Reviewerly/exHarmony huggingface dataset:
Integrated AHP-IOWA, POWA Framework for Ideal Cloud Provider Selection and Optimum Resource Management

The lack of a common framework often complicates the process of provider selection and marginal resource allocation decision. The nonlinear relationships among selection criteria greatly impact the decision-making process. The paper address the critical issue by proposing a centralised Quality of Experience (QoE) and Quality of Service (QoS)- CQoES framework. The framework considers customised priority criteria, determine the relative importance of each criterion and intelligently assign relative weights to each criterion. The framework assists the service provider to in decision making for marginal resources. To achieve the objective, we employ the Analytical Hierarchical Process (AHP), Induced OWA (IOWA) operator, Probabilistic OWA (POWA) operator, user-based collaborative filtering method with enhanced top KNN algorithm. The method handles complex nonlinear relationship of the selection criteria. It signifies consumer's customised criteria in relation to other criteria, then reorders inputs based on the ordered-inducing variable. The proposed method smartly unifies the provider's probabilistic information and the attitudinal characteristics for marginal resource allocation. To demonstrate the effectiveness of the approach, we present two scenarios and use a real cloud and other web service dataset. The experimental results show that the proposed system handles the issue of service selection and marginal resource allocation decision.
System Optimization of Federated Learning Networks With a Constrained Latency

This paper investigates a wireless federated learning (FL) network with limited communication bandwidth, where multiple mobile clients train their individual models with the help of one central server. We consider the practical communication scenarios, where the clients should complete the local computation and model upload within a defined latency. By jointly exploiting the dynamic characteristics of wireless channels and computational capability at the clients, we optimize the federated learning network by maximizing the number of active clients under the constraints of both latency and bandwidth. Specifically, we propose two bandwidth allocation (BA) schemes, where <i>scheme I</i> is based on the instantaneous channel state information (CSI), while <i>scheme II</i> employs the particle swarm optimization (PSO) method, based on the statistical CSI. Simulation results on the test accuracy and convergence rate are finally provided to demonstrate the advantages of the proposed optimization schemes for the considered FL network.
Smartphone Inertial Sensors for Human Locomotion Activity Recognition based on Template Matching and Codebook Generation

In the recent past, recognition of human locomotion activities has become a growing research area. Health monitoring, detection of a crowd’s behavior and indoor-localization are some examples of the fields that benefit from the diversity of this research area. Locomotion activity recognition while using the data from smartphone sensors has become a popular approach because it provides the user-independence. While working with smartphone sensors, where it helps to rectify the annoyance of the subject, it creates difficulty in the recognition of the activity due to smartphone’s possible random orientations. In this paper, a combination of techniques like template matching and codebook generation is proposed that not only eliminates the random orientation factor but also lessens the computational complexity of the activity recognition. Experimental results are evaluated over publicly available benchmark MobiAct dataset which include a combination of smartphone embedded accelerometer, gyroscope sensors and orientation data. Our model has secured an accuracy of 87.50% and 81.73% over static and dynamic activities respectively.
Small object detection in remote sensing images based on super-resolution

Accurate objects detection in remote sensing images is very important, because security, transportation, and rescue applications in military and civilian fields require fully analyzing and using these images. To address the problem that many small-sized objects in remote sensing images are difficult to detect, this paper proposes an improved S2ANET-SR model based on S2A-NET network. In this paper, the original and reduced image are fed to the detection network at the same time, and then a super-resolution enhancement module for the reduced image is designed to enhance the feature extraction of small objects, after that, the perceptual loss and texture matching loss is proposed as supervision. Extensional experiments are conducted to evaluate the performance on the general remote sensing dataset DOTA, and the results show that our proposed method can achieve 74.47% mAP, which is 0.79% better than the accuracy of S2A-NET.
DCT-GAN: Dilated Convolutional Transformer-Based GAN for Time Series Anomaly Detection

Time series anomaly detection (TSAD) is an essential problem faced in several fields, e.g., fault detection, fraud detection, and intrusion detection, etc. Although TSAD is a crucial problem in anomaly detection, few solutions in anomaly detection are suitable for it at present. Recently, some researchers use GAN-based methods such as TAnoGAN and TadGAN to solve TSAD problem. However, problems such as model collapse, low generalization capability and poor accuracy still exist. In this article, we proposed a Dilated Convolutional Transformer-based GAN (DCT-GAN) to enhance accuracy and improve generalization capability of the model. Specifically, DCT-GAN utilize several generators and a single discriminator to alleviate the mode collapse problem. Each generator consists of a dilated convolutional neural network and a Transformer block to obtain fine-grained and coarse-grained information of the time series, which is a useful component to improve generalization capability. We also use weight-based mechanism to balance these generators. Experiments verify the effectiveness of our method and each part of DCT-GAN.
Spatial-driven features based on image dependencies for person re-identification

