Reviewerly/exHarmony huggingface dataset:
On the Feasibility of Linking Attack to Google/Apple Exposure Notification Framework

Digital contact-tracing (DCT) applications have been installed on more than 188 M smartphones worldwide as an effective mechanism for monitoring contact with COVID-19 infected individuals. DCT is promising not only for COVID-19, but also for preparing for a possible future large-scale pandemic. The DCT framework is unique in that it combines Bluetooth Low Energy (BLE) communications with cryptography techniques to track exposure on a large scale while protecting user privacy. The objective of this study is to assess the risk of the linking attack to the DCT frameworks; i.e., linking individuals to the identifiers contained in BLE broadcast frames that are supposed to be anonymized. Specifically, we target Google/Apple’s Exposure Notification (GAEN), which is the representative implementation of DCT. Our extensive experiments demonstrate that passively collected rolling proximity identifiers (RPIs) contained in the BLE frames can be linked to face photos which could lead to the exposure of privacy information with high accuracy, including infection status. We also demonstrate that an attacker with a few number of devices can correctly link RPIs and the images of the target person with a success rate of 86% at a rate of 5,000 users per hour. Based on these results, we propose countermeasures to reduce the inherent privacy risk of the GAEN framework.
Learning Heterogeneous Interaction Strengths by Trajectory Prediction with Graph Neural Network

Dynamical systems with interacting agents are universal in nature, commonly modeled by a graph of relationships between their constituents. Recently, various works have been presented to tackle the problem of inferring those relationships from the system trajectories via deep neural networks, but most of the studies assume binary or discrete types of interactions for simplicity. In the real world, the interaction kernels often involve continuous interaction strengths, which cannot be accurately approximated by discrete relations. In this work, we propose the relational attentive inference network (RAIN) to infer continuously weighted interaction graphs without any ground-truth interaction strengths. Our model employs a novel pairwise attention (PA) mechanism to refine the trajectory representations and a graph transformer to extract heterogeneous interaction weights for each pair of agents. We show that our RAIN model with the PA mechanism accurately infers continuous interaction strengths for simulated physical systems in an unsupervised manner. Further, RAIN with PA successfully predicts trajectories from motion capture data with an interpretable interaction graph, demonstrating the virtue of modeling unknown dynamics with continuous weights.
Complete characterization of non-Abelian topological phase transitions and detection of anyon splitting with projected entangled pair states

It is well-known that many topological phase transitions of intrinsic Abelian topological phases are accompanied by condensation and confinement of anyons. However, for non-Abelian topological phases, more intricate phenomena can occur at their phase transitions, because the multiple degenerate degrees of freedom of a non-Abelian anyon can change in different ways after phase transitions. In this paper, we study these new phenomena, including partial condensation, partial deconfinement and especially anyon splitting (a non-Abelian anyon splits into different kinds of the new anyon species) using projected entangled pair states (PEPS). First, we show that anyon splitting can be observed from the topologically degenerate ground states. Next, we construct a set of PEPS describing all possible degrees of freedom of the same non-Abelian anyon. From the overlaps of this set of PEPS of a given non-Abelian anyon with the ground state, we can extract the information of partial condensation. Then, we construct a central object, a matrix defined by the norms and overlaps among the PEPS in that set. The information of partial deconfinement can be extracted from this matrix. In particular, we use it to construct an order parameter which can directly detect anyon splitting. We demonstrate the power of our approach by applying it to a range of non-Abelian topological phase transitions: From $D(S_3)$ quantum double to toric code, from $D(S_3)$ quantum double to $D(Z_3)$ quantum double, from Rep($S_3$) string-net to toric code, and finally from double Ising string-net to toric code.
A Vulnerability Detection Approach for Automated Smart Contract Using Enhanced Machine Learning Techniques

Abstract A typical contract would necessitate the use of an intermediary, make a payment to them, then wait for the record to return. Through a smart contract, though, it is as easy as putting the bitcoin into the vending machine, and the products would be published instantly. Smart contracts are Blockchain-based autonomous software. On Ethereum, a vast number of smart contracts have been deployed. Meanwhile, transaction security vulnerabilities have resulted in significant financial damages and have harmed the contract layer’s ecological integrity on Blockchain. As a result, detecting contract bugs reliably and efficiently is a new yet critical problem. Currently, available identification approaches, such as Oyente and Securify, depend primarily on symbolic execution or analysis. Since symbolic execution necessitates the discovery of all executable routes or the study of dependence diagrams in a contract, these approaches are time-consuming. In this paper, we suggest SmartPol, a machine learning-based method for detecting vulnerabilities in smart contracts. First, we use a data pre-processor to clean up the data before extracting features and classifying them. Second, we present a PSOGSA-based method for data optimisation and function extraction and a TSVM-based approach for semi-supervised learning classification models to identify smart contract vulnerabilities automatically. On EtherScan, SmartPol was used to test 49512 real-world smart contracts. The experimental findings show SmartPol’s reliability and efficacy. When we use PSOGSA for function extraction and TSVM classification sets, SmartPol’s predictive precision and accuracy are over 96%, and the average prediction time is 4 seconds on each smart contract.
Web Development of Automatic Cow Feeding Machine

