Reviewerly/exHarmony huggingface dataset:
A Multirobot System for 3-D Surface Reconstruction With Centralized and Distributed Architectures

In this article, we propose an original solution to the problem of surface reconstruction of large-scale unknown environments, with multiple cooperative robots. As they progress through the 3-D environment, the robots rely on volumetric maps obtained via a TSDF representation to extract discrete incomplete surface elements (ISEs), and a list of candidate viewpoints is generated to cover them. A next-best-view planning approach, which approximately solves a traveling salesman problem (TSP) via greedy allocation, is then used to iteratively assign these viewpoints to the robots. Two multiagent architectures, a centralized one (TSP-Greedy Allocation or TSGA) and a distributed one (dist-TSGA), in which the robots locally compute their maps and share them, are developed and compared. Extensive numerical and real-world experiments with multiple aerial and ground robots in challenging 3-D environments show the flexibility and effectiveness of our surface representation of a volumetric map. The experiments also shed light on the nexus between reconstruction accuracy and surface completeness, and between total distance traveled and execution time.
Forging Emotions: a deep learning experiment on emotions and art

Affective computing is an interdisciplinary field that studies computational methods that relate to or influence emotion. These methods have been applied to interactive media artworks, but they have focused on affect detection rather than affect generation. For affect generation, computationally creative methods need to be explored that have recently been driven by the use of Generative Adversarial Networks (GANs), a deep learning method. The experiment presented in this paper, Forging Emotions, explores the use of visual emotion datasets and the working processes of GANs for visual affect generation, that is, for generating images that can convey or trigger specified emotions. This experiment concludes that the methodology used so far by computer science researchers to build image datasets for describing high-level concepts such as emotions is insufficient and proposes utilizing emotional networks of associations according to psychology research. Forging Emotions also concludes that to generate affect visually, merely corresponding to basic psychology findings, such as bright or dark colours, does not seem adequate. Therefore, research efforts should aim to understand the structure of trained GANs and compositional GANs in order to produce genuinely novel compositions that can convey or trigger emotions through the subject matter of generated images.
Anomaly Detection in Initial Climb Segments for Helicopter Operations

Helicopters are used in a variety of operations and recent studies show that the number of accidents associated with helicopters is stagnating, if not increasing. Flight data monitoring (FDM) is a useful tool to review the data retrospectively for risk mitigation. Exceedance analyses are typically used in FDM for anomaly detection. However, they typically rely on pre-defined thresholds which might vary depending on the type of operations or vehicles considered. With recent advancements in data mining techniques, many efforts have been put into anomaly detection in the commercial fixed-wing aviation and this provides a new perspective beyond traditional methods. In this research, a sequential approach is proposed to detect anomalies in initial climb segments for helicopter operations. The stepwise methodology contains three elements: trajectory pattern mining, time series length analysis, and shape analysis for identifying different levels of anomalies. To ensure the effectiveness of the methods selected, synthetic and simulated data are used for testing before applying candidate methods to the actual initial climb segments. A specific group of initial climb segments is used to demonstrate the validity of the methods chosen in this study. Our tests show that functional principal component analysis and a convolutional variational autoencoder along with DBSCAN are capable of identifying shape anomalies in flight parameters. Although the detected anomalies might not directly be associated with hazardous events, it is useful to assist helicopter operators in discovering patterns not conforming to the norms.
(Re-)Distributional Food Justice: Negotiating conflicting views of fairness within a local grassroots community

Sustainable HCI and Human-Food-Interaction research have developing interest in preventing food waste through food sharing. Sustainability requires attention to both the opportunities and challenges associated with the building of food sharing groups engaged in the redistribution of food but also in developing a wider agenda which includes, for instance, the local production of food resources. In this paper, we argue for a better understanding of the different conceptions of 'fairness' which inform volunteer and guest practice and in turn mediate community-building efforts. We examine the practices surrounding 'SharingEvent' and challenges faced to sustainability by the heterogenous, and sometimes contested, commitments of the people involved. We further consider how ICT provided opportunities for explicit examination of ideological differences concerning what 'sharing' might mean. Our findings show that community building is dependent on the negotiation of different values and purposes identified. We derive recommendations for action-oriented researchers ultimately concerned with systemic transformation.
DataLev: Mid-air Data Physicalisation Using Acoustic Levitation

