Reviewerly/exHarmony huggingface dataset:
Implementation of Distributed File Storage and Access Framework using IPFS and Blockchain

Many critical applications are designed on the distributed structure using the blockchain technology to ensure the availability, immutability, and security. However, these applications are facing the storage problem owing to the data volume growth of transaction. The number of transactions and its size in a block is growing in the blockchain day by day because of the feature of immutability and append-only. The growing nature of transactions in a block is not only making the problem for storage but also in access to the block transactions. In this paper, we propose an IPFS based blockchain storage model to solve the storage problem of transaction in a block along with access of transaction of a particular block. In the propose storage model, the miners stores transaction on IPFS distributed file system storage and get the returned IPFS hash of transaction into the block of the blockchain. The feature of the IPFS network and its resultant hash reduce the size of transactions in a block. To secure access of transaction for a particular block content-addressed (IPFS hash) storage technique has been proposed. We have applied this scheme on a transaction which includes image storage on IPFS and hash storage into the blockchain. In this paper, we have also proposed the content-addressed technique in contrast to the location addressed for the access of transaction. To implement the framework we have used anaconda python, python flask, and IPFS.
Enabling Attribute Revocation for Fine-Grained Access Control in Blockchain-IoT Systems

The attribute-based encryption (ABE) has drawn a lot of attention for fine-grained access control in blockchains, especially in blockchain-enabled tampering-resistant Internet-of-Things (IoT) systems. However, its adoption has been severely hindered by the incompatibility between the immutability of typical blockchains and the attribute updates/revocations of ABE. In this article, we propose a new blockchain-based IoT system, which is compatible with the ABE technique, and fine-grained access control is implemented with the attribute update enabled by integrating Chameleon Hash algorithms into the blockchains. We design and implement a new verification scheme over a multilayer blockchain architecture to guarantee the tamper resistance against malicious and abusive tampering. The system can provide an update-oriented access control, where historical on-chain data can only be accessible to new members and inaccessible to the revoked members. This is distinctively different from existing solutions, which are threatened by data leakage toward the revoked members. We also provide analysis and simulations showing that our system outperforms other solutions in terms of overhead, searching complexity, security, and compatibility.
A Density Ratio Approach to Language Model Fusion in End-to-End Automatic Speech Recognition

This article describes a density ratio approach to integrating external Language Models (LMs) into end-to-end models for Automatic Speech Recognition (ASR). Applied to a Recurrent Neural Network Transducer (RNN-T) ASR model trained on a given domain, a matched in-domain RNN-LM, and a target domain RNN-LM, the proposed method uses Bayes' Rule to define RNN-T posteriors for the target domain, in a manner directly analogous to the classic hybrid model for ASR based on Deep Neural Networks (DNNs) or LSTMs in the Hidden Markov Model (HMM) framework (Bourlard & Morgan, 1994). The proposed approach is evaluated in cross-domain and limited-data scenarios, for which a significant amount of target domain text data is used for LM training, but only limited (or no) {audio, transcript} training data pairs are used to train the RNN-T. Specifically, an RNN-T model trained on paired audio & transcript data from YouTube is evaluated for its ability to generalize to Voice Search data. The Density Ratio method was found to consistently outperform the dominant approach to LM and end-to-end ASR integration, Shallow Fusion.
Multi-task learning for aspect term extraction and aspect sentiment classification

Aspect sentiment classification has a dependency over the aspect term extraction. The majority of the existing studies tackle these two problems independently, i.e., while performing aspect sentiment classification, it is assumed that the aspect terms are pre-identified. However, such assumptions are neither practical nor appropriate. In this paper, we address these impractical limitations and propose a multi-task learning framework for the identification and classification of aspect terms in a unified model. At first, the proposed approach employs a BiLSTM followed by a self-attention mechanism to identify the aspect terms in a given sentence. Subsequently, the architecture utilizes a CNN framework to predict the sentiments of the identified aspect terms. We evaluate our proposed approach for the three benchmark datasets across two languages, i.e., English and Hindi. Experimental results suggest that the proposed multi-task model achieves competitive performance with reduced complexity (i.e., a single model for the two tasks compared to two separate models for each task) for both the languages.
Multi-Agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning

