Miracle-dz/UniFAR-FTUs huggingface dataset:
Techno.COM, Vol. 20, No. 3, Agustus 2021

People with diabetes worldwide continue to experience an increase with a death rate of 4.6 million in 2011 and is expected to continue to increase globally to 552 million by 2030. Prevention of diabetes may be done effectively by detecting it early. However, the problem that is often faced in analyzing diabetes is the class imbalance problem. An unbalanced class makes it difficult for the learning model to make predictions because the learning model is dominated by instances of the majority class, thus ignoring the predictions of the minority class. In this study, we try to analyze and try to overcome the problem of class imbalance by using a data level approach, namely data resampling techniques. This experiment uses the R language with the ROSE library (version 0.0-4). The Pima Indians dataset was chosen in this study because it is one of the datasets that experience class imbalance. The classification model in this study uses the decision tree algorithm C4.5, RF (Random Forest), and SVM (Support Vector Machines). From the results of experiments conducted on the SVM classification model with a resampling technique that combines over and under-sampling into a model that has the best performance with an AUC (Area Under Curve) value of 0.80
No-Audio Multimodal Speech Detection Task at MediaEval 2020

This overview paper provides a description of the No-Audio multimodal speech detection task for MediaEval 2020. Similar to the previous two editions, the participants of this task are encouraged to estimate the speaking status (i.e. person speaking or not) of individuals interacting freely during a crowded mingle event, from multimodal data. In contrast to conventional speech detection approaches, no audio is used for this task. Instead, the automatic estimation system proposed must exploit the natural human movements that accompany speech, captured by cameras and wearable sensors. Task participants are provided with cropped videos of individuals while interacting, captured by an overhead camera, and the tri-axial acceleration of each individual throughout the event, captured with a single badge-like device hung around the neck. This year’s edition of the task also focuses on investigating posible reasons for interpersonal differences in the performances obtained.
KMODES : a modified k-modes clustering algorithm

In this paper we present a newmethod for clustering categorical data sets named CL.E. KMODES. The proposed method is a modified k-modes algorithm that incorporates a new four-step dissimilarity measure, which is based on elements of the methodological framework of the ELECTRE I multicriteria method. The four-step dissimilarity measure introduces an alternative and more accurate way of assigning objects to clusters. In particular, it compares each object with each mode, for every attribute that they have in common, and then chooses the most appropriate mode and its corresponding cluster for that object. Seven widely used data sets are tested to verify the robustness of the proposed method in six clustering evaluation measures. Journal of the Operational Research Society (2009) 60, 1085–1095. doi:10.1057/jors.2009. 25 Published online 29 April 2009
Architecture-Aware Approximate Computing

Observing that many application programs from different domains can live with less-than-perfect accuracy, existing techniques try to trade off program output accuracy with performance-energy savings. While these works provide point solutions, they leave three critical questions regarding approximate computing unanswered: (i) what is the maximum potential of skipping (i.e., not performing) data accesses under a given inaccuracy bound? ; (ii) can we identify the data accesses to drop randomly, or is being architecture aware critical? ; and (iii) do two executions that skip the same number of data accesses always result in the same output quality (error)? This paper first provides answers to these questions using ten multithreaded workloads, and then presents a program slicing-based approach that identifies the set of data accesses to drop. Results indicate 8.8% performance improvement and 13.7% energy saving are possible when we set the error bound to 2%, and the corresponding improvements jump to 15% and 25%, respectively, when the error bound is raised to 4%.
AIS Electronic

Despite the increasing diffusion of agile software development methodologies (ASDM) in firms, academic studies in this field – though rising in number – are largely missing out on the implications for the individual software developer. The application of agile methodologies in software development projects has significant impact on the individual developer’s way of working. While some empirical research evaluated the advantages of ASDM for individuals, potential negative consequences for developers, who have to cope with multiple draining demands in their daily work, remains largely unexplored. Using resource depletion theory as a lens, we develop a research model to examine how the use of ASDM potentially decreases individual job satisfaction and increases turnover intention. We suggest that the effect is mediated by developers’ level of depletion expressed by psychological strain. In addition, we aim to determine who is more or less affected by depletion in an agile context introducing the capacity for self-control as a moderator. To test our model, we plan to conduct a cross-sectional field survey among software developers working in agile teams. Drawing attention to under-represented negative implications on an individual level, we want to contribute to a more balanced picture of ASDM use in theory and practice.
Hagedorn transitions in exact U_q(su(2)) S-matrix theories with arbitrary spins

