Reviewerly/exHarmony huggingface dataset:
RSSI-Based MAC-Layer Spoofing Detection: Deep Learning Approach

In some wireless networks Received Signal Strength Indicator (RSSI) based device profiling may be the only viable approach to combating MAC-layer spoofing attacks, while in others it can be used as a valuable complement to the existing defenses. Unfortunately, the previous research works on the use of RSSI-based profiling as a means of detecting MAC-layer spoofing attacks are largely theoretical and thus fall short of providing insights and result that could be applied in the real world. Our work aims to fill this gap and examine the use of RSSI-based device profiling in dynamic real-world environments/networks with moving objects. The main contributions of our work and this paper are two-fold. First, we demonstrate that in dynamic real-world networks with moving objects, RSSI readings corresponding to one fixed transmitting node are neither stationary nor i.i.d., as generally has been assumed in the previous literature. This implies that in such networks, building an RSSI-based profile of a wireless device using a single statistical/ML model is likely to yield inaccurate results and, consequently, suboptimal detection performance against adversaries. Second, we propose a novel approach to MAC-layer spoofing detection based on RSSI profiling using multi-model Long Short-Term Memory (LSTM) autoencoder—a form of deep recurrent neural network. Through real-world experimentation we prove the performance superiority of this approach over some other solutions previously proposed in the literature. Furthermore, we demonstrate that a real-world defense system using our approach has a built-in ability to self-adjust (i.e., to deal with unpredictable changes in the environment) in an automated and adaptive manner.
The Existing Problems of English Teachers’ Classroom Evaluation Language and Its Countermeasures in Junior Middle School

Stepping into the 21st century, more and more people pay attention to teacher's qualities, particularly professional qualities, but ignore the research of teachers’ classroom evaluation language. Teachers’ classroom evaluation language is a very important part of classroom teaching, which plays a crucial role in classroom organization and students’ learning. At present, many teachers ignore its positive role and the significance. Therefore, the author analyzes the existing problems of current English teachers’ classroom evaluation language in its practical use in junior middle school, gives some suggestions for improving their classroom evaluation language, and also hopes that this paper will be helpful to teachers’ classroom evaluation language in junior middle school.
Proposed Association Rules Mining Algorithm for Sensors Data Streams

Smart environments data mining that depends on sensors is considered one of the most important and recent types of data mining in recent times, but at the same time this type of mining has several challenges, including that the data coming out of the sensors is streaming data and not static in addition to that it contains a time factor in most cases, which It is neglected in mining and data analysis operations. One of the most important tools of data mining is association rules mining, which are considered among the tools of decision-making systems . In this paper we suggest a proposed algorithm called SDT-B-ARM (Data Stream Time Based Association Rules Mining) that deals with time during the mining process and finding frequent itemsets and then finding of association rules from streaming data and all of this is done without the need to store the streaming data. The proposed algorithm needs less storage space, less computational complexity, less time required than previous related algorithms, and more efficiency to obtain strong association rules, in addition to taking time into consideration during the process of finding frequent itemsets. In the proposed approach we can obtain association times which can be useful in applications where time for correlation is important and critical. In addition, the proposed algorithm can be used in a distributed or parallel system.
Persistent Watermark For Image Classification Neural Networks By Penetrating The Autoencoder

Deep neural networks for image processing, especially image classification, have become ubiquitous. To protect them as intellectual properties and standardize the commercialization of their service, watermarking schemes have been proposed to authenticate the author of models. Many black-box watermarking schemes insert a backdoor into the neural network by poisoning the training dataset. Their performance declines if the adversary who has stolen the model adds a noise reducer, in particular an autoencoder, to ruin the backdoor. To cope with this kind of piracy, we propose an enhanced watermarking scheme by using triggers that penetrates the adversary’s autoencoder. The penetrative triggers are generated from a collection of shadow models that approximate the adversary’s autoencoder, which is assumed to be hidden from the genuine host of the model. The proposed scheme is shown to be resistant to the filtering of autoencoders and significantly increase the robustness of ownership verification.
Improving the Generalization Ability of Deepfake Detection via Disentangled Representation Learning

