Reviewerly/exHarmony huggingface dataset:
Implicit variance regularization in non-contrastive SSL

Non-contrastive SSL methods like BYOL and SimSiam rely on asymmetric predictor networks to avoid representational collapse without negative samples. Yet, how predictor networks facilitate stable learning is not fully understood. While previous theoretical analyses assumed Euclidean losses, most practical implementations rely on cosine similarity. To gain further theoretical insight into non-contrastive SSL, we analytically study learning dynamics in conjunction with Euclidean and cosine similarity in the eigenspace of closed-form linear predictor networks. We show that both avoid collapse through implicit variance regularization albeit through different dynamical mechanisms. Moreover, we find that the eigenvalues act as effective learning rate multipliers and propose a family of isotropic loss functions (IsoLoss) that equalize convergence rates across eigenmodes. Empirically, IsoLoss speeds up the initial learning dynamics and increases robustness, thereby allowing us to dispense with the EMA target network typically used with non-contrastive methods. Our analysis sheds light on the variance regularization mechanisms of non-contrastive SSL and lays the theoretical grounds for crafting novel loss functions that shape the learning dynamics of the predictor's spectrum.
Do we live in a [quantum] simulation? Constraints, observations, and experiments on the simulation hypothesis

The question "What is real?" can be traced back to the shadows in Plato's cave. Two thousand years later, Rene Descartes lacked knowledge about arguing against an evil deceiver feeding us the illusion of sensation. Descartes' epistemological concept later led to various theories of sensory experiences. The concept of "illusionism", proposing that even the very conscious experience we have is an illusion, is not only a red-pill scenario found in the 1999 science fiction movie "The Matrix" but is also a philosophical concept promoted by modern tinkers, most prominently by Daniel Dennett. Reflection upon a possible simulation and our perceived reality was beautifully visualized in "The Matrix", bringing the old ideas of Descartes to coffee houses around the world. Irish philosopher Bishop Berkeley was the father of what was later coined as "subjective idealism", basically stating that "what you perceive is real". With the advent of quantum technologies based on the control of individual fundamental particles, the question of whether our universe is a simulation isn't just intriguing. Our ever-advancing understanding of fundamental physical processes will likely lead us to build quantum computers utilizing quantum effects for simulating nature quantum-mechanically in all complexity, as famously envisioned by Richard Feynman. In this article, we outline constraints on the limits of computability and predictability in/of the universe, which we then use to design experiments allowing for first conclusions as to whether we participate in a simulation chain. Eventually, in a simulation in which the computer simulating a universe is governed by the same physical laws as the simulation, the exhaustion of computational resources will halt all simulations down the simulation chain unless an external programmer intervenes, which we may be able to observe.
Enhancing phase-covariant channel performance with non-unitality

We analyze quantum communication properties of phase-covariant channels depending on their degree of non-unitality. In particular, we derive analytical formulas for minimal and maximal channel fidelity on pure states and maximal output purity. Next, we introduce a measure of non-unitality and show how to manipulate between unital and maximally non-unital maps by considering classical mixtures of quantum channels. Finally, we prove that maximal fidelity and maximal output purity increase with non-unitality and present several examples. Interestingly, non-unitality can also prolong quantum entanglement and lead to its rebirth.
A Polynomial-Time Algorithm for MCS Partial Search Order on Chordal Graphs

We study the partial search order problem (PSOP) proposed recently by Scheffler [WG 2022]. Given a graph $G$ together with a partial order over the set of vertices of $G$, this problem determines if there is an $\mathcal{S}$-ordering that is consistent with the given partial order, where $\mathcal{S}$ is a graph search paradigm like BFS, DFS, etc. This problem naturally generalizes the end-vertex problem which has received much attention over the past few years. It also generalizes the so-called ${\mathcal{F}}$-tree recognition problem which has just been studied in the literature recently. Our main contribution is a polynomial-time dynamic programming algorithm for the PSOP of the maximum cardinality search (MCS) restricted to chordal graphs. This resolves one of the most intriguing open questions left in the work of Scheffler [WG 2022]. To obtain our result, we propose the notion of layer structure and study numerous related structural properties which might be of independent interest.
OmniHorizon: In-the-Wild Outdoors Depth and Normal Estimation from Synthetic Omnidirectional Dataset

