Reviewerly/exHarmony huggingface dataset:
Smart Contract Design Considerations for SLA Compliance Assessment in the Context of IoT

One of the main drivers behind blockchain adoption is a lack of trust among entities serving a common goal, but with different interests. Following the success of Bitcoin, several blockchain platforms have emerged, such as Ethereum and Hyperledger Fabric, to enable conducting distrusted processes in a non-repudiable manner. However, it is not safe to assume the applicability of conventional software design strategies to Blockchain-based solutions. In this paper, we assume an untrusted SLA (service level agreement) relationship between an IoT service provider and its consumer. We adopt Hyperledger Fabric for the purpose of implementing SLA compliance assessment. We design a smart contract that takes blockchain unique features into consideration. The design particularly accounts for the MVCC (multiversion concurrency control) mechanism, which while effective for resolving the double spending problem, causes read-write conflicts when high transmission rates are experienced between the IoT application and the blockchain. Using a fire station event monitoring scenario, we describe our smart contract design and solution for conflicting transactions. We experimentally evaluate our solution and demonstrate clear performance improvements in terms of throughput and latency.
Multi-Hypothesis Interactions in Game-Theoretic Motion Planning

We present a novel method for handling uncertainty about the intentions of non-ego players in trajectory games, with application to motion planning for autonomous vehicles. Our method models the uncertainty about the intention of other agents by constructing multiple hypotheses about the objectives and constraints of other agents in the scene. For each candidate hypothesis, we associate a Bernoulli random variable representing the probability of that hypothesis, which may or may not be independent of the probability of other hypotheses. We leverage constraint asymmetries and feedback information patterns to incorporate the probabilities of hypotheses in a natural way. Specifically, increasing the probability associated with a given hypothesis from 0 to 1 shifts the responsibility of collision avoidance from the hypothesized agent to the ego agent. This method allows the generation of interactive trajectories for the ego agent, where the level of assertiveness or caution that the ego exhibits is directly related to the easy-to-model uncertainty it maintains about the scene.
PolyGym: Polyhedral Optimizations as an Environment for Reinforcement Learning

The polyhedral model allows a structured way of defining semantics-preserving transformations to improve the performance of a large class of loops. Finding profitable points in this space is a hard problem which is usually approached by heuristics that generalize from domain-expert knowledge. Existing search space formulations in state-of-the-art heuristics depend on the shape of particular loops, making it hard to leverage generic and more powerful optimization techniques from the machine learning domain. In this paper, we propose a shape-agnostic formulation for the space of legal transformations in the polyhedral model as a Markov Decision Process (MDP). Instead of using transformations, the formulation is based on an abstract space of possible schedules. In this formulation, states model partial schedules, which are constructed by actions that are reusable across different loops. With a simple heuristic to traverse the space, we demonstrate that our formulation is powerful enough to match and outperform state-of-the-art heuristics. On the Polybench benchmark suite, we found the search space to contain transformations that lead to a speedup of 3.39x over LLVM O3, which is 1.34x better than the best transformations found in the search space of isl, and 1.83x better than the speedup achieved by the default heuristics of isl. Our generic MDP formulation enables future work to use reinforcement learning to learn optimization heuristics over a wide range of loops. This also contributes to the emerging field of machine learning in compilers, as it exposes a novel problem formulation that can push the limits of existing methods.
Distribution-Preserving-Based Automatic Data Augmentation for Deep Image Steganalysis

In recent years, deep learning-based steganalyzers far outperformed handcrafted feature-based steganalyzers. However, a large amount of data is needed to train deep learning networks. For steganalysis tasks, the steganographic traces are subtle and the steganographic signals are difficult to be captured when the number of cover/stego pairs in the training set is insufficient. Data augmentation has been proved to be effective in improving accuracy and generalization for deep learning models. Yet not all data augmentation methods are universal for all tasks. When performing data augmentation, we argue that data distribution under the target tasks should be maintained. Since the steganalysis task is more concerned with the high-frequency signals of the images, if the high-frequency signals are unchanged, the data distribution from the perspective of steganalysis will remain largely unchanged. Based on this principle, we designed a neural network called cover augmentation network, which enriches the dataset by intelligently adding noise to the original cover to generate the augmented cover. Further, we designed a whole process of data augmentation based on the cover augmentation network. Experimental results show that the proposed data augmentation method can effectively improve the performance of steganalysis networks, and the advantage is significant at low payloads.
The Development of Central Bank Digital Currency in China: An Analysis

