Miracle-dz/UniFAR-FTUs huggingface dataset:
CTPP: A Fast and Stealth Algorithm for Searching Eviction Sets on Intel Processors

Eviction sets are essential components of the conflict-based cache side-channel attacks. However, it is not an easy task to construct eviction sets on modern Intel processors. As a promising defense against conflict-based cache side-channels, dynamic cache randomization makes the construction of eviction sets even more difficult by periodically randomizing the mapping between addresses and cache set indices. It forces attackers to develop fast search algorithms to find an eviction set at runtime with the lowest latency. Several fast search algorithms have been proposed in recent years. By using these algorithms, attackers regain the capability of launching useful attacks on dynamically randomized caches. Consequently, a detector was recently introduced to catch the fast search algorithms in action according to the uneven distribution of cache evictions. All existing fast search algorithms fail to work. We present a new eviction set search algorithm called Conflict Testing with Probe+Prune (CTPP). Based on the evaluation on six Intel processors and a behavioral cache model, CTPP is found to achieve the lowest latency in finding an eviction set in all algorithms, potentially escape from the recently proposed detector, and present a strong tolerance to environmental noise.
VisuaLLM: Easy Web-based Visualization for Neural Language Generation

VisuaLLM is a Python library that enables interactive visualization of common tasks in natural language generation with pretrained language models (using HuggingFace’s model API), with tight integration of benchmark datasets and fine-grained generation control. The system runs as a local generation backend server and features a web-based frontend, allowing simple interface configuration by minimal Python code. The currently implemented views include data visualization, next-token prediction with probability distributions, and decoding parameter control, with simple extension to additional tasks.
Reference Resolution and New Entities in Exploratory Data Visualization: From Controlled to Unconstrained Interactions with a Conversational Assistant

In the context of data visualization, as in other grounded settings, referents are created by the task the agents engage in and are salient because they belong to the shared physical setting. Our focus is on resolving references to visualizations on large displays; crucially, reference resolution is directly involved in the process of creating new entities, namely new visualizations. First, we developed a reference resolution model for a conversational assistant. We trained the assistant on controlled dialogues for data visualizations involving a single user. Second, we ported the conversational assistant including its reference resolution model to a different domain, supporting two users collaborating on a data exploration task. We explore how the new setting affects reference detection and resolution; we compare the performance in the controlled vs unconstrained setting, and discuss the general lessons that we draw from this adaptation.
Retrieval, Masking, and Generation: Feedback Comment Generation using Masked Comment Examples

In this paper, we propose a novel method, retrieval, masking, and generation, for feedback comment generation. Feedback comment generation is a task in which a system generates feedback comments such as hints or explanatory notes for language learners, given input text and position showing where to comment. In the conventional study, the retrieve-and-edit method for retrieving feedback comments in the data pool and editing the comments has been thought effective for this task. However, the performance of this method does not perform as well as other conventional methods because its model learns to edit tokens that do not need to be rewritten in the retrieved comments. To mitigate this problem, we propose a method for combining retrieval, masking, and generation based on the retrieve-and-edit method. Specifically, tokens of feedback comments retrieved from the data pool are masked, and this masked feedback comment is used as a template to generate feedback comments. The proposed method should prevent unnecessary conversion by using not retrieved feedback comments directly but masking them. Our experiments on feedback comment generation demonstrate that the proposed method outperforms conventional methods.
A Difference-aware Ensemble Method for Task-oriented Dialogue with Subjective Knowledge

We participate in the 11th Dialog System Technology Challenges (DSTC) track-5 called Task-oriented Conversational Modeling with Subjective Knowledge. Introducing subjective knowledge into task-oriented dialogue (TOD) can help the DS to understand variables of subjective user needs and to suit more dialogue scenarios. Track-5 includes several sub-tasks: 1) knowledge-seeking turn detection; 2) knowledge entity tracking; 3) knowledge entry selection; and 4) use of the selected knowledge entries for response generation. Besides the challenges of each sub-tasks own, there are two challenges across different sub-tasks. The first is that there are multiple valid knowledge entries for each knowledge-seeking turn, the accuracy of the knowledge entry selection is important for the quality of response generation. The second challenge is how to address the unseen dialogue/entities/entries in the validation and the test set. In this paper, we propose a difference-aware ensemble method to address these sub-tasks and the two challenges mentioned above. Our method helps to obtain more robust results and performs well on unseen instances. Among all the submissions for the test set, our method ranks 1st on the knowledge-seeking turn detection task and achieves 3rd on the overall automatic evaluation score. Our code and data will be released on GitHub.
Computational Analysis of Backchannel Usage and Overlap Length in Autistic Children

