Reviewerly/exHarmony huggingface dataset:
The Impact of COVID-19 on E-Learning: Context-Based Sentiment Analysis Discourse Using Text Mining

Finding the most dominant and pertinent user opinions on a certain topic is crucial to the sentiment analysis success factor. During the pandemic lockdowns around the world, the suspension of academic institutions leads to an exceptional increase in distance education. Academic institutions closed their campuses immediately to mitigate the effects of COVID-19 and prevent its pervasive spread, and educational activities were shifted to online platforms. The effectiveness of online education is a significant topic of interest for both students and their parents, especially in terms of how students and teachers perceive it and how technologically viable it is in a range of social circumstances. Before such a wide adoption of e-learning is possible, these issues must be analyzed from multiple perspectives. The present research aims to evaluate the efficacy of e-learning by examining individuals' perceptions of it. Opinions can be found on websites such as Instagram, Facebook, Twitter, etc. As social media has recently emerged as a significant means of communication. This study addresses factors connected to a significant change in the educational system. 200,000 tweets were gathered from Twitter to evaluate the opinions of Twitter users who were taking part in online learning. This study adopts VADER to analyze the subjectivity and polarity score of tweets, a topic model was also created using the LDA algorithm to determine the themes that were talked about on Twitter the most. The models have been constructed and evaluated using Word2Vec to capture the semantic relationships between words and LSTM and RNN sequential model for sentiment analysis. This study measured the efficiency of a sentiment analysis model using the accuracy metric, the conducted experiments reveal that the proposed hybrid model achieves an overall accuracy of 96.3%. The results also indicate a significant negative impact of the Covid-19 pandemic on individuals' emotions, with 64.4% of the analyzed tweets displaying negative sentiments. These findings provide valuable insights into the relationship between global events and individual emotions on social media platforms.
User-Centric Advertisement using Software Sensors Technique

Contextual advertising is one of the most critical components in the economic system of internet due to increase internet publisher’s income highly dependent on the user-centric advertisement that is displayed on the sites according to the user context during interaction with the multiple sites. Previous contextual advertisement research work generally emphasises on investigating either to the keyword they type, content of the sites or uses any other application from the network hence, this finding has identified work when being extended through the user’s context. In this work we have looked at users’ profile information and user preferences to reach the users according to their context. These smart devices are ready with all capabilities to give useful contexts including information about physical environment, social connection, user internal and external context. These logical contexts beyond just content of the web pages, search keywords, and profile information are well used and organized for user-centric advertising. Here we are also arguing the appearances of the logical contexts which are available on the user browser, profile and visibly define the challenges of results from these logical contexts to improve the advertisement. We present a user-centric advertisement architecture and model that collects to integrate the users’ profile context and activity context to select, generate and to present advertisement with context. Finally, we discuss to implement the aspects of design and one specific application and outline our plans for future.
Secure Architecture for Electronic Commerce Applications Running over the Cloud

E-commerce and mobile commerce are two new business methodologies that utilize the cloud. A new technology called cloud computing uses the Internet to process and store data from a network of distant computers that are dispersed around the globe. Any online transaction must have security as a necessary component. Therefore, one of the main issues with the cloud is security. If electronic commerce's security is breached, customers can lose trust in it. An unauthorized individual should not have access to or be able to intercept a customer's personal information while it is being transmitted. Data integrity is a major problem since personal information shouldn't be changed before, during, or even after it is at rest on the network. The suggested solution ensures the protection of personal data and the avoidance of security problems. We have developed a solution in this study to address issues with privacy, confidentiality, and the integrity of data stored in the cloud, among other security-related issues. The suggested method employs over-encryption that is double encryption, to avoid the various security issues. It can be inferred from the results that ECC (secp256r1) utilised less time for encryption operation as compared to others asymmetric algorithms with small dispersion from means and recorded results behaviour of data is consistent because data point tends to be very close, Decryption operation ElGamal during of time was smaller than ECC and RSA with small consistent behaviour.
Comparative Analysis of Facial Recognition Models to Record Classroom attendance with one click

