Reviewerly/exHarmony huggingface dataset:
Temporal Complementary Learning for Video Person Re-Identification

This paper proposes a Temporal Complementary Learning Network that extracts complementary features of consecutive video frames for video person re-identification. Firstly, we introduce a Temporal Saliency Erasing (TSE) module including a saliency erasing operation and a series of ordered learners. Specifically, for a specific frame of a video, the saliency erasing operation drives the specific learner to mine new and complementary parts by erasing the parts activated by previous frames. Such that the diverse visual features can be discovered for consecutive frames and finally form an integral characteristic of the target identity. Furthermore, a Temporal Saliency Boosting (TSB) module is designed to propagate the salient information among video frames to enhance the salient feature. It is complementary to TSE by effectively alleviating the information loss caused by the erasing operation of TSE. Extensive experiments show our method performs favorably against state-of-the-arts. The source code is available at https://github.com/blue-blue272/VideoReID-TCLNet.
A Tutorial on Population-based Simulated Kalman Filter

&#x0D; &#x0D; &#x0D; &#x0D; Simulated Kalman Filter (SKF) is an estimation-based optimization algorithm which is established based on the Kalman filtering framework. Even since the SKF algorithm is introduced in 2015, there is no tutorial been published on SKF. One may find that the equations and flowchart of the algorithm is not easy to understand. Hence, this paper provides a tutorial on SKF algorithm that emphasizes on a numerical example for easy and intuitive explanations. This tutorial would be important to those who work on the fundamentals and applications of SKF as well as to students who are new to optimization research.&#x0D; &#x0D; &#x0D; &#x0D;
Upper bounds on the private capacity for bosonic Gaussian channels

Recently, there have been considerable progresses on the bounds of various quantum channel capacities for bosonic Gaussian channels. Especially, several upper bounds for the classical capacity and the quantum capacity on the bosonic Gaussian channels, via a technique known as quantum entropy power inequality, have been shed light on understanding the mysterious quantum-channel-capacity problems. However, upper bounds for the private capacity on quantum channels are still missing for the study on certain universal upper bounds. Here, we derive upper bounds on the private capacity for bosonic Gaussian channels involving a general Gaussian-noise case through the conditional quantum entropy power inequality.
PVSNet: Pixelwise Visibility-Aware Multi-View Stereo Network

Recently, learning-based multi-view stereo methods have achieved promising results. However, they all overlook the visibility difference among different views, which leads to an indiscriminate multi-view similarity definition and greatly limits their performance on datasets with strong viewpoint variations. In this paper, a Pixelwise Visibility-aware multi-view Stereo Network (PVSNet) is proposed for robust dense 3D reconstruction. We present a pixelwise visibility network to learn the visibility information for different neighboring images before computing the multi-view similarity, and then construct an adaptive weighted cost volume with the visibility information. Moreover, we present an anti-noise training strategy that introduces disturbing views during model training to make the pixelwise visibility network more distinguishable to unrelated views, which is different with the existing learning methods that only use two best neighboring views for training. To the best of our knowledge, PVSNet is the first deep learning framework that is able to capture the visibility information of different neighboring views. In this way, our method can be generalized well to different types of datasets, especially the ETH3D high-res benchmark with strong viewpoint variations. Extensive experiments show that PVSNet achieves the state-of-the-art performance on different datasets.
Power Grid Fault Diagnosis Based on Improved Deep Belief Network

Abstract It is of great significance to quickly and accurately identify power system faults. This paper introduces the idea of deep learning into power system fault diagnosis research and proposes a fault diagnosis model based on improved deep confidence network. Construct a set of 30-dimensional original features that can reflect the fault characteristics of the power system as the model input, and the fault diagnosis result is the model output. Use multi-layer Boltzmann machines to train the mapping relationship between grid faults and system features. Finally, the extreme learning machine is used to supervise the labeled samples to modify the network parameters. Different system failure scenarios were set up on the New England 10-machine 39-node system to test the diagnosis ability of the model. Simulation results show that the improved deep belief network has a strong feature extraction capability. The improved deep belief network has higher accuracy and faster speed in fault categories, fault areas, and fault locations than common artificial intelligence methods.
Optimal characterization of Gaussian channels using photon-number-resolving detectors

