Reviewerly/exHarmony huggingface dataset:
CONTROLE DE CAOS NO MODELO NEURONAL DE HINDMARSH-ROSE COM PARÂMETROS INCERTOS

In bioengineering there is a great motivation in studying the Hindmarsh-Rose (HR) neuron model due to the fact that it represents well the biological neuron, making it possible to simulate several behaviors of a real neuron, including periodic, aperiodic and chaotic behaviors, for example. Based on this model, this article proposes applying a linear optimal control design to the uncertain and chaotic behavior established by changes in the parameters of the system. To do so, the mathematical system of the RH model and its chaotic behavior are presented; afterwards, the fixed parametersare replaced by uncertain ones, and the chaotic dynamics of the system is investigated. At last, the linear optimal control is proposed as a method for controlling the chaotic behavior of the model, and numerical simulations are presented to show the efficiency of this proposal.
On the convergence of optimizer-activation pairs

The effect of training of deep neural network depends on the selection of the activation function and the optimizer, because the different activation functions lead to distinct loss curvature and the different optimizers will have different performance in distinct curvatures. In this paper, we select different combinations of activation functions and optimizers, seek to select the best combination under the same experiment setting, and take a general discussion for the efficiency of these combinations finally. Moreover, to guarantee fair comparison the hyperparameters tuning is conducted.
Research on drive control method of induction motor based on random forest

The induction motor is one of the key hardware for the normal operation of mechanical equipment. However, due to the poor performance of the controller designed by the traditional method, it is easy to cause the drive control instability. Therefore, the induction motor drive control method based on random forest is studied. This method obtains in advance the working characteristics of induction motors in high and low temperature environments; considering the working characteristics, a new induction motor drive controller is designed based on the random forest algorithm; and the induction motor drive control is realized by setting fuzzy rules. The experimental results show that in the high temperature and low temperature test environment, the drive control method studied in this study has regularity in the control wave amplitude and the control effect is more stable. It can be seen that the random forest theory meets the requirements of this research.
On Correspondence between Selective CPS Transformation and Selective Double Negation Translation

A double negation translation (DNT) embeds classical logic into intuitionistic logic. Such translations correspond to continuation passing style (CPS) transformations in programming languages via the Curry-Howard isomorphism. A selective CPS transformation uses a type and effect system to selectively translate only nontrivial expressions possibly with computational effects into CPS functions. In this paper, we review the conventional call-by-value (CBV) CPS transformation and its corresponding DNT, and provide a logical account of a CBV selective CPS transformation by defining a selective DNT via the Curry-Howard isomorphism. By using an annotated proof system derived from the corresponding type and effect system, our selective DNT translates classical proofs into equivalent intuitionistic proofs, which are smaller than those obtained by the usual DNTs. We believe that our work can serve as a reference point for further study on the Curry-Howard isomorphism between CPS transformations and DNTs.
Richer Aggregated Features for Optical Flow Estimation with Edge-aware Refinement

Recent CNN-based optical flow approaches have a separated structure of feature extraction and flow estimation. The core task of optical flow is finding the corresponding points while rich representation is just the key part of such matching problems. However, the prior work usually pays more attention to the design of flow decoder than the feature extraction. In this paper, we present a novel optical flow estimation network to enrich the feature representation of each pyramid level, with a hierarchical dilated architecture and a bottom-up aggregation scheme. In addition, inspired by edge guided classical methods, we bring the edge-aware idea into our approach and propose an edge-aware refinement (EAR) subnetwork to handle motion boundaries. Using the same decoding structure as PWC-Net, our network outperforms it by a large margin and leads all its derivatives both on KITTI-2012 and KITTI-2015. Further performance analysis proves the effectiveness of proposed ideas.
Ultrasound for Gaze Estimation

Most eye tracking methods are light-based. As such they can suffer from ambient light changes when used outdoors. It has been suggested that ultrasound could provide a low power, fast, light-insensitive alternative to camera based sensors for eye tracking. We designed a bench top experimental setup to investigate the utility of ultrasound for eye tracking, and collected time of flight and amplitude data for a range of gaze angles of a model eye. We used this data as input for a machine learning model and demonstrate that we can effectively estimate gaze (gaze RMSE error of 1.021 ± 0.189\(^{\circ }\) with an adjusted \(R^{2}\) score of 89.92 ± 4.9).
Transaction from informative technologies to convergent ones: basis for students’ forming foreign languages communicative competence

