Reviewerly/exHarmony huggingface dataset:
Blockchain-Enabled Joint Resource Allocation for Virtualized Video Service Functions

Power information is an important guarantee for energy security. As an important technical means of safety management and risk control, video monitoring is widely used in the power industry. Power video monitoring system uses efficient processing of multimodal video data and automatically identifies abnormal events and equipment status, replacing human monitoring with machine. Video monitoring data of power substations usually contain both visual information and auditory information, and the data types are diversified. The multimodal video data provides a rich underlying data source for the intelligent monitoring function, but it requires multiple service forms for efficient processing. Most intelligent edge monitoring equipment are only equipped with lightweight computing resources and limited battery supply, limited resources, and weak local processing data capabilities. Power video monitoring system has the characteristics of distribution, openness, interconnection, and intellectualization. Its intelligent edge video equipment is widely distributed, which also brings convenience and also brings security risks in terms of data security and reliability. For the outdoor multimodal power video monitoring system scenario, this paper adopts the edge-cloud distributed system architecture to solve the problem of resource shortage and adopts the first proposed service function virtualization (SFV) to solve the problem of multimodal video data processing. At the same time, the problem of security protection is solved by introducing blockchain to establish trust among intelligent video equipment and service providers. Under the security protection of virtualized service consortium blockchain (VSCB), virtualization technology is introduced into the service function chain (SFC) to realize SFV and solve the resource optimal allocation problem of multimodal video data processing. The work mainly involves the joint mapping of virtual resources, physical resources, and the joint optimization of computing and communication resources. Problems such as large state space and high dimensionality of action space have an impact on resource allocation. The stochastic optimization problem of resource allocation is established as a Markov decision process (MDP) model, and SFV technology is used to optimize cost and delay. The resource allocation optimization algorithm (RAOA-A3C) based on asynchronous advantage actor-critic algorithm (A3C) is proposed. Simulation experiments show that the RAOA-A3C proposed in this paper is more suitable for high-dynamic, multidimensional, and distributed power video monitoring system scenario and has achieved better optimization results in reducing time delay and deployment costs.
Cross-lingual Adaptation for Recipe Retrieval with Mixup

Cross-modal recipe retrieval has attracted research attention in recent years, thanks to the availability of large-scale paired data for training. Nevertheless, obtaining adequate recipe-image pairs covering the majority of cuisines for supervised learning is difficult if not impossible. By transferring knowledge learnt from a data-rich cuisine to a data-scarce cuisine, domain adaptation sheds light on this practical problem. Nevertheless, existing works assume recipes in source and target domains are mostly originated from the same cuisine and written in the same language. This paper studies unsupervised domain adaptation for image-to-recipe retrieval, where recipes in source and target domains are in different languages. Moreover, only recipes are available for training in the target domain. A novel recipe mixup method is proposed to learn transferable embedding features between the two domains. Specifically, recipe mixup produces mixed recipes to form an intermediate domain by discretely exchanging the section(s) between source and target recipes. To bridge the domain gap, recipe mixup loss is proposed to enforce the intermediate domain to locate in the shortest geodesic path between source and target domains in the recipe embedding space. By using Recipe 1M dataset as source domain (English) and Vireo-FoodTransfer dataset as target domain (Chinese), empirical experiments verify the effectiveness of recipe mixup for cross-lingual adaptation in the context of image-to-recipe retrieval.
Face synthesis with generative adversarial networks

Nowadays, machine learning has become a trend in computer programming techniques. It is a learning method that builds a model based on training data, in order to make predictions or decisions without being explicitly programmed to do so. Generative adversarial network (GAN) is the most advanced technology in unsupervised learning. The GAN is composed of a generator and a discriminator. Generator aims to generate realistic samples and discriminators to identify real data and generated fake data. GAN has been successfully implemented for solving a variety of tasks such as face synthesis, facial attribute manipulation, and image to image translation. Face synthesis gains great attention recently, especially after the emerge of Deepfakes. With this technology, realistic images or media can be easily created. In this paper, we will start with the introduction of GAN and then summarize and analyze five popular methods for face synthesis using GAN technology. Finally, we discuss some problems of existing methods and point out a future direction for face synthesis.
Domain Generalization for Language-Independent Automatic Speech Recognition

