Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Implicit Feedback-Based Recommendation through Multi-Behavior Alignment

Recommender systems that learn from implicit feedback often use large volumes of a single type of implicit user feedback, such as clicks, to enhance the prediction of sparse target behavior such as purchases. Using multiple types of implicit user feedback for such target behavior prediction purposes is still an open question. Existing studies that attempted to learn from multiple types of user behavior often fail to: (i) learn universal and accurate user preferences from different behavioral data distributions, and (ii) overcome the noise and bias in observed implicit user feedback. To address the above problems, we propose multi-behavior alignment (MBA), a novel recommendation framework that learns from implicit feedback by using multiple types of behavioral data. We conjecture that multiple types of behavior from the same user (e.g., clicks and purchases) should reflect similar preferences of that user. To this end, we regard the underlying universal user preferences as a latent variable. The variable is inferred by maximizing the likelihood of multiple observed behavioral data distributions and, at the same time, minimizing the Kullback? Leibler divergence (KL-divergence) between user models learned from auxiliary behavior (such as clicks or views) and the target behavior separately. MBA infers universal user preferences from multi-behavior data and performs data denoising to enable effective knowledge transfer. We conduct experiments on three datasets, including a dataset collected from an operational e-commerce platform. Empirical results demonstrate the effectiveness of our proposed method in utilizing multiple types of behavioral data to enhance the prediction of the target behavior.
Cell Lineage Statistics with Incomplete Population Trees

Cell lineage statistics is a powerful tool for inferring cellular parameters, such as division rate, death rate or the population growth rate. Yet, in practice such an analysis suffers from a basic problem: how should we treat incomplete lineages that do not survive until the end of the experiment? Here, we develop a model-independent theoretical framework to address this issue. We show how to quantify fitness landscape, survivor bias and selection for arbitrary cell traits from cell lineage statistics in the presence of death, and we test this method using an experimental data set in which a cell population is exposed to a drug that kills a large fraction of the population. This analysis reveals that failing to properly account for dead lineages can lead to misleading fitness estimations. For simple trait dynamics, we prove and illustrate numerically that the fitness landscape and the survivor bias can in addition be used for the non-parametric estimation of the division and death rates, using only lineage histories. Our framework provides universal bounds on the population growth rate, and a fluctuation-response relation which quantifies the reduction of population growth rate due to the variability in death rate. Further, in the context of cell size control, we obtain generalizations of Powell's relation that link the distributions of generation times with the population growth rate, and show that the survivor bias can sometimes conceal the adder property, namely the constant increment of volume between birth and division.
Two to Five Truths in Non-Negative Matrix Factorization

In this paper, we explore the role of matrix scaling on a matrix of counts when building a topic model using non-negative matrix factorization. We present a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly improve the quality of a non-negative matrix factorization. The results parallel those in the spectral graph clustering work of \cite{Priebe:2019}, where the authors proved adjacency spectral embedding (ASE) spectral clustering was more likely to discover core-periphery partitions and Laplacian Spectral Embedding (LSE) was more likely to discover affinity partitions. In text analysis non-negative matrix factorization (NMF) is typically used on a matrix of co-occurrence ``contexts'' and ``terms"counts. The matrix scaling inspired by LSE gives significant improvement for text topic models in a variety of datasets. We illustrate the dramatic difference a matrix scalings in NMF can greatly improve the quality of a topic model on three datasets where human annotation is available. Using the adjusted Rand index (ARI), a measure cluster similarity we see an increase of 50\% for Twitter data and over 200\% for a newsgroup dataset versus using counts, which is the analogue of ASE. For clean data, such as those from the Document Understanding Conference, NL gives over 40\% improvement over ASE. We conclude with some analysis of this phenomenon and some connections of this scaling with other matrix scaling methods.
Data Protection and Security Issues With Network Error Logging

Network Error Logging helps web server operators detect operational problems in real-time to provide fast and reliable services. This paper analyses Network Error Logging from two angles. Firstly, this paper overviews Network Error Logging from the data protection view. The ePrivacy Directive requires consent for non-essential access to the end devices. Nevertheless, the Network Error Logging design does not allow limiting the tracking to consenting users. Other issues lay in GDPR requirements for transparency and the obligations in the contract between controllers and processors of personal data. Secondly, this paper explains Network Error Logging exploitations to deploy long-time trackers to the victim devices. Even though users should be able to disable Network Error Logging, it is not clear how to do so. Web server operators can mitigate the attack by configuring servers to preventively remove policies that adversaries might have added.
A Chip-Firing Game for Biocrust Reverse Succession

