Reviewerly/exHarmony huggingface dataset:
Aspect sentiment classification using syntactic neighbour based attention network

Aspect Level Sentiment Classification (ALSC) deals with classifying the sentiment polarity towards a particular aspect or target. The performance of any method in ALSC is primarily driven by its capability to map the aspect or target term to the correct sentiment context words. The dependency tree's syntactical information is crucial for correctly mapping the appropriate context terms to the aspect term. Thus, recent top-performing methods use the graph neural network (GNN) to incorporate the syntactical knowledge of the dependency tree. However, the architecture of such methods is quite complex with the computationally expensive GNNs. In this work, we propose a method syntactic neighbour-based attention network (SNBAN), that is architecturally simple, takes less computational time and is efficient. SNBAN has a novel and simple architecture that is based on Bi-GRU (Bi-directional Gated Recurrent unit). Specifically, SNBAN can exploit both semantic and syntactic knowledge of the input sentence using two muti-head attentions (MHATT) where the first and second MHATT handle the semantic and syntactic knowledge of the input sentence respectively. The experiments show that the proposed SNBAN significantly outperforms the non-syntactic knowledge-based baselines. The experiments also show that the proposed SNBAN takes a significantly reduced average training time in comparison to GNNs and at the same time performs with comparable accuracy and F1 score against the GNN based methods.
Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Swarm intelligence has become a hot research field of artificial intelligence. Considering the importance of swarm intelligence for the future development of artificial intelligence, we discuss and analyze swarm intelligence from a broader and deeper perspective. In a broader sense, we are talking about not only bio-inspired swarm intelligence, but also human-machine hybrid swarm intelligence. In a deeper sense, we discuss the research using a three-layer hierarchy: in the first layer, we divide the research of swarm intelligence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence; in the second layer, the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence; and in the third layer, we review single-population, multi-population and human-machine hybrid models from different perspectives. Single-population swarm intelligence is inspired by biological intelligence. To further solve complex optimization problems, researchers have made preliminary explorations in multi-population swarm intelligence. However, it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive intelligent behavior that meets the needs of human cognition. Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence. In addition to single-population swarm intelligence, we thoroughly review multi-population and human-machine hybrid swarm intelligence in this paper. We also discuss the applications of swarm intelligence in optimization, big data analysis, unmanned systems and other fields. Finally, we discuss future research directions and key issues to be studied in swarm intelligence.
Cyber-Physical-Social Awareness Platform for Comprehensive Situation Awareness

Cyber-physical-social computing system integrates the interactions between cyber, physical, and social spaces by fusing information from these spaces. The result of this fusion can be used to drive many applications in areas such as intelligent transportation, smart cities, and healthcare. Situation Awareness was initially used in military services to provide knowledge of what is happening in a combat zone but has been used in many other areas such as disaster mitigation. Various applications have been developed to provide situation awareness using either IoT sensors or social media information spaces and, more recently, using both IoT sensors and social media information spaces. The information from these spaces is heterogeneous and, at their intersection, is sparse. In this paper, we propose a highly scalable, novel Cyber-physical-social Awareness (CPSA) platform that provides situation awareness by using and intersecting information from both IoT sensors and social media. By combining and fusing information from both social media and IoT sensors, the CPSA platform provides more comprehensive and accurate situation awareness than any other existing solutions that rely only on data from social media and IoT sensors. The CPSA platform achieves that by semantically describing and integrating the information extracted from sensors and social media spaces and intersects this information for enriching situation awareness. The CPSA platform uses user-provided situation models to refine and intersect cyber, physical, and social information. The CPSA platform analyses social media and IoT data using pretrained machine learning models deployed in the cloud, and provides coordination between information sources and fault tolerance. The paper describes the implementation and evaluation of the CPSA platform. The evaluation of the CPSA platform is measured in terms of capabilities such as the ability to semantically describe and integrate heterogenous information, fault tolerance, and time constraints such as processing time and throughput when performing real-world experiments. The evaluation shows that the CPSA platform can reliably process and intersect with large volumes of IoT sensor and social media data to provide enhanced situation awareness.
A Hardware-Friendly High-Precision CNN Pruning Method and Its FPGA Implementation

