Reviewerly/exHarmony huggingface dataset:
Retrieve-and-Edit Domain Adaptation for End2End Aspect Based Sentiment Analysis

End-to-end aspect based sentiment analysis (E2E-ABSA) aims to jointly extract aspect terms and predict aspect-level sentiment for opinion reviews. Though supervised methods show effectiveness for E2E-ABSA tasks, the annotation cost is extremely high due to the necessity of fine-grained labels. Recent attempts alleviate this problem using the domain adaptation technique to transfer the word-level common knowledge across domains. However, the biggest issue in domain adaptation, i.e., how to transfer the domain-specific words like <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pizza</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">delicious</i> in the source “Restaurant” to the target “Laptop” domain, has not been resolved. In this paper, we propose a novel domain adaptation method to address this issue by enhancing the transferability of domain-specific source words in a retrieve-and-edit way. Specifically, for all source words, we first retrieve the transferable prototypes from unlabeled target data via their syntactic and semantic roles. We then edit the source words to enhance their transferability by absorbing the knowledge carried in prototypes. Finally, we design an end-to-end framework to jointly accomplish cross-domain aspect term extraction and aspect-level sentiment classification. We conduct extensive experiments on four real-world datasets. The results prove that, by introducing transferable prototypes, our method significantly outperforms the state-of-the-art methods, achieving an absolute 3.95% F1 increase over the best baseline.
Extraction of Prediction Rules of Code Smell using Decision Tree Algorithm

Code Smell is a set of information of a source code that indicates any serious problem in the software. To detect the code smell prediction rule, we have applied a Decision tree algorithm. For this objective, we have taken two code smell datasets namely Blob-class and Data-class from Fontana et al. These datasets were prepared from 74 open-source systems. To calculate the performance measurement of the decision tree model on each dataset, we applied 5-fold cross-validation that divides datasets into training set and testing set and further splitting the training set into two part, one part is used for training and second part is used for validation. We have applied grid search for hyper-parameter tuning followed by extraction of decision rules to detect code smell instances. We have achieved the highest accuracy of 97.62% in both class predictions.
Tracking Paths

We consider several problems dealing with tracking of moving objects (e.g., vehicles) in networks. Given a graph G=(V,E) and two vertices s,t∈V, a set of vertices T⊆V is a tracking set for G (w.r.t. paths from s to t), if one can distinguish between any two paths from s to t by the order in which the vertices of T appear (or do not appear) in them. We prove that the problem of finding a minimum-cardinality tracking set w.r.t. shortest paths from s to t is NP-hard and even APX-hard. On the other hand, for the common case where G is planar, we present a 2-approximation algorithm for this problem. We also describe how to preprocess G (and its tracking set w.r.t. shortest paths from s to t), so that given a sequence of visited trackers, one can reconstruct the traversed path P in O(|P|) time. Moreover, we consider the following related problem: Given a graph G, two vertices s and t, and a set of forbidden vertices F⊆V−{s,t}, find a minimum-cardinality set of trackers V∗⊂V, such that a shortest path P from s to t passes through a forbidden vertex if and only if it passes through a vertex of V∗. We present a polynomial-time (exact) algorithm for this problem.
Being a sociable designer: reimagining the role of designers in social innovation

(2022). Being a sociable designer: reimagining the role of designers in social innovation. CoDesign: Vol. 18, Special issue: Designing for Reimagined Communities. Guest Editors: Lynn-Sayers McHattie and Brian Dixon, pp. 135-150.
Scalable Extended Reality: A Future Research Agenda

Extensive research has outlined the potential of augmented, mixed, and virtual reality applications. However, little attention has been paid to scalability enhancements fostering practical adoption. In this paper, we introduce the concept of scalable extended reality (XRS), i.e., spaces scaling between different displays and degrees of virtuality that can be entered by multiple, possibly distributed users. The development of such XRS spaces concerns several research fields. To provide bidirectional interaction and maintain consistency with the real environment, virtual reconstructions of physical scenes need to be segmented semantically and adapted dynamically. Moreover, scalable interaction techniques for selection, manipulation, and navigation as well as a world-stabilized rendering of 2D annotations in 3D space are needed to let users intuitively switch between handheld and head-mounted displays. Collaborative settings should further integrate access control and awareness cues indicating the collaborators’ locations and actions. While many of these topics were investigated by previous research, very few have considered their integration to enhance scalability. Addressing this gap, we review related previous research, list current barriers to the development of XRS spaces, and highlight dependencies between them.
Black Bird Attack: A Vital Threat to Blockchain Technology

