Reviewerly/exHarmony huggingface dataset:
Right to Digital Privacy: A Technological Intervention of Blockchain and Big Data Analytics

Big Data is emerging as the most valuable asset for businesses in the digital world. Data enters the online world along with the privacy concerns attached to it. Technology invasion has adversely affected the user's right to privacy. However, blockchain technology and big data analytics play vital roles in creating a balance between the security of data and the enhancement of productivity of platforms dealing with big data. However, there is a palpable refutation between big data security and the widespread use of big data. This paper reviews the existing studies and focuses on issues in the big data privacy and security landscape. The paper emphasizes the significance of technologies on big data. This study covers the challenges that the technologies relating to big data face in the data privacy landscape. The paper highlights various privacy-friendly mechanisms created to safeguard privacy. The paper suggests certain privacy-friendly tools and techniques that can ensure big data security. The objective of this paper is to present a comprehensive review of the impact of blockchain and data analytics technology on big data ensuring data protection. The paper also highlights various existing challenges and suggests privacy preservation mechanisms in big data. Finally, the study concludes with ideas that can be adopted.
Text Identification from Handwritten Data using Bi-LSTM and CNN with FastAI

Text extraction is critical for any analysis in a document processing system. Text extraction is the process of recognizing text data from an image. The handcrafted elements used by traditional handwriting recognition systems require a lot of prior knowledge. Convolutional approaches can be used to train optical character recognition (OCR) systems, although doing so requires a lot of training data. Deep learning approaches are the main focus of handwriting recognition research, which has recently produced ground-breaking results. But the exponential expansion of handwritten text and the accessibility of vast computational power need an improvement in predictive performance and more study. To enable the automatic extraction of distinguishing features from handwritten characters and phrases, Convolutional Neural Networks (CNNs), a subset of Deep Learning technology, are especially adept at comprehending the structure of handwritten letters and phrases. The disadvantages of this approach include increased time and resource requirements. The proposed design is based on CNN with Bi-LSTM, is used to identify the text from the handwritten images.
Large-scale non-negative subspace clustering based on Nyström approximation

Large-scale subspace clustering usually drops the requirements of the full similarity matrix and Laplacian matrix but constructs the anchor affinity matrix and uses matrix approximation methods to reduce the clustering complexity. However, the existing anchor affinity matrix calculation methods only consider the global structure of data. Moreover, directly using the anchor affinity matrix to approximate the full Laplacian matrix cannot guarantee the best low-rank approximation, which affects the clustering results. To address these problems, this paper proposes a large-scale non-negative subspace clustering method based on Nyström approximation. Firstly, we modify the objective function of the anchor affinity matrix by adding the local structure term, taking into account both the local and global data characteristics for better affinity learning. Secondly, a matrix iterative update rule is derived to optimize the objective function according to the non-negative Lagrangian relaxation, and its rationality and convergence are proved. Finally, two effective Laplacian matrix decomposition methods based on Nyström approximation are designed to obtain more accurate eigenvectors to improve the clustering quality. The proposed algorithms are tested on various benchmark datasets. The experimental results show that our methods have competitive performance compared with state-of-the-art large-scale clustering algorithms.
Anomaly detection in NetFlow network traffic using supervised machine learning algorithms

Anomaly detection is an important method for monitoring network traffic where is important to successfully distinguish normal traffic from abnormal traffic. For this purpose, one could use the existing classification algorithms as a part of the machine learning (ML) process. In this paper, some of the classification algorithms (Stochastic Gradient Descent (SGD), Support Vector Machines (SVM), K-Nearest Neighbor (K-NN), Gaussian Naive Bayes (GNB), Decision Tree (DT), Random Forest (RF), AdaBoost (AB)) were tested on the public UNSW-NB15 dataset. Different encoding methods and ratios of training and test data resulted in the optimal parameters classifiers. Due to the imbalanced distribution of normal and abnormal network traffic data, both standard performance scores and additional classification performance scores (F2-score, Area Under ROC Curve (AUC)) were used, that better describe the obtained results. The RF Classifier with F2-score = 97.68% and AUC score = 98.47% obtained the best results using a representative subset within the original dataset due to the shorter duration of the computations. Features in the referential dataset were reduced by 82% and selected following the structure of the NetFlow data stream. Concerning similar studies, this paper compares several algorithms for anomaly detection and selects the best one for NetFlow data streams. The F2-score and AUC metric is applied, which achieves very high accuracy compared to classic metrics that do not show realistic accuracy in imbalanced datasets. Less time was spent using Label enoding (LE) with the same accuracy compared to One-hot (OH) encoding used in similar research. The novelty introduced by this paper is in the optimization of the ML process and influence of the ratio of data for learning and testing, different encoding methods of categorical features, and feature reduction on the NetFlow data streams.
Reading Region: Effective Solutions in Promotion of Books and Reading