Human face recognition is the hottest topic in this era due to its importance in identity authentication and security checking. It has grown a momentous spot among all biometric-based systems. It can be used for verification and scrutiny to prove the identity of a person and sense individuals, singly. Great success has been achieved recently on face recognition comparison by using different models. But in this research, the HOG (Histogram Oriented Gradient) and Vgg (Visual Geometry Group) algorithms are selected as these algorithms are common and still widely accessible on the internet, and these are usually used out of the box for various applications. In this paper, we examine the accuracy and time efficiency between the HOG (Histogram Oriented Gradient) and machine learning model. Also, this paper conferred these pre-trained models and outlines which results in high-tech performance without the need for any prevailing hardware. Their features are observed and the result of each model is estimated. Their comparative analysis of the output of both of these models with the execution time and the number of repetitions of the practical algorithms is offered. It also constructed two simple applications for face recognition-based algorithms. By using these application testing data is taken from the live stream/camera in the form of image and compared through training images stored in the faces database and their time and accuracy are noted during this process and their performance is evaluated based on time and accuracy.
A Tutorial On Creating a Blockchain and Cryptocurrency with Consensus Protocol in Python

The concept of decentralization has gained a lot of focus when it comes to Blockchain. The Blockchain technology is a decentralized peer to peer distributed ledger. Many industries have been using Distributed Ledgers (DTL) before the blockchain technology. But the state of the art technology has overshadow the use of all DTLs due to its immutable nature. With the use of this technology new horizons of innovation has been explored. Bitcoin, the first cyrptocurrency, has used the blockchain technology which has received extensive attentions. In this paper, we have addressed the mechanism of the blockchain technology and a detailed tutorial of how to implement it practically. Specifically we have focused on the practical implementation of how to create a blockchain, mine a block and create a cryptocurrency. Secondly, there is detailed discussion on major platform i.e. bitcoin in which blockchain has been explored a lot. Finally towards the end, the Proof of Work Consensus algorithm is elaborated in detail. Our goal is to help readers easily understand the mechanism along with the important features of the blockchain without having to read all the blockchain specifications and application or the state-of-the-art papers that generally describe the system.
Foreground Object Search by Distilling Composite Image Feature

Foreground object search (FOS) aims to find compatible foreground objects for a given background image, producing realistic composite image. We observe that competitive retrieval performance could be achieved by using a discriminator to predict the compatibility of composite image, but this approach has unaffordable time cost. To this end, we propose a novel FOS method via distilling composite feature (DiscoFOS). Specifically, the abovementioned discriminator serves as teacher network. The student network employs two encoders to extract foreground feature and background feature. Their interaction output is enforced to match the composite image feature from the teacher network. Additionally, previous works did not release their datasets, so we contribute two datasets for FOS task: S-FOSD dataset with synthetic composite images and R-FOSD dataset with real composite images. Extensive experiments on our two datasets demonstrate the superiority of the proposed method over previous approaches. The dataset and code are available at https://github.com/bcmi/Foreground-Object-Search-Dataset-FOSD.
Multi-body Depth and Camera Pose Estimation from Multiple Views

Traditional and deep Structure-from-Motion (SfM) methods typically operate under the assumption that the scene is rigid, i.e., the environment is static or consists of a single moving object. Few multi-body SfM approaches address the reconstruction of multiple rigid bodies in a scene but suffer from the inherent scale ambiguity of SfM, such that objects are reconstructed at inconsistent scales. We propose a depth and camera pose estimation framework to resolve the scale ambiguity in multi-body scenes. Specifically, starting from disorganized images, we present a novel multi-view scale estimator that resolves the camera pose ambiguity and a multi-body plane sweep network that generalizes depth estimation to dynamic scenes. Experiments demonstrate the advantages of our method over state-of-the-art SfM frameworks in multi-body scenes and show that it achieves comparable results in static scenes. The code and dataset are available at https://github.com/andreadalcin/MultiBodySfM.
Label-Free Event-based Object Recognition via Joint Learning with Image Reconstruction from Events

Recognizing objects from sparse and noisy events becomes extremely difficult when paired images and category labels do not exist. In this paper, we study label-free event-based object recognition where category labels and paired images are not available. To this end, we propose a joint formulation of object recognition and image reconstruction in a complementary manner. Our method first reconstructs images from events and performs object recognition through Contrastive Language-Image Pretraining (CLIP), enabling better recognition through a rich context of images. Since the category information is essential in reconstructing images, we propose category-guided attraction loss and category-agnostic repulsion loss to bridge the textual features of predicted categories and the visual features of reconstructed images using CLIP. Moreover, we introduce a reliable data sampling strategy and local-global reconstruction consistency to boost joint learning of two tasks. To enhance the accuracy of prediction and quality of reconstruction, we also propose a prototype-based approach using unpaired images. Extensive experiments demonstrate the superiority of our method and its extensibility for zero-shot object recognition. Our project code is available at https://github.com/Chohoonhee/Ev-LaFOR.
Causal-DFQ: Causality Guided Data-free Network Quantization