Generalizing the quantum sine-Gordon and sausage models, we construct exact S-matrices for higher spin representations with quantum U_q(su(2)) symmetry, which satisfy unitarity, crossing-symmetry, and the Yang-Baxter equations with minimality assumption, i.e. without any unnecessary CDD factor. The deformation parameter q is related to a coupling constant. Based on these S-matrices, we derive the thermodynamic Bethe ansatz equations for q a root of unity in terms of a universal kernel where the nodes are connected by graphs of non-Dynkin type. We solve these equations numerically to find out Hagedorn-like singularities in the free energies at some critical scales and find a universality in the critical exponents, all near 0.5 for different values of the spin and the coupling constant.
The Dirac-Delta Rogue Wave

This paper presents a double spatio-temporal localized Dirac-delta solution for the linear wave equation. The solution arises from the interference of sinusoidal waves with frequencies that vary as a function of the time of emission. It is shown that the time-dependent frequency function required to produce a localized Dirac-delta wave is exclusively determined by the dispersion relation of the medium in which the wave is traveling. Both numerical and exact analytical solutions for a typical physical scenario are obtained and displayed. The application of this result to possible practical uses is discussed.
A Geometry of entanglement and entropy

This paper explores the fundamental relationship between the geometry of entanglement and von Neumann entropy, shedding light on the intricate nature of quantum correlations. We provide a comprehensive overview of entanglement, highlighting its crucial role in quantum mechanics. Our focus centers on the connection between entanglement, von Neumann entropy, a measure of the information content within quantum systems and the geometry of composite Hilbert spaces. We discuss various methods for quantifying and characterizing entanglement through a geometric perspective and elucidate how this connection unveils the nature of quantum entanglement, offering valuable insights into the underlying structure of quantum systems. This study underscores the significance of geometry as a key tool for understanding the rich landscape of quantum correlations and their implications across various domains of physics and information theory. An example of entanglement as an indispensable resource for the task of state teleportation is presented at the end.
Multifractal Formalism from Large Deviations

It has often been observed that the Multifractal Formalism and the Large Deviation Principles are intimately related. In fact, Multifractal Formalism was heuristically derived using the Large Deviations ideas. In numerous examples in which the multifractal results have been rigorously established, the corresponding Large Deviation results are valid as well. Moreover, the proofs of multifractal and large deviations are remarkably similar. The natural question then is whether under which conditions multifractal formalism can be deduced from the corresponding large deviations results. More specifically, given a sequence of random variables $\{ {X_n} \}_{n\in\N}$, satisfying a Large Deviation Principle, what can be said about the multifractal nature of the level sets $K_\alpha=\{\omega: \lim_{n} \frac{X_n(\omega)}{n}=\alpha\}$. Under some technical assumptions, we establish the upper and lower bounds for multifractal spectra in terms of the large deviation rate functions, and show that many known results of multifractal formalism are covered by our setup.
Effects of group size and noise on cooperation in population evolution of dynamic groups

In a large population, the agents temporally form group of the Public Goods Game (PGG) one after another, and size of one group is randomly distributed at $g\in [g_l,g_h]$. Players in it have two strategies to be chosen to cooperate, or to defect for playing the PGG. Based on this structure we investigate the evolution of cooperation in PGG as a function of the noise level underlying strategy adoptions with the group size distribution. In the process of the evolution, the payoffs of cooperators(defectors) have related to the strategy selection implemented by the death-birth process. When at the little value of the noise ($K=0.1$), if the system has different initial concentrations of cooperation, a hybrid state with cooperators and defectors, and AllC state are observed. Moreover, the critical parameter of the multiplication factor $r$ is obtained, above the critical value of $r_c$ it induces a totally cooperative behavior, which is fitted well with analytical expressions. The larger uncertainty with the increase of the noise level is, the more he hesitate and unsure how to choose which strategy when a moderate multiplication factor $r$ is set. So, with a greater value of noise strength, a larger value of $r$ induces a totally cooperative behavior; however a small value of $r$ leads to the mixed states of cooperators and defectors, and between the intermediate values of $r$ players probably randomly choose to cooperate or to defect in an unstable sphere, where a hysteresis effect occurs for a bigger size distribution of group.
Extremal affine surface areas in a functional setting

We introduce extremal affine surface areas in a functional setting. We show their main properties. Among them are linear invariance, isoperimetric inequalities and monotonicity properties. We establish a new duality formula, which shows that the maximal (resp. minimal) inner affine surface area of an $s$-concave function on $\mathbb{R}^n$ equals the maximal (resp. minimal) outer affine surface area of its Legendre polar. We estimate the ``size"of these quantities: up to a constant depending on $n$ and $s$ only, the extremal affine surface areas are proportional to a power of the integral of $f$. This extends results obtained in the setting of convex bodies. We recover and improve those as a corollary to our results.
Frustratingly Simple Prompting-based Text Denoising