The article presents the review of speeches at the V International Book Forum “Reading Region: Dialogue with book”, held on September 16—17, 2022 in Novosibirsk as part of the International Festival “Book Siberia”. Using the method of thematic analysis, the authors identified three trends on promotion of reading by all actors of the book industry: writers, book publishers, libraries and the state. The first trend is characterized by participation in socio-cultural projects of state structures and the Russian Book Union. The second is related to the understanding of modern authors of their role, their participation in the literary process and regional book publishing. The book space in the speakers' speeches was considered as an intellectual partner of the state in the socio-cultural development of the regions. The third trend is formed under the influence of libraries, which, responding to the challenges of the present time, offer the traditional forms of work (book exhibitions, festivals, fairs, book presentations, meetings with writers, club associations, creative competitions) and innovative forms (online initiatives, reading aloud campaigns, local history electronic resources, large-scale projects with partnership interaction of various organizations) for user involvement. The paper presents the domestic and foreign experience of national, regional, as well as scientific and technical libraries. The authors formulate the problems of modern book infrastructure and make suggestion on how to overcome them. The authors believe that in order to effectively develop the entire infrastructure related to the promotion of reading, there should be conducted support to writers, publishers, bookselling networks and libraries. It is necessary to strengthen the partnership between the state, society and business in all areas of the book industry. The review can be useful for both practitioners of book industry and librarianship and for theorists of humanitarian knowledge.
Semi-supervised deep embedded clustering with pairwise constraints and subset allocation

Semi-supervised deep clustering methods attract much attention due to their excellent performance on the end-to-end clustering task. However, it is hard to obtain satisfying clustering results since many overlapping samples in industrial text datasets strongly and incorrectly influence the learning process. Existing methods incorporate prior knowledge in the form of pairwise constraints or class labels, which not only largely ignore the correlation between these two supervision information but also cause the problem of weak-supervised constraint or incorrect strong-supervised label guidance. In order to tackle these problems, we propose a semi-supervised method based on pairwise constraints and subset allocation (PCSA-DEC). We redefine the similarity-based constraint loss by forcing the similarity of samples in the same class much higher than other samples and design a novel subset allocation loss to precisely learn strong-supervised information contained in labels which consistent with unlabeled data. Experimental results on the two industrial text datasets show that our method can yield 8.2%-8.7% improvement in accuracy and 13.4%-19.8% on normalized mutual information over the state-of-the-art method.
Mutual channel prior guided dual-domain interaction network for single image raindrop removal

Removing raindrops adhering to lenses or glass is challenging since raindrops have more complex internal structures and optical effects than rain streaks. Existing raindrop removal methods typically employ single-channel masks for raindrop representation. However, these methods ignore the complementarity of priors between different channels, thereby losing the color and transparency information of raindrops. Moreover, simply applying the same processing across different pixels is insufficient to deal with spatially-varying degradation. To address these limitation, we propose a novel Mutual channel prior guided Dual-domain Interaction Network (MDINet) for single image raindrop removal. Specifically, to effectively guide the restoration of the raindrop region, we propose a mutual channel prior by modeling the multi-channel raindrop soft mask, in which the enriched color and transparency information of raindrops are represented and learned by our model. To tackle the spatially-varying degradation, we design the dual-domain interaction block to capture global low-frequency content and local high-frequency details. Simultaneously, a spatially adaptive modulation block is devised to handle complicated and diverse raindrops. Moreover, we propose a contrastive discrimination strategy to motivate the restored image to approach the clean image and stay away from the raindrop image in the representation space. Extensive experiments on benchmark datasets and real-world images indicate that the proposed method achieves qualitative and quantitative superiority against state-of-the-art methods.
M3GAN: A masking strategy with a mutable filter for multidimensional anomaly detection

With the advent of the big data era, the detection of anomalies in time series data, especially multidimensional time series data, has received a great deal of attention from researchers in many fields, e.g., industry, medicine, and aerospace. Multidimensional time series anomaly detection (MTSAD) methods aim to discover anomalies. They play an important early warning role in protecting people from possible property damage and personal injury. Many current GAN-based anomaly detection methods suffer from poor robustness, poor generalization, and low accuracy. The M2GAN (a GAN framework based on a masking strategy for multidimensional anomaly detection) proposed in this paper uses the masking method to diversify the data and improve the robustness of the model. This paper further proposes the M3GAN (M2GAN with a mutable filter), which can utilize the different characteristics of different filters to select a suitable model for the features of the data, thus enhancing the generalization ability and improving the accuracy. The experiments prove the effectiveness of the proposed method.
Pearl: Physical Environment based Augmented Reality Lenses for In-Situ Human Movement Analysis