Model quantization, which aims to compress deep neural networks and accelerate inference speed, has greatly facilitated the development of cumbersome models on mobile and edge devices. There is a common assumption in quantization methods from prior works that training data is available. In practice, however, this assumption cannot always be fulfilled due to reasons of privacy and security, rendering these methods inapplicable in real-life situations. Thus, data-free network quantization has recently received significant attention in neural network compression. Causal reasoning provides an intuitive way to model causal relationships to eliminate data-driven correlations, making causality an essential component of analyzing data-free problems. However, causal formulations of data-free quantization are inadequate in the literature. To bridge this gap, we construct a causal graph to model the data generation and discrepancy reduction between the pre-trained and quantized models. Inspired by the causal understanding, we propose the Causality-guided Data-free Network Quantization method, Causal-DFQ, to eliminate the reliance on data via approaching an equilibrium of causality-driven intervened distributions. Specifically, we design a content-style-decoupled generator, synthesizing images conditioned on the relevant and irrelevant factors; then we propose a discrepancy reduction loss to align the intervened distributions of the pre-trained and quantized models. It is worth noting that our work is the first attempt towards introducing causality to data-free quantization problem. Extensive experiments demonstrate the efficacy of Causal-DFQ. The code is available at Causal-DFQ.
Metric3D: Towards Zero-shot Metric 3D Prediction from A Single Image

Reconstructing accurate 3D scenes from images is a long-standing vision task. Due to the ill-posedness of the single-image reconstruction problem, most well-established methods are built upon multi-view geometry. State-of-the-art (SOTA) monocular metric depth estimation methods can only handle a single camera model and are unable to perform mixed-data training due to metric ambiguity. Meanwhile, SOTA monocular methods trained on large mixed datasets achieve zero-shot generalization by learning affine-invariant depths, which cannot recover real-world metrics. In this work, we show that the key to a zero-shot single-view metric depth model lies in the combination of large-scale data training and resolving the metric ambiguity from various camera models. We propose a canonical camera space transformation module, which explicitly addresses the ambiguity problems and can be effortlessly plugged into existing monocular models. Equipped with our module, monocular models can be stably trained over 8 millions of images with thousands of camera models, resulting in zero-shot generalization to in-the-wild images with unseen camera settings. Experiments demonstrate SOTA performance of our method on 7 zero-shot benchmarks. Notably, our method won the championship in the 2nd Monocular Depth Estimation Challenge. Our method enables the accurate recovery of metric 3D structures on randomly collected internet images, paving the way for plausible single-image metrology. The potential benefits extend to downstream tasks, which can be significantly improved by simply plugging in our model. For example, our model relieves the scale drift issues of monocular-SLAM (Fig. 1), leading to high-quality metric scale dense mapping. The code is available at https://github.com/YvanYin/Metric3D.
Dancing in the Dark: A Benchmark towards General Low-light Video Enhancement

Low-light video enhancement is a challenging task with broad applications. However, current research in this area is limited by the lack of high-quality benchmark datasets. To address this issue, we design a camera system and collect a high-quality low-light video dataset with multiple exposures and cameras. Our dataset provides dynamic video pairs with pronounced camera motion and strict spatial alignment. To achieve general low-light video enhancement, we also propose a novel Retinex-based method named Light Adjustable Network (LAN). LAN iteratively refines the illumination and adaptively adjusts it under varying lighting conditions, leading to visually appealing results even in diverse real-world scenarios. The extensive experiments demonstrate the superiority of our low-light video dataset and enhancement method. Our dataset is available at https://github.com/ciki000/DID.
ORC: Network Group-based Knowledge Distillation using Online Role Change

In knowledge distillation, since a single, omnipotent teacher network cannot solve all problems, multiple teacher-based knowledge distillations have been studied recently. However, sometimes their improvements are not as good as expected because some immature teachers may transfer the false knowledge to the student. In this paper, to overcome this limitation and take the efficacy of the multiple networks, we divide the multiple networks into teacher and student groups, respectively. That is, the student group is a set of immature networks that require learning the teacher’s knowledge, while the teacher group consists of the selected networks that are capable of teaching successfully. We propose our online role change strategy where the top-ranked networks in the student group are able to promote to the teacher group at every iteration. After training the teacher group using the error samples of the student group to refine the teacher group’s knowledge, we transfer the collaborative knowledge from the teacher group to the student group successfully. We verify the superiority of the proposed method on CIFAR-10, CIFAR-100, and ImageNet which achieves high performance. We further show the generality of our method with various backbone architectures such as ResNet, WRN, VGG, Mobilenet, and Shufflenet. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Conceptual and Hierarchical Latent Space Decomposition for Face Editing

