Reviewerly/exHarmony huggingface dataset:
Design of Morse Message Transmission Control Algorithm Based on Machine Learning

The design of Morse code transmission control algorithm based on the machine learning are carried out due to the Morse message system that lacks flexibility and fails to meet the requirements of the reporting training at low and medium speeds. According to the characteristics of the report, the messages are classified into four categories, which all are designed report control algorithms or strategies, which solves the shortcomings caused by fixed bit rate and fixed ratio reporting in an effective way, and at the same time, offers a new idea for the overall design of the system.
Signature Image Improvement with Gradient Adaptive Morphology

The paper presents a method for improving signature images, by using a directional field guided morphology. The method adapts a circular or linear structural element and its orientation. There is presented the comparison of the algorithm with the results of binarization process and with the existing post-processing algorithms, such as the morphological opening, closing and median filtering. The experiments show that the authors’ method significantly improves the quality of images by removing unwanted artifacts.
Cache-aware algorithm for multidimensional correlations

Abstract In the paper discusses a speed of a convolution or correlation functions in case of a big size of input data. When a data volume exceeds a cache size, a significant part of an execution time is spent on a loading data into the cache. A performance of correlation is explored an example of receiving a long radio signal by the use of comparison with a big number of patterns. To decrease this spending a method of reordering calculation and memory operations and is proposed. The method uses cache aware approach to construct cache friendly algorithms. A decreasing of execution time (in some case) achieves up to 44%
Calculation of Mean Data on Gini Relationship by Data Mining Method

Data clustering is one of the main tasks of data mining which have to show the hidden patterns in unlabeled data. Due to inherent complexity and weakness of basic clusterings, a considerable amount of research has nowadays turned to ensemble based clusterings. Because of effectiveness of weighting in classifier ensemble it is expected that the usage of weighting can be effective in clustering ensemble. In classifier ensemble, the vote of each classifier is related to its accuracy. There, the accuracy of each classifier is approximated by testing the classifier over a test data set, but the accuracy of clustering can't be approximated at all; because it lacks supervision and also a well-known measure for estimation of accuracy. Here, research test the leukemia data using the Gini relation on meta-hierarchical algorithms and use the data mining relation to evaluate the accuracy of the algorithms over other algorithms.
Improved N-dimensional data visualization from hyper-radial values

Higher-dimensional data, which is becoming common in many disciplines due to big data problems, are inherently difficult to visualize in a meaningful way. While many visualization methods exist, they are often difficult to interpret, involve multiple plots and overlaid points, or require simultaneous interpretations. This research adapts and extends hyper-radial visualization, a technique used to visualize Pareto fronts in multi-objective optimizations, to become an n-dimensional visualization tool. Hyper-radial visualization is seen to offer many advantages by presenting a low-dimensionality representation of data through easily understood calculations. First, hyper-radial visualization is extended for use with general multivariate data. Second, a method is developed by which to optimally determine groupings of the data for use in hyper-radial visualization to create a meaningful visualization based on class separation and geometric properties. Finally, this optimal visualization is expanded from two to three dimensions in order to support even higher-dimensional data. The utility of this work is illustrated by examples using seven datasets of varying sizes, ranging in dimensionality from Fisher Iris with 150 observations, 4 features, and 3 classes to the Mixed National Institute of Standards and Technology data with 60,000 observations, 717 non-zero features, and 10 classes.
Object Tracking Via Context Information And Correlation Filer Learning

A novel object tracking method based on context correlation information and discriminative correlation filters is here proposed. This method explores discriminant correlation filters for scale translation and scale estimation within an asymmetric Siamese neural network to improve the adaptability of object trackers to target scale variation, and combines global context information in the discriminant correlation filters to locate the target accurately to improve the tracking performance in complex background. Extensive experiments demonstrate the superior performance of the proposed approach over the approaches.
A No-Reference Network Video Quality Assessment Method Based on Video Content Characteristics

A novel model of no-reference network video quality assessment is proposed in this study. First, a new definition of video content complexity is represented by the type of frame, the type of macro block, and the motion vector that consists of payload information. The proposed assessment model consists of a coding distortion module and a transmission distortion module. The coding distortion module adds the proposed complexity to a standard G.1070 model. The transmission distortion module describes the impact of frame loss rate, concentrative degree of frame loss and video content complexity on video quality. Several experiment results are presented to show that video content characteristics have a considerable influence on perceived quality. Thus, the proposed model provides a promising metric for video quality assessment.
CMA-ES based fuzzy clustering approach for MRI images segmentation

