Reviewerly/exHarmony huggingface dataset:
Feasibility Study on Optical Image Modulation Based Parallax Generator for Monocular Visual Odometry

We propose a method to improve the accuracy of Monocular Visual Odometry (MVO) by slowly modulating the lens using a miniature Optical Image Stabilizer (OIS) mounted on a smartphone. This method can be applied to the existing MVO or Structure from Motion (SfM) algorithm and can solve the reduction in accuracy due to the scaling issue and the lack of pixel parallax. In addition, OIS control can be performed at the same time, which can increase the accuracy against the vibration of the camera body. In this research, the lens was controlled to move horizontally with respect to the ground at a sine wave of 1 Hz without OIS control. The experimental results show that the accuracy of the monocular camera can be greatly improved.
Patience Control: A Modular and Dynamic Construction of Composite Controllers

The problem of designing controllers for achieving composite goals is well studied in the literature and is considered an open problem. In this paper we propose a controller architecture that builds on approaches for designing reactive decision making systems developed in the field of computer science. Specifically, we propose an extension of the Behavioural Programming (BP) approach that is tailored to allow controller design. As the name of the paper suggests, we identify the issue of "patience" as a key feature of the new approach - in many composite control problems it is not possible to advance all goals at all time, i.e., some goals need to "patiently" wait while others are being advanced. We demonstrate how the proposed approach is capable of handling elaborated multi-objective situations that include patience in an intuitive and effective way.
Using the principles and approaches of flexible methodology (agile) in the planning and implementation of the educational process

The educatonal program development process is challenging with many stakeholders: students, teachers, parents, administrators, employers, and the government. Each stakeholder has compettve priorites; Quality, risk, resources and costs limitaton. Large complex projects require the management of a skilled and experienced project manager. It is the preparaton of curricula for joint internatonal educatonal programs with the use of Agile tools and technologies. The main "customer" in this program is a student, and the ultmate goal of each project within the program-the competence of the student in the labor market afer receiving the diploma. Therefore, this program can be considered difcult, and it is constantly evolves, because the demands of the labor market of China is constantly changing and developing.
Adversarially Robust Deep Image Super-Resolution Using Entropy Regularization

Image super-resolution has been widely employed in various applications with boosted performance thanks to the deep learning techniques. However, many deep learning-based models are highly vulnerable to adversarial attacks, which is also applied to super-resolution models in recent studies. In this paper, we propose a defense method that is formulated as an entropy regularization loss for model training, which can be augmented to the original training loss of super-resolution models. We show that various state-of-the-art super-resolution models trained with our defense method are more robust against adversarial attacks than their original versions. To the best of our knowledge, this is the first attempt of adversarial defense for deep super-resolution models.
Image Inpainting with Onion Convolutions

Recently deep learning methods have achieved a great success in image inpainting problem. However, reconstructing continuities of complex structures with non-stationary textures remains a challenging task for computer vision. In this paper, a novel approach to image inpainting problem is presented, which adapts exemplar-based methods for deep convolutional neural networks. The concept of onion convolution is introduced with the purpose of preserving feature continuities and semantic coherence. Similar to recent approaches, our onion convolution is able to capture long-range spatial correlations. In general, the implementation of modules with such ability in low-level features leads to impractically high latency and complexity. To address this limitations, the onion convolution suggests an efficient implementation. As qualitative and quantitative comparisons show, our method with onion convolutions outperforms state-of-the-art methods by producing more realistic, visually plausible and semantically coherent results.
Psychologists' Information Culture Development during Professional Training at a University

The relevance of information culture development among psychologists is due to the increasing requirements for future specialists in the process of mastering professional activities.Modern society is constantly putting forward new requirements for University graduates in terms of competitiveness as well as social, professional and personal competence.The problem of informatization and digitalization of the education system arouses active interest from the part of the state; the World Wide Web has become the basis for network technologies and expanded the possibilities concerning mass access to information making them almost limitless.Informatization and digitalization of modern education system not only arouses active interest from the part of the state, but in the context of a global pandemic, a becomes essential for remote interaction with students and professional training in the context of distance learning.The Internet, being the basis of network technologies, makes it possible to expand the scope of mass access to information, and the means and methods of working with the information space are being actively developed.Informatization of the education system creates opportunities for a timely and flexible response to changes in the social educational order.Working in the information space of an educational institution under a qualified teacher's control makes it possible to shape the student's information culture meaningfully and purpusefully.The developed information culture acts as a powerful personal and psychologivally prophylactic resource in practical professional activity, contributing to its further professional development and preventing emotional burnout.
Performance Analysis of GAN Architecture for Effective Facial Expression Synthesis

