Reviewerly/exHarmony huggingface dataset:
VR-Surv: a VR-Based Privacy Preserving Surveillance System

The recent advances in smart city infrastructure have provided support for a higher adoption of surveillance cameras as a mainstream crime prevention measure. However, a consequent massive deployment raises concerns about privacy issues among citizens. In this paper, we present VR-Surv, a VR-based privacy aware surveillance system for large scale urban environments. Our concept is based on conveying the semantics of the scene uniquely, without revealing the identity of the individuals or the contextual details that might violate the privacy of the entities present in the surveillance area. For this, we create a virtual replica of the areas of interest, in real-time, through the combination of procedurally generated environments and markerless motion capture models. The results of our preliminary evaluation revealed that our system successfully conceals privacy-sensitive data, while preserving the semantics of the scene. Furthermore, participants in our user study expressed higher acceptance to being surveilled through the proposed system.
Design Futuring for Love, Friendship, and Kinships: Five Perspectives on Intimacy

Human relationships, intimacy and the role of technology within it constantly change, catapulted in 2020 by COVID-19. We take this social rupture as an opportunity to reimagine possible futures for love, friendship, and kinships. Through design futuring and related approaches, we offer five prompts we developed for imagining alternative futures exploring a diverse range of intimacies. Through generating responses to the prompts, we offer alternative intimate futures as well as reflections on how such 'prompts for futuring' can be generative for design research. Our work extends calls for diversifying design futuring, imploring design researchers to consider diverse and inclusive ways of designing for futures, especially for human relationships and intimacy.
Toward Handling the Complexities of Non-Anthropomorphic Hands

Virtual reality allows us to operate bodies that differ substantially from our own. However, avatars with different topologies than the human form require control schemes and interfaces that effectively translate between user and avatar. In this position paper, we discuss the concept of "non-anthropomorphic designs" that are inhuman in not just appearance, but in topology and/or motion. We examine current implementations of real and virtual non-anthropomorphic hands (NAHs), finding that existing NAHs generally rely on one-to-one or reductionist control strategies that limit their possible forms. We discuss the structure of a functional NAH system and design considerations for each component, including metrics for evaluating NAH system performance. The terminology and design considerations presented here support future research on NAHs in virtual and physical reality, as well as virtual and physical tool design, the body schema, and novel control interfaces and mappings.
Self-Supervised Text Erasing with Controllable Image Synthesis

Recent efforts on text erasing have shown promising results. However, existing methods require rich yet costly label annotations to obtain robust models, which limits their use for practical applications. To this end, we study an unsupervised scenario by proposing a novel Self-supervised Text Erasing (STE) framework that jointly learns to synthesize training images with erasure ground-truth and accurately erase texts in the real world. We first design a style-aware image synthesis function to generate synthetic images with diverse styled texts based on two synthetic mechanisms. To bridge the text style gap between the synthetic and real-world data, a policy network is constructed to control the synthetic mechanisms by picking style parameters with the guidance of two specifically designed rewards. The synthetic training images with ground-truth are then fed to train a coarse-to-fine erasing network. To produce better erasing outputs, a triplet erasure loss is designed to enforce the refinement stage to recover background textures. Moreover, we provide a new dataset (called PosterErase), which contains 60K high-resolution posters and is more challenging for the erasing task. The proposed method has been extensively evaluated with both PosterErase and the widely-used SCUT-Enstext dataset. Notably, on PosterErase, our method achieves 5.07 in terms of FID, with a relative improvement of 20.9% over existing supervised baselines.
Interaction with Touch-Sensitive Knitted Fabrics: User Perceptions and Everyday Use Experiments

Recent work has investigated the construction of touch-sensitive knitted fabrics, capable of being manufactured at scale, and having only two connections to external hardware. Additionally, several sensor design patterns and application prototypes have been introduced. Our aim is to start shaping the future of this technology according to user expectations. Through a formative focus group study, we explore users’ views of using these fabrics in different contexts and discuss potential concerns and application areas. Subsequently, we take steps toward addressing relevant questions, by first providing design guidelines for application designers. Furthermore, in one user study, we demonstrate that it is possible to distinguish different swipe gestures and identify accidental contact with the sensor, a common occurrence in everyday life. We then present experiments investigating the effect of stretching and laundering of the sensors on their resistance, providing insights about considerations necessary to include in computational models.
Fitness-Based Grey Wolf Optimizer Clustering Method for Spam Review Detection

