Reviewerly/exHarmony huggingface dataset:
Moving Objects Segmentation Based on DeepSphere in Video Surveillance

Segmentation of moving objects from video sequences plays an important role in many computer vision applications. In this paper, we present a background subtraction approach based on deep neural networks. More specifically, we propose to employ and validate an unsupervised anomaly discovery framework called "DeepSphere" to perform foreground objects detection and segmentation in video sequences. DeepSphere is based on both deep autoencoders and hypersphere learning methods to isolate anomaly pollution and reconstruct normal behaviors in spatial and temporal context. We exploit the power of this framework and adjust it to perform foreground objects segmentation. We evaluate the performance of our proposed method on 9 surveillance videos from the Background Model Challenge (BMC 2012) dataset, and compare that with a standard subspace learning technique, Robust Principle Component Analysis (RPCA) as well as a Deep Probabilistic Background Model (DeepPBM). Experimental results show that our approach achieved successful results than other existing ones.
Adversarial Margin Maximization Networks

The tremendous recent success of deep neural networks (DNNs) has sparked a surge of interest in understanding their predictive ability.Unlike the human visual system which is able to generalize robustly and learn with little supervision, DNNs normally require a massive amount of data to learn new concepts.In addition, research works also show that DNNs are vulnerable to adversarial examples-maliciously generated images which seem perceptually similar to the natural ones but are actually formed to fool learning models, which means the models have problem generalizing to unseen data with certain type of distortions.In this paper, we analyze the generalization ability of DNNs comprehensively and attempt to improve it from a geometric point of view.We propose adversarial margin maximization (AMM), a learning-based regularization which exploits an adversarial perturbation as a proxy.It encourages a large margin in the input space, just like the support vector machines.With a differentiable formulation of the perturbation, we train the regularized DNNs simply through back-propagation in an end-to-end manner.Experimental results on various datasets (including MNIST, CIFAR-10/100, SVHN and ImageNet) and different DNN architectures demonstrate the superiority of our method over previous state-of-the-arts.Code and models for reproducing our results will be made publicly available.
Multiple object tracking in soccer videos using topographic surface analysis

Multiple object tracking is still a challenging problem in computer vision even though there have been several attempts lately to resolve the tracking problem in the framework of deep neural networks. In this paper, a novel method for multiple object tracking in soccer videos, which often contain complicated interactions between players with severe occlusions, is introduced. To do this, we propose to interpret the extracted foreground regions in a given frame as the topographic surface. This gives a great help to reliably chase target players by accurately providing the boundary lines of each object even with occlusions. Color similarity and spatial proximity are subsequently employed to refine the estimated position of target players for continuous tracking over whole video sequences. Experimental results on various soccer videos, which are taken of the actual games with the wide-angle camera, demonstrate that the proposed method is effective for tracking multiple players in the dynamic scene of the soccer video.
gQoS: A QoS-Oriented GPU Virtualization with Adaptive Capacity Sharing

Currently, the virtualization technologies for cloud computing infrastructures supporting extra devices, such as GPU, require additional development and refinement. This requirement is particularly evident in the area of resource sharing and allocation under some performance constraints, like the quality of service (QoS) guarantee, in light of the closed GPU platform. This deficiency significantly limits the applicability range of the cloud platform, which aims to support the efficient and fluent execution of business and academic workloads. This paper introduces gQoS, an adaptive virtualized GPU resource capacity sharing system under the QoS target, which can share and allocate the virtualized GPU resource among workloads adaptively, guaranteeing the QoS level with stability and accuracy. We evaluate the workloads and compare our gQoS strategy with other allocation strategies. The experiments show that our strategy guarantees much better accuracy and stability in QoS control and that the total GPU resource utilization under gQoS can be rewarded with at most a 25.85 percent reduction compared with other strategies.
Germ Destroyer - A Gamified System to Increase the Hand Washing Duration in Shared Bathrooms

Washing hands is important for public health as it prevents spreading germs to other people. One of the most important factors in cleaning hands is the hand washing duration. However, people mostly do not wash their hands for a long enough time leading to infections and diseases for themselves and others. To counter this, we present "Germ Destroyer", a system consisting of a sensing device which can be mounted on the water tap and a mobile application providing gameful feedback to encourage users to meet the recommended duration. In the mobile application, users kill germs and collect points by washing their hands. Through a laboratory study (N=14) and a 10-day in-the-wild study (363 hand washing sessions), we found that Germ Destroyer enhances the enjoyment of hand washing, reduces the perceived hand washing duration, almost doubles the actual hand washing duration, and has the potential to reduce the risk of infection.
A Unified Scheme for Distance Metric Learning and Clustering via Rank-Reduced Regression

