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Cache Side Channel Attacks (SCAs) have gained a lot of attention in the recent past. Since, these attacks exploit the caching hardware vulnerabilities, they are fast and dangerous. Detection of cache side channel attacks is an important step towards mitigating against such hostile entities. Researchers have already proposed different techniques to detect cache side channel attacks. This paper provides a detailed survey of literature related to the state-of-the-art detection techniques for cache based side channel attacks. We identify a set of important characteristics that can be used to characterize a CSCA (cache side channel attack) detection technique. We use the identified features to compare and contrast the most important detection techniques and provide the important observations. We also identify some of the challenges that the research community will have to resolve in future to improve the efficiency of cache side channel detection techniques. To the best of our knowledge, this is the first work to do such a study. We believe that this paper will prove useful to researchers in the area of systems security.

Building and managing customer loyalty who buy fashion by e-commerce platforms is the key to making a profit. This research aims to explain the effect of electronic service quality and electronic word-of-mouth on electronic loyalty with electronic satisfaction and electronic trust as intervening. The research method used is verification with a sample of 400 consumers who have done fashion shopping by e-commerce platforms in the Jakarta, Depok, Tangerang, and Bekasi areas, so we apply the purposive sampling method. Data analysis used CB-SEM with LISREL 8.71. The results report that electronic service quality and electronic word-of-mouth positively and significantly impact electronic loyalty and indirectly through electronic satisfaction and electronic trust. The research outcomes become input for e-commerce fashion businesses to make decisions to keep and increase electronic loyalty.

In this paper, we propose a Bidirectional Attention Network (BANet), an end-to-end framework for monocular depth estimation (MDE) that addresses the limitation of effectively integrating local and global information in convolutional neural networks. The structure of this mechanism derives from a strong conceptual foundation of neural machine translation, and presents a light-weight mechanism for adaptive control of computation similar to the dynamic nature of recurrent neural networks. We introduce bidirectional attention modules that utilize the feed-forward feature maps and incorporate the global context to filter out ambiguity. Extensive experiments reveal the high degree of capability of this bidirectional attention model over feed-forward baselines and other state-of-the-art methods for monocular depth estimation on two challenging datasets - KITTI and DIODE. We show that our proposed approach either outperforms or performs at least on a par with the state-of-the-art monocular depth estimation methods with less memory and computational complexity.

The Covid-19 pandemic forced Higher Education classes to shift to online education. This sudden change required universities to respond rapidly, adapting their classes to remote courses maintaining quality educational delivery. In this context, our university implemented the Digital Flexible Model (DFM) to continue with its academic offer. The challenge involved moving 55,000 classes per week to the DFM, attending 90,000 students with 9,400 professors, all in one week. At the end of the semester, a strategic survey was applied to a statistical sample of professors and students from the Engineering School to get feedback about their experience with the DFM. The results highlight takeaways to improve the instructional model and offer interesting reflections by gender from the experience of faculty and students.

Several studies suggest that women are inspired by female role models. Observing women with successful development in their profession inspires other women to pursue professional growth as well, even in STEM areas, where there is less female participation. This research presents a successful case of a program with two differentiating characteristics: the use of social networks to present a group of female leaders like a role model in STEM, and an international strategic link. The objective of the program was to strengthen women leadership in the STEM educational environment, as well as to identify the determining factors for this strengthening. In the webinar program developed over a 6-month period, more than 700 people were impacted, with more than 22,000 views. Through feedback surveys and data regarding audience obtained from the webinars transmitted by social networks, the impact and needs of teachers were shown. The results indicate that the majority of the audience teaches classes in elementary and middle levels, however, it is observed that teachers from all educational levels participated. The presence of female role models teaching in STEM programs and their extensive experience in current social happenings are two elements that favor interest and participation in these events.

The purpose of this study is to explore how a high-impact outreach project with renowned international organizations contributes to the development of students’ self-confidence and leadership in STEM areas. The conclusion of this research, using quantitative measures, yielded outstanding results in two skills expected to develop working on projects related to STEM. In addition, other solid results showed that students who develop this type of projects increase the perspective of their professional approach and they inspire students to continue participating in projects that challenge them both personally and professionally.

Gender equity and inclusion have been relevant topics in the agenda of the companies, organizations and universities. As a university and particularly with engineering, science and technology programs, it is relevant to implement actions that encourage this culture of gender equity in a STEM environment. The main initiatives are related to mentoring programs, which seek to develop and empower women through women themselves. The research aims to identify the factors that influence the impact generated by the mentoring process in the different stakeholders: teachers, researchers and high school students. The mentoring model is composed of three key elements: (a) massive session with life testimonies of engineering and science students, (b) massive session with the testimonies of mentors and mentees, and (c) the mentoring sessions in small groups or one-to-one. The methodology is of a mixed nature and is composed of 4 stages: (1)design of the mentoring program based on the research of literature review and interviews with companies that has experience in mentoring programs, (2) implementation of the mentoring program with high school students and women STEM professors mentors, (3) evaluation of the impact of the program with interviews with mentors, observation, focus groups with mentors and mentees, and surveys, (4) definition of the impact factors and improvement areas of the program. The findings in the different stages allowed to integrate the factors that impact the mentoring process and its involved stakeholders. Some of the factors that influence the impact of the mentoring program are: the role of the mentor and its characteristics, the mentee profile, its commitments and interests, the impact of the life testimony both professional and personal, the type of needs that arise for the mentees and for the mentors, the time of attention, and the interaction.

In August 2019, the national initiative Women in Engineering and Sciences (MIC) was launched with the objective of raising awareness and monitoring gender equity, equal opportunities and women empowerment. This project, led by a group of professors, hopes to accompany, support and join efforts, for a stronger impact within our academic community. This study presents the work of the linking committee towards the empowerment of women engineers through linked experiences. The methodology of this work includes: 1) integrating a multi-campus team to work in the engagement and linking committee, 2) identifying internal efforts towards the empowerment of women, as well as external efforts in companies, organizations and other universities through research and interviews, and mapping the networking ecosystem 3) analyze the information to identify key partners for collaborative projects, and 4) create a community by implementing Lean In circles (leanin.org) and replicate the project at other campuses. The work done and the reflections hope to be a roadmap and source of inspiration to other engineering institutions working towards a culture of gender equality and women empowerment.

Expanding Non Markovian Reward Decision Processes (NMRDP) into Markov Decision Processes (MDP) enables the use of state of the art Reinforcement Learning (RL) techniques to identify optimal policies. In this paper an approach to exploring NMRDPs and expanding them into MDPs, without the prior knowledge of the reward structure, is proposed. The non Markovianity of the reward function is disentangled under the assumption that sets of similar and dissimilar trajectory batches can be sampled. More precisely, within the same batch, measuring the similarity between any couple of trajectories is permitted, although comparing trajectories from different batches is not possible. A modified version of the triplet loss is optimised to construct a representation of the trajectories under which rewards become Markovian.