Reviewerly/exHarmony huggingface dataset:
Rotationally-Temporally Consistent Novel View Synthesis of Human Performance Video

Novel view video synthesis aims to synthesize novel viewpoints videos given input captures of a human performance taken from multiple reference viewpoints and over consecutive time steps. Despite great advances in model-free novel view synthesis, existing methods present three limitations when applied to complex and time-varying human performance. First, these methods (and related datasets) mainly consider simple and symmetric objects. Second, they do not enforce explicit consistency across generated views. Third, they focus on static and non-moving objects. The fine-grained details of a human subject can therefore suffer from inconsistencies when synthesized across different viewpoints or time steps. To tackle these challenges, we introduce a human-specific framework that employs a learned 3D-aware representation. Specifically, we first introduce a novel siamese network that employs a gating layer for better reconstruction of the latent volumetric representation and, consequently, final visual results. Moreover, features from consecutive time steps are shared inside the network to improve temporal consistency. Second, we introduce a novel loss to explicitly enforce consistency across generated views both in space and in time. Third, we present the Multi-View Human Action (MVHA) dataset, consisting of near 1200 synthetic human performance captured from 54 viewpoints. Experiments on the MVHA, Pose-Varying Human Model and ShapeNet datasets show that our method outperforms the state-of-the-art baselines both in view generation quality and spatio-temporal consistency.
Privacy-Preserving in Defending against Membership Inference Attacks

The membership inference attack refers to the attacker's purpose to infer whether the data sample is in the target classifier training dataset. The ability of an adversary to ascertain the presence of an individual constitutes an obvious privacy threat if relate to a group of users that share a sensitive characteristic. Many defense methods have been proposed for membership inference attack, but they have not achieved the expected privacy effect. In this paper, we quantify the impact of these choices on privacy in experiments using logistic regression and neural network models. Using both formal and empirical analyses, we illustrate that differential privacy and L2 regularization can effectively prevent member inference attacks.
Multi-stage hybrid embedding fusion network for visual question answering

Multimodal fusion is a crucial component of Visual Question Answering (VQA), which involves joint understanding and semantic integration between visual and textual information. Existing VQA learning frameworks focus mainly on Latent Embedding Fusion (LEF) method, by projecting visual and textual features into a common latent space, and fusing them with element-wise multiplication. In this paper, we intend to achieve multiple and fine-grained multimodal interactions for enhancing fusion performance. To this end, we propose a Multi-stage Hybrid Embedding Fusion (MHEF) network to fulfill our improvements in two folds: First, we introduce a Dual Embedding Fusion (DEF) approach that transforms one modal input into the reciprocal embedding space before integration, and the DEF is further incorporated with the LEF to form a novel Hybrid Embedding Fusion (HEF). Second, we design a Multi-stage Fusion Structure (MFS) for the HEF to form the MHEF network, so as to obtain diverse and better fusion features for answer prediction. By jointly training the multi-stage framework, we can not only improve the performance in each single stage, but also obtain additional accuracy improvements by integrating all prediction results from each stage. Extensive experiments verify both our proposed HEF and MFS are beneficial to multi-modal fusion. The full MHEF model outperforms the baseline LEF model with 2% accuracy improvements, and achieves promising performance on the VQA-v1 and VQA-v2 datasets.
Bridging the Divide: Exploring the use of digital and physical technology to aid mobility impaired people living in an informal settlement

Living in informality is challenging. It is even harder when you have a mobility impairment. Traditional assistive products such as wheelchairs are essential to enable people to travel. Wheelchairs are considered a Human Right. However, they are difficult to access. On the other hand, mobile phones are becoming ubiquitous and are increasingly seen as an assistive technology. Should therefore a mobile phone be considered a Human Right? To help understand the role of the mobile phone in contrast of a more traditional assistive technology – the wheelchair, we conducted contextual interviews with eight mobility impaired people who live in Kibera, a large informal settlement in Nairobi. Our findings show mobile phones act as an accessibility bridge when physical accessibility becomes too challenging. We explore our findings from two perspective – human infrastructure and interdependence, contributing an understanding of the role supported interactions play in enabling both the wheelchair and the mobile phone to be used. This further demonstrates the critical nature of designing for context and understanding the social fabric that characterizes informal settlements. It is this social fabric which enables the technology to be useable.
Improving utility and security of the shuffler-based differential privacy