One of emerging challenges in Medical image analysis is clustering. Fuzzy C-means (FCM) algorithm is one of the most popular clustering algorithms because it is efficient, straightforward, and easy to implement. However, FCM is sensitive to initialization and is easily trapped in local optima, such a drawback could be overcome by evolutionary algorithms. This paper is dedicated to implement a fuzzy strategy evolutionary approach based to optimize the centers of the clusters by minimizing the objective function of the FCM algorithm. This approach is based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) optimization algorithm to find the optimum values of the centers of the clusters in order to classify Magnetic Resonance Imaging (MRI) brain images. The proposed approach has been validated against both simulated and clinical MRI and it has yield competitive results when compared to FCM algorithms. Results show that the proposed algorithm has obtained reasonable segmentation of white matter, gray matter, and cerebrospinal fluid from MRI data.
Target Points Tracking Control for Autonomous Cleaning Vehicle Based on the LSTM Network

In order to efficiently and exactly in tracking the desired path points, autonomous cleaning vehicles have to adapt their own behavior according to the perceived environmental information. This paper proposes a target points tracking control algorithm based on the Long Short-Term Memory network, which can generate the speed and yaw rate to arrive at the target point in real time. The target point is obtained by a parameter named foresight distance that is deduced based on the fuzzy control, whose inputs are the speed and yaw rate of the vehicle at the current point. The effectiveness of the proposed algorithm is illustrated by the simulation and field experiments. Compared with other classical algorithms, this algorithm can track the point sequence on straight path and multiple curvature path more accurately. The field experiment indicates the proposed controller is efficient in following the pre-defined path points, furthermore, it can make the autonomous cleaning vehicle run smoothly in the path which is disturbed by bounded disturbances. The distance errors can meet the actual requirement of the cleaning vehicle during the tracking process.
The Ventura Story

Ventura Publisher was the first desktop publishing software package to run on IBM PC compatible computers. Developed in the mid-1980s, Ventura was also the first desktop publishing application to incorporate features such as style sheets and automatic page layouts, allowing for long document formatting. This paper provides an autobiographical sketch of Lee Lorenzen, one of the founders of Ventura Software and developers of Ventura Publisher. Lorenzen outlines his early career in software and the founding story of Ventura Software and their innovative application. He also describes the early competitive landscape in desktop publishing software, including Aldus PageMaker, and then contrasts it with the key long document publishing features of Ventura Publisher such as style sheets, fast hyphenation and justification, automated layout algorithms, and database publishing.
Future prediction with automatically extracted morphosemantic patterns

In the following paper, we investigated the usefulness of future reference sentence patterns in the prediction of the unfolding of future events. To obtain such patterns we first collected sentences that have any reference to the future from newspapers and Web news. Based on this collection, we developed a novel method for automatic extraction of frequent patterns from such sentences. The extracted patterns, consisting of multilayer semantic information and morphological information, were implemented in the formation of a general model of linguistically expressed future. To fully assess the performance of the proposed method we performed a number of evaluation experiments. In the first experiment, we evaluated the automatic extraction of future reference sentence patterns with the proposed extraction algorithm. In the second set of experiments, we estimated the effectiveness of those patterns and applied them to automatically classify sentences into future referring and other. The final model was then tested for performance in retrieving a new set of future reference sentences from a large news corpus. The obtained results confirmed that the proposed method outperformed state-of-the-art method in fully automatic retrieval of future reference sentences. Lastly, we applied the method in practice to confirm its usefulness in two tasks. The first is to support human readers in the everyday prediction of unfolding future events. In the second task, we developed a fully automatic prototype method for future prediction and tested its performance using the tasks included in the official Future Prediction Competence Test. The results indicate that the prototype system outperforms natural human foreseeing capability.
End-to-End Feature Learning for Text-Independent Speaker Verification

Deep neural networks (DNNs) have found widespread use in text-independent speaker verification, especially the convolutional models with triplet loss. However, the training efficiency and the quality of learned features are not sufficiently good. In this paper, we present an end-to-end framework to train speaker verification models efficiently. In details, we introduce redesigned residual blocks in neural network architecture and propose a way of selecting hard triplets to improve original triplet loss function. Furthermore, the effects of hyperparameters and framing strategy in input pipeline are investigated for fine-tuning. Experimental results on the Librispeech and AISHELL-2 datasets demonstrate that the proposed method can reduce the verification equal error rate by greater than 20% relatively, which confirms the advantage of proposed methods comparing to methods in previous work.
Security aspects of the Cayley hash function using discrete heisenberg group