A language-independent automatic speech recognizer (ASR) is one that can be used for phonetic transcription in languages other than the languages in which it was trained. Language-independent ASR is difficult to train, because different languages implement phones differently: even when phonemes in two different languages are written using the same symbols in the international phonetic alphabet, they are differentiated by different distributions of language-dependent redundant articulatory features. This article demonstrates that the goal of language-independence may be approximated in different ways, depending on the size of the training set, the presence vs. absence of familial relationships between the training and test languages, and the method used to implement phone recognition or classification. When the training set contains many languages, and when every language in the test set is related (shares the same language family with) a language in the training set, then language-independent ASR may be trained using an empirical risk minimization strategy (e.g., using connectionist temporal classification without extra regularizers). When the training set is limited to a small number of languages from one language family, however, and the test languages are not from the same language family, then the best performance is achieved by using domain-invariant representation learning strategies. Two different representation learning strategies are tested in this article: invariant risk minimization, and regret minimization. We find that invariant risk minimization is better at the task of phone token classification (given known segment boundary times), while regret minimization is better at the task of phone token recognition.
Linguistic Radicalisation of Right-Wing and Salafi Jihadist Groups in Social Media: a Corpus-Driven Lexicometric Analysis

Abstract Social media groups, for example on Facebook, WhatsApp or Telegram, allow for direct exchange, communication and interaction, as well as networking of different individuals worldwide. Such groups are also used to spread propaganda and thus allow for self-radicalisation or mutual radicalisation of their members. The article reports selected results from a research project analysing online communication processes of extremist groups. Based on data from group discussions in social media, corpus linguistic analyses were carried out, examining quantitative relationships between individual lexical elements and occurring regularities. To this end, four different corpora were built. These consist of data collected in right-wing and Salafi jihadist groups of a low or medium radicalisation level on Facebook and VKontakte via fake profiles, and of group communication in forums, messenger apps and social networks of highly radicalised persons, which were extracted from files of (e.g. terrorism) cases prosecuted in Germany. Quantitative linguistic analyses of social media data continue to be challenging due to the heterogeneity of the data as well as orthographic and grammatical errors. Nevertheless, it was possible to identify phenomenon specific sociolects that point to different levels of linguistic radicalisation. Based on the results of the analyses, the article discusses the prospects, problems and pitfalls of lexicometric analyses of online communication, especially as a tool for understanding radicalisation processes.
Image Deblurring Algorithm Based on the Gaussian-Scale Mixture Expert Field Model

With the proliferation of portable digital products, image quality degradation has received a lot of attention. As the most common phenomenon in image degradation, the issue of image deblurring is the focus of much attention. Blind motion blur removal is the main target of this paper. The heavy-tailed distribution is the most dominant statistical feature of natural images. However, most image deblurring methods use a gradient prior with fixed parameters to recover a clear image, which leads to loss of details in the recovered clear image and does not consider the higher order prior of the natural image. Therefore, this paper proposes a new regularized image recovery model based on the Gaussian-scale mixture expert field(GSM-FOE) model. First, the GSM-FoE model learns filters and corresponding parameters with higher order prior information of images by training images in a natural image library; second, these learning results are used to guide the image recovery process. The GSM-FoE model and gradient-fidelity based image recovery model is proposed, which can be used with an iterative re-weighted least squares (IRLS) method. Experiments demonstrate that the suggested recovery approach is simple to use and successful at reducing blur and noise, as well as suppressing ringing effects while preserving image information. Moreover, the image restoration method performs well for large blurring kernels. The results fully reflect the effectiveness and robustness of the proposed method for complex noise scenarios. The quality of the generated images is significantly better than that of several classical methods.
Detecting Rogue Access Points Using Client-agnostic Wireless Fingerprints

The broadcast nature of wireless media makes WLANs easily attacked by rogue Access Points (APs) . Rogue AP attacks can potentially cause severe privacy leakage and financial loss. Hardware fingerprinting is the state-of-the-art technology to detect rogue APs since an attacker would find it difficult to set up a rogue AP with specific hardware fingerprints. However, existing hardware fingerprints not only depend on the AP but also depend on the client, significantly limiting their application scenarios. In this work, we investigate two novel client-agnostic fingerprints, which can be extracted using commercial off-the-shelf WiFi devices, to detect rogue APs. One is the power amplifier non-linearity fingerprint and the other is the frame interval distribution fingerprint . These two fingerprints remain consistent over time and space for the same AP but vary across different APs even with the same brand, model, and firmware. We use the fingerprint similarity between the candidate AP and the authorized AP for device authentication in typical indoor environments. We have also proposed a threshold-improved authentication scheme to improve the robustness of our system in dynamic environments. Our schemes can be implemented without modifying the infrastructural APs and can work well with new clients without rebuilding the fingerprint database. We evaluate our scheme in both in-lab and field scenarios, by analyzing 18 million WiFi packets. Results show that our scheme achieves an overall 96.55% positive detection rate and a 4.31% false alarm rate. Moreover, the threshold-improved authentication scheme can further reduce the false alarm rate by 13.0%-44.8% for dynamic environments.
‘Not Water or Air’: The Legality of Network Disruptions in Ethiopia