This letter introduces a hybrid algorithm of deep reinforcement learning (RL) and Force-based motion planning (FMP) to solve distributed motion planning problem in dense and dynamic environments. Individually, RL and FMP algorithms each have their own limitations. FMP is not able to produce time-optimal paths and existing RL solutions are not able to produce collision free paths in dense environments. Therefore, we first tried improving the performance of recent RL approaches by introducing a new reward function that not only eliminates the requirement of a pre supervised learning (SL) step but also decreases the chance of collision in crowded environments. That improved things, but there were still a lot of failure cases. So, we developed a hybrid approach to leverage the simpler FMP approach in stuck, simple and high-risk cases, and continue using RL for normal cases in which FMP can't produce optimal path. Also, we extend GA3CCADRL algorithm to 3D environment. Simulation results show that the proposed algorithm outperforms both deep RL and FMP algorithms and produces up to 50% more successful scenarios than deep RL and up to 75% less extra time to reach goal than FMP.
Operational neural networks

Abstract Feed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons are well-known universal approximators. However, their learning performance varies significantly depending on the function or the solution space that they attempt to approximate. This is mainly because of their homogenous configuration based solely on the linear neuron model. Therefore, while they learn very well those problems with a monotonous, relatively simple and linearly separable solution space, they may entirely fail to do so when the solution space is highly nonlinear and complex. Sharing the same linear neuron model with two additional constraints (local connections and weight sharing), this is also true for the conventional convolutional neural networks (CNNs) and it is, therefore, not surprising that in many challenging problems only the deep CNNs with a massive complexity and depth can achieve the required diversity and the learning performance. In order to address this drawback and also to accomplish a more generalized model over the convolutional neurons, this study proposes a novel network model, called operational neural networks (ONNs), which can be heterogeneous and encapsulate neurons with any set of operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. Finally, the training method to back-propagate the error through the operational layers of ONNs is formulated. Experimental results over highly challenging problems demonstrate the superior learning capabilities of ONNs even with few neurons and hidden layers.
Performance Optimization of Federated Learning over Wireless Networks

In this paper, the problem of training federated learning (FL) algorithms over a realistic wireless network is studied. In particular, in the considered model, wireless users perform an FL algorithm that trains their local FL models using their own data and send the trained local FL models to a base station (BS) that will generate a global FL model and send it back to the users. Since all training parameters are transmitted over wireless links, the quality of the training will be affected by wireless factors such as packet errors and availability of wireless resources. Meanwhile, due to the limited wireless bandwidth, the BS must select an appropriate subset of users to execute the FL learning algorithm so as to build a global FL model accurately. This joint learning, wireless resource allocation, and user selection problem is formulated as an optimization problem whose goal is to minimize an FL loss function that captures the performance of the FL algorithm. To address this problem, a closed-form expression for the expected convergence rate of the FL algorithm is first derived to quantify the impact of wireless factors on FL. Then, based on the expected convergence rate of the FL algorithm, the optimal transmit power for each user is derived, under a given user selection and uplink resource block (RB) allocation scheme. Finally, the user selection and uplink RB allocation is optimized so as to minimize the FL loss function. Simulation results show that the proposed joint federated learning and communication framework can reduce the FL loss function value by up to 10% and 16%, respectively, compared to 1) an optimal user selection algorithm with random resource allocation and 2) a random user selection and resource allocation algorithm.
Identity-Based Privacy Preserving Remote Data Integrity Checking for Cloud Storage

Although cloud storage service enables people easily maintain and manage amounts of data with lower cost, it cannot ensure the integrity of people's data. In order to audit the correctness of the data without downloading them, many remote data integrity checking (RDIC) schemes have been presented. Most existing schemes ignore the important issue of data privacy preserving and suffer from complicated certificate management derived from public key infrastructure. To overcome these shortcomings, this article proposes a new Identity-based RDIC scheme that makes use of homomorphic verifiable tag to decrease the system complexity. The original data in proof are masked by random integer addition, which protects the verifier from obtaining any knowledge about the data during the integrity checking process. Our scheme is proved secure under the assumption of computational Diffie-Hellman problem. Experiment result exhibits that our scheme is very efficient and feasible for real-life applications.
Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction

Knowledge Graph (KG) embeddings are a powerful tool for predicting missing links in KGs. Existing techniques typically represent a KG as a set of triplets, where each triplet (h, r, t) links two entities h and t through a relation r, and learn entity/relation embeddings from such triplets while preserving such a structure. However, this triplet representation oversimplifies the complex nature of the data stored in the KG, in particular for hyper-relational facts, where each fact contains not only a base triplet (h, r, t), but also the associated key-value pairs (k, v). Even though a few recent techniques tried to learn from such data by transforming a hyper-relational fact into an n-ary representation (i.e., a set of key-value pairs only without triplets), they result in suboptimal models as they are unaware of the triplet structure, which serves as the fundamental data structure in modern KGs and preserves the essential information for link prediction. To address this issue, we propose HINGE, a hyper-relational KG embedding model, which directly learns from hyper-relational facts in a KG. HINGE captures not only the primary structural information of the KG encoded in the triplets, but also the correlation between each triplet and its associated key-value pairs. Our extensive evaluation shows the superiority of HINGE on various link prediction tasks over KGs. In particular, HINGE consistently outperforms not only the KG embedding methods learning from triplets only (by 0.81-41.45% depending on the link prediction tasks and settings), but also the methods learning from hyper-relational facts using the n-ary representation (by 13.2-84.1%).
Particle swarm optimization with adaptive learning strategy

Population diversity maintenance is a crucial task for preventing a particle swarm optimization (PSO) algorithm from being trapped in local optima. A learning strategy is an effective means of improving population diversity. However, for the canonical PSO algorithm, the learning strategy focuses mainly on the global best particle, which leads to a loss of diversity. To increase the population diversity and strengthen the global search ability in PSO, this paper proposes a PSO algorithm with an adaptive learning strategy (PSO-ALS). To better promote the performance of the learning strategy, the swarm is adaptively grouped into several subswarms. The particles in each subswarm are further classified into ordinary particles and the locally best particle, and two different learning strategies without an explicit velocity are devised for updating the particles to increase the population diversity. Thus, the global optimum is determined by comparing the fitness values of the updated best particles in each subswarm. The proposed algorithm is compared with state-of-the-art PSO variants. The experimental results illustrate that the performance of PSO-ALS is promising and competitive in terms of enhanced population diversity and global search ability.
Deep density-based image clustering

Recently, deep clustering, which is able to perform feature learning that favors clustering tasks via deep neural networks, has achieved remarkable performance in image clustering applications. However, the existing deep clustering algorithms generally need the number of clusters in advance, which is usually unknown in real-world tasks. In addition, the initial cluster centers in the learned feature space are generated by k-means. This only works well on spherical clusters and probably leads to unstable clustering results. In this paper, we propose a two-stage deep density-based image clustering (DDC) framework to address these issues. The first stage is to train a deep convolutional autoencoder (CAE) to extract low-dimensional feature representations from high-dimensional image data, and then apply t-SNE to further reduce the data to a 2-dimensional space favoring density-based clustering algorithms. In the second stage, we propose a novel density-based clustering technique for the 2-dimensional embedded data to automatically recognize an appropriate number of clusters with arbitrary shapes. Concretely, a number of local clusters are generated to capture the local structures of clusters, and then are merged via their density relationship to form the final clustering result. Experiments demonstrate that the proposed DDC achieves comparable or even better clustering performance than state-of-the-art deep clustering methods, even though the number of clusters is not given.
Ultrafast optical integration and pattern classification for neuromorphic photonics based on spiking VCSEL neurons

Abstract In today’s data-driven world, the ability to process large data volumes is crucial. Key tasks, such as pattern recognition and image classification, are well suited for artificial neural networks (ANNs) inspired by the brain. Neuromorphic computing approaches aimed towards physical realizations of ANNs have been traditionally supported by micro-electronic platforms, but recently, photonic techniques for neuronal emulation have emerged given their unique properties (e.g. ultrafast operation, large bandwidths, low cross-talk). Yet, hardware-friendly systems of photonic spiking neurons able to perform processing tasks at high speeds and with continuous operation remain elusive. This work provides a first experimental report of Vertical-Cavity Surface-Emitting Laser-based spiking neurons demonstrating different functional processing tasks, including coincidence detection and pattern recognition, at ultrafast rates. Furthermore, our approach relies on simple hardware implementations using off-the-shelf components. These results therefore hold exciting prospects for novel, compact and high-speed neuromorphic photonic platforms for future computing and Artificial Intelligence systems.
Conversational agents in business: A systematic literature review and future research directions