This paper presents Pearl, a mixed-reality approach for the analysis of human movement data in situ. As the physical environment shapes human motion and behavior, the analysis of such motion can benefit from the direct inclusion of the environment in the analytical process. We present methods for exploring movement data in relation to surrounding regions of interest, such as objects, furniture, and architectural elements. We introduce concepts for selecting and filtering data through direct interaction with the environment, and a suite of visualizations for revealing aggregated and emergent spatial and temporal relations. More sophisticated analysis is supported through complex queries comprising multiple regions of interest. To illustrate the potential of Pearl, we developed an Augmented Reality-based prototype and conducted expert review sessions and scenario walkthroughs in a simulated exhibition. Our contribution lays the foundation for leveraging the physical environment in the in-situ analysis of movement data.
DataParticles: Block-based and Language-oriented Authoring of Animated Unit Visualizations

Unit visualizations have been widely used in data storytelling within interactive articles and videos. However, authoring data stories that contain animated unit visualizations is challenging due to the tedious, time-consuming process of switching back and forth between writing a narrative and configuring the accompanying visualizations and animations. To streamline this process, we present DataParticles, a block-based story editor that leverages the latent connections between text, data, and visualizations to help creators flexibly prototype, explore, and iterate on a story narrative and its corresponding visualizations. To inform the design of DataParticles, we interviewed 6 domain experts and studied a dataset of 44 existing animated unit visualizations to identify the narrative patterns and congruence principles they employed. A user study with 9 experts showed that DataParticles can significantly simplify the process of authoring data stories with animated unit visualizations by encouraging exploration and supporting fast prototyping.
Towards Leveraging AI-based Moderation to Address Emergent Harassment in Social Virtual Reality

Extensive HCI research has investigated how to prevent and mitigate harassment in virtual spaces, particularly by leveraging human-based and Artificial Intelligence (AI)-based moderation. However, social Virtual Reality (VR) constitutes a novel social space that faces both intensified harassment challenges and a lack of consensus on how moderation should be approached to address such harassment. Drawing on 39 interviews with social VR users with diverse backgrounds, we investigate the perceived opportunities and limitations for leveraging AI-based moderation to address emergent harassment in social VR, and how future AI moderators can be designed to enhance such opportunities and address limitations. We provide the first empirical investigation into re-envisioning AI's new roles in innovating content moderation approaches to better combat harassment in social VR. We also highlight important principles for designing future AI-based moderation incorporating user-human-AI collaboration to achieve safer and more nuanced online spaces.
Are Embodied Avatars Harmful to our Self-Experience? The Impact of Virtual Embodiment on Body Awareness

Virtual Reality (VR) allows us to replace our visible body with a virtual self-representation (avatar) and to explore its effects on our body perception. While the feeling of owning and controlling a virtual body is widely researched, how VR affects the awareness of internal body signals (body awareness) remains open. Forty participants performed moving meditation tasks in reality and VR, either facing their mirror image or not. Both the virtual environment and avatars photorealistically matched their real counterparts. We found a negative effect of VR on body awareness, mediated by feeling embodied in and changed by the avatar. Further, we revealed a negative effect of a mirror on body awareness. Our results indicate that assessing body awareness should be essential in evaluating VR designs and avatar embodiment aiming at mental health, as even a scenario as close to reality as possible can distract users from their internal body signals.
What does it mean to cycle in Virtual Reality? Exploring Cycling Fidelity and Control of VR Bicycle Simulators

Creating highly realistic Virtual Reality (VR) bicycle experiences can be time-consuming and expensive. Moreover, it is unclear what hardware parts are necessary to design a bicycle simulator and whether a bicycle is needed at all. In this paper, we investigated cycling fidelity and control of VR bicycle simulators. For this, we developed and evaluated three cycling simulators: (1) cycling without a bicycle (bikeless), (2) cycling on a fixed (stationary) and (3) moving bicycle (tandem) with four levels of control (no control, steering, pedaling, and steering + pedaling). To evaluate all combinations of fidelity and control, we conducted a controlled experiment (N = 24) in indoor and outdoor settings. We found that the bikeless setup provides the highest feeling of safety, while the tandem leads to the highest realism without increasing motion sickness. Moreover, we discovered that bicycles are not essential for cycling in VR.
Grab It, While You Can: A VR Gesture Evaluation of a Co-Designed Traditional Narrative by Indigenous People

