Reviewerly/exHarmony huggingface dataset:
Autonomous Transaction Model for E-Commerce Management Using Blockchain Technology

A blockchain is an advanced technology that can power over a decentralized network. The authors bring it up to design the autonomous transaction system for e-commerce applications; because of the dramatic increase in IoT devices, communication between physical things is enabled. This brings more efficiency and accuracy, which benefits the outsiders while human interaction reduces. There is a big challenge in data storage after payment in the e-commerce application. Blockchain presents an appropriate platform for the distributed data storage; it also protects the data from outsiders. The authors create blocks that check and record each transaction that took place in the e-commerce application. Blockchain is going to protect the user's privacy from outsiders/banks that are being violated. The authors deliver this research in this paper in terms of the method with detailed design and full implementation. The system captures the user data, processes it, and gives a visual representation of the processed data.
A Secure Data Sharing Based on Key Aggregate Searchable Encryption in Fog-Enabled IoT Environment

Cloud server and fog computing have been utilized to manage enormous volumes of data with low service transmission time in Internet of Things (IoT) environment. However, untrustworthy relationships between network entities and cloud server cause a lot of security concerns. Many researches have been studied based on traditional cryptosystems for data sharing systems. But they lead to key management difficulty and do not ensure the data owner's authority by depending on Trusted Third Party (TTP). Therefore, we use key aggregate searchable encryption (KASE), which enables owners to exercise their rights in data management and sharing without the help of TTP and resolves the key management problem. In this paper, we propose a secure data sharing system based on KASE in fog-enabled IoT environment using blockchain. We prove the resistance of the proposed scheme against various attacks through informal analysis and Automated Validation of Internet Security Protocols and Applications (AVISPA) simulation tool. We prove the guarantee of secure mutual authentcation using Burrows-Abadi-Needham (BAN) logic. Furthermore, we compare security features and performance of the proposed scheme with existing schemes through Multiprecision Integer and Rational Arithmetic Cryptographic Library (MIRACL).
Robust medical zero‐watermarking algorithm based on Residual‐DenseNet

To solve the problem of poor robustness of existing traditional DCT-based medical image watermarking algorithms under geometric attacks, a novel deep learning-based robust zero-watermarking algorithm for medical images is proposed. A Residual-DenseNet is designed, which took low-frequency features after discrete cosine transformation of medical images as labels and applied skip connections and a new objective function to strengthen and extract high-level semantic features that can effectively distinguish different medical images and binarise them to get robust hash vectors. Then, these hash vectors are bound with the chaotically encrypted watermark to generate the corresponding keys to complete the generation of watermark. The proposed algorithm neither modified the original medical image in the watermark generation stage nor required the original medical image in the watermark extraction stage. Moreover, the proposed algorithm is also suitable for multiple watermarks. Experimental results show that the proposed algorithm has good robust performance under both conventional and geometric attacks.
Are we building on the rock? on the importance of data preprocessing for code summarization

Code summarization, the task of generating useful comments given the code, has long been of interest. Most of the existing code summarization models are trained and validated on widely-used code comment benchmark datasets. However, little is known about the quality of the benchmark datasets built from real-world projects. Are the benchmark datasets as good as expected? To bridge the gap, we conduct a systematic research to assess and improve the quality of four benchmark datasets widely used for code summarization tasks. First, we propose an automated code-comment cleaning tool that can accurately detect noisy data caused by inappropriate data preprocessing operations from existing benchmark datasets. Then, we apply the tool to further assess the data quality of the four benchmark datasets, based on the detected noises. Finally, we conduct comparative experiments to investigate the impact of noisy data on the performance of code summarization models. The results show that these data preprocessing noises widely exist in all four benchmark datasets, and removing these noisy data leads to a significant improvement on the performance of code summarization. We believe that the findings and insights will enable a better understanding of data quality in code summarization tasks, and pave the way for relevant research and practice.
When to enter the metaverse: business leaders offer perspectives