Abstract Network disruption has become commonplace in Ethiopia in the past few years. Be it for preventing exam leaks, the spread of disinformation, or to fight off cyber-attacks, the government has repeatedly disrupted communication networks. However, the legal basis with which the government often shuts down the Internet or disrupts other means of digital communications remains unclear. Despite a recent attempt by the Federal Attorney General to offer some legal justification, the legality of network disruptions under Ethiopia law is questionable. This short article considers the legality of network disruptions under Ethiopian law. Having rejected the legal justifications of the Attorney General, this article argues that the current cybercrime legislation offers a rather sound legal basis for certain forms of network disruption in Ethiopia. It further considers the pertinence of rules dealing with network disruption introduced in the cybercrime Bill (2020). The article suggests that the Bill’s network disruption rules are mostly progressive, but there remains the need for a freestanding legal framework equipped with appropriate safeguards against arbitrary practices.
Passive Target Motion Analysis in 3D Using Multipath Time Delay Measurements

In this paper, an underwater target motion analysis (TMA) is done using noise corrupted bearing angle, elevation angle and multi-path time delay measurements, without manoeuvring the ownship. Here, TMA is performed by fusing the measurements with the process model using Gaussian filters. The performances of the filters are compared in terms of root mean squared errors (RMSEs) of position and velocity, computational time and track loss count. In addition, a comparison is made between classical angle only tracking (AOT) (with ownship manoeuvre) with that of TMA with bearing, elevation angles and multipath time delay measurements. It is found that multipath time delay measurement makes the filters to estimate the target position more accurately (compared to AOT) without any ownship maneuver.
Decision tree model in supervised learning

Machine learning has become a hot topics in the past few years. The challenge of generic classification is akin to the problem of medical diagnosis. On a case or an object, measurements are taken. We then wish to guess which class the case belongs to based on these measurements. Machine learning may appear to ordinary people to be too advanced; nonetheless, it is used in almost every aspect of our life. One of the most significant machine learning models is the decision tree. To classify trial sets of data, it employs a tree-like model of decisions and outcomes. We investigate the basic theories, techniques, and applications of decision trees in this article.
THINKING ISSUES: Algorithms of anger

column Free Access Share on THINKING ISSUES: Algorithms of anger: lost down the rabbit-hole! Author: Tony Clear Auckland University of Technology Auckland University of TechnologyView Profile Authors Info & Claims ACM InroadsVolume 13Issue 2June 2022 pp 6–8https://doi.org/10.1145/3530014Online:17 May 2022Publication History 0citation153DownloadsMetricsTotal Citations0Total Downloads153Last 12 Months153Last 6 weeks32 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 Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
Single image deraining via deep residual attention and encoder-decoder network

Single image rain removal remains a crucial and challenging low-level image processing task while significantly for outdoor-based high-level computer vision tasks. Recently, deep convolutional neural networks (CNNs) have become the mainstream structure of removing rain streaks and obtained remarkable performance. However, most of the existing CNN-based methods mainly pay attention to completely removing rain streaks while neglecting the restoration of details after deraining, which suffer from poor visual performance. In this paper, we propose a deep residual attention and encoder-decoder network to overcome the above shortcoming. Specifically, we develop an excellent basic block that contains dual parallel paths which are called rain removal network and detail restore network, respectively, to perform entirely and in-depth mapping relationships from rain to no-rain. The upper rain removal network is composed of dilated convolution and channel attention. This combination can explore the correlation between features from the dimensions of spatial and channel. Meanwhile, for the lower detail restore network, we construct a simple yet effective symmetrical encoder-decoder structure to prevent the loss of global structures information and encourage the details back. Furthermore, our network is end-to-end trainable, easy to implement and without giant parameter quantity. Extensive experiments on synthetic and real-world datasets have shown that our DRAEN achieves better accuracy and visual improvements against recent state-of-the-art methods.
Quantum Receiver Enhanced by Adaptive Learning

Adaptive quantum receiver designed by machine learning is demonstrated for discriminating multiple nonorthogonal coherent states, achieving reduced error rates of 20% (50%) over existing quantum (classical) receivers.
Walsh–Hadamard transforms of generalized <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e35" altimg="si6.svg"><mml:mi>p</mml:mi></mml:math>-ary functions and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e40" altimg="si7.svg"><mml:mi>C</mml:mi></mml:math>-finite sequences

