Reviewerly/exHarmony huggingface dataset:
Noise Models in Classification: Unified Nomenclature, Extended Taxonomy and Pragmatic Categorization

This paper presents the first review of noise models in classification covering both label and attribute noise. Their study reveals the lack of a unified nomenclature in this field. In order to address this problem, a tripartite nomenclature based on the structural analysis of existing noise models is proposed. Additionally, a revision of their current taxonomies is carried out, which are combined and updated to better reflect the nature of any model. Finally, a categorization of noise models is proposed from a practical point of view depending on the characteristics of noise and the study purpose. These contributions provide a variety of models to introduce noise, their characteristics according to the proposed taxonomy and a unified way of naming them, which will facilitate their identification and study, as well as the reproducibility of future research.
Experience

This paper presents our experience in adopting recent research results of mobile phone based indoor/outdoor detection (IODetector) to support the real world business of on-demand food delivery. The real world deployment of the adopted IODetector involves three phases spanning 20 months, during which the deployment scales from a feasibility study across a few areas of interest to a city-wide trial in Shanghai, and eventually to nationwide deployment over 367 cities in China. Iterative development has been performed throughout different deployment phases to excel the IODetector. Large scale evaluation and comparative A/B testing suggest key value of adopting indoor/outdoor detection in the real world business. We also present the lessons learned from the deployment experience including real world know-hows, practical limits and constraints, as well as discussions on design alternatives. We believe this paper provides insights to guide future efforts in translating research results to industry adoptions.
PlatoGL: Effective and Scalable Deep Graph Learning System for Graph-enhanced Real-Time Recommendation

Recently, graph neural network (GNN) approaches have received huge interests in recommendation tasks due to their ability of learning more effective user and item representations. However, existing GNN-based recommendation models cannot support real-time recommendation where the model keeps its freshness by continuously training the streaming data that users produced, leading to negative impact on recommendation performance. To fully support graph-enhanced large-scale recommendation in real-time scenarios, a deep graph learning system is required to dynamically store the streaming data as a graph structure and enable the development of any GNN model incorporated with the capabilities of real-time training and online inference. However, such requirements rule out existing deep graph learning solutions. In this paper, we propose a new deep graph learning system called PlatoGL, where (1) an effective block-based graph storage is designed with non-trivial insertion/deletion mechanism for updating the graph topology in-milliseconds, (2) a non-trivial multi-blocks neighbour sampling method is proposed for efficient graph query, and (3) a cache technique is exploited to improve the storage stability. We have deployed PlatoGL in Wechat, and leveraged its capability in various content recommendation scenarios including live-streaming, article and micro-video. Comprehensive experiments on both deployment performance and benchmark performance~(w.r.t. its key features) demonstrate its effectiveness and scalability. One real-time GNN-based model, developed with PlatoGL, now serves the major online traffic in WeChat live-streaming recommendation scenario.
A Deep Reinforcement Learning Quality Optimization Framework for Multimedia Streaming over 5G Networks

Media applications are amongst the most demanding services. They require high amounts of network capacity as well as computational resources for synchronous high-quality audio–visual streaming. Recent technological advances in the domain of new generation networks, specifically network virtualization and Multiaccess Edge Computing (MEC) have unlocked the potential of the media industry. They enable high-quality media services through dynamic and efficient resource allocation taking advantage of the flexibility of the layered architecture offered by 5G. The presented work demonstrates the potential application of Artificial Intelligence (AI) capabilities for multimedia services deployment. The goal was targeted to optimize the Quality of Experience (QoE) of real-time video using dynamic predictions by means of Deep Reinforcement Learning (DRL) algorithms. Specifically, it contains the initial design and test of a self-optimized cloud streaming proof-of-concept. The environment is implemented through a virtualized end-to-end architecture for multimedia transmission, capable of adapting streaming bitrate based on a set of actions. A prediction algorithm is trained through different state conditions (QoE, bitrate, encoding quality, and RAM usage) that serves the optimizer as the encoding values of the environment for action prediction. Optimization is applied by selecting the most suitable option from a set of actions. These consist of a collection of predefined network profiles with associated bitrates, which are validated by a list of reward functions. The optimizer is built employing the most prominent algorithms in the DRL family, with the use of two Neural Networks (NN), named Advantage Actor–Critic (A2C). As a result of its application, the ratio of good quality video segments increased from 65% to 90%. Furthermore, the number of image artifacts is reduced compared to standard sessions without applying intelligent optimization. From these achievements, the global QoE obtained is clearly better. These results, based on a simulated scenario, increase the interest in further research on the potential of applying intelligence to enhance the provisioning of media services under real conditions.
Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU's fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.
Improved Dragonfly Optimization Algorithm for Detecting IoT Outlier Sensors