This paper introduces a novel perspective on the automated essay scoring (AES) task, challenging the conventional view of the ASAP dataset as a static entity. Employing simple text denoising techniques using prompting, we explore the dynamic potential within the dataset. While acknowledging the previous emphasis on building regression systems, our paper underscores how making minor changes to a dataset through text denoising can enhance the final results.
Mobile-Env: An Evaluation Platform and Benchmark for LLM-GUI Interaction

The User Interface (UI) is pivotal for human interaction with the digital world, facilitating efficient control of machines, information navigation, and complex task completion. To achieve easy, efficient, and free interactions, researchers have been exploring the potential of encapsulating the traditional Programming Language Interfaces (PLIs) and Graphical User Interfaces (GUIs) into Natural Language Interfaces (NLIs). However, due to the limited capabilities of small models, traditional work mainly focuses on tasks for which only a single step is needed. This largely constrains the application of NLIs. Recently, Large Language Models (LLMs) have exhibited robust reasoning and planning abilities, yet their potential for multi-turn interactions in complex environments remains under-explored. To assess LLMs as NLIs in real-world graphical environments, we introduce the GUI interaction platform, Mobile-Env, specifically on mobile apps. Mobile-Env enhances interaction flexibility, task extensibility, and environment adaptability compared with previous environments. A GUI task set based on WikiHow app is collected on Mobile-Env to form a benchmark covering a range of GUI interaction capabilities. We further conduct comprehensive evaluations of LLM agents, including various versions of GPT, LLaMA 2, and AgentLM, on WikiHow task set to acquire insights into the potentials and challenges of LLMs in GUI interactions.
Beyond Spatio-Temporal Representations: Evolving Fourier Transform for Temporal Graphs

We present the Evolving Graph Fourier Transform (EFT), the first invertible spectral transform that captures evolving representations on temporal graphs. We motivate our work by the inadequacy of existing methods for capturing the evolving graph spectra, which are also computationally expensive due to the temporal aspect along with the graph vertex domain. We view the problem as an optimization over the Laplacian of the continuous time dynamic graph. Additionally, we propose pseudo-spectrum relaxations that decompose the transformation process, making it highly computationally efficient. The EFT method adeptly captures the evolving graph's structural and positional properties, making it effective for downstream tasks on evolving graphs. Hence, as a reference implementation, we develop a simple neural model induced with EFT for capturing evolving graph spectra. We empirically validate our theoretical findings on a number of large-scale and standard temporal graph benchmarks and demonstrate that our model achieves state-of-the-art performance.
Transition in the ancestral reproduction rate and its implications for the site frequency spectrum

Consider a supercritical birth and death process where the children acquire mutations. We study the mutation rates along the ancestral lineages in a sample of size $n$ from the population at time $T$. The mutation rate is time-inhomogenous and has a natural probabilistic interpretation. We use these results to obtain asymptotic results for the site frequency spectrum associated with the sample.
PhyPlan: Compositional and Adaptive Physical Task Reasoning with Physics-Informed Skill Networks for Robot Manipulators

Given the task of positioning a ball-like object to a goal region beyond direct reach, humans can often throw, slide, or rebound objects against the wall to attain the goal. However, enabling robots to reason similarly is non-trivial. Existing methods for physical reasoning are data-hungry and struggle with complexity and uncertainty inherent in the real world. This paper presents PhyPlan, a novel physics-informed planning framework that combines physics-informed neural networks (PINNs) with modified Monte Carlo Tree Search (MCTS) to enable embodied agents to perform dynamic physical tasks. PhyPlan leverages PINNs to simulate and predict outcomes of actions in a fast and accurate manner and uses MCTS for planning. It dynamically determines whether to consult a PINN-based simulator (coarse but fast) or engage directly with the actual environment (fine but slow) to determine optimal policy. Evaluation with robots in simulated 3D environments demonstrates the ability of our approach to solve 3D-physical reasoning tasks involving the composition of dynamic skills. Quantitatively, PhyPlan excels in several aspects: (i) it achieves lower regret when learning novel tasks compared to state-of-the-art, (ii) it expedites skill learning and enhances the speed of physical reasoning, (iii) it demonstrates higher data efficiency compared to a physics un-informed approach.
LuaTaint: A Static Taint Analysis System for Web Interface Framework Vulnerability of IoT Devices

