Reviewerly/exHarmony huggingface dataset:
E-Canteen System

Abstract: E-canteen System is designed to reduce time of both customers and staffs. This project reduces people's work because everything is automatic. The main advantage of ordering online is that ordering is very easy for customers and employees. When customers go to main menu page, they can see an updated interactive menu with all available options, and prices will vary according to the options selected. Once the food item is selected, it is added to their order and customers can view the details at any time before checkout. This provides an instant visual overview of what has been selected. When ordering from the web page, it is entered into the database and then received in real time by the web-based application on the restaurant side. In this system, all items in the order are displayed along with their status. For the convenience of our customers, our system also includes a prereservation function. Our restaurant management system keeps track of everyone who uses the restaurant for lunch. The solution is designed to meet the needs of all potential customers. Our solutions can be used to provide restaurants for the entire organization.
Text Reader for Visually Impaired Person using Image Processing/Open-CV

Abstract: The main issue that visually impaired individualsconfront these days is that they are unable to do text recognition on their own, forcing them to rely on others for day-to-day tasks such as reading newspapers, writing letters, referring to books, and so on. This issue may erode their confidence because they are unable to cope on their own. Theproject's ultimate goal is to assist visually challenged personswith text recognition. This goal is accomplished by creating amodule that converts text into speech and speaks it through the provided headphone/speaker. The code is written in Python after importing pytesseract and gtts. For character recognition, this project employs the concept of image processing and the OCR approach.
Sentiment Analysis Using Twitter Data

Abstract: Twitter is a popular social media platform where users express their thoughts and opinions on various topics. Sentiment analysis, a subfield of natural language processing, aims to automatically classify these opinions into positive, negative, or neutral categories. In this research paper, we explore the effectiveness of different sentiment analysis techniques on Twitter data. We analyse the impact of feature selection, machine learning algorithms, and pre-processing techniques on sentiment analysis performance. Our results show that the combination of feature selection and machine learning algorithms improves the accuracy of sentiment analysis on Twitter data. We also identify the challenges and limitations of sentiment analysis on Twitter data, such as the use of sarcasm and irony in tweets. There has been lot of work in the field of sentiment analysis of twitter data. This survey focuses mainly on sentiment analysis of twitter data which is helpful to analyse the information in the tweets where opinions are highly unstructured, heterogeneous and are either positive or negative, or neutral in some cases.
Social Media Spam Comments Detection Analysis using Machine Learning

Abstract: The results published by Google's famous video distribution platform Social Media are attracting more and more users every day. However, this success has also attracted many malicious users who try to promote their videos or infect them with viruses and malware. As we know Social Media has a tool to measure comments so spam is increasing very fast which is why the owner's comment section is closed. Creating a classification system for automatic spam filtering is very difficult because words are short and often contain slang words, symbols, and abbreviations. In this article, we consider several best practices for detecting and analyzing spam messages.
Enhancement of Low Light Videos using Artificial Intelligence

Abstract: Enhancing night videos can be challenging due to low light conditions and the resulting noise and loss of details. One way to enhance night videos is by using a combination of fusion and color enhancement techniques followed by preprocessing. One popular fusion technique for night videos is called Exposure Fusion, which involves blending multiple images of different exposure levels to create a final image that is well-lit and has good contrast. Color enhancement techniques can also be used to improve the quality of night videos. This involves adjusting the color balance and saturation to create a more pleasing and natural-looking image. Super resolution is a technique used to enhance the resolution of an image or video. It involves generating a high-resolution image or video from a low-resolution input, often using deep learning-based approaches. In the context of enhancing a night video, super resolution can be used to improve the visibility of details and reduce noise in low-light conditions.
Secure Decentralised Data Storage and Hiding

Abstract: Data is the most valuable thing anybody may have in this technological age, yet encryption alone may not be enough to protect it. Numerous times, sensitive information has been leaked, which has been fatal for numerous organisations. The system works by dividing data into several pieces and dispersing them among peer devices in order to safeguard the data as the complete data will not be present at any single node. The main idea is to leverage a distributed P2P on a local network to store parts of encrypted data across the network and hiding these data chunks by using steganography. A working prototype is constructed to demonstrate the feasibility of the proposed approach.
Secured E-voting System Using Blockchain Technology

