Reviewerly/exHarmony huggingface dataset:
Research on Target Tracking Problem of Fixed Scene Video Surveillance Based on Unlabeled Data

Aiming at the problem that there are enough data in target tracking based on video surveillance, but the number of annotations is seriously insufficient or the quality is not high, this paper proposes a solution to the problem of target tracking based on unlabeled data. The target detection network with pre-trained weights is used to perform target detection on the objects in the unlabeled video. When the target is detected and the confidence is higher than 95%, the frame is extracted as the "first frame" of the KCF tracker. The KCF tracker will provide a large amount of labeled data as a training set to realize the training of the target tracking network MDNet. This paper achieves effective target tracking in fixed scene video surveillance based on unlabeled data and verifies that adding manually labeled data to the video surveillance data set will further improve network performance and prove the superiority and achievability of the proposed method.
Scalable Algorithms for Multiple Network Alignment

Related DatabasesWeb of Science You must be logged in with an active subscription to view this.Article DataHistorySubmitted: 17 June 2020Accepted: 19 April 2021Published online: 05 August 2021Keywordsmultiple network alignment, network alignment, low-rank tensor, $k$-partite matching, $k$-dimensional matchingAMS Subject Headings05C85, 90B10, 68R10Publication DataISSN (print): 1064-8275ISSN (online): 1095-7197Publisher: Society for Industrial and Applied MathematicsCODEN: sjoce3
Explicit Artin maps into <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e19" altimg="si19.svg"><mml:msub><mml:mrow><mml:mi mathvariant="normal">PGL</mml:mi></mml:mrow><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math>

Let G⊂PGL2(Fq) where q is any prime power, and let Q∈Fq(x) such that Fq(x)/Fq(Q) is a Galois extension with group G. By explicitly computing the Artin map on unramified degree-1 primes in Fq(Q) for various groups G, interesting new results emerge about finite fields, additive polynomials, and conjugacy classes of PGL2(Fq). For example, by taking G to be a unipotent group, one obtains a new characterization for when an additive polynomial splits completely over Fq. When G=PGL2(Fq), one obtains information about conjugacy classes of PGL2(Fq). When G is the group of order 3 generated by 11−10, one obtains a natural tripartite symbol on Fq with values in Z/3Z. Some of these results generalize to PGL2(K) for arbitrary fields K.
Learning frame-level affinity with video-level labels for weakly supervised temporal action detection

Weakly supervised temporal action detection aims at localizing actions with only video-level labels rather than lots of frame-level labels. To this end, previous methods train a classification network for mining discernible action frames as detection results. However, the classification network is known to only concentrate on local discernible frames rather than the entire action instance. Therefore, substantial numbers of indiscernible action frames are not detected and the detection results are incomplete. To alleviate this issue, we propose a novel method to facilitate the detection of indiscernible frames based on learning frame-level affinities. In the proposed method, we design a network (named Affinity Network) for predicting affinities between pairs of adjacent frames. Then, the affinities are used as transition probabilities to propagate local responses to indiscernible frames. As a result, the responses of indiscernible frames can be enhanced and the detection of them can be facilitated. For learning the network, we propose strategies to synthesize frame-pair and video-pair training samples, which are conducive to learn frame-level affinities with only video-level labels. The experimental results on THUMOS14 dataset and ActivityNet1.2 dataset show that the detection performance of our framework outperforms most previous weakly supervised action detection methods, and is even as competitive as some fully supervised action detection methods.
Multi-GPU-based Convolutional Neural Networks Training for Text Classification

Text classification is a common problem in Natural Language Processing (NLP). In recent years, Convolutional Neural Networks (CNNs) have achieved quite promising results in many NLP tasks such as text classification. However, training CNNs is a computationally intensive task, which limits their use in large scale applications. In this paper, we are focusing on the improvement of the training cost of the text classification CNN. We implement a distibuted CNN in which the training process is divided across multiple Graphics Processing Units (GPUs) and the final result is aggregated by the fusion of each trained model with the use of an effective communication strategy. Our proposed model was tested on different multiple-GPUs environments. We achieved a good speedup compared to the sequential CNN training. Its accuracy is also very competitive.
A nanophotonic interface for tin-vacancy spin qubits in diamond