Person re-identification (Re-ID) aims to search for the same pedestrian in different cameras, which is a crucial research direction in pattern recognition. Recent deep learning methods have advanced the development of Re-ID. However, the existing approaches easily result in performance degradation in the case of larger scene data because they do not adequately consider the spatial dependencies of both the inter-image and the intra-image. The paper proposes a novel Spatial-Driven Network (SDN) to learn particularly discriminative features with abundant semantic information from both the inter-image and the intra-image dependencies for person Re-ID. Firstly, we design a global-correlation attention module to capture the inter-image dependencies among a series of different pedestrian images. Secondly, we present a local-correlation attention module to compute the intra-image dependencies from any pair of pixels within each pedestrian image. Furthermore, we propose a specific network integration mechanism, which carefully combines the above two complementary modules to match well the solution of the spatial dependency problem. We implement numerous experiments to assess the proposed SDN on mainstream person Re-ID databases. The results demonstrate that the proposed SDN outperforms most of the state-of-the-art methods in typical key criteria.
Relation construction for aspect-level sentiment classification

Aspect-level sentiment classification aims to obtain fine-grained sentiment polarities of different aspects in one sentence. Most existing approaches handle the classification by acquiring the importance of context words towards each given aspect individually, and ignore the benefits brought by aspect relations. Since the sentiment of one aspect can be deduced through their relationship according to other aspects, in this paper, we propose a novel relation construction multi-task learning network (RMN), which is the first attempt to extract aspect relations as an auxiliary classification task. RMN generates aspect representations through graph convolution networks with a semantic dependency graph and utilizes the bi-attention mechanism to capture the relevance between the aspect and the context. Unlike conventional multi-task learning methods that need extra datasets, we construct an auxiliary relation-level classification task that extracts aspect relations from the original dataset with shared parameters. Extensive experiments on five public datasets from SemEval 14, 15, 16 and MAMS show that our RMN improves about 0.09% to 0.8% on accuracy and about 0.04% to 1.19% on F1 score, compared to several comparative baselines.
Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation

Domain shift, the mismatch between training and testing data characteristics, causes significant degradation in the predictive performance in multi-source imaging scenarios. In medical imaging, the heterogeneity of population, scanners and acquisition protocols at different sites presents a significant domain shift challenge and has limited the widespread clinical adoption of machine learning models. Harmonization methods which aim to learn a representation of data invariant to these differences are the prevalent tools to address domain shift, but they typically result in degradation of predictive accuracy. This paper takes a different perspective of the problem: we embrace this disharmony in data and design a simple but effective framework for tackling domain shift. The key idea, based on our theoretical arguments, is to build a pretrained classifier on the source data and adapt this model to new data. The classifier can be fine-tuned for intra-site domain adaptation. We can also tackle situations where we do not have access to ground-truth labels on target data; we show how one can use auxiliary tasks for adaptation; these tasks employ covariates such as age, gender and race which are easy to obtain but nevertheless correlated to the main task. We demonstrate substantial improvements in both intra-site domain adaptation and inter-site domain generalization on large-scale real-world 3D brain MRI datasets for classifying Alzheimer's disease and schizophrenia.
Binary imbalanced data classification based on diversity oversampling by generative models

In many practical applications, the data are class imbalanced. Accordingly, it is very meaningful and valuable to investigate the classification of imbalanced data. In the framework of binary imbalanced data classification, the synthetic minority oversampling technique (SMOTE) is the best-known oversampling method. However, for each positive sample, SMOTE generates only k synthetic samples on the lines between the positive sample and its k-nearest neighbors, resulting in three drawbacks: (1) SMOTE cannot effectively extend the training field of positive samples; (2) the generated positive samples lack diversity; (3) SMOTE does not accurately approximate the probability distribution of the positive samples. Therefore, two binary imbalanced data classification methods named BIDC1 and BIDC2 based on diversity oversampling by generative models are proposed. The BIDC1 and BIDC2 conduct diversity oversampling using extreme learning machine autoencoder and generative adversarial network, respectively. Extensive experiments on 26 data sets are conducted to compare the two methods with 14 state-of-the-art methods using five metrics: F-measure, G-means, AUC-area, MMD-score, and Silhouette-score. The experimental results demonstrate that the two methods outperform the other 14 methods.
Performance comparison of CNN, QNN and BNN deep neural networks for real-time object detection using ZYNQ FPGA node

