Reviewerly/exHarmony huggingface dataset:
A neighborhood prior constrained collaborative representation for classification

With the development of machine learning and computer vision, classification technology is becoming increasingly important. Due to the advantage in efficiency and effectiveness, collaborative representation-based classifiers (CRC) have been applied to many practical cognitive fields. In this paper, we propose a new neighborhood prior constrained collaborative representation model for pattern classification. Compared with the naive CRC models which approximate the test sample with all the training data globally, our proposed methods emphasize the guidance of the neighborhood priors in the coding process. Two different kinds of neighbor priors and the models’ weighted extensions are explored from the view of sample representation ability and relationships between the samples. Consequently, the contributions of different samples can be distinguished adaptively and the obtained representations can be more discriminative for the recognition. Experimental results on several popular databases can verify the effectiveness of our proposed methods in comparison with other state-of-the-art classifiers.
Internet restrictions in Uganda: examining their impact on journalism

(2021). Internet restrictions in Uganda: examining their impact on journalism. Information, Communication & Society: Vol. 24, Era or Error of Transformation? Assessing Afrocentric Attributes to Digitalization Guest Editors: Bruce Mutsvairo, Massimo Ragnedda and Kristin Skare Orgeret, pp. 490-506.
Noise resilient compilation policies for quantum approximate optimization algorithm

Quantum approximate optimization algorithm (QAOA) is a promising quantum-classical hybrid algorithm to solve hard combinatorial optimization problems using noisy quantum devices. The multiqubit CPHASE gates used in the quantum circuit for QAOA are commutative i.e., the order of the gates can be altered without changing the output state. This re-ordering leads to the execution of more gates in parallel and a smaller number of additional SWAP gates to compile the QAOA circuit resulting in lower circuit-depth and gate-count. A less number of gates generally indicates a lower accumulation of gate-errors, and a reduced circuit-depth means less decoherence time for the qubits. However, near-term quantum devices exhibit significant variations in the gate success probabilities. Variation-aware compilation policies (i.e. putting most gate operations on qubits with higher gate success probabilities) can enhance the probability of successful program execution on the hardware. The greater flexibility of QAOA-circuits offer better scope of optimization with QAOA-tailored compilation policies. This paper presents an argument for compilation policies to exploit the unique characteristics of QAOA-circuits alongside the variation-awareness of the noisy devices. We present two procedures - variation-aware qubit placement (VQP) and variation-aware iterative mapping (VIM) that can improve the circuit success probability quite significantly (≈8.408X on average) for a set of QAOA-MaxCut problems on ibmq_16_melbourne.
A Semisupervised Learning Scheme with Self-Paced Learning for Classifying Breast Cancer Histopathological Images

The unavailability of large amounts of well-labeled data poses a significant challenge in many medical imaging tasks. Even in the likelihood of having access to sufficient data, the process of accurately labeling the data is an arduous and time-consuming one, requiring expertise skills. Again, the issue of unbalanced data further compounds the abovementioned problems and presents a considerable challenge for many machine learning algorithms. In lieu of this, the ability to develop algorithms that can exploit large amounts of unlabeled data together with a small amount of labeled data, while demonstrating robustness to data imbalance, can offer promising prospects in building highly efficient classifiers. This work proposes a semisupervised learning method that integrates self-training and self-paced learning to generate and select pseudolabeled samples for classifying breast cancer histopathological images. A novel pseudolabel generation and selection algorithm is introduced in the learning scheme to generate and select highly confident pseudolabeled samples from both well-represented classes to less-represented classes. Such a learning approach improves the performance by jointly learning a model and optimizing the generation of pseudolabels on unlabeled-target data to augment the training data and retraining the model with the generated labels. A class balancing framework that normalizes the class-wise confidence scores is also proposed to prevent the model from ignoring samples from less represented classes (hard-to-learn samples), hence effectively handling the issue of data imbalance. Extensive experimental evaluation of the proposed method on the BreakHis dataset demonstrates the effectiveness of the proposed method.
Source apportionment of soil heavy metals using robust spatial receptor model with categorical land-use types and RGWR-corrected in-situ FPXRF data

