Reviewerly/exHarmony huggingface dataset:
Covert Communications through Imperfect Cancellation

We propose a method for covert communications using an IEEE 802.11 OFDM/QAM packet as a carrier. We show how to hide the covert message so that the transmitted signal does not violate the spectral mask specified by the standard, and we determine its impact on the OFDM packet error rate (PER). We show conditions under which the hidden signal is not usable and those under which it can be retrieved with a usable bit error rate (BER). The hidden signal is extracted by cancellation of the OFDM signal in the covert receiver. We explore the effects of the hidden signal on OFDM parameter estimation and the covert signal BER. We test the detectability of the covert signal with and without cancellation. We conclude with an experiment where we inject the hidden signal into Over-The-Air (OTA) recordings of 802.11 packets and demonstrate the effectiveness of the technique using that real-world OTA data.
Image-Caption Pair Replacement Algorithm towards Semi-supervised Novel Object Captioning

Novel object captioning task is focused on generating a complete caption of the bounding boxes that are not seen in the training images, according to these bounding boxes and the image context. Since the training datasets for this task contains no description with bounding boxes in the reference caption, then the image-caption pair synthesis strategy is trained to generate more pseudo-label. However, due to the problem of incorrect pseudo-label in the current image-caption pair replacement strategy, we propose an Image-Caption Pair Replacement Algorithm towards semi-supervised novel object captioning (I-CPRA). I-CPRA includes two sub module: bounding box scaling algorithm and two-stage semantic graph structure. Specifically, in the image replacement strategy, to address the problem of the strict resolution and aspect ratio replacement condition between novel objects and source objects, we propose a bounding box scaling algorithm. This increases the number of candidate objects for the novel object replacement of the model and thus increasing the number of synthetic images. Additionally, in order to address the problem of contextual logical errors when descriptions are directly replaced, we propose a two-stage semantic graph structure to reduce phrase collocation errors in the context, relying on co-occurring semantic adjacency associations. Experiment results show that I-CPRA outperforms baseline models by 1.3%, 2.1% and 1.1% in terms of SPICE, METEOR and CIDEr on heldout MS COCO datasets, respectively.
Underwater imaging in turbid environments: generation model, analysis, and verification

Optical imaging usually shows significant differences in underwater environments with different characteristics. The general underwater imaging model ignores the influences of turbidity on the attenuation of light. For turbid water with different particle types and concentrations, we introduced background light correction and turbidity factors to express the influences. A parametric turbid underwater image generation model based on dark channel prior theory was proposed, in which, the transmission map changes with the turbidity while the image tone changes with the background light correction. Based on experiments, the high similarity between generated images and real images confirmed that our model can fit the imaging in different bodies of water. Thus, the model can be used to generate a greater diversity of samples of turbid underwater images. We also applied the model to turbid underwater image enhancement. The image quality evaluation results indicated that the model can enhance turbid underwater images well.
Teaching a New Dog Old Tricks: Contrastive Random Walks in Videos with Unsupervised Priors

This paper focuses on self-supervised representation learning in videos with guidance from multimodal priors. Where the temporal dimension is commonly used as supervision proxy for learning frame-level or clip-level representations, a number of works have recently shown how to learn local representations in space and time through cycle-consistency. Given a starting patch, the contrastive goal is to track the patch in subsequent frames, followed by a backtracking to the original frame with the starting patch as goal. While effective for down-stream tasks such as segmentation and body joint propagation, affinities between patches need to be learned from scratch. This setup not only requires many videos for self-supervised optimization, it also fails when using smaller patches and more connections between consecutive frames. On the other hand, there are multiple generic cues from multiple modalities that provide valuable information about how patches should propagate in videos, from saliency and optical flow to photometric center biases. To that end, we introduce Guided Contrastive Random Walks. The main idea is to employ well-known multimodal priors to provide fixed prior affinities. We outline a general framework where prior affinities are combined with learned affinities to guide the cycle-consistency objective. Empirically, we show that Guided Contrastive Random Walks result in better spatio-temporal representations for two down-stream tasks. More importantly, when using smaller patches and therefore more connections between patches, our approach further improves, while the unguided baseline can no longer learn meaningful representations.
The Culture national project and book culture

