Reviewerly/exHarmony huggingface dataset:
Understanding the joint effects of internal and external anthropomorphic cues of intelligent customer service bot on user satisfaction

As one specific type of chatbot, Intelligent Customer Service Bot (ICSB) satisfies users by providing uninterrupted customer service and reducing response time. However, users still prefer to interact with humans rather than robots. In previous studies, scholars have focused more on enhancing the anthropomorphic characteristics of chatbot by treating anthropomorphic characteristics as a general concept. Few literatures have studied the influence of both external and internal features on user satisfaction and the possible interactive effects between the two dimensions of chatbot anthropomorphic cues. Therefore, this paper studies the main effects of internal anthropomorphic constructs (cognitive empathy and emotional empathy) and external anthropomorphic constructs (virtual appearance) of ICSB on user satisfaction, as well as the two-way and three-way interaction effects among them. With a scenario-based survey of 331 respondents, the results confirm the hypotheses about the main effects, the two-way and three-way interaction effects in the model.
Digitalisation and commercial bank performance: A test of heterogeneity from Chinese commercial banks

Although digitalisation is a reality for companies and contributes to value creation, few studies have examined its impact on commercial bank performance. The objective of this paper is to analyse how banks' digitisation contributes to their performance. The study finds that digitisation has a non-linear dynamic relationship with commercial bank performance, being first inhibited and then facilitated. Moreover, the impact of digitisation on bank performance is heterogeneous, with small and medium-sized banks, those with a high share of non-interest income and those with a high customer concentration benefiting more from digitisation.
Distinguishability-based genuine nonlocality with genuine multipartite entanglement

A set of orthogonal multipartite quantum states is said to be distinguishability-based genuinely nonlocal (also genuinely nonlocal, for abbreviation) if the states are locally indistinguishable across any bipartition of the subsystems. This form of multipartite nonlocality, although more naturally arising than the recently popular ``strong nonlocality'' in the context of local distinguishability, receives much less attention. In this work, we study the distinguishability-based genuine nonlocality of a typical type of genuine multipartite entangled states---the $d$-dimensional Greenberger-Horne-Zeilinger (GHZ) states, featuring systems with local dimension not limited to two. In the three-partite case, we find the existence of small genuinely nonlocal sets consisting of these states: we show that the cardinality can at least scale down to linear in the local dimension $d$, with the linear factor $l=1$. Specifically, the method we use is semidefinite programming and the GHZ states to construct these sets are special ones which we call ``GHZ lattices''. This result might arguably suggest a significant gap between the strength of strong nonlocality and the distinguishability-based genuine nonlocality. Moreover, we put forward the notion of $(s,n)$-threshold distinguishability and, utilizing a similar method, we successfully construct (2,3)-threshold sets consisting of GHZ states in three-partite systems.
Graph matching for knowledge graph alignment using edge-coloring propagation

Knowledge graph (KG) is a kind of structured human knowledge of modeling the relationships between real-world entities. High quality KG is of crucial importance for many knowledge-based applications, e.g., question answering, recommender systems, etc. This paper studies the problem of entity alignment in KGs to promote knowledge fusion. Existing methods model the semantic representation of entities by using graph structural information or attribute information of the KG and then align the entities across different domains by calculating the distances between entities' embeddings. However, these methods only consider the node-to-node similarity in the alignment procedure while the edge-to-edge similarity is ignored. Our research hypothesis is that the graph edge alignment information is critical in entity alignment. We reformulate the knowledge entity alignment as a quadratic assignment problem (QAP) by adding relation alignment under the one-to-one mapping constraint. To solve the notorious QAP in a large-scale heterogeneous graph like KG, we propose a model, dual neighborhood consensus network (DNCN), which approximately decomposes the QAP into two small-scale linear assignment problems, i.e., entity alignment and relation alignment. After that, an edge-coloring propagation method is proposed to refine the coarse entity alignment result using the relation correspondence. Theoretical proof shows that this method can guarantee the isomorphism between local sub-graphs. The performance of DNCN is evaluated using the DBP15K and DWY100K benchmarks. Experimental results show that DNCN achieves the best performance on the DBP15K benchmark, and is computationally efficient. Ablation studies verify the importance of graph edge alignment information.
A brief review of contrastive learning applied to astrophysics

