Reviewerly/exHarmony huggingface dataset:
Links between Contractibility and Fixed Point Property for Khalimsky Topological Spaces

Given a Khalimsky (for short, K-) topological space X, the present paper examines if there are some relationships between the contractibility of X and the existence of the fixed point property of X. Based on a K-homotopy for K-topological spaces, we firstly prove that a K-homeomorphism preserves a K-homotopy between two K-continuous maps. Thus, we obtain that a K-homeomorphism preserves K-contractibility. Besides, the present paper proves that every simple closed K-curve in the n-dimensional K-topological space, S C K n , l , n ≥ 2 , l ≥ 4 , is not K-contractible. This feature plays an important role in fixed point theory for K-topological spaces. In addition, given a K-topological space X, after developing the notion of K-contractibility relative to each singleton { x } ( ⊂ X ) , we firstly compare it with the concept of K-contractibility of X. Finally, we prove that the K-contractibility does not imply the K-contractibility relative to each singleton { x 0 } ( ⊂ X ) . Furthermore, we deal with certain conjectures involving the (almost) fixed point property in the categories KTC and KAC, where KTC (see Section 3) (resp. KAC (see Section 5)) denotes the category of K-topological (resp. KA-) spaces, KA-) spaces are subgraphs of the connectedness graphs of the K-topology on Z n .
On the Tender Line Separating Generalizations and Boundary-Case Exceptions for the Second Incompleteness Theorem Under Semantic Tableaux Deduction

Our previous research has studied the semantic tableaux deductive methodology, of Fitting and Smullyan, and observed that it permits boundary-case exceptions to the Second Incompleteness Theorem, when multiplication is viewed as a 3-way relation (rather than as a total function). It is known that tableaux methodologies do prove a schema of theorems, verifying all instances of the Law of the Excluded Middle. But yet we show that if one promotes this schema of theorems into formalized logical axioms, then the meaning of the pronoun “I” in our self-referencing engine changes, and our partial evasions of the Second Incompleteness Theorem come to a complete halt.
Böbrek Tümör Segmentasyonu İçin Unet ve Unet-ResNet Modellerinin Karşılaştırılması

Kidney cancer is one of the types of cancer that can be difficult to diagnose and can be very complicated for physicians to diagnose. Especially in recent years, many new treatment methods for kidney cancer have been developed and some of them are still under development by scientists. These studies enable new treatment modalities for kidney cancer patients. In addition, renal tumors are one of the most insidious progressive tumor types. Many times it can be mistaken for other diseases. Especially until the last stage, patients may not even have a serious complaint. Therefore, conducting such studies is very important for early diagnosis. In this study, it is tried to segmentation with deep learning methods in order to help people who are dealing with difficulties of kidney cancer diagnosis. For this reason, Unet and Unet-ResNet models were compared. The Unet-ResNet model achieved 90.2% success for renal tumor segmentation, while the Unet model achieved 44.3% success for renal tumor segmentation. These results shed light on how successful and necessary the Unet-ResNet model can be in particular in studies on image segmentation.
Image Registration SIFT Algorithm Based on Adaptive Adjustment of Grayscale Weight

In view of the low color contrast of the foreground in the image of aero-engine blades, this paper optimizes the SIFT algorithm which was commonly used in image registration. The SSIM index and FSIM index in the image similarity evaluation standard are added to calculate. Using the ESFS index as the judgment standard for analysing the similarity between the color image and the corresponding grayscale image. To calculate a weight ratio which is better than the traditional algorithm. Experiments show that the proposed algorithm outperforms the traditional algorithm in saving the blade images ' information, and can acquire more feature point pairs in the subsequent SIFT registration process.
Ideas and perspectives: Proposed best practices for collaboration at cross-disciplinary observatories

Abstract. Interdisciplinary science affords new opportunities but also presents new challenges for biogeosciences collaboration. Since 2007, we have conducted site-based interdisciplinary research in central PA, USA, at the Susquehanna Shale Hills critical zone observatory. Early in our collaboration, we realized the need for some best practices that could guide our project team. While we found some guidelines for determining authorship on papers, we found fewer guidelines describing how to collaboratively establish field sites, share instrumentation, share model code, and share data. Thus, we worked as a team to develop a best practices document that is presented here. While this work is based on one large team project, we think many of the themes are universal, and we present our example to provide a building block for improving the function of interdisciplinary biogeoscience science teams.
Solar Radiation Estimations Using the Territorial Climatological Measurements in Vhembe District, Limpopo Province for Solar Energy Potential Estimation and Use