Facial expressions are an important means of communication among human beings, as they convey different meanings in a variety of contexts. All human facial expressions, whether voluntary or involuntary, are formed as a result of movement of different facial muscles. Despite their variety and complexity, certain expressions are universally recognized as representing specific emotions - for instance, raised eyebrows in combination with an open mouth are associated with surprise, whereas a smiling face is generally interpreted as happy. Deep learning-based implementations of expression synthesis have demonstrated their ability to preserve essential features of input images, which is desirable. However, one limitation of using deep learning networks is that their dependence on data distribution and the quality of images used for training purposes. The variation in performance can be studied by changing the optimizer and loss functions, and their effectiveness is analysed based on the quality of output images obtained.
Introspective Learning by Distilling Knowledge from Online Self-explanation

In recent years, many methods have been proposed to explain individual classification predictions of deep neural networks. However, how to leverage the created explanations to improve the learning process has been less explored. The explanations extracted from a model can be used to guide the learning process of the model itself. Another type of information used to guide the training of a model is the knowledge provided by a powerful teacher model. The goal of this work is to leverage the self-explanation to improve the learning process by borrowing ideas from knowledge distillation. We start by investigating the effective components of the knowledge transferred from the teacher network to the student network. Our investigation reveals that both the responses in non-ground-truth classes and the class-similarity information in teacher’s outputs contribute to the success of the knowledge distillation. Motivated by the conclusion, we propose an implementation of introspective learning by distilling knowledge from online self-explanations. The models trained with the introspective learning procedure outperform the ones trained with the standard learning procedure, as well as the ones trained with different regularization methods. When compared to the models learned from peer networks or teacher networks, our models also show competitive performance and requires neither peers nor teachers.
MSL-DE

Lexical Substitution has a noticeable evolution in the literature, mainly in the data sources used for the generation of substitutes that feed the process. Of course, dictionaries and thesauri are widely used for grouping synonyms in their structure, but the polysemy of words prevents a direct exchange of terms without analyzing the context. Vector space models, such as embeddings, are used to represent substitutes and also contexts. However, the representation of words considering only contextual factors, in many cases, may incur the approximation of terms that are not exactly synonyms. The characteristics mentioned above suggest that the simultaneous use of dictionaries and embeddings is a promising alternative for the process. Thus, we present a method using information contained in merged dictionaries, in addition to their linguistic relations structured in taxonomies. The method measures the preservation of the meaning of the sentence with the potential synonym by observing its frequency of application in small contexts. In addition, we also consider a complete context to generate input from vector operations highlighting the best synonyms in a previously selected set. The results show the efficiency of the method, surpassing many methods consolidated in the literature in the prediction of the best substitute for words contained in instances of a known benchmark.
Coordinative Scheduling of Computation and Communication in Data-parallel Systems

For many data-parallel computing systems like Spark, a job usually consists of multiple computation stages and inter-stage communication (i.e., coflows). Many efforts have been done to schedule coflows and jobs independently. The simple combination of coflow scheduling and job scheduling, however, would prolong the average job completion time (JCT) due to the conflict. For this reason, we propose a new abstraction of scheduling unit, named coBranch, which takes the dependency between computation stages and coflows into consideration, to schedule coflows and jobs jointly. Besides, mainstream coflow schedulers are order-preserving, i.e., all coflows of a high-priority job are prioritized than those of a low-priority job. We observe that the order-preserving constraint incurs low inter-job parallelism. To overcome the problem, we employ an urgency-based mechanism to schedule coBranches, which aims to decrease the average JCT by enhancing the inter-job parallelism. We implement the urgency-based coBranch Scheduling (BS) method on Apache Spark, conduct prototype-based experiments, and evaluate the performance of our method against the shortest-job-first critical-path method and the FIFO method. Results show that our method achieves around 10 and 15 percent reduction in the average JCT, respectively. Large-scale simulations based on the Google trace show that our method performs better and reduces JCT by 23 and 35 percent, respectively.
The Use of E-learning Tools and Log Data in a Course on Basic Logic