Customers nowadays rely heavily on online reviews when making buying decisions. Various internet websites, including Amazon, Yelp, Google Plus, BookMyShow, Facebook, Twitter, and others, allow users to generate massive amounts of data. The information is gathered through feedback/reviews, comments, and tweets. Companies can leverage this information to improve the quality of their products. Spam reviews are created pretentiously by some businesses and people to promote or degrade the popularity of any product, organization, or person due to their reliance on these online reviews. Thus, identifying spam or nonspam review by the naked eye is nearly impossible. Classifying the reviews manually is also highly speculative. Hence, to overcome this issue, a fitness-based Grey Wolf Optimizer (FGWOK) clustering method is proposed in this paper to identify spam reviews. The fitness-based grey wolf optimization (GWO) is used to obtain the optimal cluster heads in the proposed method. In the fitness-based GWO, the position of the grey wolves is updated in two phases. In the first phase, all the search agents update their position using the contemporary GWO, and in the second phase, the fitness is evaluated to update the position. To prove that the proposed strategy is effective, three spam data sets, namely synthetic spam reviews, movie reviews, and Yelp hotel and restaurant reviews, have been used in our work. The reported results are compared with the existing state-of-art metaheuristic clustering methods like a genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), k-Means, and artificial bee colony clustering (ABCK) method. The experimental and statistical analysis results show that the proposed FGWOK algorithm outperforms current methodologies.
The Subjectivities of Wearable Sleep-Trackers - A Discourse Analysis

Self-reported quality and duration of sleep in Western populations is declining. The interest in wearable sleep-trackers that are promising better sleep is growing. By wearing a device day and night the sleeper is continuously connected to a more-than-human network. The mass-adoption of sleep-tracking devices has an impact on the personal, social and cultural meaning of sleep. This study looks at the discourse forming around wearable sleep-trackers. This extended abstract presents how non-human subjectivities are accounted for in this discourse. Through a posthuman discourse analysis of textual and visual artefacts from interviews, academic research and popular media, six distinct roles for these non-human social agents were identified: ‘Teacher’, ‘Informant’, ‘Companion’, ‘Therapist’, ‘Coach’ and ‘Mediator’. This characterisation is a first step to understanding sleep-trackers as social agents, reorganising personal and contextual relationships with the sleeping self.
Harmonized Banner Creation from Multimodal Design Assets

Designing aesthetically pleasing single-page graphic designs (”banners”) that appeal to the target recipients is non-trivial and requires considerable human effort. Designers often start by collecting assets like background images, inspirational banners, and relevant taglines/textual phrases, and iterate over variants of the designs by testing different combinations of these assets for their current task. To expand creative processes beyond professionals, it is crucial to accelerate this tedious process of creating design variants. To this end, we propose an algorithm that takes as input multiple image and text elements to generate several visually pleasing banner designs harmonized for text colors, font styles, and the placement of these multimodal assets. The generated layout accounts for background image saliency and uses this to select typographic and color variants yielding the harmonized outputs. We demonstrate the effectiveness of the proposed algorithm in generating aesthetic banners with the help of a crowdsourced experiment.
Share Your Values! Community-Driven Embedding of Ethics in Research

Ethically-defensible research requires wide-ranging, holistic, and deep consideration. It is often overseen by Research Ethics Committees, Institutional Research Boards or equivalents but not all organisations have these and where they do, their degree of independence from organisational priorities varies (perhaps leading to research that would create reputational or other difficulties for organisations being left unpublished or unacknowledged). Conflicts of interest can therefore be left unmanaged, participants may be exploited, and society may not benefit. In this paper, we claim that publishing communities (e.g. scholarly conferences) can play a larger role in supporting improved ethical practice by defining and communicating the ethical values of their community’s collective identity and aspirations. This approach is not prescriptive like procedural ethics nor as broad as general research ethics codes (both are important) but offers a tangible way to unify ethics concerns across research contexts.
Expert Evaluation of Haptic Virtual Reality User Interfaces for Medical Landmarking

Three-dimensional (3D) models have widely been used in medical diagnosis and planning tasks. Haptic virtual reality (VR) interfaces implemented by using VR equipment and haptic devices have previously been proposed for these medical 3D manipulation tasks. They have been found to be faster and more accurate in a medical marking task with novel users, compared with the traditional 2D interaction technique that uses a mouse and a 2D display. In this study, we recruited medical experts who do the medical landmarking task as part of their daily work to examine the performance of haptic VR interfaces and to investigate experts’ user experience. There were no statistically significant differences between the haptic user interfaces and the mouse-based 2D interface in terms of task completion time and marking accuracy. Based on experts’ subjective data, haptic VR interfaces showed great potential for medical work because of the natural input methods and haptic feedback.
Particle swarm optimization algorithm with proportional factor based on Nash equilibrium

