Reviewerly/exHarmony huggingface dataset:
MPass: Bypassing Learning-based Static Malware Detectors

Machine learning (ML) based static malware detectors are widely deployed, but vulnerable to adversarial attacks. Unlike images or texts, tiny modifications to malware samples would significantly compromise their functionality. Consequently, existing attacks against images or texts will be significantly restricted when being deployed on malware detectors. In this work, we propose a hard-label black-box attack MPass against ML-based detectors. MPass employs a problem-space explainability method to locate critical positions of malware, applies adversarial modifications to such positions, and utilizes a runtime recovery technique to preserve the functionality. Experiments show MPass outperforms existing solutions and bypasses both state-of-the-art offline models and commercial ML-based antivirus products.
D$^{2}$PSG: Multi-Party Dialogue Discourse Parsing as Sequence Generation

Conversational discourse analysis aims to extract the interactions between dialogue turns, which is crucial for modeling complex multi-party dialogues. As the benchmarks are still limited in size and human annotations are costly, the current standard approaches apply pretrained language models, but they still require randomly initialized classifiers to make predictions. These classifiers usually require massive data to work smoothly with the pretrained encoder, causing severe data hunger issue. We propose two convenient strategies to formulate this task as a sequence generation problem, where classifier decisions are carefully converted into sequence of tokens. We then adopt a pretrained T5 1 model to solve this task so that no parameters are randomly initialized. We also leverage the descriptions of the discourse relations to help model understand their meanings. Experiments on two popular benchmarks show that our approach outperforms previous state-of-the-art models by a large margin, and it is also more robust in zero-shot and few-shot settings.
A Fast and High-Precision Ellipse Detection Method Based on the Candy’s Theorem

Ellipse detection is one of the important parts of intelligent manufacturing, and the efficient detection of elliptical contours corresponding to hole parts is the prerequisite before performing practical applications. Aiming at enhancing the efficiency in ellipse detection, a fast and high-precision ellipse detection method based on the Candy’s theorem is proposed, in which the constrained simple polygon constraint and the Candy’s theorem constraint are used to improve the efficiency of ellipse detection. The theory of the proposed ellipse detection method is described in detail. The comparison experiments with six popular ellipse detection methods are performed based on three public datasets, which are to verify the ellipse detection ability of the proposed method in improving detection speed and precision. And a small hole detection experiment is performed with the constructed experimental setup, which is to verify the effectiveness of the proposed method to detect small holes in practical applications. The proposed method can provide referenced methods and theories in the field of ellipse detection.
UW-CycleGAN: Model-Driven CycleGAN for Underwater Image Restoration

The formation of underwater images is a complex physical process that often suffers from various degradation factors, such as blurriness, low contrast, and color casts, which pose challenges for underwater object detection and recognition tasks. Because of the absence of reference images, learning-based methods that rely on unpaired images have been employed to enhance the underwater images. However, these methods may lose their effectiveness in real-world complex underwater environments. In this paper, we propose a model-driven cycle-consistent generative adversarial network (CycleGAN) model, which is inspired by the underwater image formation model to estimate the background light, transmission map, scene depth, and attenuation coefficient directly. Comprehensive experiments have demonstrated that our approach surpasses the compared underwater image restoration methods in both qualitative and quantitative aspects, providing restored images with satisfactory color saturation and brightness. We also conduct experiments on underwater object detection to illustrate the effectiveness of our CycleGAN in improving the detection accuracy. All our source codes and data are available at https://github.com/Duanlab123/UW-CycleGAN.
Marx and Technology

The revolutionary changes in digital technologies that ushered in the postdigital context originate with the industrial machinery Karl Marx and his collaborator and comrade Friedrich Engels observed and analysed in the mid to late 1800s. Their approach sees technology as neither neutral nor deterministic, but as one site of contestation and struggle among others in the overall totality of capital. The matter at hand is the class whose interests guide the production, distribution, and consumption of technologies.
Postdigital Education

Postdigital education involves a critical perspective on the integration of digital technology in education, and encompasses considerations of technology, pedagogy, biology, politics, economics, the environment and, crucially, social justice. It positions all educational situations as emerging from complex combinations of digital, social, and material activity. This conception supports educators to take up critical perspectives on the datafication of educational activity and to question the implications of data-driven practices for diverse groups of people, paying particular attention to those who are marginalised. This implies a need for awareness of ethical considerations, labour conditions, corporate interests, environmental harms and other political-economic concerns. It also implies a need for collective, transdisciplinary work in shaping education towards shared values such as inclusion, care, and social justice.
Using Structural Equation Modeling to Explore Patients’ and Healthcare Professionals’ Expectations and Attitudes Towards Socially Assistive Humanoid Robots in Nursing and Care Routine