Deepfake refers to a deep learning based technology which can synthesize visually realistic face images/videos. The misuse of this technology poses a great threat to the society. Although numerous approaches have been proposed to detect Deepfake forgeries, their generalization ability on unseen datasets is limited. In this paper, we propose a new approach that detects human face forgeries by automatically locating the forgery-related region to make the final decision. The proposed network contains two modules, including: the disentanglement module to extract forgery relevant information and the classification module to detect the manipulation artifacts from various regions at different scales. The experiment results on three widely used Deepfake datasets show that the proposed approach can achieve high detection accuracies and outperforms several state-of-the-arts methods especially when evaluated on the unseen datasets.
Recursive residual atrous spatial pyramid pooling network for single image deraining

In recent years, removing rain streaks from a single image has been a significant issue for outdoor vision tasks. In this paper, we propose a novel recursive residual atrous spatial pyramid pooling network to directly recover the clear image from rain image. Specifically, we adopt residual atrous spatial pyramid pooling (ResASPP) module which is constructed by alternately cascading a ResASPP block with a residual block to exploit multi-scale rain information. Besides, taking the dependencies of deep features across stages into consideration, a recurrent layer is introduced into ResASPP to model multi-stage processing procedure from coarse to fine. For each stage in our recursive network we concatenate the stage-wise output with the original rainy image and then feed them into the next stage. Furthermore, the negative SSIM loss and perceptual loss are employed to train the proposed network. Extensive experiments on both synthetic and real-world rainy datasets demonstrate that the proposed method outperforms the state-of-the-art deraining methods.
The effects of pretraining intervention in immersive embodied virtual reality cardiopulmonary resuscitation training

Providing preparatory information is a pretraining intervention method suitable for high cognitive loads and high-pressure training environments and can significantly improve training effects. In this study, we investigated the impact of the media immersion level and pretraining intervention on cardiopulmonary resuscitation (CPR) training. We conducted a 2 [immersive embodied virtual reality (IEVR) vs. video] by 2 [pretraining intervention vs. no pretraining intervention] between-subject randomised experiment in Yixin, China. The results demonstrated that regardless of the media immersion level, embodied training combined with body movements is effective in improving self-efficacy, knowledge retention, and skill transfer. Although the subjects’ self-efficacy and CPR knowledge were significantly improved after training, the media level did not significantly improve knowledge retention and self-efficacy. Our study suggested that the media immersion level and pretraining intervention strategy have a significant interaction effect on the level of skill transfer. Moreover, the pretraining intervention promoted skills transfer in IEVR training but did not have a similar effect on video embodied training. These results indicate that incorporating a pretraining intervention by providing preparatory information can enhance the learning outcomes in IEVR training.
Attention-Based Self-Supervised Learning Monocular Depth Estimation With Edge Refinement

Learning depth from a single image extracted from unlabeled videos has been attracting significant attention in the past few years. In this work, we propose a new depth estimation neural network with edge refinement to predict depth. First, we introduce a dual attention module into depth prediction module to integrate global information into local features and improve local features’ capability of representation. Second, to increase the details between objects in scenes, we propose a subnetwork to predict edges in four directions and combine the predicted depth and edges to increase the details by propagation operation. Besides, we integrate the gradients of the image into the photometric reprojection loss to handle the confusion caused by changing brightness. We conduct experiments on KITTI datasets and show that our network achieves the state-of-art result.
An Accessible Smart Home Based on Integrated Multimodal Interaction

Our homes are becoming increasingly sensorized and smarter. However, they are also becoming increasingly complex, making accessing them and their advantages difficult. Assistants have the potential for improving the accessibility of smart homes, by providing everyone with an integrated, natural, and multimodal way of interacting with the home’s ecosystem. To demonstrate this potential and contribute to more environmentally friendly homes, in the scope of the project Smart Green Homes, a home assistant highly integrated with an ICT (Information and communications technology) home infrastructure was developed, deployed in a demonstrator, and evaluated by seventy users. The users’ global impression of our home assistant is in general positive, with 61% of the participants rating it as good or excellent overall and 51% being likely or very likely to recommend it to others. Moreover, most think that the assistant enhances interaction with the smart home’s multiple devices and is easy to use by everyone. These results show that a home assistant providing an integrated view of a smart home, through natural, multimodal, and adaptive interaction, is a suitable solution for enhancing the accessibility of smart homes and thus contributing to a better living ambient for all of their inhabitants.
Towards understanding theoretical advantages of complex-reaction networks

