Reviewerly/exHarmony huggingface dataset:
Augmented Reality Technology for People Living with Dementia and their Care Partners

We designed and implemented an Augmented Reality system, called the My Daily Routine (MDR) system, to demonstrate technology that may aid people living with dementia and their care partners. Although people with dementia are often dependent on their care partners in their daily lives, their independence may be enhanced using Augmented Reality. The MDR system consists of a website and a HoloLens Augmented Reality (AR) application. The care partner can display and customize reminder content using the website. When wearing a Microsoft HoloLens AR device running MDR, a person with dementia will be able to receive personalized reminders in the form of text, images, videos, displayed three dimensional models, voice messages, or music. Customization controls the choice of reminders and their timing; for example, reminders can be issued when an object is detected, at a certain time, or when a command is voiced. MDR can also display the names of common objects and navigation instructions. Through use of the HoloLens' powerful spatial mapping capabilities and Microsoft's experimental World Locking Tools, the indoor navigation system in MDR is accurate and easy to set up.
A new approach to assess the degree of contamination and determine sources and risks related to PTEs in an urban environment: the case study of Santiago (Chile)

In 2017, a geochemical survey was carried out across the Commune of Santiago, a local administrative unit located at the center of the namesake capital city of Chile, and the concentration of a number of major and trace elements (53 in total) was determined on 121 topsoil samples. Multifractal IDW (MIDW) interpolation method was applied to raw data to generate geochemical baseline maps of 15 potential toxic elements (PTEs); the concentration-area (C-A) plot was applied to MIDW grids to highlight the fractal distribution of geochemical data. Data of PTEs were elaborated to statistically determine local geochemical baselines and to assess the spatial variation of the degree of soil contamination by means of a new method taking into account both the severity of contamination and its complexity. Afterwards, to discriminate the sources of PTEs in soils, a robust Principal Component Analysis (PCA) was applied to data expressed in isometric log-ratio (ilr) coordinates. Based on PCA results, a Sequential Binary Partition (SBP) was also defined and balances were determined to generate contrasts among those elements considered as proxies of specific contamination sources (Urban traffic, productive settlements, etc.). A risk assessment was finally completed to potentially relate contamination sources to their potential effect on public health in the long term. A probabilistic approach, based on Monte Carlo method, was deemed more appropriate to include uncertainty due to spatial variation of geochemical data across the study area. Results showed how the integrated use of multivariate statistics and compositional data analysis gave the authors the chance to both discriminate between main contamination processes characterizing the soil of Santiago and to observe the existence of secondary phenomena that are normally difficult to constrain. Furthermore, it was demonstrated how a probabilistic approach in risk assessment could offer a more reliable view of the complexity of the process considering uncertainty as an integral part of the results.
A Hybrid Quality-of-Experience Taxonomy for Mixed Reality IoT (XRI) Systems

Mixed Reality (XR) and the Internet-of-Things (IoT) are two rapidly advancing paradigms, gaining maturity toward the near term, in both industry, government and other organizations, and consumer scenarios. These domains are converging simultaneously, leading to XR systems with IoT embedded capabilities in smart environments, and IoT systems with more immersive, engaging, and adaptive interfaces and use cases. Synergies between these system design platforms are currently being explored, although there remains the need for a clear treatment of the human-factor and quality of experience perspectives of these hybrid XR and IoT (XRI) systems. This work contributes a new taxonomy derived from synthesis of usability literature and other design considerations within these disciplines toward a framework for XRI system design, development, and evaluation. It is hoped that this enables future researchers and developers of XRI systems to create more impactful, functional, and usable XRI across multiple domains in the near future.
Robust Learning From Noisy Web Images Via Data Purification for Fine-Grained Recognition

Manually labeling fine-grained datasetsis laborious and typically requires domain-specific expert knowledge. Conversely, a vast amount of web data is relatively easy to obtain with nearly no human effort. Therefore, learning from noisy web data for fine-grained tasks is attracting increasing attention in recent years. However, the presence of noise in web images is a huge obstacle for training robust fine-grained recognition models. To this end, we propose a novel approach to identify noisy images as well as specifically distinguish in- and out-of-distribution samples. It can purify the noisy web training set by discarding out-of-distribution noise and relabeling in-distribution noisy samples. Then we can train the model on the purified dataset to alleviate the harmful effects of noise and make the most of web images to achieve better performance. Extensive experiments on three commonly used fine-grained datasets demonstrate that our approach is far superior to current state-of-the-art web-supervised methods. The data and source code of this work have been made publicly available at: <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/NUST-Machine-Intelligence-Laboratory/Dataset-Purification</uri> .
Structure-aware adaptive bilateral texture filtering

