Miracle-dz/UniFAR-FTUs huggingface dataset:
Fool the COOL - On the Robustness of Deep Learning SAR ATR Systems

Over the last years, deep learning automatic target recognition systems have become very popular for synthetic aperture radar images. These systems achieve very high classification rates with common datasets, like the Moving and Stationary Target Acquisition and Recognition (MSTAR) data. A point that is normally not considered is the robustness of these systems, which typically use a softmax layer without rejection class for classification. It has been reported in the past that small variations in the training and test data of deep neural networks might lead to a change in the result. To avoid this situation, several methods to increase the robustness are presented in this paper. These methods vary from simple, like training with noisy samples, to changes in the network structure, particularly the Competitive Overcomplete Output Layer (COOL) is proposed. The COOL gives an output value that also represents a confidence, but with a larger variation than the softmax output. To evaluate the robustness, the DeepFool algorithm is used to creates adversarial examples, i.e. images that look similar to the original data, but cause a wrong classification result. This algorithm is applied to the known training data of the different networks.
SSL-SP: A Semi-Supervised-Learning-Based Stream Partitioning Method for Scale Iterated Scheduling in Time-Sensitive Networks

The demands of reliable and real-time communication in industrial automation systems drive growing attention on Time-Sensitive Networks (TSNs) due to its guarantee of low latency and deterministic transmission. Current works explore iterated scheduling of time-triggered (TT) streams in TSNs mainly by random stream partitioning and graph-based stream partitioning. However, random partitioning obtains limited performance, while the edge weights of graph-based partitioning heavily depend on prior domain knowledge. In this article, we propose a semi-supervised-learning-based stream partitioning (SSL-SP) method for scale iterated scheduling of TT streams in TSNs. SSL-SP automatically discovers stream dependence and clusters streams of similar representation. Besides, we design an evaluation metric for TT stream relevance with no requirements on prior knowledge. Furthermore, we construct a low-rank representation of each stream by sparse encoding. A T2S2 dataset on time-triggered stream scheduling is constructed as a comparison benchmark. Simulations demonstrate that SSL-LP achieves the best schedulability rate (86.6%) compared with the state-of-the-art stream partitioning methods.
Student perceptions of emergency remote civil engineering pedagogy

Transitioning to online learning (OL) is more challenging for engineering education because hands-on course delivery requirements are compromised. Like most universities around the globe, the institution to which the authors are affiliated shifted to emergency remote teaching in spring 2020 because of the coronavirus disease 2019 (COVID-19) pandemic. The researchers queried undergraduate students enrolled in the Civil and Environmental Engineering Department about their perceptions of OL during the pandemic and asked whether the students would attend online classes in the future. The students did not evince a clear inclination for either face-to-face (F2F) classes or OL (52%-48%) regardless of the proximity between their residence and the campus. The participating students were also almost equally divided irrespective of their gender when asked if OL was better than F2F learning. Moreover, the students registered a 50% positive response when asked whether they would enroll in optional online classes in the future. Most students who did not desire OL nevertheless enrolled in online classes in the summer session of 2020. However, student preference for OL tended to decline with the increase in the number of registered courses. Furthermore, overachieving and underachieving students favored F2F classes, and male students were more inclined to OL than female students. Other factors that may have influenced student predilections for OL during the COVID-19 lockdown include access to emotional support, information technology support, and reliable internet connection; the availability of an undisturbed space at home; and the nature and methodology of online examinations.
ODMDEF: On-Device Multi-DNN Execution Framework Utilizing Adaptive Layer-Allocation on General Purpose Cores and Accelerators

