visual anticipation task. However, previous knowledge distillation techniques generally transfer information within the same modality. To bridge the gap between the visual and text modalities during the distillation process, we devise a novel cross-modal contrastive distillation (CCD) scheme, which facilitates knowledge distillation between teacher and student in heterogeneous modalities with the proposed cross-modal distillation loss. We evaluate our method on the Tasty Videos dataset. CCD improves the anticipation performance of the visual-alone student model by a large margin of 40.2% relatively in BLEU4. Our approach also outperforms the state-of-the-art approaches by a large margin.
A Sliding Window Scheme for Online Temporal Action Localization

Most online video understanding tasks aim to immediately process each streaming frame and output predictions frame-by-frame. For extension to instance-level predictions of existing online video tasks, Online Temporal Action Localization (On-TAL) has been recently proposed. However, simple On-TAL approaches of grouping per-frame predictions have limitations due to the lack of instance-level context. To this end, we propose Online Anchor Transformer (OAT) to extend the anchor-based action localization model to the online setting. We also introduce an online-applicable post-processing method that suppresses repetitive action proposals. Evaluations of On-TAL on THUMOS'14, MUSES, and BBDB show significant improvements in terms of mAP, and our model shows comparable performance to the state-of-the-art offline TAL methods with a minor change of the post-processing method. In addition to mAP evaluation, we additionally present a new online-oriented metric of early detection for On-TAL, and measure the responsiveness of each On-TAL approach.
Identifying Anomalous Flight Trajectories by leveraging ensembled outlier detection framework

Increased traffic density with a greater degree of increased automation in aviation is expected within the next decade. Therefore, airspace capacity will become more congested and result in increasing challenges for detecting conflicts between aerial vehicles. Furthermore, because these vehicles rely on surrounding vehicles following a planned path, it is essential to identify flights not following a planned direction. In this paper, we utilize an ensemble of the existing outlier detection approaches for identifying the anomalous flight trajectories. In the initial step, flight trajectories are preprocessed to extract and process vital features, with the next step of having twenty different outlier detection algorithms assembled to classify trajectories. Throughout our extensive experiments and comparison studies, promising results are shown including the effectiveness of different anomaly detection algorithms and how utilizing feature engineering can improve the results of these outlier detection methods.
Query Translation Optimization and Mathematical Modeling for English-Chinese Cross-Language Information Retrieval

Abstract The basic theory of the neural network model used in this research is particle swarm optimization, applied to English-Chinese translation retrieval and model optimization. The decoded form obtains the translation task sequence. This topic uses TensorFlow to complete the construction of the translation system. This research solves the typical lack of semantic information in the hierarchical model. The demonstration of examples shows that the neural network model based on particle swarm optimization can significantly improve the quality of machine translation.
Extending Developer Experience Metrics for Better Effort-Aware Just-In-Time Defect Prediction

Developers use defect prediction models to efficiently allocate limited resources for quality assurance and appropriately make a plan for software quality improvement activities. Traditionally, defect predictions are conducted at the module level, such as the class or file level. However, a more recent trend is to perform defect prediction for a single or consecutive commits to the repository, which is known as just-in-time (JIT) defect prediction. JIT defect prediction finds error-prone changes instead of error-prone modules, and as a result, the developer only needs to investigate error-prone changed lines instead of the entire module. When building JIT defect prediction models, researchers used various metrics, including developer experience metrics which measure the developer’s experiences. Despite the fact that software defectiveness is likely to be affected by the experience of developers, developer metrics were understudied in the literature. In this work, we investigate the impact of various novel developer experience metrics and their combinations on JIT defect prediction. Our experimental results are positive. We found that it is possible to improve the cost-effectiveness of defect prediction models consistently and statistically significantly by using our new developer experience metrics.
Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries

Learning under a continuously changing data distribution with incorrect labels is a desirable real-world problem yet challenging. A large body of continual learning (CL) methods, however, assumes data streams with clean labels, and online learning scenarios under noisy data streams are yet underexplored. We consider a more practical CL task setup of an online learning from blurry data stream with corrupted labels, where existing CL methods struggle. To address the task, we first argue the importance of both diversity and purity of examples in the episodic memory of continual learning models. To balance diversity and purity in the episodic memory, we propose a novel strategy to manage and use the memory by a unified approach of label noise aware diverse sampling and robust learning with semi-supervised learning. Our empirical validations on four real-world or synthetic noise datasets (CI-FAR10 and 100, mini-WebVision, and Food-101N) exhibit that our method significantly outperforms prior arts in this realistic and challenging continual learning scenario. Code and data splits are available in https://github.com/clovaai/puridiver.
Security Evaluation of Initialization Phases and Round Functions of Rocca and AEGIS