Abstract Reliable tools to extract patterns from high-dimensionality spaces are becoming more necessary as astronomical data sets increase both in volume and complexity. Contrastive Learning is a self-supervised machine learning algorithm that extracts informative measurements from multidimensional data sets, which has become increasingly popular in the computer vision and Machine Learning communities in recent years. To do so, it maximizes the agreement between the information extracted from augmented versions of the same input data, making the final representation invariant to the applied transformations. Contrastive Learning is particularly useful in astronomy for removing known instrumental effects and for performing supervised classifications and regressions with a limited amount of available labels, showing a promising avenue towards Foundation Models. This short review paper briefly summarizes the main concepts behind contrastive learning and reviews the first promising applications to astronomy. We include some practical recommendations on which applications are particularly attractive for contrastive learning.
Combining Smart Construction Objects–Enabled Blockchain Oracles and Signature Techniques to Ensure Information Authentication and Integrity in Construction

Reliable and accurate information is crucial for decision making in construction projects. However, stakeholders driven by profit have the potential to manipulate information, compromising information authentication and integrity (IAI). Even worse, digitization in the construction industry has made IAI extremely volatile, e.g., by easily copying, modifying, and falsifying. This study aims to ensure IAI by proposing a four-layer blockchain-based framework for combining smart construction objects (SCOs)–enabled oracles and hash-based digital signature techniques to protect both on-chain and off-chain information. Four deployed smart contracts provide three assurance mechanisms, i.e., signature verification, public data validation, and SCO cross-validation, which have been tested to improve the tampering detection accuracy by 19%, 11%, and 27%, respectively. The contribution of this study is to illustrate how a reliable and flexible blockchain oracle system can be established with limited resources to handle the concomitant IAI problem and provide an in-depth understanding of the IAI assurance mechanisms from the proposed framework. Future research can be conducted to reduce the signature size, enhance scalability, and further increase detection accuracy.
Distributed maximum correntropy Kalman filter with state equality constraints in a sensor network with packet drops

This study presents the design of a distributed Kalman filter with excellent performance in a sensor network with packet drops and non-Gaussian noise. The proposed filter incorporates the maximum correntropy criterion and state equality constraint information. The maximum correntropy criterion is employed to handle non-Gaussian noise by utilizing higher-order statistics. To achieve superior estimation performance, the equality constrained filter is obtained by incorporating state equality constraint information. Furthermore, to enhance the consistency in the estimation of each node, the equality constrained distributed maximum correntropy Kalman filter is established through the covariance interaction algorithm. Finally, the effectiveness of the proposed filter is verified by simulation results.
Adequate phase unwrapping based on 2<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si107.svg" display="inline" id="d1e351"><mml:mi>π</mml:mi></mml:math> cycle invariant between shifted phase maps

To obtain an adequate unwrapping solution for reliable phase measurement under noisy circumstances, a novel unwrapping algorithm is proposed. This algorithm extracts abundant valid pixels and rejects noise based upon revealing a property of 2π invariant, which is implied between wrapped phase maps with shifted fringe patterns. Distinct from conventional algorithms that use residues or minimum norm solution to attenuate the impacts of noise affected areas, this technique is free from the uncertainties arising from inadequate resources or over-smoothing effects during the unwrapping process. The performance of the proposed algorithm is demonstrated and confirmed through both computer simulations and practical experiments carried out using a fringe projection system. Superior capability in handling noise affected phase images is then achieved.
A context-aware smart product-service system development approach and application case