IoT devices are currently facing continuous malicious attacks due to their widespread use. Among these IoT devices, web vulnerabilities are also widely exploited because of their inherent characteristics, such as improper permission controls and insecure interfaces. Recently, the embedded system web interface framework has become highly diverse, and specific vulnerabilities can arise if developers forget to detect user input parameters or if the detection process is not strict enough. Therefore, discovering vulnerabilities in the web interfaces of IoT devices accurately and comprehensively through an automated method is a major challenge. This paper aims to work out the challenge. We have developed an automated vulnerability detection system called LuaTaint for the typical web interface framework, LuCI. The system employs static taint analysis to address web security issues on mobile terminal platforms to ensure detection coverage. It integrates rules pertaining to page handler control logic within the taint detection process to improve its extensibility. We also implemented a post-processing step with the assistance of large language models to enhance accuracy and reduce the need for manual analysis. We have created a prototype of LuaTaint and tested it on 92 IoT firmwares from 8 well-known vendors. LuaTaint has discovered 68 unknown vulnerabilities.
Fusion Encoder Networks

In this paper we present fusion encoder networks (FENs): a class of algorithms for creating neural networks that map sequences to outputs. The resulting neural network has only logarithmic depth (alleviating the degradation of data as it propagates through the network) and can process sequences in linear time (or in logarithmic time with a linear number of processors). The crucial property of FENs is that they learn by training a quasi-linear number of constant-depth feed-forward neural networks in parallel. The fact that these networks have constant depth means that backpropagation works well. We note that currently the performance of FENs is only conjectured as we are yet to implement them.
Cooperation and Control in Delegation Games

Many settings of interest involving humans and machines -- from virtual personal assistants to autonomous vehicles -- can naturally be modelled as principals (humans) delegating to agents (machines), which then interact with each other on their principals' behalf. We refer to these multi-principal, multi-agent scenarios as delegation games. In such games, there are two important failure modes: problems of control (where an agent fails to act in line their principal's preferences) and problems of cooperation (where the agents fail to work well together). In this paper we formalise and analyse these problems, further breaking them down into issues of alignment (do the players have similar preferences? ) and capabilities (how competent are the players at satisfying those preferences? ). We show -- theoretically and empirically -- how these measures determine the principals' welfare, how they can be estimated using limited observations, and thus how they might be used to help us design more aligned and cooperative AI systems.
Experimental identification of force, velocity, and nematic order relationships in active nematic cell monolayers

Cell alignment often forms nematic order, which can lead to anomalous collective cell flow due to the so-called active force. Although it is appreciated that cell migration is driven by traction force, a quantitative evaluation of the relationships between the traction force, the nematic patterning, and the cell flow velocity is still elusive. Here we have found that cellular traction force aligns almost perfectly and is proportional in amplitude to the gradient of the nematic order tensor, not only near the topological defects but also globally. Furthermore, the flow in the monolayer was best described by adding nonlinear forces and a diffusion term derived from symmetry considerations. These nonlinear active forces enhance density instability but suppress bending instability, explaining why cell accumulation and dispersion can occur in neural progenitor cell culture while their ordering pattern is stable.
HSONet: A Siamese foreground association-driven hard case sample optimization network for high-resolution remote sensing image change detection

In the later training stages, further improvement of the models ability to determine changes relies on how well the change detection (CD) model learns hard cases; however, there are two additional challenges to learning hard case samples: (1) change labels are limited and tend to pointer only to foreground targets, yet hard case samples are prevalent in the background, which leads to optimizing the loss function focusing on the foreground targets and ignoring the background hard cases, which we call imbalance. (2) Complex situations, such as light shadows, target occlusion, and seasonal changes, induce hard case samples, and in the absence of both supervisory and scene information, it is difficult for the model to learn hard case samples directly to accurately obtain the feature representations of the change information, which we call missingness. We propose a Siamese foreground association-driven hard case sample optimization network (HSONet). To deal with this imbalance, we propose an equilibrium optimization loss function to regulate the optimization focus of the foreground and background, determine the hard case samples through the distribution of the loss values, and introduce dynamic weights in the loss term to gradually shift the optimization focus of the loss from the foreground to the background hard cases as the training progresses. To address this missingness, we understand hard case samples with the help of the scene context, propose the scene-foreground association module, use potential remote sensing spatial scene information to model the association between the target of interest in the foreground and the related context to obtain scene embedding, and apply this information to the feature reinforcement of hard cases. Experiments on four public datasets show that HSONet outperforms current state-of-the-art CD methods, particularly in detecting hard case samples.
Induced moduli oscillation by radiation and space expansion in a higher-dimensional model

We investigate the cosmological expansion of the 3D space in a 6D model compactified on a sphere, beyond the 4D effective theory analysis. We focus on a case that the initial temperature is higher than the compactification scale. In such a case, the pressure for the compact space affects the moduli dynamics and induces the moduli oscillation even if they are stabilized at the initial time. Under some plausible assumptions, we derive the explicit expressions for the 3D scale factor and the moduli background in terms of analytic functions. Using them, we evaluate the transition times between different cosmological eras as functions of the model parameters and the initial temperature.