Miracle-dz/UniFAR-FTUs huggingface dataset:
Gradually structured data

Dynamically-typed languages offer easy interaction with ad hoc data such as JSON and S-expressions; statically-typed languages offer powerful tools for working with structured data, notably algebraic datatypes, which are a core feature of typed languages both functional and otherwise. Gradual typing aims to reconcile dynamic and static typing smoothly. The gradual typing literature has extensively focused on the computational aspect of types, such as type safety, effects, noninterference, or parametricity, but the application of graduality to data structuring mechanisms has been much less explored. While row polymorphism and set-theoretic types have been studied in the context of gradual typing, algebraic datatypes in particular have not, which is surprising considering their wide use in practice. We develop, formalize, and prototype a novel approach to gradually structured data with algebraic datatypes. Gradually structured data bridges the gap between traditional algebraic datatypes and flexible data management mechanisms such as tagged data in dynamic languages, or polymorphic variants in OCaml. We illustrate the key ideas of gradual algebraic datatypes through the evolution of a small server application from dynamic to progressively more static checking, formalize a core functional language with gradually structured data, and establish its metatheory, including the gradual guarantees.
Perception of Ultrasound Haptic Focal Point Motion

Ultrasound haptic patterns can be rendered by continuously moving an ultrasonic focal point. It is not known how this focal point motion affects haptic perception. We present two psychophysical experiments investigating the perception of an ultrasound haptic focal point moving along a circular path. Our first experiment finds that a sensation of motion is perceived at speeds up to 17 revs–1, similar to the ‘flutter’ sensation. Our second experiment found a linear relationship between movement speed and perceived intensity up to this speed. Our findings give new insight into ultrasound haptic perception and give designers empirical evidence to inform contactless haptic feedback design.
FaceTrack: Asymmetric Facial and Gesture Analysis Tool for Speech Language Pathologist Applications

In this paper, a novel tool for facial and gesture analysis aiming to quantify different subjective measures employed in the Speech Language Pathologist area is proposed. Through an input video (from a simple monocular camera) showing a person's face, the developed tool can track the movements and expressions from it in order to extract useful morphological and gestural parameters that are of interest in different fields of study, such as, Speech Language Pathologist, Neurology, etc. A modified version of a 3D face model, named Candide-3, is employed in the tracking stage. Since the original 3D model cannot handle asymmetrical facial movements, a new set of animation units was implemented in order to effectively track asymmetrical gestures. To enhance the tracking accuracy, a fusion scheme is proposed in the facial gesture tracking stage by means of the combination of the 3D face model previously described and facial landmarks detected using deep learning models. This tool will be made open source, both the software application (oriented to health professionals, no need to have any programming knowledge), and the source code for the computer vision community. Several perceptual experiments were carried out, achieving promising results.
Similarity Analysis of Visual Sketch-based Search for Sounds

Searching through a large audio database for a specific sound can be a slow and tedious task with detrimental effects on creative workflow. Listening to each sample is time consuming, while textual descriptions or tags may be insufficient, unavailable or simply unable to meaningfully capturing certain sonic qualities. This paper explores the use of visual sketches that express the mental model associated with a sound to accelerate the search process. To achieve this, a study was conducted to collect data on how 30 people visually represent sound, by providing hand-sketched visual representations for a range of 30 different sounds. After augmenting the data to a sparse set of 855 samples, two different autoencoder were trained. The one finds similar sketches in latent space and delivers the associated audio files. The other one is a multimodal autoencoder combining both visual and sonic cues in a common feature space but lacks on having no audio input for the search task. These both were then used to implement and discuss a visual query-by-sketch search interface for sounds.
A Generic Few-Shot Solution for Food Shelf-Life Prediction using Meta-Learning

Checking the quality of agricultural produce at every step of its supply chain is the need of the hour to reduce food wastage. Manual checking of food quality at every step can be inconsistent and time consuming. Automation of food quality detection, using non-invasive imagery based techniques, needs availability of ample amount of annotated data to train models. Collecting such data in large quantity in a controlled lab setting is an expensive affair. More-over, providing a point solution for every individual food item by training food item specific models is an impractical solution. Thus, there is a need for a mechanism which would capture the common meta-level visual degradation properties across a set of food items belonging to a specific category and use this meta-knowledge to predict the quality of a new food item belonging to that category with a paucity of training data. To address this challenge, as a part of the preliminary work, we conduct an initial set of experiments to demonstrate the applicability of existing Model Agnostic Meta-Learning (MAML) algorithm for fruit freshness detection task. The results indicate that for such a task, meta-learning can serve to be a more generic and efficient solution than using few-shot transfer-learning technique and traditional ML based approaches requiring explicit feature engineering.
Audio-Visual Interactive Art: Investigating the effect of gaze-controlled audio on visual attention and short term memory