Building quantum networks requires efficient coupling of solid-state quantum emitters to photonic devices. Tinvacancy center (SnV) has attracted much interest for having long spin coherence times at temperatures above 1 K. Employing SnV as an optically addressable qubit requires integration with photonic structures to both route the emitted photons and enhance the light-matter interaction. We present incorporation of high-quality SnV centers with narrow linewidths in suspended diamond waveguides. Furthermore, we fabricate photonic crystal cavities in diamond with embedded SnV centers. We observe strong intensity enhancement of the photon emission when the cavity is resonant with the color center. Time-resolved photoluminescence measurements confirm that this effect is due to radiative Purcell enhancement of the spontaneous emission. Finally, we demonstrate Stark tuning of transition frequency of SnV centers, essential for multiemitter applications. These results are a significant step toward color-center-based quantum information processing applications without the need for dilution refrigerators.
An Overview of Multimedia Technologies in Current Era of Internet of Things (IoT)

Throughout the digital era of internet of things, various forms of media such as email, audio, picture, and video have been able to reach the network arena. Reasons behind this include easier data transmission and storage over a longer span of time, and this is amply assisted by the vast cheap availability of services such as large frequencies. This progression has raised numerous difficulties contradicting the previous’ prosperity. One of the most testing issues is the absence of security of the information. Numerous strategies in computerized watermarking were created as and when required, to counter this test, yet one single methodology that would take care of the security issues for all the media types has neglected to exist till now. We have recommended a few strategies which fulfill the previously mentioned need. They are spread range watermarking, non-disavowal absent watermarking, and assault portrayal. The main strategy is tied in with inserting the watermark in any indispensable areas of the archive so that watermark can’t be evacuated subtly, while the subsequent one proposes watermarking plans for information dispersion and the last technique improves the strength of the watermark by portraying the assault utilizing reference watermark.
Shadow-Mapping for Unsupervised Neural Causal Discovery

An important goal across most scientific fields is the discovery of causal structures underling a set of observations. Unfortunately, causal discovery methods which are based on correlation or mutual information can often fail to identify causal links in systems which exhibit dynamic relationships. Such dynamic systems (including the famous coupled logistic map) exhibit ‘mirage’ correlations which appear and disappear depending on the observation window. This means not only that correlation is not causation but, perhaps counter-intuitively, that causation may occur without correlation. In this paper we describe Neural Shadow-Mapping, a neural network based method which embeds high-dimensional video data into a low-dimensional shadow representation, for subsequent estimation of causal links. We demonstrate its performance at discovering causal links from video-representations of dynamic systems.
Lorentz and Gale–Ryser theorems on general measure spaces

Based on the Gale–Ryser theorem [2, 6], for the existence of suitable $(0,1)$ -matrices for different partitions of a natural number, we revisit the classical result of Lorentz [4] regarding the characterization of a plane measurable set, in terms of its cross-sections, and extend it to general measure spaces.
A Survey on Simulation Environments for Reinforcement Learning

Most of the recent studies of reinforcement learning and robotics basically employ computer simulation due to the advantages of time and cost. For this reason, users have to spare time for investigation in order to choose optimal environment for their purposes. This paper presents a survey result that can be a guidance in user’s choice for simulation environments. The investigation result includes features, brief historical backgrounds, license policies and formats for robot and object description of the eight most popular environments in robot RL studies. We also propose a quantitative evaluation method for those simulation environments considering the features and a pragmatic point of view.
Application of GUI Matlab in physics: Planetary motion (Kepler’s Law)

Abstract This study aimed to create a teaching material using an application with a Graphical User Interface (GUI) provided on MATLAB. This teaching material is intended for Physics subject. Creating a GUI with MATLAB was made using the guide facility provided in MATLAB. The MATLAB GUI has a relatively small file size so that it can speed up the running program commands. This research aimed to create an application that is easy and effective to be used by users without having to understand the complexity of computing MATLAB. The application consists of a main menu, a GUI that can be run according to the materials needed by the users, as well as a menu to close the application. The physical matter contained in it is planetary motion (Kepler’s law). Physics learning materials that can be used as a reference in running the GUI program, e-modules containing procedures for creating the GUI by using MATLAB for learning, as well as the MATLAB source code used in every programming. For further research, the author expects the development of this study by using the e-module contained in the application as a material for creating GUI MATLAB.
Establishing forensics capabilities in the presence of superuser insider threats

