Reviewerly/exHarmony huggingface dataset:
Surveying Consumer Understanding &amp; Sentiment Of VR

Since the resurgence of consumer-grade virtual reality (VR), VR has successfully established itself on the consumer market. As with any emerging technology, differences can exist between how industry / academia view the technology and how consumers perceive it. We present results from a survey (N=210) conducted into consumer perception and attitudes towards VR. We report sentiment towards VR is positive. We show the associations linked with VR by our respondents match the defining characteristics of VR identified by experts in the literature (a fully virtual view, immersion, and head-worn technology). We identify 3 additional concepts associated with VR by our respondents: video games, futurism, and price. However, our results also show consumer expectations for VR are fixated around "VR for gaming" and suggest VR has to an extent been pigeonholed as primarily being a gaming device.
GAIA: A Transfer Learning System of Object Detection that Fits Your Needs

Transfer learning with pre-training on large-scale datasets has played an increasingly significant role in computer vision and natural language processing recently. However, as there exist numerous application scenarios that have distinctive demands such as certain latency constraints and specialized data distributions, it is prohibitively expensive to take advantage of large-scale pre-training for per-task requirements. In this paper, we focus on the area of object detection and present a transfer learning system named GAIA, which could automatically and efficiently give birth to customized solutions according to heterogeneous downstream needs. GAIA is capable of providing powerful pre-trained weights, selecting models that conform to downstream demands such as latency constraints and specified data domains, and collecting relevant data for practitioners who have very few datapoints for their tasks. With GAIA, we achieve promising results on COCO, Objects365, Open Images, Caltech, CityPersons, and UODB which is a collection of datasets including KITTI, VOC, WiderFace, DOTA, Clipart, Comic, and more. Taking COCO as an ex-ample, GAIA is able to efficiently produce models covering a wide range of latency from 16ms to 53ms, and yields AP from 38.2 to 46.5 without whistles and bells. To benefit every practitioner in the community of object detection, GAIA is released at https://github.com/GAIA-vision.
DNN Watermarking: Four Challenges and a Funeral

The demand for methods to protect the Intellectual Property Rights (IPR) associated to Deep Neural Networks (DNNs) is rising. Watermarking has been recently proposed as a way to protect the IPR of DNNs and track their usages. Although a number of techniques for media watermarking have been proposed and developed over the past decades, their direct translation to DNN watermarking faces the problem of the embedding being carried out on functionals instead of signals. This originates differences not only in the way performance, robustness and unobtrusiveness are measured, but also on the embedding domain, since there is the possibility of hiding information in the model behavior. In this paper, we discuss these dissimilarities that lead to a DNN-specific taxonomy of watermarking techniques. Then, we present four challenges specific to DNN watermarking that, for their practical importance and theoretical interest, should occupy the agenda of researchers in the next years. Finally, we discuss some bad practices that negatively affected research in media watermarking and that should not be repeated in the case of DNNs.
Xi-Vector Embedding for Speaker Recognition

We present a Bayesian formulation for deep speaker embedding, wherein the xi-vector is the Bayesian counterpart of the x-vector, taking into account the uncertainty estimate. On the technology front, we offer a simple and straightforward extension to the now widely used x-vector. It consists of an auxiliary neural net predicting the frame-wise uncertainty of the input sequence. We show that the proposed extension leads to substantial improvement across all operating points, with a significant reduction in error rates and detection cost. On the theoretical front, our proposal integrates the Bayesian formulation of linear Gaussian model to speaker-embedding neural networks via the pooling layer. In one sense, our proposal integrates the Bayesian formulation of the i-vector to that of the x-vector. Hence, we refer to the embedding as the xi-vector, which is pronounced as /zai/ vector. Experimental results on the SITW evaluation set show a consistent improvement of over 17.5% in equal-error-rate and 10.9% in minimum detection cost.
Shorter Signatures Based on Tailor-Made Minimalist Symmetric-Key Crypto

Signature schemes based on the MPC-in-the-head approach (MPCitH) have either been designed by taking a proof system and selecting a suitable symmetric-key primitive (Picnic, CCS16), or starting with an existing primitive such as AES and trying to find the most suitable proof system (BBQ, SAC19 or Banquet, PKC21). In this work we do both: we improve certain symmetric-key primitives to better fit existing signature schemes, and we also propose a new signature scheme that combines a new, minimalist one-way function with changes to a proof system to make their combination even more efficient. Our concrete results are as follows.
Blind Channel Codes Recognition via Deep Learning

