Reviewerly/exHarmony huggingface dataset:
Online Multi-view Subspace Learning with Mixed Noise

Multi-view learning reveals the latent correlation between different input modalities and has achieved outstanding performances in many fields. Recent approaches aim to find a low-dimensional subspace to reconstruct each view, in which the gross residual or noise follows either Gaussian or Laplacian distribution. However, the noise distribution is often more complex in practical applications, and a deterministic distribution assumption is incapable of modeling it. Additionally, referring to time-changed data, e.g., videos, the noise is temporal smooth, preventing us from processing the data with the whole input, as have generally been done in many existing multi-view learning methods. To tackle these problems, a novel online multi-view subspace learning is proposed in this paper. Particularly, our proposed method not only estimates a transformation for each view to extract the correlation among various views, but also introduces a Mixture of Gausssians (MoG) model into the multi-view data, successfully exploiting numbers of Gaussian Distributions to adaptively fit a wider range of the complex noise. Furthermore, we further design a novel online Expectation Maximization (EM) algorithm, being capable of efficiently processing the dynamic data. Experimental results substantiate the effectiveness and superiority of our approach.
Online education as a “Mental Institution”

Work on situated cognition and affectivity holds that cognitive and affective processes always occur within, depend upon, and, perhaps, are even partially constituted by the surrounding social and environmental contexts. What some philosophers call a ‘mental institution’ consists of various toolsand technologies that help people to solve a particular problem and scaffold their cognitive and affective processes in various ways. Examples include legal systems, scientific practice, and educational systems. I propose that insofar as it centers around technology and involves a particular set of rules, practices, and shared expectations, the online learning environment can be understood as a mental institution. It shapes not only what and how students learn, but also how they understand the value and fundamental aims of learning. I argue that insofar as this mental institution both reflects and perpetuates neoliberal aims and values, it is in danger of limiting students’ communicative abilities and soliciting them to view higher education in distorted, limiting ways.
Violence recognition using convolutional neural network: A survey

Violence detection is a challenging task in the computer vision domain. Violence detection framework depends upon the detection of crowd behaviour changes. Violence erupts due to disagreement of an idea, injustice or severe disagreement. The aim of any country is to maintain law and order and peace in the area. Violence detection thus becomes an important task for authorities to maintain peace. Traditional methods have existed for violence detection which are heavily dependent upon hand crafted features. The world is now transitioning in to Artificial Intelligence based techniques. Automatic feature extraction and its classification from images and videos is the new norm in surveillance domain. Deep learning platform has provided us the platter on which non-linear features can be extracted, self-learnt and classified as per the appropriate tool. One such tool is the Convolutional Neural Networks, also known as ConvNets, which has the ability to automatically extract features and classify them in to their respective domain. Till date there is no survey of deciphering violence behaviour techniques using ConvNets. We hope that this survey becomes an exclusive baseline for future violence detection and analysis in the deep learning domain.
Stability and memory-loss go hand-in-hand: three results in dynamics and computation

The search for universal laws that help establish a relationship between dynamics and computation is driven by recent expansionist initiatives in biologically inspired computing. A general setting to understand both such dynamics and computation is a driven dynamical system that responds to a temporal input. Surprisingly, we find memory-loss a feature of driven systems to forget their internal states helps provide unambiguous answers to the following fundamental stability questions that have been unanswered for decades: what is necessary and sufficient so that slightly different inputs still lead to mostly similar responses? How does changing the driven system’s parameters affect stability? What is the mathematical definition of the edge-of-criticality? We anticipate our results to be timely in understanding and designing biologically inspired computers that are entering an era of dedicated hardware implementations for neuromorphic computing and state-of-the-art reservoir computing applications.
Context-Based Confidentiality Analysis for Industrial IoT

In this research paper, we present an approach for an analysis process, which can find confidentiality issues in data-exchange, on the architectural level of Industrial Internet of Things software systems. Existing approaches provide an insufficient definition of dataflow or lack support of finely granulated information for providing confidentiality. Based on an existing modeling and analysis process for Data-Driven Software Architecture, we extend the role-based approach for access control, with a model to model transformation utilizing a context-based approach. Using a case study based evaluation we show that our approach works accurately and scales in a way that is feasible for big organizations.
Role-Play in Virtual Reality

