Miracle-dz/UniFAR-FTUs huggingface dataset:
Programming with Aspectual Components

Aspect-oriented programming (AOP) controls tangling of concerns by isolating aspects that cross-cut each other into building blocks. Component-based programming (CBP) supports software development by isolating reusable building blocks that can be assembled and connected in many different ways. We show how AOP and CBP can be integrated by introducing a new component construct for programming class collaborations, called aspectual component. Aspectual components extend adaptive plug-and-play components (AP&P) with a modification interface that turns them into an effective tool for AOP. A key ingredient of aspectual components is that they are written in terms of a generic data model, called a participant graph, which is later mapped into a data model. We introduce a new property of this map, called instance-refinement, to ensure the proper deployment of components. We show how aspectual components can be implemented in Java, and demonstrate that aspectual components improve the AspectJ language for AOP from Xerox PARC.
Providing early warning for ﬂooding in the Yellowstone National Park using ambient seismic noise

We utilize twelve-year ambient seismic noise (ASN) recordings to measure near-surface seismic velocity variations ( dv/v ) within the YNP ﬂooding watersheds. We have observed that the annual peaks of dv/v variations have two to three months lead-time in comparison to anomalous water discharges. Our analysis indicates that the annual cycle of dv/v is highly correlated with the eﬀective precipitation, which takes into account the snowpack loading and melting water inﬁltration processes. The annual peaks of dv/v are likely caused by abnormal melt water, which exceed the water storage capacity of the land. Furthermore, the best-ﬁt poroelastic model provides further evidence that precipitation factors primarily inﬂuence the variations of the seismic ﬁeld. Our analysis successfully explains the indicative changes in dv/v prior to the 2014 and 2022 YNP ﬂoods. This study demonstrates the potential of using seismic observables to monitor
Experimental implementation of an efﬁcient test of quantumness

A test of quantumness is a protocol where a classical user issues challenges to a quantum device to determine if it exhibits nonclassical behavior, under certain cryptographic assumptions. Recent attempts to implement such tests on current quantum computers rely on either interactive challenges with efﬁcient veriﬁcation or noninteractive challenges with inefﬁcient (exponential time) veriﬁcation. In this paper, we execute an efﬁcient noninteractive test of quantumness on an ion-trap quantum computer. Our results signiﬁcantly exceed the bound for a classical device’s success.
Design Choices for Enhancing Noisy Student Self-Training

Semi-supervised learning approaches train on small sets of labeled data in addition to large sets of unlabeled data. Self-training is a semi-supervised teacher-student approach that often suffers from “confirmation bias” that occurs when the student model repeatedly overfits to incorrect pseudo-labels given by the teacher model for the unlabeled data. This bias impedes improvements in pseudo-label accuracy across self-training iterations, leading to unwanted saturation in model performance after just a few iterations. In this work, we study multiple design choices to improve the Noisy Student self-training pipeline and reduce confirmation bias. We showed that our proposed Weighted SplitBatch Sampler and Dataset-Adaptive Techniques for Model Calibration and Entropy-Based Pseudo-Label Selection provided performance gains over existing design choices across multiple datasets. Finally, we also study the extendability of our enhanced approach to Open Set unlabeled data (containing classes not seen in labeled data). The source code can be licensed for use via email.
The optical flow-based analysis of human behavior-specific system

To detect illegal behaviors, cameras have to be mounted in public space to analyze human behavior to determine whether it is illegal. In this thesis, we focus on detecting smoking and drinking in certain public spaces as the illegal behaviors. Comparing with other works, our work does not need to establish the background in advance to classify human behavior. The proposed system consists of four modules including face region extraction, multiple hand samples extraction, features extraction, and behavior analysis. We extract three features from each human behavior. They are the touching time between the face and hand samples, smoke detection, and handheld object detection. Then a decision tree is employed to classify the human behavior by using the extracted three features. Experimental result demonstrates that the proposed method can be suited and successfully applied in many environments under various conditions, such as different illumination intensities, different backgrounds, and the different habits exhibited by human.
Beyond Performance: Quantifying and Mitigating Label Bias in LLMs