Things receive digital intelligence by being connected to the Internet and by adding sensors. With the use of real-time data and this intelligence, things may communicate with one another autonomously. The environment surrounding us will become more intelligent and reactive, merging the digital and physical worlds thanks to the Internet of things (IoT). In this paper, an optimal methodology has been proposed for distinguishing outlier sensors of the Internet of things based on a developed design of a dragonfly optimization technique. Here, a modified structure of the dragonfly optimization algorithm is utilized for optimal area coverage and energy consumption reduction. This paper uses four parameters to evaluate its efficiency: the minimum number of nodes in the coverage area, the lifetime of the network, including the time interval from the start of the first node to the shutdown time of the first node, and the network power. The results of the suggested method are compared with those of some other published methods. The results show that by increasing the number of steps, the energy of the live nodes will eventually run out and turn off. In the LEACH method, after 350 steps, the RED-LEACH method, after 750 steps, and the GSA-based method, after 915 steps, the nodes start shutting down, which occurs after 1227 steps for the proposed method. This means that the nodes are turned off later. Simulations indicate that the suggested method achieves better results than the other examined techniques according to the provided performance parameters.
Retracted: Object detection and recognition using deep learning‐based techniques

Abstract The above article from IET Communications, published online on 17 October 2022 in Wiley Online Library ( wileyonlinelibrary.com ), has been retracted by agreement between the Interim Editor‐in‐Chief, Jian Ren, the Institution of Engineering and Technology (the IET) and John Wiley and Sons Ltd. This article was published as part of a Guest Edited special issue. Following an investigation, the IET and the journal have determined that the article was not reviewed in line with the journal's peer review standards and there is evidence that the peer review process of the special issue underwent systematic manipulation. Accordingly, we cannot vouch for the integrity or reliability of the content. As such we have taken the decision to retract the article. The authors have been informed of the decision to retract.
Digital interventions at scale

research-article Free AccessDigital interventions at scale: lessons from NPTEL and IIT Madras B.S. degree program Authors: Jayakrishnan Warriem Indian Institute of Technology Madras, Chennai, India Indian Institute of Technology Madras, Chennai, IndiaView Profile , Andrew Thangaraj Indian Institute of Technology Madras, Chennai, India Indian Institute of Technology Madras, Chennai, IndiaView Profile , Madhavan Mukund Chennai Mathematical Institute, Chennai, India Chennai Mathematical Institute, Chennai, IndiaView Profile , Bharathi Balaji Indian Institute of Technology Madras, Chennai, India Indian Institute of Technology Madras, Chennai, IndiaView Profile Authors Info & Claims Communications of the ACMVolume 65Issue 11November 2022 pp 54–57https://doi.org/10.1145/3550473Published:20 October 2022Publication History 1citation4,212DownloadsMetricsTotal Citations1Total Downloads4,212Last 12 Months4,212Last 6 weeks1,340 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
ALGORITHMS IN THE COURTS: IS THERE ANY ROOM FOR A RULE OF LAW?

