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关键词： Memory management   Mice   Arrays   Random access memory   System-on-chip   Hash functions   Computer science   Estimator   sketches   generic counter   network measurement   compression   self-adaptive  

摘要： The Sketch is a compact data structure useful for network measurements. However, to cope with the high speeds of the current data plane, it needs to be held in the small on-chip memory (SRAM). Therefore, the product of the counter size and the number of counters must be below a certain limit. With small counters, some will overflow. With large counters, the total number of counters will be small, but each counter will be shared by more flows, leading to poor accuracy. To address this issue, we propose a generic technique: self-adaptive counters (SEAD Counter). When the value of the counter is small, it works as a standard counter. When the value of the counter is large however, we increment it using a predefined probability, so as to represent this large value. Moreover, in the SEAD Counter, the probability decreases when the value increases. We show that this technique can significantly improve the accuracy of counters. This technique can be adapted to different circumstances. We theoretically analyze the improvements achieved by the SEAD Counter. We further show that our SEAD Counter can be extended to three typical sketches and Bloom filters. We conduct extensive experiments on three real datasets and one synthetic dataset. The experimental results show that, compared with the state-of-the-art, sketches using the SEAD Counter improve the accuracy by up to 13.6 times, while the Bloom filters using SEAD Counter can reduce the false positive rate by more than one order of magnitude.

关键词： Portfolios   Training   Training data   Cybernetics   Parallel algorithms   Tuning   Computer science   Automatic portfolio construction (APC)   generative adversarial approach   parallel algorithm portfolio   parameter tuning  

摘要： Since automatic algorithm configuration methods have been very effective, recently there is increasing research interest in utilizing them for automatic solver construction, resulting in several notable approaches. For these approaches, a basic assumption is that the given training set could sufficiently represent the target use cases such that the constructed solvers can generalize well. However, such an assumption does not always hold in practice since in some cases, we might only have scarce and biased training data. This article studies effective construction approaches for the parallel algorithm portfolios that are less affected in these cases. Unlike previous approaches, the proposed approach simultaneously considers instance generation and portfolio construction in an adversarial process, in which the aim of the former is to generate instances that are challenging for the current portfolio, while the aim of the latter is to find a new component solver for the portfolio to better solve the newly generated instances. Applied to two widely studied problem domains, that is, the Boolean satisfiability problems (SAT) and the traveling salesman problems (TSPs), the proposed approach identified parallel portfolios with much better generalization than the ones generated by the existing approaches when the training data were scarce and biased. Moreover, it was further demonstrated that the generated portfolios could even rival the state-of-the-art manually designed parallel solvers.

关键词： mHealth   adherence   HIV   antiretroviral therapy   intervention development   mobile phone  

摘要： Background: The HIV epidemic in the Philippines is one of the fastest growing epidemics globally, and infections among men who have sex with men are rising at an alarming rate. The World Health Organization recommends the use of mobile health (mHealth) technologies to engage patients in care and ensure high levels of adherence to antiretroviral therapy (ART). Existing mHealth interventions can be adapted and tailored to the context and population served. Objective: This study aims to create a locally tailored intervention using a mobile phone platform to support treatment adherence for HIV patients on ART in the Philippines. Methods: A mixed methods approach guided by the Behavior Change Wheel framework was used to adapt an existing mHealth adherence support platform for the local setting and target population. A literature review, retrospective clinical record review, and focus group discussions with patients were conducted to understand the drivers of ART adherence and tailor the intervention accordingly. The resulting intervention was pilot-tested for 8 weeks, followed by focus group discussions with patients who received the intervention to assess the acceptability of the design. Results: Key issues contributing to nonadherence included side effects, lack of behavioral skills for pill taking, social support, mental health, and substance use. Patients identified mHealth as an acceptable mode of intervention delivery and wanted mHealth services to be highly personalizable. The study team, clinicians, and software developers integrated these findings into the intervention, which included a menu of services as follows: pill reminders, health tips, adherence feedback, appointment reminders, and symptom reporting. During the pilot phase, technical issues in the interactive voice response system (IVRS) were identified and addressed. Patients who participated in the pilot phase expressed a preference for SMS text messaging over the IVRS. Patients responded positively to

关键词： Task analysis   Noise measurement   Neurons   Machine learning algorithms   Computer science   Genetic algorithms   Autoencoder   class imbalance   classification   neural network   undersampling  

摘要： Machine learning models have gained popularity nowadays for their potential to solve real-life issues when trained on pertinent data. In many cases, the real-life data are class imbalanced and hence the corresponding machine learning models trained on the data tend to perform poorly on metrics like precision, recall, AUC, F1, and G-mean score. Since class imbalance issue poses serious challenges to the performance of trained models, a multitude of research works have addressed this issue. Two common data-based sampling techniques have mostly been proposed-undersampling the data of the majority class and oversampling the data of the minority class. In this article, we focus on the former approach. We propose two novel algorithms that employ neural network-based approaches to remove majority samples that are found to reside in the vicinity of the minority samples, thereby undersampling the former to remove (or alleviate) the imbalance issue. We delineate the proposed algorithms and then test the proposed algorithms on some publicly available imbalanced datasets. We then compare the performance of our proposed algorithms to other popular undersampling algorithms. Finally, we conclude that our proposed algorithms outperform most of the existing undersampling approaches on most performance metrics.