With advancements in information and communication technologies (ICTs), one of the epitomes of ICTs are smart product-service systems (SPSSs), which have attracted much research attention in recent years. The capture and analysis of users' dynamic situations are crucial for generating personalized services. Thus the context awareness ability with better self-adaptation and capability to mine information on implicit needs appears to be promising to cope with this issue. However, approaches for the development of a model that integrates context analysis and SPSS have not been fully explored. Therefore, this study presents a new perspective with the incorporation of context awareness, and then proposes a conceptual framework for an SPSS with context awareness (CA-SPSS) and the approach to develop this system. Specifically, the approach consists of four steps. The first step is context acquisition to create a wireless sensor network with wearable devices to acquire the physiological data of users. Second, context modeling procedures are set up on a cloud platform, such as for data collection, storage, and analysis. Moreover, machine learning datasets with information obtained through multiple channels, such as user, physical, sensed and inferred contexts, are generated. Afterwards, neural network models are used for context reasoning to determine the modalities for the users. To produce the appropriate service contents for each user modality level, a multi-criteria decision making process is employed to form customized service strategies for context distribution. To implement the proposed approach, an SPSS case for supporting non-professional sports competitions is developed and assessed in terms of the model performance and user satisfaction. The results show that the model deployed in the system has a better performance than traditional machine learning models. According to the findings from the user experiments, the subjects are very satisfied with the personalized service bundles and intelligence, but find that the reliability is suffering. The case study shows that the proposed CA-SPSS approach contributes to extending the available definitions of context in the literature and effectively guides the further development of SPSSs, especially in sports and mobile health.
Herding in the non-fungible token (NFT) market

In this study, we empirically examine the existence and dynamics of herding in the burgeoning market of non-fungible tokens (NFT) with transaction-level data from November 23, 2017 to April 27, 2021. We adopt both macro- and micro-approaches to detect herding and find supportive evidence of the existence of herding in this market, the dynamics of which appears to be event-driven. A large inflow of newcomers or inexperienced investors can serve as a trigger of herding. Herding in the NFT market tends to arise when the return on Ethereum increases, but it tends to diminish as the return on Bitcoin increases. Meanwhile, unlike in traditional asset markets, herding in NFT markets does not appear to happen across submarkets (OpenSea, Atomic, Cryptokitties, Godsunchained, and Decentraland). Active investors herd to trade NFTs within different collections and follow the market consensus when they are making investment decisions on NFTs listed on OpenSea. Results from macro- and micro-approaches validate and complement one another, plotting a profile on how investors herd in the NFT market.
Symmetric hypergraph states: entanglement quantification and robust Bell nonlocality

Abstract Quantum hypergraph states are the natural generalization of graph states. Here we investigate and analytically quantify entanglement and nonlocality for large classes of quantum hypergraph states. More specifically, we connect the geometric measure of entanglement of symmetric hypergraphs to their local Pauli stabilizers. As a result we recognize the resemblance between symmetric graph states and symmetric hypergraph states. This explains both the exponentially increasing violation of local realism for infinitely many classes of hypergraph states and robustness towards particle loss.
Non-redundant shift-invariant complex wavelet transform and fractional gorilla troops optimization-based deep convolutional neural network for video watermarking

