Reviewerly/exHarmony huggingface dataset:
A Multitemporal Scale and Spatial–Temporal Transformer Network for Temporal Action Localization

Temporal action localization plays an important role in video analysis, which aims to localize and classify actions in untrimmed videos. Previous methods often predict actions on a feature space of a single temporal scale. However, the temporal features of a low-level scale lack sufficient semantics for action classification, while a high-level scale cannot provide the rich details of the action boundaries. In addition, the long-range dependencies of video frames are often ignored. To address these issues, a novel multitemporal-scale spatial–temporal transformer (MSST) network is proposed for temporal action localization, which predicts actions on a feature space of multiple temporal scales. Specifically, we first use refined feature pyramids of different scales to pass semantics from high-level scales to low-level scales. Second, to establish the long temporal scale of the entire video, we use a spatial–temporal transformer encoder to capture the long-range dependencies of video frames. Then, the refined features with long-range dependencies are fed into a classifier for coarse action prediction. Finally, to further improve the prediction accuracy, we propose a frame-level self-attention module to refine the classification and boundaries of each action instance. Most importantly, these three modules are jointly explored in a unified framework, and MSST has an anchor-free and end-to-end architecture. Extensive experiments show that the proposed method can outperform state-of-the-art approaches on the THUMOS14 dataset and achieve comparable performance on the ActivityNet1.3 dataset. Compared with A2Net (TIP20, Avg{0.3:0.7}), Sub-Action (CSVT2022, Avg{0.1:0.5}), and AFSD (CVPR21, Avg{0.3:0.7}) on the THUMOS14 dataset, the proposed method can achieve improvements of 12.6%, 17.4%, and 2.2%, respectively.
Target Detection Using Transformer: A Study Using DETR

Transformer has been proposed to augment the attention mechanism in neural networks without using recurrence and convolutions. Starting with machine translation, it graduated to vision transformer. Among the vision transformers, we explore the DEtection TRansformer (DETR) model proposed in the End-to-end Object Detection with Transformers paper by the team at Facebook AI. The authors have demonstrated interesting object detection results from the DETR model. That triggered the curiosity to use the model for detection of custom objects. Here, we are presenting the way to fine-tune the pre-trained DETR model over custom dataset. The fine-tuning results demonstrate significant improvement with respect to number of training epochs, both visibly as well as statistically.
Keypoint-Based Detection and Region Growing-Based Localization of Copy-Move Forgery in Digital Images

The issues caused due to image manipulations are common these days. Furthermore, it causes severe troubles in news broadcasting, social media, and digital media forensics. Mainly, the types of image manipulations are divided into four; they are splice forgery, copy-move, morphing, and retouching. Among them, copy-move forgery is one of the most challenging manipulations to detect since it does not change image characteristics while performing the copy-move forgery operation. In this paper, we propose a copy-move forgery detection and localization scheme that detect forgery regions in the image even though the forgery image undergoes translation, scaling, and rotation attacks. The scheme uses the SIFT algorithm for keypoints extraction from the forgery image, DBSCAN to cluster these keypoints, and Hu's invariant moments are used to identify similarity between two suspicious regions in the image. Lastly, a region growing is performed around these detected regions to localize copy-move forgery regions. The scheme has experimented with a CoMoFoD dataset which is publicly available, and the result shows that the proposed scheme outperforms the state-of-the-art non-deep learning-based copy-move forgery techniques in terms of recall, FNR, F1-score, and also in computational time.
RNCE: A New Image Segmentation Approach

Semantic image segmentation based on deep learning is gaining popularity because it is giving promising results in medical image analysis, automated land categorization, remote sensing, and other computer vision applications. Many algorithms have been designed in recent years, yet there is scope for further improvement in computer vision research. We have proposed a unique ensemble method called Ranking and Nonhierarchical Comparison Ensemble (RNCE) for semantic segmentation of landcover images based on the Ranking and Nonhierarchical Comparison methodology. Our approach has been tested on pretrained models showing improved accuracy and mean IoU with respect to the existing method. The code is available at: https://github.com/vekash2021/RNCE.git .
Streaming Platforms Based on Blockchain Technology: A Business Model Impact Analysis