Complex-valued neural networks have attracted increasing attention in recent years, while it remains open on the advantages of complex-valued neural networks in comparison with real-valued networks. This work takes one step on this direction by introducing the complex-reaction network with fully-connected feed-forward architecture. We prove the universal approximation property for complex-reaction networks, and show that a class of radial functions can be approximated by a complex-reaction network using the polynomial number of parameters, whereas real-valued networks need at least exponential parameters to reach the same approximation level. For empirical risk minimization, we study the landscape and convergence of complex gradient descents. Our theoretical result shows that the critical point set of complex-reaction networks is a proper subset of that of real-valued networks, which may show some insights on finding the optimal solutions more easily for complex-reaction networks.
The Future of Cloud Computing: Blockchain-Based Decentralized Cloud/Fog Solutions – Challenges, Opportunities, and Standards

By allowing the creation of fully decentralized cloud/fog technologies that reduce costs by producing predictable outcomes without needing any intermediary, smart contracts and blockchain have had the opportunity to alter the present shape of cloud markets. In addition, many recommend current requirements for the creation of fully integrated decentralized cloud solutions that would allow large vendors to comply with these kinds of solutions and avoid proprietary hardware. We claim that certain analysis contributes to the advancement of cloud systems not only by figuring out implementation incompatibilities and alternative solutions to development issues in the field but also through evaluating predetermined rules and proposing great possibilities for interoperability.
A Study on Challenges of Big Data and Their Approaches in Present Environment

With the advent of the World Wide Web, big data has gained immense popularity among the research community. This data originates from various sources such as e-commerce sites, stock exchange data, social networking sites, and data stored at weather stations through satellite among others. The data generated by these sources is huge and is referred to as big data due to its sheer volume, variety, and velocity. Most of the data generated is unstructured and heterogeneous. The traditional data analytic tools, relational database management systems, and data warehousing systems are insufficient to meet the increasing computing demand arising out of big data. Processing this huge data is a challenging task that involves high storage devices, complex computing, and powerful processing. This paper discusses several challenges posed by big data in the current scenario and studies different approaches to address these challenges.
Face Aggregation Network For Video Face Recognition

Typical approaches for video face recognition aggregate faces in a feature space to obtain a single feature representing the entire video. Unlike most previous approaches, we aggregate the faces directly in order to additionally obtain a single representative face as an intermediate output, from which a more discriminative feature vector is extracted. To overcome the limitation of a fixed number of input images of the state of the art in face aggregation, we incorporate a permutation invariant U-Net architecture capable of processing an arbitrary number of frames, which is employed in a generative adversarial network. We demonstrate the effectiveness of our method on three popular benchmark datasets for video face recognition. Our approach outperforms the baselines on the YouTube Faces dataset, obtaining an accuracy of 96.62%. Besides, we show that our method is robust against motion blur.
Research on the Algorithm of Motion Track Recognition in Football Video

Abstract It is relatively difficult to obtain the real-time information of the football position in the connected area formed by the ball and the line of the stadium in the football match video. This paper proposes a method for identifying the football trajectory. The least squares algorithm is applied to the football movement trajectory recognition function, and the Viterbi algorithm detection is screened according to the movement recognition function, and the football movement trajectory information is complemented for the error of the football movement in the football video. Finally, the results of an example show that the method proposed in this paper can accurately identify the changes in the motion trajectory in the video.
Grayscale And Normal Guided Depth Completion With A Low-Cost Lidar

In this paper, we introduce DenseLivox, a dataset with dense and accurate depth as ground truth. To our best knowledge, it is the first dataset with dense ground truth designed for LiDAR depth completion using a low-cost LiDAR. Also, we develop a simple yet effective multi-task learning network to tackle the problem of depth completion. Compared to the works in the literature, our model’s uniqueness is that it completes a depth map, a normal map, and a grayscale image simultaneously. To address the area with heavy noises, we use modified Huber loss to smooth these outliers’ effect. We evaluate our method on DenseLivox and show that accuracy is greatly improved with the grayscale and normal guidance. Our method outperforms other depth-only methods and is comparable to the methods that take RGB and depth as input.
Transresnet: Transferable Resnet For Domain Adaptation

Although Deep Convolutional Neural Network (DCNN) has been admittedly witnessed as an enormous success in a wide range of applications, most of them require sufficient annotations with time-consuming and labor-exhausting efforts. Existing domain adaptation (DA) approaches delve into designing an effective loss module to minimize the distribution gap between the source and target domains. However, few studies pay attention to improve the backbone or network architecture for DA issues. In this paper, we propose a new backbone for DA specially, i.e., Transferable ResNet (TransResNet). TransResNet remedies the residual block in ResNet, separating source and target input features and highlighting more transferable channels in each block. It can be easily applied to all kinds of DA methods, without adding any extra learning parameters. We conduct substantial experiments on two general DA datasets and embed TransResNet into two seminal DA methods, including DANN and CDAN. Experimental results demonstrate TransResNet improves the transferability of the architecture, indicating that it is a great substitute for ResNet as a network backbone in DA issues.
Robust Tracking For Motion Blur Via Context Enhancement