The author analyzes various definitions of the concept of "culture" in Russia’s federal cultural projects with the focus on building cultural environment, educating cultural creators and implementing digital technologies. The digital culture is a subculture within the national Russian culture; it competes with the book culture, a traditional subculture that ensures semantic intergenerational continuity and preserves the national cultural and historical heritage. In the mo-dern period, the emergence of the book market, machine printing, spread of bib-liophilia, professionalization of librarianship, legal regulations, etc., resulted in the formation of the bibliosphere in industrial civilizations, a supersystem (system of systems) that ensures production, preservation, use and further development of national book cultures. The mature bibliosphere of the early twentieth century was formed by socio-cultural institutions (systems, or branches of the book busi-ness), namely: publishing, printing industry, book trade, librarianship, and biblio-graphy. The concept of "book mind" (Bibliologos) was introduced to denote the book subculture of the humanitarian intelligentsia. As the book subculture, bibliologos makes a strategic resource of the industrial risk society. The general conclusion of the study is that the current national project "The Culture" is incomplete, since the federal project does not comprise the book subculture federal project. The author suggests that the new edition of the national project has to be developed to include, along with the digital culture, the book subculture repre-sented in the idea of Bibliologos.
MultiCLU: Multi-stage Context Learning and Utilization for Storefront Accessibility Detection and Evaluation

In this work, a storefront accessibility image dataset is collected from Google street view and is labeled with three main objects for storefront accessibility: doors (for store entrances), doorknobs (for accessing the entrances) and stairs (for leading to the entrances). Then MultiCLU, a new multi-stage context learning and utilization approach, is proposed with the following four stages: Context in Labeling (CIL), Context in Training (CIT), Context in Detection (CID) and Context in Evaluation (CIE). The CIL stage automatically extends the label for each knob to include more local contextual information. In the CIT stage, a deep learning method is used to project the visual information extracted by a Faster R-CNN based object detector to semantic space generated by a Graph Convolutional Network. The CID stage uses the spatial relation reasoning between categories to refine the confidence score. Finally in the CIE stage, a new loose evaluation metric for storefront accessibility, especially for knob category, is proposed to efficiently help BLV users to find estimated knob locations. Our experiment results show that the proposed MultiCLU framework can achieve significantly better performance than the baseline detector using Faster R-CNN, with +13.4% on mAP and +15.8% on recall, respectively. Our new evaluation metric also introduces a new way to evaluate storefront accessibility objects, which could benefit BLV group in real life.
Student Attendance System via Face Recognition

Abstract: When attendance is done manually, it takes a lot of effort and time. Student attendance is a part of educational institute which is most challenging part of the virtual platform. There was also a lot of proxy attendance due to lack of a good attendance system. The face is the most important part of human body which identifies a human. In this paper, we describe an efficient algorithm of haar cascade using an open-source image processing framework known as OpenCV. Therefore, in this paper, we work on an attendance system via a face detection algorithm in real-time using the frontal face recognition concept.
Four-player GroupGAN for weak expression recognition via latent expression magnification

Facial expression recognition has a wide range of applications in the real world. Although many existing deep learning methods have achieved remarkable success, weak expression recognition remains a challenging task because of the significant domain gap between a weak expression and its peak expression counterpart. One idea to solve this problem is to find an effective way to bridge the gap between the two domains by either transfer learning or cross-domain image synthesis. In this paper, we propose a Group Generative Adversarial Network (GroupGAN) that recognizes weak facial expression by magnifying the expressions to stronger or peak ones. Different from the traditional GAN which typically has only one generator and one discriminator, the proposed GroupGAN has one generator, one extractor and two discriminators. Similar to the “two-player game” analogy of the traditional GAN, in our setting the generator along with feature extractor act as one group to compete with the other group of the two distinct discriminators. Extensive experiments show that the proposed GroupGAN significantly improves the performance of weak expression recognition, and is able to magnify weak expressions, thus facilitating many expression-related vision tasks like sketch recognition.
A New Comparative Study of Dimensionality Reduction Methods in Large-Scale Image Retrieval

Indexing images by content is one of the most used computer vision methods, where various techniques are used to extract visual characteristics from images. The deluge of data surrounding us, due the high use of social and diverse media acquisition systems, has created a major challenge for classical multimedia processing systems. This problem is referred to as the ‘curse of dimensionality’. In the literature, several methods have been used to decrease the high dimension of features, including principal component analysis (PCA) and locality sensitive hashing (LSH). Some methods, such as VA-File or binary tree, can be used to accelerate the search phase. In this paper, we propose an efficient approach that exploits three particular methods, those being PCA and LSH for dimensionality reduction, and the VA-File method to accelerate the search phase. This combined approach is fast and can be used for high dimensionality features. Indeed, our method consists of three phases: (1) image indexing within SIFT and SURF algorithms, (2) compressing the data using LSH and PCA, and (3) finally launching the image retrieval process, which is accelerated by using a VA-File approach.
Realization of Future Teacher’s Mental Space in the Process of Bite-Sized Learning