The classical bilateral filter is designed for preserving the structure of the image by utilizing the range and spatial kernel. However, its straightforward application on texture smoothing is not viable as certain modifications are required in a bilateral filter to exploit it as a precise tool for texture smoothing. It is worth noting that with numerous rectifications, several methods have been developed over the last few decades that employ a bilateral filter as a precise tool for texture smoothing. Although these methods are precise in preserving significant structural information, a loss in the sharpness of the structures transpires simultaneously. Moreover, these methods do smooth texture efficiently but at the same time, they also blur some prominent structures. In this paper, we have designed a novel structure-aware bilateral filter that incorporates the structural information throughout texture smoothing. The filtering is executed on individual pixel from the scale map by employing the spatial kernel. The experimental section reveals the supremacy of the proposed method with respect to texture smoothing and structure preservation. We have also made an effort to demonstrate the proposed method's efficiency in several applications, namely, edge detection, detail enhancement, and texture transfer.
A survey on semantic question answering systems

Abstract Recently, many question answering systems that derive answers from linked data repositories have been developed. The purpose of this survey is to identify the common features and approaches of the semantic question answering (SQA) systems, although many different and prototype systems have been designed. The SQA systems use a formal query language like SPARQL and knowledge of a specific vocabulary. This paper analyses different frameworks, architectures, or systems that perform SQA and classifies SQA systems based on different criteria.
A Peer-2-Peer Management and Secure Policy of the Energy Internet in Smart Microgrids

A peer-to-peer (P2P) energy trading (ET) scheme has been presented in numerous scenarios within the industrial Internet of Things (IIoT), such as vehicle-to-grid network, microgrids, and energy harvesting network. These scenarios present security and privacy issues because of the lack of trust and transparency in the energy markets. In this article, a secure ET system named energy blockchain (EBC) is proposed in order to overcome the security issues. An EBC is suitable for P2P ET situations in which trusted intermediaries are not needed. Similarly, a credit-based payment method to help eliminate ET delays due to transaction confirmation delays on the EBC is proposed. In addition, an optimal pricing method according to the Stackelberg game has been suggested for credit-based loans. According to a real dataset, security analysis and numerical outcomes indicate that the suggested blockchain and payment layout based on credit are both effective and safe in IIoT.
Gated Adversarial Network Based Environmental Enhancement Method for Driving Safety Under Adverse Weather Conditions

The adverse weather conditions have brought considerable difficulties in vision-based applications, which are closely related to the driving safety of autonomous vehicles. However, to date, the greater part of the existing environmental perception studies are under ordinary conditions, and the method to deal with the adverse weather conditions was ignored. Hence, this paper proposes an all-in-one gated adversarial network (AIO-GAN) to improve the performance of vision-based environment perception algorithms under adverse weather conditions, including in rain, haze, lack of light, etc. Three key technical contributions are made. At first, the deep learning based gated transformer module was proposed to classify the input mixed images to different collections by passing them through different branches. Second, the multi-branch based variational autoencoder-generative adversarial network was proposed to solve the ill-pose problem of the solution. Third, high-level weight sharing encoders was given out to guarantee the stability and the high quality of the training process. In this way, the unified clean-style images can be achieved, even if the mixed multi-modal images are transferred from the source domain of complex weather scenes. Extensive experimental results show that the proposed method has achieved better performance than state-of-the-arts and improved the accuracy of target detection.
Identifying Microscopic Augmented Images using Pre-Trained Deep Convolutional Neural Networks