High-density samples are usually a prerequisite for obtaining high-precision source apportionment results in large-scale areas. In-situ field portable X-ray fluorescence spectrometry (FPXRF) is a fast and cheap way to increase the sample size of soil heavy metals (HMs). Moreover, categorical land-use types may be closely associated with source contributions. However, the above information has rarely been incorporated into the source apportionment. In this study, robust geographically weighted regression (RGWR) was first used to correct the spatially varying effect of the related soil factors (e.g., soil water and soil organic matter) on in-situ FPXRF in an urban-rural fringe of Wuhan City, China, and the correction accuracy of RGWR was compared with those of the traditionally non-spatial multiple linear regression (MLR) and basic GWR. Then, the effect of land-use types on HM concentrations was partitioned using analysis of variance (ANOVA). Last, based on the robust spatial receptor model (i.e., robust absolute principal component scores/RGWR [RAPCS/RGWR]), this study proposed RAPCS/RGWR with categorical land-use types and RGWR-corrected in-situ FPXRF data (RAPCS/RGWR_LU&FPXRF), and its performance was compared with those of RAPCS/RGWR with none or one kind of auxiliary data. Results showed that (i) the performances of the correction models for in-situ FPXRF data were in the order of RGWR > GWR > MLR, and the relative improvement of RGWR over MLR ranged from 52.6% to 70.71% for each HM; (ii) categorical land-use types significantly affected the concentrations of soil Zn, Cu, As, and Pb; (iii) the highest estimation accuracy for source contributions was obtained by RAPCS/RGWR_LU&FPXRF, and the lowest estimation accuracy was obtained by basic RAPCS/RGWR. It is concluded that land-use types and RGWR-corrected in-situ FPXRF data are closely associated with the source contribution, and RAPCS/RGWR_LU&FPXRF is a cost-effective source apportionment method for soil HMs in large-scale areas.
Interactive Machine Learning for Data Exfiltration Detection: Active Learning with Human Expertise

Data exfiltration is a serious threat to organizations. Such exfiltrations cause breach events that can lead to millions of dollars of loss. Perimeter defense is not enough by itself since successful exploits from insiders can also be very damaging. Internal network user activities need to be monitored to detect malicious actions. Automatic machine learning methods can be applied for network anomaly detection, but they create a lot of false alarms. Domain experts can identify malicious users, but they are unable to process large volumes of data. Interactive machine learning (iML) deals with this tradeoff by creating an efficient collaboration between domain experts and machine learning algorithms. Previous research in iML has focused mainly on collaboration with non-experts. The design and requirements for expertise-driven iML have yet to be delineated for cybersecurity applications. In this research, we proposed an Active Learning (AL) model trained with outputs from a liberal (outputting many false alarms as well as possible hits) anomaly detection (AD) criterion to study expert-iML collaboration in anomaly detection. The results showed that: iML in this context can prune false alarms and minimize misses; the performance/compatibility tradeoff that typically occurs in conventional machine learning updates may be less salient in iML. We suggest that compatibility between experts and algorithms can be improved by presenting information about feature relevance during the training process.
Counterfactual Explanation and Causal Inference In Service of Robustness in Robot Control

We propose an architecture for training generative models of counterfactual conditionals of the form, 'can we modify event A to cause B instead of C?', motivated by applications in robot control.Using an 'adversarial training' paradigm, an image-based deep neural network model is trained to produce small and realistic modifications to an original image in order to cause user-defined effects.These modifications can be used in the design process of image-based robust control -to determine the ability of the controller to return to a working regime by modifications in the input space, rather than by adaptation.In contrast to conventional control design approaches, where robustness is quantified in terms of the ability to reject noise, we explore the space of counterfactuals that might cause a certain requirement to be violated, thus proposing an alternative model that might be more expressive in certain robotics applications.So, we propose the generation of counterfactuals as an approach to explanation of black-box models and the envisioning of potential movement paths in autonomous robotic control.Firstly, we demonstrate this approach in a set of classification tasks, using the well known MNIST and CelebFaces Attributes datasets.Then, addressing multi-dimensional regression, we demonstrate our approach in a reaching task with a physical robot, and in a navigation task with a robot in a digital twin simulation.
Design and Implementation of a Blockchain-Based E-Health Consent Management Framework

Transformation of data into knowledge is the hallmark of modern medicine. As an evolving field of medicine, e-Health involves the electronic processing of a patient's personal, medical and other health-related data to improve healthcare delivery. Data captured from clinical interactions between patients and their care providers, as well as health data collected through medical sensors, provide a rich source of data, known as patient medical records (PMRs), which can be processed in various ways to enhance the delivery of healthcare services. However, indiscriminate processing of PMRs could potentially result in the violation of the security or privacy of patients. To ensure that PMRs are not processed in ways that could be harmful to the security or privacy of the patients, modern data protection regulations, such as the European General Data Protection Regulation (GDPR), requires healthcare service provides to obtain the consent of a patient for any processing operation on their PMRs. The mechanism by which a patient exercises their right to control who can process their PMRs, when and for what purpose, is referred to as consent management in e-Health. Existing health information technology systems do not provide adequate support for consent management; there is a lack of transparency and auditability in the existing systems to monitor and ensure that healthcare service providers comply to the relevant data protection regulations in processing PMRs. The emerging blockchain technology offers an opportunity to design an e-Health consent management system that is compliant with modern data protection regulations such as GDPR. In this paper, we present the design and implementation of an e-Health consent management framework, based on the state-of-the-art blockchain technologies, for processing PMRs. Our analysis confirms that our system satisfies the requirements for consent management in e-Health.
Online and Self-Learning Approach to the Identification of Fuzzy Neural Networks

