Reviewerly/exHarmony huggingface dataset:
Improving Activity Recognition while Reducing Misclassification of Unknown Activities

Automated recognition of Activities of daily living is a significant task since it allows monitoring patients remotely using wearable devices. It could also help doctors and specialists to easily track the health status of elderly people and of patients suffering from a variety of geriatric diseases. Most of the literature regarding activity recognition does not face the problem of classifying activities of interest performed among activities that are not, i.e., unknown activities for the classifier. In this paper, we propose a novel method, based on the acceleration data recorded by wearable smartwatches, aimed at improving the activity recognition accuracy in presence of unknown activities (i.e., in real-world settings). The approach is based on an ensemble of different classifiers, a filtering procedure, and a final voting mechanism. The empirical evaluation, carried out on 8 different subjects, shows that our approach improves the overall F1 score by 5.5%, increases the recognition of unknown activities by 11.1%, and decreases the amount of data wrongly classified as an unknown activity by 15.5%. The first two observed differences are respectively statistically significant (Wilcoxon test p-value<0.01).
AHPSO: Altruistic Heterogeneous Particle Swarm Optimisation Algorithm for Global Optimisation

This paper introduces a new particle swarm optimisation variant: the altruistic heterogeneous particle swarm optimisation algorithm (AHPSO). The algorithm conceptualises particles as energy-driven agents with bio-inspired altruistic behaviour. In our approach, particles possess a current energy level and an activation threshold and are in one of two possible states (active or inactive) depending on their energy levels at time t. The idea of altruism is used to form lending-borrowing relationships among particles to change an agent's state from inactive to active, and the main search mechanism exploits this idea. Diversity in the swarm, which prevent premature convergence, is maintained via agent states and the level of altruistic behaviour particles exhibit. The performance of AHPSO was compared with 11 metaheuristics and 12 state-of-the-art PSO variants using the CEC'17 and CEC'05 test suites at 30 and 50 dimensions. The AHPSO algorithm outperformed all 23 comparison algorithms on both benchmark test suites at both 30 and 50 dimensions.
CLC: Noisy Label Correction via Curriculum Learning

Deep neural networks reveal their usefulness through learning from large amounts of data. However, unless the data is correctly labeled, it may be very difficult to properly train a neural network. Labeling the large set of data is a time-consuming and labor-intensive task. To overcome the risk of mislabeling, several methods that are robust against the label noise have been proposed. In this paper, we propose an effective label correction method called Curriculum Label Correction (CLC). With reference to the loss distribution from self-supervised learning, CLC identifies and corrects noisy labels utilizing curriculum learning. Our experimental results verify that CLC shows outstanding performance especially in a harshly noisy condition, 91.06% test accuracy on CIFAR-10 at a noise rate of 0.8. Code is available at https://github.com/LJY-HY/CLC.
HeteFL: Network-Aware Federated Learning Optimization in Heterogeneous MEC-Enabled Internet of Things

Federated learning (FL) is an effective paradigm for training a machine-learning model based on data distributed at a large quantity of users in Internet of Things (IoT) without sharing their raw data. However, federated optimization of the global model in heterogeneous IoT—while considering the heterogeneity among users and limited network constraints—remains to be an open challenge. In this article, we propose a novel adaptive federated optimization algorithm, Adp-FedProx, to achieve the optimal learning performance within the limited computation and communication resources at the edge. In particular, we analyze how the training loss is affected by each user’s global update frequency and the time and energy used for learning by obtaining the novel convergence bound of federated training loss in heterogeneous IoT. With our proposed algorithm, all users can dynamically adjust their number of local iterations in each global interval and will not drop out during the training process for resource exhaustion, so as to impair the negative effect of heterogeneity among users and guarantee the convergence of the training model. In addition, we can get the optimal learning performance by minimizing the gap between the final loss function and the optimal one within limited resources. Finally, extensive numerical results demonstrate the algorithmic advantages in adapting system heterogeneity and admirable performance of the proposed methodologies in speeding up FL 5%–10% and reduce the energy consumption in training about 10% compared with FedProx.
A Hybrid Blockchain Implementation to Ensure Data Integrity and Interoperability for Public Service Organisations