This article presents the development and testing of a system for interactive art. The system utilises eye tracking technology to detect the eye movements of people looking at a painting, and then uses this data to trigger sounds related to the painting. The system was developed in collaboration with a visual artist and a musician with the ultimate aim to integrate it into a new piece of interactive art. Described here in detail is the design and development of a prototype which was tested using a copy of a painting by Hieronymus Bosch: The Garden of Earthly Delights. Through this system we tested whether gaze-controlled audio affects visual attention and short term memory. The final system was integrated into a contemporary piece of interactive art currently exhibited in Ronneby, southern Sweden.
Fusion of Acoustic and Linguistic Information using Supervised Autoencoder for Improved Emotion Recognition

Automatic recognition of human emotion has a wide range of applications and has always attracted increasing attention. Expressions of human emotions can apparently be identified across different modalities of communication, such as speech, text, mimics, etc. The "Multimodal Sentiment Analysis in Real-life Media' (MuSe) 2021 challenge provides an environment to develop new techniques to recognize human emotions or sentiments using multiple modalities (audio, video, and text) over in-the-wild data. The challenge encourages to jointly model the information across audio, video and text modalities, for improving emotion recognition. The present paper describes our attempt towards the MuSe-Sent task in the challenge. The goal of the sub-challenge is to perform turn-level prediction of emotions within the arousal and valence dimensions. In the paper, we investigate different approaches to optimally fuse linguistic and acoustic information for emotion recognition systems. The proposed systems employ features derived from these modalities, and uses different deep learning architectures to explore their cross-dependencies. Wide range of acoustic and linguistic features provided by organizers and recently established acoustic embedding wav2vec 2.0 are used for modeling the inherent emotions. In this paper we compare discriminative characteristics of hand-crafted and data-driven acoustic features in a context of emotional classification in arousal and valence dimensions. Ensemble based classifiers were compared with advanced supervised autoendcoder (SAE) technique with Bayesian Optimizer hyperparameter tuning approach. Comparison of uni- and bi-modal classification techniques showed that joint modeling of acoustic and linguistic cues could improve classification performance compared to individual modalities. Experimental results show improvement over the proposed baseline system, which focuses on fusion of acoustic and text based information, on the test set evaluation.
PID Tuning for Vision Based Mobile Robots Formation Control

Multiple autonomous robots are more and more used in fully automated warehouses where they can operate safely sharing the same space using a simple obstacle avoidance strategy and transport objects from one area to another. For transporting large or heavy objects, it is sometimes interesting to use several mobile robots cooperating physically and keeping the same geometric formation. In this case, the control of the robots’ formation is a crucial issue. In this context, our work proposes a strategy based on an ARTAG marker tracking for building and maintaining a Leader-Follower (LF) configuration using no other communication means than indirect communication via tracking. The tracking is using a PID (Proportional-Integral-Differential) controller for the following process and AR track Alvar vision system for the recognition and localization process. The proposed method only needs distance and angle errors information during the tracking. We defined the total error during the following phase as the performance measurement to be minimized. For PID tuning, the manual tuning method is used and compared with classical PID and conventional PID tuning methods including Ziegler-Nichols method, Tyreus-Luyben method and Cohen-Coon method. Experiments are validated and conducted on two real mobile robots, the master robot carrying the marker is manually controlled and the follower is keeping its position relative to the master using the AR tag tracking.
Advances in Multimodal Behavioral Analytics for Early Dementia Diagnosis: A Review

Clinical diagnosis of dementia is typically delayed and limited in accuracy, despite assessing cognitive impairments through neurological exams, brain imaging, and functional tests such as Activities of Daily Living. Recent advances in digital health and multimodal behavioral analytics are beginning to provide more sensitive, objective, unobtrusive and continuous assessment of functional abilities while people remain in a familiar setting such as their home, at work, or in the community. These new techniques analyze natural behaviors like speech, language, gait, eye gaze, hand movements, and facial expressions. This review compares existing clinical assessment methods with emerging behavioral analytic techniques that offer powerful capabilities for earlier and more precise diagnosis of dementia. It summarizes state-of-the-art multimodal behavioral analytics research for dementia diagnosis, including predictive features present in different human behaviors and the performance advantages of combining them into multimodal diagnostic systems. The many behavioral predictors documented in the literature are interpreted as deriving from six common cognitive deficits that are well known hallmarks of dementia. The review also discusses long-term trends in multimodal behavioral analytics research, and the five main areas requiring future work to realize the promise of earlier, more accurate, and widely accessible dementia diagnostic systems.
Cross-modal Assisted Training for Abnormal Event Recognition in Elevators

