Reviewerly/exHarmony huggingface dataset:
Efficient and Privacy-Preserving Aggregated Reverse kNN Query Over Crowd-Sensed Data

The aggregated reverse kNN (ARkNN) query aims to identify one query record with the maximum influence set and has become a powerful tool to support optimal decision-making in crowdsensing. Considering data privacy and query privacy, ARkNN queries should be performed in a private manner. Unfortunately, existing schemes cannot support privacy-preserving ARkNN queries over crowd-sensed data. To address this issue, we propose two efficient and privacy-preserving ARkNN query schemes with different security levels, named the BARQ scheme and the EARQ scheme, where the former can only protect data privacy while the latter can protect both data privacy and query privacy. Specifically, we first formalize the models of privacy-preserving ARkNN queries and propose our BARQ scheme based on a random response (RR) frequency oracle. Then, we design a privacy-preserving hardware-assisted reverse kNN query determination (PRkD) scheme for privately determining whether a query record is among the RkNN of a data record. After that, we present our EARQ scheme by leveraging the PRkD scheme to protect query privacy and integrating the RR frequency oracle to protect data privacy. In addition, our rigorous security analysis demonstrates that the BARQ scheme can well protect data privacy, and the EARQ scheme can protect both data privacy and query privacy. Extensive experimental results illustrate that they have high accuracy in query results and are efficient in computational costs and communication overheads.
Quantum Computing at IQM

Classical information processing has dramatically changed the lives of almost everyone over the last century. Now we are on the verge of a new information processing revolution, quantum computing. In this chapter, we present an overview of the main differences between classical and quantum computing, followed by a discussion of the foundational concepts of quantum computing and an introduction to quantum algorithms. We then briefly report on the latest developments in the field, focusing on the software-hardware co-design approach currently pursued at IQM. Finally, we provide an insight into the future developments and opportunities of quantum computing.
Knowledge Representation of Training Data With Adversarial Examples Supporting Decision Boundary

Deep learning (DL) has achieved tremendous success in recent years in many fields. The success of DL typically relies on a considerable amount of training data and the expensive model optimization process. Therefore, a trained DL model and its corresponding training data have become valuable assets whose intellectual property (IP) needs to be protected. Once a DL model or its training dataset is released, there is currently no mechanism for the entity that owns one part to establish a clear relationship with the other. In this paper, we aim to reveal the integrated relationship between a given DL model and the corresponding training dataset, by framing the problem of knowledge representation of a dataset with respect to DL models trained on it: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">how to effectively represent the knowledge transferred from a training dataset to a DL model?</i> Our basic idea is that the knowledge transferred from a training dataset to a DL model can be uniquely represented by the model’s decision boundary. Therefore, we design a novel generation method that utilizes geometric consistency to find the samples supporting the decision boundary, which can serve as the proxy for the knowledge representation. We evaluate our method in three different cases: IP audit of training data, IP audit of DL models, and adversarial knowledge distillation. The experimental results show that our method can improve the performance of existing works in all cases, which confirm that our method can effectively represent the knowledge transferred from a training dataset to a DL model.
On the Role of Taylor’s Formula in Machine Learning

The classical Taylor’s formula is an elementary tool in mathematical analysis and function approximation. Its role in the optimization theory, whose data-driven variants have a central role in machine learning training algorithms, is well-known. However, utilization of Taylor’s formula in the derivation of new machine learning methods is not common and the purpose of this article is to introduce such use cases. Both a feedforward neural network and a recently introduced distance-based method are used as data-driven models. We demonstrate and assess the proposed techniques empirically both in unsupervised and supervised learning scenarios.
Exploring Audio Processing in Mixed Reality to Boost Motivation in Piano Learning

As technology becomes increasingly important in education, including music, many VR (virtual reality) and AR (augmented reality) applications have been developed to improve skills and knowledge in playing musical instruments, such as piano. However, these applications mainly utilize MIDI input in validating notes. This research explores the potential implementation of mixed reality to enhance piano learning through audio processing capabilities while maintaining users’ motivation. The research used HoloLens 2 device and FFT (Fast Fourier Transform) method with peak detection, and compared various window functions to determine the most accurate one. Blackman-Harris performed the best with a 97.28% accuracy rate when tested on complex songs. The application was also tested by 31 participants in the campus and evaluated using HMSAM (Hedonic-Motivation System Adoption Model), revealing that curiosity and joy were the most significant factors influencing the use of the application. The effectiveness of the learning was moderate with an increase of 31.28%. Although there were limitations in the use of audio processing, it could still be utilized and improved further to keep users motivated in learning piano.
Directional Wavelet Packets for Image Processing