We execute the average running time of the various binary strings of different length and evaluate the other useful characteristics of a hash function which is to be Pseudo-Randomness of a hash function,which can assigned Uniformly over the range [0,p) to reduce the chance of occurring collisions. We also verify the security properties of the hash function using different attacks.
The specificity of psychologism in the works of E.M. Remark (on the material of the work “Shadows in Paradise”)

The article analyzes the methods of psychologism in the work of Erich Maria Remarque on the example of his late work “Shadows in Paradise”, less well known and studied in comparison with the glorified and repeatedly filmed novels “On the Western Front Without Changes”, “Three Comrades” or “Triumphal Arch” . The work clarifies the very concept of “psychologism”, examines its characteristic features, highlights the main forms of psychologism. The specific examples demonstrate the role of the portrait, color painting, sound writing, artistic details, the speech of the characters and the peculiarities of their non-verbal communication in the work of the German writer. It is proved that the whole set of methods of psychologism helps the awareness of the problematics of the work. Problems, largely due to the historical and political situation of the time in which the author lived and wrote, but not limited to it. After all, the situations of existential crisis and moral choice, in which Remarque's characters often find themselves, are typical of any era. The ability of the writer to reveal the inner world of his characters and understand how they are guided in their actions, sometimes irrational, - this is what makes his work relevant and interesting to readers even today, in the 21st century.
Human Tumble Action Recognition Using Spiking Neuron Network

We present a human tumble action recognition method based on Spiking Neuron Network. The approach adopts a method which can convert a skeleton sequence into a 2D image by equating the corresponding X, Y, Z components of the joint coordinates to the R, G, B components of the image pixels. After the conversion, the final images are fed to a recognized Spiking Neuron Network (SNN) model which is based on mechanisms with increased biological plausibility instead of being subsequently converted by a rate-based network. The SNN architecture uses leaky-integrate-and-fire (LIF) neurons, spike-timing-dependent plasticity (STDP), lateral inhibition and intrinsic plasticity. We do not present any labels to the network. Using this unsupervised learning scheme, the architecture achieves 85% accuracy on our dataset. Experimental results demonstrate that our method means a lot especially when using the unsupervised spike-timing-dependent plasticity algorithm.
A New Learning-Based One Shot Detection Framework for Natural Images

Nowadays, existing object detection methods based on deep learning usually need vast amounts of training data and cannot deal with unseen classes of objects well. In this paper, we propose a new framework that applies one-shot learning to object detection. During the training period, the network learns an ability from known object classes to compare the similarity of two image parts. For the image of a new category, selective search seeks proposals in the first step. Then the comparison based on traditional feature is used to screen out some inaccurate proposals. Next, our deep learning model can extract features and measure the similarity through feature fusion (which means concatenating the channels of two feature maps in this paper). After these steps, we can obtain a temporary result. Based on this result and some proposals related to it, we refine the proposals through the intersection. Then we conduct second-round detection with new proposals and improve the accuracy. Experiments on different datasets demonstrate that our method is effective and has a certain transferability.
Design of Continuous Hydrogenation Control System Based on WinCC Configuration Software

In this paper, the automatic control system is composed of WinCC configuration software and SIEMENS PLC. The SIEMENS PLC S7-300 module acts as the main control unit, adjusting the opening degree of each valve through PID, controlling various raw material flow, reactor temperature, pressure, liquid level and other factors to achieve continuous hydrogenation reaction. Using the PC software WinCC, real-time monitoring and control can be performed on the PC side. At the same time, the data monitored in the reaction can be automatically stored in the SQL Server 2005 database, and the data in the database can be invoked to query in real time in the form of electronic reports or trend curves, and can be exported and printed at any time.
Can Language Inference Support Metadata Generation?

As more papers get included in Digital collections satisfying information needs is becoming harder. In particular, when the user searches for information beyond bibliographic metadata. The situation is even worse when the information need requires a key aspect of a paper that first needs to be annotated for indexing purposes and thus, allow searching. For instance, in the biomedical field this might apply to structured abstracts, e.g. ‘background’, ‘objectives’, ‘results’, ‘methods’ and ‘conclusion’. Current state-of-the-art deep learning approaches can only succeed if a sufficiently large amount of annotated data is available for training purposes. However, annotating several thousands of documents is not only expensive, but due to the limited availability of experts often even infeasible. To alleviate this problem, we explore the use of Language Inference as a universal feature that once applied to a limited number of annotated documents can help to achieve high accuracy to generate the desired metadata. We show through our experiments the degree of success on the difficult task of generating the structured metadata of biomedical papers and its performance stability as we increase the number of examples. We compare our suggested approach with deep learning approaches such as Doc2Vec and show that language inference is up to two orders of magnitude better achieving up to 0.82 F1 scores.
Overview of the San Francisco Seawall Earthquake Safety Program

