Thanks to the high potential for profit, trading has become increasingly attractive to investors as the cryptocurrency and stock markets rapidly expand. However, because financial markets are intricate and dynamic, accurately predicting prices remains a significant challenge. The volatile nature of the cryptocurrency market makes it even harder for traders and investors to make decisions. This study presents a machine learning model based on classification to forecast the direction of the cryptocurrency market, i.e., whether prices will increase or decrease. The model is trained using historical data and important technical indicators such as the Moving Average Convergence Divergence, the Relative Strength Index, and Bollinger Bands. We illustrate our approach with an empirical study of the closing price of Bitcoin. Several simulations, including a confusion matrix and Receiver Operating Characteristic curve, are used to assess the model's performance, and the results show a buy/sell signal accuracy of over 92%. These findings demonstrate how machine learning models can assist investors and traders of cryptocurrencies in making wise/informed decisions in a very volatile market.

Feature selection is an essential process in machine learning, especially when dealing with high-dimensional datasets. It helps reduce the complexity of machine learning models, improve performance, mitigate overfitting, and decrease computation time. This paper presents a novel feature selection framework, shap-select. The framework conducts a linear or logistic regression of the target on the Shapley values of the features, on the validation set, and uses the signs and significance levels of the regression coefficients to implement an efficient heuristic for feature selection in tabular regression and classification tasks. We evaluate shap-select on the Kaggle credit card fraud dataset, demonstrating its effectiveness compared to established methods such as Recursive Feature Elimination (RFE), HISEL (a mutual information-based feature selection method), Boruta and a simpler Shapley value-based method. Our findings show that shap-select combines interpretability, computational efficiency, and performance, offering a robust solution for feature selection.

--Forest fires pose a significant threat to the environment, human life, and property. Early detection and response are crucial to mitigating the impact of these disasters. However, traditional forest fire detection methods are often hindered by our reliability on manual observation and satellite imagery with low spatial resolution. This paper emphasizes the role of transfer learning in enhancing forest fire detection in India, particularly in overcoming data collection challenges and improving model accuracy across various regions. We compare traditional learning methods with transfer learning, focusing on the unique challenges posed by regional differences in terrain, climate, and vegetation. Transfer learning can be categorized into several types based on the similarity between the source and target tasks, as well as the type of knowledge transferred. One key method is utilizing pre-trained models for efficient transfer learning, which significantly reduces the need for extensive labeled data. We outline the transfer learning process, demonstrating how researchers can adapt pre-trained models like MobileNetV2 for specific tasks such as forest fire detection. India is home to a vast and diverse range of forests, covering over 70 million hectares of land [1]. These forests are crucial not only for the country's ecosystem and biodiversity but also provide livelihoods for millions of people, particularly in rural areas. However, India's forests are facing a growing threat from forest fires, which can have devastating consequences for the environment, human life, and property [2]. Forest fires are a major concern in India, particularly during the summer months when temperatures are high and humidity is low. According to the Indian government, forest fires affect over 50, 000 hectares of land annually, causing significant economic losses and damage to the environment [3]. The country's forests are also home to a wide range of wildlife, including many endangered species which are threatened by fires. Figure 1 illustrates some images of the Uttarakhand, India, forest fire. Early detection and response are critical to mitigating the impact of forest fires. Traditional methods of forest fire detection, such as manual observation and satellite imagery with low spatial resolution, are often limited in their ability to detect fires quickly and accurately [4]. Manual observation is time-consuming and labour-intensive and may not be feasible in remote or inaccessible areas [5]. Satellite imagery with low spatial resolution may not be able to detect small fires or fires in areas with dense vegetation. In recent years, advances in deep learning and computer vision have enabled the development of more effective methods for forest fire detection. Convolutional neural networks (CNNs), in particular, have shown great promise in image classification tasks [6]-[10], including fire detection [4].