Abstract: A website is a collection of web pages and related content that is identified by a common domain name and published on at least one web server. Examples of notable websites are Google, Facebook, Amazon, and Wikipedia. All publicly accessible websites collectively constitute the World Wide Web. There are also private websites that can only be accessed on a private network, such as a company's internal website for its employees. Websites are typically dedicated to a particular topic or purpose, such as news, education, commerce, entertainment, or social networking. Hyperlinking between web pages guides the navigation of the site, which often starts with a home page. Users can access websites on a range of devices, including desktops, laptops, tablets, and smartphones. The app used on these devices is called a web browser. With the help of this information, we create a website for providing all of the information to the reader.
QGA-QGCNN:A model of Quantum Gate Circuit Neural Network optimized by Quantum Genetic Algorithm

Abstract Using global optimization algorithm to optimize the initial weights and thresholds of traditional neural network model can effectively address the problems of premature convergence and lower accuracy. However, the shortcomings such as slower convergence speed and poor local search ability still exist. In order to solve these problems, a neural network model QGA-QGCNN using a Quantum Genetic Algorithm (QGA) to optimize Quantum Gate Circuit Neural Network (QGCNN) is proposed in this paper. In QGA-QGCNN, the initial parameters of QGCNN are optimized for the strong global optimization ability and faster convergence speed by using a QGA. In the proposed model, the normalized results of the predicted sample attributes are transformed into quantum states as the input of the network, and the quantum rotation gate is used to rotate the phase and control the reversal of the target qubits. Finally, the output of the network is obtained by entanglement of multiple qubits in the quantum Controlled-NOT (CNOT) gate. When dealing with more complex problems, the QGCNN model based on quantum computing has specific parallel computing capabilities and can give full play to its ability to blur uncertain problems, thereby improving detection performance. We use the authoritative 10% KDD-CUP99 data set in the field of network intrusion detection to conduct simulation experiments on the proposed QGA-QGCNN model. Experimental results show that the proposed intrusion detection model has lower false alarm rate and significantly accuracy compared to conventional attack detection models. And GQCNN optimized by QGA also improves the convergence performance of the model.
Platform Logic: Understanding the Influence of AI on Information Dissemination Mechanisms of Chinese New Media Platforms

The development of technologies in the field of artificial intelligence is redefining the modes of information dissemination. However, it is hard to understand its influence from the perspective of the platform alone. Based on “platform logic” defined by José van Dijck, this article analyzes information dissemination from perspectives of underlayer platform, mid-layer monitoring, and upper-layer application and explores the influence of artificial intelligence and the changes in the patterns of information dissemination of Chinese new media platforms. In this paper, CiteSpace software is used to analyze the literature on artificial intelligence information dissemination on CNKI and the development trend of artificial intelligence information dissemination is summarized and discussed.
Using Artificial Intelligence to Predict Customer Satisfaction with E-Payment Systems during the COVID-19 Pandemic

This study explores the impact of electronic payment systems on Saudi Arabia’s customer satisfaction during the COVID-19 pandemic. Descriptive analytical approach of a sample of 1,025 people living in Saudi Arabia was used to answer the study questions and test its hypotheses. Then, a new hybrid fuzzy inference system (HyFIS) is proposed to predict customer satisfaction during COVID-19 pandemic. The proposed system contemplates customer resistance (CR), access to technology (AT), privacy (PV), costs (CT), and speed of efficiency (SE) as the input variables and customer satisfaction (CS) as the output variable. Various statistical tests are utilized to determine the efficiency of input variables in the obtained data. The statistical tests are multicollinearity tests, reliability and validity, ordinal least square (OLS), fixed effect, and random development. As a result, we can determine each input variable’s direct and indirect impact on the CS. Under OLS, fixed effect, and unexpected effect, the SE, CT, PV, AT, and CR considerably impact EP. The EP has been shown to have substantial positive indirect implications. Under OLS, fixed effect, and random effect, the CT, PV, and CR are found to have a significant positive impact on CS. In addition, the AT has a substantial impact on CS in a fixed effect indirect effect. The results of HyFIS were compared to those of the adaptive network-based fuzzy inference system (ANFIS). The results reveal that HyFIS outperforms ANFIS in predicting CS based on the error criterion.
Measuring the Prevalence of Anti-Social Behavior in Online Communities

