Reviewerly/exHarmony huggingface dataset:
Fast and Temporal Consistent Video Style Transfer

Style transfer is a subset of image transformation problem in which the output is a combination of the style of a reference image and the content of an input image. Despite recent advantages in image processing, video style transfer is still a challenging problem. One can implement such methods on a video, considering each frame independently. However, an unpleasant flickering effect will be observable in the output. Here, we propose a method to properly transfer a reference style to the input frames while preserving the temporal consistency. Thus, the flickering effect will be considerably mitigated. The proposed method is compared to two video style transfer methods using Sintel dataset. Results show that the proposed method keeps a better trade-off between temporal consistency and spatial losses.
Research on Multispectral 3D Panoramic Vision Device

The existing 3D vision methods do not fulfil people's requirements for good synchronization, high robustness, compactness and low energy consumption, and both indoor and outdoor applications. Based on our previous snapshot narrowband multi-spectral imaging [1-3], Dynamic 3D morphology recovery of parallelism variance [4,5] and monocular small distortion panoramic optical imaging [6]. This project proposes the research of multi-spectral three-dimensional panoramic vision devices. We will panoramic annular lens band imaging system audition small reflective surface into trans panoramic band imaging system, using micro mirror reflection time and integration of primary mirror reflection of optical components to replace the heavy annular lens, and with the integral optimization design of light path, after the successful integration mini folding trans panoramic look imaging system. The combination of multi-spectrum and panoramic camera can provide a 3D vision method and sensor with good real-time performance, high robustness, compact and low energy consumption, indoor and outdoor, for the perception of unstructured environment and understanding of complex scenes for the robot.
A blockchain-based access control scheme for smart home

Abstract With the advancement of technology and economy, the scale of the smart home industry has exploded. While improving people’s lives, smart home systems are facing threats to privacy leaks, malicious attacks, and structural security. Effective security mechanisms are very important for protecting valuable data in smart home systems. Access control helps information systems prevent malicious access, thereby reducing the risk of privacy leakage. Blockchain can provide security support for the Internet of Things system due to its advantages of decentralization and immutability. Therefore, in this paper, a smart home access control scheme based on blockchain is proposed. The scheme uses Hyperledger Fabric and implements access control strategies through smart contracts. By designing a hybrid access control model based on dynamic attribute-based access control and static access control matrix, the remote access control initiated by the user through the Internet and the access control between local devices are well guaranteed at the same time. Through safety analysis and performance evaluation, the feasibility of the proposed scheme is demonstrated.
GILDA++: Grassmann Incremental Linear Discriminant Analysis

Linear Discriminant Analysis (LDA) is an important supervised dimensionality reduction method. Traditional LDA makes use of the eigenvalue decomposition of the scatter matrices based on the entire dataset. However, in some settings the whole dataset may not be available at once. Our approach considers an incremental LDA framework where the model receives training data in the form of chunks for subsequent analysis. We propose the Grassmann-Incremental Linear Discriminant Analysis (GILDA++) using the proxy matrix optimization method (PMO). The PMO method does not directly optimize a matrix on the manifold, but uses an auxiliary or proxy matrix in ambient space which is retracted to the closest location on the manifold along the loss minimizing geodesic. PMO makes use of an LDA objective by incrementally updating the scatter matrices to handle chunks of data. It makes use of automatic differentiation and stochastic gradient descent to find the lower dimensional LDA projection matrix. GILDA++ is able to handle chunk data, where each chunk has new samples from existing classes or novel classes. Our experiments demonstrate that GILDA++ outperforms the prevailing incremental LDA methods in various datasets.
Deep-Facial Feature-Based Person Re-identification for Authentication in Surveillance Applications

Nowadays, a large network of cameras is predominantly used in public places which provide enormous video data. These data are monitored manually and may be utilized only when the need arises to ascertain the facts. Automating the system can improve the quality of surveillance and be useful for high-level surveillance tasks like person identification, suspicious activity detection or undesirable event prediction for timely alerts. In this paper, we proposed a model that can Re-identify a person from a single camera tracking environment. This system will automatically extract face features of the person and generate the Unique Id for each person when it enters for the first time in the monitored area. Its face features are stored in the database which will help to Re-identify the person whenever the same person appears again. The challenges faced by the system are occlusion, pose, light conditions, and face orientation. The proposed system highlights, effect of different deep neural networks for Person Re-identification and compares based on the accuracy, GPU usage, Speed, Number of faces detected by overcoming the challenges like illumination and occlusion. The advantage of the system is it doesn′t require the database of people in advance for recognition and it will be helpful for criminal identification for crime control and prevention.
Introducing Immersive Learning into Special Education Settings: A Comparative Review of Two Studies