Backchanneling (e.g., right , okay , uhhuh ) during a dialogue signals that a person is engaged and following along with what is being said. Although backchannels often overlap with other utterances, they are not interpreted as an attempt to take the floor when used successfully. Limited work has been done on investigating the frequency and overlap length of backchan-nels in the language of Autistic children. After controlling for age, sex, and IQ, we found that Autistic children used significantly less backchannels than their Typically Developing (TD) peers. Additionally, we found that Autistic children were less likely than TD children to produce a backchannel with a greater overlap length.
Textual Evidence Extraction for ESG Scores

With the growing importance of environmental, social, and governance (ESG) information, ESG scores, which have been rated and published by various institutions, are used for investment decisions or corporate evaluation. The evidence for rating high or low ESG scores, however, is often vague and unclear. In this paper, we propose a method to extract the textual evidence of ESG scores by automatically labeling sentences with information related to ESG. Specifically, we constructed two labeling models for ESG and ESG sentiment, and extracted sentences with high confidence levels using the two models. At first, to label ESG-related information, we developed the annotation corpus using Japanese annual securities reports. Then, we constructed the labeling models by fine-tuning a large language model that was pre-trained on financial documents. The experimental results showed that the macro average F1 scores using the BERT model pre-trained on Japanese financial documents, were 0.874 for ESG labeling and 0.797 for ESG sentiment labeling respectively. These values were higher than those obtained using the comparative models that were pre-trained on Wikipedia documents only. We also confirmed that textual evidence for the ESG scores can be effectively extracted for the companies not included in the training dataset.
DCU/TCD-FORGe at WebNLG’23: Irish rules! (WegNLG 2023)

In this paper, we describe the submission of Dublin City University (DCU) and Trinity College Dublin (TCD) for the WebNLG 2023 shared task. We present a fully rule-based pipeline for generating Irish texts from DBpedia triple sets which comprises 4 components: triple lexicalisation, generation of noninflected Irish text, inflection generation, and post-processing.
Visual Question Generation in Bengali

The task of Visual Question Generation (VQG) is to generate human-like questions relevant to the given image. As VQG is an emerging research field, existing works tend to focus only on resource-rich language such as English due to the availability of datasets. In this paper, we propose the first Bengali Visual Question Generation task and develop a novel transformer-based encoder-decoder architecture that generates questions in Bengali when given an image. We propose multiple variants of models - (i) image-only: baseline model of generating questions from images without additional information, (ii) image-category and image-answer-category: guided VQG where we condition the model to generate questions based on the answer and the category of expected question. These models are trained and evaluated on the translated VQAv2.0 dataset. Our quantitative and qualitative results establish the first state of the art models for VQG task in Bengali and demonstrate that our models are capable of generating grammatically correct and relevant questions. Our quantitative results show that our image-cat model achieves a BLUE-1 score of 33.12 and BLEU-3 score of 7.56 which is the highest of the other two variants. We also perform a human evaluation to assess the quality of the generation tasks. Human evaluation suggests that image-cat model is capable of generating goal-driven and attribute-specific questions and also stays relevant to the corresponding image.
Paella: Low-latency Model Serving with Software-defined GPU Scheduling

Model serving systems play a critical role in multiplexing machine learning inference jobs across shared GPU infrastructure. These systems have traditionally sat at a high level of abstraction---receiving jobs from clients through a narrow API and relying on black-box GPU scheduling mechanisms when dispatching them. Fundamental limitations in the built-in GPU hardware scheduler, in particular, can lead to inefficiency when executing concurrent jobs. The current abstraction level also incurs system overheads that are similarly most significant when the GPU is heavily shared. In this paper, we argue for co-designing the model compiler, local clients, and the scheduler to bypass the built-in GPU scheduler and enable software control of kernel execution order. Doing so enables the use of arbitrary scheduling algorithms and reduces system overheads throughout the critical path of inference.
Modeling Thick Filament Activation Suggests a Molecular Basis for Force Depression