Insider threats are paving ways to the headlines of security articles and reports across the globe. It's a common practice across organizations to have designated employees as administrators with complete administrative or superuser capabilities for the IT infrastructure. In this paper, we argue that superusers with all the administrative and access control capabilities may escape from the scrutiny of forensic investigation and may also become a major obstacle in the process of evidence collection. Through this work, we open a discussion on forensic aspects of insider threats with a particular focus on superuser forensics. We identify the anti-forensic administrative privileges of the superusers and discuss the sheer forensic repercussions with the help of four generic insider threat cases. As our primary contribution, we identify and define the four important requirements for a superuser-immune solution. These requirements include denying and logical access to the potential forensic artifacts, timely synchronization and integrity of evidential artifacts, and ensuring the execution of legitimate code/service and notification capabilities. Based on the identified requirements, we propose a forensic compliant mechanism, "Log-of-logs server" to countermeasure the inherent anti-forensic capabilities of the superuser. We showcase that the proposed framework effectively helps in establishing forensic capabilities for superusers. We also present the security analysis of our framework and discuss its forensic feasibility.
Using Deep Learning Models Combined with Crowd Emotion Models to Identify Abnormal Behaviors in Crowds

Abstract Aiming at the problem that the definition of crowd abnormal behavior detection is ambiguous and difficult to combine with context semantics, an algorithm using OCC human emotion model combined with crowd entropy is proposed. First calculate the crowd entropy for the crowd, and determine whether the entropy value is abnormal, if it is abnormal, further extract the optical flow OF and HOG. Then project it into two-dimensional vector data, send it to CNN for local feature extraction and combine with OCC model to achieve the description of crowd emotions. Finally, predict whether the abnormality occurs according to the judgment factor. Verified on the data set, this method shows a high accuracy.
Attention-oriented Brain Storm Optimization for Multimodal Optimization Problems

Population-based methods are often used to solve multimodal optimization problems. By combining niching or clustering strategy, the state-of-the-art approaches generally divide the population into several subpopulations to find multiple solutions for a problem at hand. However, these methods only guided by the fitness value during iterations, which are suffering from determining the number of subpopulations, i.e., the number of niche areas or clusters. To compensate for this drawback, this paper presents an Attention-oriented Brain Storm Optimization (ABSO) method that introduces the attention mechanism into a relatively new swarm intelligence algorithm, i.e., Brain Storm Optimization (BSO). By converting the objective space from the fitness space into "attention" space, the individuals are clustered and updated iteratively according to their salient values. Rather than converge to a single global optimum, the proposed method can guide the search procedure to converge to multiple "salient" solutions. The preliminary results show that the proposed method can locate multiple global and local optimal solutions of several multimodal benchmark functions. The proposed method needs less prior knowledge of the problem and can automatically converge to multiple optimums guided by the attention mechanism, which has excellent potential for further development.
Influence of tolerance to uncertainty on personal and professional development of pilots during aviation specialists’ training

The application of a new pragmatic educational model in the information environment of the higher technical education institution, which corresponds to the innovative and perspective model of society development, determined the transition from the process approach in the organisation of professional training of future aviation specialists to a reflective and effect–oriented one. It forms the basis of training objectives related to providing the country with specialists in aviation industry. The aim of the study is to theoretically substantiate, develop and experimentally test the effectiveness of the professional training model for future specialists under the conditions of uncertainty of the educational process of higher education institutions on the basis of a reflective approach. The result of the experimental stage of the study, as the foundation of the personal and professional development of the student, confirmed the effectiveness of the model based on the reflective approach. The systemically important core of the study considered the goals and results implemented in the educational and informational environment of the National Aviation University with the help of appropriately constructed psychological and pedagogical conditions, which were implemented at each stage of professional training with the innovative educational technologies.
Gaussian optical networks for one-dimensional anyons

We study the dynamics of bosonic and fermionic anyons defined on a one-dimensional lattice, under the effect of Hamiltonians quadratic in creation and annihilation operators, also called Gaussian Hamiltonians. These anyonic models are obtained from deformations of the standard bosonic or fermionic commutation relations via the introduction of a nontrivial exchange phase between different lattice sites. We study the effects of the anyonic exchange phase on the usual bosonic and fermionic bunching behaviors. We show how to exploit the inherent Aharonov-Bohm effect exhibited by these particles to build a deterministic, entangling two-qubit gate and prove quantum computational universality in these systems. We define coherent states for bosonic anyons and study their behavior under two-mode passive, Gaussian devices. In particular we prove that, for a particular value of the exchange factor, an anyonic mirror can generate cat states, an important resource in quantum information processing with continuous variables.
Accelerating Federated Edge Learning

