Reviewerly/exHarmony huggingface dataset:
Perceptions of university teachers and students on the use of Blackboard Collaborate as a teaching tool during virtual learning due to the COVID-19 pandemic

The COVID-19 pandemic, challenged university authorities and teachers to consider how to continue teaching their courses in distance learning. On one hand, most higher education teachers found it necessary to develop skills to teach and evaluate in virtual learning environments in a short time. On the other hand, students had to continue developing both their technological and social skills, as well as their autonomy, since distance education requires greater commitment and discipline [1] y [2]. Blackboard Collaborate, with its video conferencing, text, voice, chat, and interactive whiteboard is a synchronous tool that allows teachers and students to interact in real time. This qualitative study used a phenomenological design to analyze the perceptions of 10 teachers and 25 students in their final year at university, on the impact and challenges of the use of Blackboard Collaborate as a teaching-learning tool in the framework of education in line in the pandemic context. Qualitative data was collected and analyzed. Two instruments were implemented, semi-structured interviews were used with teachers and an online survey with students. It was found that the use of the Blackboard Collaborate is perceived by both study groups as a tool that facilitates the teaching - learning process. It allows interaction, the development of general competences and to monitor the progress of students' activities during the synchronous sessions. However, this is not considered by the participants as a tool that facilitates bonding during class because it might be affected by technical and connectivity difficulties. A platform that is used in 90 countries reaching 100 million users in educational settings including higher education and schools use Blackboard Collaborate as a tool for teaching [3]. The relevance of this study lies in the fact that there are no previous studies carried out in Peru and Latin America that explore, contrast, and show both, the perceptions of teachers and students on the use of the Blackboard Collaborate tool.
The role of the Embodiment Director in virtual reality film production

This paper proposes the role of an emerging job role within the virtual reality (VR) film production crew. This role we call the Embodiment Director, involves assisting the VR Film Director in the accomplishment of true immersive film experiences through the use of game engines and VR peripherals. The Embodiment Director will manage the inception of haptics and stimuli technologies that allow for the embodiment of humans within a virtual environment, and they must guarantee precise synchronicity between physical and virtual counterparts, while overseeing the safety use of software and hardware during the entire production process of the VR film experiences. This paper offers a contemporary review of the key creative roles within traditional and virtual film production, in order to generate a concise and valid argument for the role of the Embodiment Director supported by autoethnography.
Cultural Sustainability and (Post-)digital Transformation(s) in the Context of Aesthetic, Arts, and Cultural Education

This chapter discusses questions of digitalization in the context of aesthetics, arts, and cultural education, and cultural sustainability. We first outline in what respect (post-)digitalization becomes relevant with regards to the UNESCO Sustainable Development Goals. (Post-) digitalization, we argue, must be understood as an integral part of aesthetics, arts, and cultural education, because it has already brought forth (new) cultural techniques relevant in the understanding of and thus the participation in one’s own culture and on a global scale. After introducing the concept of post-digitalization, we present two case studies from research projects within this research area Digitalization & Arts and Cultural Education, analyzing creative coding in online “learning ecologies” (Post-Digital Art Practices in Cultural Education) and the virtual character Hatsune Miku (Post-Internet Art(s) Education Research) respectively. Following their preliminary findings, we focus on collective creative practices as (new) post-digital cultural techniques and on distributed production effects that arise in post-digital settings. Questions of structure, of implicit power relations and hierarchy, and of new processes of subjectivation are addressed in particular. We argue that it is precisely aesthetics, arts, and cultural education that have the potential to contribute to how we not only understand (post-)digitality but also shape it towards cultural sustainability.
Real-Time Behavioral Biometric Information Security System for Assessment Fraud Detection

Online education has become a major delivery method in education. Many schools have adopted online delivery of courses. This has exposed schools to greater information security risks, such as assessment cheating, identity theft, and loss of sensitive student information. To counter emerging new types of attacks, risks, and vulnerabilities, the protection measures must be able to adapt to the evolving behaviors of both user and attacker by learning new emerging vulnerabilities and behaviors of users. We propose a new Real-time Behavioral Biometric Information Security (RBBIS) architecture that non-invasively builds behavioral profiles on the fly using deep-learning approaches. The method learns behaviors of students to validate users and detect intrusion, identity theft, and assessment fraud. This greatly improves the current limitations of the existing user authentication approaches of online education platforms. RBBIS was evaluated using CNN deep-learning keystroke behavior biometric analysis and compared with various existing machine learning algorithms: j48, Naive Bayes, and Multi Layered Perceptron (MLP). The results show that our deep-learning method performed best with a Convolutional Neural Network (CNN) with 92.45% of accuracy, whereas Naive Bayes, j48 and MLP achieved accuracies of 68.87%, 73.38% and 77.11%, respectively.
Dual-Anonymous Off-Line Electronic Cash for Mobile Payment