Abstract Information technologies became an important part of our life and education. Traditionally, these technologies are mastered in the general education course of informatics and are applied in the wide range of disciplines study in both primary and in higher education. At the same time, recently there were significant changes in the technological sphere, which also affect the education. In particular, an important area of development is “Smart education and e-learning”, their possibilities are being intensively studied all over the world. However, the most significant changes in the sphere of information technology are the process of traditional technologies convergence and the technologies new quality formation: convergence of material and information technologies, convergence of information and cognitive technologies. Convergent technologies largely determine the priority areas of the society socio-economic development. This gives the reason to talk about the expediency of their development within the framework of general education. Modern trends in the modernization of educational programs require the application of teaching a foreign language modern methods. Converged technologies play an important role in foreign language teaching and are one of the best teaching tools. The use of convergent technologies is effective in increasing the level of teaching a foreign language, developing the students’ creative abilities in foreign language classes.
An Improved CNN-Based Completion Method for Power Grid Middle Platform Data

Abstract The transmission of power data would be likely to be interrupted or interfered, which results in the middle platform data missing. Missing data reconstruction plays a key role in power data processing, based on which the quality and utilization of power data have been enhanced. In traditional power data filling methods, only a single data distribution was considered, the correlation of power data in time and space was ignored. In this paper, an improved Convolutional Neural Network (CNN) method for filling power data was presented, and a CNN structure was designed. Through the unsupervised training of CNN, this method mines the correlation of data from the dimensions of time and space, and efficiently completes the missing data through the constraints of time continuity and space continuity. The completion results show that our method can fill the missing data efficiently, furthermore, experimental evaluations validate the performs of the proposed method.
Characterizing the Impact of TCP Coexistence in Data Center Networks

The switch fabrics of today's data centers carry traffic controlled by a variety of TCP congestion control algorithms. This leads us to ask: how does the coexistence of multiple variants of TCP on shared switch fabric impacts the performance achieved by different applications in data centers? To answer this question, we conducted an extensive set of experiments with coexisting TCP variants on Leaf-Spine and Fat-Tree switch fabrics. We executed common data center workloads, which include streaming, MapReduce, and storage workloads, using four commonly used TCP variants, namely BBR, DCTCP, CUBIC, and New Reno. We also extensively executed iPerf workloads using these 4 TCP variants to purely study the impact of the coexistence of TCP variants on each other's performance without incorporating the network behavior of the application layer. Our experiments resulted in a large set of network traces comprised of 160 billion packets (we will release these traces after publication of this work). We present comprehensive observations from these traces that have important implications in ensuring optimal utilization of data center switch fabric and in meeting the network performance needs of application layer workloads.
Transfer Learning Methods for Extracting, Classifying and Searching Large Collections of Historical Images and Their Captions

This paper is about the creation of an interactive software tool and dataset useful for exploring the unindexed 11-volume set, Pompei: Pitture e Mosaici (PPM), a valuable resource containing over 20,000 annotated historical images of the archaeological site of Pompeii, Italy. The tool includes functionalities such as a word search, and an images and captions similarity search. Searches for similarity are conducted using transfer learning on the data retrieved from the scanned version of PPM. Image processing, convolutional neural networks and natural language processing also had to come into play to extract, classify, and archive the text and image data from the digitized version of the books.
Vizualization of SAR Categories Using Complex Valued Deep Learning

This paper presents a visualization SAR patch categorization using complex valued deep learning approach. SAR patch categorization of a SAR product provides image categories with a sharp border between categories. This paper presents visualization of SAR categories using gradient weighted class activation mapping of final convolutional layer in order to produce a coarse localization map highlighting important region. The reconstructed categorization provides visually smoother categorization than the classical patch based deep learning categorization.
Restoration and Enhancement of Historical Stereo Photos Through Optical Flow

Restoration of digital visual media acquired from repositories of historical photographic and cinematographic material is of key importance for the preservation, study and transmission of the legacy of past cultures to the coming generations. In this paper, a fully automatic approach to the digital restoration of historical stereo photographs is proposed. The approach exploits the content redundancy in stereo pairs for detecting and fixing scratches, dust, dirt spots and many other defects in the original images, as well as improving contrast and illumination. This is done by estimating the optical flow between the images, and using it to register one view onto the other both geometrically and photometrically. Restoration is then accomplished by data fusion according to the stacked median, followed by gradient adjustment and iterative visual consistency checking. The obtained output is fully consistent with the original content, thus improving over the methods based on image hallucination. Comparative results on three different datasets of historical stereograms show the effectiveness of the proposed approach, and its superiority over single-image denoising and super-resolution methods.
Mutual Authentication of IoT Devices Using Kronecker Product on Secure Vault

