Reviewerly/exHarmony huggingface dataset:
A self-adaptive local metric learning method for classification

Measuring distance among data point pairs is a necessary step among numerous counts of algorithms in machine learning, pattern recognition and data mining. In the local perspective, the emphasis of all existing supervised metric learning algorithms is to shrink similar data points and to separate dissimilar ones in the local neighborhoods. This provides learning more appropriate distance metric in dealing with the within-class multi modal data. In this article, a new supervised local metric learning method named Self-Adaptive Local Metric Learning Method (SA-LM2) has been proposed. The contribution of this method is in five aspects. First, in this method, learning an appropriate metric and defining the radius of local neighborhood are integrated in a joint formulation. Second, unlike the traditional approaches, SA-LM2 learns the parameter of local neighborhood automatically thorough its formulation. As a result, it is a parameter free method, where it does not require any parameters that would need to be tuned. Third, SA-LM2 is formulated as a SemiDefinite Program (SDP) with a global convergence guarantee. Fourth, this method does not need the similar set S, the focus here is on local areas' data points and their separation from dissimilar ones. Finally, results of SA-LM2 are less influenced by noisy input data points than the other compared global and local algorithms. Results obtained from different experiments indicate the outperformance of this algorithm over its counterparts.
Towards Utilizing Touch-sensitive Physical Keyboards for Text Entry in Virtual Reality

Text entry is a challenge for Virtual Reality (VR) applications. In the context of immersive VR Head-Mounted Displays, text entry has been investigated for standard physical keyboards as well as for various hand representations. Specifically, prior work has indicated that minimalistic fingertip visualizations are an efficient hand representation. However, they typically require external tracking systems. Touch-sensitive physical keyboards allow for on-surface interaction, with sensing integrated into the keyboard itself. However, they have not been thoroughly investigated within VR. Our work brings together the domains of VR text entry and touch-sensitive physical keyboards. Specifically, we propose to utilize touch-sensitive physical keyboards for text entry as an alternative sensing mechanism for tracking user's fingertips and study its performance in a preliminary user study.
The Shepard–Risset Glissando: Identifying the Origins of Metaphorical Auditory Vection and Motion Sickness

We recently showed that auditory illusions of self-motion can be induced in the absence of physically accurate spatial cues (Mursic et al., 2017). The current study was aimed at identifying which features of this auditory stimulus (the Shepard-Risset glissando) were responsible for this metaphorical auditory vection, as well as confirming anecdotal reports of motion sickness for this stimulus. Five different types of auditory stimuli were presented to 31 blindfolded, stationary participants through a loudspeaker array: (1) a descending Shepard-Risset glissando; (2) a descending discrete Shepard scale; (3) a descending sweep signal; (4) a phase-scrambled version of (1) (auditory control type 1); and (5) white noise (auditory control type 2). We found that the auditory vection induced by the Shepard-Risset glissando was stronger than both types of auditory control, and the discrete Shepard scale stimulus. However, vection strength was not found to differ between the Shepard-Risset glissando and the sweep signal. This suggests that the continuous, gliding structure of both these auditory stimuli was integral to the induction of vection. Consistent with anecdotal reports that the Shepard-Risset glissando is also capable of generating motion sickness (as measured by the Fast Motion Sickness Scale and the Simulator Sickness Questionnaire), the likelihood and severity of sickness for these stimuli was found to increase with the strength of the auditory vection.
Compact Triplet Loss for person re-identification in camera sensor networks

The triplet loss in deep learning has achieved promising results for person re-identification (re-ID) in camera sensor networks. However, it neglects the relationship among pedestrian images captured from different sensors, which results in a relatively large intra-class variation. In this paper, we propose a novel loss function named Compact Triplet Loss (CTL) for training Convolutional Neural Networks (CNNs), which not only decreases the intra-class variation but also increases the inter-class variation to improve the generalization ability of person re-ID model. Specifically, CTL simultaneously considers three aspects for pedestrian representations. It pushes the pedestrian images to be closer to their corresponding centers and meanwhile forces different centers are away from each other. In addition, CTL forces the distance between the positive sample pair is smaller than that of the negative sample pair. Finally, we integrate the proposed CTL and the cross-entropy loss to perform multi-task learning. We evaluate the proposed method on Market1501, DukeMTMCreID and CUHK03, and the experimental results reveal our method exceeds other state-of-the-art methods by a large margin.
In-Situ Labeling for Augmented Reality Language Learning