Purpose Once Facebook transformed into Meta, a race to enter the metaverse began in all sectors of the economy. Being first has financial, technical and strategic costs, even if it can allow creating barriers to prevent others entering and establishing one’s brand as innovative. Being late, however, enables companies to benefit from more mature infrastructure and learning opportunities, but they risk being followers rather than leaders and missing opportunities. This study aims to discuss when organizations should consider entering the metaverse first, or whether they should come to it later. Design/methodology/approach To identify these conditions, 15 business strategy experts ranked 25 statements against each other about the metaverse and first- and late-entrant strategies. Findings When comparing the points of view of the 15 experts, four perspectives emerged. Three perspectives state that being a first mover can lead to a sustainable competitive advantage when organizations have strong capabilities regarding research and development, change management, learning and managing knowledge. The fourth perspective is skeptical that entering first can lead to a competitive advantage, given the high level of uncertainty surrounding the development of the metaverse. Practical implications A list of considerations when deciding to enter the metaverse is provided to managers. Originality/value This paper shifts the discourse on the metaverse from a technology-driven discussion to a strategic-asset-and-capabilities discussion.
A Stitch in Time Saves Nine: A Train-Time Regularizing Loss for Improved Neural Network Calibration

Deep Neural Networks (dnns) are known to make over-confident mistakes, which makes their use problematic in safety-critical applications. State-of-the-art (sota) calibration techniques improve on the confidence of predicted labels alone, and leave the confidence of non-max classes (e.g. top-2, top-5) uncalibrated. Such calibration is not suitable for label refinement using post-processing. Further, most sota techniques learn a few hyper-parameters post-hoc, leaving out the scope for image, or pixel specific calibration. This makes them unsuitable for calibration under domain shift, or for dense prediction tasks like semantic segmentation. In this paper, we argue for intervening at the train time itself, so as to directly produce calibrated dnn models. We propose a novel auxiliary loss function: Multi-class Difference in Confidence and Accuracy (mdca), to achieve the same. mdca can be used in conjunction with other application/task specific loss functions. We show that training with mdca leads to better calibrated models in terms of Expected Calibration Error (ece), and Static Calibration Error (sce) on image classification, and segmentation tasks. We report ece (sce) score of 0.72 (1.60) on the cifar 100 dataset, in comparison to 1.90 (1.71) by the sota. Under domain shift, a ResNet-18 model trained on pacs dataset using mdca gives a average ece (sce) score of 19.7 (9.7) across all domains, compared to 24.2 (11.8) by the sota. For segmentation task, we report a 2× reduction in calibration error on pascal-voc dataset in comparison to Focal Loss [32]. Finally, mdca training improves calibration even on imbalanced data, and for natural language classification tasks.
Towards Unbiased Label Distribution Learning for Facial Pose Estimation Using Anisotropic Spherical Gaussian

Facial pose estimation refers to the task of predicting face orientation from a single RGB image. It is an important research topic with a wide range of applications in computer vision. Label distribution learning (LDL) based methods have been recently proposed for facial pose estimation, which achieve promising results. However, there are two major issues in existing LDL methods. First, the expectations of label distributions are biased, leading to a biased pose estimation. Second, fixed distribution parameters are applied for all learning samples, severely limiting the model capability. In this paper, we propose an Anisotropic Spherical Gaussian (ASG)-based LDL approach for facial pose estimation. In particular, our approach adopts the spherical Gaussian distribution on a unit sphere which constantly generates unbiased expectation. Meanwhile, we introduce a new loss function that allows the network to learn the distribution parameter for each learning sample flexibly. Extensive experimental results show that our method sets new state-of-the-art records on AFLW2000 and BIWI datasets.
Partially Does It: Towards Scene-Level FG-SBIR with Partial Input

We scrutinise an important observation plaguing scene-level sketch research - that a significant portion of scene sketches are “partial”. A quick pilot study reveals: (i) a scene sketch does not necessarily contain all objects in the corresponding photo, due to the subjective holistic interpretation of scenes, (ii) there exists significant empty (white) regions as a result of object-level abstraction, and as a result, (iii) existing scene-level fine-grained sketch-based image retrieval methods collapse as scene sketches become more partial. To solve this “partial” problem, we advocate for a simple set-based approach using optimal transport (OT) to model cross-modal region associativity in a partially-aware fashion. Importantly, we improve upon OT to further account for holistic partialness by comparing intra-modal adjacency matrices. Our proposed method is not only robust to partial scene-sketches but also yields state-of-the-art performance on existing datasets.
Identifying core “responsible leadership” practices for SME restaurants

