Reviewerly/exHarmony huggingface dataset:
A Comprehensive Review on Social Botnet Detection Techniques

In modern era, social bots have become more prevalent as automated social actors as a result of the growth of services on web and fame of online social networks (OSN). Social media sites such as Facebook, YouTube, Twitter, Telegram, LinkedIn, etc. are populated with enormous numbers of “social bots”. “Social bots” can be typically believed of as artificial social media accounts used by bad actors to sway public opinion. They are praised for having the capacity to connect with human users and create content on their own. These players can take on a variety of harmful activities, such as eavesdroppers on people's conversations, con artists, spoofers, disseminators of false information, manipulators of the stock market, astroturfers, and content scavengers (spammers, spreaders of malware, etc.). Therefore, the study describes the possible risks posed by harmful social bots, evaluates detection methods using a methodological classification, various available data sources and suggests directions for future study. The findings can assist OSN managers and researchers in comprehending the potentially harmful effects of harmful social bots and can strengthen the current countermeasures.
Knowledge Graph Curation from Text Via Ontologies

Huge-scale knowledge graphs have become widespread on the web and are useful for a plethora of tasks. Knowledge graphs which are powered by machine learning use Natural Language Processing to build an extensive view of nodes, edges and labels by semantic enrichment in order to automate tasks like data structuring, text analysis and merging of data models. This research study put forth a semantically-driven and knowledge-centric strategy, which integrates machine learning and semantic inference for curating knowledge graphs from text using ontologies. The proposed framework amalgamates knowledge from the sources: Wikidata, DBpedia and Mediawiki to instill heterogeneity. The proffered framework transcends the baseline models by yielding an accuracy value of 98.22Cumulative Gain (NDCG) value of 0.97.
Prospects for the Application of Restoring Electric Arc Coatings in the Repair of Machines and Mechanisms

An electric arc sprayer has been improved, which makes it possible to improve the quality of composite coatings by increasing the particle velocity in a high-temperature heterophase jet and reducing its opening angle, which leads to an increase in the CMM during spraying from 0.63 to 0.74. The optimal temperature-time parameters of pre-recrystallization heat treatment are established, which provide a further increase in the hardness of coatings due to the grinding of subgrains to the nanoscale size inclusive. The possibility of thermal stabilization of the polygonization substructure of electric are coatings obtained by spraying 12×18n10t wire by their additional plastic deformation is shown.
A Robust Captcha Scheme for Web Security

The internet has grown increasingly important in everyone's everyday lives due to the availability of numerous web services such as email, cloud storage, video streaming, music streaming, and search engines. On the other hand, attacks by computer programmes such as bots are a common hazard to these internet services. Captcha is a computer program that helps a server-side company determine whether or not a real user is requesting access. Captcha is a security feature that prevents unauthorised access to a user's account by protecting restricted areas from automated programmes, bots, or hackers. Many websites utilise Captcha to prevent spam and other hazardous assaults when visitors log in. However, in recent years, the complexity of Captcha solving has become difficult for humans too, making it less user friendly. To solve this, we propose creating a Captcha that is both simple and engaging for people while also robust enough to protect sensitive data from bots and hackers on the internet. The suggested captcha scheme employs animated artifacts, rotation, and variable fonts as resistance techniques. The proposed captcha technique proves successful against OCR bots with less than 15% accuracy while being easier to solve for human users with more than 98% accuracy.
Estimation of Breast Density from Human Mammograms using U-Nets

Breast cancer, a potentially fatal condition, affects one in twenty-eight women in India. Breast density, which is defined as the ratio of the volume of breast tissue to that of fatty tissue, is an important criterion in measuring the likelihood of a person developing breast cancer. In breast cancer, it is almost always the breast tissue that turns cancerous, not the fatty tissue. Women who have dense breast tissue, are four to six times more likely to develop breast cancer than women who do not have dense breast tissue. In our study, we examine breast mammograms and aim to develop a technique to automatically quantify breast density objectively, which is important in the prediction of the risk of breast cancer. While increased breast density greatly predisposes the risk of breast cancer, it is important to assess the risk quantitatively, which is difficult. In our technique, we develop a technique based on U-Net to segment breast tissues. We aim to classify the breasts based on the amount of breast tissue they have compared with the total volume of the breast. Therefore, we utilize a training database of 20 human-segmented images to train our algorithm and test it on a separate set of 15 human-segmented images. Examining the accuracy of the segmentation, we find that we obtain an accuracy of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.79\pm 0.12$</tex> in the Dice similarity coefficient. The qualitative images have also been examined and as may be observed from the qualitative results. A fully replicable, open-source code that everyone can use to segment breast tissue from mammogram images is the project's final goal.
Analysis of Translational and Tensor Factorization Knowledge Graph Embedding models