Augmented Reality is a promising interaction paradigm for learning applications. It has the potential to improve learning outcomes by merging educational content with spatial cues and semantically relevant objects within a learner's everyday environment. The impact of such an interface could be comparable to the method of loci, a well known memory enhancement technique used by memory champions and polyglots. However, using Augmented Reality in this manner is still impractical for a number of reasons. Scalable object recognition and consistent labeling of objects is a significant challenge, and interaction with arbitrary (unmodeled) physical objects in AR scenes has consequently not been well explored. To help address these challenges, we present a framework for in-situ object labeling and selection in Augmented Reality, with a particular focus on language learning applications. Our framework uses a generalized object recognition model to identify objects in the world in real time, integrates eye tracking to facilitate selection and interaction within the interface, and incorporates a personalized learning model that dynamically adapts to student's growth. We show our current progress in the development of this system, including preliminary tests and benchmarks. We explore challenges with using such a system in practice, and discuss our vision for the future of AR language learning applications.
A Lightweight Neural Network Based on AlexNet-SSD Model for Garbage Detection

As the theory of deep learning develops, object detection technology has been widely used in all fields. How to find objects accurately and quickly is one of the key technologies. A usage scenario to be solved is proposed here, that is how to facilitate object detection technology in waste sorting. Hence, in this paper, a lightweight deep learning model is proposed. The basic network architecture of SSD(Single Shot MultiBox Detector) is changed to AlexNet. In this way, the capacity on object detection of SSD is remained, and the model parameters are greatly reduced. The experimental results show that the modified model can recognize the categories of waste accurately.
Feel the Globe: Enhancing the Perception of Immersive Spherical Visualizations with Tangible Proxies

Recent developments in the commercialization of virtual reality open up many opportunities for enhancing human interaction with three-dimensional objects and visualizations. Spherical visualizations allow for convenient exploration of certain types of data. Our tangible sphere, exactly aligned with the sphere visualizations shown in VR, implements a very natural way of interaction and utilizes senses and skills trained in the real world. In a lab study, we investigate the effects of the perception of actually holding a virtual spherical visualization in hands. As use cases, we focus on surface visualizations that benefit from or require a rounded shape. We compared the usage of two differently sized acrylic glass spheres to a related interaction technique that utilizes VR controllers as proxies. On the one hand, our work is motivated by the ability to create in VR a tangible, lightweight, handheld spherical display that can hardly be realized in reality. On the other hand, gaining insights about the impact of a fully tangible embodiment of a virtual object on task performance, comprehension of patterns, and user behavior is important in its own right. After a description of the implementation we discuss the advantages and disadvantages of our approach, taking into account different handheld spherical displays utilizing outside and inside projection.
Generating insights through data preparation, visualization, and analysis: Framework for combining clustering and data visualization techniques for low-cardinality sequential data

In this paper, we introduce a novel approach for identifying and testing relationships and patterns on the types of sequential data that are broadly present in a number of different real-world scenarios and environments. The proposed two-phase framework combines data preparation, data visualization and clustering techniques in an innovative way. The first phase of the framework explores the large amount of sequential data in stages that can be undertaken iteratively. Those stages include data preparation, counting and value-based ordering, distribution visualization, and subsequence length determination, confirmation and re-visualization. The second phase of the framework explores sequence differences, based on motifs, between data cohorts that are created using descriptive attributes, and visualizes the changes over time and different attribute values. To illustrate the analytical power of the proposed framework, we present a comprehensive example that applies the framework on a large formally-maintained research data set collected and managed by the US Census Bureau. The framework, and the presented example, utilize visualization as an analytics tool and not just a presentation accessory.
A Systematic Analysis of Real-World Energy Blockchain Initiatives

The application of blockchain technology to the energy sector promises to derive new operating models focused on local generation and sustainable practices, which are driven by peer-to-peer collaboration and community engagement. However, real-world energy blockchains differ from typical blockchain networks insofar as they must interoperate with grid infrastructure, adhere to energy regulations, and embody engineering principles. Naturally, these additional dimensions make real-world energy blockchains highly dependent on the participation of grid operators, engineers, and energy providers. Although much theoretical and proof-of-concept research has been published on energy blockchains, this research aims to establish a lens on real-world projects and implementations that may inform the alignment of academic and industry research agendas. This research classifies 131 real-world energy blockchain initiatives to develop an understanding of how blockchains are being applied to the energy domain, what type of failure rates can be observed from recently reported initiatives, and what level of technical and theoretical details are reported for real-world deployments. The results presented from the systematic analysis highlight that real-world energy blockchains are (a) growing exponentially year-on-year, (b) producing relatively low failure/drop-off rates (~7% since 2015), and (c) demonstrating information sharing protocols that produce content with insufficient technical and theoretical depth.
Heterogeneous-Length Text Topic Modeling for Reader-Aware Multi-Document Summarization

