Reviewerly/exHarmony huggingface dataset:
Online feature selection for multi-source streaming features

Multi-source streaming feature selection in an online manner has attracted considerable attention, from researchers because it can reduce the dimensionality of heterogeneous big data. However, traditional online algorithms such as Alpha-investing, Online Streaming Feature Selection (OSFS), Online Group Feature Selection (OGFS) and Scalable and Accurate OnLine Approach (SAOLA) consider only a single data source with fixed instances, and are not directly applicable to multi-source data. Multi-source Causal Feature Selection (MCFS) can search for an invariant set in multiple interventional datasets. However, fixed feature spaces are restrained, and exactly these same features are required among multi-source data. To overcome these limitations, we propose a novel method known as Multi-source Streaming Feature Selection (MSFS) to tackle the feature selection problem for multi-source streaming features. The MSFS algorithm addresses a new feature from a random source in three phases: relevance, intra-source redundancy, and inter-source redundancy analyses. That is, MSFS attempts to mine the potential relationships among different data sources rather than only independently consider each data source. In particular, each new feature is analyzed online using the overlapping instances from all data sources, and the Markov blanket (MB) of the target variable is dynamically adjusted. To evaluate the performance of the MSFS algorithm, we compare it with that of the abovementioned algorithms on 14 datasets and two real-world scenarios. The results demonstrate that MSFS outperforms the existing algorithms in classification accuracy and number of selected features.
Unboxing the digital forensic investigation process

As digital forensics continues to play an important role in criminal investigations, its investigative work must be underpinned with well-defined and robust methodologies. Over the last 20 years, a substantial body of research has been produced to define and codify the digital forensic investigation process and the stages/sub-processes involved. Whilst current digital forensic investigation process models provide a solid foundation, it is argued that existing attempts often only focus on those physical tasks, which a practitioner must carry out at any given stage of an examination, omitting to identify those core thought processes, decisions and behaviours that form part of effective investigative practices. This work presents the Digital Forensic Workflow Model (DFWM), a novel approach to the structuring and definition of the procedures and tasks involved in the digital forensic investigation process starting from the initial 'Review of Client Requirements & Planning' stage, right through to the 'Evaluation of Deployed Workflow' stage. The DFWM contributes to the digital forensic management toolbox, where it enables the identification and management of risk and supports error mitigation at each stage of the workflow. The paper demonstrates how the DFWM functions as a framework for unboxing the digital forensic investigation process based on the investigative strategy of the particular case, providing a detailed structure and depiction of the physical and investigative tasks and decisions. From a research perspective, DFWM is a descriptive starting point, and future empirical studies may expand and provide further detail to the various physical and cognitive tasks and associated risks during the DF workflow.
Merged Differential Grouping for Large-Scale Global Optimization

The divide-and-conquer strategy has been widely used in cooperative co-evolutionary algorithms to deal with large-scale global optimization problems, where a target problem is decomposed into a set of lower-dimensional and tractable subproblems to reduce the problem complexity. However, such a strategy usually demands a large number of function evaluations to obtain an accurate variable grouping. To address this issue, a merged differential grouping (MDG) method is proposed in this article based on the subset–subset interaction and binary search. In the proposed method, each variable is first identified as either a separable variable or a nonseparable variable. Afterward, all separable variables are put into the same subset, and the nonseparable variables are divided into multiple subsets using a binary-tree-based iterative merging method. With the proposed algorithm, the computational complexity of interaction detection is reduced to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\max \{n,n_{ns}\times \log _{2} k\})$ </tex-math></inline-formula> , where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n_{ns}(\leq n)$ </tex-math></inline-formula> , and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k( &lt; n)$ </tex-math></inline-formula> indicate the numbers of decision variables, nonseparable variables, and subsets of nonseparable variables, respectively. The experimental results on benchmark problems show that MDG is very competitive with the other state-of-the-art methods in terms of efficiency and accuracy of problem decomposition.
Adversarial Attack and Defence Strategies for Deep-Learning-Based IoT Device Classification Techniques

Concurrent advancements in machine learning (ML) and Internet of Things have allowed several interesting interdisciplinary applications, such as classification tasks based on data generated by smart devices for applications, such as security, resource allocation, activity and task classification. However, these applications can be vulnerable to attacks by adversarial examples. The first contribution of this article is the development of a white-box adversarial attack mechanism to generate adversarial examples for data obtained from smart meters installed in residential houses. For the second contribution, we present an analysis to demonstrate that the statistical properties of adversarial datapoints are indistinguishable from those of the true datapoints. The attack is developed specifically for deep-learning-based models used to perform appliance classification in smart home environments. The statistical indistinguishability of the adversarial datapoints from the true datapoints indicates that non ML-based solutions may not be able to tackle the challenge posed by adversarial examples. As the final contribution, we evaluate the effectiveness of defence mechanisms for white-box adversarial attacks on the proposed attack mechanism, and show that while they can reduce the potency of the attack, the original models still remain significantly affected by the adversarial attack. The effectiveness of the proposed techniques is demonstrated on two publicly available data sets: 1) United Kingdom-domestic appliance-level electricity smart meter data set and 2) the Personalized Retrofit Decision Support Tools For U.K. Homes Using Smart Home Technology data set.
Traffic Object Detection and Recognition Based on the Attentional Visual Field of Drivers