This paper considers the blind recognition of the type and the encoding parameters of channel codes from the Gaussian noisy signals. Specifically, based on the recurrent neural network (RNN), the attention mechanism, and the residual neural network (ResNet), three universal recognizers are proposed to identify the type, rate, and length of the target channel codes, with a training set generated by a small portion of all the possible code parameters. The proposed architectures need near zero a priori knowledge about the target channel code, and only require the length of the received signal to be dozen times of the codeword length. Numerical experiments show that the proposed deep learning methods own strong generalization to identify channel codes from the testing samples not generated by the encoding parameters utilized for the training set.
3D Object Detection from a Single Fisheye Image Without a Single Fisheye Training Image

Existing monocular 3D object detection methods have been demonstrated on rectilinear perspective images and fail in images with alternative projections such as those acquired by fisheye cameras. Previous works on object detection in fisheye images have focused on 2D object detection, partly due to the lack of 3D datasets of such images. In this work, we show how to use existing monocular 3D object detection models, trained only on rectilinear images, to detect 3D objects in images from fisheye cameras, without using any fisheye training data. We outperform the only existing method for monocular 3D object detection in panoramas on a benchmark of synthetic data, despite the fact that the existing method trains on the target non-rectilinear projection whereas we train only on rectilinear images. We also experiment with an internal dataset of real fisheye images.
Hybrid Paper-Digital Interfaces: A Systematic Literature Review

Past research recognized that paper has many advantages over digital devices, such as affordability, tangibility, and flexibility. Paper, however, also lacks many of the functionalities available in digital technologies, such as access to online resources and the ability to display interactive content. Prior research therefore identified opportunities for fusing the two mediums into a combined interface. This work presents a literature review on this form of innovation - technologies that bridge the paper-digital gap. First, we synthesize an understanding of paper and its relationship with digital devices through the lens of past works. Then, we outline the state-of-the-art for paper-digital interfaces and highlight possible use cases and implementation approaches. Last, we discuss design considerations and future work for developing paper-digital interfaces. Our work may be beneficial for HCI researchers interested in the development of hybrid paper-digital interfaces, and more broadly in embedding digital functionalities in everyday objects.
Multi-scale Self-calibrated Network for Image Light Source Transfer

Image light source transfer (LLST), as the most challenging task in the domain of image relighting, has attracted extensive attention in recent years. In the latest research, LLST is decomposed three sub-tasks: scene reconversion, shadow estimation, and image re-rendering, which provides a new paradigm for image relighting. However, many problems for scene reconversion and shadow estimation tasks, including uncalibrated feature information and poor semantic information, are still unresolved, thereby resulting in insufficient feature representation. In this paper, we propose novel down-sampling feature self-calibrated block (DFSB) and up-sampling feature self-calibrated block (UFSB) as the basic blocks of feature encoder and decoder to calibrate feature representation iteratively because the LLST is similar to the recalibration of image light source. In addition, we fuse the multi-scale features of the decoder in scene reconversion task to further explore and exploit more semantic information, thereby providing more accurate primary scene structure for image re-rendering. Experimental results in the VIDIT dataset show that the proposed approach significantly improves the performance for LLST. Codes have been released at https://github.com/mdswyz/MCN-light-source-transfer.
Securing Proof-of-Work Ledgers via Checkpointing

Distributed ledgers based on Proof-of-Work (PoW) are typically vulnerable when mining participation is low. During these periods, an attacker with a mining majority can mount serious attacks, like double spending or transaction censorship. Our work explores mechanisms to secure a ledger against such adversaries and puts forth the first rigorous study of checkpointing as a protection from 51% attacks. The core idea is to employ an external set of parties to finalize blocks after their creation. This idea takes the form of checkpointing and timestamping, the former ensuring low latency in a federated setting and the latter being fully decentralized. Crucially, we identify and protect against a (previously undocumented) attack, "block lead", thus our scheme is the first to guarantee liveness.
A survey of video violence detection

As one of the important applications of intelligent video surveillance, violent behaviour detection (VioBD) plays a crucial role in public security and safety. As a particular type of behaviour recognition, VioBD aims to identify whether the behaviours that occurred in the scene is aggressive, such as fighting and assault. To comprehensively analyse the current state and predict the future trend of VioBD research, we survey the existing approaches of VioBD in this work. First, we briefly introduce the basic principle and the challenges of VioBD; Then, we category the existing approaches according to their framework, including the traditional framework, end-to-end deep learning framework, and hybrid deep learning framework. Finally, we introduce the public datasets for evaluating the performance of VioBD approaches and compare their performances on these datasets. Besides, we also summarise the open problems in VioBD and predict its future trends.
Analytics of Similar-Sounding Names from the Web with Phonetic Based Clustering