Multiscale models aiming to connect muscle’s molecular and cellular function have been difficult to develop, in part, due to a lack of self-consistent multiscale data. To address this gap, we measured the force response from single skinned rabbit psoas muscle fibers to ramp shortenings and step stretches performed on the plateau region of the force-length relationship. We isolated myosin from the same muscles and, under similar conditions, performed single molecule and ensemble measurements of myosin’s ATP-dependent interaction with actin using laser trapping and in vitro motility assays. We fit the fiber data by developing a partial differential equation model that includes thick filament activation, whereby an increase in force on the thick filament pulls myosin out of an inhibited state. The model also includes a series elastic element and a parallel elastic element. This parallel elastic element models a titin-actin interaction proposed to account for the increase in isometric force following stretch (residual force enhancement). By optimizing the model fit to a subset of our fiber measurements, we specified seven unknown parameters. The model then successfully predicted the remainder of our fiber measurements and also our molecular measurements from the laser trap and in vitro motility. The success of the model suggests that our multiscale data are self-consistent and can serve as a testbed for other multiscale models. Moreover, the model captures the decrease in isometric force observed in our muscle fibers after active shortening (force depression), suggesting a molecular mechanism for force depression, whereby a parallel elastic element combines with thick filament activation to decrease the number of cycling cross-bridges. SIGNIFICANCE Connecting the molecular and cellular scales of muscle contraction would assist in, e.g., the treatment of genetic muscle diseases, the development of heart drugs, and the design of prostheses. The history dependence of muscle contraction, having no clear molecular basis, has remained an obstacle in making this connection for the seventy years since its discovery. We measured the force- and motion-generating capacity of rabbit psoas muscle from the scale of single molecules to single cells. We developed a mathematical model that, when fit to some of the cellular measurements, predicted the remaining cellular measurements and also the molecular measurements. The model’s ability to capture muscle’s history dependence suggests a unified description of muscle contraction from the molecular to cellular scale.
Comparative reservoir competence of inbred Peromyscus leucopus, C57BL/6J, and C3H/HeN for Borrelia burgdorferi B31

The Lyme disease spirochete Borrelia burgdorferi causes a range of acute and chronic maladies in humans and other incidental hosts infected with the pathogen. However, a primary vertebrate reservoir, Peromyscus leucopus, is reported not to have reduced fitness following infection. One hypothesis for this observation is that P. leucopus and B. burgdorferi exist symbiotically: P. leucopus restrain their immune response against the microbe and enable the enzootic cycle while B. burgdorferi avoids causing damage to the host. While aspects of this hypothesis have been tested, the exact interactions that occur between P. leucopus and B. burgdorferi during infection remain understudied. Here we compared infection of P. leucopus with B. burgdorferi B31 with infection of the traditional B. burgdorferi murine models—C57BL/6J and C3H/HeN Mus musculus, which develop signs of inflammation akin to human disease. We find that B. burgdorferi were able to reach much higher burdens in M. musculus, and that the overall kinetics of infection differed between the two rodent species. We also found that P. leucopus remained infectious to larval Ixodes scapularis for a far shorter period than either M. musculus strain. In line with these observations, we found that P. leucopus does launch a modest but sustained inflammatory response against B. burgdorferi in the skin, which we hypothesize leads to reduced bacterial viability and infectivity in these hosts. Similarly, we also observe evidence of inflammation in infected P. leucopus hearts. These observations provide new insight into reservoir species and the B. burgdorferi enzootic cycle. Author Summary The bacteria that cause Lyme disease, Borrelia burgdorferi, must alternate between infecting a vertebrate host—usually rodents or birds—and ticks. In order to be successful in that endeavor the bacteria must avoid being killed by the vertebrate host before it can infect a new larval tick. In this work we examine how B. burgdorferi and one of its primary vertebrate reservoirs, Peromyscus leucopus, interact during an experimental infection. We find that B. burgdorferi appear to colonize its natural host less successfully than conventional laboratory mouse models which aligns with a sustained seemingly anti-bacterial response by P. leucopus against the microbe. These data question long-held assumptions about P. leucopus immunology and could potentially serve as a foundation to uncover ways to disrupt the spread of B. burgdorferi in nature.
Physical influences on phytoplankton ecology : models and observations