Generative Adversarial Networks (GANs) can produce photo-realistic results using an unconditional image-generation pipeline. However, the images generated by GANs (e.g., StyleGAN) are entangled in feature spaces, which makes it difficult to interpret and control the contents of images. In this paper, we present an encoder-decoder model that decomposes the entangled GAN space into a conceptual and hierarchical latent space in a self-supervised manner. The outputs of 3D morphable face models are leveraged to independently control image synthesis parameters like pose, expression, and illumination. For this purpose, a novel latent space decomposition pipeline is introduced using transformer networks and generative models. Later, this new space is used to optimize a transformer-based GAN space controller for face editing. In this work, a StyleGAN2 model for faces is utilized. Since our method manipulates only GAN features, the photo-realism of Style-GAN2 is fully preserved. The results demonstrate that our method qualitatively and quantitatively outperforms baselines in terms of identity preservation and editing precision.
Social Diffusion: Long-term Multiple Human Motion Anticipation

We propose Social Diffusion, a novel method for short-term and long-term forecasting of the motion of multiple persons as well as their social interactions. Jointly forecasting motions for multiple persons involved in social activities is inherently a challenging problem due to the interdependencies between individuals. In this work, we leverage a diffusion model conditioned on motion histories and causal temporal convolutional networks to forecast individually and contextually plausible motions for all participants. The contextual plausibility is achieved via an order-invariant aggregation function. As a second contribution, we design a new evaluation protocol that measures the plausibility of social interactions which we evaluate on the Haggling dataset, which features a challenging social activity where people are actively taking turns to talk and switching their attention. We evaluate our approach on four datasets for multi-person forecasting where our approach outperforms the state-of-the-art in terms of motion realism and contextual plausibility.
SAFE: Sensitivity-Aware Features for Out-of-Distribution Object Detection

We address the problem of out-of-distribution (OOD) detection for the task of object detection. We show that residual convolutional layers with batch normalisation produce Sensitivity-Aware FEatures (SAFE) that are consistently powerful for distinguishing in-distribution from out-of-distribution detections. We extract SAFE vectors for every detected object, and train a multilayer perceptron on the surrogate task of distinguishing adversarially perturbed from clean in-distribution examples. This circumvents the need for realistic OOD training data, computationally expensive generative models, or retraining of the base object detector. SAFE outperforms the state-of-the-art OOD object detectors on multiple benchmarks by large margins, e.g. reducing the FPR95 by an absolute 30.6% from 48.3% to 17.7% on the OpenImages dataset.
Perceptual Artifacts Localization for Image Synthesis Tasks

Recent advancements in deep generative models have facilitated the creation of photo-realistic images across various tasks. However, these generated images often exhibit perceptual artifacts in specific regions, necessitating manual correction. In this study, we present a comprehensive empirical examination of Perceptual Artifacts Localization (PAL) spanning diverse image synthesis endeavors. We introduce a novel dataset comprising 10, 168 generated images, each annotated with per-pixel perceptual artifact labels across ten synthesis tasks. A segmentation model, trained on our proposed dataset, effectively localizes artifacts across a range of tasks. Additionally, we illustrate its proficiency in adapting to previously unseen models using minimal training samples. We further propose an innovative zoom-in inpainting pipeline that seamlessly rectifies perceptual artifacts in the generated images. Through our experimental analyses, we elucidate several invaluable downstream applications, such as automated artifact rectification, non-referential image quality evaluation, and abnormal region detection in images. The dataset and code are released here: https://owenzlz.github.io/PAL4VST
Story Visualization by Online Text Augmentation with Context Memory

Story visualization (SV) is a challenging text-to-image generation task for the difficulty of not only rendering visual details from the text descriptions but also encoding a long-term context across multiple sentences. While prior efforts mostly focus on generating a semantically relevant image for each sentence, encoding a context spread across the given paragraph to generate contextually convincing images (e.g., with a correct character or with a proper background of the scene) remains a challenge. To this end, we propose a novel memory architecture for the Bi-directional Transformer framework with an online text augmentation that generates multiple pseudo-descriptions as supplementary supervision during training for better generalization to the language variation at inference. In extensive experiments on the two popular SV benchmarks, i.e., the Pororo-SV and Flintstones-SV, the proposed method significantly outperforms the state of the arts in various metrics including FID, character F1, frame accuracy, BLEU-2/3, and R-precision with similar or less computational complexity.