Working Effectively with People who are Blind or Visually Impaired

This brochure on peoples who are blind or visually impaired and The Americans with Disabilities Act (ADA) is one of a series on human resources practices and workplace accommodations for persons with disabilities edited by Susanne M. Bruyère, Ph.D., CRC, SPHR, Director, Program on Employment and Disability, School of Industrial and Labor Relations – Extension Division, Cornell University

How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

An Adaptive Virtual Training System Based on Universal Design

The increasing complexity of manufacturing environments requires effective training systems to prepare the operation personnel for their tasks. Several training systems have been proposed. A common approach is the application of virtual environments to train interactions with an industrial machine in a safe, attractive, and efficient way. However, these training systems cannot adapt to the requirements of an increasingly diversified workforce. This paper introduces an approach for the design of an adaptive virtual training system based on the idea of universal design. The system is based on a two-step approach that consists of an initial adaptation to the user capabilities and real-time adaptations during training based on measurements of the user. The adaptations concern the use of different representations of lessons with different complexity and interaction modalities. The proposed approach provides a flexible training system that can adapt to the needs of a broad group of users

Learning Fast and Slow: PROPEDEUTICA for Real-time Malware Detection

In this paper, we introduce and evaluate PROPEDEUTICA, a novel methodology and framework for efficient and effective real-time malware detection, leveraging the best of conventional machine learning (ML) and deep learning (DL) algorithms. In PROPEDEUTICA, all software processes in the system start execution subjected to a conventional ML detector for fast classification. If a piece of software receives a borderline classification, it is subjected to further analysis via more performance expensive and more accurate DL methods, via our newly proposed DL algorithm DEEPMALWARE. Further, we introduce delays to the execution of software subjected to deep learning analysis as a way to "buy time" for DL analysis and to rate-limit the impact of possible malware in the system. We evaluated PROPEDEUTICA with a set of 9,115 malware samples and 877 commonly used benign software samples from various categories for the Windows OS. Our results show that the false positive rate for conventional ML methods can reach 20%, and for modern DL methods it is usually below 6%. However, the classification time for DL can be 100X longer than conventional ML methods. PROPEDEUTICA improved the detection F1-score from 77.54% (conventional ML method) to 90.25%, and reduced the detection time by 54.86%. Further, the percentage of software subjected to DL analysis was approximately 40% on average. Further, the application of delays in software subjected to ML reduced the detection time by approximately 10%. Finally, we found and discussed a discrepancy between the detection accuracy offline (analysis after all traces are collected) and on-the-fly (analysis in tandem with trace collection). Our insights show that conventional ML and modern DL-based malware detectors in isolation cannot meet the needs of efficient and effective malware detection: high accuracy, low false positive rate, and short classification time.Comment: 17 pages, 7 figure

Design of Equipment Rack with TRIZ Method to Reduce Searching Time in Change Over Activity (Case Study : PT. Jans2en Indonesia)

Janssen is a manufacturing plant that works in furniture assembly. Component shortages often occurs, it will cause the increase of work in process (WIP) in assembly section. In previous studies, we analyze the root causes with FMEA and then it is resulted that router section is the constraint of the system. There are many non value added activities such as searching and transportation caused by a messy condition of work places and the devices that aren’t put in the right place. The impact is that the time allocated for every change over is higher than before. There are many components that are worked by the router section, so improvements are needed to minimize changes in over time. 5S method and the use of a new design of rack by TRIZ method are suggested for fixing the conditions of work environment. It is expected to eliminate non value added activities and changes in over time. Result shows that we can reduce non value activities in change over of regular components up to 41% and the elimination of this time is 41,6%. The non value activities in changeover of new items is 36,6% and this elimination of time is 53,3%. Key word : change over, kaizen, design, TRIZ metho

Knowledge Transfer Needs and Methods

INE/AUTC 12.3

Online support for shop-floor operators using body movements tracking

This paper presents results of experiments on the integration of human operators and automation in the context of assembly systems. Several experiments have been conducted to evaluate the use of augmented reality applications in order to provide dynamic work instructions and online feedback information to a shop-floor operator. Based on these results, an experimental platform has been developed so that the shop-floor operator and the automated system behave as a closed-loop system