Reviewerly/exHarmony huggingface dataset:
Hierarchical RANSAC-Based Rotation Averaging

In this letter, we present a novel rotation averaging pipeline, which is performed in a hierarchical manner. Unlike the traditional rotation averaging methods which focus on designing robust loss function to get rid of the impacts of the relative rotation outliers, here the outliers are detected and filtered by leveraging the well-known robust model estimation procedure, RANdom SAmple Consensus (RANSAC). During the RANSAC process, the minimal set is randomly sampled by random tree spanning on the Epipolar-geometry Graph (EG). As the RANSAC estimation result is sensitive to the size of minimal set, the EG is clustered into several sub-graphs, and the inner- and inter-cluster RANSAC-based rotation averaging are performed hierarchically. In addition, both random generation and optimal selection of the minimal set are performed in a weighted manner to make the rotation averaging pipeline more robust. Ablation studies and comparison experiments on the 1DSfM and San Francisco (SNF) datasets demonstrate the effectiveness of our proposed method.
A Novel Parameter Estimation for Polynomial Phase Signals Using the Spectrum Phase

A novel approach for the parameter estimation of polynomial phase signals (PPSs) is proposed. Unlike the existing methods, the proposed method uses the spectrum phase (SP) rather than the traditional instantaneous phase (IP) for parameter estimation. First, we present an unwrapping procedure for the SP. Second, we derive a method that can convert the unwrapped SP into the IP and then perform polynomial regression on the IP to obtain the estimates of the parameters. Finally, we use the O'Shea refinement strategy to refine the estimates to achieve the Cramér-Rao lower bound. The simulation results show that the proposed technique has a lower signal-to-noise ratio threshold and is less complex than the existing methods for high-order PPSs.
Accessibility in 360° video players

Abstract Accessibility is a key requirement for any multimedia tool and application. With the current trend towards immersive experiences, such as Virtual Reality (VR) and 360 o video, it becomes key that these environments are adapted to be fully accessible. However, until recently the focus has been mostly on adapting the existing techniques to fit immersive displays, rather than considering new approaches for accessibility designed specifically for these increasingly relevant media experiences. This paper surveys a wide range of 360 o video players and examines the features they include for dealing with accessibility, such as Subtitles, Audio Description, Sign Language, User Interfaces and other interaction features, like voice control and support for multi-screen scenarios. These features have been chosen based on guidelines from standardization contributions, like in the World Wide Web Consortium (W3C) and the International Communication Union (ITU), and from research contributions for making 360° video consumption experiences accessible. The in-depth analysis has been part of a research effort towards the development of a fully inclusive and accessible 360° video player. The paper concludes by discussing how the newly developed player has gone above and beyond the existing solutions and guidelines, by providing accessibility features that meet the expectations for a widely used immersive medium, like 360° video.
Quantum Multiparty Privacy Set Intersection Cardinality

In this brief, we present a novel quantum protocol for Multiparty Privacy Set Intersection Cardinality (MPSI-CA) and design the corresponding quantum circuits. The proposed protocol demonstrates two primary aspects of quantum advantage: the parallelism of quantum computing and the randomness of quantum measurement. Compared with the classical related protocols, the proposed MPSI-CA protocol can ensure higher security (i.e., unconditional security) and achieve better communication complexity (i.e., O(t <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )).
Evolving behaviour trees for supervisory control of robot swarms

Abstract Supervisory control of swarms is essential to their deployment in real-world scenarios to both monitor their operation and provide guidance. We explore mechanisms by which humans can provide supervisory control to swarms to improve their performance. Rather than have humans guess the correct form of supervisory control, we use artificial evolution to learn effective human-readable strategies. Behaviour trees are applied to represent human-readable decision strategies which are produced through evolution. These strategies can be thoroughly tested and can provide knowledge to be used in the future in a variety of scenarios. A simulated set of scenarios are investigated where a swarm of robots have to explore varying environments and reach sets of objectives. Effective supervisory control strategies are evolved to explore each environment using different local swarm behaviours. The evolved behaviour trees are examined in detail alongside swarm simulations to enable clear understanding of the supervisory strategies. We conclude by identifying the strengths in accelerated testing and the benefits of this approach for scenario exploration and training of human operators.
Natural Gradient Shared Control