This paper is a study of the use of e-learning tools and log data in evaluating and further developing a course on basic logic. It is a continuation of earlier studies involving practical experiments with students of communication from Aalborg University. Two tools are involved: Syllog for training syllogistic reasoning and Proplog for training basic propositional logic. The data are logged anonymously during the course, as well as during the individual exam. Using the log data, we have obtained important insights into the effects of the lectures and exercises. We argue that the log data from using the two tools can be transformed into useful learning analytics. Careful studies based on log data can provide useful information on how the quality of the course. On this basis, it can be suggested how the course can be improved using the learning analytics based on the log data. This is evident from studies carried out over seven to eight years using log data from the use of Syllog and recent studies based on log data from the use of Proplog, also show how insight based on the log data may lead us to improvements of the course. During the present study we have developed a method by which we can determine whether the students will handle one kind of symbolic logic test better than another.
Design a Neural Controller to Control Rescue Quadcopter in Hang Status

Quadcopters can be used for various applications in many fields including service, life, security, military… This is a research of oriented application development prospects in the near future because of the quadcopters ability to move flexibly regardless of the terrain and can support human issues that quadcopter on the ground do not. However, due to the motion characteristics in the air, so the quadcopter has certain limitations, including moving the static problem, the suspend state of the quadcopter. This is a complex subject and many scientists are interested in studying the development of large size quadcopters that can carry both people and heavy equipment. This paper analyzes some problems associated with quadcopter motion in a static state and designs a neural controller as the basis for developing more advanced applications in practice to manufacture the big quadcopter for load. The simulation results illustrate the problem and explain the relevance of the theory.
Identification of cut-points in test parameters for classification of industrial products

Abstract The paper considers the problem of classification of industrial products with increased quality requirements by production batches. Identification of homogeneous batches of industrial products is one of the most important problems in the way of improving the reliability and extending the lifetime. Homogeneity of product batches ensures the reliability of destructive test results. A more consistent operation of elements with identical characteristics is also provided. We solve the problem of determining the cut-point values of test parameters for further construction of a rule-based classifier.
Crime types in the digital realm

Crime that involves digital technology is often regarded as something relatively new and somewhat mysterious, whereas it is actually a relatively simple concept. In this chapter, I will try to simplify computer crime by identifying the similarities with 'conventional' crime and explain some of the differences. Cybercrime can be classified into two categories based on the means of the attack, which may inform responsibilities and resource allocation with an organisation: cyber-enabled crime comprising traditional crimes that are increased in scale or boundary by the use of digital technology, cyber-dependent crime comprising offences for which a digital technology is required, that is, they are usually perpetrated against a computer or a system that holds information.
Imaging Inscriptions

Abstract The granite obelisk with inscriptions in Hieroglyphic and Greek which was transported by W. J. Bankes from Philae in Egypt to his home at Kingston Lacy in Dorset in 1829 played an important role in the story of the decipherment of hieroglyphs by Young and Champollion. Coinciding with the launch of the Rosetta Spacecraft mission in 2014, new digital images of the inscriptions on the base of the obelisk were made by a team from the Centre for the Study of Ancient Documents, University of Oxford, using the technique of Reflectance Transformation Imaging (RTI) and allowed improved versions of the texts to be corrected and verified.
Multifaceted Privacy

Recent works in social network stream analysis have shown that a user's online persona attributes (e.g., location, gender, ethnicity, political interest, etc.) can be accurately inferred from the topics the user writes about or engages with. Revealing a user's sensitive attributes could represent a privacy threat to some individuals. Microtargeting (e.g., the Cambridge Analytica scandal), surveillance, and discriminating ads are examples of threats to user privacy caused by sensitive attribute inference. In this paper, we propose Multifaceted privacy, a novel privacy model that aims to obfuscate a user's sensitive attributes while publicly preserving the user's public persona. To achieve multifaceted privacy, we build Aegis, a prototype client-centric social network stream processing system that helps preserve multifaceted privacy, and thus allowing social network users to freely express their online personas without revealing their sensitive attributes of choice. Aegis continuously suggests topics and hashtags to social network users to write about in order to obfuscate their sensitive attributes and hence confuse content-based sensitive attribute inferences. Our experiments show that adding as few as 0 to 4 obfuscation posts (depending on how revealing the original post is) successfully hides a user sensitive attributes without changing the user's public persona attributes.
Intelligent and autonomous wheelchair design