Abstract Healthcare systems around the world are currently witnessing various challenges, including population aging and workforce shortages. As a result, the existing, overworked staff are struggling to meet the ever-increasing demands and provide the desired quality of care. One of the promising technological solutions that could complement the human workforce and alleviate some of their workload, are socially assistive humanoid robots. However, despite their potential, the implementation of socially assistive humanoid robots is often challenging due to low acceptance among key stakeholders, namely, patients and healthcare professionals. Hence, the present study first investigated the extent to which these stakeholders accept the use of socially assistive humanoid robots in nursing and care routine, and second, explored the characteristics that contribute to higher/lower acceptance within these groups, with a particular emphasis on demographic variables, technology expectations, ethical acceptability, and negative attitudes. In study 1, conducted on a sample of 490 healthcare professionals, the results of structural equation modeling showed that acceptance is driven primarily by aspects of ethical acceptability, although education and technology expectations also exert an indirect effect. In study 2, conducted on a sample of 371 patients, expectations regarding capabilities and attitudes towards the social influence of robots emerged as important predictors of acceptance. Moreover, although acceptance rates differed between tasks, both studies show a relatively high acceptance of socially assistive humanoid robots. Despite certain limitations, the study findings provide essential knowledge that enhances our understanding of stakeholders’ perceptions and acceptance of socially assistive humanoid robots in hospital environments, and may guide their deployment.
Modernization of Databases in the Cloud Era: Building Databases that Run Like Legos

Utilizing cloud for common and critical computing infrastructures has already become the norm across the board. The rapid evolvement of the underlying cloud infrastructure and the revolutionary development of AI present both challenges and opportunities for building new database architectures and systems. It is crucial to modernize database systems in the cloud era, so that next generation cloud native databases may run like legos-they are adaptive, flexible, reliable, and smart towards dynamic workloads and varying requirements. That said, we observe four critical trends and requirements for the modernization of cloud databases: embracing cloud-native architecture, full integration with cloud platform and orchestration, co-design for data fabric, and moving towards being AI augmented. Modernizing database systems by adopting these critical trends and addressing key challenges associated with them provide ample opportunities for data management communities from both academia and industry to explore. We will provide an in-depth case study of how we modernize PolarDB with respect to embracing these four trends in the cloud era. Our ultimate goal is to build databases that run just like playing with legos, so that a database system fits for rich and dynamic workloads and requirements in a self-adaptive, performant, easy-/intuitive-to use, reliable, and intelligent manner.
Portals: A Showcase of Multi-Dataflow Stateful Serverless

Serverless applications spanning the cloud and edge require flexible programming frameworks for expressing compositions across the different levels of deployment. Another critical aspect for applications with state is failure resilience beyond the scope of a single dataflow graph that is the current standard in data streaming systems. This paper presents Portals, an interactive, stateful dataflow composition framework with strong end-to-end guarantees. Portals enables event-driven, resilient applications that span across dataflow graphs and serverless deployments. The demonstration exhibits three scenarios in our multi-dataflow streaming-based system: dynamically composing a stateful serverless application; an interactive cloud and edge serverless application; and a Portals browser playground.
MINT: Detecting Fraudulent Behaviors from Time-Series Relational Data

The e-commerce platforms, such as Shopee, have accumulated a huge volume of time-series relational data, which contains useful information on differentiating fraud users from benign users. Existing fraud behavior detection approaches typically model the time-series data with a vanilla Recurrent Neural Network (RNN) or combine the whole sequence as a single intention without considering the temporal behavioral patterns, row-level interactions, and different view intentions. In this paper, we present MINT, a M ultiview row- IN teractive T ime-aware framework to detect fraudulent behaviors from time-series structured data. The key idea of MINT is to build a time-aware behavior graph for each user's time-series relational data with each row represented as an action node. We utilize the user's temporal information to construct three different graph convolutional matrices for hierarchically learning the user's intentions from different views, that is, short-term, medium-term, and long-term intentions. To capture more meaningful row-level interactions and alleviate the over-smoothing issue in a vanilla time-aware behavior graph, we propose a novel gated neighbor interaction mechanism to calibrate the aggregated information by each action node. Since the receptive fields of the three graph convolutional layers are designed to grow nearly exponentially, our MINT requires many fewer layers than traditional deep graph neural networks (GNNs) to capture multi-hop neighboring information, and avoids recurrent feedforward propagation, thus leading to higher training efficiency and scalability. Our extensive experiments on the large-scale e-commerce datasets from Shopee with up to 4.6 billion records and a public dataset from Amazon show that MINT achieves superior performance over 10 state-of-the-art models and provides better interpretability and scalability.
MagicScaler: Uncertainty-Aware, Predictive Autoscaling