On-device DNN processing has been common interests in the field of autonomous driving research. For better accuracy, both the number of DNN models and the model-complexity have been increased. To properly respond to this, hardware platforms structured with multicore-based CPUs and DNN accelerators have been released, and the GPU is generally used as an accelerator. When multiple DNN workloads are sporadically requested, the GPU can be easily oversubscribed, thereby leading to an unexpected performance bottleneck. We propose an on-device CPU-GPU co-scheduling framework for multi-DNN execution to remove the performance barrier precluding DNN executions from being bounded by the GPU. Our framework fills up the unused CPU cycles with DNN computations to ease the computational burden of the GPU. To provide seamless computing environment for the two different core types, the framework formats each layer execution according to the computational methods supported by CPU and GPU cores. To cope with irregular arrivals of DNN workloads, and to accommodate their fluctuating demands for hardware resources, our framework dynamically selects the best fit core type after making a comparative judgement between the current availabilities of the two core types. During the core selection time, offline-trained prediction models are utilized to get precisely predicted execution time of the issued layer. Our framework mitigates the fact that even the same DNN models can have large performance deviations due to the nature of the process scheduler of the underlying OS which is GPU-agnostic. In addition, the framework minimizes the memory copy overhead inevitably occurring in the data synchronization phase between the heterogeneous cores. To do so, we further analyze GPU-to-CPU and CPU-to-GPU data transfer cases separately, and then apply the solution that best suits each case. For multi-DNN inference jobs with the NVIDIA Jetson AGX Xavier platform, our framework speeds up the execution time by up to 46.6% over the GPU-only solution.
Synchronous E-learning in Higher Education during the COVID-19 Pandemic

Since the beginning of the COVID-19 pandemic, students around the world have seen their schooling completely disrupted. Their teachers had to reorganize in a hurry to be able to give their classes synchronously but at a distance. Thanks to strong political incentives, both at the national and university levels, many tools have been massively deployed: digital learning management systems (e.g. Moodle), collaborative digital platforms (e.g. Google Meets, Microsoft Teams and Zoom), social networks (e.g. Facebook and Twitter) and even the telephone. While the COVID-19 pandemic has highlighted the essential role of digital technologies in higher education, major issues arise regarding the quality of distance education, the learning process itself and the assessment of knowledge and skills. In this study, 81 engineering students in France were followed during several periods of containment in order to provide some answers to these questions. The results of the various knowledge tests carried out at a distance show that the students obtained local scores similar to those expected from face-to-face teaching. According to the results of the satisfaction surveys, for 91.4% of students who had sufficient hardware and software resources, the synchronous approach to e-learning presented few barriers. For the 8.6% of students affected by the digital divide, telephone communications and social networks played a major role in the learning process.
Studying the Students’ Learning in LoT@UNED

The instructional design of experimental subjects with a distance methodology in Engineering disciplines is nowadays a challenge. One of the most useful applications is the use of remote and virtual laboratories technologies. The learning process must transparently integrate these technologies. It is also essential to be able to analyze the students learning and acceptance of this technology. In particular, the IoT paradigm involves the acquisition of practical skills and knowledge from the use of devices/sensors to the management of cloud environments, by including data storage and processing. A Laboratory of Things platform at UNED (called LoT@UNED) has been developed to cover all phases of a life-cycle development, both hardware infrastructures and software applications. This work focuses on studying the influence among several acceptance indicators during the learning/teaching process of students for the cyber-security topic, and with respect to their intention of employing LoT@UNED for other learning purposes. To achieve this, a new structural equation models (SEM) has been proposed, as well as an statistical analysis is included from a confirmatory point of view.
Node ranking strategy in virtual network embedding: An overview

Network virtualization (NV) is a highprofile way to solve the ossification problem of the nowadays Internet, and be able to support the diversified network naturally. In NV, Virtual Network Embedding (VNE) problem has been widely considered as a crucial issue, which is aimed to embed Virtual Networks (VNs) onto the shared substrate networks (SNs) efficiently. Recently, some VNE approaches have developed Node Ranking strategies to drive and enhance the embedding efficiency. Node Ranking Strategy rank/sort the nodes according to the attributes of the node, including both residual local attributes (CPU, Bandwidth, storage, Etc.) and the global topology attributes (Number of neighborhood Nodes, Delay to other nodes, Etc.). This paper presents an overview of Node Ranking Strategies in Virtual Network Embedding, and possible directions of VNE Node Ranking Strategy.
Bandit-Based Monte Carlo Optimization for Nearest Neighbors

The celebrated Monte Carlo method estimates an expensive-to-compute quantity by random sampling. Bandit-based Monte Carlo optimization is a general technique for computing the minimum of many such expensive-to-compute quantities by adaptive random sampling. The technique converts an optimization problem into a statistical estimation problem which is then solved via multi-armed bandits. We apply this technique to solve the problem of high-dimensional <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbors, developing an algorithm which we prove is able to identify exact nearest neighbors with high probability. We show that under regularity assumptions on a dataset of <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> points in <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-dimensional space, the complexity of our algorithm scales logarithmically with the dimension of the data as <inline-formula> <tex-math notation="LaTeX">$O\left({(n+d)\log ^{2} \frac {nd}{\delta }}\right)$ </tex-math></inline-formula> for error probability <inline-formula> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula>, rather than linearly as in exact computation requiring <inline-formula> <tex-math notation="LaTeX">$O(nd)$ </tex-math></inline-formula>. We corroborate our theoretical results with numerical simulations, showing that our algorithm outperforms both exact computation and state-of-the-art algorithms such as kGraph, NGT, and LSH on real datasets.
Pedagogical Innovations With A Gender Approach To Increase Computer Programming Self-Efficacy In Engineering Students