With the development of multimedia processing approaches along with the Internet leads to various issues, like data tampering, data piracy, and illicit dissemination. The quick progression of fast communication networks for digital video transmission has formulated the prerequisite copyright protection for these media. On the other hand, the development of Internet technologies has directed the accessibility of images, audio, and videos in different structures. Moreover, unlicensed users manipulate the utilization of multimedia by broadcasting them on numerous internet sites to get money fraudulently without the involvement of the original copyright holder. Besides, watermarking is a process utilized to hide the watermark sign present in the multimedia statistics that are not perceptible to an intruder for manipulating any information. This paper devised a novel video watermarking technique which uses an optimized deep learning technique. Here, the binary pixel map generation is performed using DCNN for self-embedding in input video. In addition, weights as well as bias of DCNN are trained by designed Fractional Gorilla Troops Optimization-Deep Convolutional Neural Network algorithm, thus FGTO_DCNN algorithm is developed. Accordingly, the FGTO technique is newly introduced by integrating FC along with GTO algorithm. Moreover, non-redundant Shift Invariant Complex Wavelet Transform (SICWT) is employed for extracted key frame and thus watermark image is fed into embedding process. After that, inverse non-redundant SICWT is applied for embedding video, which is further passed to key frame extraction in extraction stage. Finally, non-redundant SICWT is utilized for obtaining extracted watermark image. The developed FGTO-driven DCNN model attained improved performance than other existing video watermarking techniques with Mean Square Error (MSE) of 0.0437, Normalized Correlation Coefficient (NCC) of 0.9998, high Peak Signal-to-Noise Ratio (PSNR) value of 51.93 dB, and high Weighted Signal-to-Noise Ratio (WSNR) of 49.86dB.
Multi visual feature fusion based fog visibility estimation for expressway surveillance using deep learning network

Visibility in foggy weather is of great value for traffic management and pollution monitoring. However, vision-based fog visibility estimation methods are usually based on a single image to approximate the visibility in foggy weather, and most existing data-driven machine learning models struggle to capture effective features and achieve high estimation accuracy due to the severe image degradation caused by reduced visibility and lack of real scene images. Therefore, this paper proposes a novel deep learning framework based on multi visual feature fusion for fog visibility estimation, named VENet, which comprises of two subtask networks (for fog level classification and fog visibility estimation) constructed in a cascade structure. A special feature extractor and an anchor-based regression method (ARM) are proposed to help improve the accuracy. Further, a standard Fog Visibility Estimation Image (FVEI) dataset containing 15,000 images of real fog scenes is established. This dataset greatly bridges the lack of suitable data in the field of vision-based visibility estimation. Extensive experiments have been conducted to demonstrate the performance of the proposed VENet, where the error of fog visibility estimation is less than 5% at 500 m and the fog level classification accuracy is at least 92.3%. In addition, the proposed VENet has been applied on Yunnan Xiangli and Mazhao Expressway surveillance with promising performance in practice.
A Framework for Practical Anonymous Credentials from Lattices

We present a framework for building practical anonymous credential schemes based on the hardness of lattice problems. The running time of the prover and verifier is independent of the number of users and linear in the number of attributes. The scheme is also compact in practice, with the proofs being as small as a few dozen kilobytes for arbitrarily large (say up to $$2^{128}$$ ) numbers of users with each user having several attributes. The security of our scheme is based on a new family of lattice assumptions which roughly states that given short pre-images of random elements in some set S, it is hard to create a pre-image for a fresh element in such a set. We show that if the set admits efficient zero-knowledge proofs of knowledge of a commitment to a set element and its pre-image, then this yields practically-efficient privacy-preserving primitives such as blind signatures, anonymous credentials, and group signatures. We propose a candidate instantiation of a function from this family which allows for such proofs and thus yields practical lattice-based primitives.
Weak Instances of Class Group Action Based Cryptography via Self-pairings