The People's Bank of China (PBOC) has launched an ambitious project to develop a digital currency for use in domestic, retail transactions, and is, by far, the most advanced globally in this regard. In addition to involving a diverse set of stakeholders, the PBOC established a set of fundamental principles, including privacy, inclusiveness, and conservatism, and has articulated its progress in a public document translated into English. We maintain that although both its first principles and its conclusions drawn from the research conducted by the PBOC from 2014 to date are broadly reasonable and appropriate, the PBOC has also missed some important considerations and entertained some questionable assumptions, which many central banks around the world have also done. In this analysis, we consider the strengths and weaknesses of the digital currency proposition articulated by the PBOC as it exists today, and we propose one fundamental and specific change for the PBOC and other central banks around the world: The architecture must accommodate privacy-preserving, non-custodial wallets. With this change and a related set of minor adjustments, China has an opportunity to lead the world in the implementation of a central bank digital currency (CBDC) solution that protects the authority of the central bank to implement monetary policy, preserves the role of public-sector and private-sector banking institutions, promotes the efficiency of retail transactions and businesses, satisfies regulatory objectives, and safeguards the human rights of retail consumers, including their privacy and their right to participate in the economy. We hope that the PBOC, and other central banks around the world, will have the resolve and strength of purpose to implement our proposed change and carry on with implementing a CBDC architecture that serves the interests of its users.
A Comprehensive Study on Torchvision Pre-trained Models for Fine-grained Inter-species Classification

This study aims to explore different pre-trained models offered in the Torchvision package which is available in the PyTorch library. And investigate their effectiveness on fine-grained images classification. Transfer Learning is an effective method of achieving extremely good performance with insufficient training data. In many real-world situations, people cannot collect sufficient data required to train a deep neural network model efficiently. Transfer Learning models are pre-trained on a large data set, and can bring a good performance on smaller datasets with significantly lower training time. Torchvision package offers us many models to apply the Transfer Learning on smaller datasets. Therefore, researchers may need a guideline for the selection of a good model. We investigate Torchvision pre-trained models on four different data sets: 10 Monkey Species, 225 Bird Species, Fruits 360, and Oxford 102 Flowers. These data sets have images of different resolutions, class numbers, and different achievable accuracies. We also apply their usual fully-connected layer and the Spinal fully-connected layer to investigate the effectiveness of SpinalNet. The Spinal fully-connected layer brings better performance in most situations. We apply the same augmentation for different models for the same data set for a fair comparison. This paper may help future Computer Vision researchers in choosing a proper Transfer Learning model.
Person re-identification using adversarial haze attack and defense: A deep learning framework

In this paper, the adversarial haze attack problem is addressed using the dark channel prior (DCP) de-hazing method. The adversarial attack affects rank-1 accuracy, where searching a target image against each test image is a specific search problem. To resolve this kind of problem, a feature fusion model is proposed to fuse handcrafted features and a pre-trained network model to obtain robust and discriminative features . The proposed model learns global features using transfer learning architecture whereas local features are obtained using the conventional method. Three pre-trained CNN models (AlexNet, ResNet, and Inception-v3) are used for feature extraction via transfer learning. The experiments are performed on publicly available datasets, achieving 68.6% accuracy in rank-1 with VIPER dataset and 79.6% accuracy with CHUK03 dataset. The proposed model enhances rank-1 accuracy of person re-identification when comparing with other state-of-the-art methods.
Information-Growth Attention Network for Image Super-Resolution

It is generally known that a high-resolution (HR) image contains more productive information compared with its low-resolution (LR) versions, so image super-resolution (SR) satisfies an information-growth process. Considering the property, we attempt to exploit the growing information via a particular attention mechanism. In this paper, we propose a concise but effective Information-Growth Attention Network (IGAN) that shows the incremental information is beneficial for SR. Specifically, a novel information-growth attention is proposed. It aims to pay attention to features involving large information-growth capacity by assimilating the difference from current features to the former features within a network. We also illustrate its effectiveness contrasted by widely-used self-attention using entropy and generalization analysis. Furthermore, existing channel-wise attention generation modules (CAGMs) have large informational attenuation due to directly calculating global mean for feature maps. Therefore, we present an innovative CAGM that progressively decreases feature maps' sizes, leading to more adequate feature exploitation. Extensive experiments also demonstrate IGAN outperforms state-of-the-art attention-aware SR approaches.
Lightweight blockchain to solve forgery and privacy issues of vehicle image data