We present optimal schemes, based on photon-number measurements, for Gaussian state tomography and for Gaussian process tomography. An $n$-mode Gaussian state is completely specified by $2{n}^{2}+3n$ parameters. Our scheme requires exactly $2{n}^{2}+3n$ distinct photon-number measurements to tomograph the state and is therefore optimal. Furthermore, we describe an optimal scheme to characterize Gaussian processes by using coherent-state probes and photon-number measurements. With much recent progress in photon-number-measurement experimental techniques, we hope that our scheme will be useful in various quantum information processing protocols including entanglement detection, quantum computation, quantum key distribution, and quantum teleportation. This work builds upon the work of Parthasarathy and Sengupta [Infin. Dimens. Anal. Quantum Probab. Relat. Top. 18, 1550023 (2015)].
Security Analysis on Wireless Sensor Network in the Data Center for Energy Internet of Things

In the Energy Internet of Things (EIoT), the sensors in the wireless sensor network (WSN) at the data center (DC) are prone to attacks, and easy to suffer from problems in information security management (ISM), such as poor real-time performance and high complexity.This paper clarifies the structure of the DC WSN for the EIoT, models the WSN under the LEACH, and predicts the possible types of attacks.On this basis, a realtime and dynamic ISM plan was developed for the WSN.In this plan, the structure and operations of LEACH are optimized, and amount of data transmission was reduced through information fusion.Simulation experiment shows that the proposed plan safeguards the communication between adjacent nodes and between upper and lower layers, ensures the durability of network security through real-time update of keys, and promotes the efficiency of data transmission through information fusion.
Recent progress and new methods for detecting causal relations in large nonlinear time series datasets

&amp;lt;p&amp;gt;Detecting causal relationships from observational time series datasets is a key problem in better understanding the complex dynamical system Earth. Recent methodological advances have addressed major challenges such as high-dimensionality and nonlinearity, e.g., PCMCI (Runge et al. Sci. Adv. 2019), but many more remain. In this talk I will give an overview of challenges and methods and present a novel algorithm to identify causal directions among contemporaneous (or instantaneous) relationships. Such contemporaneous relations frequently appear when time series are aggregated (e.g., at a monthly resolution). Then approaches such as Granger Causality and PCMCI fail because they currently only address time-lagged causal relations.&amp;lt;br&amp;gt;We present extensive numerical examples and results on the causal relations among major climate modes of variability. The work overcomes a major drawback of current causal discovery methods and opens up entirely new possibilities to discover causal relations from time series in climate research and other fields in geosciences.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Runge et al., Detecting and quantifying causal associations in large nonlinear time series datasets, Science Advances eeaau4996 (2019).&amp;lt;/p&amp;gt;
A remark on a success rate model for side-channel attack analysis

Abstract The success rate is the most common evaluation metric for measuring the performance of a particular side-channel attack scenario. We improve on an analytic formula for the success rate.
Information Theoretic Limits of Cardinality Estimation: Fisher Meets Shannon

Estimating the cardinality (number of distinct elements) of a large multiset is a classic problem in streaming and sketching, dating back to Flajolet and Martin's classic Probabilistic Counting (PCSA) algorithm from 1983. In this paper we study the intrinsic tradeoff between the space complexity of the sketch and its estimation error in the random oracle model. We define a new measure of efficiency for cardinality estimators called the Fisher-Shannon (Fish) number $\mathcal{H}/\mathcal{I}$. It captures the tension between the limiting Shannon entropy ($\mathcal{H}$) of the sketch and its normalized Fisher information ($\mathcal{I}$), which characterizes the variance of a statistically efficient, asymptotically unbiased estimator. Our results are as follows. We prove that all base-$q$ variants of Flajolet and Martin's PCSA sketch have Fish-number $H_0/I_0 \approx 1.98016$ and that every base-$q$ variant of (Hyper)LogLog has Fish-number worse than $H_0/I_0$, but that they tend to $H_0/I_0$ in the limit as $q\rightarrow \infty$. Here $H_0,I_0$ are precisely defined constants. We describe a sketch called Fishmonger that is based on a smoothed, entropy-compressed variant of PCSA with a different estimator function. It is proved that with high probability, Fishmonger processes a multiset of $[U]$ such that at all times, its space is $O(\log^2\log U) + (1+o(1))(H_0/I_0)b \approx 1.98b$ bits and its standard error is $1/\sqrt{b}$. We give circumstantial evidence that $H_0/I_0$ is the optimum Fish-number of mergeable sketches for Cardinality Estimation. We define a class of linearizable sketches and prove that no member of this class can beat $H_0/I_0$. The popular mergeable sketches are, in fact, also linearizable.
Adaptive Channel Estimation based on Deep Learning

Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.
Chapter 4 Creation of Knowledge Graphs