Abstract: The emergence of blockchain technology has paved the way for the development of secured e-voting systems that are transparent, immutable, and tamper-proof. In this survey report, we examine the current trends and future prospects of using blockchain technology for e-voting. We begin by exploring the key features of blockchain technology, including distributed ledger, cryptographic security, and decentralized consensus mechanisms. Next, we discuss the benefits of using blockchain technology for e-voting, including voter anonymity, transparent voting process, and immutable voting records. To gain a better understanding of the current state of the field, we conducted a survey of recent literature on blockchain-based evoting systems. The survey covers research articles, white papers, and conference proceedings published in the last five years. We analysed the data using statistical software and present our findings on the most commonly used consensus algorithms, cryptographic techniques, and blockchain architectures in e-voting systems. Our survey also highlights the challenges facing the adoption of blockchain-based e-voting systems, including scalability, usability, and regulatory issues. We conclude by discussing the future prospects of blockchain-based e-voting systems and identifying the areas that require further research and development. Overall, this survey report provides valuable insights into the current trends and future prospects of using blockchain technology for e-voting, and will be useful for researchers, policymakers, and practitioners working in this field.
Decomposing the Enigma: Subgoal-based Demonstration Learning for Formal Theorem Proving

Large language models~(LLMs) present an intriguing avenue of exploration in the domain of formal theorem proving. Nonetheless, the full utilization of these models, particularly in terms of demonstration formatting and organization, remains an underexplored area. In an endeavor to enhance the efficacy of LLMs, we introduce a subgoal-based demonstration learning framework, consisting of two primary elements: Firstly, drawing upon the insights of subgoal learning from the domains of reinforcement learning and robotics, we propose the construction of distinct subgoals for each demonstration example and refine these subgoals in accordance with the pertinent theories of subgoal learning. Secondly, we build upon recent advances in diffusion models to predict the optimal organization, simultaneously addressing two intricate issues that persist within the domain of demonstration organization: subset selection and order determination. Through the integration of subgoal-based learning methodologies, we have successfully increased the prevailing proof accuracy from 38.9\% to 44.3\% on the miniF2F benchmark. Furthermore, the adoption of diffusion models for demonstration organization can lead to an additional enhancement in accuracy to 45.5\%, or a $5\times$ improvement in sampling efficiency compared with the long-standing state-of-the-art method. Our code is available at \url{https://github.com/HKUNLP/subgoal-theorem-prover}.
Are Diffusion Models Vision-And-Language Reasoners?

Text-conditioned image generation models have recently shown immense qualitative success using denoising diffusion processes. However, unlike discriminative vision-and-language models, it is a non-trivial task to subject these diffusion-based generative models to automatic fine-grained quantitative evaluation of high-level phenomena such as compositionality. Towards this goal, we perform two innovations. First, we transform diffusion-based models (in our case, Stable Diffusion) for any image-text matching (ITM) task using a novel method called DiffusionITM. Second, we introduce the Generative-Discriminative Evaluation Benchmark (GDBench) benchmark with 7 complex vision-and-language tasks, bias evaluation and detailed analysis. We find that Stable Diffusion + DiffusionITM is competitive on many tasks and outperforms CLIP on compositional tasks like like CLEVR and Winoground. We further boost its compositional performance with a transfer setup by fine-tuning on MS-COCO while retaining generative capabilities. We also measure the stereotypical bias in diffusion models, and find that Stable Diffusion 2.1 is, for the most part, less biased than Stable Diffusion 1.5. Overall, our results point in an exciting direction bringing discriminative and generative model evaluation closer. We will release code and benchmark setup soon.
Human-Machine Comparison for Cross-Race Face Verification: Race Bias at the Upper Limits of Performance?

Face recognition algorithms perform more accurately than humans in some cases, though humans and machines both show race-based accuracy differences. As algorithms continue to improve, it is important to continually assess their race bias relative to humans. We constructed a challenging test of 'cross-race' face verification and used it to compare humans and two state-of-the-art face recognition systems. Pairs of same- and different-identity faces of White and Black individuals were selected to be difficult for humans and an open-source implementation of the ArcFace face recognition algorithm from 2019 (5). Human participants (54 Black; 51 White) judged whether face pairs showed the same identity or different identities on a 7-point Likert-type scale. Two top-performing face recognition systems from the Face Recognition Vendor Test-ongoing performed the same test (7). By design, the test proved challenging for humans as a group, who performed above chance, but far less than perfect. Both state-of-the-art face recognition systems scored perfectly (no errors), consequently with equal accuracy for both races. We conclude that state-of-the-art systems for identity verification between two frontal face images of Black and White individuals can surpass the general population. Whether this result generalizes to challenging in-the-wild images is a pressing concern for deploying face recognition systems in unconstrained environments.
DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions

Modern machine learning relies on datasets to develop and validate research ideas. Given the growth of publicly available data, finding the right dataset to use is increasingly difficult. Any research question imposes explicit and implicit constraints on how well a given dataset will enable researchers to answer this question, such as dataset size, modality, and domain. We operationalize the task of recommending datasets given a short natural language description of a research idea, to help people find relevant datasets for their needs. Dataset recommendation poses unique challenges as an information retrieval problem; datasets are hard to directly index for search and there are no corpora readily available for this task. To facilitate this task, we build the DataFinder Dataset which consists of a larger automatically-constructed training set (17.5K queries) and a smaller expert-annotated evaluation set (392 queries). Using this data, we compare various information retrieval algorithms on our test set and present a superior bi-encoder retriever for text-based dataset recommendation. This system, trained on the DataFinder Dataset, finds more relevant search results than existing third-party dataset search engines. To encourage progress on dataset recommendation, we release our dataset and models to the public.
Not wacky vs. definitely wacky: A study of scalar adverbs in pretrained language models

Vector space models of word meaning all share the assumption that words occurring in similar contexts have similar meanings. In such models, words that are similar in their topical associations but differ in their logical force tend to emerge as semantically close, creating well-known challenges for NLP applications that involve logical reasoning. Modern pretrained language models, such as BERT, RoBERTa and GPT-3 hold the promise of performing better on logical tasks than classic static word embeddings. However, reports are mixed about their success. In the current paper, we advance this discussion through a systematic study of scalar adverbs, an under-explored class of words with strong logical force. Using three different tasks, involving both naturalistic social media data and constructed examples, we investigate the extent to which BERT, RoBERTa, GPT-2 and GPT-3 exhibit general, human-like, knowledge of these common words. We ask: 1) Do the models distinguish amongst the three semantic categories of MODALITY, FREQUENCY and DEGREE? 2) Do they have implicit representations of full scales from maximally negative to maximally positive? 3) How do word frequency and contextual factors impact model performance? We find that despite capturing some aspects of logical meaning, the models fall far short of human performance.
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability

Neural networks are known to be susceptible to adversarial samples: small variations of natural examples crafted to deliberately mislead the models. While they can be easily generated using gradient-based techniques in digital and physical scenarios, they often differ greatly from the actual data distribution of natural images, resulting in a trade-off between strength and stealthiness. In this paper, we propose a novel framework dubbed Diffusion-Based Projected Gradient Descent (Diff-PGD) for generating realistic adversarial samples. By exploiting a gradient guided by a diffusion model, Diff-PGD ensures that adversarial samples remain close to the original data distribution while maintaining their effectiveness. Moreover, our framework can be easily customized for specific tasks such as digital attacks, physical-world attacks, and style-based attacks. Compared with existing methods for generating natural-style adversarial samples, our framework enables the separation of optimizing adversarial loss from other surrogate losses (e.g., content/smoothness/style loss), making it more stable and controllable. Finally, we demonstrate that the samples generated using Diff-PGD have better transferability and anti-purification power than traditional gradient-based methods. Code will be released in https://github.com/xavihart/Diff-PGD
Annotating and Detecting Fine-grained Factual Errors for Dialogue Summarization

A series of datasets and models have been proposed for summaries generated for well-formatted documents such as news articles. Dialogue summaries, however, have been under explored. In this paper, we present the first dataset with fine-grained factual error annotations named DIASUMFACT. We define fine-grained factual error detection as a sentence-level multi-label classification problem, and we evaluate two state-of-the-art (SOTA) models on our dataset. Both models yield sub-optimal results, with a macro-averaged F1 score of around 0.25 over 6 error classes. We further propose an unsupervised model ENDERANKER via candidate ranking using pretrained encoder-decoder models. Our model performs on par with the SOTA models while requiring fewer resources. These observations confirm the challenges in detecting factual errors from dialogue summaries, which call for further studies, for which our dataset and results offer a solid foundation.
Revisiting Structured Variational Autoencoders

Structured variational autoencoders (SVAEs) combine probabilistic graphical model priors on latent variables, deep neural networks to link latent variables to observed data, and structure-exploiting algorithms for approximate posterior inference. These models are particularly appealing for sequential data, where the prior can capture temporal dependencies. However, despite their conceptual elegance, SVAEs have proven difficult to implement, and more general approaches have been favored in practice. Here, we revisit SVAEs using modern machine learning tools and demonstrate their advantages over more general alternatives in terms of both accuracy and efficiency. First, we develop a modern implementation for hardware acceleration, parallelization, and automatic differentiation of the message passing algorithms at the core of the SVAE. Second, we show that by exploiting structure in the prior, the SVAE learns more accurate models and posterior distributions, which translate into improved performance on prediction tasks. Third, we show how the SVAE can naturally handle missing data, and we leverage this ability to develop a novel, self-supervised training approach. Altogether, these results show that the time is ripe to revisit structured variational autoencoders.
Unsupervised Embedding Quality Evaluation

