Reviewerly/exHarmony huggingface dataset:
Contract-Oriented Design of Distributed Applications: A Tutorial

Modern distributed applications typically blend new code with legacy (and possibly untrusted) third-party services.Behavioural contracts can be used to discipline the interaction among these services.Contract-oriented design advocates that composition is possible only among services with compliant contracts, and execution is monitored to detect (and possibly sanction) contract breaches.In this tutorial we illustrate a contract-oriented design methodology consisting of five phases: specification writing, specification analysis, code generation, code refinement, and code analysis.Specifications are written in CO 2 , a process calculus whose primitives include contract advertisement, stipulation, and contractual actions.Our analysis verifies a property called honesty: intuitively, a process is honest if it always honors its contracts upon stipulation, so being guaranteed to never be sanctioned at run-time.We automatically translate a given honest specification into a skeletal Java program which renders the contract-oriented interactions, to be completed with the application logic.Then, programmers can refine this skeleton into the actual Java application: however, doing so they could accidentally break its honesty.The last phase is an automated code analysis to verify that honesty has not been compromised by the refinement. Contract-Oriented Design of Distributed Applications: A TutorialAll the phases of our methodology are supported by a toolchain, called Diogenes.We guide the reader through Diogenes to design small contractoriented applications.
Multilayer Network Models for Coordinating Orchestration of Systems Security Engineering

Abstract Systems security engineering (SSE) faces new internal (e.g., increased digitization) and external (e.g., adversary capabilities) obstacles as systems increase in complexity and are deployed to increasingly challenging operating environments. Legacy approaches heavily rely on individual, physical, digital, or personnel domain‐specific strategies for security. Such segmented responses helped initiate efforts by the INCOSE systems security working group to identify fundamental elements of SSE. One of these fundamental elements is security orchestration, where the SSE goal is to coordinate between previously disparate security solutions. Multilayer network‐based approaches seemingly provide the logical structure and mathematical foundation to conduct security orchestration for “tightly coupled coordinated system defense in cyber‐relevant time.” Within multilayer networks, the ability to identify and manipulate cross‐domain (e.g., intralayer) connections that influence security performance measures demonstrates an enhanced level of security orchestration. As such, multilayer networks support the future of SSE efforts to mitigate real‐world complexities, innovative adversaries, and disruptive technologies. After describing security orchestration as a concept and foundational element, this paper explores how multilayer network models can enhance orchestration systems security engineering. Additionally, a demonstration case of systems security for a high consequence facility (as a complex system) is followed insights and implications for incorporating orchestration in the future of systems security.
Intermediate Block Generation for Multi-Track Sign Language Synthesis

Generating realistic Sign Language using signing avatars is a challenging task that typically involves synthesis using either procedural or pre-animated techniques like motion capture or artistic editing of signs. However, combining these two approaches is difficult. In this work, we propose a novel method for generating intermediate poses in a multi-track representation of a sign language discourse. The proposed method uses procedural generation with artistic techniques to prioritize certain aspects of the generated poses while sacrificing others to improve the overall consistency of the representation. The system is implemented as an add-on in Blender, an open-source 3D toolkit.
Analysis of Cultural Experiences before and after COVID-19 among University Students in Gyeongsangnam-do

This study analyzed the degree of impact on the cultural experience of college students before and after COVID-19 and the degree of change in the scope of cultural experience using essays written by college students in Gyeongnam. The purpose of this is to suggest the direction of cultural content development that can be prepared for college students in their 20s in Gyeongnam. As a research method, the text mining method was used as an analysis to extract meaningful keywords related to cultural experiences from essays submitted by students. This study was conducted in Gyeongsangnam-do, and through this, it is to discuss the establishment of a direction for regional cultural development that reflects the actual needs and needs of college students in their 20s living in Gyeongsangnam-do. As a result of the study, first, it is to expand from indoor culture to outdoor culture. Second, it is to expand from offline cultural activities to online cultural activities. Third, it is to strengthen regional-centered cultural life.
Realistic interactions with micro-objects in a virtual environment

The term Metaverse was introduced in 1992. Lately, the concept has become a popular buzzword in the general public, thanks to Meta. And yet, its intrinsic reliance on the convergence between enabling technologies such as virtual reality, the internet, and social networks cannot be understated. It might prove a powerful tool for enhancing data discovery and interpretation, especially considering a collaborative setup. The work presented here aims to investigate the interaction between a real user and digital objects in a virtual world to understand which aspects of attention must be focused on to obtain a natural and comfortable use. Our work involved the generation of Metaballs and the interaction with them by a user in a virtual environment. Metaballs are particular implicit surfaces of arbitrary typology, widely used in Computer Graphics to model curved objects. The ”organic” look and feel of how they interact with each other and their resemblance to soft tissues have proved a natural fit for tasks such as surgery simulation. Still, these implicit surfaces can represent even real objects at an even smaller scale, like cells of living organisms, and the ability to comfortably interact with this supersized version of real objects could open the door to new possibilities.
The influence of stereopsis on visual saliency in a proto-object based model of selective attention

