Reviewerly/exHarmony huggingface dataset:
An efficient anti-quantum lattice-based blind signature for blockchain-enabled systems

Blockchain technology has a tendency to make revolutionary changes for all walks of life with its public, distributed, decentration and unforgeable characteristics. However, with the rapid development of the quantum computer, many extant cryptographic algorithms applied in blockchain-enabled systems are vulnerable to the quantum attacks. In this paper, an anti-quantum blind signature scheme based on the lattice assumption has been proposed. As lattice cryptography is the main candidate algorithm in post-quantum cryptosystems and the blind signature scheme is widely used in e-cash and voting for creating untraceable payment system, which are more suitable for privacy preserving in blockchain-enabled systems. In the proposed scheme, the bimodal Gaussian distribution, Reject sampling and other technologies have been used to improve the security and efficiency. Then, the proposed blind signature can satisfy the properties of blindness and one-more unforgeability, and it also can prove to be safe in the random oracle model. Moreover, the efficiency analysis and comparison results show that the proposed scheme is more efficient than the similar literatures and has a more stabilized signature size than other cryptosystems.
Novel multi-label feature selection via label symmetric uncertainty correlation learning and feature redundancy evaluation

Multi-label data with high dimensionality, widely existed in the real world, bring many challenges to the applications of machine learning, pattern recognition and other fields. Scholars have proposed some multi-label feature selection methods from various aspects. However, there are few studies on the feature selection for multi-label data based on fuzzy mutual information, and most existing methods neglect the correlation between labels. In this study, we propose two novel multi-label feature selection approaches via label symmetric uncertainty correlation and feature redundancy evaluation. Firstly, we propose the concept of symmetric uncertainty correlation between labels via fuzzy mutual information, and design a label importance weight based on label symmetric uncertainty correlation learning. Further, we define a label similarity relation matrix on multi-label space via the label importance weight. Secondly, we define the symmetric uncertainty correlation between features and labels, and propose the first multi-label feature selection approach. Thirdly, considering the above-proposed method can only get a feature sequence and does not remove the redundancy features, we further propose an improved multi-label removing-redundancy feature selection approach through introducing feature redundancy evaluation. Finally, comprehensive experiments are executed to demonstrate the performance of our methods. The results illustrate that our study is better than other representative feature selection methods.
Multiscale multilevel context and multimodal fusion for RGB-D salient object detection

Red–green–blue and depth (RGB-D) saliency detection has recently attracted much research attention; however, the effective use of depth information remains challenging. This paper proposes a method that leverages depth information in clear shapes to detect the boundary of salient objects. As context plays an important role in saliency detection, the method incorporates a proposed end-to-end multiscale multilevel context and multimodal fusion network (MCMFNet) to aggregate multiscale multilevel context feature maps for accurate saliency detection from objects of varying sizes. Finally, a coarse-to-fine approach is applied to an attention module retrieving multilevel and multimodal feature maps to produce the final saliency map. A comprehensive loss function is also incorporated in MCMFNet to optimize the network parameters. Extensive experiments demonstrate the effectiveness of the proposed method and its substantial improvement over state-of-the-art methods for RGB-D salient object detection on four representative datasets.
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data

Domain adaptation (DA) offers a valuable means to reuse data and models for new problem domains. However, robust techniques have not yet been considered for time series data with varying amounts of data availability. In this paper, we make three main contributions to fill this gap. First, we propose a novel Convolutional deep Domain Adaptation model for Time Series data (CoDATS) that significantly improves accuracy and training time over state-of-the-art DA strategies on real-world sensor data benchmarks. By utilizing data from multiple source domains, we increase the usefulness of CoDATS to further improve accuracy over prior single-source methods, particularly on complex time series datasets that have high variability between domains. Second, we propose a novel Domain Adaptation with Weak Supervision (DA-WS) method by utilizing weak supervision in the form of target-domain label distributions, which may be easier to collect than additional data labels. Third, we perform comprehensive experiments on diverse real-world datasets to evaluate the effectiveness of our domain adaptation and weak supervision methods. Results show that CoDATS for single-source DA significantly improves over the state-of-the-art methods, and we achieve additional improvements in accuracy using data from multiple source domains and weakly supervised signals.
CODIT: Code Editing With Tree-Based Neural Models