In this manuscript, previously trained Convolutional neural network (CNN), Quantum Neural Network (QNN), and Binarized Neural Network (BNN) models performed employing Tensor Flow's Application Programming Interface (API) for real-time object detection and implemented on FPGA. Then, the proposed real time objects detection based on CNN, QNN and BNN Deep Neural Networks classifier mode activated on python, and then the dataset taken from PASCAL VOC. For an accuracy analysis of real time objection detection, this real time objects detection based on CNN Deep Neural Networks classifier provide 3.458% and 1.600% higher accuracy value than proposed real time objects detection. Then, the proposed real time objects detection based on CNN, QNN and BNN Deep Neural Networks classifier model verified by using the Verilog programming language in the Xilinx ISE 14.5 design tools in the ZYNQ FPGA development team. These results show the FPGA implementation of this real time objects detection based on CNN Deep Neural Networks classifier model meets the objective efficiently.
QFAST: Conflating Search and Numerical Optimization for Scalable Quantum Circuit Synthesis

We present a topology aware quantum synthesis algorithm designed to produce short circuits and to scale well in practice. The main contribution is a novel representation of circuits able to encode placement and topology using generic "gates", which allows the QFAST algorithm to replace expensive searches over circuit structures with few steps of numerical optimization. When compared against optimal depth, search based state-of-the-art techniques, QFAST produces comparable results: 1.19× longer circuits up to four qubits, with an increase in compilation speed of 3.6×. In addition, QFAST scales up to seven qubits. When compared with the state-of-the-art "rule" based decomposition techniques in Qiskit, QFAST produces circuits shorter by up to two orders of magnitude (331×), albeit 5.6× slower. We also demonstrate the composability with other techniques and the tunability of our formulation in terms of circuit depth and running time.
Assessing Generalizability of CodeBERT

Pre-trained models like BERT have achieved strong improvements on many natural language processing (NLP) tasks, showing their great generalizability. The success of pre-trained models in NLP inspires pre-trained models for programming language. Recently, CodeBERT, a model for both natural language (NL) and programming language (PL), pre-trained on code search dataset, is proposed. Although promising, CodeBERT has not been evaluated beyond its pre-trained dataset for NL-PL tasks. Also, it has only been shown effective on two tasks that are close in nature to its pre-trained data. This raises two questions: Can CodeBERT generalize beyond its pre-trained data? Can it generalize to various software engineering tasks involving NL and PL? Our work answers these questions by performing an empirical investigation into the generalizability of CodeBERT. First, we assess the generalizability of CodeBERT to datasets other than its pre-training data. Specifically, considering the code search task, we conduct experiments on another dataset containing Python code snippets and their corresponding documentation. We also consider yet another dataset of questions and answers collected from Stack Overflow about Python programming. Second, to assess the generalizability of CodeBERT to various software engineering tasks, we apply CodeBERT to the just-in-time defect prediction task. Our empirical results support the generalizability of CodeBERT on the additional data and task. CodeBERT-based solutions can achieve higher or comparable performance than specialized solutions designed for the code search and just-in-time defect prediction tasks. However, the superior performance of the CodeBERT requires a tradeoff; for example, it requires much more computation resources as compared to specialized code search approaches.
Object-aware Video-language Pre-training for Retrieval

Recently, by introducing large-scale dataset and strong transformer network, video-language pre-training has shown great success especially for retrieval. Yet, existing video-language transformer models do not explicitly fine-grained semantic align. In this work, we present Object-aware Transformers, an object-centric approach that extends video-language transformer to incorporate object representations. The key idea is to leverage the bounding boxes and object tags to guide the training process. We evaluate our model on three standard sub-tasks of video-text matching on four widely used benchmarks. We also provide deep analysis and detailed ablation about the proposed method. We show clear improvement in performance across all tasks and datasets considered, demonstrating the value of a model that incorporates object representations into a video-language architecture. The code has been released in https://github.com/FingerRec/OA-Transformer.
Time Series Anomaly Detection for Smart Grids: A Survey

With the rapid increase in the integration of renewable energy generation and the wide adoption of various electric appliances, power grids are now faced with more and more challenges. One prominent challenge is to implement efficient anomaly detection for different types of anomalous behaviors within power grids. These anomalous behaviors might be induced by unusual consumption patterns of the users, faulty grid infrastructures, outages, external cyberattacks, or energy fraud. Identifying such anomalies is of critical importance for the reliable and efficient operation of modern power grids. Various methods have been proposed for anomaly detection on power grid time-series data. This paper presents a short survey of the recent advances in anomaly detection for power grid time-series data. Specifically, we first outline current research challenges in the power grid anomaly detection domain and further review the major anomaly detection approaches. Finally, we conclude the survey by identifying the potential directions for future research.
A decolonial approach to AI in higher education teaching and learning: strategies for undoing the ethics of digital neocolonialism