Unsupervised learning has recently significantly gained in popularity, especially with deep learning-based approaches. Despite numerous successes and approaching supervised-level performance on a variety of academic benchmarks, it is still hard to train and evaluate SSL models in practice due to the unsupervised nature of the problem. Even with networks trained in a supervised fashion, it is often unclear whether they will perform well when transferred to another domain. Past works are generally limited to assessing the amount of information contained in embeddings, which is most relevant for self-supervised learning of deep neural networks. This works chooses to follow a different approach: can we quantify how easy it is to linearly separate the data in a stable way? We survey the literature and uncover three methods that could be potentially used for evaluating quality of representations. We also introduce one novel method based on recent advances in understanding the high-dimensional geometric structure of self-supervised learning. We conduct extensive experiments and study the properties of these metrics and ones introduced in the previous work. Our results suggest that while there is no free lunch, there are metrics that can robustly estimate embedding quality in an unsupervised way.
Extending Explainable Boosting Machines to Scientific Image Data

As the deployment of computer vision technology becomes increasingly common in science, the need for explanations of the system and its output has become a focus of great concern. Driven by the pressing need for interpretable models in science, we propose the use of Explainable Boosting Machines (EBMs) for scientific image data. Inspired by an important application underpinning the development of quantum technologies, we apply EBMs to cold-atom soliton image data tabularized using Gabor Wavelet Transform-based techniques that preserve the spatial structure of the data. In doing so, we demonstrate the use of EBMs for image data for the first time and show that our approach provides explanations that are consistent with human intuition about the data.
NormMark: A Weakly Supervised Markov Model for Socio-cultural Norm Discovery

Norms, which are culturally accepted guidelines for behaviours, can be integrated into conversational models to generate utterances that are appropriate for the socio-cultural context. Existing methods for norm recognition tend to focus only on surface-level features of dialogues and do not take into account the interactions within a conversation. To address this issue, we propose NormMark, a probabilistic generative Markov model to carry the latent features throughout a dialogue. These features are captured by discrete and continuous latent variables conditioned on the conversation history, and improve the model's ability in norm recognition. The model is trainable on weakly annotated data using the variational technique. On a dataset with limited norm annotations, we show that our approach achieves higher F1 score, outperforming current state-of-the-art methods, including GPT3.
Sliding Window Sum Algorithms for Deep Neural Networks

Sliding window sums are widely used for string indexing, hashing and time series analysis. We have developed a family of the generic vectorized sliding sum algorithms that provide speedup of O(P/w) for window size $w$ and number of processors P. For a sum with a commutative operator the speedup is improved to O(P/log(w)). Even more important, our algorithms exhibit efficient memory access patterns. In this paper we study the application of the sliding sum algorithms to the training and inference of the Deep Neural Networks. We demonstrate how both pooling and convolution primitives could be expressed as sliding sums and evaluated by the compute kernels with the shared structure. We show that the sliding sum convolution kernels are more efficient than the commonly used GEMM kernels on the CPU, and could even outperform their GPU counterparts.
Aerial Gym -- Isaac Gym Simulator for Aerial Robots

Developing learning-based methods for navigation of aerial robots is an intensive data-driven process that requires highly parallelized simulation. The full utilization of such simulators is hindered by the lack of parallelized high-level control methods that imitate the real-world robot interface. Responding to this need, we develop the Aerial Gym simulator that can simulate millions of multirotor vehicles parallelly with nonlinear geometric controllers for the Special Euclidean Group SE(3) for attitude, velocity and position tracking. We also develop functionalities for managing a large number of obstacles in the environment, enabling rapid randomization for learning of navigation tasks. In addition, we also provide sample environments having robots with simulated cameras capable of capturing RGB, depth, segmentation and optical flow data in obstacle-rich environments. This simulator is a step towards developing a - currently missing - highly parallelized aerial robot simulation with geometric controllers at a large scale, while also providing a customizable obstacle randomization functionality for navigation tasks. We provide training scripts with compatible reinforcement learning frameworks to navigate the robot to a goal setpoint based on attitude and velocity command interfaces. Finally, we open source the simulator and aim to develop it further to speed up rendering using alternate kernel-based frameworks in order to parallelize ray-casting for depth images thus supporting a larger number of robots.