The rule of law is one of the fundamental pillars, along with human rights and democracy, which are affected by digitalisation today. Digital technologies used for the victory of populism, the manipulation of opinions, attacks on the independence of judges, and the general instrumentalisation of the law contribute significantly to the onset of negative consequences for the rule of law. Particularly dangerous are the far-reaching consequences of the algorithmisation of decision-making, including judicial decisions. The theoretical line of this research is based on the axiological method since the rule of law, democracy, and human rights are not only the foundations of legal order, but also values recognised in many societies and supported at the individual level. The study also relied on the phenomenological method in terms of assessing the experience of being influenced by digital technologies in public and private life. The practical line of research is based on the analysis of cases of the European Court of Human Rights and the Court of Justice to illustrate the changes in jurisprudence influenced by digitalisation. This article argues that the potential weakening of the rule of law could be related to the impact of certain technologies itself, and to their impact on certain values and foundations which is significantly aggravated. Judicial independence is affected since the judges are involved in digital interactions and are influenced by technologies along personal and public lines. That technologies often belong private sector but are perceived as neutral and infallible, which is highly predictive of court decisions. This leads to a distortion of the essence of legal certainty and a shift of trust from the courts to certain technologies and their creators. The possibility of algorithmic decision-making raises the question of whether the results will be fairer, or at least as fair, as those handed down by human judges. This entails two problems, the first of which is related to the task of interpreting the law and the second of which involves the need to explain decisions. Algorithms, often perceived as reliable, are not really capable of interpreting the law, and their ability to provide proper explanations for decisions or understand context and social practices is questionable. Even partial reliance on algorithms should be limited, given the growing inability to draw a line between the human and algorithmic roles in decision-making and determine who should be responsible for the decision and to what extent.
Fuzzy Controllers for Increasing Efficiency of the Floating Dock's Operations: Design and Optimization

This chapter presents the developed by the authors combined step-by-step approach to design of fuzzy controllers (FC) for the automatic control systems (ACS) of the floating docks main operations. The proposed approach allows performing the synthesis of the structure and parameters of the Mamdani type FCs for the ACSs of the main control parameters of the floating docks operations using expert knowledge in conjunction with certain optimization procedures on the basis of mathematical programming methods. In particular, the structural-parametric optimization of the FCs is carried out on the basis of the ACS desired transients and methods of gradient descent of numerical optimization, that allows to provide high-quality indicators of control of the docking operations. In turn, the reduction of the FC rule base (RB) is performed on the basis of calculating the influence of each rule on the control process and allows reducing the total number of rules of the RB without deterioration of the quality indicators of the docking operations control. In order to study and validate the effectiveness of the developed approach, the design of the Mamdani type FC for the draft ACS of the floating dock for low-tonnage vessels is performed in this work. The designed FC has a relatively simple hardware and software implementation as well as allows to achieve high quality indicators of the draft control, high economic and operational indicators, low energy consumption, as well as high quality of the docking operations conducting, which confirms the high efficiency of the proposed approach.
Rolling Shutter Inversion: Bring Rolling Shutter Images to High Framerate Global Shutter Video

A single rolling-shutter (RS) image may be viewed as a row-wise combination of a sequence of global-shutter (GS) images captured by a (virtual) moving GS camera within the exposure duration. Although rolling-shutter cameras are widely used, the RS effect causes obvious image distortion especially in the presence of fast camera motion, hindering downstream computer vision tasks. In this paper, we propose to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">invert</i> the rolling-shutter image capture mechanism, i.e., recovering a continuous high framerate global-shutter video from two time-consecutive RS frames. We call this task the RS temporal super-resolution (RSSR) problem. The RSSR is a very challenging task, and to our knowledge, no practical solution exists to date. This paper presents a novel deep-learning based solution. By leveraging the multi-view geometry relationship of the RS imaging process, our learning based framework successfully achieves high framerate GS generation. Specifically, three novel contributions can be identified: (i) novel formulations for bidirectional RS undistortion flows under constant velocity as well as constant acceleration motion model. (ii) a simple linear scaling operation, which bridges the RS undistortion flow and regular optical flow. (iii) a new mutual conversion scheme between varying RS undistortion flows that correspond to different scanlines. Our method also exploits the underlying spatial-temporal geometric relationships within a deep learning framework, where no additional supervision is required beyond the necessary middle-scanline GS image. Building upon these contributions, this paper represents the very first rolling-shutter temporal super-resolution deep-network that is able to recover high framerate global-shutter videos from just two RS frames. Extensive experimental results on both synthetic and real data show that our proposed method can produce high-quality GS image sequences with rich details, outperforming the state-of-the-art methods.
The Hong–Ou–Mandel experiment: from photon indistinguishability to continuous-variable quantum computing