Motion blur is pervasive in object tracking, especially in applications such as unmanned aerial vehicles or pods. However, the focus of tracking research has been on generic visual tracking rather than specific scenarios, such as motion blur, which degrades the performance in these scenarios. In this work, we propose an effective method for tracking in motion blur by employing the framework of D3S (a discriminative single shot segmentation tracker). IQA (image quality assessment) and deblurring components are both introduced into the basic D3S framework to enhance context patch, which improves the tracking accuracy in blurred target tracking. Extensive experiments demonstrate that our tracker can robustly track objects, not only in blurred videos but also in other challenging scenes.
Quantum multicast communication over the butterfly network

Abstract We propose a scheme where one can exploit auxiliary resources to achieve quantum multicast communication with network coding over the butterfly network. In this paper, we propose the quantum 2-pair multicast communication scheme, and extend it to k -pair multicast communication over the extended butterfly network. Firstly, an EPR pair is shared between each adjacent node on the butterfly network, and make use of local operation and classical communication to generate entangled relationship between non-adjacent nodes. Secondly, each sender adds auxiliary particles according to the multicast number k , in which the CNOT operations are applied to form the multi-particle entangled state. Finally, combined with network coding and free classical communication, quantum multicast communication based on quantum measurements is completed over the extended butterfly network. Not only the bottleneck problem is solved, but also quantum multicast communication can be completed in our scheme. At the same time, regardless of multicast number k , the maximum capacity of classical channel is 2 bits, and quantum channel is used only once.
Reference-Based Video Colorization With Multi-Scale Semantic Fusion And Temporal Augmentation

The reference-based video colorization method hallucinates a plausible color version for a gray-scale video by referring distributions of possible colors from an input color frame, which has semantic correspondences with the gray-scale frames. The plausibility of colors and the temporal consistency are two significant challenges in this task. In this paper, we propose a novel Generative Adversarial Network (GAN) with a siamese training framework to tackle these challenges. Specifically, the siamese training framework allows us to implement temporal feature augmentation, enhancing temporal consistency. Further, to improve the plausibility of colorization results, we propose a multi-scale fusion module that correlates features of reference frames to source frames accurately. Experiments on various datasets demonstrate that our proposed method performs favorably against the state-of the-art approaches.
Automated Pipeline for Training Dataset Creation from Unlabeled Audios for Automatic Speech Recognition

In the paper, we present a software pipeline for speech recognition to automate the creation of training datasets, based on desired unlabeled audios, for low resource languages and domain-specific area. Considering the commoditizing of speech recognition, more teams build domain-specific models as well as models for local languages. At the same time, lack of training datasets for low to middle resource languages significantly decreases possibilities to exploit last achievements and frameworks in the Speech Recognition area and limits the wide range of software engineers to work on speech recognition problems. This problem is even more critical for domain-specific datasets. The pipeline was tested for building Ukrainian language recognition and confirmed that the created design is adaptable to different data source formats and expandable to integrate with existing frameworks.
Using “Deep Learning School” with Digital Technologies in Science and Technology

A digital technology of education and career guidance was created within the open free resource “Deep Learning School” (DLS). Тhe mission of DLS is recruiting and education of talented youth in the field of IT research and development. DLS technology assumes the possibility of online training; in the absence of pandemic-related restrictions, a mixed format is available. The principles of DLS as an educational technology and a technology for professional self-determination of various contingents of young people are described. Empirical material - the results of an anonymous online survey of 145 DLS listeners - specialists, undergraduate and graduate students, applicants. A qualitative analysis of open-ended questions revealed the functions of DLS, which concretize its mission as an educational technology and a technology of professional self-determination. The possibilities of online and offline components of the technology are concretized to achieve actually didactic, educational goals, goals of socialization and facilitation of professional self-determination; revealed the specificity of their use by various contingents of students. The high efficiency of the technology for recruiting talented young people with high motivation to get education and work in the field of machine intelligence is shown. The research results can be used by specialists to improve the efficiency of digital educational resources and improve the practice of career management.