Reviewerly/exHarmony huggingface dataset:
Efficient Fully Secure Computation via Distributed Zero-Knowledge Proofs

Secure computation protocols enable mutually distrusting parties to compute a function of their private inputs while revealing nothing but the output. Protocols with full security (also known as guaranteed output delivery) in particular protect against denial-of-service attacks, guaranteeing that honest parties receive a correct output. This feature can be realized in the presence of an honest majority, and significant research effort has gone toward attaining full security with good asymptotic and concrete efficiency. We present an efficient protocol for any constant number of parties n, with full security against $${t<n/2}$$ corrupted parties, that makes a black-box use of a pseudorandom generator. Our protocol evaluates an arithmetic circuit C over a finite ring R (either a finite field or $${{R={\mathbb {Z}}_{2^k}}}$$ ) with communication complexity of $${{\frac{3t}{2t+1}S + o(S)}}$$ R-elements per party, where S is the number of multiplication gates in C (namely, $${<1.5}$$ elements per party per gate). This matches the best known protocols for the semi-honest model up to the sublinear additive term. For a small number of parties n, this improves over a recent protocol of Goyal et al. (Crypto 2020) by a constant factor for circuits over large fields, and by at least an $${{\varOmega (\log n)}}$$ factor for Boolean circuits or circuits over rings. Our protocol provides new methods for applying the distributed zero-knowledge proofs of Boneh et al. (Crypto 2019), which only require logarithmic communication, for compiling semi-honest protocols into fully secure ones in the more challenging case of $$t>1$$ corrupted parties. Our protocol relies on replicated secret sharing to minimize communication and simplify the mechanism for achieving full security. This results in computational cost that scales exponentially with n. Our main protocol builds on a new honest-majority protocol for verifying the correctness of multiplication triples by making a general use of distributed zero-knowledge proofs. While the protocol only achieves the weaker notion of security with abort, it applies to any linear secret-sharing scheme and provides a conceptually simpler, more general, and more efficient alternative to previous protocols from the literature. In particular, it can be combined with the Fiat-Shamir heuristic to simultaneously achieve logarithmic communication complexity and constant round complexity.
An image encryption scheme based on public key cryptosystem and quantum logistic map

Abstract Most of existing image encryption schemes are proposed in the spatial domain which easily destroys the correlation between pixels. This paper proposes an image encryption scheme by employing discrete cosine transform (DCT), quantum logistic map and substitution-permutation network (SPN). The DCT is used to transform the images in the frequency domain. Meanwhile, the SPN is used to provide the security properties of confusion and diffusion. The SPN provides fast encryption as compared to the asymmetric based image encryption since operations with low computational complexity are used (e.g., exclusive-or and permutation). Different statistical experiments and security analysis are performed against six grayscale and color images to justify the effectiveness and security of the proposed image encryption scheme.
3D Human Shape Reconstruction from a Polarization Image

This paper tackles the problem of estimating 3D body shape of clothed humans from single polarized 2D images, i.e. polarization images. Polarization images are known to be able to capture polarized reflected lights that preserve rich geometric cues of an object, which has motivated its recent applications in reconstructing surface normal of the objects of interest. Inspired by the recent advances in human shape estimation from single color images, in this paper, we attempt at estimating human body shapes by leveraging the geometric cues from single polarization images. A dedicated two-stage deep learning approach, SfP, is proposed: given a polarization image, stage one aims at inferring the fined-detailed body surface normal; stage two gears to reconstruct the 3D body shape of clothing details. Empirical evaluations on a synthetic dataset (SURREAL) as well as a real-world dataset (PHSPD) demonstrate the qualitative and quantitative performance of our approach in estimating human poses and shapes. This indicates polarization camera is a promising alternative to the more conventional color or depth imaging for human shape estimation. Further, normal maps inferred from polarization imaging play a significant role in accurately recovering the body shapes of clothed people.
Neural Architecture Search for Robust Networks in 6G-Enabled Massive IoT Domain

6G technology enables artificial intelligence (AI)-based massive IoT to manage network resources and data with ultra high speed, responsive network, and wide coverage. However, many AI-enabled Internet-of-Things (AIoT) systems are vulnerable to adversarial example attacks. Therefore, designing robust deep learning models that can be deployed on resource-constrained devices has become an important research topic in the field of 6G-enabled AIoT. In this article, we propose a method for automatically searching for robust and efficient neural network structures for AIoT systems. By introducing a skip connection structure, a feature map with reduced front-end influence can be used for calculations during the classification process. Additionally, a novel type of densely connected search space is proposed. By relaxing this space, it is possible to search for network structures efficiently. In addition, combined with adversarial training and model delay constraints, we propose a multiobjective gradient optimization method to realize the automatic searching of network structures. Experimental results demonstrate that our method is effective for AIoT systems and superior to state-of-the-art neural architecture search algorithms.
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?

Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled by non-specialist annotators, or even specialists in a challenging task, such as in the medical field. Although deep learning models have shown significant improvements in different domains, an open issue is their ability to memorize noisy labels during training, reducing their generalization potential. As deep learning models depend on correctly labeled data sets and label correctness is difficult to guarantee, it is crucial to consider the presence of noisy labels for deep learning training. Several approaches have been proposed in the literature to improve the training of deep learning models in the presence of noisy labels. This paper presents a survey on the main techniques in literature, in which we classify the algorithm in the following groups: robust losses, sample weighting, sample selection, meta-learning, and combined approaches. We also present the commonly used experimental setup, data sets, and results of the state-of-the-art models.
Interactive, effort-aware library version harmonization

As a mixed result of intensive dependency on third-party libraries, flexible mechanisms to declare dependencies and increased number of modules in a project, different modules of a project directly depend on multiple versions of the same third-party library. Such library version inconsistencies could increase dependency maintenance cost, or even lead to dependency conflicts when modules are inter-dependent. Although automated build tools (e.g., Maven's enforcer plugin) provide partial support to detect library version inconsistencies, they do not provide any support to harmonize inconsistent library versions.
Multi-modal neural machine translation with deep semantic interactions

Based on the conventional attentional encoder-decoder framework, multi-modal neural machine translation (NMT) further incorporates spatial visual features through a separate visual attention mechanism. In this aspect, most current multi-modal NMT models first separately learn the semantic representations of text and image and then independently produce two modalities of context vectors for word predictions, neglecting their semantic interactions. In this paper, we argue that learning text-image semantic interactions is more reasonable in the sense of jointly modeling two modalities for multi-modal NMT and propose a novel multi-modal NMT model with deep semantic interactions. Specifically, our model extends the conventional multi-modal NMT by introducing the following two attention neural networks: (1) a bi-directional attention network for modeling text and image representations, where the semantic representations of text are learned by referring to the image representations, and vice versa; (2) a co-attention network for refining text and image context vectors, which first summarizes the text into a context vector, then attends it to the image for obtaining the text-aware visual context vector. The final context vector is calculated by re-attending the visual context vector to the text. Results on the Multi30k dataset for different language pairs show that our model significantly improves on the state-of-the-art baselines. We have released our code at https://github.com/DeepLearnXMU/MNMT.
AI-Enabled Blockchain-Based Access Control for Malicious Attacks Detection and Mitigation in IoE

In Internet of Everything (IoE), malicious attacks detection and mitigation are important issues. These issues can be resolved through an access control framework where two entities first authenticate each other prior to establishing any secret key for their secure communication. The sensing data of various smart devices in an IoE environment are processed securely at the nearby fog servers and at the same time legitimate users can also access the real-time data directly from designated smart devices through access control mechanism. We first discuss various attack trends in an IoE environment. After that, we discuss evolution of the blockchain technology in the IoE. An artificial intelligence based blockchain-envisioned access control framework for malicious attacks detection and mitigation has been suggested to secure the IoE environment. Finally, a blockchain-based implementation has been conducted on the proposed blockchain-envisioned access control framework for measuring the computational time needed for varying number of blocks mined in the blockchain and also for varying number of transactions per block.
The (better than expected) consequences of asking sensitive questions

Within a conversation, individuals balance competing objectives, such as the motive to gather information and the motive to create a favorable impression. Across five experimental studies (N = 1427), we show that individuals avoid asking sensitive questions because they believe that asking sensitive questions will make their conversational partners uncomfortable and cause them to form negative perceptions. We introduce the Communication Motives and Expectations Model and we demonstrate that the aversion to asking sensitive questions is often misguided. Question askers systematically overestimate the impression management and interpersonal costs of asking sensitive questions. In conversations with friends and with strangers and in both face-to-face and computer-mediated conversations, respondents formed similarly favorable impressions of conversational partners who asked sensitive questions (e.g., “How much is your salary?”) as they did of conversational partners who asked non-sensitive questions (e.g., “How do you get to work?”). We assert that individuals make a potentially costly mistake when they avoid asking sensitive questions, as they overestimate the interpersonal costs of asking sensitive questions.
Joint Layout Analysis, Character Detection and Recognition for Historical Document Digitization