This article proposes a novel online and self-learning algorithm to the identification of fuzzy neural networks, which not only learns the structure and parameters online, but also learns the threshold parameters by itself and automatically. For structure learning, a self-constructing approach including adding neurons and merging highly similar fuzzy rules is proposed based on the criteria of the system error between actual and model output and the maximum firing strength of neurons. In order to achieve the efficient merging computing, a new calculation method of similarity degree between fuzzy rules is developed. Further and more importantly, the varying width of Gaussian membership functions can be learned by itself according to the underfitting and overfitting criteria. Similarly, different from the existing constant threshold of similarity degree for merging, the varying threshold of similarity degree can be self-learned according to the real-time accuracy of model. The proposed self-learning mechanism significantly improves the model accuracy and greatly enhances the easy usability. Several benchmark examples are implemented to illustrate the effectiveness and feasible of the proposed approach.
Detecting phone‐related pedestrian distracted behaviours via a two‐branch convolutional neural network

The distracted phone-use behaviours among pedestrians, like Texting, Game Playing and Phone Calls, have caused increasing fatalities and injuries. However, the research of phone-related distracted behaviour by pedestrians has not been systemically studied. It is desired to improve both the driving and pedestrian safety by automatically discovering the phone-related pedestrian distracted behaviours. Herein, a new computer vision-based method is proposed to detect the phone-related pedestrian distracted behaviours from a view of intelligent and autonomous driving. Specifically, the first end-to-end deep learning based Two-Branch Convolutional Neural Network (CNN) is designed for this task. Taking one synchronised image pair by two front on-car GoPro cameras as the inputs, the proposed two-branch CNN will extract features for each camera, fuse the extracted features and perform a robust classification. This method can also be easily extended to video-based classification by confidence accumulation and voting. A new benchmark dataset of 448 synchronised video pairs of 53,760 images collected on a vehicle is proposed for this research. The experimental results show that using two synchronised cameras obtained better performance than using one single camera. Finally, the proposed method achieved an overall best classification accuracy of 84.3% on the new benchmark when compared to other methods.
fuseGNN

Graph convolutional neural networks (GNN) have achieved state-of-the-art performance on tasks like node classification. It has become a new workload family member in data-centers. GNN works on irregular graph-structured data with three distinct phases: Combination, Graph Processing, and Aggregation. While Combination phase has been well supported by sgemm kernels in cuBLAS, the other two phases are still inefficient on GPGPU due to the lack of optimized CUDA kernels. In particular, Aggregation phase introduces large volume of DRAM storage footprint and data movement, and both Aggregation and Graph Processing phases suffer from high kernel launching time. These inefficiencies not only decrease training throughput but also limit users from training GNNs on larger graphs on GPGPU. Although these problems have been partially alleviated by recent studies, their optimizations are still not sufficient. In this paper, we propose fuseGNN, an extension of PyTorch that provides highly optimized APIs and CUDA kernels for GNN. First, two different programming abstractions for Aggregation phase are utilized to handle graphs with different average degrees. Second, dedicated GPGPU kernels are developed for Aggregation and Graph Processing in both forward and backward passes, in which kernel-fusion along with other optimization strategies are applied to reduce kernel launching time and latency as well as exploit data reuse opportunities. Evaluation on multiple benchmarks shows that fuseGNN achieves up to 5.3× end-to-end speedup over state-of-the-art frameworks, and the DRAM storage footprint is reduced by several orders of magnitude on large datasets.
Clustering Ensemble Based on Hybrid Multiview Clustering

As an effective method for clustering applications, the clustering ensemble algorithm integrates different clustering solutions into a final one, thus improving the clustering efficiency. The key to designing the clustering ensemble algorithm is to improve the diversities of base learners and optimize the ensemble strategies. To address these problems, we propose a clustering ensemble framework that consists of three parts. First, three view transformation methods, including random principal component analysis, random nearest neighbor, and modified fuzzy extension model, are used as base learners to learn different clustering views. A random transformation and hybrid multiview learning-based clustering ensemble method (RTHMC) is then designed to synthesize the multiview clustering results. Second, a new random subspace transformation is integrated into RTHMC to enhance its performance. Finally, a view-based self-evolutionary strategy is developed to further improve the proposed method by optimizing random subspace sets. Experiments and comparisons demonstrate the effectiveness and superiority of the proposed method for clustering different kinds of data.
Soft Sensor for VFA Concentration in Anaerobic Digestion Process for Treating Kitchen Waste Based on DSTHELM

