Reviewerly/exHarmony huggingface dataset:
Practical Non-Interactive Searchable Encryption with Forward and Backward Privacy

In Dynamic Symmetric Searchable Encryption (DSSE), forward privacy ensures that previous search queries cannot be associated with future updates, while backward privacy guarantees that subsequent search queries cannot be associated with deleted documents in the past.In this work, we propose a generic forward and backward-private DSSE scheme, which is, to the best of our knowledge, the first practical and non-interactive Type-II backward-private DSSE scheme not relying on trusted execution environments.To this end, we first introduce a new cryptographic primitive, named Symmetric Revocable Encryption (SRE), and propose a modular construction from some succinct cryptographic primitives.Then we present our DSSE scheme based on the proposed SRE, and instantiate it with lightweight symmetric primitives.At last, we implement our scheme and compare it with the most efficient Type-II backward-private scheme to date (Demertzis et al., NDSS 2020).In a typical network environment, our result shows that the search in our scheme outperforms it by 2 -11× under the same security notion.
User Stories and Natural Language Processing: A Systematic Literature Review

Context: User stories have been widely accepted as artifacts to capture the user requirements in agile software development.They are short pieces of texts in a semi-structured format that express requirements.Natural language processing (NLP) techniques offer a potential advantage in user story applications.Objective: Conduct a systematic literature review to capture the current state-of-the-art of NLP research on user stories.Method: The search strategy is used to obtain relevant papers from SCOPUS, ScienceDirect, IEEE Xplore, ACM Digital Library, SpringerLink, and Google Scholar.Inclusion and exclusion criteria are applied to filter the search results.We also use the forward and backward snowballing techniques to obtain more comprehensive results.Results: The search results identified 718 papers published between January 2009 to December 2020.After applying the inclusion/exclusion criteria and the snowballing technique, we identified 38 primary studies that discuss NLP techniques in user stories.Most studies used NLP techniques to extract aspects of who, what, and why from user stories.The purpose of NLP studies in user stories is broad, ranging from discovering defects, generating software artifacts, identifying the key abstraction of user stories, and tracing links between model and user stories.Conclusion: NLP can help system analysts manage user stories.Implementing NLP in user stories has many opportunities and challenges.Considering the exploration of NLP techniques and rigorous evaluation methods is required to obtain quality research.As with NLP research in general, the ability to understand a sentence's context continues to be a challenge.
Exploring fine-grained syntactic information for aspect-based sentiment classification with dual graph neural networks

The goal of aspect-based sentiment classification (ASC) is to predict the corresponding emotion of a specific target of a sentence. In neural network-based methods for ASC, various sophisticated models such as Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) are widespread. Recently, ongoing research has integrated syntactic structures into graph neural networks (GNN) to deal with ASC tasks. However, these methods are limited due to the noise and inefficient use of information of syntactic dependency trees. This paper proposes a novel GNN based deep learning model to overcome the deficiencies of prior studies. In the proposed model, to exploit the information in the syntactic dependency trees, a novel part-of-speech (POS) guided syntactic dependency graph is constructed for a relational graph attention network (RGAT) to eliminate the noises. Further, a syntactic distance attention-guided layer is designed for a densely connected graph convolutional network (DCGCN), which can fully extract semantic dependency between contextual words. Experiments on three public datasets are carried out to evaluate the effectiveness of the proposed model. Comparing to the baselines, our model, as a best alternative, achieves state-of-arts performance.
Deconv-transformer (DecT): A histopathological image classification model for breast cancer based on color deconvolution and transformer architecture

Histopathological image recognition of breast cancer is an onerous task. Although many deep learning models have achieved good classification results on histopathological image classification tasks, these models do not take full advantage of the staining properties of histopathological images. In this paper, we propose a novel Deconv-Transformer (DecT) network model, which incorporates the color deconvolution in the form of convolution layers. This model uses a self-attention mechanism to match the independent properties of the HED channel information obtained by the color deconvolution. It also uses a method similar to the residual connection to fuse the information of both RGB and HED color space images, which can compensate for the information loss in the process of transferring RGB images to HED images. The training process of the DecT model is divided into two stages so that the parameters of the deconvolution layer can be better adapted to different types of histopathological images. We use the color jitter in the image data augmentation process to reduce the overfitting in the model training process. The DecT model achieves an average accuracy of 93.02% and F1-score of 0.9389 on BreakHis dataset, and an average accuracy of 79.06% and 81.36% on BACH and UC datasets.
Blockchain in banking and finance: A bibliometric review