In this paper, we propose an end-to-end trainable framework for restoring historical documents content that follows the correct reading order. In this framework, two branches named character branch and layout branch are added behind the feature extraction network. The character branch localizes individual characters in a document image and recognizes them simultaneously. Then we adopt a post-processing method to group them into text lines. The layout branch based on fully convolutional network outputs a binary mask. We then use Hough transform for line detection on the binary mask and combine character results with the layout information to restore document content. These two branches can be trained in parallel and are easy to train. Furthermore, we propose a re-score mechanism to minimize recognition error. Experiment results on the extended Chinese historical document MTHv2 dataset demonstrate the effectiveness of the proposed framework.
SAP-SSE: Protecting Search Patterns and Access Patterns in Searchable Symmetric Encryption

Searchable symmetric encryption (SSE) enables users to search over encrypted documents in untrusted clouds without leaking the search keywords to the clouds. Existing SSE schemes achieve high search efficiency at the expense of leaking access patterns and search patterns, where clouds can recover a large percentage of queried keywords using the leaked access patterns and search patterns. To prevent clouds from recovering users' keywords, researchers have proposed a number of solutions to protect either search patterns or access patterns. However, none of them can protect both access patterns and search patterns. Moreover, existing SSE schemes cannot work in the generic database setting that allows multiple users to write or read over encrypted documents. In this paper, we propose an efficient searchable symmetric encryption scheme, called SAP-SSE, which protects both access patterns and search patterns in the generic database setting. The main idea of protecting search patterns is to leverage re-encryption cryptosystems to shuffle index entries over multiple clouds. To protect access patterns, we distribute secure indexes to multiple clouds and then propose an index redistribution protocol that allows users to renew index entries in clouds. Furthermore, SAP-SSE provides a configurable security policy to balance security and efficiency. Formal security analysis and experimental evaluation show that SAP-SSE can prevent pattern leakage with low overhead.
Adaptive binary artificial bee colony algorithm

Metaheuristics and swarm intelligence algorithms are bio-inspired algorithms, which have long standing track record of success in problem solving. Due to the nature and the complexity of the problems, problem solving approaches may not achieve the same success level in every type of problems. Artificial bee colony (ABC) algorithm is a swarm intelligence algorithm and has originally been developed to solve numerical optimisation problems. It has a sound track record in numerical problems, but has not yet been tested sufficiently for combinatorial and binary problems. This paper proposes an adaptive hybrid approach to devise ABC algorithms with multiple and complementary binary operators for higher efficiency in solving binary problems. Three prominent operator selection schemes have been comparatively investigated for the best configuration in this regard. The proposed approach has been applied to uncapacitated facility location problems, a renown NP-Hard combinatorial problem type modelled with 0–1 programming, and successfully solved the well-known benchmarks outperforming state-of-art algorithms.
Blockchain from the Perspective of Privacy and Anonymisation: A Systematic Literature Review

The research presented aims to investigate the relationship between privacy and anonymisation in blockchain technologies on different fields of application. The study is carried out through a systematic literature review in different databases, obtaining in a first phase of selection 199 publications, of which 28 were selected for data extraction. The results obtained provide a strong relationship between privacy and anonymisation in most of the fields of application of blockchain, as well as a description of the techniques used for this purpose, such as Ring Signature, homomorphic encryption, k-anonymity or data obfuscation. Among the literature researched, some limitations and future lines of research on issues close to blockchain technology in the different fields of application can be detected. As conclusion, we extract the different degrees of application of privacy according to the mechanisms used and different techniques for the implementation of anonymisation, being one of the risks for privacy the traceability of the operations.
Governing Principles of Self-Sovereign Identity Applied to Blockchain Enabled Privacy Preserving Identity Management Systems

