Miracle-dz/UniFAR-FTUs huggingface dataset:
Parsing conjunctions in DisCoCirc

In distributional compositional models of meaning logical words require special interpretations, that specify the way in which other words in the sentence interact with each other. So far within the DisCoCat framework, conjunctions have been implemented as merging both conjuncts into a single output, however in the new framework of DisCoCirc merging between nouns is no longer possible. We provide an account of conjunction and an interpretation for the word ‘and’ that solves this, and moreover ensures certain intuitively similar sentences can be given the same interpretations.
Implicit representations of event properties within contextual language models: Searching for “causativity neurons”

This paper addresses the question to which extent neural contextual language models such as BERT implicitly represent complex semantic properties. More concretely, the paper shows that the neuron activations obtained from processing an English sentence provide discriminative features for predicting the (non-)causativity of the event denoted by the verb in a simple linear classifier. A layer-wise analysis reveals that the relevant properties are mostly learned in the higher layers. Moreover, further experiments show that appr. 10% of the neuron activations are enough to already predict causativity with a relatively high accuracy.
Breeding Fillmore’s Chickens and Hatching the Eggs: Recombining Frames and Roles in Frame-Semantic Parsing

Frame-semantic parsers traditionally predict predicates, frames, and semantic roles in a fixed order. This paper explores the ‘chicken-or-egg’ problem of interdependencies between these components theoretically and practically. We introduce a flexible BERT-based sequence labeling architecture that allows for predicting frames and roles independently from each other or combining them in several ways. Our results show that our setups can approximate more complex traditional models’ performance, while allowing for a clearer view of the interdependencies between the pipeline’s components, and of how frame and role prediction models make different use of BERT’s layers.
Vibration transfer analysis of component interfaces by a power flow mode approach

A method has been developed to dynamically characterize complex structures’ interfaces at low frequencies. The aim is to optimize vibration isolation of a main structure subjected at its junctions to forces generated by connected substructures. An eigenvalue problem is formulated by minimizing the average dissipated power flow of the system. Hence, the derived eigenvalues and eigenvectors describe the energy pattern at each given frequency. It is then possible to characterize the real interface forces and, for example, to control them by determining the appropriated external forces to apply to the structure. This method has been studied on an academic system and applied to a simple coupled structure.
When No One is Watching: Ecological Momentary Assessment to Understand Situated Social Robot Use in Healthcare

∗ Socially-Assistive Robots (SARs) hold great potential to revolutionize the way we manage chronic illness outside clinical settings, but a current limitation to their broad adoption for this purpose is the lack of “ground truth” around interactions between robots and humans in in-home settings. Such ground truth is a necessity for using robotic sensor data for machine learning models of patient activity patterns or to create AI to customize robotic interactive behavior autonomously. Traditional subjective recall-based data collection methods lack the fine-grained temporal detail to support such AI development, as well as suffering from “recall bias” effects. One potential solution to this challenge is to adapt novel forms of interaction assessment, such as ecological momentary assessment (EMA), to collect patient interaction data in real-time . Here we describe a pilot study utilizing such an EMA system with SARs. We describe the development of the EMA framework, theoretical design issues, and lessons learned. Preliminary machine learning results indicate 75-80% accuracy for detecting specific interaction modalities. We also discuss the potential utility of EMA for exploring cross-cultural differences with in-the-wild robot use, and as a tool to support participatory design research on robotics in healthcare settings.
Semi-Direct Monocular Visual-Inertial Odometry Using Point and Line Features for IoV

The precise measuring of vehicle location has been a critical task in enhancing the autonomous driving in terms of intelligent decision making and safe transportation. Internet of Vehicles (IoV) is an important infrastructure in support of autonomous driving, allowing real-time road information exchanging and sharing for localizing vehicles. Global positioning System (GPS) is widely used in the traditional IoV system. GPS is unable to meet the key application requirements of autonomous driving due to meter level error and signal deterioration. In this article, we propose a novel solution, named Semi-Direct Monocular Visual-Inertial Odometry using Point and Line Features (SDMPL-VIO) for precise vehicle localization. Our SDMPL-VIO model takes advantage of a low-cost Inertial Measurement Unit (IMU) and monocular camera, using them as the sensor to acquire the surrounding environmental information. Visual-Inertial Odometry (VIO), taking into account both point and line features, is proposed, which is able to deal with both weak texture and dynamic environment. We use a semi-direct method to deal with keyframes and non-keyframes, respectively. Dual sliding window mechanisms can effectively fuse point-line and IMU information. To evaluate our SDMPL-VIO system model, we conduct extensive experiments on both an indoor dataset (i.e., EuRoC) and an outdoor dataset (i.e., KITTI) from the real-world applications, respectively. The experimental results show that the accuracy of SDMPL-VIO proposed by us is better than the mainstream VIO system at present. Especially in the weak texture of the datasets, fast-moving datasets, and other challenging datasets, SDMPL-VIO has a relatively high robustness.
Initial Assessment of Job Interview Training System using Multimodal Behavior Analysis