We propose a formalism for shared control, which is the problem of defining a policy that blends user control and autonomous control. The challenge posed by the shared autonomy system is to maintain user control authority while allowing the robot to support the user. This can be done by enforcing constraints or acting optimally when the intent is clear. Our proposed solution relies on natural gradients emerging from the divergence constraint between the robot and the shared policy. We approximate the Fisher information by sampling a learned robot policy and computing the local gradient to augment the user control when necessary. A user study performed on a manipulation task demonstrates that our approach allows for more efficient task completion while keeping control authority against a number of baseline methods.
Thirty-Seven Years of Relational Hoare Logic: Remarks on Its Principles and History

Relational Hoare logics extend the applicability of modular, deductive verification to encompass important 2-run properties including dependency requirements such as confidentiality and program relations such as equivalence or similarity between program versions. A considerable number of recent works introduce different relational Hoare logics without yet converging on a core set of proof rules. This paper looks backwards to little known early work. This brings to light some principles that clarify and organize the rules as well as suggesting a new rule and a new notion of completeness.
Towards an understanding of technology fit and appropriation in business networks: evidence from blockchain implementations

Abstract Existing information systems research thoroughly explains how task-technology fit and appropriation affect performance on an individual or group level. This was appropriate for many years, as technology is typically used to fulfill a certain task on these levels. Today, however, companies are tightly interconnected and rely on business networks to develop, produce , and deliver products and services. They collaboratively engage in joint implementation and utilization of new technologies that are applied and integrated into their business processes. These technologies, such as the newly introduced blockchain technology, operate across business networks and, thus, unfold their benefits not only on an individual or group level, but ideally on a network level. On this level, though, knowledge of the application and performance of information technology is still scarce. To drive the performance of technology in such networks, we investigate the impact of fit and technology appropriation on a network level. Due to the technology’s expected impact and characteristics, we select blockchain technology to explore potential factors, impacting fit, appropriation and , in turn , performance . We draw upon a set of interviews with experts that have implemented blockchain solutions in large business network settings. Based on our analysis, we propose a comprehensive model elevating the Fit-Appropriation Model to a network level. We contribute to the general understanding of technology utilization and performance by extending existing theory to a network-level perspective. Using insights on blockchain implementations as our empirical base, we also provide guidance to business leaders, intending to connect their partners through blockchain technology.
High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution

Operating power systems with large amounts of renewables requires predicting future photovoltaic (PV) production with fine temporal and spatial resolution. State-of-the-art techniques combine numerical weather predictions with statistical post-processing, but their resolution is too coarse for applications such as local congestion management. In this paper we introduce computing methods for multi-site PV forecasting, which exploit the intuition that PV systems provide a dense network of simple weather stations. These methods rely entirely on production data and address the real-life challenges that come with them, such as noise and gaps. Our approach builds on graph signal processing for signal reconstruction and for forecasting with a linear, spatio-temporal autoregressive (ST-AR) model. It also introduces a data-driven clear-sky production estimation for normalization. The proposed framework was evaluated over one year on both 303 real PV systems under commercial monitoring across Switzerland, and 1000 simulated ones based on high-resolution weather data. The results demonstrate the performance and robustness of the approach: with gaps of four hours on average in the input data, the average daytime NRMSE over a six-hour forecasting horizon (in 15 min steps) and over all systems is 13.8% and 9% for the real and synthetic data sets, respectively.
Research on scalable video watermarking algorithm based on H.264 compressed domain

Digital watermarking based on the scalable part of H.264 in MPEG-4-Scalable Video Code (SVC) is a promising technology in the field of video watermarking. However, the existing watermarking algorithms for H.264/SVC framework are difficult to satisfy the requirement of copyright protection. In this work, we extend the existed framework for invisible watermarking adopting the Discrete Cosine Transform (DCT) with a scalable watermarking algorithm, in order to adapt the multi-resolution property of the H.264/SVC. The original watermark is re-constructed to different scales by the pyramid decomposition, and the re-constructed watermarks are embedded in the DCT sub-blocks by modulating their positive and negative coefficients. Additionally, the sub-blocks are adaptively selected from the macro-blocks by a designed key control mechanism. According to the test results, the embedded watermarks are invisible and robust. The proposed method has achieved the Peak Signal to Noise Ratio (PSNR) in a value no worse than 41 dB and the Normalized Correlation (NC) in a value greater than 0.85. The proposed algorithm has can resist the attacks with different quantization steps.
Global Experiences with HPC Operational Data Measurement, Collection and Analysis