This study seeks to identify the effect that in-class teamwork activities have on the technical roles assumed by female engineering students in computer programming courses. During 2018 and 2019, a mixed methodology research was conducted in 2 phases. First, a qualitative study that consisted of 15 in depth interviews with current and former female engineering students. In addition, the research team conducted non-participatory observations in 2 courses, to identify elements of group dynamics in mixed gendered classes. This qualitative phase informed the quantitative study designed in the second phase. The quantitative phase took place in two computer programming courses associated to an undergraduate engineering program: Course A, which uses traditional lecture classes, and Course B, which uses project-based learning as the teaching methodology. In course B, a questionnaire (91 responses) was applied to identify the dynamics of role assignment in their teams. A self-efficacy questionnaire on computer programming was applied to courses A (n=33) and B (n =46) to compare the experience of women in the modality of individual and group work. The results indicate that women in course B tend to assume roles of coordination and work management, unlike men who define themselves as programmers or designers. The non-parametric tests applied indicate that when comparing the start and end of the course, the men and women of both courses significantly increased their overall self-efficacy. However, if only items that include execution tasks are considered, women in course B do not significantly improve their self-efficacy, unlike men from course B and both women and men from course A. Pedagogical recommendations are presented to improve self-efficacy of female engineering students in group-work based courses in computer programming subjects.
A Data Science-based Approach for Identifying Counseling Needs in first-year Students

We continuously face high dropout rates in our faculty’s Bachelor degree programs in Computer Science and in Information Systems. This is a quite common problem across engineering and computer science departments. Economically, for the university dropout means a waste of resources. On the other hand, to the young people it means a waste of lifetime, even though they might gain some experience. Furthermore, the labor market demands for many more well-educated engineers and IT specialists than are currently output. When we want to take special care of students that are in danger of dropping out, we must ensure that we identify those that really need this support. This group is sometimes called the “murky middle”. To identify them, we not only have to differentiate them from those students that will eventually earn their degree even without counseling, but also have to distinguish them from those students that do not seriously study at all – which is quite a significant number. Data analysis techniques on students’ demographic and progress data have shown potential for an early prediction of dropout. We use logistic regression to identify factors for study success or failure, and we apply decision tree analysis to support the process of deciding which students we should invite to special counseling programs. Our models reach a similar high accuracy as previously reported studies with respect to predicting whether a student will drop out after the first semester. We also demonstrate that we are able to predict reasonably well whom we should – or should not – invite for counseling after the first semester.
Joint 3D trajectory and resource optimization for a UAV relay-assisted cognitive radio network

In this paper, we consider a new spectrum sharing scenario for a cognitive relay network, where a secondary unmanned aerial vehicle (UAV) relay receives information from the ground secondary base station (SBS) and transmits information to the ground secondary user (SU), coexisting with the primary users (PUs) at the same wireless frequency band. We investigate the optimization of the UAV relay's three-dimensional (3D) trajectory to improve the communication throughput performance of the secondary network subject to the interference constraints of the PUs. The information throughput maximization problem is studied by jointly optimizing the UAV relay's 3D trajectory and the transmit power of the SBS and the UAV, subject to the constraints on the velocity and elevation of the UAV relay, the maximum and average transmit power, and the information causality, as well as a set of interference temperature (IT) constraints. An efficient algorithm is proposed to solve the admittedly challenging non-convex problem by using the path discretization technique, the successive convex approximation technique and the alternating optimization method. Finally, simulation results are provided to show that our proposed design outperforms other benchmark schemes in terms of the throughput.
Data science meets standardized game learning analytics

