Reviewerly/exHarmony huggingface dataset:
Success Factors for Designing a Cybersecurity Exercise on the Example of Incident Response

Exercises are a resource intensive form of providing participants learning, capability testing and validation of plans and procedures. As exercises commonly are used in the field of cybersecurity, their effective and optimal organisation is important. This article aims at identifying key exercise organisation assessment indicators, regardless of exercise type or goal, on the example of cyber incident response exercises. Based on practical experience and literature on crisis management, incident response, and cybersecurity exercises, a 10-step exercise organisation process is identified. For each step, criticalities for the designers and other stakeholders of cyber incident response exercises are identified. These criticalities are also translated in a checklist of actionable indicators aimed at supporting them in measuring the quality of the exercise and implementing an exercise organisation learning loop.
IWA: Integrated gradient‐based white‐box attacks for fooling deep neural networks

The widespread application of deep neural network (DNN) techniques is being challenged by adversarial examples—the legitimate input added with imperceptible and well-designed perturbation that can fool DNNs easily in the DNN testing/deploying stage. Previous white-box adversarial example generation algorithms used the Jacobian gradient information to add the perturbation. This imprecise and inexplicit information can cause unnecessary perturbation when generating adversarial examples. This paper aims to address this issue. We first propose to apply the more informative and distilled gradient information, namely, integrated gradient, to generate adversarial examples. To further make the perturbation more imperceptible, we propose to employ the restriction combination of L 0 and L 1 / L 2 second, which can restrict the total perturbation and the perturbation points simultaneously. Meanwhile, to address the nondifferentiable problem of L 1 , we explore a proximal operation of L 1 third. On the basis of these three works, we propose two Integrated gradient-based White-box Adversarial example generation algorithms (IWA): Integrated gradient-based Finite Point Attack (IFPA) and Integrated gradient-based Universe Attack (IUA). IFPA is suitable for situations where there are a determined number of points to be perturbed. IUA is suitable for situations where no perturbation point number is preset to obtain more adversarial examples. We verify the effectiveness of the proposed algorithms on both structured and unstructured data sets, and compare them with five baseline generation algorithms. The results show that our proposed algorithms craft adversarial examples with more imperceptible perturbation and satisfactory crafting rate. L 2 restriction is suitable for unstructured data sets and L 1 restriction performs better in the structured data set.
Optimizing Alignment of Speech and Language Latent Spaces for End-To-End Speech Recognition and Understanding

The advances in attention-based encoder-decoder (AED) networks have brought great progress to end-to-end (E2E) automatic speech recognition (ASR). One way to further improve the performance of AED-based E2E ASR is to introduce an extra text encoder for leveraging extensive text data and thus capture more context-aware linguistic information. However, this approach brings a mismatch problem between the speech encoder and the text encoder due to the different units used for modeling. In this paper, we propose an embedding aligner and modality switch training to better align the speech and text latent spaces. The embedding aligner is a shared linear projection between text encoder and speech encoder trained by masked language modeling (MLM) loss and connectionist temporal classification (CTC), respectively. The modality switch training randomly swaps speech and text embeddings based on the forced alignment result to learn a joint representation space. Experimental results show that our proposed approach achieves a relative 14% to 19% word error rate (WER) reduction on Librispeech ASR task. We further verify its effectiveness on spoken language understanding (SLU), i.e., an absolute 2.5% to 2.8% F1 score improvement on SNIPS slot filling task.
Topographic Analysis of Cognitive Load in Tacit Coordination Games Based on Electrophysiological Measurements

Tacit coordination games are coordination games in which communication between the players is not possible. Various studies have shown that people succeed in these games beyond what is predicted by classical game theory. This success is attributed to the identification of focal points (also known as Schelling points). Focal points are pronounced solutions based on salient features of the game that somehow attracts the players’ attention. Experiments with tacit coordination games show that some players manage to “see” the focal points and reason about the selections made by the co-player, while others fail to do so, and might turn to guessing. According to the Cognitive Hierarchy Theory (CHT), the task of coordinating, that is, reasoning about what the other player would choose is performed on cognitive levels greater than or equal to 1. In contrast, the task of just picking an answer, without an explicit need to coordinate is done at cognitive level 0. With that in mind, our study has two main purposes. First, to examine whether the same task that is defined each time at a different cognitive level (picking or coordination) causes a different psychological cognitive load in the participating players. Second, to examine the distribution of cognitive load across the scalp during coordination tasks.
Feature Selection Optimization using ACO to Improve the Classification Performance of Web Log Data

