Reviewerly/exHarmony huggingface dataset:
Two-frequency phase-shifting method vs. Gray-coded-based method in dynamic fringe projection profilometry: A comparative review

Two-frequency phase-shifting phase unwrapping and Gray-coded-based phase unwrapping are two typical temporal phase unwrapping methods to recover discontinuous or spatially isolated surfaces in fringe projection profilometry (FPP). But in dynamic measurement, environmental noise, required number of patterns and inter-frame motion affect the performance of these two methods on measuring anti-noise ability, efficiency and robustness in varying degrees. So, it is a challenge to quantitatively compare these methods’ abilities and determine the optimum one with appropriate system parameters and under a certain condition. In this paper, a detailed comparative study is conducted for the two-frequency phase-shifting method including hierarchical, number-theoretical and heterodyne approaches and the Gray-coded-based method including traditional Gray code approach, complementary Gray code approach and Gray code approach with tripartite phase unwrapping (Tri-PU). First, the principles of six approaches are reviewed; then, their anti-noise ability was quantitatively compared based on the proposed noise model; next, theoretical comparison of measuring efficiency, comparative review of improved methods and recommendation on usage are given to boost their efficiency; finally, motion-induced error model in phase unwrapping was presented to determine the appropriate system parameters in dynamic measurement. Theoretical analysis, numerical simulation and experimental results are consistent and demonstrate that (i) Gray-coded-based method has better anti-noise ability than two-frequency phase-shifting method; (ii) two-frequency phase-shifting method is more efficient than traditional Gray-coded-based method when the number of encoding fringe periods exceeds 8; (iii) hierarchical approach and Gray-coded approach with Tri-PU have best performance to resist motion-induced error in their respective types of methods. Comparative results can give a helpful guidance to choose the “best” approach in different application scenes and quantitatively determine the projected fringe periods and imaging or projecting speed in dynamic measurement for FPP.
The Next Frontier: AI We Can Really Trust

Enormous advances in the domain of statistical machine learning, the availability of large amounts of training data, and increasing computing power have made Artificial Intelligence (AI) very successful. For certain tasks, algorithms can even achieve performance beyond the human level. Unfortunately, the most powerful methods suffer from the fact that it is difficult to explain why a certain result was achieved on the one hand, and that they lack robustness on the other. Our most powerful machine learning models are very sensitive to even small changes. Perturbations in the input data can have a dramatic impact on the output and lead to entirely different results. This is of great importance in virtually all critical domains where we suffer from low data quality, i.e. we do not have the expected i.i.d. data. Therefore, the use of AI in domains that impact human life (agriculture, climate, health, ...) has led to an increased demand for trustworthy AI. Explainability is now even mandatory due to regulatory requirements in sensitive domains such as medicine, which requires traceability, transparency and interpretability capabilities. One possible step to make AI more robust is to combine statistical learning with knowledge representations. For certain tasks, it can be advantageous to use a human in the loop. A human expert can - sometimes, of course not always - bring experience, domain knowledge and conceptual understanding to the AI pipeline. Such approaches are not only a solution from a legal point of view, but in many application areas the “why” is often more important than a pure classification result. Consequently, both explainability and robustness can promote reliability and trust and ensure that humans remain in control, thus complementing human intelligence with artificial intelligence.
BreastScreening-AI: Evaluating medical intelligent agents for human-AI interactions

In this paper, we developed BreastScreening-AI within two scenarios for the classification of multimodal beast images: (1) Clinician-Only; and (2) Clinician-AI. The novelty relies on the introduction of a deep learning method into a real clinical workflow for medical imaging diagnosis. We attempt to address three high-level goals in the two above scenarios. Concretely, how clinicians: i) accept and interact with these systems, revealing whether are explanations and functionalities required; ii) are receptive to the introduction of AI-assisted systems, by providing benefits from mitigating the clinical error; and iii) are affected by the AI assistance. We conduct an extensive evaluation embracing the following experimental stages: (a) patient selection with different severities, (b) qualitative and quantitative analysis for the chosen patients under the two different scenarios. We address the high-level goals through a real-world case study of 45 clinicians from nine institutions. We compare the diagnostic and observe the superiority of the Clinician-AI scenario, as we obtained a decrease of 27% for False-Positives and 4% for False-Negatives. Through an extensive experimental study, we conclude that the proposed design techniques positively impact the expectations and perceptive satisfaction of 91% clinicians, while decreasing the time-to-diagnose by 3 min per patient.
My AI Friend: How Users of a Social Chatbot Understand Their Human–AI Friendship