The way developers edit day-to-day code tends to be repetitive, often using existing code elements. Many researchers have tried to automate repetitive code changes by learning from specific change templates which are applied to limited scope. The advancement of deep neural networks and the availability of vast open-source evolutionary data opens up the possibility of automatically learning those templates from the wild. However, deep neural network based modeling for code changes and code in general introduces some specific problems that needs specific attention from research community. For instance, compared to natural language, source code vocabulary can be significantly larger. Further, good changes in code do not break its syntactic structure. Thus, deploying state-of-the-art neural network models without adapting the methods to the source code domain yields sub-optimal results. To this end, we propose a novel tree-based neural network system to model source code changes and learn code change patterns from the wild. Specifically, we propose a tree-based neural machine translation model to learn the probability distribution of changes in code. We realize our model with a change suggestion engine, CODIT, and train the model with more than 24k real-world changes and evaluate it on 5k patches. Our evaluation shows the effectiveness of CODITin learning and suggesting patches. CODIT can also learn specific bug fix pattern from bug fixing patches and can fix 25 bugs out of 80 bugs in Defects4J.
LRSpeech

Speech synthesis (text to speech, TTS) and recognition (automatic speech recognition, ASR) are important speech tasks, and require a large amount of text and speech pairs for model training. However, there are more than 6,000 languages in the world and most languages are lack of speech training data, which poses significant challenges when building TTS and ASR systems for extremely low-resource languages. In this paper, we develop LRSpeech, a TTS and ASR system under the extremely low-resource setting, which can support rare languages with low data cost. LRSpeech consists of three key techniques: 1) pre-training on rich-resource languages and fine-tuning on low-resource languages; 2) dual transformation between TTS and ASR to iteratively boost the accuracy of each other; 3) knowledge distillation to customize the TTS model on a high-quality target-speaker voice and improve the ASR model on multiple voices. We conduct experiments on an experimental language (English) and a truly low-resource language (Lithuanian) to verify the effectiveness of LRSpeech. Experimental results show that LRSpeech 1) achieves high quality for TTS in terms of both intelligibility (more than $98%$ intelligibility rate) and naturalness (above 3.5 mean opinion score (MOS)) of the synthesized speech, which satisfy the requirements for industrial deployment, 2) achieves promising recognition accuracy for ASR, and 3) last but not least, uses extremely low-resource training data. We also conduct comprehensive analyses on LRSpeech with different amounts of data resources, and provide valuable insights and guidances for industrial deployment. We are currently deploying LRSpeech into a commercialized cloud speech service to support TTS on more rare languages.
The Conditional Entropy Bottleneck

Much of the field of Machine Learning exhibits a prominent set of failure modes, including vulnerability to adversarial examples, poor out-of-distribution (OoD) detection, miscalibration, and willingness to memorize random labelings of datasets. We characterize these as failures of robust generalization, which extends the traditional measure of generalization as accuracy or related metrics on a held-out set. We hypothesize that these failures to robustly generalize are due to the learning systems retaining too much information about the training data. To test this hypothesis, we propose the Minimum Necessary Information (MNI) criterion for evaluating the quality of a model. In order to train models that perform well with respect to the MNI criterion, we present a new objective function, the Conditional Entropy Bottleneck (CEB), which is closely related to the Information Bottleneck (IB). We experimentally test our hypothesis by comparing the performance of CEB models with deterministic models and Variational Information Bottleneck (VIB) models on a variety of different datasets and robustness challenges. We find strong empirical evidence supporting our hypothesis that MNI models improve on these problems of robust generalization.
Gate-based superconducting quantum computing

In this Tutorial, we introduce basic conceptual elements to understand and build a gate-based superconducting quantum computing system.
Dynamic pricing in industrial internet of things: Blockchain application for energy management in smart cities