Many people have difficulty walking, and the percentage of people with this challenge increases with age. The "mobility challenge," which we address in this project, centers on one's ability to independently move through the world. Enabling individuals to maintain their independence of mobility has significant social importance for society as a whole. While research in sensing and autonomous technology has made great strides in recent years, affordable fully autonomous systems are still a distant goal, primarily because of a lack of sensing accuracy and robustness based on off-the-shelf low-cost sensors. Self-driving vehicles being tested by companies rely heavily on expensive 3D LiDAR to locate themselves on the detailed maps they need to get around, and to identify things like pedestrians and other vehicles. In this project, we investigate an efficient sensing and perception hardware and software system design for autonomous and intelligent wheelchairs. The goal is to develop an experimental testbed with multi-modal sensors, computing systems, control and mobility systems. This affordable testbed will be a full-fledged modular platform to test and deploy latest deep learning-based algorithm without expensive hardware components.
A classification of the automorphism groups of polarized abelian threefolds over finite fields

We give a classification of maximal elements of the set of finite groups that can be realized as the automorphism groups of polarized abelian threefolds over finite fields.
Persian Document Classification Using Deep Learning Methods

Currently, the classification issue of Persian articles is sparse, limited, and non-automatic. So, information can be extracted from the structure of web pages that aids in the usual way of classifying texts. Text words are usually used as text features in text classification. As a result, text classification methods have many features. Numerous methods are used to reduce the number of features and to select related features. Based on deep learning, this paper proposes methods for extracting features of Persian texts, methods for classifying texts and finally identifying the subject matter of the collection. In this paper, the Hamshahri dataset (a sub-body containing 166,000 documents with a specific theme), one of the most prestigious Persian language resources in the field of natural language, is applied for feature extraction and text classification. The results of this paper indicate that the implementation of the classification of Persian news texts has achieved satisfactory performance.
A Wearable Appliance for Jogging Stride Frequency Calibration using Vibration Feedback

Recently, fitness and health promotion by doing regular jogging exercises are becoming popular. Having a stable and steady stride frequency is critical for the performance of jogging. This paper reports the progress of the design and implementation of a wearable appliance, namely the Intelligent Socks, for assisting the runner to keep steady stride frequency. The proposed appliance uses a set of pressure sensors to improve the accuracy of stride detection. Moreover, the socks provide vibration and sound feedback to help the runner to synchronize with the reference stride frequency. We have implemented an initial prototype and have conducted experiments to find the best way of providing vibration feedback.
A Saliency-based Weakly-supervised Network for Fine-Grained Image Categorization

Fine-grained image categorization is to recognize subcategories from each other. In actual photographs, there is often large intra-class variation and small inter-class variation, which make fine-grained image categorization a challenging task. Existing bilinear-pooling-based methods are less interpretable, and visual-attention-based methods often suffer from limited number of salient part. In this paper, we propose a saliency-based weakly-supervised network, a simple approach that can effectively preserve all the salient details of the original image. Based on the saliency sampler backbone, we apply feature pyramid network structure to combine the saliency information with high-level features and use KL-divergence for knowledge distillation. Experiments on Caltech-UCSD Birds dataset show that components of our network are efficient and reliable.
A New Framework of Feature Selection Approach for Sentiment Analysis

Abstract Undoubtedly that the huge business data could make data analysis becomes more complicated such that the decision-making process would be out of reach. This condition happens. In the fields of consumer buying behavior, A well-known method called sentiment analysis can help in extracting information about the up-to-date trends and is able to increase market value of product through improving its quality. One of the approaches in solving the sentiment analysis is feature selection technique. However, this technique contains a combinatorial behavior and the analysis of the huge data can experience uncertainty parameter. This paper describes a framework for solving the sentiment analysis based on feature selection approach using a stochastic combinatorial programming.