Data physicalisation is a technique that encodes data through the geometric and material properties of an artefact, allowing users to engage with data in a more immersive and multi-sensory way. However, current methods of data physicalisation are limited in terms of their reconfigurability and the types of materials that can be used. Acoustophoresis—a method of suspending and manipulating materials using sound waves—offers a promising solution to these challenges. In this paper, we present DataLev, a design space and platform for creating reconfigurable, multimodal data physicalisations with enriched materiality using acoustophoresis. We demonstrate the capabilities of DataLev through eight examples and evaluate its performance in terms of reconfigurability and materiality. Our work offers a new approach to data physicalisation, enabling designers to create more dynamic, engaging, and expressive artefacts.
Beyond Your Sight Using Metaverse Immersive Vision With Technology Behaviour Model

With the advancement of technology, the metaverse image is used to improve the success rate of surgical. The purpose of this study was to understand the acceptance of extended reality surgery by different populations of medical experts and patients. The main reason for using a triangular mixed model for the study is because of the small amount of relevant research data for this study, quantitative research, exploratory research, and cross-sectional research can avoid some human interference and reduce error. The results of the study data showed that the image model, interaction design, surgeon, and clarity of use of the head-mounted display with metaverse technology by the expert group were conducive to improved surgical success rates. Metaverse surgery offers opportunities for modern digital surgery and can effectively improve the expert's ability to promote the smoothness of physical indicators during the procedure. This study is pioneering the role of a metaverse in facilitating surgery from the dual perspective of medical experts and patients.
Playing the System: Can Puzzle Players Teach us How to Solve Hard Problems?

With nearly three billion players, video games are more popular than ever. Casual puzzle games are among the most played categories. These games capitalize on the players’ analytical and problem-solving skills. Can we leverage these abilities to teach ourselves how to solve complex combinatorial problems? In this study, we harness the collective wisdom of millions of players to tackle the classical NP-hard problem of multiple sequence alignment, relevant to many areas of biology and medicine. We show that Borderlands Science players propose solutions to multiple sequence alignment tasks that perform as well or better than standard approaches, while exploring a much larger area of the Pareto-optimal solution space. We also show the strategies of the players, although highly heterogeneous, follow a collective logic that can be mimicked with Behavioral Cloning with minimal performance loss, allowing the players’ collective wisdom to be leveraged for alignment of any sequences.
SIG on Data as Human-Centered Design Material

Behavioral data is ubiquitous in products, services, and systems that people interact with. It is increasingly used by design and HCI researchers and practitioners throughout their human-centered and participatory design processes. The highly dynamic nature of behavioral data makes it deeply intertwined with people, their behavior, and their experiences. Thus, it presents unique opportunities and challenges. This Special Interest Group will provide a space to reflect and discuss effective and responsible ways to engage with behavioral data in human-centered design processes. We will explore questions about the types and scale of data used, the contexts in which data is embedded and applied, the methods we rely on, and the forms of engagement of the multiple stakeholders. In doing so, our goal is to collaboratively develop a research agenda, setting the scope for an annual, international symposium on Data-Centric Design.
Generic User Behavior: A User Behavior Similarity-Based Recommendation Method

Recommender system (RS) plays an important role in Big Data research. Its main idea is to handle huge amounts of data to accurately recommend items to users. The recommendation method is the core research content of the whole RS. However, the existing recommendation methods still have the following two shortcomings: (1) Most recommendation methods use only one kind of information about the user's interaction with items (such as Browse or Purchase), which makes it difficult to model complete user preference. (2) Most mainstream recommendation methods only consider the final consistency of recommendation (e.g., user preferences) but ignore the process consistency (e.g., user behavior), which leads to the biased final result. In this article, we propose a recommendation method based on the Entity Interaction Knowledge Graph (EIKG), which draws on the idea of collaborative filtering and innovatively uses the similarity of user behaviors to recommend items. The method first extracts fact triples containing interaction relations from relevant data sets to generate the EIKG; then embeds the entities and relations in the EIKG; finally, uses link prediction techniques to recommend items for users. The proposed method is compared with other recommendation methods on two publicly available data sets, Scholat and Lizhi, and the experimental result shows that it exceeds the state of the art in most metrics, verifying the effectiveness of the proposed method.
“Dump it, Destroy it, Send it to Data Heaven”: Blind People’s Expectations for Visual Privacy in Visual Assistance Technologies