Immersive learning aims to create virtual environments for educational purposes with the facilitation of virtual reality (VR), augmented reality (AR) and similar interactive multimedia technologies. These safe, interactive, manipulative and repeatable virtual learning environments have been widely used in general education settings. In this chapter, two pioneering studies conducted by Centre for Innovative Applications of Internet and Multimedia Technologies (AIMtech Centre) at City University of Hong Kong that aimed to introduce immersive learning into special education settings are reviewed. The two studies employed immersive learning approaches to cater two different groups of children with special education needs, namely children with severe intellectual disability (SID) and those with autism spectrum disorder (ASD) respectively. Specifically, the immersive learning environment, instructional and pedagogical design, assessment and evaluation methodology and other aspects relevant to these two studies are reviewed and compared in contrast with the delivery of immersive learning in general education settings in order to provide insights and general guidelines for introducing immersive learning into special education settings.
Towards High-Dimensional Entanglement in Path: Photon-Source Produced from a Two-Dimensional Atomic Cloud

A photon source with high-dimensional entanglement is able to bring increasing capacity of information in quantum communication. The dimensionality is determined by the chosen degree of freedom of the photons and is limited by the complexity of the physical systems. Here we propose a new type of high-dimensional entangled photon source, generated via path-indistinguishable scheme from a two-dimensional atomic cloud, which is prepared in a magneto-optical trap. To verify the photon source, we demonstrate experimentally the quantum state of the single photons heralded by its partner photon, with homodyne tomographic technology.
NILOA Transparency Framework: Implications for the Publicity of Student Learning Outcomes

Assessment UpdateVolume 33, Issue 4 p. 6-14 Article NILOA Transparency Framework: Implications for the Publicity of Student Learning Outcomes Nhung Thi Tuyet Pham, Nhung Thi Tuyet PhamSearch for more papers by this author Nhung Thi Tuyet Pham, Nhung Thi Tuyet PhamSearch for more papers by this author First published: 20 July 2021 https://doi.org/10.1002/au.30263Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Volume33, Issue4July/August 2021Pages 6-14 RelatedInformation
RepUNet:A Fast Image Semantic Segmentation Model Based on Convolutional Reparameterization of Ship Satellite Images

In this paper, a fast image semantic segmentation model based on convolutional reparameterization of ship satellite images is developed. The salient features of the proposed approcach are: (1) Various semantic segmentation networks are evaluated using high-resolution ship satellite data. (2) A high-precision and high-speed semantic segmentation network, termed RepUnet, for detecting ships according to complex characteristics of the actual marine situation is developed. The semantic segmentation network of RepUnet is divided into four parts, namely encoding layer, decoding layer, convolutional reparameterization and batch normalization feature fusion. The RepUNet has the following characteristics: (1) The structure of 3*3 convolution is used as the basic skeleton, and the basic propagation speed is high. (2) The 1*1 convolution and identity branch layer are adopted to enhance feature extraction. (3) All the branches of the encoding layer are reparameterized and the parameters are embedded in the convolution kernel of 3*3. The branch structure is abandoned when inferring so that feature extraction is strengthened. Batchnormalization of each branch is fused to make the feedforward speed faster. Validation of the RepUnet demonstrates that it outperforms other semantic segmentation networks. The IoU and feedforword speed (Ffs) of 22.26% and 153.11 %, respectively are significantly higher than those achieved by the UNet.
Massively-parallel Amplitude-Only Fourier Optical Convolutional Neural Network

Here we introduce a novel amplitude-only Fourier-optical processor paradigm and demonstrate a prototype system capable of processing large-scale ~(2,000x1,000) matrices in a single time-step and 100 microsecond-short latency, for accelerating machine-learning applications.
Quantum Entanglement Percolation Analysis in Kleinberg Small World Quantum Networks

In the paper, the analysis of percolation of quantum entanglement in complex networks was given, including the preparation of long-range quantum entangled states in two-dimensional complex networks of small word. A scheme of quantum entanglement percolation optimization of quantum communication network is proposed. By introducing the localized “target-swap” quantum operation into the classical entanglement percolation protocol (CEP), the quantum entanglement percolation protocol is formed. It also shows that the quantum entanglement percolation protocol (QEP) is significantly better than CEP in the performance of long-range entanglement preparation in two-dimensional complex networks of small world.
An Blockchain-based E-voting System Applying Time Lock Encryption

