Reviewerly/exHarmony huggingface dataset:
Equivalence Checking of Quantum Circuits With the ZX-Calculus

As state-of-the-art quantum computers are capable of running increasingly complex algorithms, the need for automated methods to design and test potential applications rises.Equivalence checking of quantum circuits is an important, yet hardly automated, task in the development of the quantum software stack.Recently, new methods have been proposed that tackle this problem from widely different perspectives.One of them is based on the ZX-calculus, a graphical rewriting system for quantum computing.However, the power and capability of this equivalence checking method has barely been explored.The aim of this work is to evaluate the ZX-calculus as a tool for equivalence checking of quantum circuits.To this end, it is demonstrated how the ZX-calculus based approach for equivalence checking can be expanded in order to verify the results of compilation flows and optimizations on quantum circuits.It is also shown that the ZX-calculus based method is not complete-especially for quantum circuits with ancillary qubits.In order to properly evaluate the proposed method, we conduct a detailed case study by comparing it to two other state-of-the-art methods for equivalence checking: one based on path-sums and another based on decision diagrams.The proposed methods have been integrated into the publicly available QCEC tool (https://github.com/cda-tum/qcec)which is part of the Munich Quantum Toolkit (MQT).
Pied-Piper: Revealing the Backdoor Threats in Ethereum ERC Token Contracts

With the development of decentralized networks, smart contracts, especially those for ERC tokens, are attracting more and more Dapp users to implement their applications. There are some functions in ERC token contracts that only a specific group of accounts could invoke. Among those functions, some even can influence other accounts or the whole system without prior notice or permission. These functions are referred to as contract backdoors. Once exploited by an attacker, they can cause property losses and harm users’ privacy. In this work, we propose Pied-Piper, a hybrid analysis method that integrates datalog analysis and directed fuzzing to detect backdoor threats in Ethereum ERC token contracts. First, datalog analysis is applied to abstract the data structures and identification rules related to the threats for preliminary static detection. Then, directed fuzzing is applied to eliminate false positives caused by the static analysis. We first evaluated Pied-Piper on 200 smart contracts, which are injected with different types of backdoors. It reported all problems without false positives, and none of the injected problems was missed. Then, we applied Pied-Piper on 13,484 real token contracts deployed on Ethereum. Pied-Piper reported 189 confirmed problems, four of which have been assigned unique CVE ids while others are still in the review process. Each contract takes 8.03 seconds for datalog analysis on average, and the fuzzing engine can eliminate the false positives within one minute.
Barriers Affecting Higher Education Institutions’ Adoption of Blockchain Technology: A Qualitative Study

Despite the many benefits of blockchain technology in higher education, this technology is not widely adopted by Higher Education Institutions (HEIs). Therefore, instead of providing additional motives for adopting blockchain technology, this research tries to understand what factors discourage HEIs from merging blockchain with their procedures. The methodology used for this research is based upon qualitative research using 14 interviews with administrative and academic staff from the European Union (EU) and Canada. Our findings based on our empirical data revealed 15 key challenges to blockchain adoption by HEIs that are classified based on the technology, organization, and environment (TOE) framework. Theoretically, this study contributes to the body of knowledge relating to blockchain technology adoption. Practically, this research is expected to aid HEIs to assess the applicability of blockchain technology and pave the way for the widespread adoption of this technology in the educational field.
Efficient Learning of Interpretable Classification Rules

Machine learning has become omnipresent with applications in various safety-critical domains such as medical, law, and transportation. In these domains, high-stake decisions provided by machine learning necessitate researchers to design interpretable models, where the prediction is understandable to a human. In interpretable machine learning, rule-based classifiers are particularly effective in representing the decision boundary through a set of rules comprising input features. The interpretability of rule-based classifiers is in general related to the size of the rules, where smaller rules are considered more interpretable. To learn such a classifier, the brute-force direct approach is to consider an optimization problem that tries to learn the smallest classification rule that has close to maximum accuracy. This optimization problem is computationally intractable due to its combinatorial nature and thus, the problem is not scalable in large datasets. To this end, in this paper we study the triangular relationship among the accuracy, interpretability, and scalability of learning rule-based classifiers. The contribution of this paper is an interpretable learning framework IMLI, that is based on maximum satisfiability (MaxSAT) for synthesizing classification rules expressible in proposition logic. Despite the progress of MaxSAT solving in the last decade, the straightforward MaxSAT-based solution cannot scale. Therefore, we incorporate an efficient incremental learning technique inside the MaxSAT formulation by integrating mini-batch learning and iterative rule-learning. In our experiments, IMLI achieves the best balance among prediction accuracy, interpretability, and scalability. As an application, we deploy IMLI in learning popular interpretable classifiers such as decision lists and decision sets.
Verified NTT Multiplications for NISTPQC KEM Lattice Finalists: Kyber, SABER, and NTRU

