Reviewerly/exHarmony huggingface dataset:
Fake Account Detection on Social Media using Random Forest Classifier

As the growth of online social networks increases, everyone is associated and linked with social media. A massive amount of personal data is being attacked and stolen by cyber attackers. These fraudulent profiles also spread negative publicity, false news, and various malicious programs. One of the best ways is to detect these fake accounts, not only the best way but also it is the first step to stopping the negative news and false rumours. There are many efficient ways and techniques to detect these fake accounts. However, these techniques are based on or depend on the account features. The main aim of this research is to examine three machine learning algorithms i.e., random forest, logistic regression, and decision tree. The level of efficiency in the three mentioned methodologies are compared and the highest accuracy has been achieved for random forest classifier.
Multi-label and Multi-class Classification on a Custom Dataset using Convolution Neural Networks

A multi-label image classification is a challenging task as it has to map an input image to a vector of outputs. This work presents a single and efficient model to perform multi-label and multi-class classification using Convolutional Neural Networks (CNN). A custom dataset is collected and annotated with eight labels to test the neural network. The classes for the labels in the dataset are a blend of balanced and unbalanced data. The proposed network is a two-stage network. The first stage is used for general training, whereas the second stage is used for specific training. The results show that the usage of a single model across multiple labels results in varying performance. The number of classes and the balance in the class distribution have a direct impact on the performance of the model. The code is available at https://github.com/sridevibonthu/FML.
TraceX: A Tax Tracking Application

Income tax is the largest source of tax revenue in India. The tax is used for development of the country which in turn benefits the citizens and improves the lifestyle of the population. Yet, the number of taxpayers in India is about 82.7 million people which is only 6.25 percent of the over 132 crore population. There are various reasons which lead to Indians not paying their taxes, the major being lack of transparency of information about the way the tax is being spent by the government. An application that is completely transparent in information storage is required, which should allow each and every citizen to pay their tax to the government and be able to monitor the usage of their tax money by the government.This research work proposes a novel tax tracker application. This application works completely over the blockchain technology, which prioritises decentralised data as its key principle. This solution can solve the transparency problem by showing the use of the tax amount by the government to the users and will keep a track of the amount and its allocation for various government projects. It also helps the government have a clear vision on how their constituencies are using the allocated budget.
Text Summarization of Medical Documents using Abstractive Techniques

Medical researchers are exposed to enormous amounts of medical information in the form of medical news, clinical trial reports, research articles, etc. Researchers would need the documents' summaries that help them decide to do an in-depth study. Even though there are documents that contain abstracts. a lot of medical documents do not contain abstracts or summaries. The best solution to address this issue is abstractive text summarization. Extracting useful information and summarizing the information from medical documents inclined to the best interests of the user is a challenge that is addressed in this study. For this, several abstractive summarization techniques such as T5(Text-to-Text Transfer Transformer), BART (Bidirectional Auto-Regressive Transformer) and PEGASUS (Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-sequence) are used and based on ROUGE (Recall-Oriented Understudy for Gisting Evaluation) metrics, PEGASUS is identified to perform better than the other models achieving the highest ROUGE score of 0.37.
Review and Comparison on Types of Solar Tracking using PNT Systems

The eventual objective of the endeavour is to design a PNT-based solar tracking system associated to a microcontroller to pinpoint the location of the sun, which is used as data to ascertain the exact coordinates of the solar panel, and to communicate power usage to the user through This initiative aims at maximizing solar energy production while increasing efficiency. Using a solar panel, motor, GPS, battery, and electrical circuit, this technique will produce an uncomplicated and relatively inexpensive sun follower system. The collector is guided by photovoltaic tracers to follow the trajectory of the sun. Solar tracing technology progresses the PV plate’s renewable power extent practicable. The findings demonstrate that the automated solar mapping system is more reliable and meticulous than the stable one. This study analyses several solar tracking system techniques so that the choice the one that will monitor the sun’s rays with the greatest performance.
TARig: Adaptive template-aware neural rigging for humanoid characters

We present TARig, a template-aware neural rigging method, to automatically generates industry-standard and high-quality skeleton and skin weights for humanoid characters. The resulting skeleton consists of a widely-used humanoid template with 21 template joints and associate secondary joint sets which are generated adaptively for handling auxiliary components. To explicitly utilize the anatomic consistency among humanoid characters, we incorporate the manually annotated anatomic label of each template joint to fully supervise the rigging process in an end-to-end training scheme. Together with a shared graph neural network backbone, such semantic constraint ensures to generate precise joint positions and accurate skin weights simultaneously. Beyond the pre-defined topology of the template skeleton, we additionally learn a boneflow field to further determine the inner connections for each secondary joint set and avoid counter-anatomical skeleton construction. Extensive experiments demonstrate TARig generates high-quality rigging results and outperforms other state-of-the-art auto-rigging methods in terms of skeleton generation and skin weight estimation for humanoid characters with 8∼20 times speedup.
Traceable constant-size multi-authority credentials