This paper proposes a method of using a blockchain to solve the privacy problem and forgery of black box image data, which plays an essential role in determining the responsibility for traffic accidents and preventing accidents. Blockchain that can operate inside vehicle black box IoT or connected car is used, and for this purpose, the size is reduced for operation in low-power, low-capacity devices. By enabling consensus, security problems can be solved through a lightweight blockchain that can operate inside a black box device. As a result of the experiment, it was confirmed that the IPFS upload and download delay time increased linearly, and the proposed consensus algorithm decreased 63% compared to PBFT.
MICE: Mining Idioms with Contextual Embeddings

Idiomatic expressions can be problematic for natural language processing applications as their meaning cannot be inferred from their constituting words. A lack of successful methodological approaches and sufficiently large datasets prevents the development of machine learning approaches for detecting idioms, especially for expressions that do not occur in the training set. We present an approach, called MICE, that uses contextual embeddings for that purpose. We present a new dataset of multi-word expressions with literal and idiomatic meanings and use it to train a classifier based on two state-of-the-art contextual word embeddings: ELMo and BERT. We show that deep neural networks using both embeddings perform much better than existing approaches, and are capable of detecting idiomatic word use, even for expressions that were not present in the training set. We demonstrate cross-lingual transfer of developed models and analyze the size of the required dataset.
Stimulated parametric down-conversion for spatiotemporal metrology

A detailed analysis of the stimulated parametric down-conversion (PDC) process is performed to investigate the effects of the spatiotemporal degrees of freedom. The analysis provides information that would be useful for PDC-based metrology applications. Using a Wigner functional approach, we obtain the parametric down-converted state as the Bogoliubov transformed input state, in terms of Bogoliubov kernel functions. The result is used to consider the case for a coherent state seeding stimulated PDC. We also compute the background which is obtained from spontaneous PDC.
Model Specialization for the Use of ESRGAN on Satellite and Airborne Imagery

Training a deep learning model requires highly variable data to permit reasonable generalization. If the variability in the data about to be processed is low, the interest in obtaining this generalization seems limited. Yet, it could prove interesting to specialize the model with respect to a particular theme. The use of enhanced super-resolution generative adversarial networks (ERSGAN), a specific type of deep learning architecture, allows the spatial resolution of remote sensing images to be increased by “hallucinating” non-existent details. In this study, we show that ESRGAN create better quality images when trained on thematically classified images than when trained on a wide variety of examples. All things being equal, we further show that the algorithm performs better on some themes than it does on others. Texture analysis shows that these performances are correlated with the inverse difference moment and entropy of the images.
Rapidly adapting robot swarms with Swarm Map-based Bayesian Optimisation

Rapid performance recovery from unforeseen environmental perturbations remains a grand challenge in swarm robotics. To solve this challenge, we investigate a behaviour adaptation approach, where one searches an archive of controllers for potential recovery solutions. We propose two algorithms: (i) Swarm Map-based Optimisation (SMBO), which selects and evaluates one controller at a time, for a homogeneous swarm, in a centralised fashion; and (ii) Swarm Map-based Optimisation Decentralised (SMBO-Dec), which performs an asynchronous batch-based Bayesian optimisation to simultaneously explore different controllers for groups of robots in the swarm. A simulation study investigates adaptation of a Thymio robot swarm in a collective foraging task. First, we investigate different groups of sensory-motor disturbances, including fault to proximity sensors, ground sensors, or actuators of individual robots, with 100 unique combinations for each type. Second, we investigate changes to the surrounding environment of the swarm, where the number of available resources drops or where one robot disrupts the rest of the swarm; for each such change, we include 30 unique conditions. The viability of SMBO and SMBO-Dec is demonstrated, comparing favourably to variants of random search and gradient descent, and various ablations, and improving performance up to 80% compared to the performance at the time of fault injection within less than 30 evaluations.
Met-MLTS: Leveraging Smartphones for End-to-End Spotting of Multilingual Oriented Scene Texts and Traffic Signs in Adverse Meteorological Conditions

Intelligent systems, like driver assistance systems, remain within vehicles and help drivers by providing essential information about traffic, blockage of roads, and possible routes for safe driving. The objective of scene text spotting in a driver assistance system is to localize and recognize scene texts, signs of milestones, traffic panels, and road marks in natural scene images. However, text edges get fainted due to adverse weather conditions, like fog, rain, smog, or poor contrast. This makes the task of spotting more challenging. In this paper, we propose an end-to-end trainable deep neural network, known as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Met-MLTS</i> , that can address the issue of spotting multi-oriented text instances in scene images captured in adverse meteorological conditions. It localizes words, predicts script class, and performs word spotting for every rotated bounding box. It is a fast multilingual scene text spotter that utilizes hierarchical spatial context, channel-wise inter-dependencies, and semantic edge supervision to localize and recognize words and predict script class in scene images using smartphones. We explore inter-class interference to reduce the misclassification problem. A light-weight recognition module for multilingual character segmentation, word-level recognition, and script identification is incorporated. We demonstrate the efficacy of our spotting network on resource-constraint devices.
A cuckoo search algorithm with scale-free population topology