When collecting information, local differential privacy (LDP) alleviates privacy concerns of users because their private information is randomized before being sent it to the central aggregator. LDP imposes large amount of noise as each user executes the randomization independently. To address this issue, recent work introduced an intermediate server with the assumption that this intermediate server does not collude with the aggregator. Under this assumption, less noise can be added to achieve the same privacy guarantee as LDP, thus improving utility for the data collection task. This paper investigates this multiple-party setting of LDP. We analyze the system model and identify potential adversaries. We then make two improvements: a new algorithm that achieves a better privacy-utility tradeoff; and a novel protocol that provides better protection against various attacks. Finally, we perform experiments to compare different methods and demonstrate the benefits of using our proposed method.
DQAMLearn: Device and QoE-Aware Adaptive Multimedia Mobile Learning Framework

With the proliferation of mobile devices and online video services, mobile learning has grown both in popularity and complexity. New challenges include the multitude of mobile devices with different characteristics and limitations, as well as the exponential growth in educational multimedia content. Streaming multimedia content to mobile devices is a resource intensive task that requires significant resources such as network bandwidth, with demand expected to increase beyond future networks capacity as users adopt new technologies such as UHD, AR, VR, 3D and 360-degree video. While a number of adaptive m-learning systems have been previously proposed, none of these have thoroughly addressed the adaptation of educational multimedia content. This article proposes the novel DQAMLearn framework that aims to support mobile learner's seamless access to educational multimedia content from a variety of mobile devices with different characteristics. Moreover, as mobile users are increasingly becoming quality-aware, the framework integrates novel mechanisms for decreasing the video quality in a controlled way, with the aim to support a good learner quality of experience (QoE) even in resource constrained situations. A comprehensive subjective study was conducted to evaluate the proposed framework. The results showed that the framework enables both high learning achievement from educational multimedia clips, with 12% and 83% correct response rates for pre and post-test questionnaires, respectively, and high learner QoE with a mean video quality rating of 79.19 on a 0-100 acceptability scale.
Compact and efficient structure of 8-bit S-box for lightweight cryptography

In this paper, we design an inversion-based S-box with better hardware implementation than the AES S-box with similar cryptographic properties. The proposed S-box computation involves basically two steps, the field inversion, and the affine transformation. The constructed S-box uses a cost-efficient affine transformation with low area resources and low critical path delay (CPD). The sub-blocks of the S-box, such as field inversion in F24, are implemented based on the efficient circuits. A large number of gates, in the structure, have been implemented by 2-input NAND and 2-input NOR gates to reduce delay and area. The cryptographic strength of the proposed S-boxes is analyzed by studying the properties of S-box such as Nonlinearity, Differential uniformity (DU), Strict avalanche criterion (SAC), Algebraic degree (AD), Differential approximation probability (DAP), and Linear approximation probability (LAP) in SAGE. Security analysis of the proposed S-box shown that the structure has the security level equal to the AES S-box. Therefore, this structure can be used in the lightweight block ciphers. Also, the implementation results, in 180 nm and 65 nm CMOS technologies, show the proposed S-box is comparable in terms of area, delay, and area×delay than most of the famous S-boxes.
RNS Number Comparator Based on a Modified Diagonal Function

Number comparison has long been recognized as one of the most fundamental non-modular arithmetic operations to be executed in a non-positional Residue Number System (RNS). In this paper, a new technique for designing comparators of RNS numbers represented in an arbitrary moduli set is presented. It is based on a newly introduced modified diagonal function, whose strictly monotonic properties make it possible to replace the cumbersome operations of finding the remainder of the division by a large and awkward number with significantly simpler computations involving only a power of 2 modulus. Comparators of numbers represented in sample RNSs composed of varying numbers of moduli and offering different dynamic ranges, designed using various methods, were synthesized for the 65 nm technology. The experimental results suggest that the new circuits enjoy a delay reduction ranging from over 11% to over 75% compared to the fastest circuits designed using existing methods. Moreover, it is achieved using less hardware, the reduction of which reaches over 41%, and is accompanied by significantly reduced power-consumption, which in several cases exceeds 100%. Therefore, it seems that the presented method leads to the design of the most efficient current hardware comparators of numbers represented using a general RNS moduli set.
Every Moment Matters: Detail-Aware Networks to Bring a Blurry Image Alive

