Data center sustainability assessment has long relied on fragmented metrics like PUE and WUE. While these quantify resource efficiency, they fail to capture the effectiveness of transforming energy and water into reliable and meaningful computational work. This paper introduces Green Work Efficiency (GWE), a unified metric that relates total environmental burden to delivered IT performance. GWE integrates two dimensions: Total Resource Burden, which aggregates carbon and water costs via life-cycle normalization, and Total Useful Work, which combines raw capacity with a Work Quality Factor. Our validation reveals an 800-fold environmental burden disparity between regions that legacy metrics perceive as identical. Finally, we introduce Unified Resource Burden Assessment Framework (URBAF), a new high-level architecture blueprint designed to integrate energy, water, and carbon measurements into a single coherent assessment pipeline.

In this panel, the experience adopting and adapting computer science education to the availability of generative AI will be examined from multiple international perspectives spanning varied educational and cultural contexts: the Arab world, India, Latin America, and the US. This will help computer science educators compare notes on what they teach, how they teach, and how students engage with the emerging tools. It will provide an opportunity to discuss how institutions are updating curricula, supporting faculty, and preparing students to use generative AI effectively and responsibly. The premise of the panel is that the similarities among these contexts outweigh the educational and cultural differences. Given how generative AI is dominating the discussion of computer science curricula, the global conversation will be of interest to all computer science educators.

Adoption of Fully Autonomous Vehicles (FAVs) depends on trust, which is defined as confidence in a vehicle's dependability, safety, and predictability.In cooperative driving scenarios, trust must exceed ego vehicles to include other autonomous vehicles and their coordination.This is challenged by unexpected multi-agent interactions, diminishing human control, and limited system transparency.We hypothesize that enhancing transparency by providing information about ego vehicle, other cooperative vehicles, and road conditions can foster trust.This is achieved by visualizing vehicleto-everything (V2X) information via augmented reality (AR) interfaces.To test this in a safe environment, we conducted a withinsubjects experiment in a Virtual Reality (VR) driving simulator with AR overlays.Participants experienced three interface concepts: (A) no transparency, (B) system-level transparency (ego vehicle intentions only), and (C) environment-level transparency (cooperation intentions, planned paths, and infrastructure).Results show that environment-level transparency, despite the higher cognitive workload, enhanced trust in both ego and cooperating FAVs.

A growing number of institutions choose to apply for and maintain ABET accreditation to distinguish their computing programs in terms of curriculum, academic rigor, and overall quality. Such programs need to track ABET accreditation criteria changes to ensure their programs remain continually compliant.

Computer Science Curricula 2023 (CS2023) was released in early 2024 by the Joint Task Force of the ACM, IEEE-Computer Society, and AAAI. Viewed from an educational perspective, CS2023 reflects the decennial changes in computer science (CS) since the release of the previous curricular guidelines, Computer Science Curricula 2013. CS2023 comprises curricular content and curricular practices: the former includes updates to the computer science knowledge areas and a competency model framework while curricular practices cover other aspects such as program design and delivery issues.

Model curricula for baccalaureate computer science (CS) have been published regularly from 1968 through 2013. In early 2021, the ACM, IEEE-Computer Society, and the Association for the Advancement of Artificial Intelligence (AAAI) constituted a task force to revise these curricula, which have now been released as Computer Science 2023 Curricula (CS2023). The CS2023 curricular guidelines inform educators and administrators on the what, why, and how to cover undergraduate CS over the next decade. Like past guidelines, CS2023 provides curricular content - a knowledge model largely backward compatible with CS2013, supplemented by a competency framework influenced by Computing Curricula 2020 (CC2020) - and complementary curricular practices, which include articles by international experts on program design and delivery. Ongoing drafts of CS2023 were disseminated via the CS2023 website, along with regular publications or presentations at various computing education venues.