Large language models (LLMs) have demon-001 strated impressive adaptability to diverse tasks, 002 by relying only on context prompts contain-003 ing instructions, or minimal input-output ex-004 amples. However, recent work revealed they 005 also exhibit label bias —an undesirable pref-006 erence toward predicting certain answers over 007 others. Still, detecting and measuring this bias 008 reliably and at scale has remained relatively 009 unexplored. In this study, we evaluate differ-010 ent approaches to quantifying label bias in a 011 model’s predictions, conducting a comprehen-012 sive investigation across 279 classification tasks 013 and ten LLMs. Our investigation reveals sub-014 stantial label bias in models both before and af-015 ter debiasing attempts, as well as highlights the 016 importance of outcomes-based evaluation met-017 rics, which were not previously used in this re-018 gard. We further propose a novel label bias cal-019 ibration method tailored for few-shot prompt-020 ing, which outperforms recent calibration ap-021 proaches for both improving performance and 022 mitigating label bias. Nevertheless, our results 023 emphasize that label bias in the predictions of 024 LLMs remains a barrier to their reliability. 1 025
The Hubble Flow around the Centaurus A/M83 Galaxy Complex

We present Hubble Space Telescope ACS images and color-magnitude diagrams for 24 nearby galaxies in and near the constellation of Centaurus with radial velocities VLG < 550 km s-1. Distances are determined based on the luminosities of stars at the tip of the red giant branch that range from 3.0 to 6.5 Mpc. The galaxies are concentrated in two spatially separated groups around Cen A (NGC 5128) and M83 (NGC 5236). The Cen A group itself has a mean distance of 3.76 ± 0.05 Mpc, a velocity dispersion of 136 km s-1, a mean harmonic radius of 192 kpc, and an estimated orbital/virial mass of (6.4-8.1) × 1012 M⊙. This elliptical-dominated group is found to have a relatively high mass-to-light ratio: M/LB = 125 M⊙/L⊙. For the M83 group we derived a mean distance of 4.79 ± 0.10 Mpc, a velocity dispersion of 61 km s-1, a mean harmonic radius of 89 kpc, and an estimated orbital/virial mass of (0.8-0.9) × 1012 M⊙. This spiral-dominated group is found to have a relatively low M/LB = 34 M⊙/L⊙. The radius of the zero-velocity surface around Cen A lies at R0 = 1.40 ± 0.11 Mpc, implying a total mass within R0 of MT = (6.0 ± 1.4) × 1012 M⊙. This value is in good agreement with the Cen A orbital/virial mass estimates and provides confirmation of the relatively high M/LB of this elliptical-dominated group. The centroids of both groups, as well as surrounding field galaxies, have very small peculiar velocities, <25 km s-1, with respect to the local Hubble flow with H0 = 68 km s-1 Mpc-1.
Herd immunity: basic concept and relevance to public health immunization practices.

Specific immunization has long been a basic tool in medical practice and in local, state, national and international public health. However, the protection afforded by such immunization has been largely limited to the persons immunized because: 1) the usual sources of infection are extra-human (tetanus, yellow fever); 2) spread is primarily by indirect means (typhoid); or 3) those immunized may remain at least partially susceptible to infection and, hence, continue as potential links in the future spread of agents transmitted by contact (diphtheria, pertussis, Salk-type inactivated polio vaccine). An important exception has been vaccination against smallpox which does induce solid though temporary resistance to infection. Presumably as the result of systematic vaccination, endemic smallpox has disappeared from large areas
An Overview of Aquaponic Systems: Aquaculture Components