摘要： "I cannot work on what I cannot see…" ---Katherine Goble, Hidden Figures

关键词： Agriculture 4   0   Operator safety   Muscoskeletal disorders   Vibration risk   Machine learning   Ergonomics  

摘要： While being a fundamental driver of competitiveness in agroindustry, technological innovation has also introduced new critical elements related, for example, to the sustainability of the production processes as well as to the safety of workers. In such regard, the advent of the 4th industrial revolution (Agriculture 4.0) based on digitalization, is an unprecedented opportunity of rethinking the role of innovation in a new human-centric perspective. In particular, the establishment of an interconnected work environment and the augmentation of the operator's physical, sensorial, and cognitive capabilities, are two technologies which can be effectively employed for substantially improving the ergonomics and safety conditions on the workplace. This paper approaches such topic referring to the vibration risk, which is a well-known cause of work-related pathologies, and proposes an original methodology for mapping the risk exposure of the operators to the activities performed. A miniaturized wearable device is employed to collect vibration data, and the signals obtained are segmented in time windows and processed in order to extract the significant features. Finally, a machine learning classifier has been developed to recognize the worker's activity and to evaluate the related exposure to vibration risks. To validate the methodology proposed, an experimental analysis in real operating conditions has been finally carried out by monitoring the activities performed by a team of workers during harvesting operations. The results obtained demonstrate the feasibility and the effectiveness of the methodology proposed.

关键词： Scientometrics   Research trends   Time-series prediction   Deep learning   Computer science  

摘要： Thousands of research papers are being published every day, and among all these research works, one of the fastest-growing fields is computer science (CS). Thus, learning which research areas are trending in this particular field of study is advantageous to a significant number of scholars, research institutions, and funding organizations. Many scientometric studies have been done focusing on analyzing the current CS trends and predicting future ones from different perspectives as a consequence. Despite the large datasets from this vast number of CS publications and the power of deep learning methods in such big data problems, deep neural networks have not yet been used to their full potential in this area. Therefore, the objective of this paper is to predict the upcoming years' CS trends using long short-term memory neural networks. Accordingly, CS papers from 1940 and their corresponding fields of study from the microsoft academic graph dataset have been exploited for solving this research trend prediction problem. The prediction accuracy of the proposed method is then evaluated using RMSE and coefficient of determination (R-2) metrics. The evaluations show that the proposed method outperforms the baseline approaches in terms of the prediction accuracy in all considered time periods. Subsequently, adopting the proposed method's predictions, we investigate future trending areas in computer science research from various viewpoints.

关键词： course analysis   clustering   classification   heat maps  

摘要： In this paper we consider courses of a Computer Science degree in an Italian university from the year 2011 up to 2020. For each course, we know the number of exams taken by students during a given calendar year and the corresponding average grade;we also know the average normalized value of the result obtained in the entrance test and the distribution of students according to the gender. By using classification and clustering techniques, we analyze different data sets obtained by pre-processing the original data with information about students and their exams, and highlight which courses show a significant deviation from the typical progression of the courses of the same teaching year, as time changes. Finally, we give heat maps showing the order in which exams were taken by graduated students. The paper shows a reproducible methodology that can be applied to any degree course with a similar organization, to identify courses that present critical issues over time. A strength of the work is to consider courses over time as variables of interest, instead of the more frequently used personal and academic data concerning students.

关键词： death penalty   capital punishment   legitimacy   Brazil   insecurity   crime  

摘要： The primary purpose of this study was to examine public support for the death penalty in Brazil and to determine factors that influence such support. Currently, Brazil has the death penalty for cases involving war crimes, genocides, terrorism, and crime against humanity. The country's constitution, however, prohibits the use of the death penalty in ordinary crimes. Analyzing individual-level cross-sectional data collected by the American Barometer Survey, we found that a significant majority of Brazilians support the death penalty, with more than two thirds expressing greater support. In terms of factors, it was revealed that frequency of murders, sense of insecurity, and perceived institutional legitimacy largely influence support for the death penalty. In addition, ethnicity, religiosity, and religious affiliation affect citizens' attitudes toward the death penalty. The findings of this study have serious implications for research and practice.

关键词： Ethics   Training   Codes   Computer science   Reflection   Europe   Robots   Engineering   ethical competence   evaluation rubric   final degree project  

摘要： Ethics is taking a relevant role in this 21st century, in most cases going hand in hand with scientific-technological advances and the social and cultural implications. However, to date, the evidence on which a university in the European Higher Education Area (EHEA) can rely to incorporate the ethical perspective in the educational curriculum successfully is still scarce. To increase the body of knowledge in engineering ethics, this article presents and evaluates the initiative called 'Tecnoetica' implemented at the University of Deusto, Spain. This initiative proposes to include training in ethical principles, dilemmas, and issues from a holistic and participatory standpoint in which lecturers and students are involved. Because of the initiative, an evaluation rubric is presented as the main contribution from which other centers, universities and/or faculties can benefit.