Experimental work suggests that biological soil crusts, dominant primary producers in drylands and tundra, are particularly vulnerable to disturbances that cause reverse ecological succession. To model successional transitions in biocrust communities, we propose a resource-firing game that captures succession dynamics without specifying detailed function forms. The model is evaluated in idealized terrestrial ecosystems, where disturbances are modeled as a reduction in available resources that triggers inter-species competition. The resource-firing game is executed on a finite graph with nodes representing species in the community and a sink node that becomes active when every species is depleted of resources. First, we discuss the theoretical basis of the resource-firing game, evaluate it in the light of existing literature, and consider the characteristics of a biocrust community that has evolved to equilibrium. We then examine the dependence of resource-firing and game stability on species richness, showing that high species richness increases the probability of very short and long avalanches, but not those of intermediate length. Indeed, this result suggests that the response of the community to disturbance is both directional and episodic, proceeding towards reverse succession in bursts of variable length. Finally, we incorporate the spatial structure of the biocrust community into a Cayley Tree and derive a formula for the probability that a disturbance, modeled as a random attack, initiates a large species-death event.
Interactive Concept Learning for Uncovering Latent Themes in Large Text Collections

Experts across diverse disciplines are often interested in making sense of large text collections. Traditionally, this challenge is approached either by noisy unsupervised techniques such as topic models, or by following a manual theme discovery process. In this paper, we expand the definition of a theme to account for more than just a word distribution, and include generalized concepts deemed relevant by domain experts. Then, we propose an interactive framework that receives and encodes expert feedback at different levels of abstraction. Our framework strikes a balance between automation and manual coding, allowing experts to maintain control of their study while reducing the manual effort required.
Investigating the Software Engineering Roadmap for Smart City Infrastructure Development: Goals and Challenges

In today's world, many cities are embracing cutting-edge technology and transforming into"smart cities". These emerging innovations are revolutionizing the standard of living for people, and as a result, smart city infrastructure development has become a major focus for city planners and policymakers worldwide. The goal is to create more livable, sustainable, and efficient urban environments, and software engineering plays a crucial role in achieving this. In this article, we will delve into what makes a city"smart"and what it means for the future. We will explore the software engineering roadmap for smart city infrastructure development, highlighting the goals and challenges that come with this innovative approach to urban planning. Our aim is to provide valuable insights into the importance of software engineering in achieving successful smart city infrastructure development. As cities continue to grow and evolve, it is essential to adopt new technologies that can help us build smarter, more sustainable communities. Smart city initiatives are paving the way for a brighter future, and software engineering is at the forefront of this movement. By understanding the software engineering roadmap for smart city infrastructure development, we can work towards creating more livable, efficient, and sustainable urban environments for generations to come.
Code Execution with Pre-trained Language Models

Code execution is a fundamental aspect of programming language semantics that reflects the exact behavior of the code. However, most pre-trained models for code intelligence ignore the execution trace and only rely on source code and syntactic structures. In this paper, we investigate how well pre-trained models can understand and perform code execution. We develop a mutation-based data augmentation technique to create a large-scale and realistic Python dataset and task for code execution, which challenges existing models such as Codex. We then present CodeExecutor, a Transformer model that leverages code execution pre-training and curriculum learning to enhance its semantic comprehension. We evaluate CodeExecutor on code execution and show its promising performance and limitations. We also demonstrate its potential benefits for code intelligence tasks such as zero-shot code-to-code search and text-to-code generation. Our analysis provides insights into the learning and generalization abilities of pre-trained models for code execution.
Wooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs

Maintaining high standards for user safety during daily railway operations is crucial for railway managers. To aid in this endeavor, top- or side-view cameras and GPS positioning systems have facilitated progress toward automating periodic inspections of defective features and assessing the deteriorating status of railway components. However, collecting data on deteriorated status can be time-consuming and requires repeated data acquisition because of the extreme temporal occurrence imbalance. In supervised learning, thousands of paired data sets containing defective raw images and annotated labels are required. However, the one-class classification approach offers the advantage of requiring fewer images to optimize parameters for training normal and anomalous features. The deeper fully-convolutional data descriptions (FCDDs) were applicable to several damage data sets of concrete/steel components in structures, and fallen tree, and wooden building collapse in disasters. However, it is not yet known to feasible to railway components. In this study, we devised a prognostic discriminator pipeline to automate one-class damage classification using the deeper FCDDs for defective railway components. We also performed ablation studies of the deeper backbone based on convolutional neural networks (CNNs). Furthermore, we visualized deterioration features by using transposed Gaussian upsampling. We demonstrated our application to railway inspection using a video acquisition dataset of railway track from backward view at a cloudy and sunny scene. Finally, we examined the usability of our approach for prognostics and future work on railway inspection.
Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey.