Mobile devices have become near-ubiquitous tools in our daily lives. Following this trend, mobile commence is developed rapidly which in turns stimulates interests in mobile payment. Some prominent examples include Google’s Wallet, WeChat Pay, and Apple Pay. Most of these technologies, however, are designed for users to be able to pay conveniently to the business. In other words, they are designed with the business to user model in mind. Besides, an active network connection with an external payment server is required either from payer or payee during transaction. Our work intends to supplement existing solutions, which allows payment to be made in an off-line and dual-anonymous manner. In doing so, a dual-anonymous off-line electronic cash scheme is proposed by utilizing BBS+ signature. The feature of our scheme is dual-anonymous payment, which means that both the payer and the payee in any transaction cannot be identified even all other users and the payment server collude. Through security proof and performance analysis, we also demonstrate that the security of the proposed scheme can be reduced to standard assumptions and it is suitable for applications in mobile commerce.
SARNet: Self-attention Assisted Ranking Network for Temporal Action Proposal Generation

Temporal action detection is a fundamental yet challenging video understanding task. The calculation of confidence score for each generated action proposals remains the bottleneck of this task. Given that the continuity of videos is beneficial for self-supervised learning, in this paper we propose Self-attention Assisted Ranking Network (SARNet), which uses a self-attention mechanism to assist the ranking and retrieval of generated proposals. Our method incorporates a discriminative and a generative constraint to train the self-attention weight. Extensive experiments on THUMOS14 demonstrate that our method achieves a considerable improvement of average recall with a small number of proposals, and brings the mAP up to 30% at tIoU threshold 0.7 for the first time.
High-Speed Fault Attack Resistant Implementation of PIPO Block Cipher on ARM Cortex-A

In ICISC'20 conference, PIPO (Plug-In and Plug-Out) was proposed as an efficient block cipher for secure communication in IoT (Internet of Things) environment.Although PIPO equips easily high-order masking implementation because of small non-linear operations and short rounds, PIPO is still vulnerable to fault attack.For resisting the fault attack, block cipher implementation should be applied to the fault attack countermeasure.However, these techniques make the performance of computationallyintensive cryptographic algorithms slower in constrained devices.To improve this, we propose the first fast and secure software of PIPO block cipher co-designed with ARM/NEON processor for high-speed secure communication.For accelerating the performance, we present an optimal implementation of PIPO block cipher in ARM/NEON processor, respectively, and design the Interleaved way utilizing two cores.With the proposed optimal techniques, we provide the high-speed secure software.In addition, we present an interleaving random-shuffling technique, which optimizes random-shuffling by utilizing two cores.For ensuring the resistance of fault attacks, we validated the fault resistance with the computation and instruction fault model.We utilize the intra-instruction-redundancy and known ciphertext to detect them.Through the proposed contributions, the fast software for PIPO block cipher achieves the fastest performance than previous related studies.The secure software with the fault countermeasures is nearly 3 times faster than the reference implementation without any fault attack countermeasures.In addition, our secure software achieved performance improvement of 301% and 463% compared to the existing best work (HIGHT and revised CHAM).As a result, our fast and secure software for PIPO block cipher achieved the fastest performance by co-designing with two cores compared to previous work that is utilized only one core.Our software can be utilized for high-speed encrypted communication and CTR-DRBG in ARMv8-based IoT devices.
Deep Learning Aided Physical-Layer Security: The Security Versus Reliability Trade-Off

This paper considers a communication system whose source can learn from channel-related data, thereby making a suitable choice of system parameters for security improvement.The security of the communication system is optimized using deep neural networks (DNNs).More explicitly, the associated security vs reliability trade-off problem is characterized in terms of the symbol error probabilities and the discrete-input continuous-output memoryless channel (DCMC) capacities.A pair of loss functions were defined by relying on the Lagrangian and on the monotonic-function based techniques.These were then used for managing the learning/training process of the DNNs for finding near-optimal solutions to the associated non-convex problem.The Lagrangian technique was shown to approach the performance of the exhaustive search.We concluded by characterizing the security vs reliability trade-off in terms of the intercept probability vs the outage probability.
Intelligent Traffic Control System Using WSN: A Perspective