loop system, nutrient effluence is virtually non-existent, allowing agriculture to take a large step toward environmental sustainability. Moreover, fish, plant, and waste solids can all be captured and converted into fertilizer products for additional sale. These benefits make aquaponics a viable option for gardeners and producers who have limited space or reside in cities, giving more people access to locally-produced, healthy foods.
Ieee Transactions on Image Processing: to Appear Penalized Maximum-likelihood Image Reconstruction Using Space-alternating Generalized Em Algorithms

|Most expectation-maximization (EM) type algorithms for penalized maximum-likelihood image reconstruction converge slowly, particularly when one incorporates additive background eeects such as scatter, random coincidences , dark current, or cosmic radiation. In addition, regularizing smoothness penalties (or priors) introduce parameter coupling, rendering intractable the M-steps of most EM-type algorithms. This paper presents space-alternating generalized EM (SAGE) algorithms for image reconstruction , which update the parameters sequentially using a sequence of small \hidden" data spaces, rather than simultaneously using one large complete-data space. The sequential update decouples the M-step, so the maximization can typically be performed analytically. We introduce new hidden-data spaces that are less informative than the conventional complete-data space for Poisson data and that yield significant improvements in convergence rate. This acceleration is due to statistical considerations, not numerical overre-laxation methods, so monotonic increases in the objective function are guaranteed. We provide a general global convergence proof for SAGE methods with nonnegativity constraints .
Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Using Neuro Fuzzy Modeling

Electromagnetic interference effects of transmission lines on nearby metallic structures such as pipelines, communication lines, or railroads are a real problem, which can place both operator safety and structure integrity at risk. The level of these voltages can be reduced to a safe value in accordance with the IEEE standard 80 by designing a proper mitigation system. This paper presents a Fuzzy algorithm that can predict the level of the metallic conductor voltage. The model outlined in this paper is both fast and accurate and can accurately predict the voltage magnitude even with changing system parameters (soil resistivity, fault current, separation distance, mitigated or unmitigated system). Simulation results for three different scenarios, confirm the capability of the proposed Fuzzy system model in modeling and predicting the total voltage and are found to be in good agreement with data obtained from the CDEGS software.
Effects of high hydrostatic pressure on Clostridium sporogenes spores

Spores of Clostridium sporogenes were found to be resistant to ultra high pressure, with treatments of 600 MPa for 30 min at 20 °C causing no significant inactivation. Combination treatments including heat and pressure applied simultaneously (e.g. 400 MPa at 60 °C for 30 min) or sequentially (e.g. 80 °C for 10 min followed by 400 MPa for 30 min) proved more effective at inactivating spores. Pressure cycling (e.g. 60 MPa followed by 400 MPa at 60 °C) also reduced spore numbers. Overall, these pressure treatments resulted in less than a 3 log reduction, and it was concluded that the spores could not be inactivated by pressure alone. This could indicate that for the effective inactivation of bacterial spores, high pressure technology may have to be used in combination with other preservation methods.
This paper is included in the Proceedings of the 32nd USENIX Security Symposium.

Over 33% of vehicles sold in 2021 had integrated autonomous driving (AD) systems. While many adversarial machine learning attacks have been studied against these systems, they all require an adversary to perform speciﬁc (and often unrealistic) actions, such as carefully modifying trafﬁc signs or projecting malicious images, which may arouse suspicion if discovered. In this paper, we present A CERO , a robustness-guided framework to discover adversarial maneuver attacks against autonomous vehicles (AVs). These maneuvers look innocent to the outside observer but force the victim vehicle to violate safety rules for AVs, causing physical consequences, e.g., crashing with pedestrians and other vehicles. To optimally ﬁnd adversarial driving maneuvers, we formalize seven safety requirements for AD systems and use this formalization to guide our search. We also formalize seven physical constraints that ensure the adversary does not place themselves in danger or violate trafﬁc laws while conducting the attack. A CERO then leverages trajectory-similarity metrics to cluster successful attacks into unique groups, enabling AD developers to analyze the root cause of attacks and mitigate them. We evaluated A CERO on two open-source AD software, openpilot and Autoware, running on the CARLA simulator. A CERO discovered 219 attacks against openpilot and 122 attacks against Autoware. 73 . 3% of these attacks cause the victim to collide with a third-party vehicle, pedestrian, or static object.
Seasonal Prediction of Korean Surface Temperature in July and February Based on Arctic Sea Ice Reduction