This chapter introduces how Knowledge Graphs are generated. The goal is to gain an overview of different approaches that were proposed and find out more details about the current prevalent ones. After reading this chapter, the reader should have an understanding of the different solutions available to generate Knowledge Graphs and should be able to choose the mapping language that best suits a certain use case.
Research and Implementation of Handwritten Numbers Recognition System Based on Neural Network and Tensorflow Framework

Abstract It is difficult for a computer to recognize handwritten numbers with a high accuracy because of many factors such as shape, size and others. This paper applies a neural network based on Tensorflow framework to implement the handwritten numbers recognition system. The system is compiled and run in vim under Mac operating system with an 8g RAM. This system first builds a neural network, then trains the model on the MNIST dataset to output the accuracy of handwritten numbers recognition. We can input the handwritten picture and get the prediction result through this system. Then we optimize this system on neural network, activation function, loss function, learning rate and generalization to improve the accuracy. Finally, we prove that this system has a higher accuracy closed to 98% for recognizing.
Digital Surveillance Systems for Tracing COVID-19: Privacy and Security Challenges with Recommendations

Coronavirus disease 2019, i.e. COVID-19 has imposed the public health measure of keeping social distancing for preventing mass transmission of COVID-19. For monitoring the social distancing and keeping the trace of transmission, we are obligated to develop various types of digital surveillance systems, which include contact tracing systems and drone-based monitoring systems. Due to the inconvenience of manual labor, traditional contact tracing systems are gradually replaced by the efficient automated contact tracing applications that are developed for smartphones. However, the commencement of automated contact tracing applications introduces the inevitable privacy and security challenges. Nevertheless, unawareness and/or lack of smartphone usage among mass people lead to drone-based monitoring systems. These systems also invite unwelcomed privacy and security challenges. This paper discusses the recently designed and developed digital surveillance system applications with their protocols deployed in several countries around the world. Their privacy and security challenges are discussed as well as analyzed from the viewpoint of privacy acts. Several recommendations are suggested separately for automated contact tracing systems and drone-based monitoring systems, which could further be explored and implemented afterwards to prevent any possible privacy violation and protect an unsuspecting person from any potential cyber attack.
Gazing behavior exhibited by people with low vision while navigating streets

“Low vision” is defined as uncorrectable vision loss that interferes with daily activities. The gazing behavior exhibited by people with low vision as they walk is particularly important because it illustrates how they utilize information in various environments. In this study, eight participants with low vision and eight fully sighted participants were asked to navigate a roadway and a sidewalk wearing a mobile eye-tracking device. Their gazing behavior and walking behavior were recorded and analyzed. As a result, on the roadway, participants with low vision walked while fixing their gazes mainly on the road surface and white traffic lines around 10 meters ahead, as well as on the buildings around 39 meters ahead. On the sidewalk, on the other hand, they walked while fixing their gaze on mainly the road surface and tactile paving seven to eight meters ahead, as well as on the buildings around 24 meters ahead. These findings suggest that people with low vision use not only nearby objects but also distant objects as visual cues. In addition, the importance of using continuous road surface markers, such as white traffic lines and tactile paving, to enable people with low vision to walk outdoors safely is suggested.
Polyseme-Aware Vector Representation for Text Classification

Representation models for text classification have recently shown impressive performance. However, these models neglect the importance of polysemous words in text. When polysemous words appear in a text, imprecise polysemous word embeddings will produce low-quality text representation that results in changing the original meaning of the text. To address this problem, in this paper, we present a more effective model architecture, the polyseme-aware vector representation model (PAVRM), to generate more precise vector representations for words and texts. The PAVRM can effectively identify polysemous words in a corpus with a context clustering algorithm. Additionally, we propose two methods to construct polysemous word representations, PAVRM-Context and PAVRM-Center. Experiments conducted on three standard text classification tasks and a custom text classification task demonstrate that the proposed PAVRM can be effectively introduced into existing models to generate higher-quality word and text representations to achieve better classification performance.
Pengembangan E-Modul Otomatisasi Tata Kelola Kepegawaian Berbasis Android Pada Materi Semester Gasal Kelas XI OTKP 2 di SMK PGRI 2 Sidoarjo

Learning resources is one of the needs in the learning process. To ensure the learning process conducted effectively, learning resources must fulfill requirement of curriculum, such as by using technology to get facilities of access and flexibility in learning. This journal aims to develop learning resources, determine the appropriateness of learning resources, and determine student responses through the development of the Governance Staffing Automation Based on Android and Scientific e-module on Class XI OTKP 2 Odd Semester Material at SMK PGRI 2 Sidoarjo. To carry out these developments, a 4-D research model was used with data collection in the form of material expert validation, language expert validation, graphic expert validation and student responses. The results of the study stated that the e-modules developed in the category were very suitable to be used as learning resources in terms of validations result of material feasibility is 96,57%, language feasibility 95,71%, and feasibility of graphic 92,67%. If averaged, the result of product feasibility obtained a percentage of 94,98%. Then, the student response results obtained score of 94,58%.
Video Representation Learning by Recognizing Temporal Transformations