Knowledge graph embedding is used to find a real value vector representation of entities and relationship that is utilized to do the link prediction task. Knowledge Graph comprises the real world entities but many relationship between the set of two entities are missing that effect the utilization of knowledge graph for the application such as information retrieval and question-and-answering. The translation distance embedding model and tensor factorization based model focuses on predicting the missing relationship between the set of two entities by utilizing learned embedding. This paper illustrates, analyzes and evaluates the models over the benchmark dataset FB15k.
Comparison of Machine Learning-based anomaly detectors for Controller Area Network

This paper presents a comparative analysis of different Machine Learning-based detection algorithms designed for Controller Area Network (CAN) communication on three different datasets. This work focuses on addressing the current limitations of related scientific literature, related to the quality of the publicly available datasets and to the lack of public implementations of the detection solutions presented in literature. Since these issues are preventing the reproducibility of published results and their comparison with novel detection solutions, we remark that it is necessary that all security researchers working in this field start to address them properly to advance the current state-of-the-art in CAN intrusion detection systems. This paper strives to solve these issues by presenting a comparison of existing works on publicly available datasets.
Virtual Laboratory Complex for Studying the Theoretical Foundations of Construction and Analysis of Electromechanical Systems

The article discusses the process of developing a virtual complex for the study of direct current electromechanical systems with energy converters. For the first time, the technology of virtual stands of electromechanical systems was created in the form of mobile applications for gadgets running the Android OS. Conducted modeling of the electromechanical system in the Android Studio environment using the Kotlin programming language. A software application was created to work under the control of the Android OS and relevant studies of dynamic modes were carried out. The mobile application provides the opportunity to conduct research in real time; imitations of researched simulated objects with a high degree of realism; the possibilities of interactive action on the studied simulated processes, as well as the organization of feedbacks.
CNN-Based Post-Processing Filter for Video Compression with Multi-Scale Feature Representation

In this paper, we propose a convolutional neural network (CNN)-based post-processing filter for video compression with multi-scale feature representation. The discrete wavelet transform (DWT) decomposes an image into multi-frequency and multi-directional sub-bands, and can figure out artifacts caused by video compression with multi-scale feature representation. Thus, we combine DWT with CNN and construct two sub-networks: Step-like sub-band network (SLSB) and mixed enhancement network (ME). SLSB takes the wavelet subbands as input, and feeds them into the Res2Net group (R2NG) from high frequency to low frequency. R2NG consists of Res2Net modules and adopts spatial and channel attentions to adaptively enhance features. We combine the high frequency sub-band output with the low frequency sub-band in R2NG to capture multi-scale features. ME uses mixed convolution composed of dilated convolution and standard convolution as the basic block to expand the receptive field without blind spots in dilated convolution and further improve the reconstruction quality. Experimental results demonstrate that the proposed CNN filter achieves average 2.13%, 2.63%, 2.99%, 4.8%, 3.72% and 4.5% BD-rate reductions over VTM 11.0-NNVC anchor for Y channel on A1, A2, B, C, D and E classes of the common test conditions (CTC) in AI, RA and LDP configurations, respectively.
Dual-stream Self-attention Network for Image Captioning

Self-attention based encoder-decoder models achieve dominant performance in image captioning. However, most existing image captioning models (ICMs) only focus on modeling the relation between spatial tokens, while channel-wise attention is neglected for getting visual representation. Considering that different channels of visual representation usually denote different visual objects, it may lead to poor performance in terms of object and attribute words in the captioning sentences generated by the ICMs. In this paper, we propose a novel dual-stream self-attention module (DSM) to alleviate the above issue. Specifically, we propose a parallel self-attention based module that simultaneously encodes visual information from the spatial and channel dimensions. Besides, to obtain channel-wise visual features effectively and efficiently, we introduce a group self-attention block with linear computational complexity. To validate the effectiveness of our model, we conduct extensive experiments on the standard IC benchmarks including MSCOCO and Flickr30k. Without bells and whistles, the proposed model performs new SOTAs containing 135.4 CIDEr score on MSCOCO and 70.8 CIDEr score on Flickr30k.
A Privacy-Preserving and End-to-End-Based Encrypted Image Retrieval Scheme