With the advent of advancements in the power sector, various new methods have been devised to meet modern society’s electricity needs. To cope with these large sets of electronic device’s current requirements, better energy distribution is needed. Smart Grid (SG) facilitates energy providers to distribute electricity efficiently to the user according to their particular requirements. Recent advancements enable SG to monitor, analyze, control and coordinate for the demand and supply of electricity efficiency and energy saving. SG also allows two-way real-time communication between utilities and customers using cloud and Fog enabled infrastructures. SG minimizes management and operations cost, electricity theft, electricity losses, and maximize user comfort by giving the user choice about their energy use. It also facilitates Renewable Energy Resources (RER) and electric vehicles. Blockchain is a promising technology, provides the necessary features to solve most of these issues. Current Issues include saving a large amount of data, deletion, tampering, and revision of data. It also eliminates the necessity of intermediaries. Inherent security, along with the distributed nature, makes it a perfect candidate for improving the overall services. The rules of the smart contract are automatically enforced upon execution. Smart contracts are enhanced in a way that per-unit price is calculated dynamically based upon RER and utilities generated energy units in the overall grid. The system is also automated in a way that electricity is transferred from one resident (or service) to another resident according to their requirements. The exchange of energy is done via a smart contract after checking the needs of each participant. Each participant defines their requirements at the time of the registration and can update these thresholds. The privacy protection scheme has higher security, shown by theoretical security analysis. The main contributions of our work are two-fold; Using smart contracts to automate the bidding process for transactions based upon supply and demand for energy in smart cities. Secondly, at the same time, using hyper ledger fabric and composer to leveraging Blockchain to uphold privacy, anonymity, and confidentiality at the same time giving the users ability to have dynamic pricing based on supply and demand.
Multi-population following behavior-driven fruit fly optimization: A Markov chain convergence proof and comprehensive analysis

Fruit fly optimization algorithm (FOA) is a well-known optimization algorithm with a well-designed structure and superiority of fewer parameters, more effortless adjustment, and competitive and fast computation time. Up till now, FOA has been effectively applied to numerous fields, such as financial forecast and medicine, and it achieves favorable results. However, some aspects need to be enhanced when dealing with some function optimization cases. This method is inclined to falling into local optima with a slow convergence. In this paper, the following behavior-driven multi-population FOA is proposed to relieve these drawbacks, which combines the following mechanism of artificial fish swarm algorithm with the unique searching ability of different types of fruit flies. The chaotic global disturbance is introduced to improve the global exploration ability of the original FOA and reduce the probability of advanced FOA falling into the local extreme. The proposed FOA, it is compared with the advanced FOA with several well-established algorithms and the latest improved optimizers in different dimensions horizontally and vertically to substantiate the effectiveness of the. It is also applied advanced FOA to two practical engineering optimization projects. Systematic analysis and experimental data indicate that the advanced FOA variant outperforms the original FOA and the latest improved algorithms. An online repository will support this research at http://aliasgharheidari.com for any communication and guidance for future works.
Lightweight Cryptographic Protocols for IoT-Constrained Devices: A Survey

Internet of Things (IoT) is an emergent and evolving technology, interconnecting the cyber and physical worlds. IoT technology finds applications in a broad spectrum of areas such as homes, health, water and sanitation, transportation, and environmental monitoring. However, the endless opportunities and benefits of IoT come with many security challenges due to the reduced computation, communication, storage, and energy capabilities of the IoT smart devices. Several computationally lightweight cryptographic protocols exist for these resource-constrained IoT smart devices. However, lightweight solutions render the resource-rich ends of the IoT systems (e.g., edge, fog, or cloud modes) vulnerable as nodes at those ends have the capacity for computationally heavier cryptographic protocols, and they operate in relatively more malicious environments. This asymmetric computational nature of IoT systems requires security protocols that can adapt to the resource availability at the node that they operate. This survey describes the IoT structure, computational capabilities of the devices at the end, edge, fog, and cloud platforms, and classifies existing lightweight cryptographic protocols. The comparative analysis of the existing lightweight cryptographic solutions along with their advantages, drawbacks, and vulnerabilities highlights the need for elastic cryptographic protocols which are capable of adapting to the asymmetric capabilities of the different nodes in IoT systems.
Lightweight Searchable Encryption Protocol for Industrial Internet of Things

