Reviewerly/exHarmony huggingface dataset:
The Aesthetics of Language

We all find aesthetics pleasing. Attractive people get a head start in their careers. Attractive packaging grabs our attention. Given this effect aesthetic has been an area of intense research both in psychology and in consumer behavior. Yet although we know much about the aesthetic appeal of physical objects how about the aesthetics in text? For example, the header is the introduction to an electronic message. One appeal of the header is the emotion it conveys to the readers. Messages that evoke emotions are likely to be more effective. To examine this thesis the authors propose two models: one to measure the aesthetics of headers and the other to measure the effectiveness of the message. To build the universal emotion detection model, the authors use ROBERTA as the main architecture and use the task control method to merge and train the model on multiple emotion datasets.
Using Cloud Control Matrix to evaluate trust in cloud providers

The increase in the number of users of cloud has led to an increase in the number of providers of this technology. The decision to migrate resources to cloud should be made after going through a process that provides relevant information about the security, availability, financial values involved, implicitly the trust that consumers can have in cloud service providers. In the first part of this article is presented the main methods for verifying trust in suppliers, following the proposal of a framework model for analyzing the parameters that can provide information on trust in CSP. In the second part of the article is proposed, a method of using CCM (Cloud control matrix) and CAIQ-Lite (Consensus Assessment Initiative Questionnaire) tools to determine a minimum level of CSP confidence.
Developing an Interactive 360 Walkthrough of MME Shop and Laboratory for ME136P, ME123L, and ME137L

Mapúa University's woodworking shop, metalworking shop, and the Universal Testing Machine (UTM) laboratory are facilities that help engineering students, particularly the School of Mechanical and Manufacturing Engineering (SMME), in learning the parts, operations, and safety precautions of different machines and tools. With the use of a 360° camera and a virtual tour building platform, an interactive virtual walkthrough of the facilities was developed as an informational guide for students enrolled in ME136P, ME123L, and ME137L. The researchers conducted an assessment to see the effectiveness of the virtual walkthrough and a survey to get insights from students who are finished with the courses. The analyzed data from the assessment showed that the virtual walkthrough is an effective informational guide since students who used it had a significantly higher scores compared to those that did not. Most of the students who took the survey thought that the virtual walkthrough really assisted in providing necessary information about the machines and tools used in the shop and laboratory courses.
Deductive Verification via the Debug Adapter Protocol

We propose a conceptual integration of deductive program verification into existing user interfaces for software debugging. This integration is well-represented in the "Debug Adapter Protocol", a widely-used and generic technology to integrate debugging of programs into development environments. Commands like step-forward and step-in are backed by steps of a symbolic structural operational semantics, and the different paths through a program are readily represented by multiple running threads of the debug target inside the user interface. Thus, existing IDEs can be leveraged for deductive verification debugging with relatively little effort. We have implemented this scheme for SecC, an auto-active program verifier for C, and discuss its integration into Visual Studio Code.
Hierarchical feature similarity integration method for data sets based on deep learning

Abstract Under the background of the rapid rise of open-source software and the gradual popularization of various software development tools, a large amount of development activity data has been accumulated on the Internet. In the process of using these data to construct data sets, due to their poor traceability and narrow application scope, the quality of data in development activities is not high and the accuracy of analysis results is not high. The application of the hierarchical feature similarity integration method of data sets can make the multi-version and multi-level development smoother and more orderly. In this paper, a hierarchical feature similarity integration method based on hierarchical deep learning is proposed for data sets. Firstly, the dynamic mesh partitioning method is used to divide the sparse and dense regions in the space, which reduces the scale of data detection and shortens the execution time of detection. Then, through the hierarchical deep learning process, the professional knowledge and the distribution information of data attribute value are fused to realize the detection of discrete data in the database. Experimental results show that this method can accurately complete the detection of discrete data in the database in a relatively short time, and has more application advantages than traditional methods.
Psychology and modern society interaction as a way of mutual development. The genesis of the research of determinants of professional stability of a future psychologist personality: conceptualization and empirical referents

In order to implement Ukraine's strategic course for integration into the European Union, to ensure Ukraine's full integration into European political, economic and legal space and to create preconditions for Ukraine's membership in the European Union. The Decree of the President of Ukraine approved the Strategy of Ukraine's integration into the European Union. The main areas of cultural, educational, scientific and technical integration are the implementation of European norms and standards in education, science and technology and the dissemination of their own cultural, scientific and technological achievements in the EU.
A Fully Convolutional Network based on Double Attention for Saliency Object Detection

