Miracle-dz/UniFAR-FTUs huggingface dataset:
Integrated Sensing, Communication, and Computation Over-the-Air: MIMO Beamforming Design

To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data transmission, the integrated sensing and communication (ISAC) technique enables simultaneous data collection and delivery in the physical layer. By exploiting the analog-wave addition property in a multi-access channel, over-the-air computation (AirComp) has been proposed as a communication approach that also enables function computation. The promising performances of ISAC and AirComp motivate the current work on developing a framework called integrated sensing, communication, and computation over-the-air (ISCCO). Two schemes are designed to support multiple-input-multiple-output (MIMO) ISCCO simultaneously, namely the separated and shared schemes. The separated scheme splits antenna array for radar sensing and AirComp, while all the antennas transmit a joint waveform for both radar sensing and AirComp in the shared scheme. The performance of radar sensing is evaluated by the mean squared error (MSE) of the estimated target response matrix, while the MSE of the estimated function is adopted as the metric to evaluate the performance of the coupled communication and computation in AirComp. The design challenge of MIMO ISCCO lies in the joint optimization of beamformers at both the IoT devices and the server, which results in a non-convex problem. To solve this problem, an algorithmic solution based on the technique of semidefinite relaxation is proposed. The results reveal that the beamformer at each sensor needs to account for supporting dual-functional signals in the shared scheme, while dedicated beamformers for sensing and AirComp are needed to mitigate the mutual interference between the two functionalities in the separated scheme. The application of ISCCO on target location estimation is further demonstrated via simulation.
Effects of the Minimum Wage on Small Firms in China: Spillover Effects from Large Firms*

Abstract This study investigates how minimum wage affects small firms through spillover effects from large firms. Using firm-level panel data from Anhui Province in China, we find that after a minimum wage increase, small firms will reduce workers’ wages and create jobs due to the inflow of displaced workers from large firms. This spillover effect is larger for micro firms and private firms, where minimum wage compliance tends to be lower. We also find that high-tech small firms are more affected than low-tech ones because of their greater demand for skilled labor. Our findings not only highlight the unintended consequences of minimum wage on small firms in China, but also help to explain the ambiguous employment effects of minimum wage on the covered sector in developing countries.
Proxyeconomics, a theory and model of proxy-based competition and cultural evolution

Competitive societal systems by necessity rely on imperfect proxy measures. For instance, profit is used to measure value to consumers, patient volumes to measure hospital performance, or the journal impact factor to measure scientific value. While there are numerous reasons why proxies will deviate from the underlying societal goals, they will nevertheless determine the selection of cultural practices and guide individual decisions. These considerations suggest that the study of proxy-based competition requires the integration of cultural evolution theory and economics or decision theory. Here, we attempt such an integration in two ways. First, we describe an agent-based simulation model, combining methods and insights from these disciplines. The model suggests that an individual intrinsic incentive can constrain a cultural evolutionary pressure, which would otherwise enforce fully proxy-oriented practices. The emergent outcome is distinct from that with either the isolated economic or evolutionary mechanism. It reflects what we term lock-in, where competitive pressure can undermine the ability of agents to pursue the shared social goal. Second, we elaborate the broader context, outlining the system-theoretic foundations as well as some philosophical and practical implications, towards a broader theory. Overall, we suggest such a theory may offer an explanatory and predictive framework for diverse subjects, ranging from scientific replicability to climate inaction, and outlining strategies for diagnosis and mitigation.
High-quality host plant diets partially rescue female fecundity from a poor early start

Nutrition is a dynamic environmental factor and compensatory growth may help animals handle seasonal fluctuations in their diets. Yet, how the dynamic changes in nutrition affect female reproduction is understudied. We took advantage of a specialist insect herbivore, Narnia femorata Stål (Hemiptera: Coreidae), that feeds and reproduces on cactus across three seasons. We first examined how cactus quality can affect female reproductive success. Then, we investigated the extent to which reproductive success can be improved by a switch in diet quality at adulthood. We placed N. femorata juveniles onto prickly pear cactus pads with early-season (low-quality) or late-season (high-quality) fruit and tracked survivorship and development time. A subset of the females raised on low-quality diets were provided with an improved adult diet to simulate a seasonal change in diet. Adult female survival and egg production were tracked over time. All fitness-related traits were lower for females fed low-quality diets compared with females fed high-quality diets. However, when females had access to an improved adult diet, egg production was partially rescued. These findings show that a seasonal improvement in diet can enhance reproduction, but juvenile nutrition still has lasting effects that females cannot overcome.
Population viability assessment of an endangered raptor using detection/non-detection data reveals susceptibility to anthropogenic impacts