Given that very few action recognition datasets collected in elevators contain multimodal data, we collect and propose our multimodal dataset investigating passenger safety and inappropriate elevator usage. Moreover, we present a novel framework (RGBP) to utilize multimodal data to enhance unimodal test performance for the task of abnormal event recognition in elevators. Experimental results show that the best network architecture with the RGBP framework effectively improves the unimodal inference performance on the Elevator RGBD dataset by 4.71% (accuracy) and 4.95% (F1 score) with respect to the pure RGB model. In addition, our RGBP framework outperforms two other methods for ”multimodal training and unimodal inference”: MTUT [1] and the two-stage method based on depth estimation.
Development of an Interactive Human/Agent Loop using Multimodal Recurrent Neural Networks

The development of expressive embodied conversational agent (ECA) still remains a big challenge. During an interaction partners continuously adapt their behaviors one to the other [7]. Adaptation mechanisms may take different forms such as the choice of same vocabulary and grammatical form [31], imitation and synchronization [7]. The aim of my PhD project is to improve the interaction between human and agent. The key idea is to create an interactive loop between human and agent which allows the virtual agent to continuously adapt its behavior according to its partner’s behavior. The main idea is to learn how dyad of humans adapt their behaviors and implement it into human-agent interaction. My work, based on recurrent neural network, focuses on nonverbal behavior generation and addresses several scientific locks like the multimodality, the intra-personal temporality of multimodal signals or the temporality between partner’s social cues. We plan to build a model learned in an end-to-end fashion that generates behaviors considering both acoustic and visual modalities.
Automatic acoustic classification of feline sex

This paper presents a novel method for classifying the feline sex based on the respective vocalizations. Due to the size of the available dataset, we rely on tree-based classifiers which can efficiently learn classification rules in such poor data availability cases. More specifically, this work investigates the ability of random forests and boosting classifiers when trained with a wide range of acoustic features derived both from time and frequency domain. The considered classifiers are evaluated using standardized figures of merit including f1-score, recall, precision, and accuracy. The best-performing classifier was the CatBoost, while the obtained results are in line with the state-of-the-art accuracy levels in the field of animal sex classification.
Data Aggregation Of Tweets And Topic Modelling Based On The Twitter Dataset

Twitter is one of the most popular online social networks. It has a relatively simple data model and an intuitive API to access Twitter data. This makes it easy to collect social data and analyse the patterns of online behaviour. Twitter has an impactful presence among politicians, entrepreneurs, news agencies, public figures, and this makes it a crucial playground for social discussion. The topics discussed on Twitter often lead to or are the cause of social events. Therefore, a lot of information can be inferred from Twitter data. This can be used by NGOs, government agencies or policymakers to develop meaningful understanding and respond to the emerging trends. In this project, I will discuss a method to aggregate tweets related to Elon Musk and Tesla from Twitter servers using the Twitter API in the form of a web crawler. The data obtained from the web crawler will be combined with a ready-made dataset containing similar information, and the datasets will be merged together. After collecting relevant tweet information, I will perform topic modelling using Latent Dirichlet Allocation (LDA) on his tweets to find out the most common topics tweeted by Elon Musk.
Wikipedia Edit-a-thons and Editor Experience: Lessons from a Participatory Observation

Wikipedia is one of the most important sources of encyclopedic knowledge and among the most visited websites on the internet. As a peer-produced knowledge repository, Wikipedia is dependent on its community of contributors. A healthy contributor community and a steady stream of new editors from diverse backgrounds are especially vital for the platform’s future in its endeavour of closing knowledge gaps and combating biases and a lack of diversity that Wikipedia suffers from. Edit-a-thons are social activities aiming to improve content and create new articles on Wikipedia with the purpose of recruitment and onboarding of newcomers. Although edit-a-thons have been facilitated and hosted for many years now, little is known how editors experience such events. In this paper, we study editors experience during a virtual edit-a-thon by applying an ethnomethodological perspective. We use a participatory observation to study incidents of motivation and frustration occurring during the collaborative online writing event. Moreover, we use Hofstede’s 6D Model of National Culture to explore what influence culture has on participants’ actions, expressed feelings and thoughts while interacting with the administrator and with each other. Our findings indicate that the type of motivational factors is very diverse and varies from general motivation to fill in knowledge gaps, in the beginning, to share good resources for citations at later stages of the edit-a-thon. However, participants also experience moments of frustration, especially concerning the usability of the editing interface and when navigating a complex bureaucracy of policies and procedures. Finally, our analysis shows that cultural idiosyncrasies can intensify the frustrating experience of social challenges.
Teacher Working Conditions, Teacher Commitment, and Charter Schools