Optimal Transport (OT) is a mathematical framework that first emerged in the eighteenth century and has led to a plethora of methods for answering many theoretical and applied questions. The last decade has been a witness to the remarkable contributions of this classical optimization problem to machine learning. This paper is about where and how optimal transport is used in machine learning with a focus on the question of scalable optimal transport. We provide a comprehensive survey of optimal transport while ensuring an accessible presentation as permitted by the nature of the topic and the context. First, we explain the optimal transport background and introduce different flavors (i.e. mathematical formulations), properties, and notable applications. We then address the fundamental question of how to scale optimal transport to cope with the current demands of big and high dimensional data. We conduct a systematic analysis of the methods used in the literature for scaling OT and present the findings in a unified taxonomy. We conclude with presenting some open challenges and discussing potential future research directions. A live repository of related OT research papers is maintained in https://github.com/abdelwahed/OT_for_big_data. git.
Popularity Debiasing from Exposure to Interaction in Collaborative Filtering

Recommender systems often suffer from popularity bias, where popular items are overly recommended while sacrificing unpopular items. Existing researches generally focus on ensuring the number of recommendations (exposure) of each item is equal or proportional, using inverse propensity weighting, causal intervention, or adversarial training. However, increasing the exposure of unpopular items may not bring more clicks or interactions, resulting in skewed benefits and failing in achieving real reasonable popularity debiasing. In this paper, we propose a new criterion for popularity debiasing, i.e., in an unbiased recommender system, both popular and unpopular items should receive Interactions Proportional to the number of users who Like it, namely IPL criterion. Under the guidance of the criterion, we then propose a debiasing framework with IPL regularization term which is theoretically shown to achieve a win-win situation of both popularity debiasing and recommendation performance. Experiments conducted on four public datasets demonstrate that when equipping two representative collaborative filtering models with our framework, the popularity bias is effectively alleviated while maintaining the recommendation performance.
Social Value Orientation and Integral Emotions in Multi-Agent Systems

Human social behavior is influenced by individual differences in social preferences. Social value orientation (SVO) is a measurable personality trait which indicates the relative importance an individual places on their own and on others' welfare when making decisions. SVO and other individual difference variables are strong predictors of human behavior and social outcomes. However, there are transient changes human behavior associated with emotions that are not captured by individual differences alone. Integral emotions, the emotions which arise in direct response to a decision-making scenario, have been linked to temporary shifts in decision-making preferences. In this work, we investigated the effects of moderating social preferences with integral emotions in multi-agent societies. We developed Svoie, a method for designing agents which make decisions based on established SVO policies, as well as alternative integral emotion policies in response to task outcomes. We conducted simulation experiments in a resource-sharing task environment, and compared societies of Svoie agents with societies of agents with fixed SVO policies. We find that societies of agents which adapt their behavior through integral emotions achieved similar collective welfare to societies of agents with fixed SVO policies, but with significantly reduced inequality between the welfare of agents with different SVO traits. We observed that by allowing agents to change their policy in response to task outcomes, agents can moderate their behavior to achieve greater social equality. \end{abstract}
Detecting and Reasoning of Deleted Tweets before they are Posted

Social media platforms empower us in several ways, from information dissemination to consumption. While these platforms are useful in promoting citizen journalism, public awareness etc., they have misuse potentials. Malicious users use them to disseminate hate-speech, offensive content, rumor etc. to gain social and political agendas or to harm individuals, entities and organizations. Often times, general users unconsciously share information without verifying it, or unintentionally post harmful messages. Some of such content often get deleted either by the platform due to the violation of terms and policies, or users themselves for different reasons, e.g., regrets. There is a wide range of studies in characterizing, understanding and predicting deleted content. However, studies which aims to identify the fine-grained reasons (e.g., posts are offensive, hate speech or no identifiable reason) behind deleted content, are limited. In this study we address this gap, by identifying deleted tweets, particularly within the Arabic context, and labeling them with a corresponding fine-grained disinformation category. We then develop models that can predict the potentiality of tweets getting deleted, as well as the potential reasons behind deletion. Such models can help in moderating social media posts before even posting.
Anatomically Detailed Simulation of Human Torso