Visual assistance technologies provide people who are blind with access to information about their visual surroundings by digitally connecting them to remote humans or artificial intelligence systems that describe visual content such as objects, people, scenes, and text observed in their live image/video feeds. Prior work has revealed that users have concerns about how such technologies handle private visual content captured in their image/video feeds. Yet, it remains unclear how users want technologies to manage such private content. To fill this gap, we interviewed 16 totally blind individuals to learn about their expectations for visual privacy when using visual assistance technologies. Our findings reveal three overarching user-centered expectations associated with visual privacy-preservation in this domain, as well as the broader ethical challenges involved with developing AI-based privacy-preserving visual assistance technologies.
Using Bidirectional Prompt Learning in NLP Few Shot Tasks

Few-Shot Learning, a subfield of machine learning, aims to solve the problem of learning new tasks with only a small amount of annotated data. Compared to traditional supervised learning, Few-Shot Learning is more challenging because there are very few training samples available during the training process, which means that the model must learn quickly and generalize to new samples. Prompt learning is a recently emerged training paradigm in natural language processing, which can quickly leverage the language capabilities of large pre-trained language models to achieve fast start-up. Based on the prompt learning paradigm, this paper proposes to use its conjugate tasks to further enhance the model's ability in few-shot learning. The experimental results show that the proposed method can effectively improve the performance of the model on multiple datasets and can be combined quickly with other methods for joint optimization.
Deeper learning through interactions with students in natural language

This chapter reviews work on building adaptive educational systems with natural language interaction, to note current challenges and discuss potential solutions based on recent advances in Artificial Intelligence. Interacting with students in natural language has many advantages. It encourages deeper, conceptual learning as students are required to explain their reasoning and reflect on their basic approach to solving a problem. It also provides deeper insights into the learning process by revealing students’ reasoning and mental model construction processes, including potential misconceptions, thereby enabling better detection of opportunities to scaffold students’ learning by providing immediate corrective feedback, which is most effective. Furthermore, natural language interactions give students the opportunity to develop the language of their target professional communities and therefore much-needed communication skills. When conversational AI systems for learning are augmented with virtual agents, they have affordances to social agency which allegedly leads to more engaging, satisfying, and effective learning experiences. This chapter primarily focuses on dialogue-based intelligent tutoring systems (ITSs) with one-on-one tutoring. We also make some conjectures about the length and density of interactive, intense learning sessions such as those students experience when interacting one-on-one with a dialogue-based ITS.
Interactive machine translation for the language modernization and spelling normalization of historical documents

Abstract Historical documents are an important part of our cultural heritage. Among other task related to their processing, it is important to modernize their language in order to make them accessible to a broader audience and to achieve an orthography consistency to reduce the linguistic variation inherent in them. Language modernization and spelling normalization have those goals in mind. However, they still have a long way to go. Thus, in order to help scholars generate error-free modernizations/normalizations when the quality is essential, we propose an interactive framework based on interactive machine translation. In this work, we deployed two different interactive protocols into these tasks. We evaluated our proposal under simulated environments, observing significant reductions of the human effort.
Predicting the Change Impact of Resolving Defects by Leveraging the Topics of Issue Reports in Open Source Software Systems

Upon receiving a new issue report, practitioners start by investigating the defect type, the potential fixing effort needed to resolve the defect and the change impact. Moreover, issue reports contain valuable information, such as, the title, description and severity, and researchers leverage the topics of issue reports as a collective metric portraying similar characteristics of a defect. Nonetheless, none of the existing studies leverage the defect topic, i.e., a semantic cluster of defects of the same nature, such as Performance, GUI, and Database , to estimate the change impact that represents the amount of change needed in terms of code churn and the number of files changed. To this end, in this article, we conduct an empirical study on 298,548 issue reports belonging to three large-scale open-source systems, i.e., Mozilla, Apache, and Eclipse, to estimate the change impact in terms of code churn or the number of files changed while leveraging the topics of issue reports. First, we adopt the Embedded Topic Model (ETM), a state-of-the-art topic modelling algorithm, to identify the topics. Second, we investigate the feasibility of predicting the change impact using the identified topics and other information extracted from the issue reports by building eight prediction models that classify issue reports requiring small or large change impact along two dimensions, i.e., the code churn size and the number of files changed. Our results suggest that XGBoost is the best-performing algorithm for predicting the change impact, with an AUC of 0.84, 0.76, and 0.73 for the code churn and 0.82, 0.71, and 0.73 for the number of files changed metric for Mozilla, Apache, and Eclipse, respectively. Our results also demonstrate that the topics of issue reports improve the recall of the prediction model by up to 45%.
Tax System: Power Packed by Blockchain