Predictive autoscaling is a key enabler for optimizing cloud resource allocation in Alibaba Cloud's computing platforms, which dynamically adjust the Elastic Compute Service (ECS) instances based on predicted user demands to ensure Quality of Service (QoS). However, user demands in the cloud are often highly complex, with high uncertainty and scale-sensitive temporal dependencies, thus posing great challenges for accurate prediction of future demands. These in turn make autoscaling challenging---autoscaling needs to properly account for demand uncertainty while maintaining a reasonable trade-off between two contradictory factors, i.e., low instance running costs vs. low QoS violation risks. To address the above challenges, we propose a novel predictive autoscaling framework MagicScaler , consisting of a Multi-scale attentive Gaussian process based predictor and an uncertainty-aware scaler. First, the predictor carefully bridges the best of two successful prediction methodologies---multi-scale attention mechanisms, which are good at capturing complex, multi-scale features, and stochastic process regression, which can quantify prediction uncertainty, thus achieving accurate demand prediction with quantified uncertainty. Second, the scaler takes the quantified future demand uncertainty into a judiciously designed loss function with stochastic constraints, enabling flexible trade-off between running costs and QoS violation risks. Extensive experiments on three clusters of Alibaba Cloud in different Chinese cities demonstrate the effectiveness and efficiency of MagicScaler , which outperforms other commonly adopted scalers, thus justifying our design choices.
The Story of GraphLab - From Scaling Machine Learning to Shaping Graph Systems Research (VLDB 2023 Test-of-Time Award Talk)

The GraphLab project spanned almost a decade and had profound academic and industrial impact on large-scale machine learning and graph processing systems. There were numerous papers written describing the innovations in GraphLab including the original vertex-centric [8] and edge-centric [3] programming abstractions, high-performance asynchronous execution engines [9], out-of-core graph computation [6], tabular graph-systems [4], and even new statistical inference algorithms [2] enabled by the GraphLab project. This work became the basis of multiple PhD theses [1, 5, 7]. The GraphLab open-source project had broad academic and industrial adoption and ultimately lead to the launch of Turi. In this talk, we tell the story of GraphLab, how it began and the key ideas behind it. We will focus on the approach to achieving scalable asynchronous systems in machine learning. During our talk, we will explore the impact that GraphLab has had on the development of graph processing systems, graph databases, and AI/ML; Additionally, we will share our insights and opinions into where we see the future of these fields heading. In the process, we highlight some of the lessons we learned and provide guidance for future students.
Solving Hard Variants of Database Schema Matching on Quantum Computers

With quantum computers now available as cloud services, there is a global quest for applications where a quantum advantage can be shown. Naturally, data management is a candidate domain. Workable solutions require the design of hybrid quantum algorithms, where a quantum computing unit (a QPU) and classical computing (via CPUs) cooperate towards solving a problem. This demo illustrates such an end-to-end solution targeting NP-hard variants of database schema matching. Our demo is intended to be educational (and hopefully inspiring), allowing participants to explore the critical design decisions, such as the handover between phases of QPU- and CPU-based computation. It will also allow participants to experience hands-on - through playful interaction - how easily problem sizes exceed the limitations of today's QPUs.
Risks of Digital Exclusion

Teaching in primary schools means taking the heterogeneity of students’ backgrounds, skills, and capacities into account and offering them a commensurately wide variety of learning opportunities. This is particularly the case when it comes to digital-based instruction, which often entails individualized learning and greater responsibility of learners for their own learning processes. To enable participation and reduce risks of exclusion – for students with disabilities and in the context of the digital divide – teachers’ learning support is of great importance. Despite the potential of teacher support for learning with digital media, few studies have investigated this topic. This paper introduces a research study using videography to analyze the learning support given by preservice teachers during active media work. The findings indicate that teachers tend to provide mainly direct instruction, and significantly less diagnostic support aimed at fostering the learning process. The results do not confirm the idea that digital teaching formats provide technical rather than content support. Furthermore, they show that digital learning formats enable participation by all the students in the class. These results are discussed with a view to risks of exclusion and implications for teacher professionalization.
An Algorithmic Approach towards Remote Sensing Imagery Data Restoration Using Guided Filters in Real-Time Applications