Background The charter school movement relies on teachers as critical components. Teacher commitment is an important aspect of teachers’ lives, because it is an internal force for teachers to grow as professionals. It is also considered one of the crucial factors in influencing various educational outcomes, including teacher effectiveness, teacher retention, and student learning. However, no empirical studies have examined teacher commitment in charter schools. Purpose To address this knowledge gap, this study compares organizational and professional commitment of teachers in charter schools and traditional public schools (TPSs) and explores how these differences are associated with teachers’ characteristics, school contextual factors, and working conditions in the two types of schools. Research Design This study utilizes quantitative analyses of national data from the 2007– 2008 School and Staffing Survey. Hierarchical linear models were developed to examine whether teacher commitment differs between charter schools and TPSs; how teacher characteristics, school contextual factors, and teachers’ perceptions of working conditions contribute to the difference; and finally, whether these variables differentially influence teacher commitment in charter schools and TPSs. Conclusions On average, teachers in charter schools experienced lower levels of organizational commitment than teachers in TPSs, but similar levels of professional commitment. Teacher working conditions explained a large amount of the variance in between-school teacher commitment, suggesting that improving principal leadership, increasing opportunities for professional development, and alleviating teachers’ workload would be effective ways to promote teacher commitment in charter schools.
Impeded Attainment? The Role of State Exit Examination-Alternative Route Policy Combinations

Background To allay public concerns that state graduation examination mandates might unfairly hinder some students’ educational attainment prospects, most states with exit exam requirements offer alternative routes to earning a regular high school diploma. In spite of poor public documentation of alternative route usage rates, some exit exam states’ alternative credentialing policies have been linked to their relatively high reported graduation rates. However, there is little empirical evidence that these alternative route policies blunt the reported negative effects of exit exam requirements on diploma attainment in the general student population. Purpose We investigate the consequences of several distinct state exit exam-alternative route graduation policy combinations on the subsequent educational attainment of tenth graders in the graduating class of 2004. Research Design Data for our study are drawn from the cohort of U.S. tenth graders (N = 13,636) sampled by the Education Longitudinal Study of 2002–2006. We use logistic regression models to estimate the relationships between state exam difficulty-alternative route policy combinations and two educational attainment outcomes: high school diploma acquisition and postsecondary school enrollment. Conclusions While we find no relationship between exit exam policies and students’ subsequent postsecondary school enrollment, we conclude that students subject to relatively difficult state exit exams are less likely to earn a regular high school diploma than those not subject to an exam requirement. Estimating our models in the subsample of English language learner students as a model plausibility check, we observe marginally significant, but sizable, negative effects of both minimum-competency and more-difficult exit exams on diploma attainment. Our results suggest that alternative route options neither eliminate, nor appreciably attenuate, more-difficult exit exams’ negative impact on diploma attainment in the general student population or among English language learners.
Lesson Study in a Turnaround School: Local Knowledge as a Pressure-Balanced Valve for Improved Instruction

Background Federal and state accountability policies attempt to improve educational outcomes but have been blamed for a breadth of ills, including minimizing local knowledge and reducing teachers’ ability to respond to contextual needs. Teachers in high-needs schools, especially, feel the effects of constrained curricula and increased testing, resulting in increased workload and anxiety. Purpose This article explores the impact of professional development on teachers and students in a time of high-stakes accountability. Specifically, we ask: Does Lesson Study impact teachers’ instruction and students’ achievement in writing? And how do pressures imposed by policy impact efficacy and collaboration in a high-needs school? Research Design This study uses data from 20 Lesson Study meetings at a high-needs, “Turnaround” school and considers changes in students’ writing achievement. The mixed-method approach and high-stakes context offer a unique contribution to Lesson Study research. Conclusions Findings indicate that instruction changed and students’ writing significantly improved, with the mean growth percentile increasing from the 30th to the 46th percentile on state assessments. Further, we found that during the Lesson Study process, teachers moved through six Stages of Transformation in response to a high-pressure context, moving from feelings of anger and blame-shifting to eventual feelings of empowerment.