Public service organisations (PSOs) have a need to collaborate with each other in order to provide a seamless user experience, mitigate fraud and increase operational efficiency at a reduced tax payer's cost. However, legacy systems built over decades using different technology stacks, make collaboration challenging and hinder efficiency. Distributed ledger technologies (DLTs) have been proposed as a trust and efficiency mechanism for several inter organisation collaborative tasks. They can reduce friction in operations and help achieve a more streamlined and unified public service infrastructure. However, most of the work are either based on theory or purely conceptual and lack implementation. In this paper, we elaborate on the design and implementation of a proof of concept (POC) of a blockchain based interoperability and data sharing system that allows PSOs to seamlessly collaborate and share information in real time. The POC is based on the Registration of Overseas Entities and Beneficial Owners (ROEBO) legislation in the UK and demonstrates interactions between Companies House UK and other PSOs in a hybrid blockchain setting.
Skeleton-Split Framework using Spatial Temporal Graph Convolutional Networks for Action Recognition

There has been a dramatic increase in the volume of videos and their related content uploaded to the internet. Accordingly, the need for efficient algorithms to analyse this vast amount of data has attracted significant research interest. This work aims to recognize activities of daily living using the ST-GCN model, providing a comparison between four different partitioning strategies: spatial configuration partitioning, full distance split, connection split, and index split. To achieve this aim, we present the first implementation of the ST-GCN framework upon the HMDB-51 dataset. Additionally, we show that our proposals have achieved the highest accuracy performance on the UCF-101 dataset using the ST-GCN framework than the state-of-the-art approach.
Gender Identification from Speech Recognition Using Machine Learning Techniques and Convolutional Neural Networks

Gender identification represents a fundamental component of speech recognition and automatic interacting sound responding systems. Identifying the voice gender minimizes the computational loads of these systems for additional processing. Standard approaches for gender estimation from the speech have broadly relied on the extraction of speech features and classification tasks. This paper proposes a technique for gender identification of speech samples using the speech recognition process. The proposed technique extracts essential voice features like Mean, Zero-Crossing, Standard Deviation, and Amplitude, as well as 12 most significant features from every voice sample, and combines them to create voice feature vectors. The proposed technique uses several machine and deep learning methods such as Random Forest, KNN, Logistic Regression, Decision Tree, and CNNs, in order to classify the voice vectors into Male and Female classes. After comparing the evaluation metrics results of all classifiers, the proposed technique finds out that the CNN model is the best classifier used to classify the voice vectors with a higher precision value of 1.0.
Extracting Software Change Requests from Mobile App Reviews

The use of mobile apps is increasing rapidly. These apps have thousands of reviews which are widely acknowledged as a valuable resource for the community involved in the development of mobile apps. In this study, we contend that these reviews can be used to generate software change request document for improving mobile apps. A pre-requisite for generating such a document is the identification of Software Change Requests (SCR) from the user reviews. However, the manual processing of these large number of reviews to identify SCRs is a resource intensive task. However, most of the existing studies have focused on the identification of bugs. Whereas, a few studies have been conducted to identify change requests and its localization from mobile apps review, which substantially different from extracting SCR. To that end, we have scrapped review of seven Mobile Apps and developed a dataset that can be used for training of machine learning techniques for the automatic identification of SCRs. A key feature of the approach is that we have documented the annotation guidelines that are used to distinguish between SCR and non-SCR sentences. These guidelines can be used to enhance the developed dataset, as well as to develop new datasets. As another contribution, we have evaluated the effectiveness of five supervised learning techniques for their ability to identify SCR sentences from user reviews. The study shows that Logistic Regression achieved a nearly perfect F1 score of 0.97 for extracting SCR from textual reviews.
Comparative Study of Sentiment Analysis of Type 1 and Type 2 Diabetic Patients Using Apache Flume and Hive

The Hadoop ecosystem platform has been used to perform sentiment analysis of the opinion regarding type 1 and type 2 diabetes. This research work concentrates on exploring the public opinion about assumptions, thinking and behavior towards the condition of type 1 and type 2 diabetes. Tweets from twitter have been taken as dataset for sentiment analysis for the research. After conducting a set of repetitive experiments, it has been concluded that people feel more negative about type 2 diabetes. They are more pessimistic about the diet and treatment of type 2 diabetes. Present paper is divided mainly into four sections. The first section, gives an overview of the sentiment analysis and diabetes. The proposed work and methodology have been included in the second section. The third section contains the whole experiment work to support the results. In the fourth section, results regarding opinions about type 1 and type 2 diabetes have been illustrated using bar charts obtained from the experiments followed by concluding remarks, future scope and significance of the research.
Enhancing expansion of rooftop PV systems through Mixed Integer Linear Programming and Public Tender Procedures