With increasing attention to online anti-social behaviors such as personal attacks and bigotry, it is critical to have an accurate accounting of how widespread anti-social behaviors are. In this paper, we empirically measure the prevalence of anti-social behavior in one of the world's most popular online community platforms. We operationalize this goal as measuring the proportion of unmoderated comments in the 97 most popular communities on Reddit that violate eight widely accepted platform norms. To achieve this goal, we contribute a human-AI pipeline for identifying these violations and a bootstrap sampling method to quantify measurement uncertainty. We find that 6.25% (95% Confidence Interval [5.36%, 7.13%]) of all comments in 2016, and 4.28% (95% CI [2.50%, 6.26%]) in 2020-2021, are violations of these norms. Most anti-social behaviors remain unmoderated: moderators only removed one in twenty violating comments in 2016, and one in ten violating comments in 2020. Personal attacks were the most prevalent category of norm violation; pornography and bigotry were the most likely to be moderated, while politically inflammatory comments and misogyny/vulgarity were the least likely to be moderated. This paper offers a method and set of empirical results for tracking these phenomena as both the social practices (e.g., moderation) and technical practices (e.g., design) evolve.
TrojViT: Trojan Insertion in Vision Transformers

Vision Transformers (ViTs) have demonstrated the state-of-the-art performance in various vision-related tasks. The success of ViTs motivates adversaries to perform backdoor attacks on ViTs. Although the vulnerability of traditional CNNs to backdoor attacks is well-known, backdoor attacks on ViTs are seldom-studied. Compared to CNNs capturing pixel-wise local features by convolutions, ViTs extract global context information through patches and attentions. Na\"ively transplanting CNN-specific backdoor attacks to ViTs yields only a low clean data accuracy and a low attack success rate. In this paper, we propose a stealth and practical ViT-specific backdoor attack $TrojViT$. Rather than an area-wise trigger used by CNN-specific backdoor attacks, TrojViT generates a patch-wise trigger designed to build a Trojan composed of some vulnerable bits on the parameters of a ViT stored in DRAM memory through patch salience ranking and attention-target loss. TrojViT further uses minimum-tuned parameter update to reduce the bit number of the Trojan. Once the attacker inserts the Trojan into the ViT model by flipping the vulnerable bits, the ViT model still produces normal inference accuracy with benign inputs. But when the attacker embeds a trigger into an input, the ViT model is forced to classify the input to a predefined target class. We show that flipping only few vulnerable bits identified by TrojViT on a ViT model using the well-known RowHammer can transform the model into a backdoored one. We perform extensive experiments of multiple datasets on various ViT models. TrojViT can classify $99.64\%$ of test images to a target class by flipping $345$ bits on a ViT for ImageNet.Our codes are available at https://github.com/mxzheng/TrojViT
LAB-Net: LAB Color-Space Oriented Lightweight Network for Shadow Removal

This paper focuses on the limitations of current over-parameterized shadow removal models. We present a novel lightweight deep neural network that processes shadow images in the LAB color space. The proposed network termed "LAB-Net", is motivated by the following three observations: First, the LAB color space can well separate the luminance information and color properties. Second, sequentially-stacked convolutional layers fail to take full use of features from different receptive fields. Third, non-shadow regions are important prior knowledge to diminish the drastic color difference between shadow and non-shadow regions. Consequently, we design our LAB-Net by involving a two-branch structure: L and AB branches. Thus the shadow-related luminance information can well be processed in the L branch, while the color property is well retained in the AB branch. In addition, each branch is composed of several Basic Blocks, local spatial attention modules (LSA), and convolutional filters. Each Basic Block consists of multiple parallelized dilated convolutions of divergent dilation rates to receive different receptive fields that are operated with distinct network widths to save model parameters and computational costs. Then, an enhanced channel attention module (ECA) is constructed to aggregate features from different receptive fields for better shadow removal. Finally, the LSA modules are further developed to fully use the prior information in non-shadow regions to cleanse the shadow regions. We perform extensive experiments on the both ISTD and SRD datasets. Experimental results show that our LAB-Net well outperforms state-of-the-art methods. Also, our model's parameters and computational costs are reduced by several orders of magnitude. Our code is available at https://github.com/ngrxmu/LAB-Net.
On Unsupervised Training of Link Grammar Based Language Models

In this short note we explore what is needed for the unsupervised training of graph language models based on link grammars. First, we introduce the ter-mination tags formalism required to build a language model based on a link grammar formalism of Sleator and Temperley [21] and discuss the influence of context on the unsupervised learning of link grammars. Second, we pro-pose a statistical link grammar formalism, allowing for statistical language generation. Third, based on the above formalism, we show that the classical dissertation of Yuret [25] on discovery of linguistic relations using lexical at-traction ignores contextual properties of the language, and thus the approach to unsupervised language learning relying just on bigrams is flawed. This correlates well with the unimpressive results in unsupervised training of graph language models based on bigram approach of Yuret.
Crash Introduction to Python