Web mining plays a salient role in today’s era of an interconnected network to comprehend the manner in which users browse the websites. Web mining can be classified into three main categories such as web content mining, web structure mining, and web usage mining. Web structure mining deals with the analysis of the structure of web sites, web pages while web content mining deals with the analysis of the contents on the web pages. Web usage mining processes web server log files for the applications of various functionalities such as classification, clustering, and association rules on weblog data. In this paper, the authors presented the classification of data on the server log files by applying various popular and most widely used approaches of classification on the dataset. Then, based on the highest predictive accuracy, the random forest algorithm was further optimized using ant colony optimization (ACO) techniques and the authors have observed that the classification accuracy can be improved using ACO on the results of the random forest algorithm.
Review of information asymmetry in banking in the Russian Federation

Purpose The purpose of this study is to identify the prospects and main directions for improving Russian legislation on the protection of the rights of consumers of financial services taking into account the specifics of information asymmetry in banking. Design/methodology/approach Using the method of economic and legal analysis, the essence of information asymmetry in banking in the Russian Federation was considered. Taking into account international experience, the analysis of the legislation of the Russian Federation in the existing regulatory and legal field is carried out. A forecast of probable changes in the field of legal regulation of information asymmetry issues in banking was also carried out. Findings This paper deals with cases when information asymmetry can be recognized as unfair behavior. The main features of information asymmetry in banking on the part of credit institutions in terms of banks’ failure to provide information on the content of banking services on the right to refuse additional services have been studied. Originality/value This study suggests that the current Russian legislation does not provide the necessary protection for consumers of financial services from information asymmetry. Based on a comprehensive analysis of legislation and judicial practice, the information asymmetry in this paper is delimited as an economic and legal concept; the prerequisites and main forms of information asymmetry in banking are determined. The main provisions and conclusions of this study can be used in legislative activities when developing provisions on the protection of the rights of financial services consumers.
Federated Learning for Driver Status Monitoring

We apply a novel concept for distributed learning to the problem of driver status monitoring. The main benefit is that only local, in-vehicle training data is used, thus privacy sensitive pictures of the driver do not leave the vehicle. We show the challenges of this application, in particular in the distribution of the data and apply different, recent techniques of federated learning. In result, we show for our data set that federated learning can achieve almost same performance as classical learning, where all data is collected and processed in a single learning applications.
SAAS parallel task scheduling based on cloud service flow load algorithm

In cloud platform applications, the user's goal is to obtain high-quality application services, while the service provider's goal is to obtain revenue by performing the tasks submitted by the user. The platform built by the service provider's application resources needs to improve the mapping between service requests and resources to achieve higher value. Through the current situation of resource management in the cloud environment, it is found that many task scheduling and resource allocation algorithms are still affected by factors such as the diversity, dynamics, and multiple constraints of resources and tasks. This paper focuses on Software as a Service (SaaS) applications' task scheduling and resource configuration in a dynamic and uncertain cloud environment. It is a challenging online scheduling problem to automatically and intelligently allocate user task requests that continually reach SaaS applications to appropriate resources for execution. To this end, a real-time task scheduling method based on deep reinforcement learning is proposed, which automatically and intelligently allocates user task requests that continually reach SaaS applications to appropriate resources for execution. In this way, the limited virtual machine resources rented by SaaS providers can be used in a balanced and efficient manner. In the experiment, by comparing with other five task scheduling algorithms, it is proved that the algorithm proposed in this paper not only improves the execution efficiency of better deploying workflow in IaaS public cloud, but also makes the resources provided by SaaS are used in a balanced and efficient manner.
Discrete-Time Analysis of Wireless Blockchain Networks

Blockchain (BC) technology can revolutionize future networks by providing a distributed, secure, and unalterable way to boost collaboration among operators, users, and other stakeholders. Its implementations have traditionally been supported by wired communications, with performance indicators like the high latency introduced by the BC being one of the key technology drawbacks. However, when applied to wireless communications, the performance of BC remains unknown, especially if running over contention-based networks. In this paper, we evaluate the latency performance of BC technology when the supporting communication platform is wireless, specifically we focus on IEEE 802.11ax, for the use case of users’ radio resource provisioning. For that purpose, we propose a discrete-time Markov model to capture the expected delay incurred by the BC. Unlike other models in the literature, we consider the effect that timers and forks have on the transaction confirmation latency.
Span-Level Emotion Cause Analysis by BERT-based Graph Attention Network

