Reviewerly/exHarmony huggingface dataset:
Visual analytics and information extraction of geological content for text-based mineral exploration reports

A large amount of continuously increasing textual geoscience data is stored and not fully utilized. Text mining enables the discovery and analysis of valuable information,and presents valuable insights hidden in geological texts. This research aims to use text mining and visualization techniques to obtain content words -for the purpose of visually analyzing geological reports. The framework proposed in this study can enable researchers to quickly understand key information and improve the transmission efficiency of geological reports. First, we implemented an improved keyword extraction algorithm comprising the term frequency-inverse document frequency and word length to improve the accuracy of geological keyword extraction. Second, we extracted and visualized the relative importance as well as the links between content words that can represent the key information of geoscience reports using word-level information analysis and multidimensional scaling analysis. Finally, the keyword relevance and mutual clustering relations were visualized through graphs to provide an intuitive representation of the current state of the reports.
Voice spoofing detector: A unified anti-spoofing framework

Voice controlled systems (VCS) in Internet of Things (IoT), speaker verification systems, voice-based biometrics, and other voice-assistant-enabled systems are vulnerable to different spoofing attacks i.e., replay, cloning, cloned-replay, etc. VCS are not only susceptible to these attacks in a non-network environment, but they are also vulnerable to multi-order spoofing attacks in networked IoT. Additionally, deepfakes with artificially generated audio pose a great threat to the all systems having voice-interfaces. Most of the existing countermeasures against these voice spoofing attacks work for only one specific attack (e.g. voice replay) and fail to generalize this for other classes of spoofing attacks. Additionally, generalization is also crucial for cross-corpora evaluation. Thus, there exists a need to develop a unified voice anti-spoofing framework capable of detecting multiple spoofing attacks. This work presents a unified anti-spoofing framework that uses novel (ATCoP-GTCC) features to combat the variety of voice spoofing attacks. The proposed novel acoustic-ternary co-occurrence patterns (ATCoP) encode the co-occurrence of similar patterns between the center and neighboring samples. Our experiments demonstrate that ATCoP can better capture the microphone induced distortions in replays, unnatural prosody and algorithmic artifacts in cloned samples, and both the distortions and artifacts in cloned-replays including compression on multi-hop attacks in the spoofing samples. The performance of ATCoP could be further enhanced by the Gammatone cepstral coefficients. To evaluate the effectiveness of the proposed anti-spoofing system for multi-order replay and cloned-replay attacks detection, we created a diverse voice spoofing detection corpus (VSDC) containing multi-order replay and cloned-replay audios against the bonafide and cloned audio recordings, respectively. Experimental results obtained on VSDC, ASVspoof 2019, Google’s LJ Speech, and YouTube deepfakes datasets illustrate the effectiveness of the proposed system in terms of accurate detection for a variety of voice spoofing attacks.
Face masks reduce interpersonal distance in virtual reality

During the COVID-19 pandemic several behavioral measures have been implemented to reduce viral transmission. While these measures reduce the risk of infections, they may also increase risk behavior. Here, we experimentally investigate the influence of face masks on physical distancing. Eighty-four participants with or without face masks passed virtual agents in a supermarket environment to reach a target while interpersonal distance was recorded. Agents differed in wearing face masks and age (young, elderly). In addition, situational constraints varied in whether keeping a distance of 1.5 m required an effortful detour or not. Wearing face masks (both self and other) reduced physical distancing. This reduction was most prominent when keeping the recommended distance was effortful, suggesting an influence of situational constraints. Similarly, increased distances to elderly were only observed when keeping a recommended distance was effortless. These findings highlight contextual constraints in compensation behavior and have important implications for safety policies.
Designing Word Filter Tools for Creator-led Comment Moderation

Online social platforms centered around content creators often allow comments on content, where creators moderate the comments they receive. As creators can face overwhelming numbers of comments, with some of them harassing or hateful, platforms typically provide tools such as word filters for creators to automate aspects of moderation. From needfinding interviews with 19 creators about how they use existing tools, we found that they struggled with writing good filters as well as organizing and revisiting their filters, due to the difficulty of determining what the filters actually catch. To address these issues, we present FilterBuddy, a system that supports creators in authoring new filters or building from existing filter lists, as well as organizing their filters and visualizing what comments are captured over time. We conducted an early-stage evaluation of FilterBuddy with YouTube creators, finding that participants see FilterBuddy not just as a moderation tool, but also a means to organize their comments to better understand their audiences.
Density clustering with divergence distance and automatic center selection