Concerning the growth rate in Renewable Energy share, especially in the EU, it is fact that Rooftop PV systems have a significant role to play. Nevertheless, it is also accepted that with current policies, schemes and scientific methods, today's Renewable Energy growth rate is still far from the desired one. Based on that, this study proposes a novel integration of a Mixed-Integer Linear Programming based model in a Public Tender Procedure, which enables the bulk installation and thus, the rapid expansion of Rooftop PV systems, in a least-cost manner. The main objective and contribution of this paper is to provide an economic solution to interested Public Bodies for utilizing the rapid growth of rooftop PV systems through novel mechanisms and hence, to better achieve the desired Renewable Energy targets. To further highlight the capabilities of the proposed method, the results from a real-life project, showed that the bulk installation of PV systems, in 404 public schools with a total PV capacity of 4.8 MWp, can be realized at an optimum cost. The main achievements are: (i) high-level competition, due to the ability of the proposed model to select multiple PV Installers, (ii) a 35% lower project cost compared to the initial estimated budget and (iii) a significantly lower unit cost of about 0.7 million €/MWp, compared to about 0.91 and 1.54 million €/MWp obtained from other similar projects. Finally, the outcomes of the proposed MILP model are validated by those obtained from an exhaustive optimal solution search procedure.
A Framework of Cross-Modal Learning for Solving Geometry Problems

This paper presents a framework of deep cross-modal learning networks for solving geometry problems. Existing geometry solvers either focus on single-modal problems or multi-modal problems, and they cannot fit each other. In this paper, we propose a cross-modal learning architecture for solving geometry problems, which can effectively solve both text and image-text geometry problems. To attack the representation of cross-modal features is a key challenge in understanding geometry problems that this paper adopts a shared encoder, in which the text and (or) image features are masked by using self-attention units and a multi-layer transformer is used to realize the interaction between cross-modal features. Further, we adopt a shared decoder to decode the single-modal features or the series sequence of multi-modal features according to the input of the encoder is single-modal or multi-modal problems. The representation of the decoder is transferred to task-specific heads for geometry relations extraction, theorems reasoning and geometry problem solving. The proposed algorithm can produce the solutions of geometry problems in a readable way. The experimental results shown that the proposed algorithm has good property and performance in solving geometry problems.
BATS: A Blockchain-based Authentication and Trust Management System in Vehicular Networks

As the backbone of the connected and autonomous vehicles (CAVs), vehicular ad-hoc networks (VANETs) enable a variety of applications for sharing traffic information and road safety messages. However, the possibility of malicious activity in such an untrusted environment necessitates evaluating the credibility of sent messages by vehicles. In this article, we put forward a blockchain-based authentication and trust management system for vehicular networks. This architecture incorporates multiple local blockchains for enabling distributed V2X communication and a global trust score blockchain for evaluating the credibility of sent messages from vehicles as illustrated in the teaser figure. To empower our proposed architecture, we propose a novel Dual Consensus Method (DCM) consisted of local consensus for event blockchains and global consensus for the trust score blockchain. DCM enables real-time and concurrent operation of different blockchains in our architecture. We employ Proof of Elapsed Time (PoET) consensus method for the global consensus. For the local consensus, we propose a new consensus method named Restricted Practical Byzantine Fault Tolerance (RPBFT). Through our design, a vehicle's information for decision-making about an event is not limited to the neighboring vehicles and RSUs. It has access to both recent data regarding an event and vehicles' trust scores in a fully decentralized and private fashion. Performance evaluation of our proposed method reveals its capability to calculate and store trust sores via secure, decentralized, and real-time operation.
A Transformational Approach to Managing Data Model Evolution of Web Services

The communication of web services is typically organized through APIs, which rely on a common data model shared among system components. Over time, this data model must be changed in order to accommodate new or changing requirements, and the components including the data they are operating on must be migrated. In practice however, not all affected components can be migrated instantly and at the same time. A common approach is to plan data model changes in a backward compatible fashion, which causes serious maintenance problems and is a common cause of technical debt. We propose an alternative solution, using a translation layer serving as a round-trip migration service, responsible for the lossless translation of object-oriented data model instances of different versions. We present a framework which offers a version-aware interface definition language (IDL) for APIs, a typed JavaScript-based language using the IDL definition, and a run-time environment. From a methodological point of view, development is supported by a catalog which comprises a set of typical evolution scenarios along with corresponding round-trip migration strategies. We showcase the applicability of our approach via a case study of a real-world e-commerce web application, and evaluate correctness through automated testing.
Fast Non-Parametric Conditional Density Estimation using Moment Trees