Because of the success of deep learning in multiple sectors, it is gaining unquestionable acceptance in the therapeutic field also. The key challenge is determining how to construct a deep CNN model with an inadequate amount of training data. One key question is whether transfer learning and fine-tuning can be employed in biomedical image analysis to lessen the load of manual data labelling while still producing a complete deep representation for the task. In this paper, we compare the performance of transfer learning and machine learning for nuclei categorization to answer this question statistically. The used machine learning approaches are Decision Tree, Support Vector Machine, Quadratic Discriminant Analysis, K Neighbors, Ada-Boost, Gaussian Naïve Bayes, Logistic Regression, Extra Trees, Random Forest, Histogram Gradient Boosting. This paper shows how to recognize nuclei from microscope pictures using a deep learning model and an image processing-based processing flow. Convolutional neural networks and Inception Resnet V2 deep networks have been used to generate better results. The data augmentation is also done to address the paucity of data.
Orchestrating Bag-of-Tasks Applications with Dynamically Spawned Tasks in a Distributed Environment

As the distributed computing paradigm continues to evolve due to advances such as cloud and fog computing, the need for effective workload allocation on distributed resources has become more imperative than ever. However, workload orchestration in distributed environments poses many challenges, especially in the case where the workload comprises jobs consisting of parallel component tasks. A particularly challenging scenario in the context of such bag-of-tasks applications, is the case where a component task produces an output that does not meet a predefined Quality of Service (QoS) threshold. In such a case, a new task is dynamically spawned at runtime, in order to carry out the additional computational work required for producing an acceptable result. In order to leverage data locality, the dynamically spawned task must be assigned to the same resource as the previous task that failed to meet the QoS requirements. Consequently, it is important to examine how a such dynamically changing workload affects the performance and load balancing of the distributed resources. To this end, in this paper we investigate the orchestration of bag-of-tasks applications with dynamically spawned tasks in a distributed environment. Three task routing techniques are studied and compared using simulation, under various load cases.
Credit card fraud detection using a hierarchical behavior-knowledge space model

With the advancement in machine learning, researchers continue to devise and implement effective intelligent methods for fraud detection in the financial sector. Indeed, credit card fraud leads to billions of dollars in losses for merchants every year. In this paper, a multi-classifier framework is designed to address the challenges of credit card fraud detections. An ensemble model with multiple machine learning classification algorithms is designed, in which the Behavior-Knowledge Space (BKS) is leveraged to combine the predictions from multiple classifiers. To ascertain the effectiveness of the developed ensemble model, publicly available data sets as well as real financial records are employed for performance evaluations. Through statistical tests, the results positively indicate the effectiveness of the developed model as compared with the commonly used majority voting method for combination of predictions from multiple classifiers in tackling noisy data classification as well as credit card fraud detection problems.
UNLT: Urdu Natural Language Toolkit

Abstract This study describes a Natural Language Processing (NLP) toolkit, as the first contribution of a larger project, for an under-resourced language—Urdu. In previous studies, standard NLP toolkits have been developed for English and many other languages. There is also a dire need for standard text processing tools and methods for Urdu, despite it being widely spoken in different parts of the world with a large amount of digital text being readily available. This study presents the first version of the UNLT (Urdu Natural Language Toolkit) which contains three key text processing tools required for an Urdu NLP pipeline; word tokenizer, sentence tokenizer, and part-of-speech (POS) tagger. The UNLT word tokenizer employs a morpheme matching algorithm coupled with a state-of-the-art stochastic n -gram language model with back-off and smoothing characteristics for the space omission problem. The space insertion problem for compound words is tackled using a dictionary look-up technique. The UNLT sentence tokenizer is a combination of various machine learning, rule-based, regular-expressions, and dictionary look-up techniques. Finally, the UNLT POS taggers are based on Hidden Markov Model and Maximum Entropy-based stochastic techniques. In addition, we have developed large gold standard training and testing data sets to improve and evaluate the performance of new techniques for Urdu word tokenization, sentence tokenization, and POS tagging. For comparison purposes, we have compared the proposed approaches with several methods. Our proposed UNLT, the training and testing data sets, and supporting resources are all free and publicly available for academic use.
A Trajectory Released Scheme for the Internet of Vehicles Based on Differential Privacy