Distance metric learning aims to learn a positive semidefinite matrix such that similar samples are preserved with small distances while dissimilar ones are mapped with big values above a predefined margin. It can facilitate to improve the performance of certain learning tasks. In this article, distance metric learning and clustering are integrated into an unified framework via rank-reduced regression. First, distance metric learning is proved to be consistent with rank-reduced regression, which provides a new perspective to learn structured regularization matrices. Second, orthogonal and non-negative rank-reduced regression problems are addressed individually for clustering, and the corresponding algorithms with proved convergence are proposed. Finally, both distance metric learning and clustering are addressed simultaneously in the problem formulation, which may trigger some new insights for learning an effective clustering oriented low-dimensional embedding. To show the superior performance of the proposed method, we compare it with several state-of-the-art clustering approaches. And, extensive experiments on the test datasets demonstrate the superiority of the proposed method.
TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model

Multi-robot task allocation (MRTA) problems have been studied extensively in the past decades. As a result, several classifications have been proposed in the literature targeting different aspects of MRTA, with often a few commonalities between them. The goal of this paper is twofold. First, a comprehensive overview of early work on existing MRTA taxonomies is provided, focusing on their differences and similarities. Second, the MRTA problem is modelled using an Entity-Relationship (ER) conceptual formalism to provide a structured representation of the most relevant aspects, including the ones proposed within previous taxonomies. Such representation has the advantage of (i) representing MRTA problems in a systematic way, (ii) providing a formalism that can be easily transformed into a software infrastructure, and (iii) setting the baseline for the definition of knowledge bases, that can be used for automated reasoning in MRTA problems.
High Throughput Architecture for KLEIN Block Cipher in FPGA

In recent times, lightweight cryptographic algorithms have drawn a lot of attention to the researchers for securing the fast and small-computing devices. However, different algorithms have been developed to fulfill the requirements, and there has not been much research on transforming these algorithms to Field Programmable Gate Arrays (FPGAs) with minimal optimization. These reprogrammable devices are highly attractive options for hardware implementations of encryption algorithms. A strong focus is placed on high-throughput implementations, which are required to support security for current and future high bandwidth applications. In this paper, two different architectures are designed for resource-constrained environments. Among them, parallel processing implementation achieves high throughput of 2070.39 and 1646.12 Mbps on xc5vlx50t-3ff1136 and xc4vlx25-12ff668 devices, respectively. While in the other design, 8-bits datapath architecture signifies the use for low I/O port devices. All results are simulated and verified for different devices of Xilinx Spartan & Virtex families.
Self-supervised Face-Grouping on Graphs

We propose a novel self-supervised method for fine-tuning deep face representations called Face-Grouping on Graphs. We apply our method to automatic face grouping, where characters are to be separated based on their identity. To solve this problem, a graph structure with positive and negative edges over a set of face-tracks based on their temporal overlap and similarity constraints is in- duced, which requires no manual labor. We compute feature repre- sentations over sub-sequences of each track (sub-tracks) in order to obtain robust features whilst being able to utilize information contained in face variance. Each sub-track is given the ability to exchange information with adjacent sub-tracks via a typed graph neural network running over the induced graph. This allows us to push each representation in a direction in feature space that groups all representations of the same character together and separates representations of different characters. We show that our method is capable of improving clustering accuracy on popular video face clustering datasets The Big Bang Theory and Buffy the Vampire Slayer by 4.9% and 17.0% respectively compared to baseline performance, and 0.52% respective 5.55% com- pared to state-of-the-art methods. Additionally, we achieve 19.0% absolute increase in B3 F-Score on Harry Potter 1 (ACCIO) over other state-of-the-art unsupervised methods. We provide perfor- mance metrics on all episodes of The Big Bang Theory and Buffy the Vampire Slayer to enable further comparison in the future.
Subcarrier Domain of Multicarrier Continuous-Variable Quantum Key Distribution