Streaming platforms have established themselves in the global economy as a powerful source of revenue generation, owing to the advancement of mobile and broadband networks, as well as the popularity of smartphones. Blockchain technology has been used in the ecosystem of streaming platforms to develop solutions that provide transparency and traceability features about the services provided. This paper investigates how the characteristics of decentralized storage, immutability, and traceability have influenced the business model of streaming platforms. To achieve this goal, it was investigated in state-of-the-art, studies that addressed the development and validation of computational solutions with blockchain technology observing the following criteria: (i) type of network, (ii) data storage description, (iii) payment system with blockchain technology and (iv) transparency in audience metrics using smart contracts. Based on the results found, there is a lack of studies that assess the financial and governance feasibility of maintaining all of a streaming platform’s functionalities using blockchain technology.
A Deep Learning Approach for Sentiment and Emotional Analysis of Lebanese Arabizi Twitter Data

Arabizi is an Arabic dialect that is represented in Latin transliteration and is commonly used in social media and other informal settings. This work addresses the problem of Arabizi text identification and emotional analysis based on Lebanese dialect. The work starts with the extraction and construction of a dataset and uses two machine learning models. The first is based on fastText for learning the embeddings while the second uses a combination of recurrent and dense deep learning models. The proposed approaches were attempted on the Arabizi dataset that we extracted and curated from Twitter. We attempted our results with six classical machine learning approaches using separate sentiment and emotion analysis. We achieved the highest result in literature for the binary sentiment analysis with an F1 score of 81%. We also present baseline results for the 3-class sentiment classification of Arabizi tweets with an F1 score of 64%, and for emotion classification of Arabizi tweets with an f1 score of 61%.
An Ensemble Approach for Moving Vehicle Detection and Tracking by Using Ni Vision Module

In the recent years, surveillance systems and video monitoring have been largely used for the management of traffic. Acquired images and video clips from the road traffic can be utilized in the Lab VIEW program environment. LabVIEW Vision Assistant is focusing on to discover the moving vehicles. This approach finds a formation of resemblance in the frames for vehicle and non-vehicle objects, while the vehicles tracking progress through image sequences. For improving the adaptive background mixture model, there is indeed of background subtraction method. In addition, it constructs the system more precisely with rapid learning also. Therefore, its performance shows the adaptability on the occurrence of any real time videos. This evolve system robustly detect the vehicles on resolving the background objects which help to track the vehicles effectively. The various types of attributes related to moving vehicles are extracted which are used in feature extraction techniques for tracking the vehicles. The extracted features utilize in the module of LabVIEW environment and Vision Assistant module works mainly in the detection of moving vehicles objects. This proposed work can help in reducing the cost of traffic monitoring systems and real automation of traffic observation systems.
Multi-class Weather Classification Using Single Image via Feature Fusion and Selection

Weather classification using multiple classes is the most sought technique which has many potential applications. It is extremely difficult to get discriminative features from weather images due to diverse nature of weather conditions. In this paper, we have tried to capture the discriminative features by using feature fusion and feature reduction/selection methods. The proposed method uses combination of Histogram of Gradient (HOG) & deep features, feature selection/reduction and classification. Extensive experiments on the benchmark datasets were carried out using various features extraction and selection/reduction methods in conjunction with various classifiers. The extensive experimental evaluation demonstrates fusion of HOG & DenseNet-161 features with linear SVM classifier achieves the best classification accuracy of 99.65% & 95.2% for MCWRD and MWI dataset respectively. Our method has outperformed the state-of-the-art methods for both datasets. Our method is scalable, as it generates variety of solutions. Based on available compute, user can pick & choose the relevant method.
Analyzing Hydro-Estimator INSAT-3D Time Series with Outlier Detection

Precipitation plays an important role in various applications like agriculture, health care, disaster management, forest health and others. These applications are directly or indirectly affected by precipitation. In order to predict precipitation, it becomes important to analyze the pattern followed by the event. The generated pattern should be accurate to obtain the prediction at par. Presence of outliers in any data significantly affects the generated pattern, time required and accuracy in prediction. Treating missing values is not sufficient to get higher accuracy in prediction. In this paper, we present the performance evaluation of Support vector regression (SVR), Seasonal auto regressive integrated moving average (SARIMA), and Bi-directional LSTM (Bi-LSTM) for prediction of hydro estimator values (HEM), in domain of machine learning, statistical and deep learning approaches respectively. We have made a comparison between the generated pattern and prediction with and without outliers. We have identified a suitable outlier detection technique by comparing the performance of Cluster Based Local Outlier Factor (CBLOF), Histogram based outlier Detection (HBOS), K nearest neighbour (KNN), Isolation Forest for detecting outliers. The performance on prediction techniques are compared using RMSE and MAE measures. The implementation is done over a big data architecture, since the data is, Big data’.
A Segmentation Based Robust Fractional Variational Model for Motion Estimation