As we move into the exascale era, supercomputers grow larger, denser, more heterogeneous, and ever more complex. Operating such machines reliably and efficiently requires deep insight into the operational parameters of the machine itself as well as its supporting infrastructure. To fulfill this need, early adopter sites have started the development and deployment of Operational Data Analytics (ODA) frameworks allowing the continuous monitoring, archiving, and analysis of near realtime performance data from the machine and infrastructure levels, providing immediately actionable information for multiple operational uses. To understand their ODA goals, requirements, and use cases, we have conducted a survey among eight early adopter sites from the US, Europe, and Japan that operate top 50 high-performance computing systems. We have assessed the technologies leveraged to build their ODA frameworks, identified use cases and other push and pull factors that drive the sites' ODA activities, and report on their operational lessons.
Using artificially generated pictures in customer-facing systems: an evaluation study with data-driven personas

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A Universe Inside the MRI Scanner: An In-Bore Virtual Reality Game for Children to Reduce Anxiety and Stress

The feeling of lying in the restricted bore of an MRI scanner, surrounded by loud noises and not allowed to move, is a psychological burden for patients. Children especially have problems to lie still during the examination and sometimes exhibit intense fear reactions. Therefore, children are frequently sedated before the examination. We introduce the concept and implementation of a virtual reality (VR) game as a medication-free alternative for children, aiming to reduce anxiety, stress, and boredom during MRI examinations. Playing or just watching an animated space story in VR provides distraction and relaxation to the patients. We pursued a child-centered, participatory design process and derived design requirements from the results of two workshops with children in a hospital school (N = 15) and a primary school (N = 14), as well as a focus group test with a prototype. We propose VR games as a solution to enhance the well-being of children during medical interventions and show a way to approach a user-centered and targeted game design.
Learning to Generate Customized Dynamic 3D Facial Expressions

Recent advances in deep learning have significantly pushed the state-of-the-art in photorealistic video animation given a single image. In this paper, we extrapolate those advances to the 3D domain, by studying 3D image-to-video translation with a particular focus on 4D facial expressions. Although 3D facial generative models have been widely explored during the past years, 4D animation remains relatively unexplored. To this end, in this study we employ a deep mesh encoder-decoder like architecture to synthesize realistic high resolution facial expressions by using a single neutral frame along with an expression identification. In addition, processing 3D meshes remains a non-trivial task compared to data that live on grid-like structures, such as images. Given the recent progress in mesh processing with graph convolutions, we make use of a recently introduced learnable operator which acts directly on the mesh structure by taking advantage of local vertex orderings. In order to generalize to 4D facial expressions across subjects, we trained our model using a high resolution dataset with 4D scans of six facial expressions from 180 subjects. Experimental results demonstrate that our approach preserves the subject’s identity information even for unseen subjects and generates high quality expressions. To the best of our knowledge, this is the first study tackling the problem of 4D facial expression synthesis.
Deep Semantic-Preserving Reconstruction Hashing for Unsupervised Cross-Modal Retrieval

Deep hashing is the mainstream algorithm for large-scale cross-modal retrieval due to its high retrieval speed and low storage capacity, but the problem of reconstruction of modal semantic information is still very challenging. In order to further solve the problem of unsupervised cross-modal retrieval semantic reconstruction, we propose a novel deep semantic-preserving reconstruction hashing (DSPRH). The algorithm combines spatial and channel semantic information, and mines modal semantic information based on adaptive self-encoding and joint semantic reconstruction loss. The main contributions are as follows: (1) We introduce a new spatial pooling network module based on tensor regular-polymorphic decomposition theory to generate rank-1 tensor to capture high-order context semantics, which can assist the backbone network to capture important contextual modal semantic information. (2) Based on optimization perspective, we use global covariance pooling to capture channel semantic information and accelerate network convergence. In feature reconstruction layer, we use two bottlenecks auto-encoding to achieve visual-text modal interaction. (3) In metric learning, we design a new loss function to optimize model parameters, which can preserve the correlation between image modalities and text modalities. The DSPRH algorithm is tested on MIRFlickr-25K and NUS-WIDE. The experimental results show that DSPRH has achieved better performance on retrieval tasks.
Extended RECTANGLE Algorithm Using 3D Bit Rotation to Propose a New Lightweight Block Cipher for IoT