Blockchain is a disruptive, decentralized, replicable, distributed ledger technology with the potential to change the conventional business landscape in banking and finance. This study consists of a bibliometric review and a content analysis of the academic literature dealing with the antecedents, the applications, and the consequences of the adoption of blockchain-based technologies in these highly interconnected industries. With a final sample of 154 papers published between 2009 and 2021, we map their influential aspects, in terms of trending topic, authors and target journals. We then provide a bibliographic analysis, which consists of co-authorship, cartographic, co-citation and coupling analyses. Finally, we identify the main literature streams and the future research agenda, which may serve as a valid starting point for future research in the field for both scholars and practitioners.
Toward a Photonic Demonstration of Device-Independent Quantum Key Distribution

The security of quantum key distribution (QKD) usually relies on that the users' devices are well characterized according to the security models made in the security proofs. In contrast, device-independent QKD-an entanglement-based protocol-permits the security even without any knowledge of the underlying quantum devices. Despite its beauty in theory, device-independent QKD is elusive to realize with current technologies. Especially in photonic implementations, the requirements for detection efficiency are far beyond the performance of any reported device-independent experiments. In this Letter, we report a proof-of-principle experiment of device-independent QKD based on a photonic setup in the asymptotic limit. On the theoretical side, we enhance the loss tolerance for real device imperfections by combining different approaches, namely, random postselection, noisy preprocessing, and developed numerical methods to estimate the key rate via the von Neumann entropy. On the experimental side, we develop a high-quality polarization-entangled photon source achieving a state-of-the-art (heralded) detection efficiency about 87.5%. Although our experiment does not include random basis switching, the achieved efficiency outperforms previous photonic experiments involving loophole-free Bell tests. Together, we show that the measured quantum correlations are strong enough to ensure a positive key rate under the fiber length up to 220 m. Our photonic platform can generate entangled photons at a high rate and in the telecom wavelength, which is desirable for high-speed generation over long distances. The results present an important step toward a full demonstration of photonic device-independent QKD.
Extendable Multiple Nodes Recurrent Tracking Framework With RTU++

Recently, tracking-by-detection has become a popular paradigm in Multiple-object tracking (MOT) for its concise pipeline. Many current works first associate the detections to form track proposals and then score proposalns by manual functions to select the best. However, long-term tracking information is lost in this way due to detection failure or heavy occlusion. In this paper, the Extendable Multiple Nodes Tracking framework (EMNT) is introduced to model the association. Instead of detections, EMNT creates four basic types of nodes including correct, false, dummy and termination to generally model the tracking procedure. Further, we propose a General Recurrent Tracking Unit (RTU++) to score track proposals by capturing long-term information. In addition, we present an efficient generation method of simulated tracking data to overcome the dilemma of limited available data in MOT. The experiments show that our methods achieve state-of-the-art performance on MOT17, MOT20 and HiEve benchmarks. Meanwhile, RTU++ can be flexibly plugged into other trackers such as MHT, and bring significant improvements. The additional experiments on MOTS20 and CTMC-v1 also demonstrate the generalization ability of RTU++ trained by simulated data in various scenarios.
The Alleviation of Perceptual Blindness During Driving in Urban Areas Guided by Saccades Recommendation

In advanced industrial applications, computational visual attention models (CVAMs) could predict visual attention very similarly to actual human attention allocation. This has been used as a very important component of technology in advanced driver assistance systems (ADAS). Given that the biological inspiration of the driving-related CVAMs could be obtained from skilled drivers in complex driving conditions, in which the driver’s attention is constantly directed at various salient and informative visual stimuli by alternating the eye fixations via saccades to drive safely, this paper proposes a saccade recommendation strategy to enhance the driving safety under urban road environment, particularly when the driver’s vision is often impaired by the visual crowding. The altered and directed saccades are collected and optimized by extracting four innate features from human dynamic vision. A neural network isdesigned to classify preferable saccades to reduce perceptual blindness due to visual crowding under urban scenes. A state-of-the-art CVAM is firstly adopted to localize the predicted eye fixation locations (EFLs) in driving video clips. Besides, human subjects’ gaze at the recommended EFLs is measured via an eye-tracker. The time delays between the predicted EFLs and drivers’ EFLs are analyzed under different driving conditions, followed by the time delays between the predicted EFLs and the driver’s hand control. The visually safe margin is then measured by mediating the driving speed and the total delay. Experimental results demonstrate that the recommended saccades can effectively reduce the amount of perceptual blindness, which is known to be of help to further improve road driving safety.
A hybrid algorithm based on grey wolf optimizer and differential evolution for UAV path planning