Some animals including humans use stereoscopic vision which reconstructs spatial information about the environment from the disparity between images captured by eyes in two separate adjacent locations. Like other sensory information, such stereoscopic information is expected to influence attentional selection. We develop a biologically plausible model of binocular vision to study its effect on bottom-up visual attention, i.e., visual saliency. In our model, the scene is organized in terms of proto-objects on which attention acts, rather than on unbound sets of elementary features. We show that taking into account the stereoscopic information improves the performance of the model in the prediction of human eye movements with statistically significant differences.
Technology and Innovation in Avatar: The Way of Water

This talk provides a comprehensive look at the innovative technology and workflows developed for Avatar: The Way of Water. The talk will cover the extensive research and development with a focus on three key topics: depth-compositing, the new facial system (APFS) and finally the Loki integrated solver.
Incorporating saturation bands into MR Fingerprinting reduces streaking artefacts

Magnetic Resonance Fingerprinting (MRF) enables fast quantitative MR imaging, which is appealing in prostate cancer diagnostics. In pelvic MR Fingerprinting, blood flow in the femoral vessels causes strong streak-like artefacts when a radial sampling strategy is used. Here we develop a strategy to incorporate saturation bands into MRF sequences. Our phantom study shows quantification of areas outside the saturation band are not significantly affected. Meanwhile, qualitative analysis of in vivo experiments indicates a marked reduction in streak intensity when saturation is applied.
CBV-sensitive layer-fMRI in the human auditory cortex at 7T: Challenges and capabilities

Laminar-specific fMRI with CBV-sensitive VASO is valuable for neuroscientific questions on hierarchical information processing. While VASO fMRI has already proven its utility in other brain areas, it has not yet been successfully applied in the auditory cortex due to several additional technical challenges in this region. Here, we explore multiple sequences and their effectiveness to mitigate these challenges. Our purpose is to develop an experimental setup that future neuroscientific studies can be built on. Ultimately, we found a stable parameter set for the usage of layer-fMRI VASO in the auditory cortex and validated it in a group of participants.
Tennisball: slew-efficient trajectory design for 3D-radial imaging

What is the most efficient path to traverse a sphere? For a spherical gap-minimizing efficiency definition and a particular path length, the answer happens to resemble the seam lines of a tennis ball. Here we explore properties of this general class of spherical trajectories and investigate their application to slew-constrained ZTE imaging. Static and motion-resolved imaging results demonstrate that Tennisball trajectories can achieve competitive performance with existing methods but with ~30-40% lower peak slew.
Combining Shape Descriptor and Image Ranking to the Identification of Alzheimer’s Disease in Anatomical Regions using T1-weighted MR Images

Alzheimer's Disease is one of the most prevalent types of dementia that affects human behaviour and cognitive skills. The progression of this dementia occurs at different rates in distinct parts of the brain, thus also affecting their brain shape. We proposed an approach based on shape feature extraction and image ranking to identify which regions are more predictive for each level of the AD progression. For each stage, we highlighted the sub-cortical regions that suggested a strong correlation in the AD levels. In fact, the results showed a predictive pattern, which aligns to the state-of-the-art.
Free-running Simultaneous 3D Cardiac T1 Mapping and Cine Imaging Using a Linear Tangent Space Alignment Model

Cardiac T1 mapping allows assessment of tissue characteristics and functioning of the heart. However, existing methods are limited in terms of spatial resolution and coverage due to limitations in acquisition speed and the presence of cardiac and respiratory motion. This work proposes a new reconstruction framework for simultaneous, high-resolution 3D cardiac T1 mapping and cine imaging of the heart at 3T from sparsely sampled k-space data.
A proposed method to objectively quantify hippocamapal dentation through midlayer AUC analysis

We present a novel method to objectively quantify a morphological feature known as hippocampal dentation from ultra high resolution 3D T2w images.
Optimal Transport driven Cycle-consistent Generative Adversarial Network (OT-CycleGAN) for an accurate MR water-fat separation