Abstract The subcarrier domain of multicarrier continuous-variable quantum key distribution (CVQKD) is defined. In a multicarrier CVQKD scheme, the information is granulated into Gaussian subcarrier CVs and the physical Gaussian link is divided into Gaussian sub-channels. The subcarrier domain injects physical attributes to the description of the subcarrier transmission. We prove that the subcarrier domain is a natural representation of the subcarrier-level transmission in a multicarrier CVQKD scheme. We also extend the subcarrier domain to a multiple-access multicarrier CVQKD setting. We demonstrate the results through the adaptive multicarrier quadrature-division (AMQD) CVQKD scheme and the AMQD-MQA (multiuser quadrature allocation) multiple-access multicarrier scheme. The subcarrier domain representation provides a general apparatus that can be utilized for an arbitrary multicarrier CVQKD scenario.
Towards Increased Accessibility of Meme Images with the Help of Rich Face Emotion Captions

In recent years, there has been an explosion in the number of memes being created and circulated in online social networks. Despite their rapidly increasing impact on how we communicate online, meme images are virtually inaccessible to the visually impaired users. Existing automated assistive systems that were primarily devised for natural photos in social media, overlook the specific fine-grained visual details in meme images. In this paper, we concentrate on describing one such prominent visual detail: the meme face emotion. We propose a novel automated method that enables visually impaired social media users to understand and appreciate meme face emotions with the help of rich textual captions. We first collect a challenging dataset of meme face emotion captions to support future research in face emotion understanding. We design a two-stage approach that significantly outperforms baseline approaches across all the standard captioning metrics and also generates richer discriminative captions. By validating our solution with the help of visually impaired social media users, we show that our emotion captions enable them to understand and appreciate one of the most popular classes of meme images encountered on the Internet for the first time. Code, data, and models are publicly available.
Elicitation Study of Body Gestures for Locomotion in HMD-VR Interfaces in a Sitting-Position

Proxy gestures have proven to be a powerful tool and widely used for desktop and smartphone based user interactions. However, regarding its use for virtual travel in Virtual environments (VEs), specific limitation arises concerning the gestures naturalness, intuitiveness and the fatigue involved while performing the gesture in the real space. The proxy gestures must be natural, demand less effort and have the ability to move long virtual distances without colliding with the real-world boundaries. In this paper, we present a gesture elicitation study to find the most natural and intuitive body-gestures for the virtual travel task in three different VEs in a sitting position. This paper discusses our two experiments. In experiment 1, we extract the most natural and intuitive gesture by asking participants to perform gestures for virtual travel in three different VEs including selection and manipulation of objects in different difficulty levels. This is followed by the experiment 2, where a new group of 40 participants evaluated the extracted gestures based on appropriateness, ease of use of the gesture, effort and user preference. We identified the leaning gesture suitable for the virtual travel task in VE1 and VE3 and the pointing gesture suitable for the VE2. We discuss the results and qualitative findings of both experiments in this paper.
Action Recognition with Bootstrapping based Long-range Temporal Context Attention

Actions always refer to complex vision variations in a long-range redundant video sequence. Instead of focusing on limited range sequence, i.e. convolution on adjacent frames, in this paper, we proposed an action recognition approach with bootstrapping based long-range temporal context attention. Specifically, due to vision variations of the local region across frames, we target at capturing temporal context by proposing the Temporal Pixels based Parallel-head Attention (TPPA) block. In TPPA, we apply the self-attention mechanism between local regions at the same position across temporal frames to capture the interaction impacts. Meanwhile, to deal with video redundancy and capture long-range context, the TPPA is extended to the Random Frames based Bootstrapping Attention (RFBA) framework. While the bootstrapping sampling frames have the same distribution of the whole video sequence, the RFBA not only captures longer temporal context with only a few sampling frames but also has comprehensive representation through multiple sampling. Furthermore, we also try to apply this temporal context attention to image-based action recognition, by transforming the image into "pseudo video" with the spatial shift. Finally, we conduct extensive experiments and empirical evaluations on two most popular datasets:UCF101 for videos andStanford40 for images. In particular, our approach achieves top-1 accuracy of $91.7%$ in UCF101 and mAP of $90.9%$ in Stanford40.
Creating Virtual Reality and Augmented Reality Development in Classroom: Is it a Hype?