Postquantum cryptography requires a different set of arithmetic routines from traditional public-key cryptography such as elliptic curves. In particular, in each of the lattice-based NISTPQC Key Establishment finalists, every state-ofthe-art optimized implementation for lattice-based schemes still in the NISTPQC round 3 currently uses a different complex multiplication based on the Number Theoretic Transform. We verify the NTT-based multiplications used in NTRU, Kyber, and SABER for both the AVX2 implementation for Intel CPUs and for the pqm4 implementation for the ARM Cortex M4 using the tool CryptoLine. e extended CryptoLine and as a result are able to verify that in six instances multiplications are correct including range properties.We demonstrate the feasibility for a programmer to verify his or her high-speed assembly code for PQC, as well as to verify someone else’s high-speed PQC software in assembly code, with some cooperation from the programmer.
A New Hate Speech Detection System based on Textual and Psychological Features

Hate speech often spreads on social media and harms individuals and the community.Machine learning models have been proposed to detect hate speech in social media; however, several issues presently limit the performance of current approaches.One challenge is the issue of having diverse comprehensions of hate speech constructs which will lead to many speech categories and different interpretations.In addition, certain language-specific features, and short text issues, such as Twitter, exacerbate the problem.Moreover, current machine learning approaches lack universality due to small datasets and the adoption of a few features of hateful speech.This paper develops and builds new feature sets based on frequencies of textual tokens and psychological characteristics.Then, the study evaluates several machine learning methods over a large dataset.Results showed that the Random Forest and BERT methods are the most valuable for detecting hate speech content.Furthermore, the most dominant features that are helpful for hate speech detection methods combine psychological features and Term-Frequency Inverse Document-Frequency (TFIDF) features.Therefore, the proposed approach could identify hate speech on social media platforms like Twitter.
Time-optimal holonomic quantum computation

A three-level system can be used in a $\mathrm{\ensuremath{\Lambda}}$-type configuration in order to construct a universal set of quantum gates through the use of non-Abelian nonadiabatic geometrical phases. Such construction allows for high-speed operation times which diminish the effects of decoherence. This might be, however, accompanied by a breakdown of the validity of the rotating-wave approximation (RWA) due to the comparable timescale between counter-rotating terms and the pulse length, which greatly affects the dynamics. Here, we investigate the trade-off between dissipative effects and the RWA validity, obtaining the optimal regime for the operation of the holonomic quantum gates.
Quantum simulations of hydrodynamics via the Madelung transformation

Developing numerical methods to simulate efficiently nonlinear fluid dynamics on universal quantum computers is a challenging problem. In this paper, a generalization of the Madelung transform is defined to solve quantum relativistic charged fluid equations interacting with external electromagnetic forces via the Dirac equation. The Dirac equation is discretized into discrete-time quantum walks which can be efficiently implemented on universal quantum computers. A variant of this algorithm is proposed to implement simulations using current noisy intermediate scale quantum (NISQ) devices in the case of homogeneous external forces. High resolution (up to $N={2}^{17}$ grid points) numerical simulations of relativistic and nonrelativistic hydrodynamical shocks on current IBM NISQs are performed with this algorithm. This paper demonstrates that fluid dynamics can be simulated on NISQs, and opens the door to simulating other fluids, including plasmas, with more general quantum walks and quantum automata.
Step-Ahead Error Feedback for Distributed Training with Compressed Gradient

Although the distributed machine learning methods can speed up the training of large deep neural networks, the communication cost has become the non-negligible bottleneck to constrain the performance. To address this challenge, the gradient compression based communication-efficient distributed learning methods were designed to reduce the communication cost, and more recently the local error feedback was incorporated to compensate for the corresponding performance loss. However, in this paper, we will show that a new "gradient mismatch" problem is raised by the local error feedback in centralized distributed training and can lead to degraded performance compared with full-precision training. To solve this critical problem, we propose two novel techniques, 1) step ahead and 2) error averaging, with rigorous theoretical analysis. Both our theoretical and empirical results show that our new methods can handle the "gradient mismatch" problem. The experimental results show that we can even train faster with common gradient compression schemes than both the full-precision training and local error feedback regarding the training epochs and without performance loss.
Integrating Continuous and Teleporting VR Locomotion into a Seamless ‘HyperJump’ Paradigm