The COVID-19 pandemic has had a significant socio-economic impact on the world. Specifically, social distancing has impacted many activities that were previously conducted face-to-face. One of these is the training that students receive for job interviews. Thus, we introduce a job interview training system that will give students the ability to continue receiving this type of training. Our system recognizes the nonverbal behaviors of an interviewee, namely gaze, facial expression, and posture using a Tobii eye tracker and cameras. The system compares the recognition results with those of models of exemplary nonverbal behaviors of an interviewee and highlights the behaviors that need improvement while playing back the interview recording. Most current interview training systems require high-end Hardware and Software and are not designed for general users, and there are few systems using CG agents to give feedback. The purpose of this study was to conduct an initial evaluation of the usefulness of this system and to identify areas for improvement. The results of the initial evaluation of the system indicate that improvements in the recognition accuracy of nonverbal behaviors and the quality of the interaction with the CG agent are needed.
Younger and Older Adults’ Perceptions on Role, Behavior, Goal and Recovery Strategies for Managing Breakdown Situations in Human-Robot Dialogues

Mutual understanding of the dialogue purpose and role of participants is vital to co-create meaning and satisfactory outcomes during human and social robot dialogues. This study explored how younger and older adults perceive roles, goals and behaviors of dialogue participants when breakdown situations occur during co-creation of meaning in a dialogue. The aim was to identify intuitive mechanisms that a social robot can use to manage breakdown situations. A set of dialogues were authored, based on activity-theoretical models of human activity and recorded in a Wizard-of-Oz setup with a Nao robot. Results of 8 (four younger and four old adults) participant interviews after observing the recorded dialogues are presented. Thematic analysis of interviews resulted into three themes of perceptions and expectations on roles and relationship, the robot’s understanding of the situation and relevant behavioral norms. Themes were translated into design implications for developing activity-centric dialogue systems to seamlessly manage breakdown situations. Future work includes implementation and further study of the proposed design implications for human robot dialogues.
Conveying the Perception of Humor Arising from Ambiguous Grammatical Constructs in Human-Chatbot Interaction

Chatbots have long been advocated for computer-assisted language learning systems to support learners with conversational practice. A particular challenge in such systems is explaining mistakes stemming from ambiguous grammatical constructs. Misplaced modifiers, for instance, do not make sentences ungrammatical, but introduce ambiguity through the misplacement of an adverb or prepositional phrase. In certain cases, the ambiguity gives rise to humor, which can serve to illustrate the mistake itself. We conducted an online experiment with 400 native English speakers to explore the use of a chatbot to harness such humor. In an interaction resembling an advanced grammar exercise, the chatbot presented participants with a phrase containing a misplaced modifier, explained the ambiguity in the phrase, acknowledged (or ignored) the humor that the ambiguity gave rise to, and suggested a correction. Participants then completed a questionnaire, rating the chatbot with respect to ten traits. A quantitative analysis showed a significant increase in how participants rated the chatbot’s personality, humor, and friendliness when it acknowledged the humor arising from the misplaced modifier. This effect was observed whether the acknowledgment was conveyed using verbal, nonverbal (emoji), or mixed cues.
Classification of pseudocalcium visual responses from mouse retinal ganglion cells

Abstract Recently, a detailed catalog of 32 retinal ganglion cell (RGC) visual response patterns in mouse has emerged. However, the 10,000 samples required for this catalog—based on fluorescent signals from a calcium indicator dye—are much harder to acquire from the extracellular spike train recordings underlying our bionic vision research. Therefore, we sought to convert spike trains into pseudocalcium signals so that our data could be directly matched to the 32 predefined, calcium signal-based groups. A microelectrode array (MEA) was used to record spike trains from mouse RGCs of 29 retinas. Visual stimuli were adapted from the Baden et al. study; including moving bars, full-field contrast and temporal frequency chirps, and black–white and UV-green color flashes. Spike train histograms were converted into pseudocalcium traces with an OGB-1 convolution kernel. Response features were extracted using sparse principal components analysis to match each RGC to one of the 32 RGC groups. These responses mapped onto of the 32 previously described groups; however, some of the groups remained unmatched. Thus, adaptation of the Baden et al. methodology for MEA recordings of spike trains instead of calcium recordings was partially successful. Different classification methods, however, will be needed to define clear RGC groups from MEA data for our bionic vision research. Nevertheless, others may pursue a pseudocalcium approach to reconcile spike trains with calcium signals. This work will help to guide them on the limitations and potential pitfalls of such an approach.
Protocol for a quasi-experimental study examining the effect of a ball skills intervention on four domains of preschooler development