More and more user comments like Tweets are available, which often contain user concerns. In order to meet the demands of users, a good summary generating from multiple documents should consider reader interests as reflected in reader comments. In this article, we focus on how to generate a summary from multi-document documents by considering reader comments, named as reader-aware multi-document summarization (RA-MDS). We present an innovative topic-based method for RA-MDA, which exploits latent topics to obtain the most salient and lessen redundancy summary from multiple documents. Since finding latent topics for RA-MDS is a crucial step, we also present a Heterogeneous-length Text Topic Modeling (HTTM) to extract topics from the corpus that includes both news reports and user comments, denoted as heterogeneous-length texts. In this case, the latent topics extract by HTTM cover not only important aspects of the event, but also aspects that attract reader interests. Comparisons on summary benchmark datasets also confirm that the proposed RA-MDS method is effective in improving the quality of extracted summaries. In addition, experimental results demonstrate that the proposed topic modeling method outperforms existing topic modeling algorithms.
Non-malleable Secret Sharing in the Computational Setting: Adaptive Tampering, Noisy-Leakage Resilience, and Improved Rate

We revisit the concept of non-malleable secret sharing (Goyal and Kumar, STOC 2018) in the computational setting. In particular, under the assumption of one-to-one one-way functions, we exhibit a computationally private, threshold secret sharing scheme satisfying all of the following properties. Previous constructions achieved information-theoretic security, sometimes even for arbitrary access structures, at the price of at least one of the following limitations: (i) Non-malleability only holds against one-time tampering attacks; (ii) Non-malleability holds against a bounded number of tampering attacks, but both the choice of the tampering functions and of the sets used for reconstruction is non-adaptive; (iii) Information rate asymptotically approaching zero; (iv) No security guarantee in the presence of leakage.
Knowledge Extraction from XCSR Based on Dimensionality Reduction and Deep Generative Models

This paper proposes a novel learning classifier system (LCS) framework named ELSDeCS (Encoding, Learning, Sampling, and Decoding Classifier System) which can employ any dimensionality reduction method as pre-processing of learning and has its own components for extracting interpretable rule representations. We also propose two LCSs as examples of ELSDeCS. The first is DCAXCSR2, which is a revised version of the conventional system, and the second is VAEXCSR, which employs a deep generative model for dimensionality reduction. The experimental results on a classification task of handwritten digits show that only VAEXCSR can extract useful rule representations thanks to its robustness of decoding newly generated samples.
Reconciling Being in-Control vs. Being Helped for the Execution of Complex Movements in VR

Performing motor tasks in virtual environments is best achieved with motion capture and animation of a 3D character that participants control in real time and perceive as being their avatar in the virtual environment. A strong Sense of Embodiment (SoE) for the virtual body not only relies on the feeling that the virtual body is their own (body ownership), but also that the virtual body moves in the world according to their will and replicates precisely their body movement (sense of agency). Within that frame of mind our specific aim is to demonstrate that the avatar can even be programmed to be better at executing a given task or to perform a movement that is normally difficult or impossible to execute precisely by the user. More specifically, our experimental task consists in asking subjects to follow with the hand a target that is animated using non-biological motion; the unnatural nature of the movement leads to systematic errors by the subjects. The challenge here is to introduce a subtle distortion between the position of the real hand and the position of the virtual hand, so that the virtual hand succeeds in performing the task while still letting subjects believe they are fully in control. Results of two experiments (N=16) show that our implementation of a distortion function, that we name the attraction well, successfully led participants to report being in control of the movement (agency) and being embodied in the avatar (body ownership) even when the distortion was above a threshold that they can detect. Furthermore, a progressive introduction of the distortion (starting without help, and introducing distortion on the go) could even further increase its acceptance.
Sick Moves! Motion Parameters as Indicators of Simulator Sickness

We explore motion parameters, more specifically gait parameters, as an objective indicator to assess simulator sickness in Virtual Reality (VR). We discuss the potential relationships between simulator sickness, immersion, and presence. We used two different camera pose (position and orientation) estimation methods for the evaluation of motion tasks in a large-scale VR environment: a simple model and an optimized model that allows for a more accurate and natural mapping of human senses. Participants performed multiple motion tasks (walking, balancing, running) in three conditions: a physical reality baseline condition, a VR condition with the simple model, and a VR condition with the optimized model. We compared these conditions with regard to the resulting sickness and gait, as well as the perceived presence in the VR conditions. The subjective measures confirmed that the optimized pose estimation model reduces simulator sickness and increases the perceived presence. The results further show that both models affect the gait parameters and simulator sickness, which is why we further investigated a classification approach that deals with non-linear correlation dependencies between gait parameters and simulator sickness. We argue that our approach could be used to assess and predict simulator sickness based on human gait parameters and we provide implications for future research.
Exploring Constructions of Compact NIZKs from Various Assumptions