In this paper, we introduce a nonlinear robust fractional order variational framework for motion estimation from image sequences (video). Particularly, the presented model provides a generalization of integer order derivative based variational functionals and offers an enhanced robustness against outliers while preserving the discontinuity in the dense flow field. The motion is estimated in the form of optical flow. For this purpose, a level set segmentation based fractional order variational functional composed of a non-quadratic Charbonnier norm and a regularization term is propounded. The non-quadratic Charbonnier norm introduces a noise robust character in the model. The fractional order derivative demonstrates non-locality that makes it competent to deal with discontinuous information about edges and texture. The level set segmentation is carried on the flow field instead of images, which is a union of disjoint and independently moving regions such that each motion region contains objects of equal flow velocity. The resulting fractional order partial differential equations are numerically discretized using Grünwald–Letnikov fractional derivative. The nonlinear formulation is transformed into a linear system which is solved with the help of an efficient numerical technique. The results are evaluated by conducting experiments over a variety of datasets. The accuracy and efficiency of the propounded model is also depicted against recently published works.
ResUNet: An Automated Deep Learning Model for Image Splicing Localization

With the advances in digital media, images have been used as one of the most significant sources of information and communication in the modern society. However, images can be manipulated easily with the widespread availability of advanced image editing tools. Image splicing is one of the most popular image forgery methods in which a portion of one image is copied and stitched into another image for misleading the information. Hence, detection of image splicing forgery has become a significant research problem in multimedia forensic. Many splicing detection algorithms have been developed using traditional hand-crafted features, but these algorithms could not perform well on many post-processed or geometrically transformed spliced images. Hence in this paper, a new encoder-decoder architecture using convolutional neural network (CNN) is designed to locate the spliced region in an image. A hybrid CNN model called ResUNet is proposed in which encoder part of standard UNet architecture is replaced by ResNet 50 to analyze the discriminating feature between tampered and untampered regions. The proposed model is evaluated on CASIA v2.0 a benchmark dataset and experimental results demonstrate the proposed model turned out to be more efficient and accurate in splicing localization than other existing techniques.
T2CI-GAN: Text to Compressed Image Generation Using Generative Adversarial Network

The problem of generating textual descriptions for the visual data has gained research attention in the recent years. In contrast to that the problem of generating visual data from textual descriptions is still very challenging, because it requires the combination of both Natural Language Processing (NLP) and Computer Vision techniques. The existing methods utilize the Generative Adversarial Networks (GANs) and generate the uncompressed images from textual description. However, in practice, most of the visual data are processed and transmitted in the compressed representation. Hence, the proposed work attempts to generate the visual data directly in the compressed representation form using Deep Convolutional GANs (DCGANs) to achieve the storage and computational efficiency. We propose GAN models for compressed image generation from text. The first model is directly trained with JPEG compressed DCT images (compressed domain) to generate the compressed images from text descriptions. The second model is trained with RGB images (pixel domain) to generate JPEG compressed DCT representation from text descriptions. The proposed models are tested on an open source benchmark dataset Oxford-102 Flower images using both RGB and JPEG compressed versions, and accomplished the state-of-the-art performance in the JPEG compressed domain. The code will be publicly released at GitHub after acceptance of paper.
Linear and Non-Linear Filter-based Counter-Forensics Against Image Splicing Detection

Digital images are widely used as primary sources of evidence in today’s world, spanning security, forensics, and legal domains. However, image tampering poses a shallow technical skill barrier with the wide availability of sophisticated, easy-to-use image manipulation software. Tampered images are often used intentionally for unlawful and malicious purposes. One of the most common forms of image manipulation attack is image splicing, which is performed by combining regions from multiple source images to synthesize an artificial image that looks natural. Digital forensic measures have been widely explored in the literature to detect such type of image forgery. However, the recent growth of counter-forensics poses a threat to such forensic/security measures. Forensic techniques can be easily deceived by adopting counter-forensic manipulation of forged images. In this work, we explore different linear and non-linear filtering-based counter-forensic modifications to digital images and hence investigate the after-effects of those, in terms of severity of such manipulations in rendering state-of-the-art forensic splicing detection methods useless. In this paper, we implement two forensic image splicing detection techniques based on feature extraction from image along with machine learning and deep CNN with transfer learning. Then, different filtering techniques have been applied to the image dataset, investigating their effectiveness as a counter-forensic attack against image splicing detection. Experimental results show that the Gaussian filter and Average filter are the two most effective counter-forensic filtering methods against image splicing detection, suggesting the need for further strengthening the existing family of forensic techniques.
Mineral Informatics: Origins