Motion-blurred images are the result of light accumulation over the period of camera exposure time, during which the camera and objects in the scene are in relative motion to each other. The inverse process of extracting an image sequence from a single motion-blurred image is an ill-posed vision problem. One key challenge is that the motions across frames are subtle, which makes the generating networks difficult to capture them and thus the recovery sequences lack motion details. In order to alleviate this problem, we propose a detail-aware network with three consecutive stages to improve the reconstruction quality by addressing specific aspects in the recovery process. The detail-aware network firstly models the dynamics using a cycle flow loss, resolving the temporal ambiguity of the reconstruction in the first stage. Then, a GramNet is proposed in the second stage to refine subtle motion between continuous frames using Gram matrices as motion representation. Finally, we introduce a HeptaGAN in the third stage to bridge the continuous and discrete nature of exposure time and recovered frames, respectively, in order to maintain rich detail. Experiments show that the proposed detail-aware networks produce sharp image sequences with rich details and subtle motion, outperforming the state-of-the-art methods.
Cross-modal discriminant adversarial network

Cross-modal retrieval aims at retrieving relevant points across different modalities, such as retrieving images via texts. One key challenge of cross-modal retrieval is narrowing the heterogeneous gap across diverse modalities. To overcome this challenge, we propose a novel method termed as Cross-modal discriminant Adversarial Network (CAN). Taking bi-modal data as a showcase, CAN consists of two parallel modality-specific generators, two modality-specific discriminators, and a Cross-modal Discriminant Mechanism (CDM). To be specific, the generators project diverse modalities into a latent cross-modal discriminant space. Meanwhile, the discriminators compete against the generators to alleviate the heterogeneous discrepancy in this space, i.e., the generators try to generate unified features to confuse the discriminators, and the discriminators aim to classify the generated results. To further remove the redundancy and preserve the discrimination, we propose CDM to project the generated results into a single common space, accompanying with a novel eigenvalue-based loss. Thanks to the eigenvalue-based loss, CDM could push as much discriminative power as possible into all latent directions. To demonstrate the effectiveness of our CAN, comprehensive experiments are conducted on four multimedia datasets comparing with 15 state-of-the-art approaches.
X-SRL: A Parallel Cross-Lingual Semantic Role Labeling Dataset

Even though SRL is researched for many languages, major improvements have mostly been obtained for English, for which more resources are available.In fact, existing multilingual SRL datasets contain disparate annotation styles or come from different domains, hampering generalization in multilingual learning.In this work we propose a method to automatically construct an SRL corpus that is parallel in four languages: English, French, German, Spanish, with unified predicate and role annotations that are fully comparable across languages.We apply high-quality machine translation to the English CoNLL-09 dataset and use multilingual BERT to project its highquality annotations to the target languages.We include human-validated test sets that we use to measure the projection quality, and show that projection is denser and more precise than a strong baseline.Finally, we train different SOTA models on our novel corpus for monoand multilingual SRL, showing that the multilingual annotations improve performance especially for the weaker languages.
Financial Loss due to a Data Privacy Breach: An Empirical Analysis

Previous studies have explored the impact of the cybersecurity breach on the market value of the firm, but the impact of the data privacy breach on the firm performance is less explored. The terms cybersecurity breach and data privacy breach are sometimes used interchangeably, but they are two different things. In this study, we have used the event study methodology and standard market model (MM) to observe the effect of the data privacy breach on the firm performance. We have also observed the impact of firm-specific characteristics (such as firm size and firm type) and attack-specific characteristics (such as attack type and damage potency) on the cumulative abnormal return (CAR) due to privacy breaches. We have used data of 131 data privacy breaches related to US firms for the years 2012 (43 breaches), 2013 (37 breaches), and 2014 (51 breaches). We have selected the period 2012-2014 for study as we have observed the major data privacy breaches occurred during this period. The results of this study demonstrate that the data privacy breach announcement negatively affects the market value of the firm. Another interesting finding of this study is that the market values of larger firms are less adversely affected by the data privacy breach event than the smaller firms. We have also calculated the average financial loss, which comes out to be $ 229 million, $ 241 million, and $ 108 million in the year 2012, 2013, and 2014, respectively, due to these data privacy breaches.
Impact of Base Dataset Design on Few-Shot Image Classification

The quality and generality of deep image features is crucially determined by the data they have been trained on, but little is known about this often overlooked effect. In this paper, we systematically study the effect of variations in the training data by evaluating deep features trained on different image sets in a few-shot classification setting. The experimental protocol we define allows to explore key practical questions. What is the influence of the similarity between base and test classes? Given a fixed annotation budget, what is the optimal trade-off between the number of images per class and the number of classes? Given a fixed dataset, can features be improved by splitting or combining different classes? Should simple or diverse classes be annotated? In a wide range of experiments, we provide clear answers to these questions on the miniImageNet, ImageNet and CUB-200 benchmarks. We also show how the base dataset design can improve performance in few-shot classification more drastically than replacing a simple baseline by an advanced state of the art algorithm.
GNEA: A Graph Neural Network with ELM Aggregator for Brain Network Classification