As there are contradictions in the digital technology: 1) a tool for professional growth and 2) a tool for imposing a prepared scenario of achieving success on the subject, ignoring the principle of environmental compliance, our study aims to substantiate and experimentally test the effectiveness of the model of realization of modern student’s mental experience (factor in activating his systemic thinking and teleological competence). Moreover, the technology of programming as philosophizing in the BSL format is considered to be an element in the humanization of geeks culture in the situation of supposed technological singularity. In accordance with the goal, we have created a regression-correlation design for the study of the influence of the predictor variable (BSL) on the criterion variable (disclosure of mental experience). The exploratory methodology of our experimental study is determined by the content of bite sized-approbation of the author's model ‘Flipped Class’. Its conceptual idea is self-learning as creativity, which is realized in the didactics of performative action. The results of the tasks have revealed the relationships and differences in the implementation of indicators of students’ mental experience of joint e-Learning and BSL, which are visualized in correlation galaxies for future clustering. They have confirmed the leading idea of ​​our study: the disclosure of the resources inherent in human nature in the process of learning is the way for the relentless cognition of the sense of placement with a view to achieving dignity and making free choices in life.
Passive Framework of Sparse Region Duplication Detection from Digital Images

Currently, digital images are widely communicated by media using social media applications. The general public captures the digital images for preserving the family and personal memories and to share with their friends and family. Digital images have been used extensively in forensic science to present the digital images as proof in the court and law enforcement agencies, which present a loophole for the culprits to forge the digital image and change the proofs and evidence. Copy-move forgery (CMF) is among the most widely employed image manipulation methods. In this method, the area of the image is duplicated to some other part to modify its content by applying different postprocessing operations on images like blurring, color reduction, and scaling which is a challenging research problem in copy-move forgery detection (CMFD). In this paper, an efficient and effective CMFD method is presented to identify the single and multiple altered areas in an image in the presence of postprocessing operations. The proposed CMFD method divides the image into circular blocks. It computes a rotation-invariant feature vector from each circular block of the image by applying local intensity order pattern (LIOP) features. The computed feature vectors are then compared using Euclidean distance to locate the suspected image’s forged areas. The experimental results of the proposed CMFD method are reported on three standard datasets of the CMF, namely, CoMoFoD, KLTCI, and MICC-F220. The experimental analysis of the proposed CMFD method on these datasets indicates that it produces robust performance (detection accuracies of 97.29% on the CoMoFoD dataset, 98.53% on the KLTCI dataset, and 97.57% on the MICC-F220 dataset) as compared with state-of-the-art CMFD methods in terms of the standard performance evaluation parameters of the CMF.
IIBES: a proposed framework to improve the identity-based encryption system for securing federated learning

Purpose In the field of cryptography, authentication, secrecy and identification can be accomplished by use of secret keys for any computer-based system. The need to acquire certificates endorsed through CA to substantiate users for the barter of encoded communications is one of the most significant constraints for the extensive recognition of PKC, as the technique takes too much time and susceptible to error. PKC’s certificate and key management operating costs are reduced with IBC. IBE is a crucial primeval in IBC. The thought behind presenting the IBE scheme was to diminish the complexity of certificate and key management, but it also gives rise to key escrow and key revocation problem, which provides access to unauthorised users for the encrypted information. Design/methodology/approach This paper aims to compare the result of IIBES with the existing system and to provide security analysis for the same and the proposed system can be used for the security in federated learning. Findings Furthermore, it can be implemented using other encryption/decryption algorithms like elliptic curve cryptography (ECC) to compare the execution efficiency. The proposed system can be used for the security in federated learning. Originality/value As a result, a novel enhanced IBE scheme: IIBES is suggested and implemented in JAVA programming language using RSA algorithm, which eradicates the key escrow problem through eliminating the need for a KGC and key revocation problem by sing sub-KGC (SKGC) and a shared secret with nonce. IIBES also provides authentication through IBS as well as it can be used for securing the data in federated learning.
MICROBLOGGING TO IMPROVE STUDENT CREATIVITY IN WRITING ACTIVITIES

Microblogging are often used by the teacher in the learning process. Microblogging like Instagram is one of the web technologies or applications that are used for the learning process. Microblogging is not only writing a tweet on Twitter. On Instagram students also can be media for student learning writing. In addition, microblogging can be used as interesting media to learn English. Therefore this study was conducted to find out what is a significant effect of using microblogging and also find out what are student perception of the use of microblogging to elevate student creativity in a writing activity. using qualitative methods, research instruments are interview, questionnaires, and documents. The study was conducted at one of the vocational school in Bogor with eight students as participants. From the findings, it is concluded that students have some significant and have positive perceptions after using microblogging in writing activities. Most students agreed that microblogging is easy to use for writing. They also said microblogging is an interesting medium for learning English. In addition, can motivate them to write better and elevate their creativity.&#x0D; Keyword: Microblogging, Creative Writing, EFL
Demo: Visualizing USSD and IVR Usage Data with Icicle Charts