With advancements in modern technology in the current era, very large volumes of big data have been generated and collected in numerous real-life applications. These have formed a connected world comprising webs of agents, data, people, things and trust. Some of these webs have also emerged in health and smart living. As valuable information and knowledge is embedded in these rich sets of webs, web intelligence is in demand. In this paper, we focus a data science task of web content mining. In particular, we conduct big web data analytics to cluster similar-sounding names based on their phonemes. Our phonetic based clustering groups similar-sounding names together, which helps users deal with name disambiguation problems by identifying web records on the same person but with multiple similar-sounding names.
Bell Diagonal and Werner State Generation: Entanglement, Non-Locality, Steering and Discord on the IBM Quantum Computer

We propose the first correct special-purpose quantum circuits for preparation of Bell diagonal states (BDS), and implement them on the IBM Quantum computer, characterizing and testing complex aspects of their quantum correlations in the full parameter space. Among the circuits proposed, one involves only two quantum bits but requires adapted quantum tomography routines handling classical bits in parallel. The entire class of Bell diagonal states is generated, and several characteristic indicators, namely entanglement of formation and concurrence, CHSH non-locality, steering and discord, are experimentally evaluated over the full parameter space and compared with theory. As a by-product of this work, we also find a remarkable general inequality between "quantum discord" and "asymmetric relative entropy of discord": the former never exceeds the latter. We also prove that for all BDS the two coincide.
Imbalanced Data Problems in Deep Learning-Based Side-Channel Attacks: Analysis and Solution

In recent years, the threat of profiling attacks using deep learning has emerged. Successful attacks have been demonstrated against various types of cryptographic modules. However, the application of deep learning to side-channel attacks (SCAs) is often not adequately assessed because the labels that are widely used in SCAs, such as the Hamming weight (HW) and Hamming distance (HD), follow an imbalanced distribution. This study analyzes and solves the problems caused by dataset imbalance during training and inference. First, we state the reasons for the negative effect of data imbalance in classification for deep-learning-based SCAs and introduce the Kullback-Leibler (KL) divergence as a metric to measure this effect. Using the KL divergence, we demonstrate through analysis how the recently reported cross-entropy ratio loss function can solve the problem of imbalanced data. We further propose a method to solve dataset imbalance at the inference phase, which utilizes a likelihood function based on the key value instead of the HW/HD. The proposed method can be easily applied in deep-learning-based SCAs because it only needs an extra multiplication of the inverted binomial coefficients and inference results (i.e., the output probabilities) from the conventionally trained model. The proposed solution corresponds to data-augmentation techniques at the training phase, and furthermore, it better estimates the keys because the probability distributions of the training and test data are preserved. We demonstrate the validity of our analysis and the effectiveness of our solution through extensive experiments on two public databases.
Assistive Technology for Elderly Care: An Overview

Global demographic changes have resulted in a growing technological demand to meet the arisen social needs. In particular, the increasingly ageing population requires assistive technologies to stay at home for longer independently while receiving continuous healthcare. In this sense, a wide academic and industrial research is taking place, introducing these technologies in hospitals and rehabilitation centres. This paper aims at providing an overview of research projects for elderly care and assistance, focusing on cognitive and robot assistants due to their popularity in the area. More precisely, physical and/or cognitive rehabilitation are presented. This paper also discusses their limitations and the open challenges to be tackled in order to be successfully integrated in our society.
Watching Your Phone's Back

Gesture recognition on the back surface of mobile phone, not limited to the touch screen, is an enabling Human-Computer Interaction (HCI) mechanism which enriches the user interaction experiences. However, there are two main limitations in the existing Back-of-Device (BoD) gesture recognition systems. They can only handle coarse-grained gesture recognition such as tap detection and cannot avoid the air-borne propagation suffering from the interference in the air. In this paper, we propose StruGesture, a fine-grained gesture recognition system using the back of mobile phones with ultrasonic signals. The key technique is to use the structure-borne sounds (i.e., sound propagation via structure of the device) to recognize sliding gestures on the back of mobile phones. StruGesture can fully extract the structure-borne component from the hybrid Channel Impulse Response (CIR) based on Peak Selection Algorithm. We develop a deep adversarial learning architecture to learn the gesture-specific representation for robust and effective recognition. Extensive experiments are designed to evaluate the robustness over nine deployment scenarios. The results show that StruGesture outperforms the competitive state-of-the-art classifiers by achieving an average recognition accuracy of 99.5% over 10 gestures.
Expectations and challenges of using technology in education