We examined potential seasonal prediction of the Korean surface temperature using the relationships between the Arctic Sea Ice Area (SIA) in autumn and the temperature in the following July and February at 850 hPa in East Asia (EA). The Surface Air Temperature (SAT) over Korea shows a similar relationship to that for EA. Since 2007, reduction of autumn SIA has been followed by warming in Korea in July. The regional distribution shows strong correlations in the southern and eastern coastal areas of Korea. The correlations in the sea surface temperature shows the maximum values in July around the Korean Peninsula, consistent with the coastal regions in which the maximum correlations in the Korean SAT are seen. In February, the response of the SAT to the SIA is the opposite of that for the July temperature. The autumn sea ice reduction is followed by cooling over Korea in February, although the magnitude is small. Cooling in the Korean Peninsula in February may be related to planetary wave-like features. Examining the autumn Arctic sea ice variation would be helpful for seasonal prediction of the Korean surface temperature, mostly in July and somewhat in February. Particularly in July, the regression line would be useful as supplementary information for seasonal temperature prediction.
Large‐scale analysis of Drosophila core promoter function using synthetic promoters

The core promoter plays a central role in setting metazoan gene expression levels, but how exactly it “computes” expression remains poorly understood. To dissect its function, we carried out a comprehensive structure–function analysis in Drosophila. First, we performed a genome‐wide bioinformatic analysis, providing an improved picture of the sequence motifs architecture. We then measured synthetic promoters’ activities of ~3,000 mutational variants with and without an external stimulus (hormonal activation), at large scale and with high accuracy using robotics and a dual luciferase reporter assay. We observed a strong impact on activity of the different types of mutations, including knockout of individual sequence motifs and motif combinations, variations of motif strength, nucleosome positioning, and flanking sequences. A linear combination of the individual motif features largely accounts for the combinatorial effects on core promoter activity. These findings shed new light on the quantitative assessment of gene expression in metazoans.
Discovering Large Dense Subgraphs in Massive Graphs

We present a new algorithm for finding large, dense subgraphs in massive graphs. Our algorithm is based on a recursive application of fingerprinting via shingles, and is extremely efficient, capable of handling graphs with tens of billions of edges on a single machine with modest resources.We apply our algorithm to characterize the large, dense subgraphs of a graph showing connections between hosts on the World Wide Web; this graph contains over 50M hosts and 11B edges, gathered from 2.1B web pages. We measure the distribution of these dense subgraphs and their evolution over time. We show that more than half of these hosts participate in some dense subgraph found by the analysis. There are several hundred giant dense subgraphs of at least ten thousand hosts; two thousand dense subgraphs at least a thousand hosts; and almost 64K dense subgraphs of at least a hundred hosts.Upon examination, many of the dense subgraphs output by our algorithm are link spam, i.e., websites that attempt to manipulate search engine rankings through aggressive interlinking to simulate popular content. We therefore propose dense subgraph extraction as a useful primitive for spam detection, and discuss its incorporation into the workflow of web search engines.
Pervasive and Mobile Computing

The push to a connected world where people carry an always-online device which has been designed to maximise instant gratification and prompts users via notifications has lead to a surge of potentially problematic behaviour as a result. This has lead to a rising interest in addressing and understanding the addictiveness of smartphone usage, as well as for particular applications (apps). However, capturing addiction from usage involves not only assessment of potential addiction risk but also requires understanding of the complex interactions that define user behaviour and how these can be effectively isolated and summarised. In this paper, we examine the correlation of physical user interface (UI) interactions (e.g. taps and scrolls) and smartphone addiction risk using a large dataset of those smartphone events (65,093,343, N=301,024 sessions) collected from 64 users over an 8-week period with an accompanying smartphone addiction survey. Our novel method which reports on the probability of a users addiction risk and in a model case we show how it was be used to identify 57 of 64 users correctly. This supports our observations of UI events during sessions of usage being indicative of addiction risk while improving previous approaches which rely on summative data such as screen on time. Within this we also find that users only exhibit addictive behaviour in a subset of all sessions while using their smartphone.
B-Fetch: Branch Prediction Directed Prefetching for Chip-Multiprocessors