Abstract Use of conversational artificial intelligence (AI), such as humanlike social chatbots, is increasing. While a growing number of people is expected to engage in intimate relationships with social chatbots, theories and knowledge of human–AI friendship remain limited. As friendships with AI may alter our understanding of friendship itself, this study aims to explore the meaning of human–AI friendship through a developed conceptual framework. We conducted 19 in-depth interviews with people who have a human–AI friendship with the social chatbot Replika to uncover how they understand and perceive this friendship and how it compares to human friendship. Our results indicate that while human–AI friendship may be understood in similar ways to human–human friendship, the artificial nature of the chatbot also alters the notion of friendship in multiple ways, such as allowing for a more personalized friendship tailored to the user’s needs.
Quantifying the advantage of domain-specific pre-training on named entity recognition tasks in materials science

A bottleneck in efficiently connecting new materials discoveries to established literature has arisen due to an increase in publications. This problem may be addressed by using named entity recognition (NER) to extract structured summary-level data from unstructured materials science text. We compare the performance of four NER models on three materials science datasets. The four models include a bidirectional long short-term memory (BiLSTM) and three transformer models (BERT, SciBERT, and MatBERT) with increasing degrees of domain-specific materials science pre-training. MatBERT improves over the other two BERTBASE-based models by 1%∼12%, implying that domain-specific pre-training provides measurable advantages. Despite relative architectural simplicity, the BiLSTM model consistently outperforms BERT, perhaps due to its domain-specific pre-trained word embeddings. Furthermore, MatBERT and SciBERT models outperform the original BERT model to a greater extent in the small data limit. MatBERT's higher-quality predictions should accelerate the extraction of structured data from materials science literature.
Disparity-Based Multiscale Fusion Network for Transportation Detection

The transportation detection of long-distance small objects has low accuracy. In this work, we propose DMF, which is based on disparity depths. We map different disparity regions to 2D candidate regions according to the distance to solve the small-object detection problem. This method clusters disparity maps of different depths. The projected image is extracted with image features in the mapping region. On the one hand, it uses a multicluster method to unsample 2D mapping regions. On the other hand, the feature fusion of different scales is performed on each cluster region. The experimental results on two datasets show that DMF can improve the detection accuracy of small objects.
ShieldFL: Mitigating Model Poisoning Attacks in Privacy-Preserving Federated Learning

Privacy-Preserving Federated Learning (PPFL) is an emerging secure distributed learning paradigm that aggregates user-trained local gradients into a federated model through a cryptographic protocol. Unfortunately, PPFL is vulnerable to model poisoning attacks launched by a Byzantine adversary, who crafts malicious local gradients to harm the accuracy of the federated model. To resist model poisoning attacks, existing defense strategies focus on identifying suspicious local gradients over plaintexts. However, the Byzantine adversary submits encrypted poisonous gradients to circumvent existing defense strategies in PPFL, resulting in encrypted model poisoning. To address the issue, in this paper we design a privacy-preserving defense strategy using two-trapdoor homomorphic encryption (referred to as ShieldFL), which can resist encrypted model poisoning without compromising privacy in PPFL. Specially, we first present the secure cosine similarity method aiming to measure the distance between two encrypted gradients. Then, we propose the Byzantine-tolerance aggregation using cosine similarity, which can achieve robustness for both Independently Identically Distribution (IID) and non-IID data. Extensive evaluations on three benchmark datasets ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.,</i> MNIST, KDDCup99, and Amazon) show that ShieldFL outperforms existing defense strategies. Especially, ShieldFL can achieve 30%-80% accuracy improvement to defend two state-of-the-art model poisoning attacks in both non-IID and IID settings.
Error correction of a logical grid state qubit by dissipative pumping

Stabilization of encoded logical qubits using quantum error correction is crucial for the realization of reliable quantum computers. Although error-correcting codes implemented using individual physical qubits require many separate systems to be controlled, codes constructed using a quantum oscillator offer the possibility to perform error correction with a single physical entity. One powerful encoding approach for oscillators is the grid state or Gottesman–Kitaev–Preskill (GKP) encoding, which allows small displacement errors to be corrected. Here we introduce and implement a dissipative map designed for physically realistic finite GKP codes, which performs quantum error correction of a logical qubit encoded in the motion of a single trapped ion. The correction cycle involves two rounds, which correct small displacements in position and momentum. We demonstrate an extension in coherence time of logical states by a factor of three using both square and hexagonal GKP codes. The simple dissipative map used for this correction can be viewed as a type of reservoir engineering, which pumps into the manifold of highly non-classical GKP qubit states. Physical systems with continuous degrees of freedom can be used to implement quantum error correction codes. An autonomous correction protocol has now been used to extend the lifetime of a qubit encoded in the motion of a trapped ion.
Heterogeneous Network Representation Learning Approach for Ethereum Identity Identification