As Large Language Models (LLMs) become increasingly sophisticated, they raise significant security concerns, including the creation of fake news and academic misuse. Most detectors for identifying model-generated text are limited by their reliance on variance in perplexity and burstiness, and they require substantial computational resources. In this paper, we proposed a watermarking method embedding a specific watermark into the text during its generation by LLMs, based on a pre-defined signal pattern. This technique not only ensures the watermark's invisibility to humans but also maintains the quality and grammatical integrity of model-generated text. We utilize LLMs and Fast Fourier Transform (FFT) for token probability computation and detection of the signal watermark. The unique application of signal processing principles within the realm of text generation by LLMs allows for subtle yet effective embedding of watermarks, which do not compromise the quality or coherence of the generated text. Our method has been empirically validated across multiple LLMs, consistently maintaining high detection accuracy, even with variations in temperature settings during text generation. In the experiment of distinguishing between human-written and watermarked text, our method achieved an AUROC score of 0.97, significantly outperforming existing methods like GPTZero, which scored 0.64. The watermark's resilience to various attacking scenarios further confirms its robustness, addressing significant challenges in model-generated text authentication.

Model fusion research aims to aggregate the knowledge of multiple individual models to enhance performance by combining their weights. In this work, we study the inverse problem: investigating whether model fusion can be used to reduce unwanted knowledge. We investigate the effects of model fusion in three scenarios: the learning of shortcuts, social biases, and memorization of training data in fine-tuned language models. Through experiments covering classification and generation tasks, our analysis highlights that shared knowledge among models is enhanced during model fusion, while unshared knowledge is usually forgotten. Based on this observation, we demonstrate the potential of model fusion as a debiasing tool and showcase its efficacy in addressing privacy concerns associated with language models.

Non-parametric multivariate density estimation faces strong statistical and computational bottlenecks, and the more practical approaches impose near-parametric assumptions on the form of the density functions. In this paper, we leverage recent developments to propose a class of non-parametric models which have very attractive computational and statistical properties. Our approach relies on the simple function space assumption that the conditional distribution of each variable conditioned on the other variables has a non-parametric exponential family form.

This paper contributes some original thinkings on how to assess the quality of a generative model. The new evaluation metric, as defined by the distributional precision and recall statistics (PRD), overcomes a major drawback from prior-arts: that evaluation scores are almost exclusively scalar metrics. The author(s) attributes intuitive explanations to this new metric and experimentally reached a conclusion that it is able to disentangle the quality from the coverage, two critical aspects wrt the quality of a learned synthetic sampler. An efficient algorithm is also described and theoretically justified. The quality of this work seems okay, yet I am prone to a neutral-to-negative rating.

I also strongly approve of the suggestion of highlighting more the experimental results in the main paper. RLSs are well known quantities used in randomized sketching and coreset selection to identify influential samples. Similarly, it is known that sampling **and reweighting** rows of a matrix A according to their RLS produces a sketch that whp approximates the true matrix up to a small multiplicative and additive error. The authors prove that sorting the rows in descending order (by RLS), and deterministically selecting them until the RLS falls under a carefully chosen threshold is also sufficient to obtain a provably accurate sketch. Therefore, they propose deterministic RLS selection as a convenient and provably accurate rule for column selection, with improved interpretability over optimization based alternative such as lasso and elastic net.

This paper introduces Theseus, an algorithm for matching MS/MS spectra to peptide in a D.B. This is a challenging and important task. It is important because MS/MS is currently practically the only common high-throughput method to identify which proteins are present in a sample. It is challenging because the data is analog (intensity vs. m/z graphs) and extremely noisy. This work builds upon an impressive body of work that has been dedicated to this problem.

This paper builds on the work of Platanios et al. (2014, 2016) on estimating the accuracy of a set of classifiers for a given task using only unlabeled data, based on the agreement behavior of the classifiers. The current work uses a probabilistic soft logic (PSL) model to infer the error rates of the classifiers. The paper also extends this approach to the case where we have multiple related classification tasks: for instance, classifying noun phrases with regard to their membership in multiple categories, some of which subsume others and some of which are mutually exclusive. The paper shows how a PSL model can take into account these constraints among the categories, yielding better error rate estimates and higher joint classification accuracy. It is well written and the methodology seems sound.