For decades, the primary tools in alleviating the "Memory Wall" have been large cache hierarchies and dataprefetchers. Both approaches, become more challenging in modern, Chip-multiprocessor (CMP) design. Increasing the last-level cache (LLC) size yields diminishing returns in terms of performance per Watt, given VLSI power scaling trends, this approach becomes hard to justify. These trends also impact hardware budgets for prefetchers. Moreover, in the context of CMPs running multiple concurrent processes, prefetching accuracy is critical to prevent cache pollution effects. These concerns point to the need for a light-weight prefetcher with high accuracy. Existing data prefetchers may generally be classified as low-overhead and low accuracy (Next-n, Stride, etc.) or high-overhead and high accuracy (STeMS, ISB). Wepropose B-Fetch: a data prefetcher driven by branch prediction and effective address value speculation. B-Fetch leverages control flow prediction to generate an expected future path of the executing application. It then speculatively computes the effective address of the load instructions along that path based upon a history of past register transformations. Detailed simulation using a cycle accurate simulator shows a geometric mean speedup of 23.4% for single-threaded workloads, improving to 28.6% for multi-application workloads over a baseline system without prefetching. We find that B-Fetch outperforms an existing "best-of-class" light-weight prefetcher under single-threaded and multi programmed workloads by 9% on average, with 65% less storage overhead.
MAdVerse: A Hierarchical Dataset of Multi-Lingual Ads from Diverse Sources and Categories

The convergence of computer vision and advertising has sparked substantial interest lately. Existing advertisement datasets are either subsets of existing datasets with specialized annotations or feature diverse annotations without a cohesive taxonomy among ad images. Notably, no datasets encompass diverse advertisement styles or semantic grouping at various levels of granularity. Our work addresses this gap by introducing MAdVerse, an extensive, multilingual compilation of more than 50,000 ads from the web, social media websites, and e-newspapers. Advertisements are hierarchically grouped with uniform granularity into 11 categories, divided into 51 sub-categories
Retrieval-based Language Models Using a Multi-domain Datastore

Retrieval-based language models (LMs) can generalize well to unseen test domains, but typically assume access to a datastore of examples from the target domain. It remains an open question if these models are robust with more general datastores, which may include other out of domain data or cover multiple different test domains. In this paper, we study this question by constructing a multi-domain datastore, using a k NN-LM approach. We first show that, on domains that are part of the multi-domain datastore, the model is comparable to or even better than the model with an oracle test domain datastore. We also find that, on domains that are unseen during training and not part of the datastore, using a multi-domain datastore consistently outperforms an oracle single-domain datastore. Together, our results show that k NN-LM is highly robust at out-of-distribution generalization and can effectively target many domains at once, without the oracle domain knowledge assumptions included in all previous work.
Performance analysis of scalable certificate revocation schemes for ad hoc networks

Certificate revocation is a challenging task, especially in mobile network environments. Several different revocation approaches have been proposed in the literature. However, no performance evaluation and usability studies exist for mobile network environments like ad hoc networks. In this paper a comparison of two different certificate revocation schemes used in the context of ad hoc network scenarios is presented. First the state of the art in certificate revocation is presented and a motivation for its use in the context of ad hoc environments is given. A quick introduction in the theoretical background and overhead calculation of revocation approaches is presented. Further, our two revocation protocols are introduced and simulation results presented. The paper closes with an evaluation and a discussion of the results and gives an outlook on future work