Understanding the ambient scene is imperative for several applications such as autonomous driving and navigation. While obtaining real-world image data with per-pixel labels is challenging, existing accurate synthetic image datasets primarily focus on indoor spaces with fixed lighting and scene participants, thereby severely limiting their application to outdoor scenarios. In this work we introduce OmniHorizon, a synthetic dataset with 24,335 omnidirectional views comprising of a broad range of indoor and outdoor spaces consisting of buildings, streets, and diverse vegetation. Our dataset also accounts for dynamic scene components including lighting, different times of a day settings, pedestrians, and vehicles. Furthermore, we also demonstrate a learned synthetic-to-real cross-domain inference method for in-the-wild 3D scene depth and normal estimation method using our dataset. To this end, we propose UBotNet, an architecture based on a UNet and a Bottleneck Transformer, to estimate scene-consistent normals. We show that UBotNet achieves significantly improved depth accuracy (4.6%) and normal estimation (5.75%) compared to several existing networks such as U-Net with skip-connections. Finally, we demonstrate in-the-wild depth and normal estimation on real-world images with UBotNet trained purely on our OmniHorizon dataset, showing the promise of proposed dataset and network for scene understanding.
Incorporating Emotions into Health Mention Classification Task on Social Media

The health mention classification (HMC) task is the process of identifying and classifying mentions of health-related concepts in text. This can be useful for identifying and tracking the spread of diseases through social media posts. However, this is a non-trivial task. Here we build on recent studies suggesting that using emotional information may improve upon this task. Our study results in a framework for health mention classification that incorporates affective features. We present two methods, an intermediate task fine-tuning approach (implicit) and a multi-feature fusion approach (explicit) to incorporate emotions into our target task of HMC. We evaluated our approach on 5 HMC-related datasets from different social media platforms including three from Twitter, one from Reddit and another from a combination of social media sources. Extensive experiments demonstrate that our approach results in statistically significant performance gains on HMC tasks. By using the multi-feature fusion approach, we achieve at least a 3% improvement in F1 score over BERT baselines across all datasets. We also show that considering only negative emotions does not significantly affect performance on the HMC task. Additionally, our results indicate that HMC models infused with emotional knowledge are an effective alternative, especially when other HMC datasets are unavailable for domain-specific fine-tuning. The source code for our models is freely available at https://github.com/tahirlanre/Emotion_PHM.
Robustness Implies Privacy in Statistical Estimation

We study the relationship between adversarial robustness and differential privacy in high-dimensional algorithmic statistics. We give the first black-box reduction from privacy to robustness which can produce private estimators with optimal tradeoffs among sample complexity, accuracy, and privacy for a wide range of fundamental high-dimensional parameter estimation problems, including mean and covariance estimation. We show that this reduction can be implemented in polynomial time in some important special cases. In particular, using nearly-optimal polynomial-time robust estimators for the mean and covariance of high-dimensional Gaussians which are based on the Sum-of-Squares method, we design the first polynomial-time private estimators for these problems with nearly-optimal samples-accuracy-privacy tradeoffs. Our algorithms are also robust to a constant fraction of adversarially-corrupted samples.
Evaluating the Social and Psychological Factors about the Public Acceptance of Treated Wastewater Reuse: A Review

To address water scarcity, treated wastewater has been demonstrated to be a more practical and environmentally beneficial solution than untreated wastewater.Nowadays, it is well acknowledged that social marketing and advocacy are inefficient at getting people to use recycled water.It is widely understood that the key components of any reuse project's success are public attitudes and acceptance of water reuse.This paper aims to present a summary of the literature on water reuse, with a focus on issues that may influence how the public perceives water reuse.In this study, a variety of social, psychological, and demographic variables are linked to higher levels of public support for wastewater use.The level of knowledge in this field is included in the conclusion, along with advanced suggestions and policy implications for improving our understanding of the socio-psychological and technological forces behind water reuse for multiple applications.
Analysis of Game Tree Search Algorithms Using Minimax Algorithm and Alpha-Beta Pruning

An important topic of research in computer systems is the optimization of finding the optimum course of action based on different variables, such as the environment's state, the system's goal, etc. The building of the entire state search space, also known as the minimax algorithm, can result from any search algorithm's attempt to find the best feasible solution from among all known possibilities. The recursive backtracking algorithm known as Minimax is used to select the next action in a game of strategy for two players. The algorithm works well because it anticipates that your adversary will play well as well. However, as the tree's depth increases, we observe that minimax frequently investigates repetitive and unlikely situations. We'll also take a look at the minimax extension known as alpha-beta pruning, which prohibits us from considering states that won't be chosen. We will also examine a number of established techniques for resolving two-player games, such as adversarial search and other machine learning-based techniques.
Reliability Reinforcement of Forensic Affirmation using Blockchain

