Reviewerly/exHarmony huggingface dataset:
Math-odology: Methodology for teaching mathematics and tools for the implementation of virtual classes

Due to the COVID-19 pandemic that is affecting worldwide, the educational centers have been forced to change its regular classroom classes approach towards a virtual environment. This change has brought challenges and difficulties for teachers and students since this is a learning style that has not been used before for most of them. The area of Mathematics is a clear example of these challenges, since it is usually considered one of the most challenging subjects, and one that conventionally requires a classroom environment to be taught appropriately and pedagogically. Therefore, it is important to create a methodology for implementing virtual classes in Mathematics and also, search for tools to improve and enhance the learning process, while making it adaptable for the continuously changing learning environments. The proposed methodology is based on the study of pedagogical techniques, different Learning Management Systems (LMS), and online resources involved with the virtual teaching of Mathematics. To develop it, the Analysis Design Development Implementation Evaluation (ADDIE) method has been applied, while the illustration has been made using the Software Process Engineering Metamodel (SPEM), which allows developers and group members to adopt processes in a common language. This methodology was evaluated through a study case where a group of Mathematics university instructors were asked to apply the methodology in a class, while, another group was asked to impart a virtual class without it. Then, a questionnaire about their perceptions and expectations was applied to the students; and to the instructors about perceptions. Finally, after comparing the results it was concluded that Math-odology is highly applicable to adapt a mathematics class to a virtual environment. Also, it is necessary to link each activity with the right tools that contribute positively to the teaching-learning process.
A Tensor Modeling for Video Rain Streaks Removal Approach Based on the Main Direction of Rain Streaks

The algorithms for video rain streaks removal do not properly consider the influence of wind on the main direction of rain streaks. They do not rotate or only perform a rough rotation when rain streaks deviate from the vertical direction, resulting in residual rain patterns or blurred background. Therefore, a sparse tensor model based on the main direction of rain streaks is suggested for video rain streaks removal in this paper. First, the first-order directional derivative (FODD) filter is used to obtain the rain image with the best background suppression effect. Second, we calculate its histogram of oriented gradient (HOG) feature to match the rain streaks image library. The main direction of rain streaks and the rotation angle of the global model are determined by the matching result. Finally, a sparse tensor is constructed with a rotation-angle based regularization term for rain streaks removal. In addition, the tensor nuclear norm (TNN) is replaced with the tensor truncated nuclear norm (T-TNN) to ensure the global low rank of the rain free video. The alternating direction method of multipliers (ADMM) is used to work out the model. The experimental results represent the excellence of the proposed method compared with the baseline methods in terms of the value of peak signal-to-noise ratio (PSNR) and the value of structural similarity (SSIM).
Towards Accurate Vehicle Behaviour Classification With Multi-Relational Graph Convolutional Networks.

Understanding on-road vehicle behaviour from a temporal sequence of sensor data is gaining in popularity. In this paper, we propose a pipeline for understanding vehicle behaviour from a monocular image sequence or video. A monocular sequence along with scene semantics, optical flow and object labels are used to get spatial information about the object (vehicle) of interest and other objects (semantically contiguous set of locations) in the scene. This spatial information is encoded by a Multi-Relational Graph Convolutional Network (MR-GCN), and a temporal sequence of such encodings is fed to a recurrent network to label vehicle behaviours. The proposed framework can classify a variety of vehicle behaviours to high fidelity on datasets that are diverse and include European, Chinese and Indian on-road scenes. The framework also provides for seamless transfer of models across datasets without entailing re-annotation, retraining and even fine-tuning. We show comparative performance gain over baseline Spatio-temporal classifiers and detail a variety of ablations to showcase the efficacy of the framework.
Memory Augmented Design of Graph Neural Networks

The expressive power of graph neural networks (GNN) has drawn much interest recently. Most existent work focused on measuring the expressiveness of GNN through the task of distinguishing between graphs. In this paper, we inspect the representation limits of locally unordered messaging passing (LUMP) GNN architecture through the lens of \emph{node classification}. For GNNs based on permutation invariant local aggregators, we characterize graph-theoretic conditions under which such GNNs fail to discriminate simple instances, regardless of underlying architecture or network depth. To overcome this limitation, we propose a novel framework to augment GNNs with global graph information called \emph{memory augmentation}. Specifically, we allow every node in the original graph to interact with a group of memory nodes. For each node, information from all the other nodes in the graph can be gleaned through the relay of the memory nodes. For proper backbone architectures like GAT and GCN, memory augmented GNNs are theoretically shown to be more expressive than LUMP GNNs. Empirical evaluations demonstrate the significant improvement of memory augmentation. In particular, memory augmented GAT and GCN are shown to either outperform or closely match state-of-the-art performance across various benchmark datasets.
Attention-based object detection with saliency loss in remote sensing images

