Studying computer science is a journey: people start at different times, travel at different paces, and pause along the way. In this experience report, we describe a peer-led, year-long program designed to welcome students to Computer Science and Engineering as a discipline, department, and academic program. We detail the logistical, curricular, and personnel structures of this program, highlighting design choices we made to (a) open multiple ways to join the program all year, (b) de-emphasize "getting ahead", (c) prioritize reflection, and (d) connect students to existing resources. Throughout, we emphasize the critical role of peer mentors in leading and shaping this space. We share our own lessons learned, as well as reflections from students and mentors on the value of this learning community outside of formal classroom structures.

This work contributes to a better understanding of computing teachers' perceptions of themselves as role models. Role models are described as important to address under-representation, yet there is little in-depth research on how role modeling works and what university teachers in computing can model to broaden participation in the discipline. We will analyze teachers' reflections on how they may, or want to, be perceived by their students, particularly in terms of professional competencies, emotions and attitudes towards well-being. We will use and further develop an already existing framework on role modeling in computing, and we will relate our findings to existing research on computing and science identities. Modeling aspects outside the computing norm can help provide students with a wider notion of what it means to be a computer scientist. Besides developing the theoretical understanding of computing teachers as role models , our work can support various ways of developing computing teachers' competences and departments' teaching culture. The results are one way to contribute to student diversity and equitable access, and more broadly increase the relevance of computing education for sustainability.

At our large U.S. research-intensive university, Chicano/Latino and Black/African-American students have been disproportionately leaving the Computer Science and Engineering (CSE) majors at a higher rate than students without these identities. To uncover possible reasons for this, we invited students in these majors who identify as Chicano/Latino and Black/African-American to participate in focus groups. Twelve students, all identifying as Latinx/Hispanic, partici- pated in the focus groups. We identify several themes related to challenging aspects of the student experience, spanning physical campus environment, department curriculum and policies, and connections between students. We triangulate these findings with results from a survey measuring sense of belonging, confidence, and obstacles for thousands of students across eight introductory CSE courses. We discuss how these themes relate to actions that departments can take to address these challenges.

Urban areas are negatively impacted by Carbon Dioxide (CO2 ) and Nitrogen Oxide (NOx) emissions. In order to achieve a cost-effective reduction of greenhouse gas emissions and to combat climate change, the European Union (EU) introduced an Emissions Trading System (ETS) where organizations can buy or receive emission allowances as needed. The current ETS is a centralized one, consisting of a set of complex rules. It is currently administered at the organizational level and is used for fixed-point sources of pollution such as factories, power plants, and refineries. However, the current ETS cannot efficiently cope with vehicle mobility, even though vehicles are one of the primary sources of CO2 and NOx emissions. In this study, we propose a new distributed Blockchain-based emissions allowance trading system called B-ETS. This system enables transparent and trustworthy data exchange as well as trading of allowances among vehicles, relying on vehicle-to-vehicle communication. In addition, we introduce an economic incentive-based mechanism that appeals to individual drivers and leads them to modify their driving behavior in order to reduce emissions. The efficiency of the proposed system is studied through extensive simulations, showing how increased vehicle connectivity can lead to a reduction of the emissions generated from those vehicles. We demonstrate that our method can be used for full life-cycle monitoring and fuel economy reporting. This leads us to conjecture that the proposed system could lead to important behavioral changes among the drivers

Mobile devices with embedded sensors for data collection and environmental sensing create a basis for a cost-effective approach for data trading. For example, these data can be related to pollution and gas emissions, which can be used to check the compliance with national and international regulations. The current approach for IoT data trading relies on a centralized third-party entity to negotiate between data consumers and data providers, which is inefficient and insecure on a large scale. In comparison, a decentralized approach based on distributed ledger technologies (DLT) enables data trading while ensuring trust, security, and privacy. However, due to the lack of understanding of the communication efficiency between sellers and buyers, there is still a significant gap in benchmarking the data trading protocols in IoT environments. Motivated by this knowledge gap, we introduce a model for DLT-based IoT data trading over the narrowband Internet-of-Things (NB-IoT) system, intended to support massive environmental sensing. We characterize the communication efficiency of three basic DLT-based IoT data trading protocols via NB-IoT connectivity in terms of latency and energy consumption. The model and analyses of these protocols provide a benchmark for IoT data trading applications.

In 2016, 49 Black women in the United States and Canada were enrolled in computer science PhD programs. This is less than one percent of total enrollment. In the same year, only 8 Black women earned the PhD. Additionally, Black women achieving tenured and tenure-track positions are sorely lacking in computer science, computer engineering and informatics departments. In 2016, 3 black females were full professors, 14 were associate professors, and 11 were assistant professors. In comparison, white female faculty numbers included 219 full professors, 144 associate professors, and 118 assistant professors. This research studies two main factors that deter black women from pursuing and achieving PhDs in computer science through understanding and uncovering the statistics provided by the Computing Research Association's annual Taulbee survey. Additionally, we explore the misrepresentation of the "unknowns" in the statistics. The next phase of this work will include a more in-depth analysis of the personal experiences of Black and Brown women in doctoral programs in the computer sciences.