The paper presents a versatile library of quasi-analytic complex-valued wavelet packets originated from polynomial splines of arbitrary orders. The real parts of quasi-analytic wavelet packets are regular spline-based orthonormal wavelet packets derived from discretized periodic polynomial splines. Imaginary parts are the so-called complementary orthonormal wavelet packets derived from the Hilbert transforms of regular wavelet packets. The discrete Fourier transforms of quasi-analytic wavelet packets are located in either the positive or negative half-band of the frequency domain. Consequently, the discrete Fourier transforms of 2D wavelet packets, which are derived by the tensor products of 1D wavelet packets, occupy one of the quadrants of the 2D frequency domain. Such a structure results in the directionality of their real parts. The shapes of real quasi-analytic wavelet packets are close to windowed cosine waves that oscillate in several different directions at different frequencies. The paper provides a few examples of the successful application of designed quasi-analytic wavelet packets to image denoising and inpainting.
Curiously Empty Intersection of Proof Engineering and Computational Sciences

The tools and techniques of proof engineering have not yet been applied to the computational sciences. We try to explain why and investigate their potential to advance the field. More concretely, we formalize elementary group theory in an interactive theorem prover and discuss how the same technique could be applied to formalize general computational methods, such as discrete exterior calculus. We note that such formalizations could reveal interesting insights into the mathematical structure of the methods and help us implement them with stronger guarantees of correctness. We also postulate that working in this way could dramatically change the way we study and communicate computational sciences.
Learning by Redoing: An Experimental Study on the Impact of Repetition of Formative Assessments in a Formal Methods Course for Software Engineers

This study evaluates the role of tests as weekly formative assessments and investigates the impact of their repetition on the academic performance of students in a postgraduate formal methods course for professional software engineers.
DCDPI: Dynamic and Continuous Deep Packet Inspection in Secure Outsourced Middleboxes

Secure outsourced middleboxes are deployed in network function virtualization services that detect malicious activities on communications, which provides privacy-preserving deep packet inspection (DPI) over encrypted traffic. To boost filtering efficiency of packets, the two-layer middlebox architecture has been adopted in recent DPI systems. Nevertheless, state-of-the-art solutions based on two-layer architecture mainly suffer from two limitations: (i) cannot support dynamic rule addition; (ii) failed to inspect discontinuous token for rule matching. To address these limitations, this work proposes an efficient, dynamic and continuous DPI (DCDPI) system in secure outsourced middleboxes. To achieve dynamic rule addition with forward privacy, we refine a data structure called virtual binary tree (VBTree) and further introduce a variant of VBTree for DCDPI, termed VBTree+. VBTree+ supports two new desirable features: (i) taking the rule action information into consideration; (ii) achieving both rule identifier and rule action hiding. By introducing a token continuity check mechanism, DCDPI can effectively identify discontinuous tokens and categorize continuous tokens into one group. The extensive experiment over the real dataset and rule set confirms the practicality and efficiency of DCDPI. Compared to state-of-the-art works with same setting, DCDPI is 18% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 110% more efficient for a connection establishment between gateway/client and server.
The Effect of Developers’ General Intelligence on the Understandability of Domain Models: An Empirical Study

The discipline of software engineering has long studied the understandability of domain models, but always focusing on the semantic and notational characteristics of these models. However, as some authors point out, understandability is a cognitive process, where many human factors of the developers themselves are involved. In this sense, one of the human factors most studied by cognitive psychology is the Intelligence, considered as a set of skills that give each person abilities among which stand out comprehensibility and problem solving, both associated with the understandability process. In this paper we focus on Spearman’s Bifactorial Theory that proposes the use of D48 test, to obtain the so-called General Intelligence that represents a factor underlying specific mental abilities. This work proposes a theoretical model that has guided an empirical study with 102 subjects from the University of Alicante, we measure their general intelligence using the D48 test, and then these subjects perform a set of UML domain model understandability tasks, both in comprehensibility and problem solving. At this point, we also study the impact that the model understandability performance has on the final intention to adopt the model, based on the UMAM-Q test. Once the data were obtained, we applied a two-way analysis of variance, and the study obtained statistically significant results confirming that: 1) subjects with higher intelligence perform obtain better results on model understandability performance, and 2) subjects with higher model understandability also have higher compatibility and intention to adopt the model.
Secure Distributed Modular Exponentiation: Systematic Analysis and New Results