To address the problems of large storage requirements, computational pressure, untimely data supply of off-chip memory, and low computational efficiency during hardware deployment due to the large number of convolutional neural network (CNN) parameters, we developed an innovative hardware-friendly CNN pruning method called KRP, which prunes the convolutional kernel on a row scale. A new retraining method based on LR tracking was used to obtain a CNN model with both a high pruning rate and accuracy. Furthermore, we designed a high-performance convolutional computation module on the FPGA platform to help deploy KRP pruning models. The results of comparative experiments on CNNs such as VGG and ResNet showed that KRP has higher accuracy than most pruning methods. At the same time, the KRP method, together with the GSNQ quantization method developed in our previous study, forms a high-precision hardware-friendly network compression framework that can achieve "lossless" CNN compression with a 27× reduction in network model storage. The results of the comparative experiments on the FPGA showed that the KRP pruning method not only requires much less storage space, but also helps to reduce the on-chip hardware resource consumption by more than half and effectively improves the parallelism of the model in FPGAs with a strong hardware-friendly feature. This study provides more ideas for the application of CNNs in the field of edge computing.
Aspect-oriented unsupervised social link inference on user trajectory data

We focus on representation learning for social link inference based on user trajectory data. While traditional methods predict relations between users by considering hand-crafted features, recent studies first perform representation learning using network embedding or graph neural networks (GNNs) for downstream tasks such as node classification and link prediction. However, those approaches fail to capture behavioral patterns of individuals ingrained in periodical visits or long-distance movements. To better learn behavioral patterns, this paper proposes a novel scheme called ANES (Aspect-oriented Network Embedding for Social link inference). ANES learns aspect-oriented relations between users and Point-of-Interests (POIs) within their contexts. ANES is the first approach that extracts the complex behavioral pattern of users from both trajectory data and the structure of User-POI bipartite graphs. Our extensive experiments on several real-world datasets show that ANES outperforms state-of-the-art baselines.
Feature weighting in DBSCAN using reverse nearest neighbours

DBSCAN is arguably the most popular density-based clustering algorithm, and it is capable of recovering non-spherical clusters. One of its main weaknesses is that it treats all features equally. In this paper, we propose a density-based clustering algorithm capable of calculating feature weights representing the degree of relevance of each feature, which takes the density structure of the data into account. First, we improve DBSCAN and introduce a new algorithm called DBSCANR. DBSCANR reduces the number of parameters of DBSCAN to one. Then, a new step is introduced to the clustering process of DBSCANR to iteratively update feature weights based on the current partition of data. The feature weights produced by the weighted version of the new clustering algorithm, W-DBSCANR, measure the relevance of variables in a clustering and can be used in feature selection in data mining applications where large and complex real-world data are often involved. Experimental results on both artificial and real-world data have shown that the new algorithms outperformed various DBSCAN type algorithms in recovering clusters in data.
Green Federated Learning via Energy-Aware Client Selection

Federated learning (FL) is a collaborative machine learning framework to enable different clients such as Internet of Things (IoT) devices to participate in a machine learning model training process, while preserving data privacy. Client selection is critical to determine the performance of FL. Most of the existing client selection methods aim to maximize the number of selected clients, who can upload their local models before the deadline, in each global iteration, thus potentially accelerating the model convergence rate. However, these methods ignore the fact that most of the IoT devices are powered by on-board batteries and harvested green energy from the environment to prolong battery life. Hence, clients selected by these methods may not have sufficient energy to upload their local models in a global iteration or are unable to participate in the training process in the near future due to battery drainage. In this paper, we propose a novel client selection, entitled “EnerGy-AwaRe CliEnt SElection for Green FeDerated Learning (GREED)”, to optimize the trade-off between maximizing the number of selected clients and minimizing the energy drawn from batteries for the selected clients, while ensuring that all the selected clients have sufficient energy to upload their local models before the deadline. The performance of GREED is validated via extensive simulations.
FTLIoT: A Federated Transfer Learning Framework for Securing IoT

The growing number of Internet of Things (IoT) applications and connected devices has increased the chance for more cyberattacks against those applications and devices and emphasized the need to protect the IoT networks. Due to the vast network and the anonymity of the internet, it has been challenging to preserve private information and communication. Although most systems implement security devices (i.e. firewalls) to avoid this, the second line of defence, Intrusion Detection Systems (IDSs), are critical in enhancing the system's security level. This paper proposed a model that combines the two machine learning techniques, Federated and Transfer Learning, to build an IDS to secure the IoT networks with less training time and enhanced performance while preserving the user's data privacy. Deep learning algorithms, namely Deep Neural Network (DNN) and Convolutional Neural Network (CNN), are used to evaluate the performance of the proposed framework on a benchmark dataset, CSE-CIC-IDS2018, and the feasibility of adopting Federated Transfer Learning (FTL) is shown in terms of performance metrics and training and fine-tuning time. The results show that the proposed technique can increase performance and decrease training time compared to the traditional machine learning techniques.
A Unified Multi-Task Semantic Communication System with Domain Adaptation