Geospatial object detection in remote sensing images is a challenging subject since objects in remote sensing images are dense, multioriented, and multiscale. We present an attention network for object detection in remote sensing images. Through channel attention and spatial attention, the framework pays more attention to important channels and emphasizes position information of objects. Meanwhile, saliency learning is proposed to enhance objects information. Furthermore, saliency loss is added to the loss function to guide network learning in the training stage. In addition, multiscale feature module is added into the network to capture scale variations. Experimental results on public remote sensing image datasets validate the effectiveness of the proposed method.
Ранговый анализ в оценке валидности олимпиадных заданий

The rank analysis (cenological approach) based on a strict mathematical apparatus allows diagnosing the state of educational systems and processes on a scientific objective basis. The rank analysis base is applying the law of hyperbolic rank distribution of objects in systems-cenoses, which is a refined Pareto law 20/80 (Pareto-Kudrin law). Using of rank analysis to evaluate the validity of the olympiad tasks of the all-Russian Multidisciplinary Engineering Olympiad «Star», the authors demonstrate the necessity of implementing rank analysis in the practice of education quality management. They show that this law is important and appropriate to apply in the education quality management. Optimization of educational systems and processes using rank analysis consists in eliminating anomalous deviations from the hyperbolic law, which is possible only with graphical visualization of the rank distribution and its approximation. The rank distribution graph provides visibility and insight into the nature of the rank decrease. Shortcomings in the content of olympiad tasks (as well as tests), leading to a deterioration of their validity and reliability, are shown the form of distortions of the hyperbolic graph (humps, depressions, tails, degeneration of the hyperbola into other dependencies). The rank analysis allows identifying objectively several levels of validity of olympiad or test tasks carrying on a scientific basis. The paper demonstrates graphs of run distributions illustrating examples of high (natural science) and below the average (history) validity of olympiad tasks. Educational systems are cenoses, ranking objects in them are students, classes, schools, etc., and their ranked parameters are academic performance, rating in points, performance indicators, etc. Using simple mathematical tools of rank analysis and computer software bring the technologies to assess the education quality to a higher level meeting the challenges of the time.
Emergent Literacy and Reading for Children of Taipei

Children’s reading ability plays an important role in the future development and competence of a country. Taipei municipal government was aware of the importance and started a 4-year program of “ Emergent Literacy and Reading for Children” since 2003 in order to facilitate a quality environment for reading atmosphere through a variety of creative reading activities. Hoping that children can start their own journey in the world of books. The “Emergent Literacy and Reading” program has open the door to a wonderful world of reading full of imagination for the children. The efforts of this program has been devoted to the youngsters and hopefully will develop a reading culture of Taipei.
COMPARATIVE STUDY BETWEEN SCAFFOLDING TECHNIQUE AND GUIDED WRITING TO IMPROVE STUDENTS’ WRITING ABILITY AT NINTH GRADE OF SMPN 5 TOLITOLI

The objective of this research was to find out the significant difference between scaffolding technique and guided writing improving students’ writing ability at the ninth grade of SMPN 5 Tolitoli. It was a true-experimental research. It was conducted at ninth grade of SMP Negeri 5 Tolitoli. The researcher gave a pre-test to measure the students’ previous ability in writing. After gave treatment scaffolding technique and guided writing, the researcher gave post-test to the students. In analyze the data collection the researcher use SPSS 21 program. Based on data analysis showed that, there was significant effect of scaffolding technique and guided writing in students’ writing ability. The mean score of post test in scaffolding technique (84,05) was higher than the mean score of pre-test (52,00). And the mean score of post-test in guided writing (86,60) was higher than the mean score of pre-test (52,05). Although, the improvement in  guided writing was higher than scaffolding technique  but the testing of hypothesis showed that the value of t count was lower than t table (-0,755≤2.024). Hence, there was no a significance difference of students’ achievement score between who were thought scaffolding technique and guided writing at ninth the grade of SMPN 5 Tolitoli. Keywords: Comparative, scaffolding technique, guided writing, writing ability.
Pixel Machine Learning with Clonal Selection Algorithm for Lung Nodules Visualization