The affordances of computer-based virtual reality (VR) make it a natural fit for role-play. Although role-play has been commonly used in VR-based learning environments, the discussion of design details and guidelines is limited. In this chapter, the authors present a design case and aim to share their experiences of designing role-play in OpenSimulator-supported VR during a three-year design project for providing teaching training to graduate teaching assistants in STEM disciplines. The design of role-play bridges the technical and pedagogical affordances of VR. The authors discuss the design details from several aspects, including the formulation of role-play, types of role-play, and implementation. The authors also report the success and constraints in implementing role-play. With this design case, the authors intent on sharing the knowledge about the design and practice of VR-based role-play.
Monocular image depth prediction without depth sensors: An unsupervised learning method

Monocular image depth prediction is an interesting challenge in three-dimensional (3D) perception, the purpose of which is to obtain the geometric features of 3D scenes from two-dimensional (2D) images. At present, the deep learning method for monocular depth prediction has yielded good results, but this approach treats it as a supervised deep regression problem. A significant weakness of current methods is the need to collect reams of depth measurement data in actual scenarios for training. In this paper, we design a novel convolutional neural network (CNN) with an encoding and decoding structure to estimate the depth map from monocular RGB images based on basic principles of binocular stereo vision, and use rectified stereo pairs to train our network from scratch in an unsupervised learning method without any depth data. We also explore a new upsampling strategy to improve the output resolution, and introduce a new dynamic optimization strategy to enhance the training speed and prediction accuracy. Extensive experiments on the publicly available KITTI and Cityscapes datasets demonstrate that our approach is more accurate than competing methods. The findings of the proposed methodology illustrate that our CNN model can be utilized as depth completion from LIDAR images.
Learning with an augmented (unknown) class using neural networks

The wide diversity of categories encountered in big data applications means certain classes will be labelled while many other classes will remain unlabelled. A classification domain would consequently consist of known source classes for which labelled samples are available, and unknown novel classes that do not have any labelled samples. Given the assumption that novel classes are far more prevalent than source classes, it is appropriate to define an encapsulating ’unknown’ class for all such novel classes. To be practically useful, classification systems must classify over both the source and the unknown novel domains. Regularly available data often includes unlabelled samples that, intuitively, belong to both source and novel classes. Including scattered unlabelled data with source labelled data during model training provides tractable means to learn classification boundaries between source classes and the unknown class. Da et al. were the first to introduce this framework as learning with augmented class by exploiting unlabelled data or LACU [6]. In this work, we promote the LACU paradigm to the neural network research space with the first LACU-enabled neural model. Using simple neural architectures, our proposed method produces state-of-the-art results when compared to previously published LACU works. With neural networks more capable of handling large datasets, this work takes us one step closer to building big data classifiers capable of known and unknown classification.
Cross-domain Cross-modal Food Transfer

The recent works in cross-modal image-to-recipe retrieval pave a new way to scale up food recognition. By learning the joint space between food images and recipes, food recognition is boiled down as a retrieval problem by evaluating the similarity of embedded features. The major drawback, nevertheless, is the difficulty in applying an already-trained model to recognize different cuisines of dishes unknown to the model. In general, model updating with new training examples, in the form of image-recipe pairs, is required to adapt a model to new cooking styles in a cuisine. Nevertheless, in practice, acquiring sufficient number of image-recipe pairs for model transfer can be time-consuming. This paper addresses the challenge of resource scarcity in the scenario that only partial data instead of a complete view of data is accessible for model transfer. Partial data refers to missing information such as absence of image modality or cooking instructions from an image-recipe pair. To cope with partial data, a novel generic model, equipped with various loss functions including cross-modal metric learning, recipe residual loss, semantic regularization and adversarial learning, is proposed for cross-domain transfer learning. Experiments are conducted on three different cuisines (Chuan, Yue and Washoku) to provide insights on scaling up food recognition across domains with limited training resources.
Robots’ State Estimation and Observability Analysis Based on Statistical Motion Models

This article presents a generic motion model to capture mobile robots’ dynamic behaviors (translation and rotation). The model is based on statistical models driven by white random processes and is formulated into a full state estimation algorithm based on the error-state extended Kalman filtering framework (ESEKF). The major benefits of this method are its versatility, being applicable to different robotic systems without accurately modeling the robots’ specific dynamics, and the ability to estimate the robot’s (angular) acceleration, jerk, or higher order dynamic states with low delay. Mathematical analyses with numerical simulations are presented to show the properties of the statistical model-based estimation framework and reveal its connection to existing low-pass filters. Furthermore, a new paradigm is developed for robotic observability analysis by developing Lie derivatives and associated partial differentiation directly on manifolds. It is shown that this new paradigm is much simpler and more natural than existing methods based on quaternion parameterizations. It is also scalable to high-dimensional systems. A novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">thin</i> set concept is introduced to characterize the unobservable subset of the system states, providing the theoretical foundation to observability analysis of robotic systems operating on manifolds and in high dimension. Finally, extensive experiments, including full state estimation and extrinsic calibration (both POS-IMU and IMU-IMU) on a quadrotor unmanned aerial vehicle (UAV), a handheld platform, and a ground vehicle, are conducted. Comparisons with existing methods show that the proposed method can effectively estimate all extrinsic parameters, the robot’s translation/angular acceleration, and other state variables (e.g., position, velocity, and attitude) with high accuracy and low delay.
Increasing Video Perceptual Quality with GANs and Semantic Coding