The task-oriented semantic communication sys-tems have achieved significant performance gain, however, the paradigm that employs a model for a specific task might be limited, since the system has to be updated once the task is changed or multiple models are stored for serving various tasks. To address this issue, we firstly propose a unified deep learning enabled semantic communication system (U-DeepSC), where a unified model is developed to serve various transmission tasks. To jointly serve these tasks in one model with fixed parameters, we employ domain adaptation in the training procedure to specify the task-specific features for each task. Thus, the system only needs to transmit the task-specific features, rather than all the features, to reduce the transmission overhead. Moreover, since each task is of different difficulty and requires different number of layers to achieve satisfactory performance, we develop the multi-exit architecture to provide early-exit results for relatively simple tasks. In the experiments, we employ a proposed U-DeepSC to serve five tasks with multi-modalities. Simulation re-sults demonstrate that our proposed U-DeepSC achieves compa-rable performance to the task-oriented semantic communication system designed for a specific task with significant transmission overhead reduction and much less number of model parameters.
Generalizing Flow Classification for Distributed Denial-of-Service over Different Networks

With the growth in connected devices and network traffic, these systems require automated and fast approaches to achieve secure operations. Hence, machine learning-based network intrusion detection has become the state-of-the-art approach to tackle uncertainties and new attacks. However, the generalization of the models when exposed to different domains and workloads remains an open issue. In this paper, we propose using federated learning (FL) with sampling methods and feature selection to improve the generalization of the trained global model when evaluated in different network contexts. We evaluate this approach to classify network flows representing benign traffic and distributed denial-of-service attacks. Our proposed approach results in an 85% improvement compared with the naive evaluation of training in one context and evaluating others. Moreover, it presented a similar performance to a statistical algorithm with the reported generalization capability on flow-based network traffic classification. Additionally, this FL-based approach brings data privacy and distributed learning capability to the table.
Multi-Key Similar Data Search on Encrypted Storage With Secure Pay-Per-Query

Many commercial cloud service providers (CSPs) adopt pay-per-query pricing models, in which data owners are charged based on the amount of data scanned by each query. In such a data sharing model, not only the privacy preservation for the data and queries but also the trustworthiness of the underlying billing system is of the utmost importance. In this paper, we revisit multi-key searchable encryption (MKSE), an efficient and secure data search algorithm allowing a data owner to grant users the ability to retrieve data of interest over the outsourced, encrypted datasets. We first investigate which factor in existing MKSE schemes renders authorized users over-privileged such that, without risking their credits (e.g., leaking the private keys and/or the passwords for their accounts associated with a project where the shared data resides), they can allow unauthorized users to make valid queries. Unfortunately, this concern may be devastating because the queries made by unauthorized users would incur unexpected financial damage to the owner in practical pay-per-query models. We then propose a novel multi-key data search scheme that is resilient to unauthorized queries. The proposed scheme features a novel user authorization mechanism that carefully limits user privilege such that even an authorized user cannot illegally invite unauthorized users to query unless he entirely leaks his credit. We demonstrate the proposed scheme is comparable to prior work in terms of performance while achieving a higher level of security.
Improved Particle Swarm Optimization for Laser Cutting Path Planning

This research focuses on the long empty cutting path problem during the laser cutting process by employing an improved proximity method to establish the starting point set in complex closed graphics. Specifically, this work improves the particle swarm algorithm and proposes the Levy Flight, power function, and Singer map employed particle swarm optimization (LPSPSO) to avoid the disadvantages of the standard particle swarm optimization (PSO) algorithm. Specifically, the comprehensive prospect-regret theoretical model evaluation value is used as the fitness value to guide the algorithm’s evolution and adaptively adjust the parameters in the LPSPSO algorithm, including the inertia weight power function, the learning factors, and the chaotic random number based on the Singer chaotic map. Additionally, the Levy flight is introduced to disturb the particles and prevent local optimization. This is achieved by adjusting the Levy flight threshold based on the distance between the particles to prevent the Levy flight from starting prematurely and increasing the calculation burden. To verify the performance of the LPSPSO algorithm, it was challenged against three state-of-the-art algorithms on 22 benchmark test instances and a laser cutting problem, with the results revealing that the LPSPSO algorithm has a better performance and can be used to solve the empty length of the laser cutting path problem.
A natural language interface for automatic generation of data flow diagram using web extraction techniques