The physical environment in the oceans dictates not only how phytoplankton cells are dispersed and their populations intermingled, but also mediates the supply of nutrients to the surface mixed layer. In this thesis I explore both of these aspects of the interaction between phytoplankton ecology and ocean physics, and have approached this topic in two distinct but complementary ways, working with a global ocean ecosystem model, and collecting data at sea. In the first half of the thesis, I examine the role of mesoscale physical features in shaping phytoplankton community structure and influencing rates of primary production. I compare the output of a complex marine ecosystem model coupled to coarse resolution and eddy-permitting physical models. Explicitly resolving eddies resulted in marked regional variations in primary production, zooplankton and phytoplankton biomass. The same phytoplankton phenotypes persisted in both cases, and were dominant in the same regions. Global phytoplankton diversity was unchanged. However, levels of local phytoplankton diversity were markedly different, with a large increase in local diversity in the higher resolution model. Increased diversity could be attributed to a combination of enhanced dispersal, environmental variability and nutrient supply in the higher resolution model. Diversity "hotspots" associated with western boundary currents and coastal upwelling zones are sustained through a combination of all of these factors. In the second half of the thesis I describe the results of a fine scale ecological and biogeochemical survey of the Kuroshio Extension Front. I found fine scale patterns in physical, chemical and biological properties that can be linked back to both the large scale horizontal and smaller scale vertical physical dynamics of the study region. A targeted genomic analysis of samples focused on the ecology of the picoeukaryote Ostreococcus clade distributions strongly supports the model derived hypotheses about the mechanisms supporting diversity hotspots. Strikingly, two distinct clades of Ostreococcus co-occur in more than half of the samples. A "hotspot" of Ostreococcus diversity appears to be supported by a confluence of water masses containing either clade, as well as a local nutrient supply at the front and the mesoscale variability of the region.
UX Study for Origami-Inspired Foldable Robotic Mechanisms

Origami-inspired robotic mechanisms have unique advantages due to their flexibility, softness, and easy fabrication, and their great potential to democratize the wearable computing in human-computer interaction (HCI). It is interesting and intriguing to explore the functionality and user experience of the origami-inspired foldable mechanisms in HCI field. In this work, we focus on the fabrication process of origami robotic mechanism and conduct a user study on the user experience of the process as an interaction design. Especially, we adopt three typical origami robotic mechanisms with cylindrical structures: Twisted tower, Yoshimura pattern, and Kresling pattern. We standardize the fabrication processes and confirm the robotic functions of these origami structures. We ask participants to join the user study to practice origami folding for these three structures. From this study, we found the trade-off relationships among the three structures in aspects of ease of use, fabrication time, and functionality. We believe that this work can stimulate research interest in soft origami-inspired structures in HCI community.
Extracting Threat Intelligence From Cheat Binaries For Anti-Cheating

Rampant cheating remains a serious concern for game developers who fear losing loyal customers and revenue. While numerous anti-cheating techniques have been proposed, cheating persists in a vibrant (and profitable) illicit market. Inspired by novel insights into the economics behind cheat development and recent techniques for defending against advanced persistent threats (APTs), we propose a fully automated methodology for extracting “cheat intelligence” from widely distributed cheat binaries to produce a “memory access graph” that guides selective data randomization to yield immune game clients. We have implemented a prototype system for Android and Windows games, CheatFighter, and evaluated it on 86 cheats collected from a variety of real-world sources, including Telegram channels and online forums. CheatFighter successfully counteracts 80 of the real-world cheats in under a minute, demonstrating practical end-to-end protection against widespread cheating.
Apparent singularities of the finite-depth Zakharov equation

Abstract The description of weakly nonlinear water-wave evolution over a horizontal bottom by the integro-differential Zakharov equation, because of utilising the underlying Hamiltonian structure, has many advantages over direct use of the Euler equations. However, its application to finite-depth situations is not straightforward since, in contrast to the deep-water case, the kernels governing the four-wave interactions are singular, as well as the kernels in the canonical transformation that removes non-resonant interactions from the original equations of motion. At the singularities, these kernels are finite but not unique. The issue of how to use the Zakharov equation for finite depth and whether it is possible at all was debated intensely in the literature for decades but remains outstanding. Here we show that the absence of a limit of the kernels at the singularities is inconsequential, since in the equations of motion it is only the integral that matters. By applying the definition of the Dirac-$\delta$, we show that all the integrals involving a trivial manifold singularity are evaluated uniquely. Therefore, the Zakharov evolution equation and the nonlinear canonical transformation are only apparently singular. The findings are validated by application to examples where predictions based on the Zakharov equation are compared with known solutions obtained from the Euler equations.