Recent developments in Virtual Reality (VR) applications, such as hand gesture tracking, provide new opportunities to create embodied user experiences. Numerous gesture elicitation studies have been conducted. However, in most instances they lack validation of implemented gestures, as well diversity of participant groups. Our research explores the digitalization of intangible cultural heritage in collaboration with one of the San tribes in Southern Africa. The focus is on particular gestures as embodied interactions of a VR implementation of a traditional San hunting story. In this paper, we present a gesture study, which entails an in-situ elicitation of natural gestures, a co-designed integration, a VR story implementation with grasping and three mid-air gestures, and a user evaluation. Based on our findings, we discuss the anthropological value of gesture implementations determined by an indigenous community, the local usability of a grasping gesture, and in-VR gesture elicitation, as an extension of existing methods.
Supporting Novices Author Audio Descriptions via Automatic Feedback

Audio descriptions (AD) make videos accessible to those who cannot see them. But many videos lack AD and remain inaccessible as traditional approaches involve expensive professional production. We aim to lower production costs by involving novices in this process. We present an AD authoring system that supports novices to write scene descriptions (SD)—textual descriptions of video scenes—and convert them into AD via text-to-speech. The system combines video scene recognition and natural language processing to review novice-written SD and feeds back what to mention automatically. To assess the effectiveness of this automatic feedback in supporting novices, we recruited 60 participants to author SD with no feedback, human feedback, and automatic feedback. Our study shows that automatic feedback improves SD's descriptiveness, objectiveness, and learning quality, without affecting qualities like sufficiency and clarity. Though human feedback remains more effective, automatic feedback can reduce production costs by 45%.
Charagraph: Interactive Generation of Charts for Realtime Annotation of Data-Rich Paragraphs

Documents often have paragraphs packed with numbers that are difficult to extract, compare, and interpret. To help readers make sense of data in text, we introduce the concept of Charagraphs: dynamically generated interactive charts and annotations for in-situ visualization, comparison, and manipulation of numeric data included within text. Three Charagraph characteristics are defined: leveraging related textual information about data; integrating textual and graphical representations; and interacting at different contexts. We contribute a document viewer to select in-text data; generate and customize Charagraphs; merge and refine a Charagraph using other in-text data; and identify, filter, compare, and sort data synchronized between text and visualization. Results of a study show participants can easily create Charagraphs for diverse examples of data-rich text, and when answering questions about data in text, participants were more correct compared to only reading text.
Living with Sound Zones: A Long-term Field Study of Dynamic Sound Zones in a Domestic Context

Sound zone technology enables multiple people to have personal and shared listening experiences without disturbing each other. Methods for constructing sound zones have now matured enough to allow installations outside of experimental laboratories, making it essential for further development to conduct empirical studies about how people adopt, use, and interact with sound zones in, e.g., domestic settings. To that end, we conducted a four-week field study with a sound zone system in five households. Through an inductive reflexive thematic analysis, we identify three themes relating to 1) experiencing sound zones in everyday life, 2) sound zone usage patterns in households, and 3) interacting with sound zones. Based on these themes, we discuss how sound zones can be used to manage sound in homes in new ways to allow for better social coexistence and listening experiences. We present four directions for future HCI research and interaction design to comply with user needs and considerations when using this novel technology.
AdaCAD: Parametric Design as a New Form of Notation for Complex Weaving

Woven textiles are increasingly a medium through which HCI is inventing new technologies. Key challenges in integrating woven textiles in HCI include the high level of textile knowledge required to make effective use of the new possibilities they afford and the need for tools that bridge the concerns of textile designers and concerns of HCI researchers. This paper presents AdaCAD, a parametric design tool for designing woven textile structures. Through our design and evaluation of AdaCAD we found that parametric design helps weavers notate and explain the logics behind the complex structures they generate. We discuss these finding in relation to prior work in integrating craft and/or weaving in HCI, histories of woven notation, and boundary object theory to illuminate further possibilities for collaboration between craftspeople and HCI practitioners.
Felt Ethics: Cultivating Ethical Sensibility in Design Practice

We theoretically develop the ethical positions implicit in somaesthetic interaction design and, using the case study of a water faucet, illustrate our conceptual understanding of ethical sensibilities in design. We apply four lenses – the felt self, intercorporeal self, socio-cultural and political self, and entangled self – to show how our selves and ethical sensibilities are fundamentally constituted by a socially, materially, and technologically entwined world. Further, we show how ethical sensibilities are cultivated in the practice of somaesthetic interaction design. We contribute felt ethics as an approach to cultivating ethical sensibilities in design practice. The felt ethics approach is comprised of (i) a processual cultivation of ethical sensibility through analytical, pragmatic, and practical engagement, (ii) an ongoing critical attentiveness to the limits of our own bodies and lived experiences, and (iii) the rendering visible of our ethical practices as a matter of care.