The density peak clustering (DPC) algorithm is a famous density-based method for exploring, analyzing, and deriving information from data. In the case of application, it encounters the following bottlenecks. Firstly, its Euclidean distance based similarity measurement is prone to misclassification of neighbors. Secondly, its clustering results are significantly influenced by human factors (controlling the cut-off distance parameter dc and selecting the clustering center manually). Finally, the local density ρ is affected by parameter dc and cannot reflect the sparseness of the data distribution. To overcome these deficiencies, a novel density clustering algorithm, called NAPC, is proposed. First, a Divergence distance is defined to evaluate the similarity of points under the refined Euclidean distance space. Second, the specific value of dc is obtained based on the theory of Adjusted Boxplot. Then, the local density ρ is reinterpreted based on the above Divergence distance and newly assigned dc. Finally, a judgement index is given to determine which points can be regarded as the center points in the Divergence distance space. The performance of our proposal is evaluated by comparing with several existing clustering methods on various datasets. Experimental results prove that NAPC performs much better than several comparison algorithms. It can identify clusters of various shapes and spatial dimensions with minimal human intervention.
AutoRet: A Self-Supervised Spatial Recurrent Network for Content-Based Image Retrieval

Image retrieval techniques are becoming famous due to the vast availability of multimedia data. The present image retrieval system performs excellently on labeled data. However, often, data labeling becomes costly and sometimes impossible. Therefore, self-supervised and unsupervised learning strategies are currently becoming illustrious. Most of the self/unsupervised strategies are sensitive to the number of classes and can not mix labeled data on availability. In this paper, we introduce AutoRet, a deep convolutional neural network (DCNN) based self-supervised image retrieval system. The system is trained on pairwise constraints. Therefore, it can work in self-supervision and can also be trained on a partially labeled dataset. The overall strategy includes a DCNN that extracts embeddings from multiple patches of images. Further, the embeddings are fused for quality information used for the image retrieval process. The method is benchmarked with three different datasets. From the overall benchmark, it is evident that the proposed method works better in a self-supervised manner. In addition, the evaluation exhibits the proposed method's performance to be highly convincing while a small portion of labeled data are mixed on availability.
Service composition in dynamic environments: A systematic review and future directions

Distributed computing paradigms such as cloud, mobile, Internet of Things, and Fog have enabled new modalities for building enterprise architectures through service composition. The fundamental premise is that the application can benefit from functionally equivalent services that can be traded in the cloud or services repositories. These services can vary in their Quality of Services (QoS) and cost provision. Accordingly, the problem of service composition is the process of choosing a configuration of candidate services from a pool of available ones, considering QoS attribute, cost, and users’ preference. Due to the inherent dynamism in service computing environments and communication networks, the advertised QoS values might fluctuate; therefore, service composition under uncertainty is inevitable and challenges satisfying Services Level Agreement (SLA). In this paper, we present a systematic literature review to investigate and classify the existing studies in service composition under uncertainty. We identified 100 relevant studies published between the year 2007 and 2020. To the best of our knowledge, this work is the first to explicate a focused systematic review, classification, taxonomy of approaches, and trends along with their assumptions and applications; and to discuss future research directions in service composition under uncertainty.
Scientific Exploration and Explainable Artificial Intelligence

Abstract Models developed using machine learning are increasingly prevalent in scientific research. At the same time, these models are notoriously opaque. Explainable AI aims to mitigate the impact of opacity by rendering opaque models transparent. More than being just the solution to a problem, however, Explainable AI can also play an invaluable role in scientific exploration. This paper describes how post-hoc analytic techniques from Explainable AI can be used to refine target phenomena in medical science, to identify starting points for future investigations of (potentially) causal relationships, and to generate possible explanations of target phenomena in cognitive science. In this way, this paper describes how Explainable AI—over and above machine learning itself—contributes to the efficiency and scope of data-driven scientific research.
A novel improved random forest for text classification using feature ranking and optimal number of trees

Machine learning-based models like random forest (RF) have been widely deployed in diverse domains such as image processing, health care, and text processing, etc. during the past few years. The RF is a prominent technique for handling imbalanced data and performs significantly better than other machine learning models due to its parallel architecture. This study presents an improved random forest for text classification, called improved random forest for text classification (IRFTC), that incorporates bootstrapping and random subspace methods simultaneously. The IRFTC removes unimportant (less important) features, adds a number of trees in the forest on each iteration, and monitors the classification performance of RF. Classification accuracy is determined with respect to the number of trees which defines the optimal number of trees for IRFTC. Feature ranking is determined using the quality of the split in a tree. The proposed IRFTC is applied on four different benchmark datasets, binary and multiclass, to validate its performance in this study. Results indicate that IRFTC outperforms both the traditional RF, as well as, other machine learning models such as logistic regression, support vector machine, Naive Bayes, and decision trees.
The Effects of a Virtual Reality Tourism Experience on Tourist’s Cultural Dissemination Behavior