Traffic object detection and recognition systems play an essential role in Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles (AV). In this research, we focus on four important classes of traffic objects: traffic signs, road vehicles, pedestrians, and traffic lights. We first review the major traditional machine learning and deep learning methods that have been used in the literature to detect and recognize these objects. We provide a vision-based framework that detects and recognizes traffic objects inside and outside the attentional visual area of drivers. This approach uses the driver 3D absolute coordinates of the gaze point obtained by the combined, cross-calibrated use of a front-view stereo imaging system and a non-contact 3D gaze tracker. A combination of multi-scale HOG-SVM and Faster R-CNN-based models are utilized in the detection stage. The recognition stage is performed with a ResNet-101 network to verify sets of generated hypotheses. We applied our approach on real data collected during drives in an urban environment with the RoadLAB instrumented vehicle. Our framework achieved 91% of correct object detections and provided promising results in the object recognition stage.
Implementation of AdaBoost and genetic algorithm machine learning models in prediction of adsorption capacity of nanocomposite materials

Simulation of adsorption capacity of a nanocomposite material was performed in this study in order to save time and cost in performing adsorption experiments. By developing the predictive models, one can easily predict the adsorption capacity of materials in wide range of processing conditions. Here, we developed two machine learning techniques for correlation of adsorption parameters including adsorbent dosage and solution pH to the adsorption capacity of the adsorbent. One of the most important issues and concerns in issues like the one we have in this study is ensuring the generality of output models, given that the dataset is a small one. As a result of this issue, we chose simple models to improve the modeling result in this study using AdaBoost and GA optimization models. The data was collected from literature and used in the fitting and simulations. After optimizing the hyper-parameters of these models, the final results showed very high efficiency. Using the RMSE criteria, linear regression, Bayesian ridge regression, and Huber regression received error rates of 2.5, 2.3, and 1.7, respectively.
Counterfactual inference to predict causal knowledge graph for relational transfer learning by assimilating expert knowledge --Relational feature transfer learning algorithm

Transfer learning (TL) is a machine learning (ML) method in which knowledge is transferred from the existing models of related problems to the model for solving the problem at hand. Relational TL enables the ML models to transfer the relationship networks from one domain to another. However, it has two critical issues. One is determining the proper way of extracting and expressing relationships among data features in the source domain such that the relationships can be transferred to the target domain. The other is how to do the transfer procedure. Knowledge graphs (KGs) are knowledge bases that use data and logic to graph-structured information; they are helpful tools for dealing with the first issue. The proposed relational feature transfer learning algorithm (RF-TL) embodies an extended structural equation modelling (SEM) as a method for constructing KGs. Additionally, in fields such as medicine, economics, and law related to people’s lives and property safety and security, the knowledge of domain experts is a gold standard. This paper introduces the causal analysis and counterfactual inference in the TL domain that directs the transfer procedure. Different from traditional feature-based TL algorithms like transfer component analysis (TCA) and CORelation Alignment (CORAL), RF-TL not only considers relations between feature items but also utilizes causality knowledge, enabling it to perform well in practical cases. The algorithm was tested on two different healthcare-related datasets — sleep apnea questionnaire study data and COVID-19 case data on ICU admission — and compared its performance with TCA and CORAL. The experimental results show that RF-TL can generate better transferred models that give more accurate predictions with fewer input features.
Secure Logistic Regression for Vertical Federated Learning

Data island effectively blocks the practical application of machine learning. To meet this challenge, a new framework known as federated learning was created. It allows model training on a large amount of scattered data owned by different data providers. This article presents a parallel solution for computing logistic regression based on distributed asynchronous task framework. Compared to the existing work, our proposed solution does not rely on any third-party coordinator, and hence has better security and can solve the multitraining problem. The logistic regression based on homomorphic encryption is implemented in Python, which is used for vertical federated learning and prediction of the resulting model. We evaluate the proposed solution using the MNIST dataset, and the experimental results show that good performance is achieved.
Bin similarity‐based domain adaptation for fine‐grained image classification

Fine-grained classification tasks are challenging because fine-grained data sets are quite scarce. Thus, we utilized the domain adaptation method to migrate knowledge from large, labeled data sets to fine-grained target data sets. We employed the bin similarity (BS) algorithm to measure and select the approximate domains from large-scale data sets to the fine-grained target domains. Source domain feature space was divided into multiple bins and the features of the target domains were sampled to fill the bins. The most similar domains were selected based on the similarity statistics of the sample features. We implemented the BS algorithm combined with the popular convolutional neural networks, pretrained the network on the selected similar subdata sets, and subsequently fine-tuned it on the fine-grained data sets. We evaluated the BS classification model on Stanford Dogs and Oxford Flower data sets, and the results showed improved BS classification performance compared with the state-of-the-art domain adaptation methods, earth mover's distance, selective joint fine-tuning, L2 with starting point, and domain similarity for transfer learning. Furthermore, BS is a pluggable module that boosts the performance of domain adaptation.
Improved Normalized Cut for Multi-View Clustering