As the demand for carbon-neutral energy sources increases, so does the need to understand the impacts that these technologies have on the environment. Here, we assess the potential consequences of additional mortality on an Endangered raptor recently exposed to wind farms for the first time, the Black Harrier Circus maurus, one of the world’s rarest harriers. We conduct a population viability assessment using a Bayesian model integrating life-history information and annual reporting rates from detection/non-detection surveys from the South African Bird Atlas Project. Our model estimates a global population of approximately 1300 birds currently declining at 2.3% per year, and one that could collapse in under 100 years, if an average of three to five adult birds are killed annually. This level of mortality may soon exist, given the current rate of fatalities and the number of wind farms planned within the species’ distribution. In addition, we find that the population is sensitive to changes in climate. Our results highlight the critical need for appropriate placement, and adaptive management of wind farms and other infrastructure causing harrier mortality. We also show how detection/non-detection data may be used to infer population dynamics and viability, when population counts are unavailable.
Minimization of thermal insulation thickness taking into account condensation on external walls

Condensation occurs in the inner layers of construction materials at whatever point the partial pressure of water vapor diffuses and reaches its saturation pressure. Condensation, also called sweating, damages materials, reduces thermal resistance, and by increasing the total heat transfer coefficient, results in unwanted events such as increased heat loss. This study applied minimization of thermal insulation thickness with consideration given to condensation in the external walls. The calculations of heat and mass transfers in the structure elements are expressed in a graphical form. While there was an increase in the required thermal insulation thickness subsequent to an increase in the internal environment’s temperature, relative humidity, and the external environment’s relative humidity, the required thickness decreased with an increase in the external environment’s temperature. The amount of water vapor transferred varied with internal or external conditions and the thickness of the insulation. A change in the vapor diffusion resistance of the insulation material can increase the risk of condensation on the internal or external surfaces of the insulation.
Obesity and breast cancer: Preventive and therapeutic possibilities for bariatric surgery

Breast cancer is the most common and second deadliest malignancy in women. With rising obesity rates and building evidence for a strong association with obesity, the incidence of breast cancer can be expected to increase. Weight loss reduces breast cancer risk, the mechanisms of which are still poorly understood. As an effective therapy for obesity, bariatric surgery may be a powerful tool in breast cancer prevention and treatment. This review details the potential physiologic mechanisms that may underlie this association, as well as recently published studies that reinforce the link between bariatric surgery and a reduction in incident breast cancer. The use of bariatric surgery as an adjunct therapy in endometrial cancer also raises the potential for similar use in select breast cancer patients. Despite the expanding potential applications of bariatric surgery in this field, publications to date have been strictly observational, highlighting a need for future clinical trials.
Engineering a Complete Curriculum Overhaul

We present an eight-year curriculum redesign effort impacting al-most every course in our computer science department. Having not made a major update to our curriculum in two decades, complications began to arise from significant increases in enrollment and instituting multiple degrees in computing in the same department. Starting from a desire to adjust a few courses, we systematically collected a broad set of requirements and blue-sky ideas from many stakeholders, resulting in an unsatisfiable set of content, ordering, and course boundary constraints. After multiple rounds of conversation with our stakeholders in and out of the department, we evolved and relaxed several of our constraints, allowing us to develop a compromise plan for seven new courses and a new prerequisite system. We then piloted five of the new courses and collected feedback on results, iterating on these courses each semester for two years. We worked with registrars, advisors, and administrators to develop a transition plan from old to new courses. This paper presents highlights of each step of this process, a summary of the resulting curriculum design, and reflections and recommendations for other departments that may want to undertake a similar update.
How Creatively Are We Teaching and Assessing Creativity in Computing Education: A Systematic Literature Review

Previous studies investigating and identifying non-technical skills of computing show that creative skills play an important role in tackling difficult programming problems. In the field of cognitive psychology, creativity has been extensively researched, and many of these methods can be adapted for application in computing education. We conducted a systematic literature review to support research on creativity in computing education by summarizing relevant theories, instruments, and other prior work. The review encompasses all SIGCSE venues and major journals in the field, providing a perspective of the current landscape on teaching and assessing creativity, in the context of computing in higher education. We identify which papers explore creativity as a central theoretical basis, revealing eight major themes. We also found seven commonly used measurement instruments. Creativity theories that are notably absent are also discussed, as are pedagogical implications of the approaches to fostering creativity in computing education. This paper serves to support the community by highlighting the interdisciplinary aspects of creativity research applicable in computing contexts. Additionally, it provides practical guidance and implications for educators in leveraging creativity in the classroom.
Efficient and scalable core multiplexing with M³v