Abstract We extensively discuss the Hong–Ou–Mandel experiment by taking an original phase-space-based perspective. For this, we analyze time and frequency variables as quantum continuous variables in perfect analogy with position and momentum of massive particles or with the electromagnetic field’s quadratures. We discuss how this experiment can be used to directly measure the time-frequency Wigner function and implement logical gates in these variables. We also briefly discuss the quantum/classical aspects of this experiment providing a general expression for intensity correlations that make explicit the differences between a classical Hong–Ou–Mandel-like dip and a quantum one. Throughout the manuscript, we will often focus and refer to a particular system based on AlGaAs waveguides emitting photon pairs via spontaneous parametric down conversion, but our results can be extended to other analogous experimental systems and to various degrees of freedom. Graphical Abstract The Hong–Ou–Mandel experiment is a landmark in quantum optics, showing the bunching of indistinguishable bunch. In the present contribution, we give another perspective to this experiment based on a phase space representation of the continuous degrees of freedom of the single photons sent into the input arms of such interferometer. We show that the coincidence detection in the output ports of an Hong– Ou–Mandel interferometer is a direct measurement of the Wigner function of the produced photons in a given region of space, and we discuss how continuous degrees of freedom of single photons can be used in continuous variables quantum protocols, as quantum error correction and metrology. Our results open the perspective of broadening even more the applications of single photon-based quantum information-related protocols.
Distance Learning in Higher Education: The Experience of the Covid-19 Pandemic and War in Ukraine

Distance learning has become one of the most popular educational trends of the 21st century, and the COVID-19 pandemic and war in Ukraine has only accelerated the process of its integration into the education sector. &#x0D; The purpose of our work is to study the influence of the online learning format on the adaptation and academic success of students, as well as to search for promising analogues. &#x0D; The methodology. In addition to a comprehensive theoretical analysis, which included a comparison of different approaches and research, we used the method of interviewing respondents, which involved 200 first-year students from 6 Ukrainian higher education institutions (H.S. Skovoroda Kharkiv National Pedagogical University, Taras Shevchenko National University of Kyiv, V. N. Karazin Kharkiv National University, National Technical University of Ukraine Kyiv Polytechnic Institute, State Biotechnology University, Kharkiv National University of Radio Electronics). The survey was conducted online using the Google Forms platform in the period from December 19 to 26, 2021, the calculation and visualization of the received data were performed using Microsoft Office tools. Fisher's statistical test (online-tool) was used to establish differences between the indicators of academic success of the respondents of the two groups.&#x0D; Results. We decided to compare the academic success of students who study online with students included in the blended learning system. Thus, only 8% of the respondents who took the course in an online format received a mark of 5 at the end of the academic semester, while almost a quarter (25%) of the students of the second group who took the course in blended learning received the highest score. We also asked respondents to evaluate the process of their own adaptation to new conditions (distance and blended learning). The results of the survey showed that the adaptation process proceeds much easier in the conditions of the blended learning or Flipped Classroom blended learning model, while the adaptation of respondents to the online format had a number of problems. &#x0D; Conclusion. Online learning has a high potential, which is difficult to realize due to the high demands on technical support, communication problems in an unfamiliar space, and the lack of social presence of participants in the educational process. Blended learning, as a combination of full-time and distance learning, can offset the shortcomings of online learning and realize its potential. The next step in our research will be to compare the performance of another learning models.
Corrigendum: Multi-person feature fusion transfer learning-based convolutional neural network for SSVEP-based collaborative BCI

Frontiers Editorial approval.• please read through all the templates before choosing• pick the most relevant text template(s) from the following page and delete all others.• edit the text as necessary, ensuring that the original incorrect text is included for the record, please see the below.• please do not use any extra formatting when editing the templates, and only modify the red text unless absolutely necessary• submit to Frontiers following the instructions on this page.In the published article, there was an error in the article title.