Abstract Based on the global gradient-less stochastic search method of traditional particle swarm optimization algorithm, an improved particle swarm optimization algorithm for finding mixed-strategy Nash equilibrium is proposed in view of the shortcomings of prone particle overflow and unconstrained relationship between individual dimensions. In the improved evolution equation, the position function is improved to conform to the concept and properties of the combination of strategies, and the proportional factor is introduced to ensure that the numerical value of particle iteration is mapped to the solution space of normal form game. Through the test of several groups of classical examples, the results show that the algorithm not only improves efficiency and accuracy, but also solves the problems of divergence of particle and low efficiency of iteration. The experiments show that the algorithm has good practical performance.
Research on Intelligent Campus and Visual Teaching System Based on Internet of Things

The rapid development of Internet of things technology provides robust conditions for building a perfect intelligent campus. A visual teaching question answering system is essential for creating a smart campus, significantly improving education quality. However, the accuracy of the existing teaching question answering system is not high. To solve this problem, this paper proposes a visual teaching system based on a knowledge map. The system mainly includes two parts: problem processing and answer search. In the part of problem processing, combined with the pretraining language model, a new model framework is constructed to deal with the problem of entity reference recognition, entity link, and relationship extraction. By setting three kinds of classification labels, the problem is divided into simple, chain, and multientity problems. Different solutions are given to the above three classification problems in the answer search part. The experimental results show that the answer accuracy of this system is higher than other comparison methods.
Platformisation of Digital Financial Services (DFS): The Journey of DFS in the Global North and Global South

The digitization of financial transactions in both Global North and Global South has led to considerable shifts in how money is used, understood, and processed by users, banks, and fintechs. This shift from physical cash to digital media, accelerated by the COVID-19 push for digital transactions, has impacted how users perceive and use digital money and opened avenues for more data collection. This diverse panel proposes a discussion to understand the set of opportunities and challenges around the design of digital financial services (DFS) and data-driven decision-making in DFS. We will create a live working document starting before the panel to document the discussion, which develops during and after the panel. This live document will enable community to engage with a broader audience of researchers and industry, outlining processes, methods, and tools that researchers and practitioners have created to work with users to develop new equitable DFS and further exploration.
“I need to be professional until my new team uses emoji, GIFs, or memes first’’: New Collaborators’ Perspectives on Using Non-Textual Communication in Virtual Workspaces

Virtual workspaces rapidly increased during the COVID-19 pandemic, and for many new collaborators, working remotely was their first introduction to their colleagues. Building rapport is essential for a healthy work environment, and while this can be achieved through non-textual responses within chat-based systems (e.g., emoji, GIF, stickers, memes), those non-textual responses are typically associated with personal relationships and informal settings. We studied the experiences of new collaborators (questionnaire N=49; interview N=14) in using non-textual responses to communicate with unacquainted teams and the effect of non-textual responses on new collaborators' interpersonal bonds. We found new collaborators selectively and progressively use non-textual responses to establish interpersonal bonds. Moreover, the use of non-textual responses has exposed several limitations when used on various platforms. We conclude with design recommendations such as expanding the scope of interpretable non-textual responses and reducing selection time.
Collaborative Filtering Algorithm-Based Destination Recommendation and Marketing Model for Tourism Scenic Spots

The information age of rapid development of tourism industry provides abundant travel information, but it also comes with the problem of information overload, which makes it difficult to meet the growing personalized needs of people. The traditional collaborative filtering recommendation algorithm (CFA) also suffers from the problem of data sparsity when the user population increases. Therefore, this study optimizes the CFA through the similarity factor and correlation factor and enhances the tourism sense of travel experience through the satisfaction balance strategy. The experimental results show that the improved CFA method has the highest average accuracy on the overall dataset and the best recommendation performance of the satisfaction balance strategy. Overall, the recommendation model in this study is useful for attraction selection of users and marketing optimization of travel companies.
Attention Mechanism with Energy-Friendly Operations

Attention mechanism has become the dominant module in natural language processing models. It is computationally intensive and depends on massive power-hungry multiplications. In this paper, we rethink variants of attention mechanism from the energy consumption aspects. After reaching the conclusion that the energy costs of several energy-friendly operations are far less than their multiplication counterparts, we build a novel attention model by replacing multiplications with either selective operations or additions. Empirical results on three machine translation tasks demonstrate that the proposed model, against the vanilla one, achieves competitable accuracy while saving 99\% and 66\% energy during alignment calculation and the whole attention procedure. Code is available at: https://github.com/NLP2CT/E-Att.
Underwater target passive detection method based on Hough transform track-before-detect