Industrial Internet of Things (IoT) has suffered from insufficient identity authentication and dynamic network topology, thereby resulting in vulnerabilities to data confidentiality. Recently, the attribute-based encryption (ABE) schemes have been regarded as a solution to ensure data transmission security and the fine-grained sharing of encrypted IoT data. However, most of existing ABE schemes that bring tremendous computational cost are not suitable for resource-constrained IoT devices. Therefore, lightweight and efficient data sharing and searching schemes suitable for IoT applications are of great importance. To this end, In this article, we propose a light searchable ABE scheme (namely LSABE). Our scheme can significantly reduce the computing cost of IoT devices with the provision of multiple-keyword searching for data users. Meanwhile, we extend the LSABE scheme to multiauthority scenarios so as to effectively generate and manage the public/secret keys in the distributed IoT environment. Finally, the experimental results demonstrate that our schemes can significantly maintain computational efficiency and save the computational cost at IoT devices, compared to other existing schemes.
Heterogenous Adaptive Ant Colony Optimization with 3-opt local search for the Travelling Salesman Problem

The majority of optimization algorithms require proper parameter tuning to achieve the best performance. However, it is well-known that parameters are problem-dependent as different problems or even different instances have different optimal parameter settings. Parameter tuning through the testing of parameter combinations is a computationally expensive procedure that is infeasible on large-scale real-world problems. One method to mitigate this is to introduce adaptivity into the algorithm to discover good parameter settings during the search. Therefore, this study introduces an adaptive approach to a heterogeneous ant colony population that evolves the alpha and beta controlling parameters for ant colony optimization (ACO) to locate near-optimal solutions. This is achievable by introducing a set of rules for parameter adaptation to occur in order for the parameter values to be close to the optimal values by exploring and exploiting both the parameter and fitness landscape during the search to reflect the dynamic nature of search. In addition, the 3-opt local search heuristic is integrated into the proposed approach to further improve fitness. An empirical analysis of the proposed algorithm tested on a range of Travelling Salesman Problem (TSP) instances shows that the approach has better algorithmic performance when compared against state-of-the-art algorithms from the literature.
Discriminative Joint Probability Maximum Mean Discrepancy (DJP-MMD) for Domain Adaptation

Maximum mean discrepancy (MMD) has been widely adopted in domain adaptation to measure the discrepancy between the source and target domain distributions. Many existing domain adaptation approaches are based on the joint MMD, which is computed as the (weighted) sum of the marginal distribution discrepancy and the conditional distribution discrepancy; however, a more natural metric may be their joint probability distribution discrepancy. Additionally, most metrics only aim to increase the transferability between domains, but ignores the discriminability between different classes, which may result in insufficient classification performance. To address these issues, discriminative joint probability MMD (DJP-MMD) is proposed in this paper to replace the frequently-used joint MMD in domain adaptation. It has two desirable properties: 1) it provides a new theoretical basis for computing the distribution discrepancy, which is simpler and more accurate; 2) it increases the transferability and discriminability simultaneously. We validate its performance by embedding it into a joint probability domain adaptation framework. Experiments on six image classification datasets demonstrated that the proposed DJP-MMD can outperform traditional MMDs.
Inverse Distance Aggregation for Federated Learning with Non-IID Data

Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more accurate shared model which is robust to noisy and out-of distribution clients. In this work, we tackle the problem of statistical heterogeneity in data for FL which is highly plausible in medical data where for example the data comes from different sites with different scanner settings. We propose IDA (Inverse Distance Aggregation), a novel adaptive weighting approach for clients based on meta-information which handles unbalanced and non-iid data. We extensively analyze and evaluate our method against the well-known FL approach, Federated Averaging as a baseline.
NoRBERT: Transfer Learning for Requirements Classification

Classifying requirements is crucial for automatically handling natural language requirements. The performance of existing automatic classification approaches diminishes when applied to unseen projects because requirements usually vary in wording and style. The main problem is poor generalization. We propose NoRBERT that fine-tunes BERT, a language model that has proven useful for transfer learning. We apply our approach to different tasks in the domain of requirements classification. We achieve similar or better results F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -scores of up to 94%) on both seen and unseen projects for classifying functional and non-functional requirements on the PROMISE NFR dataset. NoRBERT outperforms recent approaches at classifying non-functional requirements subclasses. The most frequent classes are classified with an average F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of 87%. In an unseen project setup on a relabeled PROMISE NFR dataset, our approach achieves an improvement of 15 percentage points in average F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> score compared to recent approaches. Additionally, we propose to classify functional requirements according to the included concerns, i.e., function, data, and behavior. We labeled the functional requirements in the PROMISE NFR dataset and applied our approach. NoRBERT achieves an F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -score of up to 92%. Overall, NoRBERT improves requirements classification and can be applied to unseen projects with convincing results.