In many machine learning tasks, one tries to infer unknown quantities such as the conditional density p(Y | X) from observed ones X. Conditional density estimation (CDE) constitutes a challenging problem due to the trade-off between model complexity, distribution complexity, and overfitting. In case of online learning, where the distribution may change over time (concept drift) or only few data points are available at once, robust, non-parametric approaches are of particular interest. In this paper we present a new, non-parametric tree-ensemble-based method for CDE that reduces the problem to a simple regression task on the transformed input data and a (unconditional) density estimation. We prove the correctness of our approach and show its usefulness in empirical evaluation on standard benchmarks. We show that our method is comparable to other state-of-the-art methods, but is much faster and more robust.
Conversion Prediction with Delayed Feedback: A Multi-task Learning Approach

Online display advertising has become a vital business for large-scale E-commerce markets. As the main goal of advertisers is to reach interested customer prospects, accurate conversion prediction is essential for successful online display advertising. A particular challenge for conversion prediction is that conversions may occur long after the click events. Such delayed feedback makes it a non-trivial task to keep conversion prediction models updated and consistent with the latest customer distribution. Although several studies have been conducted to tackle the delayed feedback issue, the relationship between the early conversion and full term conversion has not been fully exploited to improve conversion prediction. In this paper, we consider conversion prediction as a multi-task learning problem by leveraging multiple conversion labels after different observation intervals. Specifically, we propose a multi-task model with an end-to-end architecture for conversion prediction. Our approach is guided by theoretical and probabilistic analysis of the early and full term conversions. Our mixture-of-experts module can integrate distinct characteristics of input features and optimize the task-specific experts. In addition, the multiple tasks are jointly learned with a regularization term to ensure the embedding consistency between tasks and prevent potential overfitting issues. In comparison with competitive benchmarks, our approach can significantly improve conversion prediction with delayed feedback and improve business performance of online display advertising.
TRIO: Task-agnostic dataset representation optimized for automatic algorithm selection

With the growing number of machine learning (ML) algorithms, the selection of the top-performing algorithms for a given dataset, task, and evaluation measure is known to be a challenging task. The human expertise required for this task has fueled the demand for automatic solutions. Meta-learning is a popular approach for automatic algorithm selection based on dataset characterization. Existing meta-learning methods often represent the datasets using predefined features and thus cannot be generalized for various ML tasks, or alternatively, learn their representations in a supervised fashion, and thus cannot address unsupervised tasks. In this study, we first propose a novel learning-based task-agnostic method for dataset representation. Second, we present TRIO, a meta-learning approach based on the proposed dataset representation, which is capable of accurately recommending top-performing algorithms for unseen datasets. TRIO first learns graphical representations from the datasets and then utilizes a graph convolutional neural network technique to extract their latent representations. An extensive evaluation on 337 datasets and 195 ML algorithms demonstrates the effectiveness of our approach over state-of-the-art methods for algorithm selection for both supervised (classification and regression) and unsupervised (clustering) tasks.
Face Mask Detection from Live-Stream Surveillance Video using Convolutional Neural Network

The coronavirus is a contagious disease and can spread very rapidly if the proper measures are not taken. Though the invention of the vaccines against coronavirus has given a sigh of relief, however, the complete eradication still looks a very long way to go. With the presence of the new variants of the coronavirus, the risk of the spread still remains. Among several guidelines given by the WHO and healthcare practitioners, facemasks have been one of the most effective ways to prevent the spread of the virus. However, some people usually ignore or forget to follow these guidelines especially in public places such as offices, shopping malls, etc. The number of people in such places is usually high and facemask is a factor to consider against the spread of the virus. Therefore, to hinder the spread of the virus, people with no facemask must be identified and notified. This research proposes a convolutional neural network-based deep learning model for detecting the people without facemasks using the frames captured from the live-stream surveillance video. The research primarily focuses on the facemask detection module of the proposed system. The data for this study contains almost 1500 images for masked and without mask faces. The proposed model has been implemented using two different optimizers. The RMSprop optimizer-based model outperforms the Adam optimizer-based model. The accuracy achieved by RMSprop based model was 92.27% and the accuracy achieved by Adam optimizer-based model was 85.1%.