The rapid development of the era gives effect to the field of education. It can be known from the presence of educational technology as a form of the development of science and technology (IPTEK). The presence of educational technology for some time certainly brings expectation to the provision of education to ease in every learning process. But behind the ease given, there are challenges that include three components of educational technology namely hardware (hardware), software (software) and brainware (users) which are now reflected from the implementation of the Computer-Based National Examination (UNBK) that cause problems in the field of education. The challenge must be addressed to realize expectation to achieve the goal of education as a whole and comprehensive.
WovenProbe: Probing Possibilities for Weaving Fully-Integrated On-Skin Systems Deployable in the Field

On-skin interfaces demonstrate great potential given their direct skin contact; however, conducting field studies of these devices outside of laboratories and in real settings remains a challenge. We conduct a research-through-design investigation using an extended woven practice for fabricating fully-integrated and untethered multi-sensor on-skin systems that are resilient, versatile, and capable of field deployment. We designed, implemented, and deployed a woven on-skin index-finger and thumb-based inertial measurement unit (IMU) sensing system for multi-hour use as a technology probe to understand the social, technical, and design facets towards moving integrated on-skin systems into a wearer’s daily life. Further, we integrate a woven NFC coil into the IMU on-skin system, which is wirelessly powered by a smartwatch substitute, signifying the potential of our woven approach for developing wirelessly powered on-skin systems for longer-term continuous wear. Our investigation and the lessons learned shed light on the opportunities for designing on-skin systems for everyday wear.
Repeat-Free Codes

In this paper we consider the problem of encoding data into repeat-free sequences in which sequences are imposed to contain any k-tuple at most once (for predefined k). First, the capacity of the repeat-free constraint are calculated. Then, an efficient algorithm, which uses two bits of redundancy, is presented to encode length- n sequences for k=2+2log(n). This algorithm is then improved to support any value of k of the form k=alog(n), for 1 <; a, while its redundancy is o(n). We also calculate the capacity of repeat-free sequences when combined with local constraints which are given by a constrained system, and the capacity of multi-dimensional repeat-free codes.
Unsupervised Object Detection with LiDAR Clues

Despite the importance of unsupervised object detection, to the best of our knowledge, there is no previous work addressing this problem. One main issue, widely known to the community, is that object boundaries derived only from 2D image appearance are ambiguous and unreliable. To address this, we exploit LiDAR clues to aid unsupervised object detection. By exploiting the 3D scene structure, the issue of localization can be considerably mitigated. We further identify another major issue, seldom noticed by the community, that the long-tailed and open-ended (sub-)category distribution should be accommodated. In this paper, we present the first practical method for unsupervised object detection with the aid of LiDAR clues. In our approach, candidate object segments based on 3D point clouds are firstly generated. Then, an iterative segment labeling process is conducted to assign segment labels and to train a segment labeling network, which is based on features from both 2D images and 3D point clouds. The labeling process is carefully designed so as to mitigate the issue of long-tailed and open-ended distribution. The final segment labels are set as pseudo annotations for object detection network training. Extensive experiments on the large-scale Waymo Open dataset suggest that the derived unsupervised object detection method achieves reasonable accuracy compared with that of strong supervision within the LiDAR visible range.
Dual-Modality Vehicle Anomaly Detection via Bilateral Trajectory Tracing

Traffic anomaly detection has played a crucial role in Intelligent Transportation System (ITS). The main challenges of this task lie in the highly diversified anomaly scenes and variational lighting conditions. Although much work has managed to identify the anomaly in homogenous weather and scene, few resolved to cope with complex ones. In this paper, we proposed a dual-modality modularized methodology for the robust detection of abnormal vehicles. We introduced an integrated anomaly detection framework comprising the following modules: background modeling, vehicle tracking with detection, mask construction, Region of Interest (ROI) backtracking, and dual-modality tracing. Concretely, we employed background modeling to filter the motion information and left the static information for later vehicle detection. For the vehicle detection and tracking module, we adopted YOLOv5 and multi-scale tracking to localize the anomalies. Besides, we utilized the frame difference and tracking results to identify the road and obtain the mask. In addition, we introduced multiple similarity estimation metrics to refine the anomaly period via backtracking. Finally, we proposed a dual-modality bilateral tracing module to refine the time further. The experiments conducted on the Track 4 testset of the NVIDIA 2021 AI City Challenge yielded a result of 0.9302 F1-Score and 3.4039 root mean square error (RMSE), indicating the effectiveness of our framework.