Internet of things provides a way to enable various Internet-enabled wireless devices to connect and communicate with each other, which is beneficial to collect and store the information on the cloud or on any server. This paper presents a novel mutual authentication technique, which uses a secure vault to store and generate session keys to empower the mutual authentication-based scheme. Secure vault is a matrix, which consists of integer values that are used to create the session keys, which are further used as authentication keys. At initial stage, the secure vault matrix is stored in the server and some part of secure vault is stored in the IoT devices, and further, the value of vault changes to provide a secure mechanism against side-channel as well as dictionary attacks. Our algorithm uses a Kronecker product method to establish the keys and helps to enhance the storage and communication cost for the resource-constrained devices. We reduced the storage cost of an authentication scheme to O(√N), and it is not required for a device to have any communication cost during the computation phase.
On the Fundamental Limits of Exact Inference in Structured Prediction

Inference in structured prediction is naturally modeled with a graph, where the goal is to recover the unknown true label for each node given noisy observations corresponding to nodes and edges. The focus of this paper is on the fundamental limits of exact recovery irrespective of computational efficiency, assuming the generative process proposed by [1]. Analyzing the fundamental limits is crucial for algorithm evaluation and development. In this regard, we establish the information-theoretic limit bounds and show that there exists a gap between the limits and the performance of the existent tractable method [2], implying the need for further development of algorithms for exact inference. The fundamental limit we suggest applies to general connected graphs and involves graphical metrics such as the Cheeger constant and the maximum degree. Finally, we reveal that the sufficient and necessary conditions derived from the limit bounds are tight up to a logarithmic factor for a wide range of graphs.
Gender Differences in Innovation Design

This study investigates aspects of the role gender plays in participatory design innovation workshops; reflecting on both the process and the output. Often when gender and design are discussed, the problems raised concern a lack of women as designers or developers [26], but there appear to be gaps in addressing full gender representation when it comes to users in the design process. In this study, a design workshop was run where participants, two men and five women were asked initially to identify or generate problems and possible digital solutions concerning their academic studies, and then to design their top self-selected solution. The workshop was recorded and transcribed, and conversation and discourse analysis were carried out which found gender to influence problem raising, language used and group practices. The paper concludes both that gender apparently plays a strong role in group dynamics with regards to design innovation; and that thematic conversation and discourse analysis provides an appropriate and insightful approach to understanding these issues.
Hubungan Antara Pola Asuh Orang Tua Dengan Hasil Belajar Siswa Di SMP Swastyastu Tolai

Parenting patterns have an influence on learning outcomes. However, how much the influence and relationship level between the parenting of the parents with the student learning outcomes in Tolasty Swastyastu Middle School have not been studied. Therefore, the goals of the study is to determine the level of relationship between parenting parents with student learning outcomes and to determine the influence of parenting parents on student learning outcomes in Tolasty Swastyastu SMP. The study used a correlation method that connects the independent variable (X), parenting pattern, and dependent variable (Y), learning outcomes. The study collected 91 students from class VII A and B and class VIII A and B. Instrument of the study used a closed questionnaire consisting of 20 questions for parenting variables. An average value of even semester report was used for the student learning outcomes variable. Data was analysed with product moment correlation index. Result of the study obtained rxy = 0.825, indicating that the level of relationship between variables X (Parenting Parents) and Y (Student Learning Outcomes) is very strong. Moreover, the correlation was positively linier. Thus, it can be conluded that parenting patterns affect student learning outcomes (=68.1%).
Free Chain: Enabling Freedom of Expression through Public Blockchains