Purpose: Tax fraudulence is not an aberration, but it is a deliberate attempt to get rid of the tax burden. The government took enormous measures to curtail tax fraud, which seems dispensable. Taxation is the largest area that needs to be explored as individuals make discrete stings while filing taxes. Since compliance-focussed systems for tax collection are missing, this department is facing difficulties in providing quality data. This chapter will cover blockchain operations in the tax system to streamline the entire process and highlight the challenges.Need of the Study: As the growth of technology is in the boom stage, research should be carried out to create more awareness regarding its usage and its possible threats. This study will spark more light on taxation by taking blockchain as the torch to improvise the vision.Design/methodology/approach: The researcher did a comprehensive analysis of the Indian Tax Department and determined whether simplifying taxation could be possible through blockchain. The study gathered concrete facts about the various challenges faced while implementing this system.Finding: Blockchain technology is rapidly making a roadmap in the taxation system, and there is tremendous potential to create a future vision with blockchain.Practical Implications: This study will accentuate the function of blockchain technology that is becoming more prominent in the global technology sphere. This study will aid scholars in elaborating on the role of blockchain technology.
Time crystals transforming frequency combs in tunable photonic oscillators

The response of a tunable photonic oscillator, consisting of an optically injected semiconductor laser, under an injected frequency comb is considered with the utilization of the concept of the time crystal that has been widely used for the study of driven nonlinear oscillators in the context of mathematical biology. The dynamics of the original system reduce to a radically simple one-dimensional circle map with properties and bifurcations determined by the specific features of the time crystal fully describing the phase response of the limit cycle oscillation. The circle map is shown to accurately model the dynamics of the original nonlinear system of ordinary differential equations and capable for providing conditions for resonant synchronization resulting in output frequency combs with tunable shape characteristics. Such theoretical developments can have potential for significant photonic signal-processing applications.
Video object segmentation with self-supervised framework for an autonomous vehicle

Self-supervision framework targets to study useful representations from unlabeled pool of data as the unlabeled data [1] is available in abundance and getting good quality labeled data tends to be costlier and time taking. The vision transformers (ViT) [2] have achieved the present state of art performance for applying self-supervision in video segmentation tasks. The literature review discusses various video segmentation frameworks using contrastive methods [3]. Similarity algorithms have been applied to get optimized attention heads to discriminate foreground and background. It involves Knowledge Distillation (KD) [4] that helps student model learn from the teacher network, in which the instructor&apos;s logits are employed to train the student. It&apos;s most recognized for being a powerful model compression technique. Data augmentation [5] method aims to obtain the accuracy by providing various aspects of the same image. Our study underlines the importance of student-teacher encoder, multi-crop training and optimal use of small patches with ViTs replicated by proposed novel distillation model which mainly concerned about the avoidance of expected trivial solutions with the help of involved predictor model and centering process. It defines the synergy between autonomous vehicle and smart cities where perceiving precise environment can be achieved with the proposed model.
Single Image Dehazing based on Additive Wavelet Transform

In this work, a single image dehazing method, which uses the multiscale products of additive wavelet transform as a prior is presented. In this method, first, the additive wavelet transform is applied to the hazy image to obtain its approximation and wavelet layers. Then the multiscale products of the approximation and detail layers of the input hazy image is calculated. The multiscale products of approximation and wavelet layers are summed up to obtain the proposed prior. Observations demonstrate that the proposed prior calculation keeps the detail information of the image, while detecting the haze. Using the proposed prior and commonly used hazy image model together, an efficient dehazing method is constructed. The comparisons between the proposed method and commonly used dehazing methods show that the proposed method has a better dehazing perfomrance than the traditional methods.
Filtered-DiskANN: Graph Algorithms for Approximate Nearest Neighbor Search with Filters

As Approximate Nearest Neighbor Search (ANNS)-based dense retrieval becomes ubiquitous for search and recommendation scenarios, efficiently answering filtered ANNS queries has become a critical requirement. Filtered ANNS queries ask for the nearest neighbors of a query’s embedding from the points in the index that match the query’s labels such as date, price range, language. There has been little prior work on algorithms that use label metadata associated with vector data to build efficient indices for filtered ANNS queries. Consequently, current indices have high search latency or low recall which is not practical in interactive web-scenarios. We present two algorithms with native support for faster and more accurate filtered ANNS queries: one with streaming support, and another based on batch construction. Central to our algorithms is the construction of a graph-structured index which forms connections not only based on the geometry of the vector data, but also the associated label set. On real-world data with natural labels, both algorithms are an order of magnitude or more efficient for filtered queries than the current state of the art algorithms. The generated indices also be queried from an SSD and support thousands of queries per second at over recall@10.