Many existing digital human models approximate the human skeletal system using rigid bodies connected by rotational joints. While the simplification is considered acceptable for legs and arms, it significantly lacks fidelity to model rich torso movements in common activities such as dancing, Yoga, and various sports. Research from biomechanics provides more detailed modeling for parts of the torso, but their models often operate in isolation and are not fast and robust enough to support computationally heavy applications and large-scale data generation for full-body digital humans. This paper proposes a new torso model that aims to achieve high fidelity both in perception and in functionality, while being computationally feasible for simulation and optimal control tasks. We build a detailed human torso model consisting of various anatomical components, including facets, ligaments, and intervertebral discs, by coupling efficient finite-element and rigid-body simulations. Given an existing motion capture sequence without dense markers placed on the torso, our new model is able to recover the underlying torso bone movements. Our method is remarkably robust that it can be used to automatically "retrofit" the entire Mixamo motion database of highly diverse human motions without user intervention. We also show that our model is computationally efficient for solving trajectory optimization of highly dynamic full-body movements, without relying any reference motion. Physiological validity of the model is validated against established literature.
Modal Homotopy Type Theory. The Prospect of a New Logic for Philosophy

The theory referred to by the—perhaps intimidating—main title of this book is an extension of Per Martin-Löf’s dependent type theory. Much philosophical work pertaining to dependent type theory takes as its starting point Martin-Löf’s meaning explanations, that is, a system of careful explanations by virtue of which type theory is notmerely an object ofmetamathematical study, but a meaningful formalism fit for the expression of logico-mathematical thought. The present book takes a different approach. David Corfield sets out to harvest philosophical fruits from the rich syntax of dependent type theory and its deep connections to category theory. This places the book in line, on the one hand, with work in linguistics utilizing dependent type theory, and on the other hand, with philosophical work on, or inspired by, category theory. The book’s subtitle points to its programmatic character. The preface and the conclusion each opens with quotations from the final paragraph of Russell 1914, ch. 2, which praises the advantages of the new logic invented by Frege and Peano over the old logic inherited from Aristotle and the schoolmen. A ‘new new logic’, as Corfield calls it (p. 163), has now appeared in the form of modal homotopy type theory, which should become the new ‘formal language of choice’ in philosophy (p. v). To get there, ‘a large-scale retraining exercise’ would be necessary (p. vi). In the process, a reconsideration of central parts of analytic philosophy would follow (p. 27). As befits a book containing such programmatic statements, it probes widely rather than deeply. Others may come after and develop individual topics in more detail. Apart from the technical material that makes up Corfield’s presentation of modal homotopy type theory itself, topics dealt with range from the methodology of philosophy to the ontology of events, from definite descriptions to the nature of present-day work in geometry. Also befitting the programmatic character of the book is the enthusiasm Corfield shows for its subject matter. This enthusiasm may indeed inspire readers to follow the call to explore the prospects of modal homotopy type theory as a new logic for philosophy. We proceed with a general discussion of modal homotopy type theory in Section 2 and a critique of Corfield’s book in Section 3.
Impact of Changing Greenhouse Gas Concentrations on Ontario's Climate

The effect of changing greenhouse gas concentrations on climate was examined. Calculations of the climate sensitivity, the warming of the Earth due to a doubling of atmospheric CO2, are discussed. Ontario was responsible for 0.35% of the world's CO2 emissions in 2019 and this amount was 20% lower than in 2005. The predictions of Global Climate Models (GCM) were compared to observations. Records since 1880 show an overall warming of about 1 C. The GCMs do not account for observed decadal temperature fluctuations and consistently overestimate the warming. Ontario's contribution to global warming is only 9.2 x 10^{-5} C/year using the Intergovernmental Panel on Climate Change recommended climate sensitivity value. Measurements of the polar ice caps reveal a decrease in the minimum September Arctic sea ice extent during 1979-2022 but the trend levelled off after 2007; while the average Antarctic sea ice extent slightly increased. Sea level increased slightly throughout the 20th century. Ontario's contribution to anthropogenic sea level rise is about 0.005 mm/year. Sea level along Ontario's Hudson Bay coast is decreasing due to isostatic rebound of the land following the last Ice Age. The change to ocean acidity due to CO2 absorption from the atmosphere is negligible compared to that due to tides, ocean depth and seasonal effects. Ontario's contribution to ocean acidification is estimated to be 6 x 10^{-6} pH/year. No changes in precipitation in North America over the 19th and 20th centuries, nor at Toronto since 1843, were found. The Great Lake levels are remarkably constant over the past century showing no evidence of a change in the incidence of flooding. No evidence was found that the frequency of extreme events such as hurricanes or tornadoes increased during recent decades. The number of forest fires in Canada and Ontario decreased during 1990 to 2020.