Purpose The purpose of this study is to identify the core responsible leadership (RL) practices that are most relevant to small- and medium-sized enterprises (SME) restaurants. Furthermore, the authors adapt scales to measure these practices and conduct a pilot study to evaluate their impact on business performance in such establishments. Design/methodology/approach Exploratory sequential mixed methods are used to fulfill the research aims. In the first phase, a set of definitions and practices associated with RL are derived from a systematic literature review. Second, a projective method of data collection is applied, involving a panel of 16 experts. Third, a fuzzy cognitive map is developed, which captures the responses of 40 owners or general managers of SME restaurants. Findings Twenty-five practices are identified from the systematic literature review. The results show the five leadership practices that match the order of importance assigned by the experts: societal orientation, ethics, stakeholder involvement, power-sharing and environmental orientation. The relevance of those five practices is validated to explain SME restaurants’ financial performance and innovation performance. Practical implications Innovation is the key to advancing business sustainability and resilience, and the results identify the specific RL practices that enable improvements to be made in innovation performance among SME restaurants. Originality/value This paper identifies the RL practices that are particularly relevant to the tourism field (specifically, the restaurant industry), offers measurement scales for those practices and provides empirical evidence of the relationship between these RL practices and business performance in SME restaurants.
Nonstabilizerness determining the hardness of direct fidelity estimation

In this work, we show how the resource theory of nonstabilizerness quantifies the hardness of direct fidelity estimation protocols. In particular, the resources needed for a direct fidelity estimation conducted on generic states, such as Pauli fidelity estimation and shadow fidelity estimation protocols, grow exponentially with the stabilizer R\'enyi entropy. Remarkably, these protocols are shown to be feasible only for those states that are useless to attain any quantum speedup or advantage. This result suggests the impossibility of estimating efficiently fidelity for generic states and, at the same time, leaves the window open to those protocols specialized at directly estimating the fidelity of particular states. We then extend our results to quantum evolutions, showing that the resources needed to certify the quality of the implementation of a given unitary $U$ are governed by the nonstabilizerness in the Choi state associated with $U$, which is shown to possess a profound connection with out-of-time order correlators.
Learning to Purification for Unsupervised Person Re-Identification

Unsupervised person re-identification is a challenging and promising task in computer vision. Nowadays unsupervised person re-identification methods have achieved great progress by training with pseudo labels. However, how to purify feature and label noise is less explicitly studied in the unsupervised manner. To purify the feature, we take into account two types of additional features from different local views to enrich the feature representation. The proposed multi-view features are carefully integrated into our cluster contrast learning to leverage more discriminative cues that the global feature easily ignored and biased. To purify the label noise, we propose to take advantage of the knowledge of teacher model in an offline scheme. Specifically, we first train a teacher model from noisy pseudo labels, and then use the teacher model to guide the learning of our student model. In our setting, the student model could converge fast with the supervision of the teacher model thus reduce the interference of noisy labels as the teacher model greatly suffered. After carefully handling the noise and bias in the feature learning, our purification modules are proven to be very effective for unsupervised person re-identification. Extensive experiments on three popular person re-identification datasets demonstrate the superiority of our method. Especially, our approach achieves a state-of-the-art accuracy 85.8\% @mAP and 94.5\% @Rank-1 on the challenging Market-1501 benchmark with ResNet-50 under the fully unsupervised setting. The code will be released.
Underwater Ranker: Learn Which Is Better and How to Be Better

In this paper, we present a ranking-based underwater image quality assessment (UIQA) method, abbreviated as URanker. The URanker is built on the efficient conv-attentional image Transformer. In terms of underwater images, we specially devise (1) the histogram prior that embeds the color distribution of an underwater image as histogram token to attend global degradation and (2) the dynamic cross-scale correspondence to model local degradation. The final prediction depends on the class tokens from different scales, which comprehensively considers multi-scale dependencies. With the margin ranking loss, our URanker can accurately rank the order of underwater images of the same scene enhanced by different underwater image enhancement (UIE) algorithms according to their visual quality. To achieve that, we also contribute a dataset, URankerSet, containing sufficient results enhanced by different UIE algorithms and the corresponding perceptual rankings, to train our URanker. Apart from the good performance of URanker, we found that a simple U-shape UIE network can obtain promising performance when it is coupled with our pre-trained URanker as additional supervision. In addition, we also propose a normalization tail that can significantly improve the performance of UIE networks. Extensive experiments demonstrate the state-of-the-art performance of our method. The key designs of our method are discussed. Our code and dataset are available at https://li-chongyi.github.io/URanker_files/.
Privacy-Preserving Point-of-Interest Recommendation based on Simplified Graph Convolutional Network for Geological Traveling