This paper proposes an intelligent traffic control system using a wireless sensor network. The proposed model is capable of basic routing and for emergencies and traffic rule violations. Here, we use IR sensors to detect the number of vehicles and help detect emergency vehicles, Image sensors for traffic rule violations, and Mic sensors to identify the emergency vehicle's frequency using an advanced algorithm. The work is done with the help of machine learning and image processing techniques.
Hardware Implementation of Yolov4-tiny for Object Detection

The high computational power of GPUs allowed for larger networks to be used in object detection applications. However, due to the huge power consumption and inefficiency when it comes to memory access and the number of bits used to represent the data, it is difficult to use them in embedded applications. Therefore, extensive research has been conducted to use FPGAs as a highly efficient substitute for GPUs to implement deep learning algorithms. As the scale and complexity of the algorithms keep increasing each year to improve their performance, it becomes even harder to implement such algorithms on an FPGA without reusing hardware resources. In this work, we implement Yolov4-tiny on a single FPGA by applying several resource sharing and optimization techniques. Our implementation shows a decrease in power consumption that ranges from 66% to 93.5% less power when compared to software. Moreover, less hardware resources and faster inference time is achieved. When comparing with the hardware implementation of networks with similar size, our design is 6.67 times faster and uses 62.5% less energy per image.
A Statistical Basis for Reporting Strength of Evidence as Pool Reduction

This article establishes a statistical basis for an evidence-reporting strategy that interprets strength of evidence in terms of a reduction in the size of a pool of relevant conceptual objects. The strategy is motivated by debates in forensic science, wherein the pool would consist of sources of forensic material. An advantage of using the pool-reduction strategy is that it highlights uncertainty that cannot be resolved by empirical considerations. It is shown mathematically to reflect a nonstandard formulation of a Bayes factor, and to extend for use in problems of general quantitative inference. A number of conventions are proposed for full effectiveness of the strategy’s implementation in practice.
DenseGAN: A Password Guessing Model Based on DenseNet and PassGAN

Password authentication has become one of the most significant authentication methods because of low cost and convenience, and its security is getting more and more attention. The vulnerability of password security mainly lies in the password construction method which inevitably has many human characteristics. With the development of deep learning, these human characteristics are more and more explored, which bring new challenges to password security. In 2019, a PassGAN password guessing model was proposed, and its performance is remarkable when the maximum training password length is 10. However, when the length is extended to 15, the performance gets worse.To address this issue, in this paper an approach is proposed to innovate the structure of PassGAN by using DenseNet, and two novel password guessing DenseGAN models are proposed, which both can generate high-quality password guesses. With the first DenseGAN model, when the maximum training password length is 15, the generated passwords were able to match 2.7–4.8% of the passwords in the testing datasets more than PassGAN. Specifically, with the second DenseGAN model, when the maximum training password length is 10, the generated passwords were able to match 0.5% of the passwords in the testing datasets more than PassGAN, when the maximum training password length is 15, the match is 6.2% to 12.5% of the passwords more than PassGAN.
Security Issues in SMTP-based Email Systems

The SMTP standard was originally published in 1982 and has since then become one of the mostly used methods for data communication. Since its publication the standard has been modified with many extensions intended to handle security issues. A selection of the most common security issues that surfaces when these extensions are used in practice is identified and discussed in relation to confidentiality, integrity, availability, and authentication. The complexity of the current situation makes it clear why organizations, mail service providers, and users have great difficulty in securely managing their email systems. Most email systems are vulnerable and cause serious security risks for individuals, organizations, and societies. The risks have grown to be critical for the digitization of our societies.
Towards an Aligned Blockchain Standard System: Challenges and Trends

As a rapidly-developing emerging technology, blockchain met the challenges of standardization. In order to analyze the systematic degree of blockchain standards, 3 developing stages of both blockchain industry and standard system are comparatively studied. The current status of blockchain standardization in the aspect of participants including ISO, ITU-T, IEEE-SA and national level organizations are summarized. A total number of 167 blockchain standard projects have been collected and studied. Highlighted topics including foundational, security, privacy & identity, application, data, test & assessment are analyzed. Finally, a blockchain standard system composed of 10 categories of blockchain standards is given, based on which the challenges and trends of blockchain standardization are analyzed.
Towards Highly Automated Machine-Learning-Empowered Monitoring of Motor Test Stands