We introduce a novel self-supervised learning approach to learn representations of videos that are responsive to changes in the motion dynamics. Our representations can be learned from data without human annotation and provide a substantial boost to the training of neural networks on small labeled data sets for tasks such as action recognition, which require to accurately distinguish the motion of objects. We promote an accurate learning of motion without human annotation by training a neural network to discriminate a video sequence from its temporally transformed versions. To learn to distinguish non-trivial motions, the design of the transformations is based on two principles: 1) To define clusters of motions based on time warps of different magnitude; 2) To ensure that the discrimination is feasible only by observing and analyzing as many image frames as possible. Thus, we introduce the following transformations: forward-backward playback, random frame skipping, and uniform frame skipping. Our experiments show that networks trained with the proposed method yield representations with improved transfer performance for action recognition on UCF101 and HMDB51.
Towards an Architecture for Reliable Capacity Provisioning for Distributed Clouds

The complexity of computing along the cloud-to-edge continuum presents significant challenges to ICT operations and in particular reliable capacity planning and resource provisioning to meet unpredictable, fluctuating, and mobile demand. This chapter presents a high-level conceptual overview of RECAP—an architectural innovation to support reliable capacity provisioning for distributed clouds—and its operational modes and functional building blocks. In addition, the major design concepts informing its design—namely separation of concerns, model-centricism, modular design, and machine learning and artificial intelligence for IT operations—are also discussed.
Superradiance from nonideal initial states: A quantum trajectory approach

Collective emission behavior is usually described by the decay dynamics of the completely symmetric Dicke states. To study a more realistic scenario, we investigate alternative initial states inducing a more complex time evolution. Superposition states of the fully inverted Dicke state and the Dicke ground state with unequal mutual weights are studied as examples as well as superradiance stemming from atoms in clusters separated by more than one wavelength. The Monte Carlo wave function method serves as a framework to study the dynamics of quantum states, which is determined by quantum jumps, on the one hand, and continuous evolution dynamics, on the other hand. We compare this method with the classical picture of a system of rate equations written for the diagonal components of the density matrix.
A New Combinatorial Coded Design for Heterogeneous Distributed Computing

Coded Distributed Computing (CDC) introduced by Li et al. in 2015 offers an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as MapReduce and Spark. In particular, increasing the computation load in the Map phase by a factor of r can create coded multicasting opportunities to reduce the communication load in the Shuffle phase by the same factor. However, the CDC scheme is designed for the homogeneous settings, where the storage, computation load and communication load on the computing nodes are the same. In addition, it requires an exponentially large number of input files (data batches), reduce functions and multicasting groups relative to the number of nodes to achieve the promised gain. We address the CDC limitations by proposing a novel CDC approach based on a combinatorial design, which accommodates heterogeneous networks where nodes have varying storage and computing capabilities. In addition, the proposed approach requires an exponentially less number of input files compared to the original CDC scheme proposed by Li et al. Meanwhile, the resulting computation-communication trade-off maintains the multiplicative gain compared to conventional uncoded unicast and asymptotically achieves the optimal performance proposed by Li et al.
Digital media users and Facebook hashtags' misinterpretations

Purpose This paper examines reasons of pragmatic functions' misinterpretation of three types of Facebook hashtags: long, short and multiple mixed hashtags. Design/methodology/approach Focus group interviews of 15 English language learners, who are also active users of Facebook and hashtags, are conducted. Thematic analysis is performed through the software Nvivo for arriving at reliable findings. Findings The findings show that unknown vocabulary in contents and lowercasing in long and short hashtags are major factors that cause misinterpretation. Also, the symbol of underscore and appropriate usage of upper and lowercasing of alphabets should be adopted in hashtag writing for the improvement in communication and successful conveyance of the intended meaning. Originality/value This paper contributes to the online hashtag writing style by finding reasons for the misinterpretations of different types of hashtags. Hashtags have been developed for adequate communication (Livingstone, 2012), but in Pakistan, hashtags are practiced as a “fashionable” trend and thus result in misinterpretation and inadequate communication among readers even of the same background (Tariq and Sarfraz, 2018). Moreover, this study focuses on the trend of hashtagging that is common among university students and particularly second-language learners because they are active users of Facebook and adopt the latest trends quickly.