The fast-growing number of high-performance computer processor and hand-held devices have paved the way for the development of Virtual Reality and Augmented Reality in terms of hardware and software in the education sector. The question of whether students can adopt these new technologies is not fully addressed. Answering this question thus plays an essential role for instructors and course designers. The objectives of this study are: (1) to investigate the feasibility of the Virtual Reality/Augmented Reality development for undergraduate students, and (2) to highlight some practical challenges when creating and sharing Virtual Reality and Augmented Reality applications from student's perspective. Study design for the coursework was given with detail. During a 16-week long, 63 Virtual Reality/Augmented Reality applications were created from a variety of topics and various development tools. 43 survey questions are prepared and administered to students for each phase of the projects to address technical difficulties. The exploration method was used for data analysis.
Batch Weight for Domain Adaptation With Mass Shift

Unsupervised domain transfer is the task of transferring or translating samples from a source distribution to a different target distribution. Current solutions unsupervised domain transfer often operate on data on which the modes of the distribution are well-matched, for instance have the same frequencies of classes between source and target distributions. However, these models do not perform well when the modes are not well-matched, as would be the case when samples are drawn independently from two different, but related, domains. This mode imbalance is problematic as generative adversarial networks (GANs), a successful approach in this setting, are sensitive to mode frequency, which results in a mismatch of semantics between source samples and generated samples of the target distribution. We propose a principled method of re-weighting training samples to correct for such mass shift between the transferred distributions, which we call batch weight. We also provide rigorous probabilistic setting for domain transfer and new simplified objective for training transfer networks, an alternative to complex, multi-component loss functions used in the current state-of-the art image-to-image translation models. The new objective stems from the discrimination of joint distributions and enforces cycle-consistency in an abstract, high-level, rather than pixel-wise, sense. Lastly, we experimentally show the effectiveness of the proposed methods in several image-to-image translation tasks.
Video Text Detection by Attentive Spatiotemporal Fusion of Deep Convolutional Features

Scene text in videos carries rich semantic information and plays an important role in various content-based video applications. Compared to text in static images, scene text in videos exhibits some distinct characteristics such as motion blur and temporal redundancy, which bring additional difficulties as well as exploitable clues to the text detection task. In this paper, we propose a novel end-to-end deep neural network for detecting scene text in the video, which combines complementary text features from multiple related frames to enhance the overall detection performance relative to single-frame detection schemes. Specifically, we first extract descriptive features from each video frame using a hierarchical convolutional neural network. Next, we spatiotemporally sample and warp supplementary features from adjacent frames surrounding the current frame using a multi-scale deformable convolution structure. We then aggregate the sampled features with an attention mechanism to adaptively focus on and augment relevant features and generate an enhanced feature representation of the current frame, which is further fed to the prediction network for localizing text candidates. The proposed model achieves state-of-the-art text detection performance on public scene text video datasets, demonstrating the superiority of the proposed multi-frame feature fusion based video text detection scheme to most single-frame and tracking-based detection schemes.
Testing Deep Neural Networks on the Same-Different Task

Developing abstract reasoning abilities in neural networks is an important goal towards the achievement of human-like performances on many tasks. As of now, some works have tackled this problem, developing ad-hoc architectures and reaching overall good generalization performances. In this work we try to understand to what extent state-of-the-art convolutional neural networks for image classification are able to deal with a challenging abstract problem, the so-called same-different task. This problem consists in understanding if two random shapes inside the same image are the same or not. A recent work demonstrated that simple convolutional neural networks are almost unable to solve this problem. We extend their work, showing that ResNet-inspired architectures are able to learn, while VGG cannot converge. In light of this, we suppose that residual connections have some important role in the learning process, while the depth of the network seems not so relevant. In addition, we carry out some targeted tests on the converged architectures to figure out to what extent they are able to generalize to never seen patterns. However, further investigation is needed in order to understand what are the architectural peculiarities and limits as far as abstract reasoning is concerned.
Internet of Things Infrastructure for Security and Safety in Public Places

We present the technologies and the theoretical background of an intelligent interconnected infrastructure for public security and safety. The innovation of the framework lies in the intelligent combination of devices and human information towards human and situational awareness, so as to provide a protection and security environment for citizens. The framework is currently being used to support visitors in public spaces and events, by creating the appropriate infrastructure to address a set of urgent situations, such as health-related problems and missing children in overcrowded environments, supporting smart links between humans and entities on the basis of goals, and adapting device operation to comply with human objectives, profiles, and privacy. State-of-the-art technologies in the domain of IoT data collection and analytics are combined with localization techniques, ontologies, reasoning mechanisms, and data aggregation in order to acquire a better understanding of the ongoing situation and inform the necessary people and devices to act accordingly. Finally, we present the first results of people localization and the platforms’ ontology and representation framework.
Machines versus Humans: The Impact of AI Chatbot Disclosure on Customer Purchases

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