Abstract Background: Preschooler inactivity and insufficient motor development have serious long-term consequences. The Chinese Ministry of Education launched a nationwide football-focused pilot project aimed at kindergartens in 2019 and issued the policy “Notice on the Establishment of National Football Kindergartens” in 2020. However, the impact of fundamental movement skills (FMS) interventions on other aspects of child development is unclear. Aim: This study will evaluate the effects of ball skills physical education projects on the development of Chinese preschoolers’ physical, motor, cognitive, and social competencies and examine the influencing factors. Method: This is a quasi-experimental study evaluating how well the “Hello Sunshine” curriculum project promotes children’s development over 10 weeks. The trial will be conducted from September 2021 to November 2021 in 12 classes from 3 kindergartens with a total of 249 children aged 4 to 6 years in Haikou, China. Pre- and posttest analyses will include tests on participants’ physical fitness, FMS, cognitive self-regulation, and emotional competence. Participants’ background information will be collected through questionnaires answered by parents and teachers. The intervention will focus on game-based basic ball skills. Findings: If this intervention provides evidence that these skills improve children’s multidimensional development, it will support the promotion of similar programs in China. We will also outline the social-ecological factors affecting the intervention’s results, providing further information for improving pedagogical methods related to preschool ball skills.
Redefining performance measures for construction project managers: an empirical evaluation

Traditional success criteria for construction projects centre on the achievement of cost, programme and quality targets. In this paper, it is argued that these simple measures are too crude to be used for gauging managers' performance in the context of today's construction project environment, as many variables outside of the manager's control can impact on outturn performance and the demands on project managers are far broader than in the past. It is argued that the industry needs to define more appropriate performance criteria for measuring project managers' performance and encouraging their professional development. These must redefine traditional success parameters to consider the knowledge, skills and behavioural inputs which contribute to superior performance. These can then be used to engender a more appropriate set of management behaviours that lead to improved project outcomes. This study has developed such a framework by examining the views of members of project teams and other organizational stakeholders through a series of focus groups. Factor analysis was used to refine a range of nine performance criteria against which managers' performance can be evaluated. These have the potential to be used as a basis for developing more appropriate reward management approaches and as a starting point for developing competency-based performance management tools in the future.
MMIC technology for spectroscopy applications

Millimeter-wave monolithic integrated circuit (MMIC) technology is now widely recognized as a key to many modern applications in safety and security, ranging from near and far-field imaging and sensing to non-invasive material inspection. In this paper, we apply our state-of-the-art MMIC technology to the analysis of gaseous media by spectroscopic techniques. The paper presents recent developments of amplifying and frequency-translating MMICs based on metamorphic HEMT technology and their application to the spectroscopic analysis of the frequency range from 250 to 330 GHz, including the important absorption line of water around 321 GHz.
Transferring Lean into government

Purpose – This paper evaluates the transfer of a Lean approach developed by a global manufacturing and logistics company into a large UK Government department. The purpose of this paper is to examine which tools and techniques are transferred and implemented into the government department together with their impact as viewed by the staff within the department in order to bring about both technical and culture change.Design/methodology/approach – The research takes a case study approach based visiting ten sites within one organisation interviewing over 250 people throughout the organisation.Findings – This paper reflects on the introduction or transfer of a Lean approach into a large government department in order to understand which tools are relevant and have had an impact. The findings indicate that the tools mostly focused on the principle of Lean related to reduction of waste and that some of the concepts such as standard work may not be appropriate for public services. On reflecting on the findings, .
A Customer Churn Detection Model for the Pay-TV Sector

The business environment today is characterized by high competition and saturated markets. Pay-tv platforms there are not an exception. Because of that, the cost to acquire new customers is much higher than the cost of retaining the existing customers. Therefore, it is important for Pay-TV platforms to keep controlled the Customer Churn. Therefore, the paper studies existing models used to predict Customer Churn in other context -like telecommunication companies customer Churn-and adapts them to the Pay-TV context. Another big problem faced in the paper is the fact that, in the data set udes in the paper there are not personal metrics, which are indispensables to solve the problem. Therefore this approach has defined new metrics in order to be able to predict customer churn.
Exploring Non-Autoregressive Text Style Transfer