Applying encryption technology to image retrieval can ensure the security and privacy of personal images. The related researches in this field have focused on the organic combination of encryption algorithm and artificial feature extraction. Many existing encrypted image retrieval schemes cannot prevent feature leakage and file size increase or cannot achieve satisfied retrieval performance. In this paper, a new end-to-end encrypted image retrieval scheme is presented. First, images are encrypted by using block rotation, new orthogonal transforms and block permutation during the JPEG compression process. Second, we combine the triplet loss and the cross entropy loss to train a network model, which contains gMLP modules, by end-to-end learning for extracting cipher-images' features. Compared with manual features extraction such as extracting color histogram, the end-to-end mechanism can economize on manpower. Experimental results show that our scheme has good retrieval performance, while can ensure compression friendly and no feature leakage.
Exploration and Research on Digital Education Scenarios from the Perspective of Metaverse

The term “metaverse” has become one of the most popular in recent years. Its immersion, low barrier, diversity, anytime, anywhere provided by technology creates a large area for educational scene investigation and research. The use of the metaverse in education creates new opportunities, promotes augmented reality, life milestones, virtual reality, and mirror worlds, among other things. Metaverse also provides new fields, virtual classrooms, and teachers for learners in the learning process, as well as a convenient way to organize scenario simulation instruction, design scientifically, and locate properly in the education scenario. Furthermore, Constructive learning results and social learning effects are realized in such a virtual learning setting, promoting the reform of the digital education mode. It promotes a deep integration of the metaverse with educational practice.
Method for Evaluation the Pattern of Internet Service Customers Based on Stylometric Analysis Oof their Text Content

Messengers and chats of Internet services (in particular, transport companies, taxi services, energy generating companies etc.) are traditional tools for customer communication with the service provider and a means of feedback. Text messages from customers are a source of operational data and can contain important information about customer preferences, and opinions about certain service elements and quality. Tools of semantic and sentiment analysis allow us to give answers to these questions. However, the authors show the stylometric analysis of chat messages gives a lot of additional information about the personality of the customer himself, who often remains anonymous. The article proposes a method of segmentation of Internet service customers in the space of their stylometric characteristics. The authors also showed that in the space of stylometric characteristics there are certain homogeneous groups that have stable both stylometric patterns. These results can be the basis for the development of a classifier that will allow classifying a new customer by the nature of writing and optimizing his service. The use of such a method by managers or chatbots, in particular, based on artificial intelligence, will increase the level of service of the customers of service of power generating companies, etc.
DesnowFormer: an effective transformer-based image desnowing network

Single image desnowing is an important and challenge task for lots of computer vision applications, such as visual tracking and video surveillance. Although existing deep learning-based methods have achieved promising results, most of them rely on the local deep features and neglect global relationship information between the local regions. Therefore, inevitably leading to over-smooth or detail loss results. To solve this issue, we design a UNet-based end-to-end architecture for image desnowing. Specially, to better characterize global information and preserve image detail, we combine Window-based Self-Attention (WSA) transformer block with Residue Spatial Attention (RSA) to build basic unit of our network. Besides, to protect the structure of the image effectively, we also introduce a Residue Channel (RC) loss to guide high-quality image restoration. Extensive experimental results on both synthetic and real-world datasets demonstrate that the proposed model achieves new state-of-the-art results.
Unlocking the Path toward Intelligent Telecom Marketplaces for Beyond 5G and 6G Networks

6G systems are perceived to be heavily softwarized, and therefore, the collaboration among all stakeholders, such as network and cloud providers, operators, application developers, service providers and device and equipment vendors, shall occur via software in a secured, distributed, and automated manner. This could be envisioned with a brokerless and immutable marketplace anchored in distributed ledger technologies, removing the need for a trusted party as a marketplace operator while avoiding this single point of failure, increasing transparency and flexibility of operational rules, and featuring automated contract negotiation and fulfillment supporting governance, delivery, and billing operations. This work goes a step forward and introduces a smart resource and service discovery application, based on intent recognition and machine learning assisted techniques, to enable the automated discovery of resources and services exposed over the marketplace. Evaluation results demonstrate the accuracy of our approach and its suitability to provide decentralized telecom marketplaces with intelligent and data-driven discovery capabilities.
Rethinking Image Deblurring via CNN-Transformer Multiscale Hybrid Architecture