This chapter kicks off with an example for a simple program in Python. Through this example, we will delve right away into the syntax of this popular programming language. There will be quite a few technical details in this example, which are probably new to anyone with no prior Python experience. The rest of the chapter will then walk the reader briefly yet methodologically through the basic Pythonic syntax. By the end of this chapter, you will be able to write simple programs in Python to solve various problems of a computational nature.
MULTI-GENERATIONAL LEARNING IN THE CONTEXT OF TOY DESIGN

Education is a vital process for everyone, it helps people to think, feel, and behave, which contributes to their success.Education is important not just for children but also for the elderly.The increasing proportion of elderlies globally raises the importance of lifelong learning, specifically elder education.Furthermore, the significance of the grandparent-grandchildren relationship increased due to two factors:The number of grand families -the nuclear family living with the grandparents -increased globally by 7.3% (Beltran, 2019), and the average lifespan of people increased.Therefore, multi-generational learning, the learning derived from old and young generation interaction, takes an important role in families and society as a whole.Past studies show that multi-generational education benefits both age groups in various ways.For children, grandparents offer life wisdom, acceptance, love, and positive effects on a child's well-being, whereas for the elderly, having strong connections with the younger generation allows them to be exposed to new ideas and information, especially technology-related knowledge.Based on the research conducted, results show that over 70% of parents and grandparents find a lack in grandparent-grandchildren interaction, reducing the chances of multi-generational learning, whereas the majority of them find indoor toys a good tool to boost grandparents-grandchildren interaction.According to the criteria stated, a toy was specially designed for elders and children to enhance the training skills, boost the entertainment aspect of existing toys, and at the same time increase multi-generational learning and interaction.Based on the feedback from users, possible improvements were identified and modified for future development.
IDENTIFYING PROTOTYPING COMPETENCIES AND ROLES IN EDUCATION

There have been recent efforts at the Singapore University of Technology and Design (SUTD) in identifying levels of design competency to support teaching and assessment, which has resulted in the development of the Design Competency Assessment (DesCA) framework.One competency area in this framework is prototyping.To understand the nature of prototyping activities, an extensive literature review was conducted along with an empirical study at SUTD to address the following research questions: How do individual instructors define prototyping?What roles do prototypes play in the design process in courses at the university?What prototyping frameworks, methods, and tools are introduced in these courses?Herein we report preliminary results based on 17 interviews with SUTD instructors from different disciplines, who teach, supervise, or are interested in prototyping.Insights from this work include: (1) variations in definitions of what a prototype is across different fields, (2) lack of intentionality in prototyping, and (3) missing associated competencies, including how to select appropriate prototyping methods and tools.Taken together, these insights will allow us to re-examine the way prototyping is taught and how prototyping is represented in the DesCA framework.
PROJECT BASED LEARNING IN THE CONTEXT OF CUTTING-EDGE ROBOTICS COMPETITION

RoboMaster is an annual robotics competition pitting student's cutting edge robot designs in a Multiplayer Online Battle Arena (MOBA) gamified shoot-out battle.The highly technical-advanced competition rules are designed to encourage innovation in robotics automation and intelligent systems while placing robot battles at centre-stage.It manages to display the beauty of engineering technology and popularize robotics to the wider audience by balancing technical challenge and entertainment value.Hong Kong University of Science and Technology (HKUST) sees RoboMaster as a unique platform for promoting STEM to the wider university student body.Through 4 years of team development and student participations, we have homegrown a group of outstanding scientific and technological engineers who have excelled in both academic research and entrepreneurial ventures.In this paper we review the development, growth and success of the 4 years of the HKUST RoboMaster ENTERPRIZE team and evaluate assessable learning outcomes of the competition as a project course.The degree of engineering design complexity and resulting the comprehensive learning outcomes, inspired the launch of a new bachelor programme in which many foundation engineering courses are replaced by year-long cornerstone project courses mirroring the RoboMaster project course.The outcome is a more individualized high-impact programme allowing students to build their engineering studies around technologies of our time.
GROUP IDEATION WITH BRAINWRITING – A COMPARISON OF CO-LOCATED AND DISTANCE COLLABORATION

This article examines the influence of physical proximity (co-location vs. distance) and the medium (paper vs. online) on idea generation.It especially focusses on a popular brainwriting techniquenamely Method 6-3-5.For this, an experiment with altogether = 134 Engineering Design students was conducted.The results show that the mode of interaction generally does not bias the quantity of ideas generated, but that the rate of graphical representations drops slightly.