The locations and users' information can be shared and interacted in the IoV (Internet of Vehicles), which provides sufficient data for traffic deployment and behavior pattern analysis.However, privacy issues had become more severe since personal or sensitive information is inclined to be revealed in a big data environment.In this work, a novel differential privacybased algorithm named DPTD (Differentially Private Trajectory Database) is proposed for trajectory database releasing.Firstly, a 3-dimensional generalized trajectory dataset is established by considering the time factor.Then, the trajectory space is divided into several planes through the timestamps, and the set of the locations on each plane is further processed by clustering and generalizing to re-form new trajectories, that is, the trajectories to be released.This method is quite favorable to prefix-tree releasing because the spatiotemporal characteristics of the trajectories can be captured and spareness problem is fixed.Besides, a Markov assumption-based prediction method is suggested in order to reduce the cost of adding noise.Unlike the traditional method that the noise is added layer by layer, the noise is only added to the odd layers based on the prediction through spatiotemporal correlation, saving approximately 50% of the privacy budget.Theoretical analysis and experimental results show that the proposed algorithm has better data availability than the compared algorithms while guaranteeing the expected privacy level.
ERDNS: Ensemble of Random Forest, Decision Tree, and Naive Bayes Kernel Through Stacking for Efficient Cross Site Scripting Attack Classification

Cross-Site Scripting (XSS) is a form of client-side code injection attack in which an attacker attempts to execute malicious scripts in the victim’s web browser by embedding dangerous code in a legitimate web page or application. During such an attack, the attacker impersonates a victim user and performs any behavior that the user is capable of, as well as accessing any of his data. If the victim user has privileged access to the application, the attacker can take complete control of the app’s features and data. XSS attacks are most common on message boards, forums, and websites that accept comments. According to a study from CDN and cloud security provider CDNetworks, attacks on web applications increased by 800% in the first six months of 2020 compared to the same time frame last year. Our approach involves the application of Machine Learning Algorithms for efficient classification of XSS attacks. The MMR and SDMR Algorithms aid in the selection of significant features from a data set without compromising classification results. The proposed ERDNS framework ensures better Detection Accuracy and Precision with least Classification Error values among all the available models.
Resisting Distributed Backdoor Attacks in Federated Learning: A Dynamic Norm Clipping Approach

With the advance in artificial intelligence and high-dimensional data analysis, federated learning (FL) has emerged to allow distributed data providers to collaboratively learn without direct access to local sensitive data. However, limiting access to individual provider’s data inevitably incurs security issues. For instance, backdoor attacks, one of the most popular data poisoning attacks in FL, severely threaten the integrity and utility of the FL system. In particular, backdoor attacks launched by multiple collusive attackers, i.e., distributed backdoor attacks, can achieve high attack success rates and are hard to detect. Existing defensive approaches, like model inspection or model sanitization, often require to access a portion of local training data, which renders them inapplicable to the FL scenarios. Recently, the norm clipping approach is developed to effectively defend against distributed backdoor attacks in FL, which does not rely on local training data. However, we discover that adversaries can still bypass this defense scheme through robust training due to its unchanged norm clipping threshold. In this paper, we propose a novel defense scheme to resist distributed backdoor attacks in FL. Particularly, we first identify that the main reason for the failure of the norm clipping scheme is its fixed threshold in the training process, which cannot capture the dynamic nature of benign local updates during the global model’s convergence. Motivated by it, we devise a novel defense mechanism to dynamically adjust the norm clipping threshold of local updates. Moreover, we provide the convergence analysis of our defense scheme. By evaluating it on four non-IID public datasets, we observe that our defense scheme effectively can resist distributed backdoor attacks and ensure the global model’s convergence. Noticeably, our scheme reduces the attack success rates by 84.23% on average compared with existing defense schemes.
Exploring Local Detail Perception for Scene Sketch Semantic Segmentation

In this paper, we aim to explore the fine-grained perception ability of deep models for the newly proposed scene sketch semantic segmentation task. Scene sketches are abstract drawings containing multiple related objects. It plays a vital role in daily communication and human-computer interaction. The study has only recently started due to a main obstacle of the absence of large-scale datasets. The currently available dataset SketchyScene is composed of clip art-style edge maps, which lacks abstractness and diversity. To drive further research, we contribute two new large-scale datasets based on real hand-drawn object sketches. A general automatic scene sketch synthesis process is developed to assist with new dataset composition. Furthermore, we propose to enhancing local detail perception in deep models to realize accurate stroke-oriented scene sketch segmentation. Due to the inherent differences between hand-drawn sketches and natural images, extreme low-level local features of strokes are incorporated to improve detail discrimination. Stroke masks are also integrated into model training to guide the learning attention. Extensive experiments are conducted on three large-scale scene sketch datasets. Our method achieves state-of-the-art performance under four evaluation metrics and yields meaningful interpretability via visual analytics.