MRI water-fat separation is an ill-posed inverse problem usually addressed using complex numerical methods. This problem is related to an MR signal physical model that includes the effects of R 2 * signal decay ratio and main magnetic field inhomogeneities (&amp;Delta;f), which induce non-linearities that are related to the ill-posedness of the inverse problem. In this work, we propose an Optimal Transport driven Cycle-consistent Generative Adversarial Networks (OT-CycleGAN) framework, which is physics-based, and could use partially labeled training data to estimate the non-linear components (R 2 * and &amp;Delta;f), and posteriorly compute the water-fat images through a conventional least-squares approach.
Teaching Paradigm Reform of Chinese-English Translation Based on PAD Class

In order to address the problems in traditional Chinese-English translation teaching, the study adopts the "condensation-expansion" and "AB double Texts" based on PAD Class teaching model, which reconstructs the traditional Chinese-English translation teaching.The study decodes the Chinese text by analyzing the sentence components; and encodes the English version by determining sentence structure, determining tense and adding modifiers.Text A is used to demonstrate by teachers and Text B is used to practice by learners.The teaching practices from 2020 till now show that the "condensation-expansion" and "AB Double Texts" based on PAD Class teaching model can effectively improve the learners' Chinese-English translation ability, cultivating learners' critical thinking and expressive abilities.
OpenSLU: A Unified, Modularized, and Extensible Toolkit for Spoken Language Understanding

Spoken Language Understanding (SLU) is one of the core components of a task-oriented dialogue system, which aims to extract the semantic meaning of user queries (e.g., intents and slots).In this work, we introduce OpenSLU, an open-source toolkit to provide a unified, modularized, and extensible toolkit for spoken language understanding.Specifically, OpenSLU unifies 10 SLU models for both single-intent and multi-intent scenarios, which support both non-pretrained and pretrained models simultaneously.Additionally, OpenSLU is highly modularized and extensible by decomposing the model architecture, inference, and learning process into reusable modules, which allows researchers to quickly set up SLU experiments with highly flexible configurations.OpenSLU is implemented based on PyTorch, and released at https://github.com/LightChen233/OpenSLU.
xPQA: Cross-Lingual Product Question Answering in 12 Languages

Product Question Answering (PQA) systems are key in e-commerce applications to provide responses to customers' questions as they shop for products.While existing work on PQA focuses mainly on English, in practice there is need to support multiple customer languages while leveraging product information available in English.To study this practical industrial task, we present xPQA, a large-scale annotated cross-lingual PQA dataset in 12 languages across 9 branches, and report results in (1) candidate ranking, to select the best English candidate containing the information to answer a non-English question; and (2) answer generation, to generate a natural-sounding non-English answer based on the selected English candidate.We evaluate various approaches involving machine translation at runtime or offline, leveraging multilingual pre-trained LMs, and including or excluding xPQA training data.We find that (1) In-domain data is essential as cross-lingual rankers trained on other domains perform poorly on the PQA task; (2) Candidate ranking often prefers runtime-translation approaches while answer generation prefers multilingual approaches; (3) Translating offline to augment multilingual models helps candidate ranking mainly on languages with non-Latin scripts; and helps answer generation mainly on languages with Latin scripts.Still, there remains a significant performance gap between the English and the cross-lingual test sets. 1 Question: does the latch come in white?Candidate: it only comes in black … Label: fully answering Answer: no, it comes
Finding words that aren’t there: Using word embeddings to improve dictionary search for low-resource languages

Modern machine learning techniques have produced many impressive results in language technology, but these techniques generally require an amount of training data that is many orders of magnitude greater than what exists for low-resource languages in general, and endangered languages in particular.However, dictionary definitions in a comparatively much more well-resourced majority language can provide a link between low-resource languages and machine learning models trained on massive amounts of majority-language training data.Promising results have been achieved by leveraging these embeddings in the search mechanisms of bilingual dictionaries of Plains Cree (nêhiyawêwin), Arapaho (Hinóno'éitíit), Northern Haida (Xaad Kíl), and Tsuut'ina (Tsúùt'ínà), four Indigenous languages spoken in North America.Not only are the search results in the majority language of the definitions more relevant, but they can be semantically relevant in ways not achievable with classic information retrieval techniques: users can perform successful searches for words that do not occur at all in the dictionary.Not only this, but these techniques are directly applicable to any bilingual dictionary providing translations between a high-and low-resource language.
Eliminating Bottlenecks in GPP Processes: Improving Documentation Flow

conducted by a micro-sequencer core we developed: the Enclosed Loop Micro-Sequencer (ELMS). The unique feature of the ELMS is that it supports the ''FOR'' loops with pre-defined iterations at the machine code level, which provides programming convenience and avoids many micro-complexities from the beginning.
Exploring the Dark Sector in MicroBooNE Through e+e- Final States

interactions in MicroBooNE is a single short proton track. We present our work on the automated reconstruction and classification of proton tracks in LArTPCs, an important step in the determination of neutrino- nucleon cross sections and the measurement of $$\Delta s$$.
Semi-Supervised Few-Shot Object Detection via Adaptive Pseudo Labeling

