Coreference Resolution in Freeling 4.0

This paper presents the integration of RelaxCor into FreeLing. RelaxCor is a coreference resolution system based on constraint satisfaction that ranked second in the CoNLL-2011 shared task. FreeLing is an open-source library for NLP with more than fifteen years of existence and a widespread user community. We present the difficulties found in porting RelaxCor from a shared task scenario to a production enviroment, as well as the solutions devised. We present two strategies for this integration and a rough evaluation of the obtained resultsPeer ReviewedPostprint (published version

Improving speaker turn embedding by crossmodal transfer learning from face embedding

Learning speaker turn embeddings has shown considerable improvement in situations where conventional speaker modeling approaches fail. However, this improvement is relatively limited when compared to the gain observed in face embedding learning, which has been proven very successful for face verification and clustering tasks. Assuming that face and voices from the same identities share some latent properties (like age, gender, ethnicity), we propose three transfer learning approaches to leverage the knowledge from the face domain (learned from thousands of images and identities) for tasks in the speaker domain. These approaches, namely target embedding transfer, relative distance transfer, and clustering structure transfer, utilize the structure of the source face embedding space at different granularities to regularize the target speaker turn embedding space as optimizing terms. Our methods are evaluated on two public broadcast corpora and yield promising advances over competitive baselines in verification and audio clustering tasks, especially when dealing with short speaker utterances. The analysis of the results also gives insight into characteristics of the embedding spaces and shows their potential applications

Data clustering using a model granular magnet

We present a new approach to clustering, based on the physical properties of an inhomogeneous ferromagnet. No assumption is made regarding the underlying distribution of the data. We assign a Potts spin to each data point and introduce an interaction between neighboring points, whose strength is a decreasing function of the distance between the neighbors. This magnetic system exhibits three phases. At very low temperatures it is completely ordered; all spins are aligned. At very high temperatures the system does not exhibit any ordering and in an intermediate regime clusters of relatively strongly coupled spins become ordered, whereas different clusters remain uncorrelated. This intermediate phase is identified by a jump in the order parameters. The spin-spin correlation function is used to partition the spins and the corresponding data points into clusters. We demonstrate on three synthetic and three real data sets how the method works. Detailed comparison to the performance of other techniques clearly indicates the relative success of our method.Comment: 46 pages, postscript, 15 ps figures include

Autonomous Learning of Speaker Identity and WiFi Geofence From Noisy Sensor Data

A fundamental building block towards intelligent environments is the ability to understand who is present in a certain area. A ubiquitous way of detecting this is to exploit unique vocal characteristics as people interact with one another in common spaces. However, manually enrolling users into a biometric database is time-consuming and not robust to vocal deviations over time. Instead, consider audio features sampled during a meeting, yielding a noisy set of possible voiceprints. With a number of meetings and knowledge of participation, e.g., sniffed wireless Media Access Control (MAC) addresses, can we learn to associate a specific identity with a particular voiceprint? To address this problem, this paper advocates an Internet of Things (IoT) solution and proposes to use co-located WiFi as supervisory weak labels to automatically bootstrap the labelling process. In particular, a novel cross-modality labelling algorithm is proposed that jointly optimises the clustering and association process, which solves the inherent mismatching issues arising from heterogeneous sensor data. At the same time, we further propose to reuse the labelled data to iteratively update wireless geofence models and curate device specific thresholds. Extensive experimental results from two different scenarios demonstrate that our proposed method is able to achieve 2-fold improvement in labelling compared with conventional methods and can achieve reliable speaker recognition in the wild

Mental Health Literacy for Adolescents with Special Needs: A Pilot Occupational Therapy Curriculum in a Middle School

In school communities, addressing student mental health and establishing safe, supportive relationships have become part of our everyday work. National statistics currently report one in five children in the United States struggles with a mental disorder (One in Five Minds, 2020). Students are presenting with increasingly complex psychiatric, behavioral, and trauma experiences that impact their ability to access education and navigate the tasks of adolescence-with life-long health results. This doctoral project has explored youth mental health prevention and promotion interventions for the occupational therapy practitioner and contributing factors such as youth marginalization, social inequity, and youth health literacy deficits. Throughout my career, I have worked in diverse settings, serving in different roles. Each experience has taught me skills and built upon core professional values to shape my practice. I have worked collaboratively with patients, parents, and peers to create programs that expanded the role of occupational therapy in a given community and have been privileged to work with individuals who have experienced significant loss or trauma. Their stories have inspired me to develop professional competencies to create safe, supportive environments that teach skills and allow healing. Through facilitating successful occupation and engagement in community, I have helped individuals define and create their next chapter of meaningful living. My three knowledge translation projects apply the occupational therapy lens to maximize effectiveness and student engagement in implementation of mental health promotion programs in a school serving adolescents with special needs. The project aims are to improve mental health literacy of adolescent students with special needs by offering a pilot curriculum within the science class of a special education setting, to increase awareness of occupational therapy contributions to a school-based mental health program for adolescents with special needs through a professional presentation at an interdisciplinary national conference, and to introduce entry-level occupational therapy graduate students to the process and outcomes of a pilot mental health literacy curriculum for adolescents in a special education setting through completion of an educational module. The projects, themselves, and their outcomes have been significantly impacted by societal changes of the COVID-19 pandemic. The mental health literacy pilot, begun in a middle school classroom, was converted and successfully delivered through virtual learning. Despite changes, both students and staff consistently reported positive increases in knowledge of mental health with effective strategies for coping in everyday life. The interdisciplinary conference proposal is currently stalled as we await the conversion of the conference to a virtual format. The presentation for occupational therapy graduate students was successfully developed for virtual delivery. The questions and dialogue with the students were insightful and hopeful about the future of occupational therapy in addressing mental health needs in school-based practice. In reflection, I can identify two aspects of achievement in these doctoral projects. In the first, my clinical expertise has been honed into an innovative, successful intervention approach for youth-supported by science and implemented with creativity and flexibility. The second is the understanding that completion of an advanced practice degree comes with a responsibility to share one’s narrative, from both a scholarly and a clinical perspective, to guide and encourage others in their practice. If the work resonates, they carry it forward in multiple directions similar to a pebble that creates ripples in a pond

41st Rocky Mountain Conference on Analytical Chemistry

Final program, abstracts, and information about the 41st annual meeting of the Rocky Mountain Conference on Analytical Chemistry, co-sponsored by the Colorado Section of the American Chemical Society and the Rocky Mountain Section of the Society for Applied Spectroscopy. Held in Denver, Colorado, August 1-5, 1999

Biometrics

Biometrics uses methods for unique recognition of humans based upon one or more intrinsic physical or behavioral traits. In computer science, particularly, biometrics is used as a form of identity access management and access control. It is also used to identify individuals in groups that are under surveillance. The book consists of 13 chapters, each focusing on a certain aspect of the problem. The book chapters are divided into three sections: physical biometrics, behavioral biometrics and medical biometrics. The key objective of the book is to provide comprehensive reference and text on human authentication and people identity verification from both physiological, behavioural and other points of view. It aims to publish new insights into current innovations in computer systems and technology for biometrics development and its applications. The book was reviewed by the editor Dr. Jucheng Yang, and many of the guest editors, such as Dr. Girija Chetty, Dr. Norman Poh, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park, Dr. Sook Yoon and so on, who also made a significant contribution to the book