Virtual reality technology has been widely applied in the tourism industry, but the literature examining the relationship between the virtual tourism experience and cultural dissemination behavior is limited. This present study examines how a virtual reality tourism experience could stimulate tourists’ cultural dissemination behavior intention. It does so by developing a moderated mediation model to explore how virtual reality tourism uses digital technology to improve tourists’ experiential value, enhances their pride, and then affects their cultural dissemination behavior intentions. Results derived from a sample of 359 respondents show that VR experiential value can stimulate tourists’ cultural dissemination behavior and that the link between VR experiential value and tourists’ cultural dissemination behavior is mediated by pride. Furthermore, the cultural value of individual collectivism moderates the relationship between VR experiential value and pride. This study extends the theoretical understanding of virtual reality tourism from the emotional perspective and also has practical implications for VR design and destination marketing.
Fast quantum algorithm for protein structure prediction in hydrophobic-hydrophilic model

In its unfolded form, a protein is a linear sequence of amino acids. Protein structure prediction attempts to find the native conformation for a given protein, which has potential applications in drug and vaccine development. Classically, protein structure prediction is an NP-complete, unsolved computational problem. Quantum computing however promises to improve upon the performance of classical algorithms. Here we develop a quantum algorithm in hydrophobic-hydrophilic model on two-dimensional square lattice to solve the problem for any sequence of length N amino acids with a quadratic speedup over its classical counterpart. This speedup is achieved using Grover's quantum search algorithm. The algorithm can be used for amino acid sequences of arbitrary length. It consists of three stages: (1) preparation of a superposition state that encodes all possible 22(N−1) conformations, (2) calculation of coordinates and energy for each possible conformation in parallel, and (3) finding the conformation with the minimal energy. The asymptotic complexity with regard to space is O(N3), while the obtained speedup is quadratic compared to the classical counterpart. We have successfully simulated the algorithm on the IBM Quantum's qasm simulator using Qiskit SDK. Also, we have further confirmed the correctness of the results by calculating theoretical probability of finding the right conformation.
Skeleton Graph-Neural-Network-Based Human Action Recognition: A Survey

Human action recognition has been applied in many fields, such as video surveillance and human computer interaction, where it helps to improve performance. Numerous reviews of the literature have been done, but rarely have these reviews concentrated on skeleton-graph-based approaches. Connecting the skeleton joints as in the physical appearance can naturally generate a graph. This paper provides an up-to-date review for readers on skeleton graph-neural-network-based human action recognition. After analyzing previous related studies, a new taxonomy for skeleton-GNN-based methods is proposed according to their designs, and their merits and demerits are analyzed. In addition, the datasets and codes are discussed. Finally, future research directions are suggested.
Attention-Guided Discriminative Region Localization and Label Distribution Learning for Bone Age Assessment

Bone age assessment (BAA) is clinically important as it can be used to diagnose endocrine and metabolic disorders during child development. Existing deep learning based methods for classifying bone age use the global image as input, or exploit local information by annotating extra bounding boxes or key points. However, training with the global image underutilizes discriminative local information, while providing extra annotations is expensive and subjective. In this paper, we propose an attention-guided approach to automatically localize the discriminative regions for BAA without any extra annotations. Specifically, we first train a classification model to learn the attention maps of the discriminative regions, finding the hand region, the most discriminative region (the carpal bones), and the next most discriminative region (the metacarpal bones). Guided by those attention maps, we then crop the informative local regions from the original image and aggregate different regions for BAA. Instead of taking BAA as a general regression task, which is suboptimal due to the label ambiguity problem in the age label space, we propose using joint age distribution learning and expectation regression, which makes use of the ordinal relationship among hand images with different individual ages and leads to more robust age estimation. Extensive experiments are conducted on the RSNA pediatric bone age data set. Without using extra manual annotations, our method achieves competitive results compared with existing state-of-the-art deep learning-based methods that require manual annotation. Code is available at https://github.com/chenchao666/Bone-Age-Assessment.
Study on Smart Home Interface Design Characteristics Considering the Influence of Age Difference: Focusing on Sliders