Autonomous navigation is significant to UAVs, especially in disaster scenarios. A Hybrid GWO and Differential Evolution (HGWODE) algorithm is developed to solve UAV path planning. In HGWODE, GWO and DE algorithms cooperate well to balance exploitation and exploration. The position-updated equation of GWO is improved, which makes alpha, beta, and delta wolves search around the alpha wolf, and omega wolves search around the top three wolves to boost the exploitation. A rank-based mutation strategy is implemented in DE algorithm to promote exploitation while maintaining the exploration capacity. We test HGWODE on CEC 2014 and UAV path planning. The proposed HGWODE is superior to GWO and several GWO variants when solving test functions and UAV path planning models. UAV path from HGWODE is smoother and shorter than its rivals.
Understanding the user satisfaction and loyalty of customer service chatbots

The artificial intelligence (AI) chatbot is emerging as a significant corporate customer-facing application, potentially increasin customer service efficiency while reducing costs. However, little work has sought to assess the quality of service they provide consumers. This study applies the e-service quality by incorporating conversational AI quality to predict users' satisfaction and loyalty to customer service chatbots. The proposed model was empirically evaluated using survey data collected from 219 users responding about their perceptions of customer service chatbots. The findings indicate that AI chatbot service recovery quality and AI chatbot conversational quality significantly influence user satisfaction. On the other hand, core AI chatbot service quality and satisfaction significantly influenced chatbot user loyalty. This study contributes to researchers and practitioners by proposing and evaluating a more comprehensive chatbot e-service quality that combines both fundamental (core service and service recovery qualities) and human-like (conversational quality) aspects of e-service. The results are of value in devising future AI chatbot services and related strategies.
Deep Hierarchical Semantic Segmentation

Humans are able to recognize structured relations in observation, allowing us to decompose complex scenes into simpler parts and abstract the visual world in multiple levels. However, such hierarchical reasoning ability of human perception remains largely unexplored in current literature of semantic segmentation. Existing work is often aware of flatten labels and predicts target classes exclusively for each pixel. In this paper, we instead address hierarchical semantic segmentation (HSS), which aims at structured, pixel-wise description of visual observation in terms of a class hierarchy. We devise H <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SSN</inf> , a general HSS framework that tackles two critical issues in this task: i) how to efficiently adapt existing hierarchy-agnostic segmentation networks to the HSS setting, and ii) how to leverage the hierarchy information to regularize HSS network learning. To address i), HSSN directly casts HSS as a pixel-wise multi-label classification task, only bringing minimal architecture change to current segmentation models. To solve ii), HSSN first explores inherent properties of the hierarchy as a training objective, which enforces segmentation predictions to obey the hierarchy structure. Further, with hierarchy-induced margin constraints, HSSNreshapes the pixel embedding space, so as to generate well-structured pixel representations and improve segmentation eventually. We conduct experiments on four semantic segmentation datasets (i.e., Mapillary Vistas 2.0, City-scapes, LIP, and PASCAL-Person-Part), with different class hierarchies, segmentation network architectures and backbones, showing the generalization and superiority of HSSN.
Knowledge Distillation with the Reused Teacher Classifier

Knowledge distillation aims to compress a powerful yet cumbersome teacher model into a lightweight student model without much sacrifice of performance. For this purpose, various approaches have been proposed over the past few years, generally with elaborately designed knowledge rep-resentations, which in turn increase the difficulty of model development and interpretation. In contrast, we empirically show that a simple knowledge distillation technique is enough to significantly narrow down the teacher-student performance gap. We directly reuse the discriminative classifier from the pre-trained teacher model for student inference and train a student encoder through feature alignment with a single ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> loss. In this way, the student model is able to achieve exactly the same performance as the teacher model provided that their extracted features are perfectly aligned. An additional projector is developed to help the student encoder match with the teacher classifier, which renders our technique applicable to various teacher and student architectures. Extensive experiments demonstrate that our technique achieves state-of-the-art results at the modest cost of compression ratio due to the added projector.
Towards Robust Vision Transformer