The early detection of lung nodules is critical to provide a better chance of survival from lung cancer. Since benign/malignant lung cancer may be caused by the growth of lung nodules, the diagnosis of an early detection of lung nodules is important. With rapidly development of advanced technology, detection of lung nodules becomes efficient by utilizing computer-aided detection (CAD) systems that can automatically detect and localize the nodules from computed tomography (CT) scans. CAD is fundamentally based on pattern recognition by extensive use of machine learning approaches which is highly interrelated to mathematical algorithms. In this study, a pixel machine learning algorithm which is developed by artificial immune system (AIS) based algorithm – Clonal Section Algorithm (CSA) is proposed for lung nodules visualization.  By using pixel machine learning algorithm, several pre-processing procedures can be avoided to prevent the loss of information from image intensities. It is found that the proposed classification algorithm using original intensity values from CT scans is able to provide reasonable visualization results for lung nodules detection.
High Performance Graph Data Imputation on Multiple GPUs

In real applications, massive data with graph structures are often incomplete due to various restrictions. Therefore, graph data imputation algorithms have been widely used in the fields of social networks, sensor networks, and MRI to solve the graph data completion problem. To keep the data relevant, a data structure is represented by a graph-tensor, in which each matrix is the vertex value of a weighted graph. The convolutional imputation algorithm has been proposed to solve the low-rank graph-tensor completion problem that some data matrices are entirely unobserved. However, this data imputation algorithm has limited application scope because it is compute-intensive and low-performance on CPU. In this paper, we propose a scheme to perform the convolutional imputation algorithm with higher time performance on GPUs (Graphics Processing Units) by exploiting multi-core GPUs of CUDA architecture. We propose optimization strategies to achieve coalesced memory access for graph Fourier transform (GFT) computation and improve the utilization of GPU SM resources for singular value decomposition (SVD) computation. Furthermore, we design a scheme to extend the GPU-optimized implementation to multiple GPUs for large-scale computing. Experimental results show that the GPU implementation is both fast and accurate. On synthetic data of varying sizes, the GPU-optimized implementation running on a single Quadro RTX6000 GPU achieves up to 60.50× speedups over the GPU-baseline implementation. The multi-GPU implementation achieves up to 1.81× speedups on two GPUs versus the GPU-optimized implementation on a single GPU. On the ego-Facebook dataset, the GPU-optimized implementation achieves up to 77.88× speedups over the GPU-baseline implementation. Meanwhile, the GPU implementation and the CPU implementation achieve similar, low recovery errors.
Security Mandates are Pervasive: An Inter-School Study on Analyzing User Authentication Behavior

Two-factor authentication (2FA) technologies are designed to increase the security and usability of authentica-tion. Adoption of 2FA hardware devices that generate one-time passwords has proven to be effective as a risk mitigating strategy. Despite 2FA addressing user data security concerns, individuals appear either disinterested or unable to adopt 2FA tools. Many institutions are now mandating 2FA to better secure their network and user data. Some have more rigid requirements than others (e.g., offering only one 2FA method vs. offering multiple 2FA options). To better understand the impact of mandatory 2FA policies, we conducted a study of the usability, adoption, and acceptability of 2FA at three different universities. In our study, using the Yubico FIDO U2F security token, we found that mandating the use of 2FA without complementary risk communication is often inadequate. In our interviews, we found that mandatory 2FA did not necessarily increase security, instead leading to less secure user behavior, such as sharing 2FA tokens, storing credentials for a longer time in public devices, and other security avoidance behaviors.
Collaborative Systems for Design Inspiration

Inspiration is a crucial activity in design and innovation, in which potential desirable solutions are explored and refined to later provide directions and inspiration in later stages of design. Designers use a plethora of inspirational methods and tools. Among them are mood boards, a visual collage of pictures, text, and objects, that is usually created collaboratively in e.g. fashion design, architecture, and marketing. Mood boards help designers identify, select, and curate visually inspiring content, to express their existing ideas but also to inspire new ideas through their combination. As mood board design becomes increasingly digital, the availability and variety of online material presents ever greater opportunities to assist designers. However, it also poses new challenges. Through interviews with professional designers we identified three of them in particular: 1) turning tacit ideas into expressible search terms, 2) synthesizing and reflecting on visual material, and 3) finding external inspiration. While existing methods for visual inspiration hint at a great potential to support conceptual innovations, the computational support to address these challenges remains lacking. My work contributes knowledge, interaction techniques, and co-creative algorithms to assist users with these challenges. First, it introduces collaborative systems that enrich images with semantic information, to help designers express vague, visual ideas and translate them into usable search terms. Second, to support visual reflection, this thesis introduces multiple levels of semantic abstraction of visual material, to inspire designers to find higher-level concepts in their own work. Third, collaboration is an integral part of physical mood board practice, yet digital mood boards are often crafted at an individual level, which deprives designers of many opportunities to challenge and expand their ideas. An artificial agent was developed that creates mood boards jointly with a designer, based on a cooperative contextual bandit algorithm. This approach conferred it the ability to make its own decisions about whether to explore or exploit the visual contents of the current mood board, and our participants, all professional designers, genuinely valued its contributions. Thanks to a grounding-based interaction approach, it had the ability to justify its contributions and to inquire about sudden changes in the designer's choices. That resulted in a system that was perceived as a contributing agent, rather than merely a tool. Finally, beyond mood board creation with individual designers, the developed collaborative systems also contributed to creative collaborations between human designers. Within these collaborations, artificial agents played a role complementary to that of designers, and were appreciated in particular when ideas were sparse, when designers felt ''stuck',' or had trouble expressing their ideas. My work highlights the immense potential of intelligent collaborative systems for inspiration-seeking and creative processes,…
Message from the General and Program Chairs FG 2020