In this paper, we explore Non-AutoRegressive (NAR) decoding for unsupervised text style transfer. We first propose a base NAR model by directly adapting the common training scheme from its AutoRegressive (AR) counterpart. Despite the faster inference speed over the AR model, this NAR model sacrifices its transfer performance due to the lack of conditional dependence between output tokens. To this end, we investigate three techniques, i.e., knowledge distillation, contrastive learning, and iterative decoding, for performance enhancement. Experimental results on two benchmark datasets suggest that, although the base NAR model is generally inferior to AR decoding, their performance gap can be clearly narrowed when empowering NAR decoding with knowledge distillation, contrastive learning, and iterative decoding.
Extracting Material Property Measurement Data from Scientific Articles

Machine learning-based prediction of material properties is often hampered by the lack of sufficiently large training data sets. The majority of such measurement data is embedded in scientific literature and the ability to automatically extract these data is essential to support the development of reliable property prediction methods. In this work, we describe a methodology for developing an automatic property extraction framework using material solubility as the target property. We create a training and evaluation data set containing tags for solubility-related entities using a combination of regular expressions and manual tagging. We then compare five entity recognition models leveraging both token-level and span-level architectures on the task of classifying solute names, solubility values, and solubility units. Additionally, we explore a novel pretraining approach that leverages automated chemical name and quantity extraction tools to generate large datasets that do not rely on intensive manual tagging. Finally, we perform an analysis to identify the causes of classification errors.
Combining Simulations and Machine Learning for Efficient Prediction of Process Parameters Evolution in Injection Moulding

In recent years, the emerging technologies in the context of Industry 4.0 have led to novel approaches in process monitoring and control, such as the introduction of Reinforcement Learning and Digital Twins. Consequently, large amounts of data, precise modelling and exhaustive simulations are required. The aim of this work is to propose a methodology based on the technique of backward selection to reduce the number of reference points in the simulation stage of manufacturing processes, enhancing the efficiency of data generation and the simplicity of the simulations. The methodology is proved in the particular case of plastic injection moulding simulations.
Universal-KD: Attention-based Output-Grounded Intermediate Layer Knowledge Distillation

Intermediate layer matching is shown as an effective approach for improving knowledge distillation (KD). However, this technique applies matching in the hidden spaces of two different networks (i.e. student and teacher), which lacks clear interpretability. Moreover, intermediate layer KD cannot easily deal with other problems such as layer mapping search and architecture mismatch (i.e. it requires the teacher and student to be of the same model type). To tackle the aforementioned problems all together, we propose Universal-KD to match intermediate layers of the teacher and the student in the output space (by adding pseudo classifiers on intermediate layers) via the attention-based layer projection. By doing this, our unified approach has three merits: (i) it can be flexibly combined with current intermediate layer distillation techniques to improve their results (ii) the pseudo classifiers of the teacher can be deployed instead of extra expensive teacher assistant networks to address the capacity gap problem in KD which is a common issue when the gap between the size of the teacher and student networks becomes too large; (iii) it can be used in cross-architecture intermediate layer KD. We did comprehensive experiments in distilling BERT-base into BERT-4, RoBERTa-large into DistilRoBERTa and BERT-base into CNN and LSTM-based models. Results on the GLUE tasks show that our approach is able to outperform other KD techniques.
Comparative Opinion Quintuple Extraction from Product Reviews

As an important task in opinion mining, comparative opinion mining aims to identify comparative sentences from product reviews, extract the comparative elements, and obtain the corresponding comparative opinion tuples. However, most previous studies simply regarded comparative tuple extraction as comparative element extraction, but ignored the fact that many comparative sentences may contain multiple comparisons. The comparative opinion tuples defined in these studies also failed to explicitly provide comparative preferences. To address these limitations, in this work we first introduce a new Comparative Opinion Quintuple Extraction (COQE) task, to identify comparative sentences from product reviews and extract all comparative opinion quintuples (Subject, Object, Comparative Aspect, Comparative Opinion, Comparative Preference). Secondly, based on the existing comparative opinion mining corpora, we make supplementary annotations and construct three datasets for the COQE task. Finally, we benchmark the COQE task by proposing a new BERT-based multi-stage approach as well as three baseline systems extended from previous methods. %The new approach significantly outperforms three baseline systems on three datasets and represents a strong benchmark for COQE. Experimental results show that the new approach significantly outperforms three baseline systems on three datasets for the COQE task.