Many attribute-based anonymous credential (ABC) schemes have been proposed, enabling users to anonymously prove possession of attributes. Recent papers introduced constant-size credentials issued by a single credential issuer for showing a subset of attributes. However, proving possession of attributes from multiple issuers typically requires independent credentials. Only aggregate signature-based attribute-based credential schemes can overcome this limitation. This paper presents new ABC schemes using aggregate signatures with randomizable tags. The schemes consider malicious credential issuers, adaptive corruptions, and collusions with malicious users. While our constructions only support selective attribute disclosures to remain compact, our approach significantly improves the complexity in time and memory for showing multiple attributes. For the first time, the cost for the prover becomes (almost) independent of the number of attributes and issuers. Whereas anonymous credentials require privacy of the user, we also propose the first schemes allowing traceability by a specific tracing authority.
Dual attentional transformer for video visual relation prediction

Video visual relation detecti on (VidVRD) is to detect visual relations among instances as well as the trajectories of the corresponding subjects and objects in the video. Most current works improve the accuracy of tracking the objects but neglect the other key challenge, predicting the reliable visual relations in the videos, a vital meant for downstream tasks further. In this paper, we propose a dual attentional transformer network (VRD-DAT) for predicting the visual relations, also known as the predicates, in multi-relation videos. Specifically, our network first respectively targets modeling action visual predicates (Act-T) and spatial locating visual relations (Spa-T) via two parallel visual transformer structures simultaneously. Then, an attentional weighting module obtains the final precise merged visual relations. We conduct extensive experiments on two public datasets, ImageNet-VidVRD and VidOR, to demonstrate our model is capable of outperforming other state-of-the-art methods effectively on the task of video visual relation prediction. Quantitative and qualitative results also show that with more accurate visual relations, the performance of the video visual relation detection task can be further boosted.
A new local structural similarity fusion-based thresholding method for homomorphic ultrasound image despeckling in NSCT domain

Ultrasound is a diagnostic imaging technique to detect various illnesses related to the body's internal organs. It is a non-invasive, safe, cost-effective, and simple imaging modality that uses high-frequency sound waves and their reflection from internal organs. However, a key drawback of using ultrasound imaging tools is that they are often contaminated by multiplicative speckle noise, which can reduce the image resolution and contrast and complicates the diagnosis of the tiny structure of lesions. Extensive studies have shown that eliminating speckle noise is a necessary pre-processing task for computer-assisted ultrasound image systems. This paper proposes a novel method for denoising, weighted thresholding, and fusion of ultrasound images in the Non-Subsampled Contourlet Transformation (NSCT) domain. The method utilizes (a) The Bayesian and Bivariate shrinkage rule in the NSCT domain, (b) A new local structural similarity-fusion-based homomorphic ultrasound image despeckling method in the NSCT domain, and (c) A weights estimation approach based on a bilateral method that helps improve noise suppression and edge preservation results. The performance of the proposed algorithm is evaluated in terms of mean squared error (MSE), peak signal-to-noise ratio, structural similarity index (SSIM), speckle suppression index, signal-to-MSE ratio, and speckle signal-to-noise ratio. Extensive computer simulation results show that the presented method performs better in subjective visual inspection than existing state-of-the-art despeckling techniques.
End-to-End Fusion Network of Deep and Hand-Crafted Features for Small Object Detection

Recent advances in deep learning have enabled state-of-the-art performance in detecting medium and large-size objects. However, small object detection remains challenging primarily due to the scarcity of information. This paper proposes an end-to-end fusion network that integrates deep and hand-crafted features to address this limitation. A fusion module based on semantic context information is designed to enhance feature discrimination ability. Additionally, we introduce a kind of feature-contrast loss to incorporate prior knowledge into the learning of deep feature according to contrastive learning. Experiments on MS COCO (34.4% <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${\mathrm {A}}{{\mathrm {P}}_{\mathrm {S}}}$ </tex-math></inline-formula> ) and PASCAL VOC (85.9% mAP) datasets demonstrate that our approach achieves improved detection accuracy over previous methods, especially for small objects.
New User Intent Discovery With Robust Pseudo Label Training and Source Domain Joint Training