Smart homes represent an effective approach to improve one's quality of life. Developing user interfaces that are both comfortable and understandable can assist users, particularly the elderly, embrace smart home technologies. It's critical to concentrate on the characteristics of smart home interface design and their impact on people of various ages. Since sliders are one of the most common components utilized in the smart home user interface, this article aimed to investigate the effects of slider design characteristics (e.g., button size, track color, and sliding orientation) on user performance and preference. Thirty-four participants were recruited for the experiment (16 for the young group, aged between 18 and 44 years; 18 for the middle-aged and elderly group, aged between 45 years and above). Our results revealed that both groups had shorter task completion time, less fixation time, and saccades on horizontal sliding orientation and larger buttons, which means better user performance. For the older group, the slider with color gradient track led to better user performance, while the track color only had less effect on the performance of the younger group. In terms of user preference, the results and performance of the older group were basically consistent, while the younger group had no significant difference in sliding orientation and track color.
Functionality-Preserving Adversarial Machine Learning for Robust Classification in Cybersecurity and Intrusion Detection Domains: A Survey

Machine learning has become widely adopted as a strategy for dealing with a variety of cybersecurity issues, ranging from insider threat detection to intrusion and malware detection. However, by their very nature, machine learning systems can introduce vulnerabilities to a security defence whereby a learnt model is unaware of so-called adversarial examples that may intentionally result in mis-classification and therefore bypass a system. Adversarial machine learning has been a research topic for over a decade and is now an accepted but open problem. Much of the early research on adversarial examples has addressed issues related to computer vision, yet as machine learning continues to be adopted in other domains, then likewise it is important to assess the potential vulnerabilities that may occur. A key part of transferring to new domains relates to functionality-preservation, such that any crafted attack can still execute the original intended functionality when inspected by a human and/or a machine. In this literature survey, our main objective is to address the domain of adversarial machine learning attacks and examine the robustness of machine learning models in the cybersecurity and intrusion detection domains. We identify the key trends in current work observed in the literature, and explore how these relate to the research challenges that remain open for future works. Inclusion criteria were: articles related to functionality-preservation in adversarial machine learning for cybersecurity or intrusion detection with insight into robust classification. Generally, we excluded works that are not yet peer-reviewed; however, we included some significant papers that make a clear contribution to the domain. There is a risk of subjective bias in the selection of non-peer reviewed articles; however, this was mitigated by co-author review. We selected the following databases with a sizeable computer science element to search and retrieve literature: IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, SpringerLink, and Google Scholar. The literature search was conducted up to January 2022. We have striven to ensure a comprehensive coverage of the domain to the best of our knowledge. We have performed systematic searches of the literature, noting our search terms and results, and following up on all materials that appear relevant and fit within the topic domains of this review. This research was funded by the Partnership PhD scheme at the University of the West of England in collaboration with Techmodal Ltd.
A large-scaled corpus for assessing text readability

This paper introduces the CommonLit Ease of Readability (CLEAR) corpus, which provides unique readability scores for ~ 5000 text excerpts along with information about the excerpt's year of publishing, genre, and other metadata. The CLEAR corpus will provide researchers interested in discourse processing and reading with a resource from which to develop and test readability metrics and to model text readability. The CLEAR corpus includes a number of improvements in comparison to previous readability corpora including size, breadth of the excerpts available, which cover over 250 years of writing in two different genres, and unique readability criterion provided for each text based on teachers' ratings of text difficulty for student readers. This paper discusses the development of the corpus and presents reliability metrics for the human ratings of readability.
Cross-modality person re-identification via multi-task learning

Despite its promising preliminary results, existing cross-modality Visible-Infrared Person Re-IDentification (VI-PReID) models incorporating semantic (person) masks simply use these person masks as selection maps to separate person features from background regions. Such models do not dedicate to extracting more modality-invariant person body features in the VI-PReID network itself, thus leading to suboptimal results in VI-PReID. Differently, we aim to better capture person body information in the VI-PReID network itself for VI-PReID by exploiting the inner relations between person mask prediction and VI-PReID. To this end, a novel multi-task learning model is presented in this paper, where person body features obtained by person mask prediction potentially facilitate the extraction of discriminative modality-shared person body information for VI-PReID. On top of that, considering the task difference between person mask prediction and VI-PReID, we propose a novel task translation sub-network to transfer discriminative person body information, extracted by person mask prediction, into VI-PReID. Doing so enables our model to better exploit discriminative and modality-invariant person body information. Thanks to more discriminative modality-shared features, our method outperforms previous state-of-the-arts by a significant margin on several benchmark datasets. Our intriguing findings validate the effectiveness of extracting discriminative person body features for the VI-PReID task.