In today’s world, the prominent thing in all stages of our daily life is information. Storing the data with the proper security is the need for every application. There are more chances of stealing the data which is useful for the organization. Nowadays, increments in cyber attacks, so the assaulter tries to modify that information. Although it contains a major influence on evidence of forensics that is essential in finding out the origin. Henceforth, it is a crucial aspect to maintain security and the source of computer-based information as it undergoes various levels throughout the investigation process. Here, it consists of a particular chain of forensics in which obtained medical reports pass throughout the different stages such as pathology staff, forensic staff, doctors, and police officers. Investigation of forensic evidence within the lab consists of knowledge and medical skills to perform inquiry to collect the data considering the death of an innocent person or any physical injury. This process which is legal helps to collect, examine, analyze and report the proof. For building a transparent structure using the stability of evidence, the technology of blockchain is suitable.
Pattern Recognition of Customer Spending Habits Using Apriori Algorithms in DataMining as an Inventory Strategy

The readiness of product inventory is very important, product shortages related to other products can make buyers disappointed and then cancel to buy products that were previously planned to be purchased at once. Sellers can experience a decrease in the number of sales to revenue. In this case, the seller needs to know the pattern of customer habits when making purchases by going through sales transaction data that has occurred. Association techniques can be used to analyze the pattern of interrelationships between items in transaction events. With the a priori algorithm as a popular association algorithm, the pattern of sales transaction data can be analyzed through the research stage. From the implementation of the algorithm with 1063 transaction data using 10% min-support and 75% min-confidence resulting in 4 association rules where 1) if you buy "kacer" and "love bird" you will buy "pentet" as much as 17% support, 2) if you buy "magpie" and "love bird" will also buy "pentet" at 16%, 3) if you buy "kacer" and "magpie" then you will buy "pentet" at 14%, 4) if you buy "anis" you will buy "pentet" of 11% with a confidence level of 76%, 81%, 84%, 77%, respectively. So, there are 5 main items that play a strong role in the rule that must be considered. Sellers can use the resulting item relationship patterns as consideration in managing inventory and structuring the items sold.
Fundraising Tracking System Using Blockchain

People no longer trust charities as a result of the lack of openness, which has caused social investment to stagnate. The donor is unaware of how his money is being used legally. Mistrust of the donor is increased by corruption. In this study, a decentralised network named Charity-Chain that is based on the Ethereum blockchain is proposed. By employing smart contract-based incentives to ensure that their impact is independently validated and available to everyone, it aids social organisations in managing initiatives transparently. For funders (philanthropic organisations, impact investors, and small donors), this makes it much simpler for them to monitor their transactions and, as a result, restore their trust in funding these types of social organisations.
Analysis of Big Data and Cloud Services with Experimental of Manually and Automated Testing

Big Data has increased much focus from the scholastic world and the IT business. In the advanced and figuring world, all together is created and gathered at a rate that quickly surpasses the limit go. Right now, more than 2 billion individuals worldwide are associated with the Internet, and more than 5 billion people possess cell phones. By 2020, 50 billion gadgets are relied upon to be associated with the Internet. Now, anticipated information creation will be 44 times more prominent than that in 2009. As data is exchanged and shared at light speed on optic fiber and remote systems, the volume of information and the speed of market development increment. In any case, the quick development rate of such substantial information creates various difficulties, for example, the fast development of information, exchange speed, different information, and security. In any case, Big Data is still in its outset arrange, and the space has not been checked on all in all. Distributed computing has opened up new open doors for testing offices. New innovation and social network patterns are making an ideal tempest of chance, empowering cloud to change inside tasks, Customer connections and industry esteem chains. To guarantee high caliber of cloud applications being worked on, designer must perform testing to analyze the quality and exactness whatever they plan. In this examination paper, we address a testing natural engineering with important key advantages, to perform execution of experiments and utilized testing strategies to improve nature of cloud applications.
Utilizing Online History Learning Resources (Kitlv, Delpher Kranten and Beeldbank) in History Learning Class X SMA Negeri 3 Palu

The use of online learning resources has a significant influence on student learning needs, especially with the distance learning system implemented at SMA Negeri 3 Palu, including the selection of student learning resources. The implementation of community service aims to provide knowledge to students in the context of utilizing online history learning resources KITLV, Delpher Kranten and Beeldbank in history learning class X SMA Negeri 3 Palu. This service activity was attended by history teachers and students, they were very enthusiastic in participating in the service activities. The resulting output is the knowledge of students and teachers regarding online learning resources that can be used for further learning activities.
Overview of Gesture Recognition Based Virtual Mouse and Keyboard