Modular exponentiation is a vital function in public key cryptography. Dozens of protocols, including encryption schemes, signature schemes, pseudorandom functions, and more, perform this operation on a secret base and/or a secret exponent. In a multiparty computation setting, these secret data might be shared over multiple parties who wish to compute this modular exponentiation in a secure and distributed way. However, whereas typical frameworks for secure multiparty computation based on secret sharing provide a basic tool box of secure distributed computation of, most importantly, randomness generation, addition, and multiplication, the status quo of secure distributed modular exponentiation is unsatisfactory. In this work, we provide a complete and comprehensive overview on existing protocols for perfectly secure distributed exponentiation differing depending on whether the inputs and outcomes are public or shared. We perform a detailed complexity computation of the currently existing protocols, observing that earlier authors have overestimated the complexity of their own protocols, and close the remaining open problem: protocols for secure distributed exponentiation with secret base, secret exponent, and public outcome. We prove that the presented protocol is universally composably secure in the presence of malicious adversaries. We finally exemplify the practical relevance of the new protocol by demonstrating how it can be used for pseudorandom generation and signing.
A Survey of Blockchain-Based Schemes for Data Sharing and Exchange

Data immutability, transparency and decentralization of blockchain make it widely used in various fields, such as Internet of things, finance, energy and healthcare. With the advent of the big data era, various companies and organizations urgently need data from other parties for data analysis and mining to provide better services. Therefore, data sharing and data exchange have become an enormous industry. Traditional centralized data platforms face many problems, such as privacy leakage, high transaction costs and lack of interoperability. Introducing blockchain into this field can address these problems, while providing decentralized data storage and exchange, access control, identity authentication and copyright protection. Although many impressive blockchain-based schemes for data sharing or data exchange scenarios have been presented in recent years, there is still a lack of review and summary of work in this area. In this paper, we conduct a detailed survey of blockchain-based data sharing and data exchange platforms, discussing the latest technical architectures and research results in this field. In particular, we first survey the current blockchain-based data sharing solutions and provide a detailed analysis of system architecture, access control, interoperability, and security. We then review blockchain-based data exchange systems and data marketplaces, discussing trading process, monetization, copyright protection and other related topics.
DCPDN: High-Performance Pedestrian Detection Networks for Difficult Conditions

Pedestrian detection is the use of computer vision technology to identify and accurately locate pedestrians in image or video data, which has a strong use value. This technology can be used as the research basis for visual tasks such as person re-identification, human pose estimation and behavior analysis, and can also be applied to industrial fields such as intelligent security, automatic driving and human-computer interaction. However, the problems of low image resolution, blurred appearance, large scale difference of pedestrians, occluded pedestrians and complex background still bring great challenges to the detection performance. To solve these problems, this paper proposes a high-performance pedestrian detection network dedicated to difficult conditions: DCPDN. Firstly, we design an optimized super-resolution reconstruction network to preprocess the image to alleviate the performance damage caused by low-resolution and blurred images. Then, to solve the multi-scale problem in pedestrian detection, we propose a weighted cross-scale feature fusion module, which adopts a hierarchical detection strategy to deal with pedestrian objects of different scales while fully fusing feature maps of different levels. Finally, to solve the occlusion problem that has plagued pedestrian detection for a long time, we design an occlusion processing module based on graph convolutional network, which can effectively use the correlation information between different parts of the human body and promote the feature expression of occluded objects. On the CityPersons dataset, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MR</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> of the detector is reduced by 6.9%, 19.2%, 8.9%, 1.9%, 3.6% and 14.2%, respectively, corresponding to different partition subsets of R, HO, A, L, M and S. On the Caltech dataset, corresponding to different divisions of R, HO, A, L and S, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MR</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> of the detector is reduced by 9.9%, 15.8%, 16.3%, 6.8% and 25.8%, respectively. The experimental results show that the performance improvement of the detector is significant on both severe occlusion (HO) and small scale (S) subsets. After testing, the algorithm has strong robustness to occluded pedestrians, and can be easily embedded in other detection frameworks. Our DCPDN is able to compete with the state of the art methods and is especially effective when dealing with the pedestrian detection problem under difficult conditions.
Toward a Personalized Clustered Federated Learning: A Speech Recognition Case Study