In this paper we study non-trivial self-pairings with cyclic domains that are compatible with isogenies between elliptic curves oriented by an imaginary quadratic order $$\mathcal {O}$$ . We prove that the order m of such a self-pairing necessarily satisfies $$m \mid \varDelta _\mathcal {O}$$ (and even $$2m \mid \varDelta _\mathcal {O}$$ if $$4 \mid \varDelta _\mathcal {O}$$ and $$4m \mid \varDelta _\mathcal {O}$$ if $$8 \mid \varDelta _\mathcal {O}$$ ) and is not a multiple of the field characteristic. Conversely, for each m satisfying these necessary conditions, we construct a family of non-trivial cyclic self-pairings of order m that are compatible with oriented isogenies, based on generalized Weil and Tate pairings. As an application, we identify weak instances of class group actions on elliptic curves assuming the degree of the secret isogeny is known. More in detail, we show that if $$m^2 \mid \varDelta _\mathcal {O}$$ for some prime power m then given two primitively $$\mathcal {O}$$ -oriented elliptic curves $$(E, \iota )$$ and $$(E',\iota ') = [\mathfrak {a}](E,\iota )$$ connected by an unknown invertible ideal $$\mathfrak {a}\subseteq \mathcal {O}$$ , we can recover $$\mathfrak {a}$$ essentially at the cost of a discrete logarithm computation in a group of order $$m^2$$ , assuming the norm of $$\mathfrak {a}$$ is given and is smaller than $$m^2$$ . We give concrete instances, involving ordinary elliptic curves over finite fields, where this turns into a polynomial time attack. Finally, we show that these self-pairings simplify known results on the decisional Diffie–Hellman problem for class group actions on oriented elliptic curves.
Orbweaver: Succinct Linear Functional Commitments from Lattices

We present Orbweaver, the first plausibly post-quantum functional commitment to achieve quasilinear prover time together with $$O(\log n)$$ proof size and $$O(\log n \log \log n)$$ verifier time. Orbweaver enables evaluation of linear maps on committed vectors over cyclotomic rings or the integers. It is extractable, preprocessing, non-interactive, structure-preserving, amenable to recursive composition, and supports logarithmic public proof aggregation. The security of our scheme is based on the k-R-ISIS assumption (and its knowledge counterpart), whereby we require a trusted setup to generate a universal structured reference string. We additionally use Orbweaver to construct a succinct polynomial commitment for integer polynomials.
MacORAMa: Optimal Oblivious RAM with Integrity

Oblivious RAM (ORAM), introduced by Goldreich and Ostrovsky (J. ACM '96), is a primitive that allows a client to perform RAM computations on an external database without revealing any information through the access pattern. For a database of size N, well-known lower bounds show that a multiplicative overhead of $$\varOmega (\log N)$$ in the number of RAM queries is necessary assuming O(1) client storage. A long sequence of works culminated in the asymptotically optimal construction of Asharov, Komargodski, Lin, and Shi (CRYPTO '21) with $$O(\log N)$$ worst-case overhead and O(1) client storage. However, this optimal ORAM is known to be secure only in the honest-but-curious setting, where an adversary is allowed to observe the access patterns but not modify the contents of the database. In the malicious setting, where an adversary is additionally allowed to tamper with the database, this construction and many others in fact become insecure. In this work, we construct the first maliciously secure ORAM with worst-case $$O(\log N)$$ overhead and O(1) client storage assuming one-way functions, which are also necessary. By the $$\varOmega (\log N)$$ lower bound, our construction is asymptotically optimal. To attain this overhead, we develop techniques to intricately interleave online and offline memory checking for malicious security. Furthermore, we complement our positive result by showing the impossibility of a generic overhead-preserving compiler from honest-but-curious to malicious security, barring a breakthrough in memory checking.
Lattice-Based Timed Cryptography

Timed cryptography studies primitives that retain their security only for a predetermined amount of time, such as proofs of sequential work and time-lock puzzles. This feature has proven to be useful in a large number of practical applications, e.g. randomness generation, sealed-bid auctions, and fair multi-party computation. However, the current state of affairs in timed cryptography is unsatisfactory: Virtually all efficient constructions rely on a single sequentiality assumption, namely that repeated squaring in unknown order groups cannot be parallelised. This is a single point of failure in the classical setting and is even false against quantum adversaries. In this work we put forward a new sequentiality assumption, which essentially says that a repeated application of the standard lattice-based hash function cannot be parallelised. We provide concrete evidence of the validity of this assumption and, to substantiate its usefulness, we show how it enables a new proof of sequential work, with a stronger sequentiality guarantee than prior hash-based schemes.