As the most robust, information rich artifacts available for analysis and exploration, minerals provide us insights about planetary origins and evolutions. The volume, variety, and velocity of mineral data, and development to extract patterns from this data have increased in past decades. We are at the precipice of a paradigm shift and “Mineral Informatics” efforts provide a roadmap to synthesize and coordinate data driven research in mineralogy and in multidisciplinary studies that use mineral data. Mineral informatics includes the study of mineral data at every step of the information cycle, starting with best practices and strategies for optimal creation, collection and compilation of data resources, the development of algorithms, models, pipelines, and visualizations to present and extract key patterns from mineral data, and accurate interpretation of the results from these algorithms and models to make interdisciplinary scientific discoveries. In this chapter, we describe the history of data driven research in mineralogy, and the key events that led to the development and adoption of mineral informatics in the scientific community.
Examining the Employees Behavior Control in Cloud Computing Performance Through the Moderating Lenses of Transformational Leadership

Cloud computing (CC) is becoming a new paradigm for Small and Medium-Sized Enterprises (SMEs) to access information systems. However, there is a dearth of empirical investigation on how SME employees utilize CC. Hence, the purpose of this study is to ascertain how employees' behavioral control and transformational leadership (TL) among SME influence successful CC performance. The 206 responses that were gathered, used in structural equation modeling (SEM) utilizing SmartPLS 4.0 to confirm the formulated hypotheses. The findings indicated that employees' behavior control (EBC) and the CC performance were positively influenced by information sharing (IS), technical ability (TA), and change readiness (CR). Additionally, the moderating effect of TL was found to strengthen the relationship among factors and plays an important role in controlling the employee behavior for successful CC performance.
An Efficient Artificial Intelligence based Human-Machine Interaction System

In today’s computer systems, the mouse is an essential input device. Touch interfaces are high-contact planes that we use on a regular basis and frequently throughout the period. As a result, the input device gets infested with bacteria and pathogens. Despite the fact that wireless mouse have eliminated the bunch of tangled wires, there is still a desire to tap the gadget. In light of the epidemic, this proposed method employs a outlying webcam or an in-built image sensor to capture arm gestures and identify fingertip detection, allowing users to execute standard mouse activities such as left click, scrolling and other mouse activities. The algorithm is trained using machine learning with the use of image sensor and the fingers are identified efficiently. As a result, this reliance on corporeal devices to manage the computational system cancels out the requirement of man-machine interface. Thus the suggested approach will prevent the proliferation of Covid-19.
An Effective Cardless Atm Transaction Using Computer Vision Techniques

Rapider responses are required from ATM banking suppliers to security requests. The current network environment has made it easier for hackers to engage in illicit activity. One of the most pressing issues confronting modern IT teams is network security. There is no doubt that ATM financial transactions should be protected against security risks by several different layers, but service providers should also incorporate security implications in their service offerings. And a lot concerning security flaws, credit card or identity card fraud, and methods for thieves and crackers to get their hands on any logical pin numbers or passwords. Identification, used in the current architecture to permit a system's access, is analogous to a username. Since passwords can be hijacked or misplaced, it is crucial to utilize authentication to confirm that the intended user is truly who their claim to be. The most recent solutions to security and privacy issues are biometric identification and authentication systems. Face recognition is the study of characteristics of a person being that can be used to recognize them. These physical characteristics include, among other things, fingerprints, faces, hand shapes, voices. This study surveys the various security system authentication methods. Top-level safety in authentication is provided by this facial biometric with QR code verification.
Early Renaissance Art Generation Using Deep Convolutional Generative Adversarial Networks

Recent developments in generative art technology have demonstrated the efficacy of neural networks trained on publicly available samples in creating lifelike visuals. However, there are situations where there aren't many training samples to choose from. It's safe to say that you won't see another picture quite like an early Renaissance masterpiece again. In this paper, a pipeline consisting of Deep Convolutional Generative Adversarial Network (DCGAN) preceeded by an augmentation setup is proposed for the automatic generation of early Renaissance-style paintings. Using a 100-dimensional noise vector as input, a neural network with deep convolutional layers is employed as the generator model. The discriminator is a deep convolutional neural network-based classifier with two output classes – authentic and counterfeit. The proposed model uses an open-source dataset of Early Renaissance artwork hand-picked from the WikiArt database. Since original early renaissance paintings are so hard to come by, a transformation process including polarization, inversion, elastic augmentation, solarization, and gaussian noise is employed to generate more samples. The significance of this research is rising high over time due to the scarcity of data and the difficulty in recreating intricate brush strokes in generative models. The investigation is motivated by an interest in Chinese landscape paintings [1] as a source of generative inquiry.