Electronic voting has been thriving for high ballot privacy and verifiability in recent eras. Especially a blockchain technology-based on cryptography is highly open and transparent for the individual transaction. It avoids election fraud which means that all parties involved do not have access to the results until a specific, predefined time in the future. In this paper, we proposed a time-lock puzzle with a blind signature authenticated e-voting system. Our work’s main objective is to provide a public-key-based authenticated encryption algorithm that takes the sender’s secret key, receiver’s public key, and designated time. Therefore, the resulting ciphertext can be decrypted only by the receiver, including specified start time, the receiver’s secret key and sender’s public key together. In this e-voting system, the system will automatically disclose the result after the end of the designated time. The time lock encryption will help to secure the elections from frauds and other unfair means. This system has implemented using python programming language and Flask framework to prove its feasibility.
Media Sensitivity Towards Cybercrimes Against Women

Retrieving reports of cybercrimes against women in two daily newspapers, the Times of India and the Hindu in the years 2014–2016 and taking a ratio of the same against the total number of cybercrimes in the same period reported by the National Crime Records Bureau, we have developed a simple media sensitivity index of cybercrimes against women. Considering the paucity of similar studies, this may reveal a new area of research which might also help to develop a more apt media sensitivity index, applicable to a range of crimes. Further, it may help to identify the thrust of media reports towards particular types of crimes as well as facilitate the measurement of media sensitivity to different crime genres.
Evolutionary Neural Architecture Search by Mutual Information Analysis

Neural architecture search (NAS) is a promising approach to fully automated machine learning that applies to arbitrary tasks. However, existing NAS methods tend to make a complex network that requires much time to train and predict. Since machine learning applications are now often run on mobile devices with limited computational resources, there is a need to obtain small, efficient, yet effective network models. This paper proposes MIA-NAS (Mutual Information Analysis Neural Architecture Search), an evolutionary NAS algorithm that extends Neural Architecture Search by Hill Climbing (NASH) by Elsken et al.. The proposed method introduces a procedure that removes layers that are less contributing to making predictions. The amount of contribution from each layer is quantified by estimating mutual information they have with true labels. The experiments showed that MIA-NAS produces smaller networks with comparative performance to the ones made by NASH. Besides, removing layers based on mutual information resulted in better performance than randomly selecting layers to be removed.
Closing the BIG-LID: An Effective Local Intrinsic Dimensionality Defense for Nonlinear Regression Poisoning

Nonlinear regression, although widely used in engineering, financial and security applications for automated decision making, is known to be vulnerable to training data poisoning. Targeted poisoning attacks may cause learning algorithms to fit decision functions with poor predictive performance. This paper presents a new analysis of local intrinsic dimensionality (LID) of nonlinear regression under such poisoning attacks within a Stackelberg game, leading to a practical defense. After adapting a gradient-based attack on linear regression that significantly impairs prediction capabilities to nonlinear settings, we consider a multi-step unsupervised black-box defense. The first step identifies samples that have the greatest influence on the learner's validation error; we then use the theory of local intrinsic dimensionality, which reveals the degree of being an outlier of data samples, to iteratively identify poisoned samples via a generative probabilistic model, and suppress their influence on the prediction function. Empirical validation demonstrates superior performance compared to a range of recent defenses.
Writing to advance knowledge: The impact of readability on knowledge diffusion in OSCM

Abstract The sophistication of academic research often creates a cognitive barrier for readers when they understand and utilize new knowledge. Cognizant of this issue, scholars in various fields, including Operations and Supply Chain Management (OSCM), have called for improved communication in academic research. Readability is a key pillar of written communication and is particularly important in OSCM. This is partially because OSCM articles tend to be diverse in terms of methods, theoretical lenses used, and author origins. OSCM is also a field where knowledge is created with the purpose of interfacing with real business problems closely and engaging with diverse external stakeholders. The purpose of this study is to explore the relationship between readability and knowledge diffusion in academic OSCM research. Using cognitive load as the theoretical lens for readability, we analyze a full‐text dataset of 1476 articles published in leading OSCM journals between 1998 and 2018. We find that readability has a positive impact on citations garnered. This relationship holds over 3‐year short‐term spans and 6‐year long‐term spans, indicating lasting effects. In addition, the results show that the impact of readability on knowledge diffusions is nonlinear. This finding confirms the additive nature of cognitive load and suggests that there is a level authors and journal editors should achieve to make their work more understandable and more likely to be diffused. Suggestions are made to help researchers reduce cognitive loads and to increase the likelihood that knowledge generated in their research is more likely to be disseminated by future researchers.
MBOX: Designing a Flexible IoT Multimodal Learning Analytics System