We study the task of span-level emotion cause analysis (SECA), which is focused on identifying the specific emotion cause span(s) triggering a certain emotion in the text. Compared to the popular clause-level emotion cause analysis (CECA), it is a finer-grained emotion cause analysis (ECA) task. In this paper, we design a BERT-based graph attention network for emotion cause span(s) identification. The proposed model takes advantage of the structure of BERT to capture the relationship information between emotion and text, and utilizes graph attention network to model the structure information of the text. Our SECA method can be easily used for extracting clause-level emotion causes for CECA as well. Experimental results show that the proposed method consistently outperforms the state-of-the-art ECA methods on benchmark emotion cause dataset.
Deep Single Fisheye Image Camera Calibration for Over 180-degree Projection of Field of View

We propose a learning-based calibration method for trigonometric function models that represent distortion with over 180° projection of field of views. Unlike perspective projection for less than 180° projection of field of views, fisheye projection such as equisolid angle projection is valid for whole world coordinates. To calibrate fisheye camera models, we define a new loss function based on camera projection effectively to optimize fisheye camera extrinsic (tilt and roll angles) and intrinsic (focal length) parameters. Our loss achieves small prediction errors throughout the ranges of parameters. Our results show that our method predicts precise fisheye camera parameters compared with conventional polynomial function models for radial distortion. This work is the first to calibrate a fisheye camera model including extrinsic and intrinsic parameters for over 180° projection of field of views from a single image to our knowledge.
TransBlast: Self-Supervised Learning Using Augmented Subspace with Transformer for Background/Foreground Separation

Background/Foreground separation is a fundamental and challenging task of many computer vision applications. The F-measure performance of state-of-the-art models is limited due to the lack of fine details in the predicted output (i.e., the foreground object), and the limited labeled data. In this paper, we propose a background/foreground separation model based on a transformer that has a higher learning capacity than the convolutional neural networks. The model is trained using self-supervised learning to leverage the limited data and learn a strong object representation that is invariant to changes. The proposed method, dubbed Trans-Blast, reformulates the background/foreground separation problem in self-supervised learning using the augmented subspace loss function. The augmented subspace loss function consists of two components: 1) the cross-entropy loss function and 2) the subspace that depends on Singular Value Decomposition (SVD). The proposed model is evaluated using three benchmarks, namely CDNet, DAVIS, and SegTrackV2. The performance of TransBlast outperforms state-of-the-art background/foreground separation models in terms of F-measure.
Rethinking Motion Representation: Residual Frames With 3D ConvNets

Recently, 3D convolutional networks yield good performance in action recognition. However, an optical flow stream is still needed for motion representation to ensure better performance, whose cost is very high. In this paper, we propose a cheap but effective way to extract motion features from videos utilizing residual frames as the input data in 3D ConvNets. By replacing traditional stacked RGB frames with residual ones, 35.6% and 26.6% points improvements over top-1 accuracy can be achieved on the UCF101 and HMDB51 datasets when trained from scratch using ResNet-18-3D. We deeply analyze the effectiveness of this modality compared to normal RGB video clips, and find that better motion features can be extracted using residual frames with 3D ConvNets. Considering that residual frames contain little information of object appearance, we further use a 2D convolutional network to extract appearance features and combine them together to form a two-path solution. In this way, we can achieve better performance than some methods which even used an additional optical flow stream. Moreover, the proposed residual-input path can outperform RGB counterpart on unseen datasets when we apply trained models to video retrieval tasks. Huge improvements can also be obtained when the residual inputs are applied to video-based self-supervised learning methods, revealing better motion representation and generalization ability of our proposal.
Multi-task Learning for Bias-Free Joint CTR Prediction and Market Price Modeling in Online Advertising

