There is growing interest in U.S. cities to shift resources towards community-led solutions to crime and disorder. However, there is a simultaneous need to provide community organizations with access to real-time data to facilitate decision making, to which only the police normally have access. In this work we present a low-cost gunshot detection system with localization that has been developed for community-based violence interruption. The distributed real-time gunshot detection sensor network is linked to a mobile phone-based alert and tasking system for exclusive use by civilian gang interventionists. Here we present details on the system architecture and gunshot detection model, which consists of an Audio Spectrogram Transformer (AST) neural network. We then combine gradient maps of the input to the AST for time of arrival identification with a Bayesian maximum a posteriori estimation procedure to identify the location of gunshots. We conduct several experiments using simulated data, open data from the commercial ShotSpotter detection system in Pittsburgh, and data collected using our devices during live-fire experiments at the Indianapolis Metropolitan Police Department (IMPD) gun firing range. We then discuss potential applications of the system and directions for future research.

Many cities using gunshot detection technology depend on expensive systems that ultimately rely on humans differentiating between gunshots and non-gunshots, such as ShotSpotter. Thus, a scalable gunshot detection system that is low in cost and high in accuracy would be advantageous for a variety of cities across the globe, in that it would favorably promote the delegation of tasks typically worked by humans to machines. A repository of audio data was created from sound clips collected from online audio databases as well as from clips recorded using a USB microphone in residential areas and at a gun range. One-dimensional as well as two-dimensional convolutional neural networks were then trained on this sound data, and spectrograms created from this sound data, to recognize gunshots. These models were deployed to a Raspberry Pi 3 Model B+ with a short message service modem and a USB microphone attached, using a software pipeline to continuously analyze discrete two-second chunks of audio and alert a set of phone numbers if a gunshot is detected in that chunk. Testing found that a majority-rules ensemble of our one-dimensional and two-dimensional models fared best, with an accuracy above 99% on validation data as well as when distinguishing gunshots from fireworks. Besides increasing the safety standards for a city's residents, the findings generated by this research project expand the current state of knowledge regarding sound-based applications of convolutional neural networks.

Some transfer students have significant challenges in adapting to the pace and depth of a competitive four-year University. According to the National Student Clearinghouse Research Center, only 60% of transfer students from community colleges have earned their baccalaureate degree four years after transferring. In this paper, we present our program designed to increase the success rate of transfer students. Our goal is to continue Binghamton University's tradition of providing an effective collegiate learning environment for all, and particularly, to increase the success rate of the transfer students in STEM fields.

Microfabrication is a critical area to many branches of science and engineering. Beginning in 2009, we undertook a broad multidisciplinary approach to bring microfabrication into all aspects of the science and engineering curriculum. This program was coupled with a comprehensive assessment activity to evaluate the program's effectiveness and continuously improve it year to year. In this paper, we report on the details of our implementation process, the techniques we have found to bring a hands-on experience to large classes and the lessons learned from our attempts to make this topic a mainstream part of engineering and science education.

This paper reports a case study of the integration of information technology (IT) in a Discovery School in South Australia, and the problems and issues that the school encountered in due process. A number of data-collection methods were used. The school was well provided for in IT. Both students and teachers were confident with the use of IT in learning and teaching. The students' use of IT was much dependent on their access to IT at home. Some learning area coordinators did not seem to recognize or place emphasis on the success of integrating IT into their own areas. Also, the principal and the teachers seemed to have different perceptions in relation to some of the problems and issues arising from integrating IT into the school. Flexible timetabling and further development of instructional strategies may be some of the keys to further enhance the school's success in using IT.