Discovering new user intents based on existing intents from constantly incoming unlabeled data is an important task in many intelligent systems deployed in the real world (e.g., dialogue systems). Since data with new intents are completely unlabeled, most current approaches employ clustering methods to generate pseudo labels to train their models. However, due to intent gaps between existing and new intents, pseudo labels generated by these models are noisy and prior knowledge from existing intents is not fully utilized. To mitigate these issues, we propose a robust Pseudo label Training and source domain Joint-training Network (PTJN) to refine the noisy pseudo labels and make full use of prior knowledge. Experimental results on three intent detection datasets show that our model is more effective and robust than state-of-the-art methods. The code and data are released at <ext-link ext-link-type="uri" xlink:href="https://github.com/Lackel/PTJN" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Lackel/PTJN</ext-link> .
Hierarchical Random Access Coding for Deep Neural Video Compression

Recently, neural video compression networks have obtained impressive results. However, previous neural video compression models mostly focus on low-delay configuration with the order of display being the same as the order of coding. In this paper, we propose a hierarchical random access coding approach that exploits bidirectionally temporal redundancy to improve the coding efficiency of existing deep neural video compression models. The proposed framework applies a video frame interpolation network to improve inter-frame prediction. In addition, a hierarchical coding structure is also proposed in this paper. Experimental results show the proposed framework improves the coding efficiency of the base deep neural model by 48.01% with the UVG dataset, 50.96% with the HEVC-class B dataset, and outperforms the previous deep neural video compression networks.
Information-theoretic analysis of Hierarchical Temporal Memory-Spatial Pooler algorithm with a new upper bound for the standard information bottleneck method

Hierarchical Temporal Memory (HTM) is an unsupervised algorithm in machine learning. It models several fundamental neocortical computational principles. Spatial Pooler (SP) is one of the main components of the HTM, which continuously encodes streams of binary input from various layers and regions into sparse distributed representations. In this paper, the goal is to evaluate the sparsification in the SP algorithm from the perspective of information theory by the information bottleneck (IB), Cramer-Rao lower bound, and Fisher information matrix. This paper makes two main contributions. First, we introduce a new upper bound for the standard information bottleneck relation, which we refer to as modified-IB in this paper. This measure is used to evaluate the performance of the SP algorithm in different sparsity levels and various amounts of noise. The MNIST, Fashion-MNIST and NYC-Taxi datasets were fed to the SP algorithm separately. The SP algorithm with learning was found to be resistant to noise. Adding up to 40% noise to the input resulted in no discernible change in the output. Using the probabilistic mapping method and Hidden Markov Model, the sparse SP output representation was reconstructed in the input space. In the modified-IB relation, it is numerically calculated that a lower noise level and a higher sparsity level in the SP algorithm lead to a more effective reconstruction and SP with 2% sparsity produces the best results. Our second contribution is to prove mathematically that more sparsity leads to better performance of the SP algorithm. The data distribution was considered the Cauchy distribution, and the Cramer-Rao lower bound was analyzed to estimate SP's output at different sparsity levels.
Scale-Semantic Joint Decoupling Network for Image-Text Retrieval in Remote Sensing

Image-text retrieval in remote sensing aims to provide flexible information for data analysis and application. In recent years, state-of-the-art methods are dedicated to “scale decoupling” and “semantic decoupling” strategies to further enhance the capability of representation. However, these previous approaches focus on either the disentangling scale or semantics but ignore merging these two ideas in a union model, which extremely limits the performance of cross-modal retrieval models. To address these issues, we propose a novel Scale-Semantic Joint Decoupling Network (SSJDN) for remote sensing image-text retrieval. Specifically, we design the Bidirectional Scale Decoupling (BSD) module, which exploits Salience Extraction Map (SEM) and Salience Suppression Map (SSM) units to adaptively extract potential features and suppress cumbersome features at other scales in a bidirectional pattern to yield different scale clues. Besides, we design the Label-supervised Semantic Decoupling (LSD) module by leveraging the category semantic labels as prior knowledge to supervise images and texts probing significant semantic-related information. Finally, we design a Semantic-guided Triple Loss (STL), which adaptively generates a constant to adjust the loss function to improve the probability of matching the same semantic image and text and shorten the convergence time of the retrieval model. Our proposed SSJDN outperforms state-of-the-art approaches in numerical experiments conducted on four benchmark remote sensing datasets.
Review on Performance Analysis of Lightweight Cryptographic Algorithm-CLEFIA

CLEFIA algorithm is a 128-bit block cipher with its key length being 128, 192 and 256 bits respectively, which is consistent to AES.CLEFIA is a light weight algorithm concerned with block cipher developed by well-known Sony company.CLEFIA name is given to this algorithm with reference to the French word clef which means "key".The block size used is of 128 bits and the key size may be 128-bit, 192 bit or 256 bits.CLEFIA operates on 128-bit block size with three different key sizes: 128-bit, 192-bit, 256-bits.In this Paper the performance analysis of Lightweight Cryptographic Algorithms based on execution time and memory use by algorithm is discussed.
Exhaustive study on post effect processing of 3D image based on nonlinear digital watermarking algorithm