In this paper, we explore the performance of an opportunistic network forwarding algorithm, namely Power and Interest Aware PeopleRank (PIPeR) in a subway mobility environment. PIPeR is known to perform well according to known metrics in delivering data taking into consideration both the interest in the disseminated data, along with power conservation. The algorithm was only tested however using pedestrian mobility models. Subway mobility is a candidate for many other useful cases for opportunistic content dissemination such as that of armed conflicts where civilian populations live in subway environments for safety without fixed network infrastructure. In this work, we implement and evaluate the PIPeR algorithm in a subway model using the AnyLogic simulator. Our results show a significant increase in the f-measure by 61% and decrease in the delay by 41% in comparison to the pedestrian mobility environment. In addition to that, we pinpoint some areas of interest where content dissemination happens vigorously yet at the expense of some increase in cost and power consumption, which we subsequently alleviate by using a particular hibernation model that decreases the power consumption by 43 % at the expense of only 6% reduction in delivery ratio.

Proactive content caching has been proposed as a promising solution to cope with the challenges caused by the rapid surge in content access using wireless and mobile devices and to prevent significant revenue loss for content providers. In this paper, we propose an end-to-end Deep Learning framework for proactive content caching that models the dynamic interaction between users and content items, particularly their features. The proposed model performs the caching task by building a probability distribution across different content items, per user, via a Deep Neural Network model and supports, both, centralized and distributed caching schemes. In addition, the paper addresses the key question: Do we need an explicit user-item pairs-based recommendation system in content caching? i.e., do we need to develop a recommendation system while tackling the content caching problem? To this end, an end-to-end Deep Learning framework is introduced. Finally, we validate our approach through extensive experiments on a real-world, public data set, coined MovieLens. Our experiments show consistent performance gains against its counterparts, where our proposed Deep Learning Caching module, dubbed as DLC, significantly outperforms state-of-the-art content caching schemes, serving as a baseline. Our code is available here: https://github.com/heshameraqi/Proactive-Content-Caching-with-Deep-Learning.

Professional accreditation in medicine and religious organizations started in the 1800s; business and engineering followed in the early 1900s. Program accreditation in computing commenced in the 1980s after computer science, informatics, and information systems programs had become widespread. In 2008, accrediting bodies in eight countries signed the Seoul Accord to set up mutual recognition of professional computing degrees. Although competency-based learning has existed for centuries, it has only made headway in computing in the last dozen years. Computing Curricula 2020 defined competency as the amalgamation of knowledge, skills, and dispositions. This working group report examines professional accreditation in computing, exploring aspects of professional accreditation criteria that support competency-based learning. The report will help educators understand professional accreditation and competency-based learning worldwide. Finally, it will also guide future efforts contributing to competency-based accreditation.

CS2013, the latest version of the ACM/IEEE Computer Society curricula is approaching its 10th anniversary. The CS202x Task Force, whose charge is to update this important document, began its work in Spring 2021. The aim of this BoF session is to seek feedback from the SIGCSE community on a draft recommendation from the Data Management knowledge area (CS2013 terminology) subcommittee. The area of Data Management has not remained static this past decade as new topics have emerged and previously minor topics have grown in popularity. These include distributed and replicated databases, the expanding role of non-relational databases, the now ubiquitous association between "back-end development" and data management services, and the elephant in any investigation of data management; Big Data. In this light, we seek to gather as many diverse views and voices as possible to help shape the curricular recommendations from the Data Management subcommittee. Additionally, we hope to enlist participants to remain engaged in this process as a guiding community to the Data Management subcommittee.

High Performance Computing (HPC) is the ability to process data and perform complex calculations at extremely high speeds. Current HPC platforms can achieve calculations on the order of quadrillions of calculations per second with quintillions on the horizon. The past three decades witnessed a vast increase in the use of HPC across different scientific, engineering and business communities, for example, sequencing the genome, predicting climate changes, designing modern aerodynamics, or establishing customer preferences. Although HPC has been well incorporated into science curricula such as bioinformatics, the same cannot be said for most computing programs. This working group will explore how HPC can make inroads into computer science education, from the undergraduate to postgraduate levels. The group will address research questions designed to investigate topics such as identifying and handling barriers that inhibit the adoption of HPC in educational environments, how to incorporate HPC into various curricula, and how HPC can be leveraged to enhance applied critical thinking and problem solving skills. Four deliverables include: (1) a catalog of core HPC educational concepts, (2) HPC curricula for contemporary computing needs, such as in artificial intelligence, cyberanalytics, data science and engineering, or internet of things, (3) possible infrastructures for implementing HPC coursework, and (4) HPC-related feedback to the CC2020 project.