Recent advances on Vision Transformer (ViT) and its improved variants have shown that self-attention-based networks surpass traditional Convolutional Neural Networks (CNNs) in most vision tasks. However, existing ViTs focus on the standard accuracy and computation cost, lacking the investigation of the intrinsic influence on model robustness and generalization. In this work, we conduct systematic evaluation on components of ViTs in terms of their impact on robustness to adversarial examples, common corruptions and distribution shifts. We find some components can be harmful to robustness. By leveraging robust components as building blocks of ViTs, we propose Robust Vision Transformer (RVT), which is a new vision transformer and has superior performance with strong robustness. Inspired by the findings during the evaluation, we further propose two new plug-and-play techniques called position-aware attention scaling and patch-wise augmentation to augment our RVT, which we abbreviate as RVT*. The experimental results of RVT on ImageNet and six robustness benchmarks demonstrate its advanced robustness and generalization ability compared with previous ViTs and state-of-the-art CNNs. Furthermore, RVT-S* achieves Top-1 rank on multiple robustness leaderboards including ImageNet-C, ImageNet-Sketch and ImageNet-R.
LineVD

Current machine-learning based software vulnerability detection methods are primarily conducted at the function-level. However, a key limitation of these methods is that they do not indicate the specific lines of code contributing to vulnerabilities. This limits the ability of developers to efficiently inspect and interpret the predictions from a learnt model, which is crucial for integrating machine-learning based tools into the software development work-flow. Graph-based models have shown promising performance in function-level vulnerability detection, but their capability for statement-level vulnerability detection has not been extensively explored. While interpreting function-level predictions through explainable AI is one promising direction, we herein consider the statement-level software vulnerability detection task from a fully supervised learning perspective. We propose a novel deep learning framework, LineVD, which formulates statement-level vulnerability detection as a node classification task. LineVD leverages control and data dependencies between statements using graph neural networks, and a transformer-based model to encode the raw source code tokens. In particular, by addressing the conflicting outputs between function-level and statement-level information, LineVD significantly improve the prediction performance without vulnerability status for function code. We have conducted extensive experiments against a large-scale collection of real-world C/C++ vulnerabilities obtained from multiple real-world projects, and demonstrate an increase of 105% in F1-score over the current state-of-the-art.
Multi-level Feature Learning for Contrastive Multi-view Clustering

Multi-view clustering can explore common semantics from multiple views and has attracted increasing attention. However, existing works punish multiple objectives in the same feature space, where they ignore the conflict between learning consistent common semantics and reconstructing inconsistent view-private information. In this paper, we propose a new framework of multi-level feature learning for contrastive multi-view clustering to address the aforementioned issue. Our method learns different levels of features from the raw features, including low-level features, high-level features, and semantic labels/features in a fusion-free manner, so that it can effectively achieve the reconstruction objective and the consistency objectives in different feature spaces. Specifically, the reconstruction objective is conducted on the low-level features. Two consistency objectives based on contrastive learning are conducted on the high-level features and the semantic labels, respectively. They make the high-level features effectively explore the common semantics and the semantic labels achieve the multi-view clustering. As a result, the proposed framework can reduce the adverse influence of view-private information. Extensive experiments on public datasets demonstrate that our method achieves state-of-the-art clustering effectiveness.
Data collection and quality challenges in deep learning: a data-centric AI perspective

Data-centric AI is at the center of a fundamental shift in software engineering where machine learning becomes the new software, powered by big data and computing infrastructure. Here, software engineering needs to be re-thought where data become a first-class citizen on par with code. One striking observation is that a significant portion of the machine learning process is spent on data preparation. Without good data, even the best machine learning algorithms cannot perform well. As a result, data-centric AI practices are now becoming mainstream. Unfortunately, many datasets in the real world are small, dirty, biased, and even poisoned. In this survey, we study the research landscape for data collection and data quality primarily for deep learning applications. Data collection is important because there is lesser need for feature engineering for recent deep learning approaches, but instead more need for large amounts of data. For data quality, we study data validation, cleaning, and integration techniques. Even if the data cannot be fully cleaned, we can still cope with imperfect data during model training using robust model training techniques. In addition, while bias and fairness have been less studied in traditional data management research, these issues become essential topics in modern machine learning applications. We thus study fairness measures and unfairness mitigation techniques that can be applied before, during, or after model training. We believe that the data management community is well poised to solve these problems.