Recently, network representation learning has been widely used to mine and analyze network characteristics, and it is also applied to blockchain, but most of the embedding methods in blockchain ignore the heterogeneity of network, so it is difficult to accurately describe the characteristics of the transaction. As smart society evolves, Ethereum makes smart contracts reality, while the mine of transaction characteristics appearing on the Ethereum platform is scarce; thus, there is an urgent need to mine Ethereum from contract and transfer. In this article, we propose a heterogeneous network representation learning method to mine implicit information inside Ethereum transactions. Specifically, we construct an Ethereum transaction network by collecting transaction data from normal and phishing Ethereum accounts. Then, we propose a walk strategy that combines timestamps and transaction amounts to represent the information that occurs at the time of a transaction. To mine the types of nodes and edges, we use a heterogeneous network representation learning method to map the transaction network to a low-dimensional space. Finally, we improve the accuracy of the embedding results in the node classification task, which has important implications for Ethereum mining as well as identity recognition.
Edge-Guided Recurrent Positioning Network for Salient Object Detection in Optical Remote Sensing Images

Optical remote sensing images (RSIs) have been widely used in many applications, and one of the interesting issues about optical RSIs is the salient object detection (SOD). However, due to diverse object types, various object scales, numerous object orientations, and cluttered backgrounds in optical RSIs, the performance of the existing SOD models often degrade largely. Meanwhile, cutting-edge SOD models targeting optical RSIs typically focus on suppressing cluttered backgrounds, while they neglect the importance of edge information which is crucial for obtaining precise saliency maps. To address this dilemma, this article proposes an edge-guided recurrent positioning network (ERPNet) to pop-out salient objects in optical RSIs, where the key point lies in the edge-aware position attention unit (EPAU). First, the encoder is used to give salient objects a good representation, that is, multilevel deep features, which are then delivered into two parallel decoders, including: 1) an edge extraction part and 2) a feature fusion part. The edge extraction module and the encoder form a U-shape architecture, which not only provides accurate salient edge clues but also ensures the integrality of edge information by extra deploying the intraconnection. That is to say, edge features can be generated and reinforced by incorporating object features from the encoder. Meanwhile, each decoding step of the feature fusion module provides the position attention about salient objects, where position cues are sharpened by the effective edge information and are used to recurrently calibrate the misaligned decoding process. After that, we can obtain the final saliency map by fusing all position attention cues. Extensive experiments are conducted on two public optical RSIs datasets, and the results show that the proposed ERPNet can accurately and completely pop-out salient objects, which consistently outperforms the state-of-the-art SOD models.
Deep learning-based detection from the perspective of small or tiny objects: A survey

Detecting small or tiny objects is always a difficult and challenging issue in computer vision. In this paper, we provide a latest and comprehensive survey of deep learning-based detection approaches from the perspective of small or tiny objects. Our survey is featured by thorough and exhaustive analysis of small or tiny object detection. We comprehensively introduce 30 existing datasets about small or tiny objects, and summarize different definitions of small or tiny objects based on different application scenarios, such as pedestrian detection, traffic signs detection, face detection, remote sensing target detection and object detection in common life. Then small or tiny object detection techniques are overviewed systematically from seven aspects, including super-resolution techniques, context-based information, multi-scale representation learning, anchor mechanism, training strategy, data augmentation, and schemes based on loss function. Finally, the detection performance of small or tiny objects on 12 popular datasets is analyzed in depth. Based on performance analysis, we also discuss the promising research directions in the future. We hope this survey could provide researchers guidance to catalyze understanding of small or tiny object detection and further facilitate research on small or tiny object detection systems.
Autoencoders

The study of psychiatric and neurologic disorders typically involves the acquisition of a wide range of different types of data, such as brain images, electronic health records, and mobile phone sensors data. Each type of data has its unique temporal and spatial characteristics, and the process of extracting useful information from them can be very challenging. Autoencoders are neural networks that can automatically learn useful features and representations from the data; this makes them an ideal technique for simplifying the process of feature engineering in machine learning studies. In addition, autoencoders can be used for dimensionality reduction, denoising data, generative modeling, and even pretraining deep learning neural networks. In this chapter, we present the fundamental concepts of autoencoders and provide an overview of how they execute these tasks. Finally, we show some exemplary applications from brain disorders research.
Efficient Decentralized Attribute Based Access Control for Mobile Clouds