The provision of privacy-preserving recommendations for geological tourist attractions is an important research area. The historical check-in data collected from location-based social networks (LBSNs) can can be utilized to mine their preferences, thereby facilitating the promotion of the geological tourism industry. However, such check-ins often contain sensitive user information that poses privacy leakage risks. To address this issue, some methods have been proposed to develop privacy-preserving point-of-interest (POI) recommendation systems. These methods commonly rely on either perturbation-based or federated learning techniques to protect users’ privacy. However, the former can hinder preference capture, while the latter remains vulnerable to privacy breaches during the parameter-sharing process. To overcome these challenges, we propose a novel privacy-preserving POI recommendation model that incorporates users’ privacy preferences based on a simplified graph convolutional neural network. Specifically, we employ a generative model to create a subset of POIs that reflect users’ preferences but do not reveal their private information, and then design a simplified graph convolutional network to analyze the high-order connectivity between users and POIs that are privacy-preserving. The resulting model enables efficient POI recommendation under strict privacy protection, which is particularly relevant to geological tourism. Experimental results on two public datasets demonstrate the effectiveness of our proposed approach.
De-Anonymizing Users Across Heterogeneous Social Computing Platforms

Many people use multiple online and social computing platforms, and choose to share varying amounts of personal information about themselves depending on the context and type of site. For example, people may be willing to share personally-identifiable details (including their real name and date of birth) on a site like Facebook, but may withhold their identity on a dating site that may be widely viewed by strangers. We study the extent to which subtle correlations in a user's activity patterns across different sites may be used to infer that two accounts correspond to the same person. We study a variety of features, including similarity of temporal access patterns, textual content, geo-tags, and social connections, finding that even very weak signals yield surprisingly accurate de-anonymization results.
White-Box Cryptography: Don’t Forget About Grey-Box Attacks

Despite the fact that all current scientific white-box approaches of standardized cryptographic primitives have been publicly broken, these attacks require knowledge of the internal data representation used by the implementation. In practice, the level of implementation knowledge required is only attainable through significant reverse-engineering efforts. In this paper, we describe new approaches to assess the security of white-box implementations which require neither knowledge about the look-up tables used nor expensive reverse-engineering efforts. We introduce the differential computation analysis (DCA) attack which is the software counterpart of the differential power analysis attack as applied by the cryptographic hardware community. Similarly, the differential fault analysis (DFA) attack is the software counterpart of fault injection attacks on cryptographic hardware. For DCA, we developed plugins to widely available dynamic binary instrumentation (DBI) frameworks to produce software execution traces which contain information about the memory addresses being accessed. For the DFA attack, we developed modified emulators and plugins for DBI frameworks that allow injecting faults at selected moments within the execution of the encryption or decryption process as well as a framework to automate static fault injection. To illustrate the effectiveness, we show how DCA and DFA can extract the secret key from numerous publicly available non-commercial white-box implementations of standardized cryptographic algorithms. These approaches allow one to extract the secret key material from white-box implementations significantly faster and without specific knowledge of the white-box design in an automated or semi-automated manner.
Learning error-correcting graph matching with a multiclass neural network

Many tasks in computer vision and pattern recognition are formulated as graph matching problems. Despite the NP-hard nature of such problems, fast and accurate approximations have led to significant progress in a wide range of applications. However, learning graph matching from observed data, remains a challenging issue. In practice, the node correspondences ground truth is rarely available. This paper presents an effective scheme for optimizing the graph matching problem in a classification context. For this, we propose a representation that is based on a parametrized model graph, and optimize the associated parameters. The objective of the optimization problem is to increase the classification rate. Experimental results on seven public datasets demonstrate the effectiveness (in terms of accuracy and speed) of our approach compared to four reference graph classifiers.
New Frontiers for Cognitive Assessment: An Exploratory Study of the Potentiality of 360° Technologies for Memory Evaluation

One of the most recent trends in technology field is the 360° technology. In order to investigate its potentiality for the evaluation of memory function, we compared participants' performance in two memory tasks between an encoding delivered through an immersive 360° environment and a non-immersive 360° one. Specifically, we investigated if the immersive 360° environment would facilitate memory encoding in comparison to the non-immersive 360° one. Forty-two participants visualized two 360° pictures of a bedroom containing specific items and items shared by the two rooms, either in the “non-immersive → immersive” order or in the “immersive → non-immersive” one. Results showed that the immersive set-up was able to strengthen the mnestic trace only when participants performed the tasks in the “non-immersive → immersive” order. A possible explanation is a potential familiarity with the technologies used to explore the 360° environments. Overall, these results only partially supported the effectiveness of immersive 360° technology in facilitating episodic memory encoding; future studies should further explore the potentiality of this innovative technology.