Abstract In this work, an exhaustive study on post effect processing of three-dimensional (3D) image is carried to solve the problem of nonlinear digital watermarking algorithm. First, through the feature space decomposition method of the host image, the embedded watermark is constructed with the full row or column rank of the matrix, and then the public key is constructed by using the existence of the unitary matrix of the full row rank and column rank matrix, so that the algorithm can embed and extract the watermark in an asymmetric way. Watermark extraction correlation coefficient ( ρ ) value is 1. When the deformation amplitude of the model is slight and the noise intensity is σ = 0.0001, the watermark can be extracted successfully, and the watermark extraction correlation coefficient ( ρ ) is 0.92. In addition, the security of the algorithm is analyzed from many angles, the theoretical analysis is given, and verified by the experimental results. The proposed 3D watermarking methods are used to examine the information capacity of various 3D meshes. The 3D watermarking methods’ resistance to noise perturbation and object cropping is also investigated.
Security Monitoring during Software Development: An Industrial Case Study

The devastating consequences of successful security breaches that have been observed recently have forced more and more software development enterprises to shift their focus towards building software products that are highly secure (i.e., vulnerability-free) from the ground up. In order to produce secure software applications, appropriate mechanisms are required for enabling project managers and developers to monitor the security level of their products during their development and identify and eliminate vulnerabilities prior to their release. A large number of such mechanisms have been proposed in the literature over the years, but limited attempts with respect to their industrial applicability, relevance, and practicality can be found. To this end, in the present paper, we demonstrate an integrated security platform, the VM4SEC platform, which exhibits cutting-edge solutions for software security monitoring and optimization, based on static and textual source code analysis. The platform was built in a way to satisfy the actual security needs of a real software development company. For this purpose, an industrial case study was conducted in order to identify the current security state of the company and its security needs in order for the employed security mechanisms to be adapted to the specific needs of the company. Based on this analysis, the overall architecture of the platform and the parameters of the selected models and mechanisms were properly defined and demonstrated in the present paper. The purpose of this paper is to showcase how cutting-edge security monitoring and optimization mechanisms can be adapted to the needs of a dedicated company and to be used as a blueprint for constructing similar security monitoring platforms and pipelines.
Defending Against Membership Inference Attacks on Beacon Services

Large genomic datasets are created through numerous activities, including recreational genealogical investigations, biomedical research, and clinical care. At the same time, genomic data has become valuable for reuse beyond their initial point of collection, but privacy concerns often hinder access. Beacon services have emerged to broaden accessibility to such data. These services enable users to query for the presence of a particular minor allele in a dataset, and information helps care providers determine if genomic variation is spurious or has some known clinical indication. However, various studies have shown that this process can leak information regarding if individuals are members of the underlying dataset. There are various approaches to mitigate this vulnerability, but they are limited in that they (1) typically rely on heuristics to add noise to the Beacon responses; (2) offer probabilistic privacy guarantees only, neglecting data utility; and (3) assume a batch setting where all queries arrive at once. In this article, we present a novel algorithmic framework to ensure privacy in a Beacon service setting with a minimal number of query response flips. We represent this problem as one of combinatorial optimization in both the batch setting and the online setting (where queries arrive sequentially). We introduce principled algorithms with both privacy and, in some cases, worst-case utility guarantees. Moreover, through extensive experiments, we show that the proposed approaches significantly outperform the state of the art in terms of privacy and utility, using a dataset consisting of 800 individuals and 1.3 million single nucleotide variants.
A hierarchical aggregation model for combat intention recognition

Combat intent recognition refers to analyzing the enemy target's state information to interpret and judge the purpose of the enemy. With the increased knowledge of combat platforms, these time-series enemy state presents multi-dimensional and massive characteristics. Using neural networks to learn enemy state information has become a research trend in the face of such traits. To address these challenges, we propose a hierarchical aggregation model to recognize the intention of the target. The bottom layer of our model is based on convolutional neural network(CNN) to perceive behavior features, and the middle layer is based on Bi-LSTM(Bi-directional long short-term memory) to aggregate the long-time interdependence information between sub-intentions. The top layer focuses on higher-level features that contribute more to the recognition of intent through the attention mechanism and finally combines the global information to recognize the intention. Extensive experimental results show the superiority of our model in that the recognition accuracy achieves 88.83%, which can solve the problem of identifying air target intent on the modern battlefield.