Welcome to the 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020). FG is the premier international conference on vision-based automatic face and body behavior analysis and applications. Since its first meeting in Zurich in 1994, the conference has been held fourteen times throughout the world. This is the 15th conference.
Motion Deblurring of Faces

This paper evaluates learning-based data-driven models for deblurring of facial images. Existing algorithms for deblurring, when used for facial images, often fail to preserve the facial shape and identity information. The best available models, which are used for general-purpose image deblurring, are pre-trained using only facial images. The Peak Signal to Noise Ratio (PSNR) Structural Similarity Index Measure (SSIM) and Time to deblur single images are the key metrics used for evaluating the models and for finding the most efficient model for deblurring facial images. From the results, the observation is that even though the PSNR value for DeblurGANv2 model is the highest, the best trade off between PSNR, SSIM, Time to deblur and visual quality is seen in DeblurGAN model.
Traffic counting by stereo camera

Computational stereo vision introduced by Marr is one of the powerful methods to extract three-dimensional (3D) information using multiple images. By applying stereo vision, many kinds of traffic monitoring systems have been developed. In this chapter, we discuss configurations and characteristic of stereo-based traffic monitoring systems especially for the roadside unit.
АНАЛІЗ ПІДХОДІВ ДЛЯ ТЕСТУВАННЯ ПСЕВДОВИПАДКОВИХ БІТОВИХ ПОСЛІДОВНОСТЕЙ

An article devoted to the systematization of scientific positions on static testing of sequences widely used in cryptographic information security systems to obtain key and additional information (random numbers, initialization vectors, etc.). Existing approaches to testing pseudo-random sequences are considered. The main advantages and disadvantages of the most well-known test packages for sequence analysis for randomness are presented. The information obtained allows you to choose the most optimal approach and tests to check the bit sequence for randomness, depending on the available characteristics of this sequence. It was found that for sequences up to 100 bits there are not enough existing statistical packets and this direction requires further and thorough research.
Perancangan Prototipe Voice User Interface(VUI) Aplikasi Presensi Karyawan Dengan Speech Recognition.

As we all know, the very easy and fast spread of COVID-19 can threaten human life. This virus is easily spread from inanimate objects that are touched by sufferers. The use of fingerprint attendance machines in the workplace can be a source of the spread of COVID-19, this needs to be considered for other alternatives to record employee attendance, for example with speech recognition technology or known as automated Speech Recognition (ASR). This technology is needed as an important component to build a Voice user interface (VUI). ASR is a technology for translating speech from the user into text. In addition, the use of ASR can improve communication between human-computer interactions, that is, users can interact with computers using voice. This research is aimed at designing prototype employee attendance application with the VUI concept and speech recognition technology. To speed up design and development process, researchers used the RAD (Rapid Application Development) method. From the results of research using the RAD method, it can be concluded that the application can be designed quickly through prototype modeling together with the user.
Through the Lens of Code Granularity: A Unified Approach to Security Policy Enforcement

A common way to characterize security enforcement mechanisms is based on the time at which they operate. Mechanisms operating before a program’s execution are static mechanisms, and mechanisms operating during a program’s execution are dynamic mechanisms. This paper introduces a different perspective and classifies mechanisms based on the granularity of program code that they monitor. Classifying mechanisms in this way provides a unified view of security mechanisms and shows that all security mechanisms can be encoded as dynamic mechanisms that operate at different levels of program code granularity. The practicality of the approach is demonstrated through a prototype implementation of a framework for enforcing security policies at various levels of code granularity on Java bytecode applications.