The development of new electric traction machines requires a complex process of experimentation due to the many factors that affect motor performance. Dedicated test benches, which are complex and vulnerable to failures during experiments, generate heterogeneous multivariate time series data collected by multiple sensors. Failures or anomalous states in these systems can slow down the development and testing process enormously. This article proposes a new and innovative approach to machine-learning-empowered monitoring and predictive maintenance for motor test benches. It allows to optimize the test process and reduce costly test bench downtime, with a self-improvement cycle to respond to new operation areas during run-time, integration of new components, continuous knowledge integration of human operators, autonomous parameter updating of machine-learning models, and hardware accelerated monitoring. Based on a first case study, we show that our procedure produces promising results based on the raw data for failure detection and failure type classification, representing an essential block of self-awareness in the system. A dedicated hardware-accelerated machine-learning online monitoring allows to meet critical time constraints and optimise power consumption. In a second case study, we demonstrate automated word-width reductions, which results in a smaller implementation of the network and reduce the needed memory bandwidth. All by keeping floating point accuracy and taking reconfigurable constant coefficient multiplication instead of generic multiplication into account.
Teaching C++ programming using automated unit testing and test‐driven development—Design and efficacy study

Abstract The best practices of agile software development have had a significant positive impact on the quality of software and time‐to‐delivery. As a result, many leading software companies employ some form of agile software development practices. Some of the most important best practices of agile software development, which have received significant attention in recent years, are automated unit testing (AUT) and test‐driven development (TDD). Both of these practices work in conjunction to provide numerous benefits. AUT leads to reduced time to test, discover bugs, fix bugs, and implement new features; wider and measurable test coverage; reproducibility, reusability, consistency, and reliability of tests; improved accuracy, regression testing, parallel testing, faster feedback cycle, reduced cost, and higher team morale. The benefits of TDD include flexible and adaptive program design, cleaner interfaces, higher code quality, maintainability, extensibility, reliability, detailed evolving specification, reduced time on bug fixes and feature implementation, reliable refactoring, and code changes, reduced cost, reduced development time, and increased programmer productivity. Unfortunately, students in introductory programming courses are generally not introduced to AUT and TDD. This leads to the development of bad programming habits and practices which become harder to change later on. By introducing the students earlier to these industry‐standard best practices, not only the motivation and interest of students in this area can be increased but also their academic success and job marketability can be enhanced. This paper presents the detailed design and efficacy study of an introductory C++ programming course designed using the principles of AUT and TDD. The paper presents the pedagogical techniques employed to build industry‐proven agile software development practices in the students. As part of the paper, all the course material including the source code for the labs and the automated unit tests are being made available to encourage people to incorporate these best practices into their curricula.
Specific Emitter Identification Based on Eye Diagram

Specific emitter identification (SEI) refers to identifying an individual emitter by extracting the features of individual differences reflected in the external structure of RF signals of the emitter. The RF fingerprint of transmitters are complex and non-linear, but the performance improvement of SEI is limited by the current cognitive level. Therefore, this paper proposes an SEI method based on the signal eye diagram, which uses CNN to realise the joint extraction of multiple complex features. Firstly, we construct the transmitter distortion model and analyse the visual representation of the distortion on the eye diagram. On this basis, an enhancement strategy is introduced to the original eye diagram. The enhanced eye diagram is used to train the network model and complete the SEI task. The proposed method overcomes the limitations of existing cognition and has high information integrity and low computational complexity. The simulation results show that the proposed method can significantly improve the performance of SEI, and the average recognition gain is about 15%.
Soft-Output Detector Using Multi-Layer Perceptron for Bit-Patterned Media Recording

As conventional data storage systems are faced with critical problems such as the superparamagnetic limit, bit-patterned media recording (BPMR) has received significant attention as a promising next-generation magnetic data storage system. However, the reduced spacing between islands at increased areal density causes severe intersymbol and intertrack interference, which degrade BPMR system performance. In this study, we introduce a soft-output detector using multi-layer perceptron to predict reliable information. A received signal is equalized and detected by the MLP detector. The MLP detector provides a well-estimated value by using the binary-cross entropy function as a loss function and the identity function as an activation function for the output layer of the MLP detector. This study investigates the received probability distributions out of the detectors and compares the performance of various versions against a conventional detector. Compared with the conventional detection, the proposed MLP detectors provide a small variance and better BER performance than the conventional detection. Simulations of MLP designs show an advantage over conventional detection. Moreover, the proposed MLP detectors with the demodulator exhibit better BER performance than the conventional detector with the demodulator.