Abstract: Researchers around the globe nowadays are concentrating on making our devices more interactive and trying to make the devices more operational with minimal physical contact. Thai research proposes an interactive computer system that uses computer vision to create a virtual keyboard and mouse using only hand gestures. We can utilize a built-in camera or an external camera to track the image of different gestures performed by the person’s hand and according to different gestures conduct mouse cursor operations such as right and left clicks, as well as move the cursor. In addition to that, we can control the console using different gestures such as one finger to select the particular alphabet and a four-finger motion gesture to perform swipe operation in either the left or right direction. This proposed system with no wire or any other external device will act as virtual keyboard and mouse. The only hardware aspect of this system is the webcam which is used to capture images whereas the coding part will be done in python programming language
Comparative Study of Different Machine Learning Classifiers Using Multiple Feature Selection Techniques for Breast Cancer Classification

Abstract: This research investigates use of several Machine Learning classifiers under feature selection methods: Without Dimensionality reduction, using Correlation Coefficient Score, using Voting Classifier, and using Tree Based Feature Selection. The different ML Classifiers used in this research are: Logistic Regression, Decision Trees, Support Vector Machine (SVM), Random Forest, K-Nearest Neighbours (KNN) and Naïve Bayes Classifier. These classification models are run on data generated from processing mammography scans to extract shape, texture, size and other spatial features from the tumour contour. The performance of these ML classifiers is evaluated by performance metrics like: Precision Score, Recall Score, F1 Score, and Accuracy Score. The dataset used for the purpose of our study was The Wisconsin Breast Cancer Dataset for both training and testing. The comparison of these results helps us better understand the nature of these classifiers for such classification problems, give us more insights on feature engineering and selection, and their potential use in clinical trials. After computing the results, we were able to get accuracy levels as high as 97.9% and were able to reach accuracy between 90- 95% in general.
Survey on Identification and Classification of Informative Tweets During Disasters

Abstract: During the time of crisis, people often post countless instructive and informative tweets on various social media platforms like Twitter. Recognizing informative tweets could be a difficult errand during the fiasco from such an enormous pool of tweets. As a solution to the current issue of sorting out enlightening tweets, we present a technique to perceive the distinguishing calamity related informative tweets from the Twitter streams utilizing the textual content. Our objective is to construct a model by using Natural Language Processing(NLP), Exploratory Data Analysis(EDA) and Support Vector Machine(SVM) and Visual Geometry Group (VGG as Deep CNN) to categorize the textual and pictorial content for the tweets. In this kernel we’ll explore classification of tweets as disaster or non- disaster, using TensorFlow and Keras. The output of the text-based model is consolidated using the late fusion technique to predict the tweet label.
Aspetti testuali e problemi linguistici (di datazione e localizzazione) dell’antica lingua del cibo

Abstract The paper focuses on some methodological, philological and linguistic problems related to the study of the most ancient culinary texts (cookbooks and practical documents) in Italian vernacular (14 th century). The most relevant problems relate to the precise dating and localization of the manuscripts. These preliminary aspects are essential in order to achieve the Atlante della lingua e dei testi della cultura gastronomica italiana dall’età medievale all’Unità which can assist in identifying the origins and history of culinary language in the history of Italian.
GRASS: A Syntactic Text Simplification System based on Semantic Representations

Automatic Text Simplification (ATS) is the process of reducing a text's linguistic complexity to improve its understandability and readability while maintaining its original information, content, and meaning. Several text transformation operations can be performed such as splitting a sentence into several shorter sentences, substitution of complex elements, and reorganization. It has been shown that the implementation of these operations essentially at a syntactic level causes several problems that could be solved by using semantic representations. In this paper, we present GRASS (GRAph-based Semantic representation for syntactic Simplification), a rulebased automatic syntactic simplification system that uses semantic representations. The system allows the syntactic transformation of complex constructions, such as subordination clauses, appositive clauses, coordination clauses, and passive forms into simpler sentences. It is based on graph-based meaning representation of the text expressed in DMRS (Dependency Minimal Recursion Semantics) notation and it uses rewriting rules. The experimental results obtained on a reference corpus and according to specific metrics outperform the results obtained by other state of the art systems on the same reference corpus.
“Mah 3 favrits”: The Use of Netspeak Language in a Selection of Literary Texts

This paper tackles the use of netspeak language features in three texts of different literary genres: “Give Me Women, Wine, and Snuff,” a poem by John Keats; Hamlet, a play by William Shakespeare; and Yellow Dog, a novel by Martin Amis. The first two works are rewritten by contemporary bloggers and satirists in netspeak as an attempt to make these classics better suit the spirit of the current age. The third example is a novel that comprises formal and standard English on the one hand and informal and netspeak language on the other. Thus, this research examines the literary effects of netspeak language features in the selected texts and investigates the main reasons behind the authors’ reliance on such a language style. To further discuss the origin and emergence of certain netspeak terms, this paper deploys some website tools such as Google Search Trends and the Corpus of Contemporary American English.