The field of business shows an increasing interest in exploring conversational agents to improve service quality and market competitiveness. Furthermore, the advances in machine learning capabilities leverage the natural language processing towards natural and straightforward dialogue experiences for industries. However, in the best of our knowledge, no literature review outlines conversational agents in the business industry, primarily taking into account computational learning capabilities. This article presents a systematic literature review that encompasses these areas looking through the use of machine learning to improve the field of business. The review followed a guideline for systematic reviews to present the literature of the last decade, emphasizing business perspectives such as domains, goals, and challenges, and computational methods for self-learning, personalization, and response generation of conversational agents. As a result, the article provides the answers of three general, three focused, and two statistical questions to address the role of artificial intelligence in conversational agents applied to business domains. In this regard, the results show that no study combines self-learning, personalization, and generative-based responses for the same business solution. Additionally, the article describes the organization of the state-of-the-art, highlighting the correlation of business perspectives and machine learning methods. The contributions of this review focus on opportunities and future research directions towards human-like conversational agents for business.
Temporal Action Detection with Structured Segment Networks

This paper addresses an important and challenging task, namely detecting the temporal intervals of actions in untrimmed videos. Specifically, we present a framework called structured segment network (SSN). It is built on temporal proposals of actions. SSN models the temporal structure of each action instance via a structured temporal pyramid. On top of the pyramid, we further introduce a decomposed discriminative model comprising two classifiers, respectively for classifying actions and determining completeness. This allows the framework to effectively distinguish positive proposals from background or incomplete ones, thus leading to both accurate recognition and precise localization. These components are integrated into a unified network that can be efficiently trained in an end-to-end manner. Additionally, a simple yet effective temporal action proposal scheme, dubbed temporal actionness grouping is devised to generate high quality action proposals. We further study the importance of the decomposed discriminative model and discover a way to achieve similar accuracy using a single classifier, which is also complementary with the original SSN design. On two challenging benchmarks, THUMOS'14 and ActivityNet, our method remarkably outperforms previous state-of-the-art methods, demonstrating superior accuracy and strong adaptivity in handling actions with various temporal structures.
Providing End-to-End Security Using Quantum Walks in IoT Networks

Internet of Things acts an essential role in our everyday lives and it definitely has the potential to grow on the importance and revolutionize our future. However, the present communication technologies have several security related issues which is required to provide secure end to end connectivity among services. Moreover, due to recent, rapid growth of quantum technologies, most common security mechanisms considered secure today may be soon imperilled. Thus, the modern security mechanisms during their construction also require the power of quantum technologies to resist various potential attacks from quantum computers. Because of its characteristics, quantum walks (QW) is considered as a universal quantum computation paradigm that can be accepted as an excellent key generator. In this regard, in this paper a new lightweight image encryption scheme based on QW for secure data transfer in the internet of things platforms and wireless networking with edge computing is proposed. The introduced approach utilises the power of nonlinear dynamic behaviour of QW to construct permutation boxes and generates pseudo-random numbers for encrypting the plain image after dividing it into blocks. The results of the conducted simulation and numerical analyses confirm that the presented encryption algorithm is effective. The encrypted images have randomness properties, no useful data about the ciphered image can be obtained via analysing the correlation of adjacent pixels. Moreover, the entropy value is close to 8, the number of the pixel change rate is greater than 99.61%, and there is high sensitivity of the key parameters with large key space to resist various attacks.
Single Image Deraining Using Bilateral Recurrent Network

Single image deraining has received considerable progress based on deep convolutional neural network (CNN). In existing deep deraining methods, CNNs are deployed to extract rain streaks while failing in learning direct mapping from rainy image to clean background image, and their architectures become more and more complicated. In this work, we first propose a single recurrent network (SRN) by recursively unfolding a shallow residual network, where a recurrent layer is adopted to propagate deep features across multiple stages. This simple SRN is effective not only in learning residual mapping for extracting rain streaks, but also in learning direct mapping for predicting clean background image. Furthermore, two SRNs are coupled to simultaneously exploit rain streak layer and clean background image layer. Instead of naive combination, we propose bilateral LSTMs, which not only can respectively propagate deep features of rain streak layer and background image layer across stages, but also bring the interplay between these two SRNs, finally forming bilateral recurrent network (BRN). The experimental results demonstrate that our BRN notably outperforms state-of-the-art deep deraining networks on synthetic datasets quantitatively and qualitatively. The proposed methods also perform more favorably in terms of generalization performance on real-world rainy dataset. All the source code and pre-trained models are available at https://github.com/csdwren/RecDerain.