The images captured from SAR sensors are inherently weakened by speckle noise. The SAR image processing community targeted this problem with many feature-based filters. Since SAR images are low-contrast images, edge retention is the most crucial aspect to consider. This helps in the efficient retrieval of information. This paper provides a two-step edge-preserving homomorphic SAR image despeckling technique that implements a guided filter as the first step, and a modified method of noise thresholding using the bivariate shrinkage rule and canny edge operator in the Discrete Orthonormal Stockwell Transform (DOST) domain as the second step. The use of a canny edge operator improves overall edge preservation after despeckling. The use of noise thresholding delivers the highest level of speckle reduction in the DOST domain. The detected edges are added to the residual part obtained after removing the noise to produce more informative content. According to several qualitative and quantitative criteria, the suggested approach is compared to some of the newest despeckling methods. The execution time of the proposed method is around 7.2679 seconds. Upon conducting qualitative and quantitative analysis, it has been determined that the proposed method surpasses all other despeckling methods that were compared.
A pragmatic and discourse analysis of hate words on social media

Abstract This paper studies some pragmatic and discursive properties of hate words employed in the comment chains of two Italian right-wing politicians’ social media accounts. The analysis focuses on hate speech directed towards two ethnic groups – African migrants and the Chinese – and an individual, the former minister of Agriculture Teresa Bellanova. Hate words are divided into two macrocategories: slurs and insulting epithets. Slurs are expressions that are consistently associated with derogatory attitudes against a group of people based on their origin/descent; insulting epithets are either offensive terms that do not attack specific identity traits or neutral words that, in certain contexts, can be offensive. Data indicate that the use of hate words is guided by pragmatic factors and discursive elements, and it changes according to the individual(s) or the groups being attacked. Hate speech on social media occurs mainly through insulting epithets, thus allowing the authors to avoid moderation and any responsibility for their utterance. The results support the idea that hate speech is a complex speech act that aims not only at derogating or expressing negative emotions but works within the framework of racist discourses as a means of creating and reinforcing political polarisation and in-group values.
Transmission Line Insulator Defect Detection Based on Swin Transformer and Context

Insulators are important components of power transmission lines. Once a failure occurs, it may cause a large-scale blackout and other hidden dangers. Due to the large image size and complex background, detecting small defect objects is a challenge. We make improvements based on the two-stage network Faster R-convolutional neural networks (CNN). First, we use a hierarchical Swin Transformer with shifted windows as the feature extraction network, instead of ResNet, to extract more discriminative features, and then design the deformable receptive field block to encode global and local context information, which is utilized to capture key clues for detecting objects in complex backgrounds. Finally, the filling data augmentation method is proposed for the problem of insufficient defects and more images of insulator defects under different backgrounds are added to the training set to improve the robustness of the model. As a result, the recall increases from 89.5% to 92.1%, and the average precision increases from 81.0% to 87.1%. To further prove the superiority of the proposed algorithm, we also tested the model on the public data set Pascal visual object classes (VOC), which also yields outstanding results.
Mapping global water-surface photovoltaics with satellite images

The recent boom in solar photovoltaics has intensified global competition for land use. Water-surface photovoltaics (WSPV) has also increased globally as an efficient alternative to land-based photovoltaics. Determining the spatio-temporally distribution of WSPVs is essential for estimating renewable energy capacity, evaluating the associated socio-environmental impacts, and managing and planning WSPV projects. However, a comprehensive inventory of WSPV locations and extent on the global scale is still lacking. To address these issues, we developed a workflow for identifying WSPVs using time-series optical satellite images and generated the first global-scale WSPV inventory map. The results demonstrate that WSPV has experienced rapid expansion with an average annual growth rate of 20.6% between 2019 and 2021. By 2021, a total of 1027 WSPV projects in 27 countries were detected, covering a water area of 272.0 km2. Asia dominates the fast-emerging WSPV development, with eleven countries that rank in the top fourteen for WSPV are located in Asia. The development of WSPV in China far exceeds that of all other countries, accounting for 95.1% of the total global WSPV area. The global installed capacity of WSPVs is estimated at 12.9 GWp, >20% of which are floating WSPVs (2.6 GWp). The results advanced the understanding of the global spatial-temporal dynamics of the recent WSPV development and will be useful for informing future global and regional renewable planning and management for policymakers and project stakeholders. It also provides a basis for further studies on assessing the potential impact of WSPVs.