In this article we show that Walsh–Hadamard transformations of generalized p-ary functions whose components are symmetric, rotation symmetric or a combination or concatenation of them are C-finite sequences. This result generalized many of the known results for regular p-ary functions. We also present a study of the roots of the characteristic polynomials related to these sequences and show that properties like balancedness and being bent are not shared by the underlying p-ary functions.
Automatic Generation of Customized Areas of Interest and Evaluation of Observers' Gaze in Portrait Videos

We present a novel framework for the evaluation of eye tracking data in portrait videos including the automatic generation of customized areas of interest (AOIs) based on facial landmarks. In contrast to previous work, our framework allows the user to flexibly create AOIs by grouping the detected landmarks. Moreover, their shape and size can be modified to better fit both the research question and the precision of the eye tracker. The framework can be used as an integrated solution to not only generate AOIs but also to evaluate viewing behavior like the overall fixation times, the similarity of scanpaths, and the number of saccades between AOIs. Other functionalities include the visualization of gaze paths and the creation of heatmaps. We demonstrate the benefits of our framework and user-defined AOI layouts via an exemplary application, i.e., the investigation of face swapping artifacts.
A novel convergent filled function algorithm for multi-dimensional global optimization

As we all know, the filled function method as an effective algorithm to solve global optimization problems can overcome the defect that the non-linear algorithm is trapped in the local minimizer and cannot jump to attach the global minimizer, which has attracted the attention of researchers. However, when solving multi-dimensional global optimization problems, the filled function often lacks good properties such as mandatory, convexity and continuity, which makes the conventional filled function algorithm to always encounter the difficulty that its convergence cannot be guaranteed. In view of this, a novel convergent filled function algorithm for multi-dimensional global optimization is proposed. In this algorithm, by the way of the coordinates rotate, the multi-dimensional global optimization problem is transformed into several one-dimensional sub-problems, and then the new filled function with good properties is constructed to solve the one-dimensional sub-problem, which leads to attachment of the convergence in algorithm. Finally, compared with the existing algorithms on test optimization problems in NETLIB library, the effectiveness of the algorithm is verified by numerical experiments.
Bearing Fault Classification Using Improved Antlion Optimizer and Extreme Learning Machine

Bearing is an important part of rotating machinery, and its early fault diagnosis and accurate classification have always been difficult in engineering application. At present, the models based on the fusion of various optimization algorithms and neural networks have become one of the emerging techniques for accurate fault identification. Firstly, an improved antlion optimizer (ALO) algorithm based on estimation of distribution algorithm (EDA) and variable-step Lévy flight strategy, abbreviated as ELALO, is proposed as a new bionic intelligence. During the initialization of population, the individuals with poor fitness are redistributed by the Gaussian probability model. In view of the stagnation of iteration, Lévy flight strategy is introduced and the adaptive change of disturbance step length is controlled. Experimental results on 4 benchmark functions show that the novel ELALO can effectively improve the solution accuracy and convergence speed, compared with the original ALO. Secondly, in order to solve the disadvantage that extreme learning machine (ELM) network is easy to fall into local optimization, this ELALO algorithm is used to initialize the weights and thresholds of its network and to form the new pattern recognition model, ELALO-ELM. Finally, the bearing data of 8 patterns from Western Reserve University are decomposed by local mean decomposition (LMD), and then the symbolic entropy (SE) of the first three product function (PF) components signals is extracted and used as the input eigenvectors. Compared with the standard ELM and ALO-ELM models, the ELALO-ELM model has better generalization and stronger robustness and it can effectively improve the efficiency of network training and the accuracy of early fault pattern classification in bearing fault diagnosis. The new ELALO-ELM model can also be used for other difficult classification problems.
Lexical-Semantic Means of Forming Evaluation of the MUSLIM WOMAN Concept in the English Internet Discourse

The present work examines the lexical-semantic means of forming an evaluation of the MUSLIM WOMAN concept in the Internet discourse, and also considers the notion of “concept” and its evaluation component. The aim is to determine the lexical-semantic means by which the MUSLIM WOMAN concept is actualized and its evaluation is formed. The scientific novelty of the study lies in the fact that for the first time the author considers not only the lexical-semantic means of actualizing this concept, but also their evaluative component. As a result of the factual material analysis, it has been found that in the English-language media, the MUSLIM WOMAN concept is actualized mainly by negative evaluation judgments that is due to the lack of sufficient knowledge about a Muslim woman in the English-language worldview.