We have seen a rise in video based user communication in the last year, unfortunately fueled by the spread of COVID-19 disease. Efficient low-latency delay of transmission of video is a challenging problem which must also deal with the segmented nature of network infrastructure not always allowing a high throughput. Lossy video compression is a basic requirement to enable such technology widely. While this may compromise the quality of the streamed video there are recent deep learning based solutions to restore quality of a lossy compressed video.
Temporal Denoising Mask Synthesis Network for Learning Blind Video Temporal Consistency

Recently, developing temporally consistent video-based processing techniques has drawn increasing attention due to the defective extend-ability of existing image-based processing algorithms (e.g., filtering, enhancement, colorization, etc). Generally, applying these image-based algorithms independently to each video frame typically leads to temporal flickering due to the global instability of these algorithms. In this paper, we consider enforcing temporal consistency in a video as a temporal denoising problem that removing the flickering effect in given unstable pre-processed frames. Specifically, we propose a novel model termed Temporal Denoising Mask Synthesis Network (TDMS-Net) that jointly predicts the motion mask, soft optical flow and the refining mask to synthesize the temporal consistent frames. The temporal consistency is learned from the original video and the learned temporal features are applied to reprocess the output frames that are agnostic (blind) to specific image-based processing algorithms. Experimental results on two datasets for 16 different applications demonstrate that the proposed TDMS-Net significantly outperforms two state-of-the-art blind temporal consistency approaches.
Online Video Object Detection via Local and Mid-Range Feature Propagation

This work proposes a new Local and Mid-range feature Propagation (LMP) method for video object detection to well capture feature correlations and reduce the redundant computation. Specifically, the proposed LMP model contains two modules with two individual propagation schemes. The local module is leveraged to propagate motion and appearance context in short term. The local module is a lightweight one to greatly reduce the redundant computation without considering local attention. On the other hand, to explore the feature correlations in long term, the mid-range module based on the non-local attention mechanism is introduced by capturing relative longer-range relationships. By incorporating these two modules, LMP enables to enrich feature representation with fast computation. The proposed method is evaluated on the ImageNet VID dataset. The proposed LMP method achieves 64.2% mAP score at speed of 28.5 FPS on desktop GPUs, which is the state-of-the-art performance among one-stage MobileNet based detectors. Source code is available at https://github.com/ktw361/Local-Mid-Propagation.
Generation of multimode quantum correlation with energy-level cascaded four-wave mixing processes

Multimode quantum correlation has important applications in the field of quantum information. In this paper, we propose a compact scheme to generate multiple quantum correlated beams based on energy-level cascaded four-wave mixing (EC-FWM) process in a single atomic medium of rubidium. Injecting two strong pump beams with different frequencies and one weak probe beam into one rubidium vapor, four possible parametrically amplified FWM (PA FWM) processes can coexist under different phase matching conditions. Within the scheme, the weak probe beam is amplified and three coherent beams are generated, which can also be seen as an eight-wave mixing (EWM) process when certain phase matching condition is satisfied. Based on the energy level system of the scheme, three different cascaded structure models are employed to study the quantum correlation between the four generated beams. Amplitude and phase quadratures squeezing and intensity squeezing of the three- and four-mode are calculated via the input–output relations of the system. We found versatile multimode squeezing can be generated in all the structures, but the degree of squeezing and its distribution among the modes are different, depending on the specific structure. Furthermore, the relation of the quantum correlation, as well as the eigenmodes and eigenvalues of the four-mode quantum states are studied.
Minimal distances for certain quantum product codes and tensor products of chain complexes

We use a map to quantum error-correcting codes and a subspace projection to get lower bounds for minimal homological distances in a tensor product of two chain complexes of vector spaces over a finite field. Homology groups of such a complex are described by the K\"unneth theorem. We give an explicit expression for the distances when one of the complexes is a linear map between two spaces. The codes in the construction, subsystem product codes and their gauge-fixed variants, generalize several known families of quantum error-correcting codes.
Varying density method for data stream clustering