Digital identity is the key element of digital transformation in representing any real-world entity in the digital form. To ensure a successful digital future the requirement for an effective digital identity is paramount, especially as demand increases for digital services. Several Identity Management (IDM) systems are developed to cope with identity effectively, nonetheless, existing IDM systems have some limitations corresponding to identity and its management such as sovereignty, storage and access control, security, privacy and safeguarding, all of which require further improvement. Self-Sovereign Identity (SSI) is an emerging IDM system which incorporates several required features to ensure that identity is sovereign, secure, reliable and generic. It is an evolving IDM system, thus it is essential to analyse its various features to determine its effectiveness in coping with the dynamic requirements of identity and its current challenges. This paper proposes numerous governing principles of SSI to analyse any SSI ecosystem and its effectiveness. Later, based on the proposed governing principles of SSI, it performs a comparative analysis of the two most popular SSI ecosystems uPort and Sovrin to present their effectiveness and limitations.
Privacy Protection for Medical Data Sharing in Smart Healthcare

In virtue of advances in smart networks and the cloud computing paradigm, smart healthcare is transforming. However, there are still challenges, such as storing sensitive data in untrusted and controlled infrastructure and ensuring the secure transmission of medical data, among others. The rapid development of watermarking provides opportunities for smart healthcare. In this article, we propose a new data-sharing framework and a data access control mechanism. The applications are submitted by the doctors, and the data is processed in the medical data center of the hospital, stored in semi-trusted servers to support the selective sharing of electronic medical records from different medical institutions between different doctors. Our approach ensures that privacy concerns are taken into account when processing requests for access to patients’ medical information. For accountability, after data is modified or leaked, both patients and doctors must add digital watermarks associated with their identification when uploading data. Extensive analytical and experimental results are presented that show the security and efficiency of our proposed scheme.
Dangers of the Defaults: A Tutorial on the Impact of Default Priors When Using Bayesian SEM With Small Samples

When Bayesian estimation is used to analyze Structural Equation Models (SEMs), prior distributions need to be specified for all parameters in the model. Many popular software programs offer default prior distributions, which is helpful for novel users and makes Bayesian SEM accessible for a broad audience. However, when the sample size is small, those prior distributions are not always suitable and can lead to untrustworthy results. In this tutorial, we provide a non-technical discussion of the risks associated with the use of default priors in small sample contexts. We discuss how default priors can unintentionally behave as highly informative priors when samples are small. Also, we demonstrate an online educational Shiny app, in which users can explore the impact of varying prior distributions and sample sizes on model results. We discuss how the Shiny app can be used in teaching; provide a reading list with literature on how to specify suitable prior distributions; and discuss guidelines on how to recognize (mis)behaving priors. It is our hope that this tutorial helps to spread awareness of the importance of specifying suitable priors when Bayesian SEM is used with small samples.
Agent-based automated dynamic SLA negotiation framework in the cloud using the stochastic optimization approach

Optimization of the broker negotiation strategy is one of the challenging issues observed in the cloud-based e-commerce negotiation framework. This strategy can be optimized either in the context of pre-request optimization or long-term optimization. Most researchers have focused on the context of pre-request optimization using various utility functions (such as time, opportunity, and competition). In the context of long-term optimization, the current state-of-the-art negotiation strategies can increase the utility value and success rate of the parties to some extent, but they do not guarantee minimization of the negotiation states (rounds) involved during the negotiation process. In addition, the existing strategies cannot react to the stochastic, rational, emotional, and unknown behaviour of the opponents due to their deterministic behaviour, which may lead to a negotiation break-off between parties. To overcome such limitations, a novel stochastic behavioural learning negotiation (SBLN) strategy is proposed to further maximize the utility value and success rate.
Why to Buy Insurance? An Explainable Artificial Intelligence Approach

We propose an Explainable AI model that can be employed in order to explain why a customer buys or abandons a non-life insurance coverage. The method consists in applying similarity clustering to the Shapley values that were obtained from a highly accurate XGBoost predictive classification algorithm. Our proposed method can be embedded into a technologically-based insurance service (Insurtech), allowing to understand, in real time, the factors that most contribute to customers’ decisions, thereby gaining proactive insights on their needs. We prove the validity of our model with an empirical analysis that was conducted on data regarding purchases of insurance micro-policies. Two aspects are investigated: the propensity to buy an insurance policy and the risk of churn of an existing customer. The results from the analysis reveal that customers can be effectively and quickly grouped according to a similar set of characteristics, which can predict their buying or churn behaviour well.