Phishing often causes more financial damages to enterprises and users than virus and worms. Proposed techniques so far mostly are to provide protection for the banks or other enterprise corporations and/or require changes in Web servers. Regular home users are left without protection against phishing, other than the traditional rudimentary email filtering. Furthermore, security toolbars on browsers have been shown to be not effective. In this paper, we propose a light-weight user white list technique to provide protection for home users. We argue that for home users, white listing is advantageous over blacklisting, a popular technology. We show it is effective and efficient while imposing little burden on users and requiring no modification of servers.

As society increasingly relies on digital technologies in many different aspects, those who lack relevant access and skills are lagging increasingly behind. Among the underserved groups disproportionately affected by the digital divide are women who are transitioning from incarceration and seeking to reenter the workforce outside the carceral system (women-in-transition). Women-in-transition rarely have been exposed to sound technology education, as they have generally been isolated from the digital environment while in incarceration. Furthermore, while women have become the fastest-growing segment of the incarcerated population in the United States in recent decades, prison education and reentry programs are still not well adjusted for them. Most programs are mainly designed for the dominant male population. Consequently, women-in-transition face significant post-incarceration challenges in accessing and using relevant digital technologies and thus have added difficulties in entering or reentering the workforce. Against this backdrop, our multi-disciplinary research team has conducted empirical research as part of technology education offered to women-in-transition in the Midwest. In this article, we report results from our interviews with 75 women-in-transition in the Midwest that were conducted to develop a tailored technology education program for the women. More than half of the participants in our study are women of color and face precarious housing and financial situations. Then, we discuss principles that we adopted in developing our education program for the marginalized women and participants’ feedback on the program. Our team launched in-person sessions with women-in-reentry at public libraries in February 2020 and had to move the sessions online in March due to COVID-19. Our research-informed educational program is designed primarily to support the women in enhancing their knowledge and comfort with technology and nurturing computational thinking. Our study shows that low self-efficacy and mental health challenges, as well as lack of resources for technology access and use, are some of the major issues that need to be addressed in supporting technology learning among women-in-transition. This research offers scholarly and practical implications for computing education for women-in-transition and other marginalized populations.

Human injuries and casualties at entertaining, religious, or political crowd events often occur due to the lack of proper crowd safety management. For instance, for a large scale moving crowd, a minor accident can create a panic for the people to start stampede. Although many smart video surveillance tools, inspired by the recent advanced artificial intelligence (AI) technology and machine learning (ML) algorithms, enable object detection and identification, it is still challenging to predict the crowd mobility in real-time for preventing potential disasters. In this paper, we propose an intelligent crowd engineering platform using mobility characterization and analytics named ICE-MoCha. ICE-MoCha is to assist safety management for mobile crowd events by predicting and thus helping to prevent potential disasters through real-time radio frequency (RF) data characterization and analysis. The existing video surveillance based approaches lack scalability thus have limitations in its capability for wide open areas of crowd events. Via effectively integrating RF signal analysis, our approach can enhance safety management for mobile crowd. We particularly tackle the problems of identification, speed, and direction detection for the mobile group, among various crowd mobility characteristics. We then apply those group semantics to track the crowd status and predict any potential accidents and disasters. Taking the advantages of power-efficiency, cost-effectiveness, and ubiquitous availability, we specifically use and analyze a Bluetooth low energy (BLE) signal. We have conducted experiments of ICE-MoCha in a real crowd event as well as controlled indoor and outdoor lab environments. The results show the feasibility of ICE-MoCha detecting the mobile crowd characteristics in real-time, indicating it can effectively help the crowd management tasks to avoid potential crowd movement related incidents.

A network of sensors and surveillance devices creates a critical infrastructure in smart cities, enabling civic authorities to render faster and more efficient services. Notably, many deployed sensor applications form linear topologies, such as road monitoring sensors (e.g., intelligent lighting, road traffic, pedestrians, etc.), railway track sensors, and pipeline sensors (e.g., sensing leaks, pressure, etc.). These linear sensors convey data to a central control station by daisy-chaining through neighbor sensors. However, it causes critical energy inefficiency and imbalance issues and creates data transmission delay, which provokes complex application and service management hassles. In this paper, we propose an Energy-Efficient Linear Sensor Architecture (ELSA) for building sensor network infrastructure of Smart Cities. We design and develop an energy-efficient Wake-up Receiving (WuRx) method on sensor nodes by using a dual-channel, an infrared (IR) based WuRx channel over a WiFi data channel to enhance the duty cycle efficiency. In addition, we build a pipelined relay algorithm to avoid any network delay performance degradation. Furthermore, we design a centrality-based algorithm to identify linear sensor networks. The experimental results show that IR WuRx ELSA performed nearly ten times better energy efficiency per node and achieved a constant network delay regardless of the number of relaying nodes in a network.