Fine grained access control is a requirement for data stored in untrusted servers like clouds. Owing to the large volume of data, decentralized key management schemes are preferred over centralized ones. Often encryption and decryption are quite expensive and not practical when users access data from resource constrained devices. We propose a decentralized attribute based encryption (ABE) scheme with fast encryption, outsourced decryption and user revocation. Our scheme is very specific to the context of mobile cloud as the storage of encrypted data and the partial decryption of ciphertexts are dependent on the cloud and users with mobile devices can upload data to the cloud or access data from it by incurring very little cost for encryption and decryption respectively. The main idea is to divide the encryption into two phases, offline preprocessing phase which is done when the device is otherwise not in use and an online phase when the data is actually encrypted with the policy. This makes encryption faster and more efficient than existing decentralized ABE schemes. For decryption outsourcing, data users need to generate a transformed version of the decryption key allowing an untrusted proxy server to partially decrypt the ciphertext without gaining any information about the plaintext. Data users can then fully decrypt the partially decrypted ciphertext without performing any costly pairing operations. We also introduce user revocation in this scheme without incurring too much additional cost in the online phase. Comparison with other ABE schemes shows that our scheme significantly reduces computation times for both data owners and data users and highly suitable for use in mobile devices.
Toward Methodological Guidelines for Process Theories and Taxonomies in Software Engineering

Software engineering is increasingly concerned with theory because the foundational knowledge comprising theories provides a crucial counterpoint to the practical knowledge expressed through methods and techniques. Fortunately, much guidance is available for generating and evaluating theories for explaining why things happen (variance theories). Unfortunately, little guidance is available concerning theories for explaining how things happen (process theories), or theories for analyzing and understanding situations (taxonomies). This paper therefore attempts to clarify the nature and functions of process theories and taxonomies in software engineering research, and to synthesize methodological guidelines for their generation and evaluation. It further advances the key insight that most process theories are taxonomies with additional propositions, which helps inform their evaluation. The proposed methodological guidance has many benefits: it provides a concise summary of existing guidance from reference disciplines, it adapts techniques from reference disciplines to the software engineering context, and it promotes approaches that better facilitate scientific consensus.
Towards Large-scale Functional Verification of Universal Quantum Circuits

We introduce a framework for the formal specification and verification of quantum circuits based on the Feynman path integral.Our formalism, built around exponential sums of polynomial functions, provides a structured and natural way of specifying quantum operations, particularly for quantum implementations of classical functions.Verification of circuits over all levels of the Clifford hierarchy with respect to either a specification or reference circuit is enabled by a novel rewrite system for exponential sums with free variables.Our algorithm is further shown to give a polynomial-time decision procedure for checking the equivalence of Clifford group circuits.We evaluate our methods by performing automated verification of optimized Clifford+T circuits with up to 100 qubits and thousands of T gates, as well as the functional verification of quantum algorithms using hundreds of qubits.Our experiments culminate in the automated verification of the Hidden Shift algorithm for a class of Boolean functions in a fraction of the time it has taken recent algorithms to simulate.
Location Privacy in Usage-Based Automotive Insurance: Attacks and Countermeasures

Usage-based insurance (UBI) is regarded as a promising way to provide accurate automotive insurance rates by analyzing the driving behaviors (e.g., speed, mileage, and harsh braking/accelerating) of drivers. The best practice that has been adopted by many insurance programs to protect users' location privacy is the use of driving speed rather than GPS data. However, in this paper, we challenge this approach by presenting a novel speed-based location trajectory inference framework. The basic strategy of the proposed inference framework is motivated by the following observations. In practice, many environmental factors, such as real-time traffic and traffic regulations, can influence the driving speed. These factors provide side-channel information about the driving route, which can be exploited to infer the vehicle's trace. We implement our discovered attack on a public data set in New Jersey. The experimental results show that the attacker has a nearly 60% probability of obtaining the real route if he chooses the top 10 candidate routes. To thwart the proposed attack, we design a privacy preserving scoring and data audition framework that enhances drivers' control on location privacy without affecting the utility of UBI. Our defense framework can also detect users' dishonest behavior (e.g., modification of speed data) via a probabilistic audition scheme. Extensive experimental results validate the effectiveness of the defense framework.