Everyone should have the right to expression and opinion without interference. Nevertheless, Internet censorship is often misused to block freedom of speech. The distributed ledger technology provides a globally shared database that is geographically distributed and that cannot be controlled by a central authority. Blockchain is an emerging technology that enabled distributed ledgers and has recently been employed for building various types of applications. This paper demonstrates a unique application of blockchain technologies to create a platform that supports the freedom of expression. The paper adapts permissionless and public blockchains in order to leverage their advantages of providing an immutable and tamper-proof digital ledger. The study shows the blockchain potential for providing censorship-resistant publication platform. The paper presents and evaluates possible methods for building such system using the Bitcoin and Ethereum blockchain networks. Our results demonstrate that the Ethereum blockchain is much more beneficial for our system as it requires much lower cost than Bitcoin. to use as a platform to allow freedom of speech.
E-government Deep Recommendation System Based on User Churn

At present, all major government-related APPs have basically achieved one-stop operation. However, due to the various categories of government affairs, how to provide users with personalized recommendation services based on user behavior is a problem that smart government needs to solve. Aiming at the problems of sparse user government behavior data and difficulty in mining hidden features, this paper proposes a two-tower model that integrates user churn. A deep neural network is constructed to characterize user item characteristics, and the influence of user churn factor on feature weights is also considered. At the same time, the random forest algorithm is introduced to weight the characteristics of user churn, and the characteristics of the two towers model are combined to achieve personalized ranking recommendation. The experimental results show that our proposed model is better than the original features, and this model has been successfully deployed in the “My Ningxia” government recommendation system, and the user experience has been significantly improved.
Separation Axioms in N<sub>nc</sub> Topological Spaces via N<sub>nc</sub> e-open Sets

Abstract The main idea of this research is to define a new neutrosophic crisp points in neutrosophic crisp topological space namely ( N nc P N ), the concept of N nc limit point was defined using ( N nc P N ), with some of its properties, the separation axioms ( N nc eτ i -space ( i = 0,1, 2) were constructed in neutrosophic crisp topological space using ( N nc P N ) and examine the relationship between them in details.
Edge Detection Algorithm Used in a 3D Sketching &amp; Photogrammetry System

Abstract A clear 3D representation is vital to prototyping in design. 3D modeling is a popular approach to design structures in 3D, resulting in accurate models that can be viewed from multiple perspectives. However, 3D modeling is too costly and rigid for designers because it has a steep learning curve. Existing heavy-weight 3D software packages are usually not suitable for ideation. Therefore, we consider using sketches to convey ideas in 3D as a more natural way for prototyping. Even if most sketches are loose and 2D, people can still identify 3D structures from them. However, most sketching tools only support drawing in 2D, motivating us to find a solution to bridging 2D sketches and 3D structures. To fill the gap, we propose a 3D sketching system that facilitates sketch creation in 3D. Users can create a sketch in 3D and view it from multiple perspectives, without having to create multiple 2D sketches. This application also supports reconstructing a point cloud from images taken from existing structures. We designed a novel edge detection algorithm derived from the Hough transform to extract borders as strokes from a point cloud reconstructed from captured images. This includes fitting multiple planes to the point cloud and creating strokes by projecting points to the planes. We also present real-world use cases to evaluate our system. Our application allows designers to prototype more rapidly, opening a gate to more user-friendly ideation processes.
Exponential Family Graph Signal Recovery under Structural Constraints

The problem of graph signal recovery from a relatively small number of noisy observations has been studied extensively in the recent on the field of graph signal processing. However, most of studies focus on the recovery under Gaussian noise which is just a special case of exponential family noise. It natural to ask whether we can accurately recover graph signals under different noise with exponential distribution. Herein, we extend the results of [8][9] to the more general case of exponential distribution.
Engineering and geological features of technogenic deposits and cultural layers in the territory of Samara

Abstract The engineering and geological features of technogenic deposits in the territory of the city of Samara have been studied. Their features are revealed and a classification is carried out. The article presents the results of a petrographic study of samples taken at the archaeological site of Staraya Samara, Khlebnaya Square, carried out in 2019. The object represents cultural layers that were formed in the XVIII-XIX centuries at the site of the alleged location of the second Samara fortress. The studies were performed under a binocular microscope at an 8.75-fold magnification, in the passing light of a polarization microscope at a 72-fold magnification, and under a digital microscope at a 10-fold magnification. It has been determined that the pebble fraction is represented by quartzite and jasper flint, the sandy and silty fractions are angular quartz fragments with an admixture of undecomposed organic remains. Organic cultural layers, which reach 7-8 meters or more in the ancient part of the city, are especially powerful. Their presence is a limiting factor in modern urban construction.
Logical foundations: Personal perspective

Abstract We illustrate the glorious history of logical foundations and discuss the uncertain future.