Blockchain technology has made significant success, but it is also vulnerable for cyberattacks. As a particular form of attacks, Black Bird 51% hash rate attack has not been studied in depth, and it deserves particular attention. It is important to analyze its core construction, origin, and perniciousness. Black Bird Attack (BBA) could jeopardize a decentralized system more than centralized one, because centralized system has higher priority to control the dataflow than its users, whereas decentralized systems are vulnerable to attack, since everyone has almost the same privilege. Black Bird Attack can potentially destroy the fairness of blockchain networks. With this in mind, we have built a blockchain network simulation to examine this form of attacks, as presented in a detailed case study. This case study demonstrates Black Bird Attack is completely possible. Since Black Bird 51% hash rate attack is particularly dangerous in the post-quantum computing era, an intensive research agenda on this topic is in urgent need.
Protecting Regression Models With Personalized Local Differential Privacy

The equation-solving model extraction attack is an intuitively simple but devastating attack to steal confidential information of regression models through a sufficient number of queries. Complete mitigation is difficult. Thus, the development of countermeasures is focused on degrading the attack effectiveness as much as possible without losing the model utilities. We investigate a novel personalized local differential privacy mechanism to defend against the attack. We obfuscate the model by adding high-dimensional Gaussian noise on model coefficients. Our solution can adaptively produce the noise to protect the model on the fly. We thoroughly evaluate the performance of our mechanisms using real-world datasets. The experiment shows that the proposed scheme outperforms the existing differential-privacy-enabled solution, i.e., 4 times more queries are required to achieve the same attack result. We also plan to publish the relevant codes to the community for further research.
A Privacy-Preserving Framework for Endorsement Process in Hyperledger Fabric

Hyperledger Fabric is a permissioned flavor of blockchain which offers various privacy features to its users. It stands out from other permissioned framework by allowing only a subset of peers called endorsing peers to execute the chaincode. This subset is given in the form of logical expression called endorsement policy. During the validation phase, every peer checks the transaction for desired endorser signatures against the endorsement policy. There are various attacks and privacy risks involved in disclosing the endorser signature and endorsement policy. In this paper we provide a framework for a secure endorsement system in Hyperledger Fabric. An efficient linkable threshold ring signature scheme is constructed, with a ZK-SNARK set membership proof, to secure the endorsements made by the endorsers. The endorsement policy is obfuscated by employing a recent construction of universal RSA accumulators and the individual membership proofs are batched through a non-interactive proof of exponentiation technique for efficient validation. We provide formal security definitions and proofs for the proposed framework.
Detection of AI-Generated Synthetic Faces

Abstract In recent years there have been astonishing advances in AI-based synthetic media generation. Thanks to deep learning methods it is now possible to generate visual data with a high level of realism. This is especially true for human faces. Advanced deep learning tools allow one to easily change some specific attributes of a real face or even create brand new identities. Although this opens up a large number of new opportunities, just think of the entertainment industry, it also undermines the trustworthiness of media content and supports the spread of fake identities over the internet. In this context, there is a fundamental need to develop robust and automatic tools capable of distinguishing synthetic faces from real ones. The scientific community is making a huge research effort in this field, proposing several interesting approaches. However, a universal detector is yet to come. Fundamentally, the research in this field is like a cat and mouse game, with new detectors that are designed to deal with powerful synthetic face generators, while the latter keep improving to produce more and more realistic images. In this chapter we will present the most effective techniques proposed in the literature for the detection of synthetic faces. We will analyze their rationale, present real-world application scenarios , and compare different approaches in terms of accuracy and generalization ability.
GraFeHTy: Graph Neural Network using Federated Learning for Human Activity Recognition

Human Activity Recognition (HAR) from sensor measurements is still challenging due to noisy or lack of la-belled examples and issues concerning data privacy. Training a traditional centralized machine learning (ML) model for HAR is constrained by infrastructure availability, network connectivity, latency issues etc. Issues regarding labels of user measurements can be tackled by using semi-supervised learning while issues regarding privacy concerns can be addressed by increasingly popular Federated Learning (FL). In this work, we propose a novel algorithm GraFeHTy, a Graph Convolution Network (GCN) trained in a federated setting to alleviate these key obstructions for HAR. We construct a similarity graph from sensor measurements for each user and apply a GCN to perform semi-supervised classification of human activities by leveraging inter-relatedness and closeness of activities. The weights of the GCN are trained using federated learning where each user performs gradient descent using their local data and share only the updated weights with a central server for aggregation. The GCN helps in accurate detection of unlabelled or noisy labels in the activity sequence by borrowing information from similar labelled nodes. The federated setting for training these models ensures that user privacy is respected by transferring only the learned representations out of the device to a central server. By avoiding transfer of raw data, the algorithm also ensures that training a HAR model is not constrained by infrastructure availability in central server or low network bandwidth from edge devices. Our proposed algorithm performs better than the baseline feed-forward federated learning model in terms of both accuracy and computational complexity.
Dataset of acceleration signals recorded while performing activities of daily living