Continuous locomotion in VR provides uninterrupted optical flow, which mimics real-world locomotion and supports path integration . However, optical flow limits the maximum speed and acceleration that can be effectively used without inducing cybersickness. In contrast, teleportation provides neither optical flow nor acceleration cues, and users can jump to any length without increasing cybersickness. However, teleportation cannot support continuous spatial updating and can increase disorientation. Thus, we designed ‘HyperJump’ in an attempt to merge benefits from continuous locomotion and teleportation. HyperJump adds iterative jumps every half a second on top of the continuous movement and was hypothesized to facilitate faster travel without compromising spatial awareness/orientation. In a user study, Participants travelled around a naturalistic virtual city with and without HyperJump (equivalent maximum speed). They followed waypoints to new landmarks, stopped near them and pointed back to all previously visited landmarks in random order. HyperJump was added to two continuous locomotion interfaces (controller- and leaning-based). Participants had better spatial awareness/orientation with leaning-based interfaces compared to controller-based (assessed via rapid pointing). With HyperJump, participants travelled significantly faster, while staying on the desired course without impairing their spatial knowledge. This provides evidence that optical flow can be effectively limited such that it facilitates faster travel without compromising spatial orientation. In future design iterations, we plan to utilize audio-visual effects to support jumping metaphors that help users better anticipate and interpret jumps, and use much larger virtual environments requiring faster speeds, where cybersickness will become increasingly prevalent and thus teleporting will become more important.
A revocable attribute-based encryption EHR sharing scheme with multiple authorities in blockchain

With the development of digital healthcare, sharing electronic medical record data has become an indispensable part of improving medical conditions. Aiming at the centralized power caused by the single attribute authority in current CP-ABE schemes and the problem that cloud servers are curious and even malicious, we design a revocable CP-ABE EHR sharing scheme with multiple authorities (MA-RABE) in blockchain. In this solution, a group of authorities complete user attribute distribution, key generation and user management through secret sharing and transactions. Besides, we innovatively implemented a distributed one-way anonymous key agreement so that other participants cannot obtain useful information from the fully hidden policy embedded in the ciphertext. Taking into account the computational overhead of a large number of bilinear operations in the decryption process, the solution also supports the cloud server to pre-decrypt the ciphertext, and the data user only needs to perform exponentiation operation once to obtain the plaintext from the pre-decryption result. Theoretical analysis and performance evaluation show that the scheme has reliable security and lower user revocation and ciphertext update overhead.
Graph neural network-based visual relationship and multilevel attention for image captioning

With the remarkable success of the image captioning tasks, visual attention methods have become a vital part of captioning models. However, most attention-based image captioning methods do not consider any relationship among regions, which play a significant role in better image understanding. We proposed an image captioning method based on local relation network using a multilevel attention approach with graph neural network. It not only fully explores the relationship between the object and the image regions but also generates significant and context-based features corresponding to every region in the image. The attention employed in our work enhances the image representation capability of our method by focusing on a given image region and its related image regions. Thus addressing the relevant contextual information, spatial locations, and deep visual features leads to improve caption generation. We verified the effectiveness of the proposed model by conducting extensive experiments on three benchmark datasets: Flickr30k, MSCOCO, and nocaps. The results show the superiority of the proposed method over the existing methods both in quantitative and qualitative manners. Detailed ablation studies are conducted to communicate how each part would contribute to the final performance.
Prospection for economic mineralization using PRISMA satellite hyperspectral remote sensing imagery: an example from central East Greenland

The PRISMA hyperspectral imaging satellite of the Italian Space Agency records in the 0.4-2.5 μm wavelength region at a spatial resolution of 30 m. This study used the PRISMA hyperspectral imagery to characterize an extensive hydrothermal alteration system in the Kap Simpson igneous complex in central East Greenland. The prospection for economic mineralization in the study area has been in the focus of the activities of several mineral exploration companies. The PRISMA data were analyzed using the Adaptive Coherence Estimator. The hyperspectral imagery mapped the spatial distribution of several alteration minerals. The hyperspectral data identified widespread iron stained zones consisting of limonite and jarosite. All these zones constitute prospection targets for the localization of economic mineralization of molybdenum, rare earth elements, gold, silver, and base metals. The study indicates the applicability of PRISMA imagery for the prospection of economic mineralization. The results should also be useful for the evaluation of the PRISMA hyperspectral imagery in geoenvironmental applications.
HAPPS: A Hidden Attribute and Privilege-Protection Data-Sharing Scheme With Verifiability