The M³ system (ASPLOS ’16) proposed a hardware/software co-design that simplifies integration between general-purpose cores and special-purpose accelerators, allowing users to easily utilize them in a unified manner. M³ is a tiled architecture, whose tiles (cores and accelerators) are partitioned between applications, such that each tile is dedicated to its own application. The M³x system (ATC ’19) extended M³ by trading off some isolation to enable coarse-grained multiplexing of tiles among multiple applications. With M³x, if source tile t₁ runs code of application p and sends a message m to destination tile t₂ while t₂ is currently not associated with p, then m is forwarded to the right place through a “slow path”, via some special OS tile. In this paper, we present M³v, which extends M³x by further trading off some isolation between applications to support “fast path” communication that does not require the said OS tile’s involvement. Thus, with M³v, a tile can be efficiently multiplexed between applications provided it is a general-purpose core. M³v achieves this goal by 1) adding a local multiplexer to each such core, and by 2) virtualizing the core’s hardware component responsible for cross-tile communications. We prototype M³v using RISC-V cores on an FPGA platform and show that it significantly outperforms M³x and may achieve competitive performance to Linux.
Developing Evidence-Based Teacher Practice Briefs with Middle School Computer Science Teachers

Engaging teachers in research has been shown to increase their self-efficacy and other factors related to increasing students' learning and growth. In this project, we partnered education researchers with computer science (CS) middle school teachers in the U.S. to develop a set of equity-focused teacher practice briefs that address a problem of practice that the teachers experience in their classrooms. Using a prescribed development process previously developed by Bell and Rhinehart (2016) for creating these briefs, teacher involved in the process saw their self-efficacy, professional networks and pedagogical content knowledge with respect to equity increase. The process allowed educators to reflect on their understanding of their particular problems of practice, while also collaboratively with expert researchers in the field to interpret previously published research results. The teachers and researcher teams created three briefs for the broader community to use. Throughout this four-month process, we learned how to improve the format for our particular needs, which included ensuring all voices were heard and equity was at the forefront of the work. In this experience report, we detail the process we used and report on lessons learned about the process from the teachers and researchers who participated. We also provide recommendations for others interested in using this method to engage in-service or pre-service teachers.
WhatShapeWhatColor: Designing a Puzzle Game to Elicit the Field Dependence-Independence Cognitive Style

Theories in cognitive psychology suggest that people differ in the way we process information as we tend to develop different cognitive styles. Recent research in Human-Computer Interaction has revealed that people who have different cognitive styles tend to develop different strategies, achieve different performance, and have different experience when interacting with information systems. Therefore, there is a need for customizing such systems to fit their users’ cognitive styles. However, the elicitation of users’ cognitive styles is a time-consuming and a non-practical process that requires human intervention. To overcome this challenge, in this paper, we present the design of WhatShapeWhatColor, which is a web-based memory puzzle game that aims to elicit Field Dependence-Independence, a well established cognitive style, implicitly and with no human intervention. Moreover, we report the results of an evaluation user-study with 108 participants, which revealed that the implicit and automatic elicitation is feasible in various game difficulty levels. Such results could increase the practicability and applicability of elicitation techniques to enable designers deliver experiences that could be adapted to their users’ cognitive preferences and needs.
Exploring the Impact of Voluntary Practice and Procrastination in an Introductory Programming Course

The effort to learn and the regulation of learning are key to successful learning. Voluntary practice has been shown to improve learning and is associated with having generally good self-regulated learning. At the same time, procrastination often slows the learning process and is associated with less than ideal regulation of learning. In this paper, we present the results of a study exploring the impact of voluntary practice and procrastination on the learning outcomes of novice programmers. We used data from an introductory programming course (CS1) at a large university and found that most students engaged in voluntary practice. However, students with higher prior performance and non-procrastinators were more likely to participate in the voluntary practice. We also found that participating in the voluntary practice did not have a significant impact on course performance. Furthermore, the study showed a weak negative correlation between procrastination and time spent on the homework and a weak negative correlation between procrastination and distributed practice. Finally, we found that non-procrastinators performed significantly better than procrastinators on the majority of homeworks.
How Computer Science and Statistics Instructors Approach Data Science Pedagogy Differently: Three Case Studies