Over the past two decades, data science or data analytics degree programs have begun to emerge, reflecting the world's demand for data specialists to make sense of the vast amounts of collected data in the sciences, engineering, business, and other domains. As degree creation has occurred mainly due to demand, ACM and other professional bodies have recently stepped in to provide curricular guidance. However, no \em shared global framework for data science as an academic discipline exists, making growth unfocused and driven by employer demands. More recently, the growth of artificial intelligence has also impacted data science programs. This working group builds on prior efforts and participant experiences to develop a global taxonomy of approaches to data science education and expectations for graduates of data science programs to \em think like data scientists.

Although high speed internet access is widespread in many countries, it is still undeniably scarce elsewhere. Opportunistic networking solutions have provided alternative connectivity models in such scenarios. One of the main opportunistic networking approaches involves leveraging social and Space Syntax information in order to minimize message forwarding costs, along with delay. Space Syntax is a domain of architecture engineering that relates spatial configuration to user mobility and thus, can provide insights into when to forward messages. In this work, we question the accuracy of existing Space syntax metrics in defining attraction points in a given urban area, and statistically demonstrate their inefficiency and lack of power awareness. We propose variations of Space Syntax metrics, and introduce a framework for integrating Space Syntax with social-based forwarding algorithms. We evaluate our framework via simulations using real traces that we collected from a university campus. Our results show an improvement in performance resulting from this integration, in terms of f-measure and utilization fairness, as well as significant reduction in the ratio of contacted uninterested nodes, cost, power consumption, and delay.

IP Television (IPTV) has created a new arena for digital advertising that has not been explored to its full potential. IPTV allows users to retrieve both on demand content as well as recommended content; however, limited research has been applied in the domain of advertising in IPTV systems. The introduction of IPTV, smart devices, and social networks led to the ability to gather a wider set of rich contextual information from multiple diverse sources. Such context could be used to enrich the decision making process of recommending advertisements to tailor better ads for users. This work attempts to create a targeted advertisement system that starts off by gathering a rich set of context information, and then provides viewers with recommended advertisements based on their current detected contextual information. In this paper, we describe our methodology, as well as our experiments designed to evaluate the proposed system. Our experimental results demonstrate both efficiency, as well as enhanced accuracy in the recommendation of context-sensitive advertisements.

Many applications are being developed to leverage the popularity of mobile opportunistic networks. However, building adaptive testbeds can be costly and challenging. This challenge motivates the need for effective opportunistic network simulators to provide a variety of opportunistic environment setups, and evaluate proposed applications and protocols with a comprehensive set of metrics. This paper presents SAROS, a simulator of opportunistic networking environments with a variety of interest distributions, power consumption distributions, imported real traces, and social network integration. The simulator provides a wide variety of evaluation metrics that are not offered by comparable simulators. Finally, SAROS also implements several opportunistic forwarding algorithms ranging from social-oblivious algorithms to interest and power-aware social-based algorithms.

Interest and social-awareness can be valuable determinants in decisions related to content delivery in mobile environments. Under certain conditions, we can deliver content with less cost and better delivery ratios, while only involving users that are interested in the type of content being delivered. However, the depletion of valuable power resources poses a deterrent to node participation in such interest-aware forwarding systems. No significant research contribution has been identified to collectively maximize the benefits of social, interest, and power awareness. In this work, we propose a new algorithm called PIPeR which integrates power awareness with an interest and socially aware forwarding algorithm called IPeR. Through simulations, we present and evaluate four modes of PIPeR. The results show that PIPeR is more fair and preserves at least 22% of the power IPeR consumes with less delay, while relying significantly on interested forwarders and with comparable cost to maintain similar delivery ratios.