The aim of this article is to use decolonial thinking, as applied in the field of AI, to explore the ethical and pedagogical implications for higher education teaching and learning. The questions driving this article are: What does a decolonial approach to AI imply for higher education teaching and learning? How can educators, researchers and students interrogate the coloniality of AI in higher education? Which strategies can be useful for undoing the ethics of digital neocolonialism in higher education? While there is work on decolonial theory in AI as well as literature on the decolonization of higher education, there is not much theorization that brings those literatures together to develop a decolonial conceptual framework for ethical AI in higher education teaching and learning. This article offers this conceptual framing and suggests decolonial strategies that challenge algorithmic coloniality and colonial AI ethics in the context of higher education teaching and learning.
Power quality disturbance classification under noisy conditions using adaptive wavelet threshold and DBN-ELM hybrid model

To solve the problems of noise interference and artificial feature extraction in power quality disturbance (PQD) classification, a new method combining adaptive wavelet threshold denoising and deep belief network fusion extreme learning machine (DBN-ELM) is proposed. Firstly, the noise content of the layer is determined by calculating the energy ratio of the wavelet coefficients of each layer, and an adaptive wavelet threshold is constructed based on the energy ratio to denoise the PQD signals. Secondly, the feature extraction capability of DBN is used to extract the feature from the PQD signals after denoising. Finally, a novel PQD classifier called DBN-ELM is constructed by integrating an ELM into a DBN, which avoids global fine-tuning of DBN and improves PQD classification efficiency. The simulation result and experimental verification show that the proposed method can effectively suppress PQD noise and performs well on DBN-ELM classification.
PubTables-1M: Towards comprehensive table extraction from unstructured documents

Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, one of the greatest challenges remains the creation of datasets with complete, unambiguous ground truth at scale. To address this, we develop a new, more comprehensive dataset for table extraction, called PubTables-1M. PubTables-1M contains nearly one million tables from scientific articles, supports multiple input modalities, and contains detailed header and location information for table structures, making it useful for a wide variety of modeling approaches. It also addresses a significant source of ground truth inconsistency observed in prior datasets called oversegmentation, using a novel canonicalization procedure. We demonstrate that these improvements lead to a significant increase in training performance and a more reliable estimate of model performance at evaluation for table structure recognition. Further, we show that transformer-based object detection models trained on PubTables-1M produce excellent results for all three tasks of detection, structure recognition, and functional analysis without the need for any special customization for these tasks. Data and code will be released at https://github.com/microsoft/table-transformer.
Blockchain for value creation in the healthcare sector

The healthcare sector is a complex ecosystem in which multiple stakeholders have to interact, and digital transformation is providing new ways for those actors to cooperate. Among the different new technologies, blockchain is one of the most discussed in a confrontation between true believers and skeptics. To shed light on the question of how blockchain technology can contribute to value creation in the healthcare sector, this paper investigates 33 companies from a sample of 2404 enterprises founded by professional incubators and accelerators. Whitepapers, websites, and weblog discussions are analyzed using content analysis. Focusing on companies financed by professional investors reduces the noise coming from short-term investments and frauds that sometimes follow blockchain initiatives. The findings show four different approaches to value creation: endogenous hedonistic value, public-private conflict mitigation, utilitarian/instrumental value, and social value.
Fast-RRT: A RRT-Based Optimal Path Finding Method

As a sampling-based pathfinding algorithm, Rapidly Exploring Random Trees (RRT) has been widely used in motion planning problems due to the ability to find a feasible path quickly. However, the RRT algorithm still has several shortcomings, such as the large variance in the search time, poor performance in narrow channel scenarios, and being far from the optimal path. In this paper, we propose a new RRT-based path find algorithm, Fast-RRT, to find a near-optimal path quickly. The Fast-RRT algorithm consists of two modules, including Improved RRT and Fast-Optimal. The former is aims to quickly and stably find an initial path, and the latter is to merge multiple initial paths to obtain a near-optimal path. Compared with the RRT algorithm, Fast-RRT shows the following improvements: (1) A Fast-Sampling strategy that only samples in the unreached space of the random tree was introduced to improve the search speed and algorithm stability; (2) A Random Steering strategy expansion strategy was proposed to solve the problem of poor performance in narrow channel scenarios; (3) By fusion and adjustment of paths, a near-optimal path can be faster found by Fast-RRT, 20 times faster than the RRT* algorithm. Owing to these merits, our proposed Fast-RRT outperforms RRT and RRT* in both speed and stability during experiments.