Data is a key asset in our interconnected and smart city. Especially, in the context of healthcare, healthcare data can facilitate remote diagnosis and medical research. Because of the potentially sensitive nature of healthcare data, privacy is a key consideration for both individuals and organizations. We can broadly categorize privacy considerations into data privacy, attribute privacy, and privilege policy privacy. To support one or more notions of privacy, the potential of solutions, such as fine-grained access control [e.g., those based on attribute-based encryption (ABE)] and blockchain in realizing data sharing has been explored. However, these approaches generally only facilitate access control of data and the traceability of the sharing process, and do not protect the attribute and privilege policy privacy of users. Therefore, in this article, we implement HAPPS, a hidden attribute and privilege-protection data-sharing scheme with verifiability. The three key building blocks of HAPPS are zero-knowledge proof, blockchain, and distributed ABE (DABE). Specifically, in our approach, we propose a new data access control strategy (i.e., attribute-hidden zero-knowledge proof—at-ZKP) to hide user identity and attributes during the authorization process. Our scheme is embedded in the blockchain and built into the decentralized sharing platform to prevent central verifier counterfeiting and support auditing. To demonstrate utility, we prove that HAPPS ensures data, attribute, and privilege policy privacy. Findings of our evaluations implemented on Ethereum and using the data set from the healthcare cost and utilization project (HCUP), we demonstrate that our scheme can share sensitive healthcare records belonging to minors (e.g., children) without the at-ZKP incurring unrealistic cost.
Live Detection of Foreign Object Debris on Runways Detection using Drones and AI

The removal of foreign objects and debris (FOD) on runways at both non-towered and tower-controlled airstrips is a universal problem.Current detection systems are relatively efficient at finding large foreign objects but are expensive to set up and tend to be inaccurate with small items.With recent developments in artificial intelligence and object detection, it has been proposed to use drones to collect images and then detect any FODs with a specific trained algorithm.This paper will discuss the required materials and method, the image collection process as well as the training and testing results analysis.The development and testing of a live detection model is also discussed.Six object tags have been selected for testing that are realistic representations of possible objects found on runways, these include Litter, Wood, Bolts, Metal, Rubber and Nuts.The collected image database provided a larger variety of images to increase the robustness of the model.The Microsoft Azure platform and Custom Vision module was selected for the deep learning and object detection component.Through the system development, two different models were created, the first with six individual classes and then a single object class model.Two different training algorithms were also used, the online general model and the local compact model.The compact model is able to rapidly analyse images however it loses overall model accuracy.The general model is significantly more accurate than the compact model, however it requires significantly more time to analyse.The single class general trained model has the lowest error rate of 10.5% with 51 correct objects detected out of 57.A live detection module was developed using a 4K HDMI video capture card to live stream the drone footage from the controller to the computer.The individual frames were then extracted and loaded through the detection module.The returned objects that have a probability above the set threshold are processed with a bounding box and given classification tag names.A GPS transmitter and receiver model captured the current coordinates of the drone and then synchronises it with the live video frame data.This was used to create a user-friendly data representation that is accurate to approximately 2.5m.
Title-and-Tag Contrastive Vision-and-Language Transformer for Social Media Popularity Prediction

Social media is an indispensable part of modern life, and social media popularity prediction (SMPP) plays a vital role in practice. In current work, the inconsistency of words in labels and titles, user feature transformation, etc have not been well noticed. In this paper, we propose a novel approach named Title-and-Tag Contrastive Vision-and-Language Transformer (TTC-VLT), combining two pre-trained vision and language transformers and other two dense feature parts for this prediction task. On one hand, in order to learn the differences between titles and tags, we design title-tag contrastive learning for title-visual and tag-visual, which separately extracts multimodal information from two types of text. On the other hand, user identification features are transformed to embedding vectors to capture user attribute details. From the extensive experiments, our approach outperforms the other methods on the social media prediction dataset. Our team achieve the 2nd place on the leader board of the Social Media Prediction Challenge 2022.
Symmetric private information retrieval supported by quantum-secure key-exchange network

Quantum key distribution provides a provably secure way for private key distribution, which enables the practical implementation of information retrieval that preserves both the user privacy and database security.