Several research studies have investigated the human activity recognition (HAR) domain to detect and recognise patterns of daily human activities. However, the accurate and automatic assessment of activities of daily living (ADLs) through machine learning algorithms is still a challenge, especially due to limited availability of realistic datasets to train and test such algorithms. The dataset contains data from 52 participants in total (26 women, and 26 men). The data for these participants was collected in two phases: 33 participants initially, and 19 further participants later on. Participants performed up to 5 repetitions of 24 different ADLs. Firstly, we provide an annotated description of the dataset collected by wearing a wrist-worn measurement device, Empatica E4. Secondly, we describe the methodology of the data collection and the real context in which participants performed the selected activities. Finally, we present some examples of recent and relevant target applications where our dataset can be used, namely lifelogging, behavioural analysis and measurement device evaluation. The authors consider the dissemination of this dataset can highly benefit the research community, and specially those involved in the recognition of ADLs, and/or in the removal of cues that reveal identity.
Virtual Reality Assisted General Education of Nuclear Chemistry and Radiochemistry

Nuclear chemistry and radiochemistry is a branch of chemistry that has high experimental risks, has high research costs, and is subject to certain prejudices. However, it has an extremely important influence on the development of human life. It is particularly important to emphasize the general education of nuclear chemistry and radiochemistry without compromising the quality of the teaching. The introduction of the general education mode into nuclear chemistry and radiochemistry overcomes the shortcomings of narrow audiences. However, single theoretical courses are limited to a slide-based lecture. Although the audience for general education is wide, it is difficult to carry out related experiments because of safety responsibility and teaching funds. In this work, VR technology was first applied to the general education of nuclear chemistry and radiochemistry, and the audiences of the course were not limited to students who major in chemistry. The research results showed that not only did the VR platform effectively improve students’ learning confidence, but also the effect of teacher–student interaction and the usability of VR equipment were recognized. This work may bring enlightenment to the popularization of nuclear chemistry and radiochemistry through new media technology.
Hiding payments in lightning network with approximate differentially private payment channels

Payment Channel Networks (PCN) form a class of techniques created to solve the scalability problems that permissionless blockchains such as Bitcoin face. The Lightning Network (LN) is the biggest PCN to date. As of September 2021, LN is an interconnected network with 27 thousand nodes and 71 thousand channels, representing a total payment capacity of 2500 bitcoins (114M USD). LN nodes establish trustless payment channels by locking funds in a funding transaction. The distribution of the funds can be modified with near-instant transactions without broadcasting them to the blockchain. By chaining channels together, it is possible for two nodes to transact without having a direct channel between them. This results in far fewer transactions needing to be broadcast and a theoretical number of off-chain transactions per second that can rival established, centralized payment systems. With off-chain transactions one can obtain more privacy than with on-chain transaction: individual payments through LN should be able to stay private. However, recent research suggests that this is not the case. Several works have shown that channel balances can be tracked with the Balance Discovery Attack (BDA). Monitoring the changes in channel balances over time enables an adversary to track individual payments in LN. Since payments can travel through channels outside the scope of influence of the payer or the payee, these BDA's form an Interdependent Privacy (IDP) risk for the participants of the LN network. This work applies approximate differential privacy to hide payment amounts of a realistic size in LN. To our knowledge, this is the first time that strong privacy guarantees in the sense of approximate differential privacy are achieved in the setting of LN. We prove the feasibility with our prototype, compatible with LN today. Our solution is evaluated in terms of cost and utility and we show that the latter is not affected by our solution.
Dynamic Classifier Alignment for Unsupervised Multi-Source Domain Adaptation

