University of Massachusetts Boston

About: University of Massachusetts Boston is a education organization based out in Boston, Massachusetts, United States. It is known for research contribution in the topics: Population & Health care. The organization has 6541 authors who have published 12918 publications receiving 411731 citations. The organization is also known as: UMass Boston.

Scaling Social Movements Through Social Media: The Case of Black Lives Matter:

Abstract: In this article, we explore the potential role of social media in helping movements expand and/or strengthen themselves internally, processes we refer to as scaling up. Drawing on a case study of B...

On real-time transactions

Abstract: Next generation real-time systems will require greater flexibility and predictability than is commonly found in today's systems. These future systems include the space station, integrated vision/robotics/AI systems, collections of humans/robots coordinating to achieve common objectives (usually in hazardous environments such as undersea exploration or chemical plants), and various command and control applications. The complexity of such systems due to timing constraints, concurrency, and distribution is high. It is accepted that the synchronization, failure atomicity, and permanence properties of transactions aid in the development of distributed systems. However, little work has been done in exploiting transactions in a real-time context. We have been attempting to categorize real-time data into classes depending on their time, synchronization, atomicity, and permanence properties. Then, using the semantics of the data and the applications, we are developing special, tailored, real-time transactions that only supply the minimal properties necessary for that class. This reduces the system overhead in supporting access to various types of data. The eventual goal is to verify that timing requirements can be met.

Differentially Private Location Protection for Worker Datasets in Spatial Crowdsourcing

Abstract: Spatial Crowdsourcing (SC) is a transformative platform that engages individuals in collecting and analyzing environmental, social, and other spatio-temporal information. SC outsources spatio-temporal tasks to a set of workers , i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations. However, current solutions require the workers to disclose their locations to untrusted parties. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We address scenarios with both static and dynamic (i.e., moving) datasets of workers. Experimental results on real-world data show that the proposed technique protects location privacy without incurring significant performance overhead.

Online Streaming Feature Selection

Abstract: We study an interesting and challenging problem, online streaming feature selection, in which the size of the feature set is unknown, and not all features are available for learning while leaving the number of observations constant. In this problem, the candidate features arrive one at a time, and the learner's task is to select a "best so far" set of features from streaming features. Standard feature selection methods cannot perform well in this scenario. Thus, we present a novel framework based on feature relevance. Under this framework, a promising alternative method, Online Streaming Feature Selection (OSFS), is presented to online select strongly relevant and non-redundant features. In addition to OSFS, a faster Fast-OSFS algorithm is proposed to further improve the selection efficiency. Experimental results show that our algorithms achieve more compactness and better accuracy than existing streaming feature selection algorithms on various datasets.

How supine postural preferences of infants can contribute toward the development of handedness

Abstract: During their first three months postpartum, infants manifest an asymmetrically lateralized head position preference, typically turned to the right. This head position preference elicits an asymmerical tonic neck reflex, which places one hand in the infant's visual field. As a result, infants have differential visual experience of their two hands. The majority of infants have more visual experience with their right hands than their left. Knowledge of which hand an infant has had more visual experinece of, as a result of its postural preference, reliably predicts the hand that will be used most in a visually-elicited reaching task at 12 weeks postpartum. Therefore, the origin of human handedness status may reside in an asymmetrical postural preference during early infancy, which biases visual experience of the hands, giving one hand an advantage in eye-hand coordination tasks.