Transferring large models in federated learning (FL) networks is often hindered by clients’ limited bandwidth. We propose <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {FedAA}$ </tex-math></inline-formula> , an FL algorithm which achieves fast convergence by exploiting the regularized Anderson acceleration (AA) on the global level. First, we demonstrate that FL can benefit from acceleration methods in numerical analysis. Second, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textsf {FedAA}$ </tex-math></inline-formula> improves the convergence rate for quadratic losses and improves the empirical performance for smooth and strongly convex objectives, compared to FedAvg, an FL algorithm using gradient descent (GD) local updates. Experimental results demonstrate that employing AA can significantly improve the performance of FedAvg, even when the objective is non-convex.
Privacy Preserving On Remote Sensing Data Using Reversible Data Hiding

Abstract The method of steganography covers message in the cover file and forms a stego file. Imagery needs a method that will boost protection, minimise distortion in the stego file and recover data without failure. There is a need for reducing transmitting time in the age of multimedia and the Internet. The main purpose of this project is to create safe contact between the sender and the recipient through emails and other modes of communication. A reversible data hiding algorithm was proposed in the integer wavelet transformation (IWT) domain to resolve the image distortion problem. This strategy aims to incorporate data into IWT coefficients of higher frequencies. After wavelet coefficients are collected by converting the integer wavelet data into high-frequency subbands HL, LH and HH, the human eyes would have fewer recognisable objects. After data is incorporated in such IWT coefficients, reverse integral wavelet transformation can occur following application. The stegno image was further encrypted by using Elliptic Curve Cryptography (ECC). The process of encryption enhances the image security during data transmission. In our proposed work inverse wavelet transformation (IWT) and Elliptic Curve Cryptography (ECC) algorithms are effectively utilized to achieve highly secure communication.
Analysis of ML Algorithms to Support Elastic Service Chaining in eHealth Vertical Applications

The efficient design of SFC-enabled eHealth applications requires an accurate provision of the underlying infrastructure. This provision requires both computing and networking resources to meet stringent QoS requirements under any conditions of service demand. Cloud providers often offer automatic elasticity strategies based on monitoring specific metrics that lead to a waste of resources, time/energy consumption, and the problem of starvation with competing services. Our findings provide evidence that proactive-based elasticity overcomes these issues, when assisted by Machine Learning (ML) methods for predicting Internet traffic load. An optimal autoscaling algorithm depends on high precision and fast predictions to provide accurate results. Thus, this paper assesses ML algorithms to support SFC-enabled eHealth vertical applications. The experimental results suggest that the evaluated models achieved similar accuracy metrics, with an MLP architecture delivering the best performance in terms of time training and average prediction time.
Analyzing the Existence of Organization Specific Languages on Twitter

The presence of organisations in Online Social Networks (OSNs) has motivated malicious users to look for attack vectors, which are then used to increase the possibility of carrying out successful attacks and obtaining either private information or access to the organisation. This article hypothesised that organisations have specific languages that their members use in OSNs, which malicious users could potentially use to carry out an impersonation attack. To prove these specific languages, we propose two tasks: classifying tweets in isolation by their author’s organisation and classifying users’ entire timelines by organisation. To accomplish both tasks, we generate a dataset of over 15 million tweets of five organisations, and we apply language dependant models to test our hypothesis. Our results and the ablation study conclude that it is possible to classify tweets and users by organisation with more than three times the performance achieved by a traditional ML algorithm, showing a substantial potential for predicting the linguistic style of tweets.
Toward a Science of Augmented Agency

Abstract To supervise and enjoy the benefits of digitalization, humanity must develop the science of augmented agency. This future science will be multidisciplinary, joining the human and computer sciences, as well as neuroscience and related fields of human science and engineering, but also going further. In fact, the novelties of digital augmentation transcend the conceptual architecture of modernity and its problematics. Digitalized humanity is truly exceptional. To describe these phenomena, science needs fresh concepts, categories, and constructs. This chapter offers proposals in this regard. It maps the emerging field of augmented agency, its core categories, mechanisms, and dilemmas, and thereby looks forward to a new science of digitally augmented agency.
Applications of Machine Learning for 5G Advanced Wireless Systems

Wireless communication is playing an important role in benefiting our daily lives. Fifth generation (5G) advanced wireless communication is anticipated to support various upcoming services. Moreover, machine learning (ML) is expected to optimize wireless systems by tackling complex problems which cannot be solved using traditional mathematical models. In this paper, applications of ML for 5G advanced wireless communication are introduced in conjunction with its research advances. Furthermore, this paper explores the challenges and future research directions of applying ML in 5G advanced wireless communication.