Spectral clustering (SC) algorithms have been successful in discovering meaningful patterns since they can group arbitrarily shaped data structures. Traditional SC approaches typically consist of two sequential stages, i.e., performing spectral decomposition of an affinity matrix and then rounding the relaxed continuous clustering result into a binary indicator matrix. However, such a two-stage process could make the obtained binary indicator matrix severely deviate from the ground true one. This is because the former step is not devoted to achieving an optimal clustering result. To alleviate this issue, this paper presents a general joint framework to simultaneously learn the optimal continuous and binary indicator matrices for multi-view clustering, which also has the ability to tackle the conventional single-view case. Specially, we provide theoretical proof for the proposed method. Furthermore, an effective alternate updating algorithm is developed to optimize the corresponding complex objective. A number of empirical results on different benchmark datasets demonstrate that the proposed method outperforms several state-of-the-arts in terms of six clustering metrics.
Fuzzy association rule mining approach to identify e-commerce product association considering sales amount

Abstract Online stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers’ choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.
EAIS: Energy-aware adaptive scheduling for CNN inference on high-performance GPUs

Recently, a large number of convolutional neural network (CNN) inference services have emerged on high-performance Graphic Processing Units (GPUs). However, GPUs are high power consumption units, and the energy consumption increases sharply along with the deployment of deep learning tasks. Although previous studies have considered the latency Service-Level-Objective (SLO) of inference services, they fail to directly take account of the energy consumption. Our investigation shows that coordinating batching and dynamic voltage frequency scaling (DVFS) settings can decrease the energy consumption of CNN inference. But it is affected by (i) larger configuration spaces; (ii) GPUs’ underutilization while data are transferred between CPUs and GPUs; (iii) fluctuating workloads. In this paper, we propose EAIS, an energy-aware adaptive scheduling framework that is comprised of a performance model, an asynchronous execution strategy, and an energy-aware scheduler. The performance model provides valid information about the performance characteristics of CNN inference services to shrink the feasible configuration space. The asynchronous execution strategy overlaps data upload and GPU execution to improve the system processing capacity. The energy-aware scheduler adaptively coordinates batching and DVFS according to fluctuating workloads to minimize energy consumption while meeting latency SLO. Our experimental results on NVIDIA Tesla M40 and V100 GPUs show that, compared to the state-of-the-art methods, EAIS decreases the energy consumption by up to 28.02% and improves the system processing capacity by up to 7.22% while meeting latency SLO. Besides, EAIS has been proved to have good versatility under different latency SLO constraints.
NetNMSP: Nonoverlapping maximal sequential pattern mining

Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.
Online teaching research in universities based on blockchain

Abstract This study explores the use of blockchain in the course design and evaluation in Chinese universities, and explores the value of teachers’ views and experiences on blockchain in the course design. Previous studies have shown that blockchain has a positive effect on the improvement of online teaching management and evaluation quality. Most of the current research focuses on the management of online teaching by using blockchain, and this qualitative case study investigates the design and evaluation of online course based on blockchain by five teachers of different specialties. Semi-structured interviews and course materials were collected from five teachers for a TPACK framework analysis. The result of the survey emphasizes that the redesign of online courses based on blockchain can improve the matching between blockchain and courses, the quality of teaching and the trust of various parties in online education. The block chain-based the online course of both theory and practice design and evaluation, which is oriented by the index method of capability evaluation, is an appropriate idea and has achieved good results. The experimental course focuses on the integration of blockchain and experimental platform. The immersive and interactive course presentation and the personalized examination based on machine learning are effective. But follow-up should pay attention to the different requirements of course design of different disciplines, using a variety of data sources for analysis, to take measures to enhance the enthusiasm of teachers to adopt new technologies.
Boosting RGB-D Saliency Detection by Leveraging Unlabeled RGB Images

Training deep models for RGB-D salient object detection (SOD) often requires a large number of labeled RGB-D images. However, RGB-D data is not easily acquired, which limits the development of RGB-D SOD techniques. To alleviate this issue, we present a Dual-Semi RGB-D Salient Object Detection Network (DS-Net) to leverage unlabeled RGB images for boosting RGB-D saliency detection. We first devise a depth decoupling convolutional neural network (DDCNN), which contains a depth estimation branch and a saliency detection branch. The depth estimation branch is trained with RGB-D images and then used to estimate the pseudo depth maps for all unlabeled RGB images to form the paired data. The saliency detection branch is used to fuse the RGB feature and depth feature to predict the RGB-D saliency. Then, the whole DDCNN is assigned as the backbone in a teacher-student framework for semi-supervised learning. Moreover, we also introduce a consistency loss on the intermediate attention and saliency maps for the unlabeled data, as well as a supervised depth and saliency loss for labeled data. Experimental results on seven widely-used benchmark datasets demonstrate that our DDCNN outperforms state-of-the-art methods both quantitatively and qualitatively. We also demonstrate that our semi-supervised DS-Net can further improve the performance, even when using an RGB image with the pseudo depth map.