Image deblurring is a representative low-level vision task that aims to estimate latent sharp images from blurred images. Recently, convolutional neural network (CNN)-based methods have dominated image deblurring. However, traditional CNN-based deblurring methods suffer from two essential issues: first, existing multiscale deblurring methods process blurred images at different scales through sub-networks with the same composition, which limits the model performance. Second, the convolutional layers fail to adapt to the input content and cannot effectively capture long-range dependencies. To alleviate the above issues, we rethink the multiscale architecture that follows a coarse-to-fine strategy and propose a novel hybrid architecture that combines CNN and transformer (CTMS). CTMS has three distinct features. First, the finer-scale sub-networks in CTMS are designed as architectures with larger receptive fields to obtain the pixel values around the blur, which can be used to efficiently handle large-area blur. Then, we propose a feature modulation network to alleviate the disadvantages of CNN sub-networks that lack input content adaptation. Finally, we design an efficient transformer block, which significantly reduces the computational burden and requires no pre-training. Our proposed deblurring model is extensively evaluated on several benchmark datasets, and achieves superior performance compared to state-of-the-art deblurring methods. Especially, the peak signal to noise ratio (PSNR) and structural similarity (SSIM) values are 32.73 dB and 0.959, respectively, on the popular dataset GoPro. In addition, we conduct joint evaluation experiments on the proposed method deblurring performance, object detection, and image segmentation to demonstrate the effectiveness of CTMS for subsequent high-level computer vision tasks.
Bidirectional String Anchors for Improved Text Indexing and Top-$K$ Similarity Search

The minimizers sampling mechanism is a popular mechanism for string sampling. However, minimizers sampling mechanisms lack good guarantees on the expected size of their samples for different combinations of their input parameters. Furthermore, indexes constructed over minimizers samples lack good worst-case guarantees for on-line pattern searches. In response, we propose bidirectional string anchors (bd-anchors), a new string sampling mechanism. Given an integer <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> , our mechanism selects the lexicographically smallest rotation in every length- <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> fragment. We show that, like minimizers samples, bd-anchors samples are approximately uniform, locally consistent, and computable in linear time. Furthermore, our experiments demonstrate that the bd-anchors sample sizes decrease proportionally to <inline-formula><tex-math notation="LaTeX">$\ell$</tex-math></inline-formula> ; and that these sizes are <i>competitive to or smaller than</i> the minimizers sample sizes. We theoretically justify these results by analyzing the expected size of bd-anchors samples. We also prove that computing a total order on the input alphabet which minimizes the bd-anchors sample size is NP-hard. We next highlight the benefits of bd-anchors in two important applications: text indexing and top- <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> similarity search. For the first application, we develop an index for performing on-line pattern searches in near-optimal time, and show experimentally that a simple implementation of our index is <i>consistently faster</i> for on-line pattern searches than an analogous implementation of a minimizers-based index; we also show that it is <i>substantially</i> faster than two classic text indexes. For the second application, we develop a heuristic for top- <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> similarity search under edit distance, and show experimentally that it is generally as <i>accurate</i> as the state-of-the-art tool for the same purpose but <i>more than one order of magnitude faster</i> .
An Exploration of State-of-the-Art Automation Frameworks for FPGA-Based DNN Acceleration

FPGA-based acceleration is considered a promising approach to improve the performance and power efficiency of Deep Neural Network (DNN) inference tasks. However, mapping a DNN onto an FPGA is not trivial. To make this easier, various automation frameworks have been proposed. Among them, FINN and Vitis AI, both developed by Xilinx, are two key players. They represent two different philosophies in designing FPGA-based DNN accelerators: dataflow-style and overlay-style architectures. Dataflow architectures are generally expected to provide better performance and power efficiency but have a major drawback in that they scale very poorly to the size of the target DNN. Advanced frameworks like FINN alleviate this drawback by transforming the target DNN into operations that can be time-multiplexed on fewer hardware resources. This approach, however, is challenging because of the difficulty in transforming the target DNN and raises a question as to whether the generated dataflow architectures retain a significant advantage over overlay architectures in terms of performance and power efficiency. This paper aims to clarify it by conducting an in-depth exploration of FINN and Vitis AI. For this purpose, we extend the FINN’s development flow to be able to use the same target hardware and DNN model to evaluate each framework. We demonstrate the effectiveness of the FPGA-based acceleration by providing a comparison with two reference platforms: an NVIDIA Jetson Nano Developer Kit with a similar power budget to our target FPGA hardware, and a high-performance desktop computer with an Intel Core i7-11700K CPU. The results show that despite the use of the time-multiplexing approach, the FINN-based accelerator can still outperform the Vitis-AI-based accelerator by a significant margin, 8.4x in terms of latency, 3.0x in terms of throughput, and 3.3x in terms of power efficiency. The outcome of the comparison with the NVIDIA Jetson Nano Developer Kit and the desktop computer is also overwhelmingly favorable to the FINN-based accelerator. This indicates that, even in the case of using automation frameworks, DNN accelerators on FPGAs can still yield significant performance and power efficiency gains compared with GPUs and CPUs.