The scale-free network is an important type of complex network. The node degrees in a scale-free network follow the power-law distribution. In the skeleton of a scale-free network, there exists a few nodes which own huge neighborhood size and play an important role in information transmission of the entire network, while most of the network nodes have few connections whose influences of information exchange are limited to a relatively low level. In this paper, we introduce a scale-free population topology into the cuckoo search (CS) algorithm to propose a novel variant, which is termed the scale-free cuckoo search (SFCS) algorithm. Unlike other CS algorithms where the individuals exchange information randomly, two properties of the scale-free network can improve the SFCS algorithm in two aspects: the possibility that the information of competent individuals quickly floods the whole population is reduced significantly, which guarantees population diversity; and the corrupt individuals can learn from competent individuals with greater probability, which is beneficial for convergence. Thus, SFCS can obtain a better trade-off between exploitation and exploration during the search process. To evaluate the effectiveness of the proposed SFCS, 58 benchmark functions with different dimensions (10-D, 30-D, and 50-D), and 21 real-world optimization problems are employed in our experiment. We compare SFCS with the basic CS algorithm, two CS variants, and five state-of-the-art optimization algorithms, and the experimental results and statistical analysis verify the superiority of SFCS in terms of solution quality and convergence speed. Furthermore, we compare SFCS with a scale-free fully informed particle swarm optimization algorithm (SFIPSO) and the results prove our scale-free idea is effective despite its simplicity. We also introduce the scale-free population topology into the differential evolution (DE) and the firefly algorithm (FA) and the experimental results show that the scale-free population topology enhance the search ability of the DE and FA. These lead us to believe that the scale-free population topology may be a new technique for improving the performance of the population-based algorithms.
Pareto based ant lion optimizer for energy efficient scheduling in cloud environment

The tremendous growth of cloud-based data centres leads to significant amount of energy. Thus, the prime concern for the cloud service providers is to generate environment friendly solution by minimizing the energy consumption. Further, due to increased demand of scientific workflow applications, cloud providers face a challenging issue of efficient workflow scheduling by minimizing the makespan. To address the above-mentioned contradictory issues, we proposed a Pareto based multi-objective discrete ant lion optimization algorithm (PBMO-DALO) to solve workflow-scheduling problem in cloud data centres with minimizing the conflicting objectives of makespan and energy consumption simultaneously. Distinguished from the original ant lion optimization algorithm, the proposed algorithm involves new encoding scheme for ants and antlions, their random walk and selection of fitter antlion for trap building to address the discrete nature of the workflow scheduling problem. Subsequently, the PBMO-DALO uses the Pareto dominance and crowding distance approach to tackle optimization of multiple objectives and to achieve the optimal solutions. The simulation results indicate that the proposed PBMO-DALO algorithm overwhelms other competing algorithms and generates good trade-off solutions with better convergence and uniform diversity. • Considered the problem of workflow scheduling in cloud environment. • Minimizing the conflicting objectives of makespan and energy consumption. • Pareto based multi-objective discrete ant lion optimization algorithm is proposed. • Gives better results in conflicting objectives and well distributed Pareto front. • Superiority of proposed algorithm is verified and compared with other algorithms.
An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddings

Many mispronunciation detection and diagnosis (MD&D) research approaches try to exploit both the acoustic and linguistic features as input. Yet the improvement of the performance is limited, partially due to the shortage of large amount annotated training data at the phoneme level. Phonetic embeddings, extracted from ASR models trained with huge amount of word level annotations, can serve as a good representation of the content of input speech, in a noise-robust and speaker-independent manner. These embeddings, when used as implicit phonetic supplementary information, can alleviate the data shortage of explicit phoneme annotations. We propose to utilize Acoustic, Phonetic and Linguistic (APL) embedding features jointly for building a more powerful MD&D system. Experimental results obtained on the L2-ARCTIC database show the proposed approach outperforms the baseline by 9.93%, 10.13% and 6.17% on the detection accuracy, diagnosis error rate and the F-measure, respectively.