A non-interactive zero-knowledge (NIZK) protocol allows a prover to non-interactively convince a verifier of the truth of the statement without leaking any other information. In this study, we explore shorter NIZK proofs for all $$\mathbf{NP }$$ languages. Our primary interest is NIZK proofs from falsifiable pairing/pairing-free group-based assumptions. Thus far, NIZKs in the common reference string model (CRS-NIZKs) for $$\mathbf{NP }$$ based on falsifiable pairing-based assumptions all require a proof size at least as large as $$O(|C| \kappa )$$ , where C is a circuit computing the $$\mathbf{NP }$$ relation and $$\kappa $$ is the security parameter. This holds true even for the weaker designated-verifier NIZKs (DV-NIZKs). Notably, constructing a (CRS, DV)-NIZK with proof size achieving an additive-overhead $$O(|C|) + \mathsf {poly}(\kappa )$$ , rather than a multiplicative-overhead $$|C| \cdot \mathsf {poly}(\kappa )$$ , based on any falsifiable pairing-based assumptions is an open problem. In this work, we present various techniques for constructing NIZKs with compact proofs, i.e., proofs smaller than $$O(|C|) + \mathsf {poly}(\kappa )$$ , and make progress regarding the above situation. Our result is summarized below. Another related but independent issue is that all (CRS, DV)-NIZKs require the running time of the prover to be at least $$|C|\cdot \mathsf {poly}(\kappa )$$ . Considering that there exists NIZKs with efficient verifiers whose running time is strictly smaller than |C|, it is an interesting problem whether we can construct prover-efficient NIZKs. To this end, we construct prover-efficient CRS-NIZKs for $$\mathbf{NP }$$ with compact proof through a generic construction using laconic functional evaluation schemes (Quach, Wee, and Wichs (FOCS'18)). This is the first NIZK in any model where the running time of the prover is strictly smaller than the time it takes to compute the circuit C computing the $$\mathbf{NP }$$ relation. Finally, perhaps of an independent interest, we formalize the notion of homomorphic equivocal commitments, which we use as building blocks to obtain the first result, and show how to construct them from pairing-based assumptions.
Incidental image memorability

Recent studies using intentional memory tasks showed that images differ consistently in memorability. Here, we used a surprise recognition memory test. We asked how incidental encoding would affect the consistency and ranking of the memorability scores. If memorability is truly an intrinsic property of the image, one should not expect large differences between incidental and intentional conditions. On the other hand, participants might process images differently when not anticipating a memory test, yielding different results. The study list of our incidental memory task was identical to our previous, intentional study. Participants were to watch the images of this list carefully as they appeared on the screen one by one (free viewing); supposedly for a study about eye movements. Afterwards, a surprise recognition memory test was administered. Despite the free viewing instructions during the study phase, the resulting incidental image memorability scores still showed high levels of consistency across participants. In addition, the overlap between the incidental and intentional memorability ranking was large. These results provide further support for the idea of memorability as an intrinsic image property and add to its ecological validity, as people rarely memorise images intentionally.
Publishing Sensitive Trajectory Data Under Enhanced l-Diversity Model

With the proliferation of location-aware devices, trajectory data have been widely collected, published, and analyzed in real-life applications. However, published trajectory data often contain sensitive attributes, so an attacker who can identify an individual from such data through record linkage, attribute linkage, or similarity attacks can gain sensitive information about this individual. To resist from these attacks, we propose a scheme called Data Privacy Preservation with Perturbation (DPPP). To protect the privacy of sensitive information, we first determine those critical location sequences that can identify specific individuals. Then we perturb these sequences by adding or deleting some moving points while ensuring the published data satisfy (l, α, β)-privacy, an enhanced privacy model from ldiversity. Our experiments on both synthetic and real-life datasets suggest that DPPP achieves better privacy while still ensuring high utility, compared with existing privacy preservation schemes on trajectory.
Towards a Framework on Accessible and Social VR in Education

In this extended abstract, we argue that for virtual reality to be a successful tool in social learning spaces (e.g. classrooms or museums) we must also look outside the virtual reality literature to provide greater focus on accessible and social collaborative content. We explore work within Computer Supported Collaborative Learning (CSCL) and social VR domains to move towards developing a design framework for socio-educational VR. We also briefly describe our work-in-progress application framework, Circles, including these features in WebVR.