Authenticated-Encryption with Associated-Data (AEAD) plays an important role in guaranteeing confidentiality, integrity, and authenticity in network communications. To meet the requirements of high-performance applications, several AEADs make use of AES New Instructions (AES-NI), which can conduct operations of AES encryption and decryption dramatically fast by hardware accelerations. At SAC 2013, Wu and Preneel proposed an AES-based AEAD scheme called AEGIS-128/128L/256, to achieve high-speed software implementation. At FSE 2016, Jean and Nikolić generalized the construction of AEGIS and proposed more efficient round functions. At ToSC 2021, Sakamoto et al. further improved the constructions of Jean and Nikolić, and proposed an AEAD scheme called Rocca for beyond 5G. In this study, we first evaluate the security of the initialization phases of Rocca and AEGIS family against differential and integral attacks using MILP (Mixed Integer Linear Programming) tools. Specifically, according to the evaluation based on the lower bounds for the number of active S-boxes, the initialization phases of AEGIS-128/128L/256 are secure against differential attacks after 4/3/6 rounds, respectively. Regarding integral attacks, we present the integral distinguisher on 6 rounds and 6/5/7 rounds in the initialization phases of Rocca and AEGIS-128/128L/256, respectively. Besides, we evaluate the round function of Rocca and those of Jean and Nikolić as cryptographic permutations against differential, impossible differential, and integral attacks. Our results indicate that, for differential attacks, the growth rate of increasing the number of active S-boxes in Rocca is faster than those of Jean and Nikolić. For impossible differential and integral attacks, we show that the round function of Rocca achieves the sufficient level of the security against these attacks in smaller number of rounds than those of Jean and Nikolić.
A Multi-step Time Series Prediction Strategy in Deep Learning: Combination of Recursive Strategy and Multi-output by Dense Layer Strategy

In practical applications, when solving the problem of time series prediction, it is often necessary to predict the data of multiple time points in the future according to the observed data. It’s a problem called multi-step time series prediction. Now there are some solutions to handle the problem, but each solution has its own advantages and is insufficient. As a result, the goal of this paper is to combine the recursive strategy and the multi-output strategy to propose a new method for solving the multi-step time series prediction problem to gain better performance than either of them alone. In the research, the author will use House Hold Energy data from Kaggle and conduct a contrast experiment to reflect the advantages of the combined strategy. The experiment results show that the combined strategy outperforms the recursive and multi-output strategies.
Recovering Thin Cloud Covered Regions in Gf Satellite Images Based on Cloudy Image Arithmetic +

We propose the Cloudy Image Arithmetic + (CIA +) for training dataset construction of thin cloud removal, which addresses the deficiency of Cloud Image Arithmetic (CIA) that cloud shadows cannot be simulated. CIA + is able to synthesize cloudy images with cloud shadows, and as in nature, the angle and intensity of the cloud shadows vary depending on the irradiation angle and cloud thickness, which achieves state-of-the-art cloudy image synthesis. The first thin cloud removal dataset on GF satellite (TCR-GF) constructed with CIA + is released to supplement public data for cloud removal. Meanwhile, we propose the Dual-attention MSGAN to remove thin clouds. The network is capable to focus on thin cloud covered regions for the coordinate attention module encodes both channel relationship and long-range dependencies with precise position information. Several qualitative and quantitative experiments validate that the Dual-attention performs excellently on our TCR-GF dataset.
Covert Channel Detection Methods

The modern networking world is being exposed to many risks more frequently every day. Most of systems strongly rely on remaining anonymous throughout the whole endpoint exploitation process. Covert channels represent risk since they ex-ploit legitimate communications and network protocols to evade typical filtering. This firewall avoidance sees covert channels frequently used for malicious communication of intruders with systems they compromised, and thus a real threat to network security. While there are commercial tools to safeguard computer networks, novel applications such as automotive connectivity and V2X present new challenges. This paper focuses on the analysis of the recent ways of using covert channels and detecting them, but also on the state-of-the-art possibilities of protection against them. We investigate observing the timing covert channels behavior simulated via injected ICMP traffic into standard network communications. Most importantly, we concentrate on enhancing firewall with detection and prevention of such attack built-in features. The main contribution of the paper is design for detection timing covert channel threats utilizing detection methods based on statistical analysis. These detection methods are combined and implemented in one program as a simple host-based intrusion detection system (HIDS). As a result, the proposed design can analyze and detect timing covert channels, with the addition of taking preventive measures to block any future attempts to breach the security of an end device.
Cross-target Stance Classification as Domain Adaptation

As in many NLP tasks, stance classification - the computational task of inferring attitudes towards a given target topic from text - often relies on supervised methods and annotated corpora. However, given the costs associated with corpus annotation, and the need to provide stance labels for every target of interest, cross-target methods (that is, the use of an existing corpus of stance towards a particular target to classify stance towards a different, previously unseen target), are of great interest. Based on these observations, in the present work we address the issue of cross-target stance classification with the aid of an existing domain adaptation method based on BERT in combination with adversarial learning and knowledge distillation, and which has been shown to be successful in the related tasks of cross-domain sentiment analysis and cross-domain author profiling. To this end, we envisage a number of experiments to compare cross-target stance classification between target pairs with different degrees of semantic relatedness, and examine how much loss is observed from single-target to cross-target stance classification settings, and attempt to identify possible ways forward.