Few-shot object detection (FSOD) aims to detect novel objects with limited annotated examples. Mainstream methods suffer from the data scarcity of novel classes with insufficient intra-class variations, which makes the trained model biased to base classes. Actually, there are massive unlabeled novel instances in the base dataset and their adequate utilization will enhance the discriminability of model to novel classes. This paper proposes a semi-supervised few-shot object detection method, which utilizes a teacher model and a pre-trained few-shot object detector to guide the learning of a student model through adaptive pseudo labeling. In particular, a class-adaptive threshold filtering (CATF) strategy is designed to deal with the class-imbalance problem of pseudo labels. And for each novel class, the threshold to select valuable pseudo labels is determined by quantile statistics of the confidence score distribution of pseudo labels. Furthermore, the pre-trained detector and the teacher model are associated with the preliminary CATF and in-depth CATF, respectively, and then the pseudo labels from the two-stream CATF are fused to provide supervisions. In this way, the knowledge of these two models is exploited, which improves the quality of pseudo labels. Under these supervisions, the student model is trained and the teacher model is correspondingly updated through parameters sharing, thus forming a positive feedback to improve the performance of both models. Besides, an attention module is integrated to the teacher and student models to enhance the feature representation of novel instances. The validations on PASCAL VOC and MS COCO show the effectiveness of the proposed method.
COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective

Detecting commonsense causal relations (causation) between events has long been an essential yet challenging task.Given that events are complicated, an event may have different causes under various contexts.Thus, exploiting context plays an essential role in detecting causal relations.Meanwhile, previous works about commonsense causation only consider two events and ignore their context, simplifying the task formulation.This paper proposes a new task to detect commonsense causation between two events in an event sequence (i.e., context), called contextualized commonsense causal reasoning.We also design a zero-shot framework: COLA (Contextualized Commonsense Causality Reasoner) to solve the task from the causal inference perspective.This framework obtains rich incidental supervision from temporality and balances covariates from multiple timestamps to remove confounding effects.Our extensive experiments show that COLA 1 can detect commonsense causality more accurately than baselines.Cause 1. Emma felt hungry.Cause 2. Emma was doing her job.Emma made a steak in the kitchen. Context-Free ✅ Which cause is more plausible?Cause 1. Emma felt hungry.Cause 2. Emma was doing her job.Emma made a steak in the kitchen. Context 1: Emma exercised for a while.
Yes, this Way! Learning to Ground Referring Expressions into Actions with Intra-episodic Feedback from Supportive Teachers

The ability to pick up on language signals in an ongoing interaction is crucial for future machine learning models to collaborate and interact with humans naturally.In this paper, we present an initial study that evaluates intraepisodic feedback given in a collaborative setting.We use a referential language game as a controllable example of a task-oriented collaborative joint activity.A teacher utters a referring expression generated by a well-known symbolic algorithm (the "Incremental Algorithm") as an initial instruction and then monitors the follower's actions to possibly intervene with intra-episodic feedback (which does not explicitly have to be requested).We frame this task as a reinforcement learning problem with sparse rewards and learn a follower policy for a heuristic teacher.Our results show that intra-episodic feedback allows the follower to generalize on aspects of scene complexity and performs better than providing only the initial statement.
On Text-based Personality Computing: Challenges and Future Directions

Text-based personality computing (TPC) has gained many research interests in NLP.In this paper, we describe 15 challenges that we consider deserving the attention of the NLP research community.These challenges are organized by the following topics: personality taxonomies, measurement quality, datasets, performance evaluation, modelling choices, as well as ethics and fairness.When addressing each challenge, not only do we combine perspectives from both NLP and social sciences, but also offer concrete suggestions.We hope to inspire more valid and reliable TPC research.
StructSP: Efficient Fine-tuning of Task-Oriented Dialog System by Using Structure-aware Boosting and Grammar Constraints

We have investigated methods utilizing hierarchical structure information representation in the semantic parsing task and have devised a method that reinforces the semantic awareness of a pre-trained language model via a two-step fine-tuning mechanism: hierarchical structure information strengthening and a final specific task.The model used is better than existing ones at learning the contextual representations of utterances embedded within its hierarchical semantic structure and thereby improves system performance.In addition, we created a mechanism using inductive grammar to dynamically prune the unpromising directions in the semantic structure parsing process.Finally, through experiments 1 on the TOP and TOPv2 (low-resource setting) datasets, we achieved state-of-the-art (SOTA) performance, confirming the effectiveness of our proposed model.