Kitchen waste is both a pollutant and an available resource.The anaerobic digestion (AD) technology can effectively treat waste and generate biogas.The concentration of volatile fatty acids (VFA) is one of the essential quality monitoring indicators in the anaerobic digestion process of kitchen waste and is difficult to measure online.In this paper, the hierarchical extreme learning machine (HELM) is applied to the soft sensor for the concentration of VFA considering the shortcomings of traditional soft sensor.The combined feature information is extracted by the multi-layer extreme learning machine-autoencoder (ELM-AE) structure in the HELM.Besides, the last layer is the combined manifold regularization extreme learning machine (ELM) and used to predict and mine the data structure of labeled data and unlabeled data.Considering that the characteristics of system operating conditions are prone to change in the actual production process of anaerobic digestion, the domain space transfer hierarchical extreme learning machine (DSTHELM) is used to solve the data drift problem caused by different initial conditions in the actual data; this is different from the traditional hierarchical extreme learning machine.This method increases the accuracy by 14.31% and 9.67% in training set and test set compared with HELM, indicating that the model possesses higher prediction accuracy and better generalization performance.
Digital Technologies at the Pre-University and University Levels

The education sector is undergoing significant challenges in the process of transforming learning models in order to face the new requirements of our hyper-connected society. Teaching students how to adequately interact as active and committed citizens in our knowmadic global society is the most challenging task facing educators nowadays. Moreover, the university must consider a student’s knowledge of digital technologies to be able to design new educational models that respond to their current needs. The aim of our research is to assess that knowledge and the use of digital technologies among college students and the relationship this has with their prior academic experience. Data collection included a validated ad-hoc questionnaire divided into 16 categories applied to 757 students of both sexes between 20 and 57 years of age. The results show that, irrespective of gender and age, those students who have used Information and Communication Technologies (ICT) in their secondary education have a better knowledge of how to use them to their advantage throughout their university learning process. In conclusion, students need to undergo a training in digital skills before entering university, so as to equip them with a higher level of digital competence.
Audiovisual saliency prediction via deep learning

Neuroscience study verifies that synchronized audiovisual stimuli would make a stronger response of visual perception than an independent stimulus. Many researches show that audio signals would affect human gaze behavior in the viewing of natural video scenes. Thus in this paper, we propose a multi-sensory framework of audio and visual signals for video saliency prediction. It mainly includes four modules: auditory feature extraction, visual feature extraction, semantic interaction between auditory feature and visual feature, and feature fusion. With the inputs of audio and visual signals, we present a network architecture of deep learning to undertake the tasks of these four modules. It is an end-to-end architecture that could interact the semantics from its learned features of audio and visual stimuli. The numerical and visual results show our method achieves a significant improvement over eleven recent saliency models that are regardless of the audio stimuli, even some of them are state-of-the-art deep learning models.
Improving Bitcoin Transaction Propagation by Leveraging Unreachable Nodes

The Bitcoin P2P network is at the core of all communications between clients. The reachable part of this network has been explored and analyzed by numerous studies. Unreachable nodes, however, are, in most part, overlooked. Nonetheless, they are a relevant part of the network and play an essential role in the propagation of messages. In this paper, we focus on transaction propagation and show that increasing the participation of unreachable nodes can potentially improve the robustness and efficiency of the network. In order to do that, we propose a few changes to the network protocol. Additionally, we design a novel transaction propagation protocol that explicitly involves unreachable nodes to provide better protection against deanonymization attacks. Our solutions are simple to implement and can effectively bring immediate benefits to the Bitcoin network.
An evolutionary technique for supporting the consensus process of group decision making

Discrepancies arise when experts have to decide the preference on alternatives for a problem. Henceforth, it is necessary to carry out a process during which they adjust their opinions in order to achieve an acceptable consensus. Usually, this process is coordinated by a moderator that makes suggestions to the participants regarding the most adequate changes. In order to simplify this process, we propose an evolutionary technique based on the search of the changes of the preferences that improve the consensus degree. We also consider that the opinions of the experts should stay closer to the original ones. Taking into account these two factors, we are able to provide useful feedback for the experts willing to get a consensus and measure how much improvement can be achieved.