The rapid rise of real-time bidding-based online advertising has brought significant economic benefits and attracted extensive research attention. From the perspective of an advertiser, it is crucial to perform accurate utility estimation and cost estimation for each individual auction in order to achieve cost-effective advertising. These problems are known as the click through rate (CTR) prediction task and the market price modeling task, respectively. However, existing approaches treat CTR prediction and market price modeling as two independent tasks to be optimized without regard to each other, thus resulting in suboptimal performance. Moreover, they do not make full use of unlabeled data from the losing bids during estimations, which makes them suffer from the sample selection bias issue. To address these limitations, we propose Multi-task Advertising Estimator (MTAE), an end-to-end joint optimization framework which performs both CTR prediction and market price modeling simultaneously. Through multi-task learning, both estimation tasks can take advantage of knowledge transfer to achieve improved feature representation and generalization abilities. In addition, we leverage the abundant bid price signals in the full-volume bid request data and introduce an auxiliary task of predicting the winning probability into the framework for unbiased learning. Through extensive experiments on two large-scale real-world public datasets, we demonstrate that our proposed approach has achieved significant improvements over the state-of-the-art models under various performance metrics.
RWWO: an effective strategy for workflow scheduling in cloud computing with predicted energy using Deep Maxout Network

Abstract Workflow scheduling is the recent researching area in the cloud environment, in which user satisfaction based on the cost and bandwidth is the most challenging task. Several research methods are devised to minimize the execution time and cost, which compromises the attributes. Hence, this research introduces an effective task scheduling mechanism in a cloud environment utilizing the Regressive Whale Water Optimization (RWWO) algorithm, which is derived by the integration of Regressive Whale Optimization (RWO) and Water Cycle Algorithm (WCA). The fitness parameters utilized are Quality of Service (QoS), resource utilization, and predicted energy. However, predicted energy is determined using Deep Maxout Network. Moreover, the proposed RWWO + Deep Maxout Network achieved a minimum task scheduling time of 0.0208, minimum task scheduling cost of 0.0017, minimum predicted energy of 0.1971, and maximum resource utilization of 0.9999.
HierMatch: Leveraging Label Hierarchies for Improving Semi-Supervised Learning

Semi-supervised learning approaches have emerged as an active area of research to combat the challenge of obtaining large amounts of annotated data. Towards the goal of improving the performance of semi-supervised learning methods, we propose a novel framework, HierMatch, a semi-supervised approach that leverages hierarchical information to reduce labeling costs and performs as well as a vanilla semi-supervised learning method. Hierarchical information is often available as prior knowledge in the form of coarse labels (e.g., woodpeckers) for images with fine-grained labels (e.g., downy woodpeckers or golden-fronted woodpeckers). However, the use of supervision using coarse-category labels to improve semi-supervised techniques has not been explored. In the absence of fine-grained labels, HierMatch exploits the label hierarchy and uses coarse class labels as a weak supervisory signal. Additionally, HierMatch is a generic-approach to improve any semi-supervised learning framework, we demonstrate this using our results on recent state-of-the-art techniques MixMatch and FixMatch. We evaluate the efficacy of HierMatch on two benchmark datasets, namely CIFAR-100 and NABirds. HierMatch can reduce the usage of fine-grained labels by 50% on CIFAR-100 with only a marginal drop of 0.59% in top-1 accuracy as compared to MixMatch.
High Accuracy RF-PUF for EM Security through Physical Feature Assistance using Public Wi-Fi Dataset

In this work, using physical features extracted from RF nonidealities in communicated EM signals, we show that radio frequency physical unclonable function (RF-PUF) performs much better compared to a solely convolutional neural network (CNN) based secure authentication method, ORACLE. For the static and quasi-static channels, respectively, we achieve 96&#x0025; and 100&#x0025; accuracy for RF-PUF compared to 87.13&#x0025; and 98.6&#x0025; accuracy for authentication using ORACLE. For the first time, RF-PUF has been applied for Wi-Fi devices to show that&#x003E; 95&#x0025; accuracy can be achieved for a wide range of transmitter and receiver separation from 2ft to 62ft both for the static and quasi-static channel, showing a peak of &#x007E;100&#x0025; within 38ft range for the static case. The design space has been explored in detail. Finally, the concept of RF-PUF has been applied for clustering to detect safe-listed devices.
A New Method Combining Pattern Prediction and Preference Prediction for Next Basket Recommendation

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers' individual shopping records, where tendency is a new concept to reflect the evolution of customers' shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.