Over the past decade, data science courses have been growing more popular across university campuses. These courses often involve a mix of programming and statistics and are taught by instructors from diverse backgrounds. In our experiences launching a data science program at a large public U.S. university over the past four years, we noticed one central tension within many such courses: instructors must finely balance how much computing versus statistics to teach in the limited available time. In this experience report, we provide a detailed firsthand reflection on how we have personally balanced these two major topic areas within several offerings of a large introductory data science course that we taught and wrote an accompanying textbook for; our course has served several thousand students over the past four years. We present three case studies from our experiences to illustrate how computer science and statistics instructors approach data science differently on topics ranging from algorithmic depth to modeling to data acquisition. We then draw connections to deeper tradeoffs in data science to help guide instructors who design interdisciplinary courses. We conclude by suggesting ways that instructors can incorporate both computer science and statistics perspectives to improve data science teaching.
A Four-year Study of a Placement Exam for an Introductory Programming Course

Introductory programming courses can be challenging due to heterogeneous student backgrounds. While novice programmers find even basic contents very demanding, experienced programmers are not sufficiently challenged. Having both novice and experienced programmers attend the same lab exercise can have a negative impact on novice programmers' self-efficacy. To create a better teaching environment for students, two different tracks were introduced in our introductory programming course, one for beginners and one for advanced students. Incoming students are offered a voluntary exam that can be used to obtain a place in the advanced track. Only one course is held throughout the semester but the advanced track does not begin lab exercises until the middle of the semester. This paper outlines the design and implementation of our course and its placement exam. It describes how, over the past four years, internal factors such as student feedback and grades, as well as external factors such as COVID-19, have influenced the design of the placement exam. Various statistics collected during this period are analysed and compared with results from the CEd literature.
Predicting Student Success in CS2: A Study of CS1 Exam Questions

A number of published studies indicate that many students who receive passing grades in CS1 may struggle in CS2. This can lead to higher attrition and failure rates in CS2, and perhaps also in subsequent courses in the curriculum. Many researchers have studied factors that lead to student success and common misconceptions that may arise in introductory courses. However, relatively little attention has been focused on the transition between CS1 and CS2. In this paper, we report on a study of a variety of types of CS1 exam questions, with the goal of finding questions that can predict a student's success (or lack of success) in CS2. Results indicate that code explanation and code completion questions can be especially good predictors, along with some code tracing questions. We discuss some of the factors that may make certain questions better predictors than others, in the process confirming some observations that have been reported by other researchers. The results from this experiment seem promising, and point to several possibilities for further research.
Evaluating Citizen Comments in Public Consultations Using Data Mining: Evaluating Citizen Comments in Public Consultations Using Data Mining: Analyzing Legislation Comments for the Greek General Commercial Registry

In the present paper, in the context of a pilot application, comments that appear on the Consultation Website of the Ministry of Development and Investments of Greece, concerning the draft provisions of the Greek General Commercial Registry, were collected and studied. Algorithms that use various data mining methods were applied in order to analyze citizens’ comments. The data set was processed with the algorithms Apriori, Naive Bayes, J4.8 and K-Means through Weka software. The aim of this study is to classify citizens’ comments, discover behavioral patterns with the use of association rules, identify groups with similar comments and recommendations using the clustering method
AI-driven prediction for the disposition of medium-risk incidents visiting emergency departments

Emergency Departments globally suffer overcrowding due to the lack of adequate capacity and/or guidelines and policies for triaging patients. Medical service quality improvement requires optimal patient prioritization according to their level of urgency. This study aims to evaluate classification models predicting the admission or discharge of incidents triaged as level 3 according to the Emergency Severity Index algorithm. As such, adult patient visits were examined from a publicly available dataset. Feature Importance was used for the assessment of each variable contribution in predictions and a subset of 196 out of 972 variables of the original dataset was sampled. XGBoost, random forest, convolutional neural network, and k-nearest neighbors algorithms were deployed and evaluated regarding hospitalization prediction. Convolutional neural network utilization required a tabular data to image transformation which was applied using the Image Data Generator to Tabular Data (IGTD) algorithm. Benchmarking among the four algorithms showed that XGBoost outperformed the others, achieving an accuracy of 0.75, an area under the receiver operating curve of 0.74 and an area under the precision-recall curve of 0.54. Overall, machine learning-based assessment of the disposition of medium-risk patients according to ESI may lead to predictive models which constitute useful decision support tools.