Unsupervised domain adaptation leverages the previously gained knowledge from a labeled source domain to tackle the task from a different but similar unlabeled target domain. Most existing methods focus on transferring knowledge from a single source domain, but the information from a single domain may be inadequate to complete the target task. Some previous studies have turned to multi-view representations to enrich the transferable information. However, they simply concatenate multi-view features, which may result in information redundancy. In this paper, we propose a dynamic classifier alignment (DCA) method for multi-source domain adaptation, which aligns classifiers driven from multi-view features via a sample-wise automatic way. As proposed, both the importance of each view and the contribution of each source domain are investigated. To determine the important degrees of multiple views, an importance learning function is built by generating an auxiliary classifier. To learn the source combination parameters, a domain discriminator is developed to estimate the probability of a sample belonging to multiple source domains. Meanwhile, a self-training strategy is proposed to enhance the cross-domain ability of source classifiers with the assistance of pseudo target labels. Experiments on real-world visual datasets show the superiority of the proposed DCA.
Evaluation of Recursive Feature Elimination and LASSO Regularization-based optimized feature selection approaches for cervical cancer prediction

Cervical cancer is one of the leading causes of premature mortality among women worldwide and more than 85% of these deaths are in developing countries. There are several risk factors associated with cervical cancer. In this research, the aim is to develop a predictive model for predicting the outcome of patient's cervical cancer results, given risk patterns from individual medical records and preliminary screening. This work presents a machine learning method using Decision Tree (DT) algorithm to analyze the risk factors of cervical cancer. Recursive Feature Elimination (RFE) and least absolute shrinkage and selection operator (LASSO) feature selection techniques were fully explored to determine the most important attributes for cervical cancer prediction. Comparative analysis of the 2 feature selection techniques were performed to show the importance of feature selection in cervical cancer prediction. Based on the result of the analysis, we can conclude that the proposed model produced the highest accuracy of 98% and 96% respectively while using DT with RFE and LASSO feature selection techniques respectively.
Detecting Offensive Content on Twitter During Proud Boys Riots

Hateful and offensive speech on online social media platforms has seen a rise in the recent years. Often used to convey humor through sarcasm or to emphasize a point, offensive speech may also be employed to insult, deride and mock alternate points of view. In turbulent and chaotic circumstances, insults and mockery can lead to violence and unrest, and hence, such speech must be identified and tagged to limit its damage. This paper presents an application of machine learning to detect hateful and offensive content from Twitter feeds shared after the protests by Proud Boys, an extremist, ideological and violent hate group. A comprehensive coding guide, consolidating definitions of what constitutes offensive content based on the potential to trigger and incite people is developed and used to label the tweets. Linguistic, auxiliary and social features extracted from these labeled tweets were used to train machine learning classifiers, which detect offensive content with an accuracy of about 92%. An analysis of the importance scores reveals that offensiveness is pre-dominantly a function of words and their combinations, rather than meta features such as punctuations and quotes. This observation can form the foundation of pre-trained classifiers that can be deployed to automatically detect offensive speech in new and unforeseen circumstances.
Quantum Error Correction

The concept of this chapter is to gradually introduce readers to the quantum error correction coding principles, starting from an intuitive description to a rigorous mathematical description. The chapter starts with Pauli operators, basic definitions, and representation of quantum errors. Although the Pauli operators were introduced in Chapter 2, they are put here in the context of quantum errors and quantum error correction. Next, basic quantum codes, such as three-qubit flip code, three-qubit phase-flip code, and Shor's nine-qubit code, are presented. The projection measurements are used to determine the error syndrome and perform corresponding error correction action. Furthermore, the stabilizer formalism and the stabilizer group are introduced. The basic stabilizer codes are described as well. The whole of the next chapter is devoted to stabilizer codes; here only the basic concepts are introduced. An important class of codes, the class of Calderbank–Shor–Steane (CSS) codes, is described next. The connection between classical and quantum codes is then established, and two classes of CSS codes, dual-containing and quantum codes derived from classical codes over GF(4), are described. The concept of quantum error correction is then formally introduced, followed by the necessary and sufficient conditions for quantum code to correct a given set of errors. Then, the minimum distance of a quantum code is defined and used to relate it to the error correction capability of a quantum code. The CSS codes are then revisited by using this mathematical framework. The next section is devoted to important quantum coding bounds, such as the Hamming quantum bound, quantum Gilbert–Varshamov bound, and quantum Singleton bound (also known as the Knill–Laflamme bound). Next, the concept of operator-sum representation is introduced, and used to provide physical interpretation and describe the measurement of the environment. Finally, several important quantum channel models are introduced, such as depolarizing channel, amplitude-damping channel, and generalized amplitude-damping channel. After the summary section, the set of problems for self-study is provided, which enables readers to better understand the underlying concepts of quantum error correction.