Various emerging context aware social-based applications and services assume constant non-disruptive connectivity. Mobile advertisers in such environments want to reach potentially interested users in a given proximity and within a specified short-duration, whether these users are connected to the network or not. While opportunistic forwarding algorithms can be leveraged for forwarding these advertisements, there is little incentive for those not interested in the ad to act as forwarders. Our goal in this paper is to leverage explicit interest, gathered from a user's social profile, and integrate it with social-based opportunistic forwarding algorithms in order to enable soft realtime opportunistic ad delivery in intermittently connected mobile networks. We propose IPeR, a fully distributed interest-aware forwarding algorithm that integrates with PeopleRank to reduce the overall cost and delay while reducing the number of contacted uninterested candidates. Our results, obtained via simulations and validated with real mobility traces coupled with user social data, are promising. In comparison to interest-oblivious socially-aware protocols such as PeopleRank, the IPeR approach reduces the cost to 70% to reach the same delivery ratio, and reduces the ratio of contacted uninterested forwarders by 23%. It also achieves an extra 70% recall and 107% accuracy with only 2% less precision.

Web caching is one of many applications argued to benefit from a switch from a client-server to a peer-to-peer architecture. Several projects suggested the use of a network of peers to provide cached web content in order to help sites survive a burst of user requests. We present a new system that targets web sites with dynamic content and allows them to use a group of a variable number of volunteer peers to provide cached web content to the clients. The main objective of our system is to increase the capacity of a web server, and to reduce the average end user latency. We implement an Internet forums application using our approach as a case study of dynamic web sites, and emulate the implementation on a single machine. We show and analyze the results of experiments we applied on the emulated system, focusing on measurements of average end user latency and the upload bandwidth used by the web server.

Enterprise web based systems require an extremely vigilant management of database resources. Database connectivity is considered to be an important crosscutting, nonfunctional requirement that inevitably impacts the overall, runtime performance measures of the running web application. It is also a requirement that needs systematic resolution at the early phases of designing the web application under development due to its crosscutting nature. Database connection pooling can be seen as one of the more efficient solutions that will enhance the run-time performance of web/database transactional activities. Our work asserts that adopting an aspect-oriented approach for database connection pooling in web based systems promises for an even better improved run time performance of web based systems. In this study we analyze the run-time effects that result from applying three approaches upon cross cutting concerns of database connectivity in a developed web application. The three approaches are: An aspect-oriented approach (AOP), an object-oriented approach (OOP) or (Pool), and sequential calls to the native database driver approach (Native). The run-time effects were translated into run-time performance measures, namely, the average response time and successful hits percentage. We were able to demonstrate the added benefit of using an aspect-oriented approach for this problem.

Abstract As mobility became the norm rather than the exception, location‐based services are playing more of a key role in assisting mobile users. In this paper, we address the challenges of providing location‐based services to users in areas of sudden population increases, such as stadiums and traffic jams. A sudden increase in the number of mobile users leads to an increasing demand for the already scarce wireless bandwidth, thus causing dramatical throughput degradation and an increase in connectivity failures. We propose a hybrid model within which a peer‐to‐peer mode is deployed to assist the cellular‐based network whenever a sudden increase in population density is sensed by the base station. Location‐based data is migrated to selected elite nodes, thus allowing other peer nodes to get their information locally. This approach is proven, through experimental results, to decrease the query response time and number of query failures. Copyright © 2006 John Wiley & Sons, Ltd.

This paper presents the state of the art research achieved in resource sharing systems, and the application of such research in the highly promising field of pervasive computing. We discuss different resource sharing systems including peer to peer computing, utility computing, cluster computing, autonomic computing and grid computing, as well as their field of applications. We subsequently illustrate how such systems along with their applications can be adopted in a pervasive computing environment. We also present the open spaces architecture which utilizes many of the previously mentioned technologies for application in the field of pervasive computing.