Software designers in many parts of the world rely on Interactive Voice Response (IVR) and Unstructured Supplementary Service Data (USSD) to create applications that work universally on basic mobile phones. In this work, we demonstrate our use of hierarchical visualizations like icicle charts to explore the usage data generated by IVR and USSD applications, helping designers understand user behavior and evaluate the efficacy of the applications’ different components. By adding interactive elements, a viewer could zoom down to to see details about users’ behavior from a certain screen, or filter users by different demographics.
An improved software defect prediction model based on grey incidence analysis and Naive Bayes algorithm

This paper aims to improve the accuracy of software defect prediction by using a prediction model based on grey incidence analysis and Naive Bayes algorithm. The model employs the Naïve Bayes as the basic classifier of the software defect prediction model. The grey incidence analysis is used to analyze the relation between software modules and ideal modules. Then, the grey correlation degree is embedded into the Naive Bayes classification model as a feature attribute. According to the comparison and analysis of NASA’s public dataset, the prediction model in this paper improves the prediction accuracy.
Improve Image Captioning by Modeling Dynamic Scene Graph Extension

Recently, scene graph generation methods have been used in image captioning to encode the objects and their relationships in the encoder-decoder framework, where the decoder selects part of the graph nodes as input for word inference. However, current methods attend to scene graph relying on ambiguous language information, neglecting the strong connections between scene graph nodes. In this paper, we propose a Scene Graph Extension (SGE) architecture to model the dynamic scene graph extension using the partly generated sentence. Our model first uses the generated words and previous attention results of scene graph nodes to make up a partial scene graph. Then we choose objects or relationships that has close connection with the generated graph to infer the next word. Our SGE is appealing in view that it is pluggable to any scene graph based image captioning method. We conduct the extensive experiments on MSCOCO dataset. The results shows that the proposed SGE significantly outperforms the baselines, resulting in a state-of-the-art performance under most metrics.
A Robust Strategy for Large-Size Optical and SAR Image Registration

The traditional template matching strategy of optical and synthetic aperture radar (SAR) is sensitive to the nonlinear transformation between two images. In some cases, the optical and SAR image pairs do not conform to the affine transformation condition. To address this issue, this study presents a novel template matching strategy which uses the One-Class Support Vector Machine (SVM) to remove outliers. First, we propose a method to construct the similarity map dataset using the SEN1-2 dataset for training the One-Class SVM. Second, a four-step strategy for optical and SAR image registration is presented in this paper. In the first step, the optical image is divided into some grids. In the second step, the strongest Harris response point is selected as the feature point in each grid. In the third step, we use Gaussian pyramid features of oriented gradients (GPOG) descriptor to calculate the similarity map in the search region. The trained One-Class SVM is used to remove outliers through similarity maps in the fourth step. Furthermore, the number of improve matches (NIM) and the rate of improve matches (RIM) are designed to measure the effect of image registration. Finally, this paper designs two experiments to prove that the proposed strategy can correctly select the matching points through similarity maps. The experimental results of the One-Class SVM in dataset show that the One-Class SVM can select the correct points in different datasets. The image registration results obtained on the second experiment show that the proposed strategy is robust to the nonlinear transformation between optical and SAR images.
DMPCANet: A Low Dimensional Aggregation Network for Visual Place Recognition

Visual place recognition (VPR) aims to estimate the geographical location of a query image by finding its nearest reference images from a large geo-tagged database. Most of the existing methods adopt convolutional neural networks to extract feature maps from images. Nevertheless, such feature maps are high-dimensional tensors, and it is a challenge to effectively aggregate them into a compact vector representation for efficient retrieval. To tackle this challenge, we develop an end-to-end convolutional neural network architecture named DMPCANet. The network adopts the regional pooling module to generate feature tensors of the same size from images of different sizes. The core component of our network, the Differentiable Multilinear Principal Component Analysis (DMPCA) module, directly acts on tensor data and utilizes convolution operations to generate projection matrices for dimensionality reduction, thereby reducing the dimensionality to one sixteenth. This module can preserve crucial information while reducing data dimensions. Experiments on two widely used place recognition datasets demonstrate that our proposed DMPCANet can generate low-dimensional discriminative global descriptors and achieve the state-of-the-art results.