Most speech recognition systems utilize cloud computing for model training and updates. Speech data, being personally identifiable information (PII), encompasses personal, privacy-sensitive, and regulated content. Relying on centralized servers or third parties can threaten confidential data, resulting in privacy breaches. Therefore, privacy issues and strict regulations (e.g., EU’s general data protection regulation, California’s CCPA, and the Privacy Act in Australia) limit the availability of large data sets. The scarcity of data sets is particularly pronounced in less-represented languages, like Persian, adversely impacting innovation and data-driven product development. To overcome the challenges posed by the scarcity of data sets and privacy concerns, for the first time, we propose a novel federated learning (FL) solution for Persian Spoken Isolated Digit Recognition. This proposed technique bridges the gap between privacy and utility by enabling the training of an algorithm using decentralized data sets stored on edge devices or servers, without the need for data exchange. Nonindependent and identically distributed data (non-IID), such as unique speaker accents, poses a challenge in speech recognition, especially in an FL setup. Regrettably, this challenge has largely been overlooked in existing techniques and methodologies. To address this, we present an innovative personalized clustered FL (PCFL) approach that successfully exploits similarities among the private data distributions and captures distinctive characteristics inherent in each client’s data in order to train models. The experimental results show that while the proposed solution significantly addresses privacy concerns, it has a negligible performance loss compared to centralized model training techniques.
Randomized Constructive Neural Network Based on Regularized Minimum Error Entropy

So far several types of random neural networks have been proposed in which optimal output weights are adjusted using the Mean Square Error (MSE) objective function. However, since many real-world phenomena do not follow a normal distribution, MSE-based methods act poorly in such cases. This paper presents a new single-layer random constructive neural network based on the regularized Minimum Error Entropy (MEE) objective function. The proposed method investigates the performance of MSE and MEE objective functions in combination using a regularization term to adjust the optimal output parameter for new nodes. Experimental results show that the proposed method performs well in the presence of both Gaussian and impulsive noise. Furthermore, due to the random assignment of the hidden layer parameters, the computational burden of the proposed method is reduced. Incremental constructive architecture of the proposed network helps optimize non-convex objective functions to achieve the desired performance. Computational comparisons indicate the superior performance of our method with several synthetic and benchmark datasets.
Multidimensional Fairness in Paper Recommendation

To prevent potential bias in the paper review and selection process for conferences and journals, most include double blind review. Despite this, studies show that bias still exists. This implicit bias may persist when recommendation algorithms for paper selection are employed instead. To address this, we describe three fair algorithms that specifically take into account author diversity in paper recommendation. In contrast to fair algorithms that only take into account one protected variable, our methods provide fair outcomes across multiple protected variables concurrently. Five demographic characteristics—gender, ethnicity, career stage, university rank, and geolocation—are included in our multidimensional author profiles. The Overall Diversity approach uses a score for overall diversity to rank publications. The Round Robin Diversity technique chooses papers from authors who are members of each protected group in turn, whereas the Multifaceted Diversity method chooses papers that initially fill the demographic feature with the highest importance. By selecting papers from a pool of SIGCHI 2017, DIS 2017, and IUI 2017 papers, we recommend papers for SIGCHI 2017 and evaluate these algorithms using user profiles with Boolean and Continuous-valued attributes. We contrast the papers that were suggested with those that were approved by the conference. We find that utilizing profiles with either Boolean or Continuous feature values, all three techniques boost diversity while experiencing just a slight decrease in paper quality or no decrease at all. Our best-performing algorithm, Multifaceted Diversity, recommends a set of papers that achieve demographic parity with the demographics of pool of authors of all submitted papers. Compared to the actual authors of the papers actually selected for the conference, the recommended paper authors are 42.50% more diverse and actually achieve a 2.45% boost in paper quality as measured by the h-index. Our approach could be applied to reduce bias in the selection of grant proposals, conference papers, journal articles, and other academic duties.
A Multisensor Fusion Method Based on Strict Velocity for Underwater Navigation System

Aiming at the inconsistency of the frame system defined by the velocity error in the underwater integrated navigation system and taking the strap-down inertial navigation system (SINS)/Doppler velocity logs (DVLs)/ultrashort baseline (USBL) integrated navigation system as the research object, a multisensor fusion method based on strict velocity is proposed. First, based on the azimuth angle, slope distance, and Doppler velocity information, a multisensor fusion model based on SINS/DVL/USBL is proposed. Second, in order to solve the problem of inaccurate calculation of the system matrix caused by the large quantization noise of the specific information, an error model based on strict velocity is proposed. According to the velocity error model, an improved Kalman filter fusion algorithm is proposed. Finally, simulations and Yangtze River tests verify that the method proposed by this question can effectively improve the accuracy of integrated navigation system, especially in the case that USBL signals cannot be received at a long distance, which has greater advantages.