Determining the solar radiation for the use of energy generation involves a number of procedures, estimations and calculations using the climatological weather data measurements. The case study was conducted in the Vhembe District area through the nine installed Weather Stations (WS). The analysis determines the minimum and maximum solar radiation equations associated with the local climate patterns in accommodating the theoretical bases and its time period changes. The paper contributes to the main project objectives on renewable (i.e. solar, wind, biomass/biogas and hydro) energy assessment for their potentials and generating at small/ micro scale in the district. These parameters are very important in estimating the solar energy radiation to the area using its extraterrestrial solar radiation per day/ weekly/ monthly and annual periods. The metrology estimations through graphical representations were performed. These provided calculations in which territorial solar energy was determined through climatic conditions and analysis found to be usable.
Informatization of task planning in project activity of students

In the educational and project activities, an important area is the formation of competencies associated with the planning, monitoring and adjustment of the work plan. An effective means of structuring a project implementation plan is to use technology for modeling business processes. Using the notation of functional modeling IDEF0, a description is given of the tasks that must be solved during the project. This makes it possible to allocate the necessary resources and the results obtained at each stage of the work, as well as present a holistic picture of all activities in the relationship of all stages. Each task, business process can be considered as an independent project with its own characteristics. It is possible to use the decomposition of business processes, which is also present in the IDEF0 notation. In educational and project activities, this approach is applied for the first time. It allows students to make the planning process simpler and more understandable by structuring the project work plan and visualizing it in the form of graphic diagrams. The variety of software and Internet services that allow you to create IDEF0 diagrams is an additional argument in favor of using the proposed methodology in educational and project activities.
Progressive Full Data Convolutional Neural Networks for Line Extraction from Anime-Style Illustrations

Anime-style comics are popular world-wide and an important industry in Asia. However, the output quantity and quality control of art workers have become the biggest obstacle to industrialization, and it is time consuming to produce new manga without the help of an intelligence assisted tool. As deep learning techniques have achieved great successes in different areas, it is worth exploring them to develop algorithms and systems for computational manga. Extracting line drawings from finished illustrations is one of the main tasks in drawing a manuscript and also a crucial task in the common painting process. However, traditional filters such as Sobel, Laplace, and Canny cannot output good results and require manual adjustments of the parameters. In order to address these problems, in this paper, we propose progressive full data convolutional neural networks for extracting lines from anime-style illustrations. Experimental results show that our progressive full data convolutional neural networks not only can learn as much as processing methods for the detailed regions, but also can accomplish the target extraction work with only a small training dataset.
Human-Computer/Device Interaction

Any interaction refers to the communication between two or more entities (be it abstract/conceptual or physical entity).Successful interaction is equated from the properties of each entity involved in the interaction as well as the capabilities of the interacting entities.With the diversified use and application of computers and specialized devices for specific tasks, such as biomechanical and biomedical devices, interaction design needs to further study the context of the tasks as well.Moreover, with the inclusion of embedded systems and smart devices, instead of focusing only on the hardware performance, the computer architecture needs to consider the opportunities.Especially, HCI can be improved as the current technologies are giving an opportunity for building smart interaction where the user interacts with devices implicitly and in less obtrusive way.In light of this, the design and architecture of an engineered product need to strive for making the product usable and used while making it useful to the user.And this can be achieved if interaction design is dictated by scrutinizing the user model with respects to the usability attributes in view of the context of its task as well as the platform capabilities and constraints as discussed in this chapter.
The Development of the myKadera System for Object Vocabulary Mastery for Students with Hearing Impairment Based on Augmented Reality

Abstract This study aims to: 1) design appropriate learning media based on Augmented Reality on the introduction of object vocabulary for hearing-impaired students, 2) perform the developed system. This research belongs to research and development (R&amp;D). This developmental research refers to the stages of learning media development by Arief Sadiman, et al and the stages of the development model by Borg &amp; Gall. This research development procedure consists of three major steps that must be passed, namely planning, development and evaluation activities. The result of this Android-based myKadera system development research is that the system has some features provided including audio, sign language videos, 3D animation, and object vocabulary quizzes that were packaged more interestingly, applicative and innovative with a more real-time visual display, supported by object vocabulary learning media books with the help of AR (augmented reality) markers. The results of the test indicate that the application meets ISO/IEC 25010 standards on aspects; (1) functional suitability; all features of the application has been successful and said to be good, which means there are no functions that fail on testing, ( 2) application compatibility; the application is 100% compatible with sub-characteristics of co-existence, various operating systems, and various types of devices used for testing, (3) performance efficiency; the application is successfully run using the AppAchhi test device. The application is said to be good without experiencing memory leak or application damage which results in force close or launch fail. The application shows an average time behavior of 0.063 seconds/thread, an average CPU usage of 20.39%, and average memory usage of 13.08 MB, and (4) usability testing obtained a percentage of 95.50%. These results indicate that the application is very feasible and meets the usability aspect.
Proposal of Online Regularization for Dynamical Structure Optimization in Complex-Valued Neural Networks