Abstract Aiming at the low detectability and concealment detection requirements of underwater targets such as underwater unmanned vehicles (UUV), an underwater target passive detection method based on Hough transform track-before-detect was proposed. In order to deal with the approach reconnaissance of UUV, the passive detection model of UUV was constructed; In order to obtain the target position information required for track-before-detect, the cross-positioning method of multiple passive sonar buoys was adopted to realize the data pre-processing and noise reduction before concealment detection; In order to improve the detection performance of passive sonar in complex underwater acoustic environment, Hough transform method and point trace correlation were used to realize the demand of low detectability of underwater targets. Simulation results show that this method can still maintain high detection probability under the conditions of low signal-to-noise ratio, multi-target and complex noise.
Using TREC for developing semantic information retrieval benchmark for Urdu

Information Retrieval (IR) systems are developed to fulfill several needs of users. As a plethora of IR techniques has been developed, therefore the evaluation of these techniques has been of paramount importance. The evaluation of these techniques require test collections, which are composed of a collection of documents, queries and relevance judgement between query–document pairs. Recognizing the importance of the evaluation of IR techniques, Text REtrieval Conference (TREC) has been regularly organized for the last three decades with an aim to develop and continuously improve these techniques. However, most of the resource development effort has been directed to English and other Western languages, whereas resource development for Urdu has received little attention, which has thwarted the development of IR techniques. Furthermore, the available benchmarks have several limitations which include smaller size, unavailability of the benchmark, inadequate candidate documents for evaluation, and merely binary relevance judgement. To that end, this study has focused on constructing the largest-ever and semantic IR benchmark for the Urdu language that strictly complies with the procedures proposed by TREC. That is, firstly, a large collection of 2,887,169 Urdu documents is scrapped and converted into the standard format proposed by TREC. Secondly, 105 queries are generated, which includes detailed descriptions of 35 queries and three variants of each query in the TREC format. Thirdly, a pooling-based approach is employed using queries, as well as their variants, to identify a candidate pool of 13,392 documents for human judgement. Finally, two experts performed 13,392 query–document comparisons and ranked them on a scale of four types of relevance: highly relevant, fairly relevant, marginally relevant and irrelevant, which is a significant enhancement from the existing benchmarks. The benchmark can be used for the evaluation of existing IR techniques, as well as for future techniques. • Large corpus of over 2,887,169 Urdu documents in the TREC defined SGML format. • A collection of 35 Urdu queries from 14 domains for assessment. • Human benchmark of candidate relevant documents using a pooling-based. • Non-binary relevance judgement at four-levels.
Efficient Hyperchaotic Image Encryption Algorithm Based on a Fast Key Generation Method and Simultaneous Permutation-Diffusion Structure

To provide faster encryption speed for protecting image data privacy, a highly efficient hyperchaotic image encryption algorithm using a fast key generation method and simultaneous permutation-diffusion structure is proposed in this paper. Firstly, we design a novel fast key generation method to obtain four key matrixes with the size of W × H (W and H are the width and height of an image) with the hyperchaotic Lorenz system only iterating 2W times. And within the key generation process, the piecewise linear chaotic map serves as a random selector to further enhance the randomness and unpredictability. Secondly, we introduce vectorization technique to parallelize the cipher blockchain-based encryption process. Hence, an image can be fast and sufficiently encrypted row by row and column by column through a four-round and interlinked diffusion process. Furthermore, during the diffusion process, the pixels are simultaneously scrambled by the Chebyshev map to enhance the antiattack capability of the proposed algorithm. Thirdly, we utilize a secure hash function to generate a plaintext-related secret key. Finally, cipher images can successfully pass common security tests with the correlation coefficients on the verge of 0, the entropies are greater than 7.99, and NPCR and UACI values are close to the expected values. Combined with other experimental results and analysis, we can safely conclude that the proposed algorithm has strong robustness against statistical, brute-force, differential, and some other common attacks. Moreover, only 0.009 s of encrypting a 256 × 256 image indicates that our algorithm has extensive application prosperity for real-time communications of massive digital images.
Persephone’s Feet: A Foot-Based Approach to Play in Virtual Reality

Persephone's Feet is a virtual reality game that questions: What if you could only garden with your feet? Exploring a series of three foot-based gestures (Tap, Hover & Shake, and Kick), this work presents a proof-of-concept for a novel virtual reality game for gardening. The game is designed as a relaxation and light exercise activity to take during breaks for the demographic of users who spend prolonged periods of time sedentary at a desk, which is becoming increasingly common for both work and education in modern society. A preliminary evaluation suggests that foot-based gestures can engender fun and motivate light exercise.