Brain networks provide essential insights into the diagnosis of functional brain disorders, such as Alzheimer’s disease (AD). Many machine learning methods have been applied to learn from brain images or networks in Euclidean space. However, it is still challenging to learn complex network structures and the connectivity of brain regions in non-Euclidean space. To address this problem, in this paper, we exploit the study of brain network classification from the perspective of graph learning. We propose an aggregator based on extreme learning machine (ELM) that boosts the aggregation ability and efficiency of graph convolution without iterative tuning. Then, we design a graph neural network named GNEA (Graph Neural Network with ELM Aggregator) for the graph classification task. Extensive experiments are conducted using a real-world AD detection dataset to evaluate and compare the graph learning performances of GNEA and state-of-the-art graph learning methods. The results indicate that GNEA achieves excellent learning performance with the best graph representation ability in brain network classification applications.
User Experience in Collaborative Extended Reality: Overview Study

Recent trends in Extended Reality technologies, including Virtual Reality and Mixed Reality, indicate that the future infrastructure will be distributed and collaborative, where end-users as well as experts meet, communicate, learn, interact with each other, and coordinate their activities using a globally shared network and meditated environments. The integration of new display devices has largely changed how users interact with the system and how those activities, in turn, change their perception and experience. Although a considerable amount of research has already been done in the fields of computer-supported collaborative work, human-computer interaction, extended reality, cognitive psychology, perception, and social sciences, there is still no in-depth review to determine the current state of research on multiple-user-experience-centred design at the intersection of these domains. This paper aims to present an overview of research work on coexperience and analyses important aspects of human factors to be considered to enhance collaboration and user interaction in collaborative extended reality platforms, including: (i) presence-related factors, (ii) group dynamics and collaboration patterns, (iii) avatars and embodied agents, (iv) nonverbal communication, (v) group size, and (vi) awareness of physical and virtual world. Finally, this paper identifies research gaps and suggests key directions for future research considerations in this multidisciplinary research domain.
No Jitter Please: Effects of Rotational and Positional Jitter on 3D Mid-Air Interaction

Virtual Reality (VR) 3D tracking systems are susceptible to minor fluctuations in signal (jitter). In this study, we explored how different levels of jitter affect user performance for 3D pointing. We designed a Fitts' Law experiment investigating target positional jitter and cursor rotational jitter at three different depth distances. Performance was negatively affected when up to \(\pm 0.5^{\circ }\) rotational jitter was applied to the controller and up to ±0.375 cm positional jitter was applied to the target. At 2.25 m distance, user performance did not improve with decreasing positional jitter or rotational jitter compared to the no jitter condition. Our results can inform the design of 3D user interfaces, controllers, and interaction techniques in VR. Specifically, we suggest a focus on counteracting controller rotational jitter as this would globally increase performance for ray-based selection tasks.
Ontological Transparency, (In)visibility, and Hidden Curricula: Critical Pedagogy Amidst Contentious Edtech

Abstract The steady migration of higher education online has accelerated in the wake of Covid-19. The implications of this migration on critical praxis—the theory-in-practice of pedagogy—deserve further scrutiny. This paper explores how teacher and student-led educational technology research and development can help rethink online critical praxis. The paper is based on a recent research project at the University of Edinburgh that speculatively explored the potential for automation in teaching, which generated insights into current and future pedagogical practice among both teachers and students. From this project emerged a series of pedagogical positions that were centred around visions of the future of teaching in response to automation: the pedagogical potential of visibility and invisibility online, transparency, and interrogating the hidden curricula of both higher education and educational technology itself. Through the surfacing of these pedagogical positions, this paper explores how critical pedagogy can be built into the broader teacher function and begins to identify the institutional structures that could potentially impede or accelerate that process.
Interaction-transformation symbolic regression with extreme learning machine

Symbolic Regression searches for a mathematical expression that fits the input data set by minimizing the approximation error. The search space explored by this technique is composed of any mathematical function representable as an expression tree. This provides more flexibility for fitting the data but it also makes the task more challenging. The search space induced by this representation becomes filled with redundancy and ruggedness, sometimes requiring a higher computational budget in order to achieve good results. Recently, a new representation for Symbolic Regression was proposed, called Interaction-Transformation, which can represent function forms as a composition of interactions between predictors and the application of a single transformation function. In this work, we show how this representation can be modeled as a multi-layer neural network with the weights adjusted following the Extreme Learning Machine procedure. The results show that this approach is capable of finding equally good or better results than the current state-of-the-art with a smaller computational cost.