We propose online-learning complex-valued neural networks (CVNN) to predict future channel states in fast fading multipath mobile communications. A communication channel is represented by complex amplitude. Then, CVNNs are intrinsically suitable for fading channel prediction by utilizing its high generalization ability in the complex-amplitude domain. In this paper, we introduce regularization dynamics to make the CVNN structure dynamically changing in accordance with the changes in the multipath situations. We demonstrate the online adaptability when the scattering environment changes.
RLDR-Pruning: Restricted Linear Dimensionality Reduction Approach for Model Compression

This paper studies pruning a trained deep neural network for resource-constrained devices. In general, pruning a trained CNN is an iterative process: start with a trained CNN; choose and prune the least important parameter; fine-tune to get another trained CNN. It is reasonable to define the least important parameter as the parameter that leads to the minimum accuracy drop after pruning and fine-tuning. However, directly searching such parameter is computationally infeasible because of fine-tuning. Therefore, current methods ignore fine-tuning when choosing parameter to prune. We take fine-tuning into consideration and propose our RLDR-pruning method. To make the searching feasible in our method, we first model the fine-tuning process and propose an one-step process called mini-tuning. Then, our RLDR-pruning replaces fine-tuning with mini-tuning when searching the least important parameter. Via experiments on classification tasks, we demonstrate that RLDR-pruning achieves significantly higher inference accuracy than existing techniques based on similar parameter optimization capabilities.
Root Methods for Dynamic Analysis of the One Class Chaotic Systems

The paper deals with the developing of the methodological backgrounds for the construction and study of nonlinear dynamical models. Such backgrounds allow us to use interval methods to define piece-wise linear models for the one class chaotic systems. Usage of proposed models is the way to find conditions of chaos occurring in dynamical systems. We prove our approach by considering the example of modeling, simulating, and studying of Mackey-Glass system. This example shows that one can use linear control theory methods to study the dynamic of time-delayed chaotic systems.
Dynamic Facial Feature Learning by Deep Evolutionary Neural Networks

Facial Action Coding System is a comprehensive and anatomical system which could encode various facial movements by the combination of basic AUs (Action Units), and makes the emotion categories much wider. Recently, deep learning has been shown its superiority on recognition tasks. Despite the powerful feature learning ability of deep learning, there are still several problems remained. Firstly, a large amount of training data is needed to fully extract features and avoid overfitting. Secondly, the parameters optimization of deep neural network is complex, and the direct guidance of the results is insufficient. In this paper, a spatiotemporal self-learning method is designed by evolutional deep neural network model, and spatial augmentation is utilized to deal with the two problems facing in practical application. The proposed method is conducted on AUs analysis task which is important for emotion identification. The 3D convolutional neural network which could learn dynamic facial features from AUs image sequences is optimized automatically for the topology and hyper-parameters by evolutional scheme. Extensive experiments demonstrated the effectiveness of EVONET (Deep Evolutionary Neural Networks) on the facial databases over alternative methods, including 3DCNNs (3D Convolutional Neural Networks), and several convolutional neural network based models.
[Neural mechanisms of visual selective attention].