Data science applications in education are quickly proliferating, partially due to the use of LMSs and MOOCs. However, the application of data science techniques in the validation and deployment of serious games is still scarce. Among other reasons, obtaining and communicating useful information from the varied interaction data captured from serious games requires specific data analysis and visualization techniques that are out of reach of most non-experts. To mitigate this lack of application of data science techniques in the field of serious games, we present T-Mon, a monitor of traces for the xAPI-SG standard. T-Mon offers a default set of analysis and visualizations for serious game interaction data that follows this standard, with no other configuration required. The information reported by T-Mon provides an overview of the game interaction data collected, bringing analysis and visualizations closer to non-experts and simplifying the application of serious games.
Active Learning for Classification With Abstention

We construct and analyze active learning algorithms for the problem of binary classification with abstention, in which the learner has an additional option to withhold its decision on certain points in the input space. We consider this problem in the <italic>fixed-cost</italic> setting, where the learner incurs a cost <inline-formula> <tex-math notation="LaTeX">$\lambda \in (0, 1/2)$ </tex-math></inline-formula> every time the abstain option is invoked. Our proposed algorithm can work with the three most commonly used active learning query models, namely, <italic>membership-query</italic>, <italic>pool-based</italic>, and <italic>stream-based</italic> models. We obtain a high probability upper-bound on the excess risk of our algorithm, and establish its minimax near-optimality by deriving matching lower-bound (modulo polylogarithmic factors). Since our algorithm relies on the knowledge of the smoothness parameters of the regression function, we also describe a new strategy to adapt to these unknown parameters in a data-driven manner under an additional <italic>quality</italic> assumption. We show that using this strategy our algorithm achieves the same performance in terms of excess risk as their counterparts with the knowledge of the smoothness parameters. We end the paper with a discussion about the extension of our results to the setting of <italic>bounded rate</italic> of abstention.
Log data traffic characterization for packet loss estimation in ALICE O2 system

At CERN, the ALICE O2 computing system has nodes, called First Level Processors (FLPs), which collect particle interaction data from the ALICE detectors and carry out local processing. Log data generated by tasks running on FLPs are sent over a network to the ELK stack server to monitor and identify anomalies in FLPs. A large amount of the log-data traffic from FLPs over this network could lead to packet losses. In this paper, we create FLPs in a testbed environment to characterize the log-data traffic generated by tasks in FLPs and fit these to time-series models and probability distributions assuming independent interarrival times. The fitted models are then used to study the end-to-end packet loss with input traffic from a large number of FLPs in a network of switches. The simulation results will help to guide the number of FLPs and traffic intensity that the network can sustain for different kinds of tasks running on FLPs.
Students’ Emotions Using an Algorithm Experimentation Tool in the New Normal

Emotions play an important role in learning. As a part of our research efforts on algorithm education, we planned to evaluate the impact of an educational system to experiment with algorithms on students’ emotions. The advent of the COVID-19 pandemic changed both the educational context and the educational process. We redesigned our study to inquire two research questions: (a) what is the emotional level of students in the current situation? , and (b) what is the emotional impact of OptimEx on students of an algorithm course? We planned an evaluation in a lab session under mixed conditions, i.e. students could choose between attending physically or virtually. We used the PANAS questionnaire, which allows assessing 20 emotions, classified into positive and negative. The outcomes show that students experienced higher emotional levels for positive emotions than negative. Furthermore, students who attended face-to-face scored stronger emotions. After the session, all students decreased their negative emotional levels, with the students who attended physically also experiencing an increase in positive emotions. Due to the pandemic situation, the study was conducted with less control than we intended, therefore it is better qualified as an exploratory study.
Teaching Software Engineering During Covid-19 Constraint or Opportunity?

Due to the Covid-19 pandemic outbreak, academic institutions around the globe have been forced to shut their gates and switch to online learning environments (OLEs). Current online academic environments may not be customized equally for different fields of instruction. This study examined the influence of Covid-19 on OLEs in Software Engineering (SE) and Computer Sciences (CS) fields, where there is allegedly great potential for creating customized OLEs. For this purpose, students and lecturers from Israeli institutions of higher education participated in an online survey that examined the opportunities and constraints involved in learning solely in online environment. Results show that generally, the degree of interactivity in the OLE and adjustment of OLE’s for CS and SE fields affect the learning experience and outcome for students. The study points to the need to customize the OLE to the CS and SE fields and emphasize “hands on” experiences rather than theoretical skills. Additionally, it can be is expanding the knowledge and skills of the students to operate at an OLE by adding mandatory provisions to the Software Engineer Book of Knowledge.