Starting in 1878 and lasting nearly four decades, San Francisco transformed hundreds of acres of tidal mudflat into a major deep water port by building a three mile long seawall, a continuous bulkhead wharf, and over forty finger piers. A century later, maritime cargo has moved on and this area of the port has transformed to serve a changing city. The Embarcadero Seawall is still there, and now protects over 500 acres of dense city and port land including portions of downtown, a baseball stadium, a waterfront promenade, a multi-modal transit corridor, regional ferry terminals, a cruise terminal, lifeline utility infrastructure, and a national historic district containing over half of the remaining finger piers. Recognizing the importance of the seawall today, the port completed initial studies for sea level rise in 2012 and earthquake vulnerability in 2016. These studies indicated that increased flooding from sea level rise is likely to inundate portions of downtown by 2050 and that the next major earthquake would likely damage most of the seawall beyond repair, impairing emergency response, risking lives, and forever altering the historic fabric. In response, the city has created the Seawall Earthquake Safety and Disaster Prevention Program, a $5B program to strengthen or replace this vital piece of infrastructure over the next 30 years. Program development is underway and the first phase of improvements is budgeted at $500M with completion by 2026. This paper will provide an overview of the program, from initial studies through planning and program development.
Learning Low Dimensional Patterns with Multi-Scale Local Representation

Manifold learning has been extensively applied to the field of data dimensionality reduction and pattern recognition in recent years since the related algorithms can efficiently retrieve the intrinsic geometric features of image data in computational intelligence. In this paper, we propose a new manifold learning model based on the nonnegative matrix factorization. In this model, α-divergence with locally linear representation is adopted as the objective function for nonnegative matrix decomposition for both basis and low dimensional representation or encoding vectors to capture features in high dimensional space. With a variable neighborhood size in the learning, the proposed model can learn the linear features and at the same time learn the local similarity of images in a neighborhood. Test results on JAFFE and ORL image datasets show that the proposed algorithms can obtain the state of art performance on the tasks of image clustering.
The surface shape testing and close-loop performance of a deflectometry system

The Shack-Hartmann wavefront sensor (SHWFS) is widely used in an adaptive optics (AO) system to measure the wavefront. The measurement accuracy of the SHWFS is limited by the micro-lens array, which is a core component of it. A deflectometry system (DS) is constructed to offer a high resolution measurement for the AO system, which could be used to test the surface shape of the deformable mirror (DM). The configuration and principle of the DS are presented. The surface shape testing and close-loop performance are analyzed in simulation and experiment. Results show that the DS has good ability in surface shape testing and close-loop correction.
New Password Embedding Technique Using Elliptic Curve Over Finite Field

In the present sophisticated digital era, safe communication of user password from one source to the other is quite difficult in client/server system. Also storing the password as it appears increases the potential risk of the security. Protection of the password is at most important in group communications to avoid the access of the illegal person to group resources. In addition, a roaming user who uses the network from different client terminals requires access to the private key. The present paper explains secure communication of password from one entity to the other. Here the password is encrypted using elliptic curve over finite field, embedded in a large random text at different selected positions, and communicated to the receiver via public channel.
Power trace functions over finite fields

We count the number of solutions to a power trace function equal to a constant and use this to find the probability of a successful attack on an authentication code proposed by Ding et al. (2005) [C. Ding, A. Salomaa, P. Solé and X. Tian, Three constructions of authentication/secrecy codes, J. Pure Appl. Algebra 196 (2005) 149–168].
A Mock-up for the Development of a Digital Edition for Ancient Greek Fragmentary Poetry

Ancient Greek poetry is an essential part of the western cultural heritage; thus, it is important that people have access to its texts and whatever relates to their understanding in a reliable and easy way. Whenever user evaluation is concerned, mock-ups are used by designers to acquire feedback from users. A mock-up is defined as a model of the final product, and may be used for demonstration, evaluation and other purposes. The authors prototyped a mock-up for focusing on the requirements of a scholarly digital edition of Archilochus. This was put under evaluation to assess its usability: it was submitted to extensive use and testing by a sample of prospective users, to better focus on the requirements from a product's perspective. Experimentation involved a group of university students, attending a Greek Philology course at Parma University. More than half of the respondents considered the mock-up a useful study support. The evaluation also pointed out that the mock-up had to be revised, so as to guarantee better cognitive simplicity of the user interface.