In this paper, a new online-offline density-based clustering method for data stream with varying density is proposed. In the online phase, the summary of data is created (often known as micro-clusters) and in the offline phase, this synopsis of data is used to form the final clusters. Finding the accurate micro-clusters is the goal of online phase. When a new data point arrives, the procedure of finding the nearest and best fit micro-cluster is the time consuming process. This procedure can lead to increase the execution time. To address this problem, a new merging algorithm is proposed. For maintaining a limited number of micro-clusters, a pruning process is applied along with the summarization process. In the existing methods, this pruning process takes too long time to remove micro-clusters whose do not receive objects frequently that cause to increase the memory usage. In this paper, to solve this problem, a new pruning algorithm is introduced. Another problem with density-based methods is that they use global parameters in the data sets with varying density that can lead to dramatic decrease in the clustering quality. In our work, to create final clusters, a new density-based algorithm that works based on only MinPts parameter is proposed for increasing the clustering quality of data sets with varying density. The performance evaluation on both synthetic and real data sets illustrates the efficiency and effectiveness of the proposed method. The experimental results show that our method can increase the clustering quality in data sets with varying density along with limited time and memory usage.
Generalized Zero-shot Learning with Multi-source Semantic Embeddings for Scene Recognition

Recognizing visual categories from semantic descriptions is a promising way to extend the capability of a visual classifier beyond the concepts represented in the training data (i.e. seen categories). This problem is addressed by (generalized) zero-shot learning methods (GZSL), which leverage semantic descriptions that connect them to seen categories (e.g. label embedding, attributes). Conventional GZSL are designed mostly for object recognition. In this paper we focus on zero-shot scene recognition, a more challenging setting with hundreds of categories where their differences can be subtle and often localized in certain objects or regions. Conventional GZSL representations are not rich enough to capture these local discriminative differences. Addressing these limitations, we propose a feature generation framework with two novel components: 1) multiple sources of semantic information (i.e. attributes, word embeddings and descriptions), 2) region descriptions that can enhance scene discrimination. To generate synthetic visual features we propose a two-step generative approach, where local descriptions are sampled and used as conditions to generate visual features. The generated features are then aggregated and used together with real features to train a joint classifier. In order to evaluate the proposed method, we introduce a new dataset for zero-shot scene recognition with multi-semantic annotations. Experimental results on the proposed dataset and SUN Attribute dataset illustrate the effectiveness of the proposed method.
Lane line recognition based on improved 2D-gamma function and variable threshold Canny algorithm under complex environment

The visual guidance of AGV (automated guided vehicle) has gradually become one of the most important perception methods. Aiming at the problem that it is difficult to extract lane line accurately when AGV is running in complex working environment (such as uneven illumination, overexposure, lane line is not obvious, etc.), a scheme of lane line recognition under complex environment is proposed. Firstly, the variable scale image correction is carried out for the uneven illumination area in ROI (region of interest), and the threshold of Canny algorithm is adjusted adaptively according to the luminance of ROI region by Fuzzy-Canny algorithm; Secondly, the edge points matching the lane width feature are extracted by the way of aerial view. Finally, the curve fitting method based on RANSAC (Random Sample Consensus) is used to fit a curve with the lowest error rate and then get the lane center curve. The experimental results show that the processing algorithm used in this paper is feasible and effective, has strong robustness and fast computing performance, and can meet the requirements of intelligent AGV in various complex environments.
Cheating immune k-out-of-n block-based progressive visual cryptography

Hou et al. introduced a (2, n) block-based progressive visual cryptographic scheme (BPVCS). In (2, n)-BPVCS, a secret image is subdivided into n non-overlapped image blocks. When t (2 ≤ t ≤ n) participants stack their shadow images, the image blocks belonged to these t participants will be recovered. Unfortunately, Hou et al.’s (2, n)-BPVCS suffers from the cheating problem. Additionally, Hou et al.’s scheme is only suitable for 2-out-of-n case, i.e., (k, n)-BPVCS where k = 2. In this paper, we propose a cheating immune (k, n)-BPVCS for k>2. The main contribution of our paper is that the proposed (k, n)-BPVCS is not only cheating immune, but also suitable for general k-out-of-n threshold, where k and n are integers with 2 ≤ k ≤ n. Theoretical analysis demonstrates that our scheme still holds progressive recovery and security, and meantime has cheating immune capability.