To model the data and functions in various computer science applications, the researcher uses a Data Flow Diagram (DFD). DFD has been constructed using [open-source software tools that provide users with different shapes and environments. However, the existing approaches require substantial human effort, the validity of the generated output is still a loophole, and they have never gained traction in practice. Our research objective is to develop a semi-automated tool for drawing complex Data Flow Diagrams in the shortest time according to the specified features of the intended system. We developed a Natural Language Interface (NLI) that allows the user to compose a query and identify the system functionality and constraints for the composition of DFD. Natural Language Processing (NLP) techniques are applied to scrapped data to extract the keywords and develop a data repository. Also, we developed rule-based algorithms to map user queries onto respective token shapes to draw the required functionality into appropriate levels of DFD. For verification, output DFDs were converted into conceptual digraphs using adjacency and permutation matrices to evaluate isomorphism. The empirical results reflect that the DFDs generated by the system are correct, complete, and significant.
Semantic representation and dependency learning for multi-label image recognition

Recently many multi-label image recognition (MLR) works have made significant progress by introducing pre-trained object detection models to generate lots of proposals or utilizing statistical label co-occurrence to enhance the correlation among different categories. However, these works have some limitations: (1) the effectiveness of the network significantly depends on pre-trained object detection models that bring expensive and unaffordable computation; (2) the network performance degrades when there exist occasional co-occurrence objects in images, especially for the rare categories. To address these problems, we propose a novel and effective semantic representation and dependency learning (SRDL) framework to learn category-specific semantic representation for each category and capture semantic dependency among all categories. Specifically, we design a category-specific attentional regions (CAR) module to generate channel/spatial-wise attention matrices to guide the model to focus on semantic-aware regions. We also design an object erasing (OE) module to implicitly learn semantic dependency among categories by erasing semantic-aware regions to regularize the network training. Extensive experiments and comparisons on two popular MLR benchmark datasets (i.e., MS-COCO and Pascal VOC 2007) demonstrate the effectiveness of the proposed framework over current state-of-the-art algorithms.
Generating diverse augmented attributes for generalized zero shot learning

Generalized Zero-Shot Learning (GZSL) has become an important research due to its powerful ability of recognizing unseen objects. Generative methods, converting conventional GZSL to fully supervised learning, can achieve competing performance, and most of them use semantic attributes plus Gaussian noise to enrich generated features. The visual features obtained in this way are consistent with the semantic description. However, the reality is that the semantic description of the visual features of the same category should be different, because the appearance of images differs from each other although they belong to a same category, i.e., mapping from semantic attributes to visual features should be a "many to many" relationship rather than "one to many". Therefore, we propose a novel method to generate diverse augmented attribute, which are subsequently utilized to synthesize features. We construct a semantic generator based on a pre-trained semantic mapper, which augments the category semantics. Using the augmented category semantics to generate visual features will result in a better fit of the generated visual features to the distribution of real features. The proposed method can well solve the pseudo diversity of visual features generated by most generative GZSL methods. We evaluate the proposed method on five popular benchmark datasets, and the results show that it can achieve the state-of-the-art performance.
Low Power and Efficient Re-Configurable Multiplier for Accelerator

Deep learning is a rising topic at the edge of technology, with applications in many areas of our lives, including object detection, speech recognition, natural language processing, and more. Deep learning's advantages of high accuracy, speed, and flexibility are now being used in practically all major sciences and technologies. As a result, any efforts to improve the performance of related techniques are worthwhile. We always have a tendency to generate data faster than we can analyse, comprehend, transfer, and reconstruct it. Demanding data-intensive applications such as Big Data. Deep Learning, Machine Learning (ML), the Internet of Things (IoT), and high- speed computing are driving the demand for "accelerators" to offload work from general-purpose CPUs. An accelerator (a hardware device) works in tandem with the CPU server to improve data processing speed and performance. There are a variety of off-the-shelf accelerator architectures available, including GPU, ASIC, and FPGA architectures. So, this work focus on designing a multiplier unit for the accelerators. This increases the performance of DNN, reduced the area and increasing the training speed of the system.