The Internet of Things (IoT) is a broad range of applications enabled by the connection of devices such as sensors, actuators, and monitors accessible through the Internet. Massive IoT device connectivity and vast data transmission have made the information susceptible to various types of attacks. Therefore, encryption is required for secure communication in an IoT ecosystem. An IoT system is constrained by its complexities that require small computing power. Thus, lightweight block cipher is chosen as the solution to IoT security issues. RECTANGLE block cipher has very efficient encryption speed performance among the existing lightweight algorithms. Although RECTANGLE achieves such high efficiency, lack of focus on its security aspect needs to be addressed. The algorithm is short of confusion and diffusion characteristics that should be offered by a block cipher as one of the cryptographic security properties. Therefore, we extended RECTANGLE using a 3D cipher to improve its security features by enhancing the algorithm confusion and diffusion properties. Security analysis and performance tests were performed to verify the strength of the proposed 3D RECTANGLE. The results show that 3D RECTANGLE performs better than its original version in terms of the correlation between data input and output with an increase of 1.58% for non-linearity results, records approximately 50% bit error rate for sensitiveness against both modifications of plaintext and key, increase of passing rate in the randomness test by 22.22%, and achieves competitive performance results against existing algorithms with 0.9516 ms execution speed and 67.26 bit/ms throughput.
Schímatos: A SHACL-Based Web-Form Generator for Knowledge Graph Editing

Knowledge graph creation and maintenance is difficult for naïve users. One barrier is the paucity of user friendly publishing tools that separate schema modeling from instance data creation. The Shapes Constraint Language (SHACL) [12], a W3C standard for validating RDF based knowledge graphs, can help. SHACL enables domain relevant structure, expressed as a set of shapes, to constrain knowledge graphs. This paper introduces Schímatos, a form based Web application with which users can create and edit RDF data constrained and validated by shapes. Forms themselves are generated from, and stored as, shapes. In addition, Schímatos, can be used to edit shapes, and hence forms. Thus, Schímatos enables end-users to create and edit complex graphs abstracted in an easy-to-use custom graphical user interface with validation procedures to mitigate the risk of errors. This paper presents the architecture of Schímatos, defines the algorithm that builds Web forms from shape graphs, and details the workflows for SHACL creation and data-entry. Schímatos is illustrated by application to Wikidata.
Waste Wizard: Exploring Waste Sorting using AI in Public Spaces

Waste sorting is getting gradually important as we produce growing amounts of waste every year. However, waste sorting has proven to be challenging due to waste complexity and lack of motivation and knowledge among citizens. In particular, waste sorting in public spaces has been found to be difficult. We present a study on waste sorting in public spaces with an automatic waste sorting bin called Waste Wizard. Waste Wizard uses machine learning to classify and sort waste. We deployed it in three public contexts: a zoo, a retail store, and a music festival. Our findings show how practices, knowledge and attitudes influence the complicated process of public waste sorting. Users also displayed playfulness in their interaction and furthermore generated ideas and wishes for public waste sorting. We discuss our findings regarding current work within sustainable HCI and implications for public waste sorting.
A Decision Procedure for Path Feasibility of String Manipulating Programs with Integer Data Type

In this paper, we propose a decision procedure for a class of string-manipulating programs which includes not only a wide range of string operations such as concatenation, replaceAll, reverse, and finite transducers, but also those involving the integer data-type such as length, indexof, and substring. To the best of our knowledge, this represents one of the most expressive string constraint languages that is currently known to be decidable. Our decision procedure is based on a variant of cost register automata. We implement the decision procedure, giving rise to a new solver \(\text {OSTRICH+}\). We evaluate the performance of \(\text {OSTRICH+}\) on a wide range of existing and new benchmarks. The experimental results show that \(\text {OSTRICH+}\) is the first string decision procedure capable of tackling finite transducers and integer constraints, whilst its overall performance is comparable with the state-of-the-art string constraint solvers.
SG-VAE: Scene Grammar Variational Autoencoder to Generate New Indoor Scenes

Deep generative models have been used in recent years to learn coherent latent representations in order to synthesize high-quality images. In this work, we propose a neural network to learn a generative model for sampling consistent indoor scene layouts. Our method learns the co-occurrences, and appearance parameters such as shape and pose, for different objects categories through a grammar-based auto-encoder, resulting in a compact and accurate representation for scene layouts. In contrast to existing grammar-based methods with a user-specified grammar, we construct the grammar automatically by extracting a set of production rules on reasoning about object co-occurrences in training data. The extracted grammar is able to represent a scene by an augmented parse tree. The proposed auto-encoder encodes these parse trees to a latent code, and decodes the latent code to a parse tree, thereby ensuring the generated scene is always valid. We experimentally demonstrate that the proposed auto-encoder learns not only to generate valid scenes (i.e. the arrangements and appearances of objects), but it also learns coherent latent representations where nearby latent samples decode to similar scene outputs. The obtained generative model is applicable to several computer vision tasks such as 3D pose and layout estimation from RGB-D data.