Because of a limited capacity of information processing in the brain, the efficient processing of visual information requires selecting only a very small fraction of visual inputs at any given moment in time. Attention is the main mechanism that controls this selection process, namely selective attention. Selective attention is the mechanism by which the subset of incoming information is preferentially processed from the complex external environment. Research on selective attention has two key issues. One is what targets (inputs) are selected by attention. There are three different types of selective attention according to its selected target: space-based, feature-based, and object-based attention. Another issue is how selective attention is generated. There are two different types of selective attention according to its generating source: top-down and bottom-up attention. In this review, these two issues are introduced to systematically discuss the neural mechanism of visual selective attention.
Active Vibration Isolation System for Drone Cameras

Drones are small unmanned aerial vehicles, often equipped with imaging system, which can be controlled remotely. During the drone flight, the camera is subjected to vibrations which hamper the quality of the captured images/videos. The high-frequency vibrations from the drone are transmitted to the camera. Conventionally, passive rubber mounts are used to isolate the camera from the drone vibrations. The passive mounts are able provide reduction in response near the resonance. However, this comes at the cost of amplification at the higher frequencies. This paper proposes an active vibration isolation system which exhibits improved performance at the higher frequencies compared to the conventional system. The system consists of introducing an intermediate active system between the drone and the conventional system (i.e., camera with passive rubber mounts). Experiments are carried out to characterize the drone vibrations are being transmitted to the camera. A test bench is developed to study the effect of these vibrations on the quality of the images captured. The quality of images captured is expressed in terms of pixel-to-signal noise ratio (PSNR). The transmissibility of the camera with passive rubber mounts is also evaluated experimentally. Three different isolation systems are proposed based on the degrees of freedom that needs to be controlled. Controller architecture for the isolation systems is designed and evaluated numerically. The response of the camera with passive isolation, when integrated with proposed isolation system, is found to reduce at high frequency without compromising the reduction in response at lower frequencies.
Lightweight alternative tech: a study of Chromebooks in the academic library classroom

Purpose The purpose of this paper is to evaluate Chromebooks as an alternative to the traditional computer laboratory for library instruction in an academic environment. The results of this study could help inform the creation of a mobile instruction lab that students and librarians quickly construct inside any classroom with minimal effort and no software to manage. Design/methodology/approach An online survey was distributed to student participants. The survey contained two demographic questions followed by seven user experience questions related to the in-classroom use of Chromebooks; most questions were quantitative in nature. Findings The majority of respondents (84 percent) strongly or somewhat agreed that Chromebooks were easy to use, and 15 percent of the respondents reported some difficulty accessing the university’s Wi-Fi system while using the devices. Research limitations/implications This introductory study was limited to a survey population primarily comprised of lower-level undergraduate students in their first two years of study. Additionally, Chromebooks were not tested in an educational environment using G Suite for Education, which could increase the opportunities for use in an academic setting. Practical implications The study’s findings, combined with the portability and long battery life of Chromebooks, make them a candidate for a mobile instruction lab. Originality/value This research seeks to explore the viability of Chromebooks as an affordable and easy to manage alternative to wired instruction rooms, using a light laptop technology that is becoming increasingly familiar to college students.
Anonymous market product classification based on deep learning

With the rapid development of Internet technology, the abuse of dark networks and anonymous technology has brought great challenges to network supervision. Therefore, it is important to study the anonymous market. In this paper, we propose a single-mode multivariate classification model for anonymous market product classification. Divide anonymous markets products into 5 categories. Our algorithm uses the word vector embedded in a convolutional neural network based on Word2vec training. Compared with the simple machine learning classification model, the accuracy of the single-mode multivariate classification model on the test set is 91.84%. By studying the classification of anonymous market products, law enforcement personnel can better supervise anonymous market of illegal products and maintain network security.
Helping Johnny to Search: Usable Encrypted Search on Webmail System

End-to-end encryption is becoming common in messaging applications. E-mail that is the one of most popular a messaging application has several standards and systems in its encryption. However, it is hard to say that e-mail encryption has spread enough. It has been pointed out that there are usability problems in message encryption. Even though various studies on message encryption and usability have been made, there is no study discussing utility function such as searching and sorting. In this paper, the search function is focused. We aim to investigate the usability problems on encrypted search. Therefore, a method that applies encrypted search transparently is proposed at first. The implementation of the proposed method is achieved using Google Chrome Extension for Gmail, and evaluation both performance and usability is conducted. We found that participants do not mind whether the search function is encrypted or not if both have almost identical UI and encrypted search operates at high speed.
Hermes: A Reversible Language for Writing Encryption Algorithms (Work in Progress)

We describe the programming language Hermes, which is designed for writing private-key encryption algorithms. Specifically, every program written in Hermes is reversible: It can run equally well forwards and backwards. This means that you only write the encryption algorithm and get the decryption algorithm for free. Hermes also ensures that all variables are cleared after use, so the memory will not contain data that can be used for side-channel attacks. Additionally, to prevent side-channel attacks that extract information from running times, control structures that may give data-dependent execution times are avoided.