Multimodal Learning Analytics (MMLA) provides opportunities for understanding and supporting collaborative problem-solving. However, the implementation of MMLA systems is challenging due to the lack of scalable technologies and limited solutions for collecting data from group work. This paper proposes the Multimodal Box (MBOX), an IoT-based system for MMLA, allowing the collection and processing of multimodal data from collaborative learning tasks. MBOX investigates the development and design for an IoT focusing on small group work in real-world settings. Moreover, MBOX promotes adaptation to different learning environments and enables a better scaling of computational resources used within the learning context.
QoI-Aware Camera Network-as-a-Service for Social Behavior Analysis

The frames captured by the camera should be of sufficient quality, generally measured in pixel density on a target (pixels per foot or pixels per meter (ppm)) to derive information suitable for social behavior analysis. Achieving 24x7 high pixel density coverage incurs a large number of cameras and exorbitant network bandwidth for large scale deployments. In this paper, we introduce additional features to the Camera Network-as-a-Service (CNaaS) concept proposed by Misra et al. to address two aspects pertinent to services related to social behavioral analysis. We present a Quality of Information-Aware (QoI-Aware) CNaaS to address the two aspects. (1) To allow the reuse of cameras which are already available in a locality to minimize the number of additional cameras required to provide the CNaaS service. (2) Inclusion and exclusion of camera node(s) into the CNaaS platform while ensuring fair opportunities to the Camera Network Owners (CNOs) which provide the same Quality of Information (QoI). The simulation results indicate that the proposed scheme is fair to nodes providing the same QoI, reduces the energy consumption, and excludes the cameras which offer lesser QoI. This will lead to an improvement in the usage of network bandwidth, and the profit of the Camera Network Service Provider (CNSP).
Capturing Uncertainty with Interval Fuzzy Logic Systems through Composite Deep Learning

In this paper, we propose a learning approach for interval Fuzzy Logic Systems (FLSs) to end up with models that are capable to cover an expected amount of uncertainty with a high accuracy by exploiting a composite learning method with quantile regression. Within this paper, we construct two interval FLSs that have a different representation of uncertainty. One of them models the uncertainty in its consequents while the other one within its antecedents that are defined with interval type-2 Fuzzy Sets (FSs). The learning approach uses a multi-objective composite loss that is formed by the mean square error for accuracy purposes along with tilted loss for enforcing the bounds of the FLSs to capture the expected amount of uncertainty. In that way, it is not only possible to learn the FLSs that represent the uncertainty within their MFs (which can be used as prediction intervals) but also to improve the regression performance since the composite loss provides a more complete representation of the data. We present the proposed learning approach alongside parameterization tricks so that they can be trained within the frameworks of deep learning while not violating the definitions of FSs. We present comparative results on benchmark datasets that have different characteristics.
Performance evaluation of GPU- and cluster-computing for parallelization of compute-intensive tasks

Purpose This paper aims to evaluate different approaches for the parallelization of compute-intensive tasks. The study compares a Java multi-threaded algorithm, distributed computing solutions with MapReduce (Apache Hadoop) and resilient distributed data set (RDD) (Apache Spark) paradigms and a graphics processing unit (GPU) approach with Numba for compute unified device architecture (CUDA). Design/methodology/approach The paper uses a simple but computationally intensive puzzle as a case study for experiments. To find all solutions using brute force search, 15! permutations had to be computed and tested against the solution rules. The experimental application comprises a Java multi-threaded algorithm, distributed computing solutions with MapReduce (Apache Hadoop) and RDD (Apache Spark) paradigms and a GPU approach with Numba for CUDA. The implementations were benchmarked on Amazon-EC2 instances for performance and scalability measurements. Findings The comparison of the solutions with Apache Hadoop and Apache Spark under Amazon EMR showed that the processing time measured in CPU minutes with Spark was up to 30% lower, while the performance of Spark especially benefits from an increasing number of tasks. With the CUDA implementation, more than 16 times faster execution is achievable for the same price compared to the Spark solution. Apart from the multi-threaded implementation, the processing times of all solutions scale approximately linearly. Finally, several application suggestions for the different parallelization approaches are derived from the insights of this study. Originality/value There are numerous studies that have examined the performance of parallelization approaches. Most of these studies deal with processing large amounts of data or mathematical problems. This work, in contrast, compares these technologies on their ability to implement computationally intensive distributed algorithms.