The current salient object detection method is based on full convolution network, and the method based on full convolution directly applies multi-layer convolution features without distinguishing, which leads to the result is not optimal because of the dispersion of redundant details; In this paper, based on the existing full convolution network, we first design attention module which can generate attention features by combining channel attention and spatial attention. We further design a boundary branch aiming at get accurate object boundaries. We selectively superimpose information from multi-layer features, optimize attention features by using low-level features, and fuse multi-scale features; This method is compared with six related methods on four common datasets. Experimental results show that the model we proposed outperforms the state-of-the-arts with less false predictions and accurate object boundaries.
Application of a Wavelet based Krylov Subspace Algorithm on Digital Signal Convergence

In the World of Communication, digital transmission has its importance in sense of speed and accuracy which is the basic requirement of current time. Nowadays, successful as well as rapid communication is the main concern of the research. It can be achieved by using different technique and methods based on theories built upon the branches of applied sciences and engineering. In this research an algorithm is presented by sequentially combining two transforms, Wavelet and Krylov. The Algorithm was formerly developed by the same and was known as WK Algorithm. In this research the Algorithm is first studied for digital signal application and results are presented and concluded by applying also with other two methods in order to verify and validate the research.
Analysing Leakage during VPN Establishment in Public Wi-Fi Networks

The use of public Wi-Fi networks can reveal sensitive data to both operators and bystanders. A VPN can prevent this. However, a machine that initiates a connection to a VPN server might already leak sensitive data before the VPN tunnel is fully established. Furthermore, it might not be immediately possible to establish a VPN connection if the network requires authentication via a captive portal, thus increasing the leakage potential. In this paper we examine both issues. For that, we analyse the behaviour of native and third-party VPN clients on various platforms, and introduce a new method called selective VPN bypassing to avoid captive portal deadlocks.
Deep Image Watermarking with Recover Module

Image watermarking based on deep learning has been proposed in the last few years. Typical framework for image watermarking consists of embedding network, extracting network, and attack stimulating module. Adversarial discriminator is sometimes used to make watermarked images much more similar to cover images. To improve the robustness of CNN-based work against attacks of different type and strength, we proposed a novel model, introducing recover module into the framework to compensate some damaged watermark information during attacks and improve extracting accuracy. For non-differentiable JPEG compression, we propose a new approximation approach based on previous methods. Experimental results show that the proposed model performs better than the state of the art in bit accuracy of message extraction while the image quality does not deteriorate.
Good and bad boundaries in ultrasound compounding: preserving anatomic boundaries while suppressing artifacts

Ultrasound compounding is to combine sonographic information captured from different angles and produce a single image. It is important for multi-view reconstruction, but as of yet there is no consensus on best practices for compounding. Current popular methods inevitably suppress or altogether leave out bright or dark regions that are useful and potentially introduce new artifacts. In this work, we establish a new algorithm to compound the overlapping pixels from different viewpoints in ultrasound.Inspired by image fusion algorithms and ultrasound confidence, we uniquely leverage Laplacian and Gaussian pyramids to preserve the maximum boundary contrast without overemphasizing noise, speckles, and other artifacts in the compounded image, while taking the direction of the ultrasound probe into account. Besides, we designed an algorithm that detects the useful boundaries in ultrasound images to further improve the boundary contrast.We evaluate our algorithm by comparing it with previous algorithms both qualitatively and quantitatively, and we show that our approach not only preserves both light and dark details, but also somewhat suppresses noise and artifacts, rather than amplifying them. We also show that our algorithm can improve the performance of downstream tasks like segmentation.Our proposed method that is based on confidence, contrast, and both Gaussian and Laplacian pyramids appears to be better at preserving contrast at anatomic boundaries while suppressing artifacts than any of the other approaches we tested. This algorithm may have future utility with downstream tasks such as 3D ultrasound volume reconstruction and segmentation.
Are Ellipses Important for Machine Translation?

Abstract This article describes an experiment to evaluate the impact of different types of ellipses discussed in theoretical linguistics on Neural Machine Translation (NMT), using English to Hindi/Telugu as source and target languages. Evaluation with manual methods shows that most of the errors made by Google NMT are located in the clause containing the ellipsis, the frequency of such errors is slightly more in Telugu than Hindi, and the translation adequacy shows improvement when ellipses are reconstructed with their antecedents. These findings not only confirm the importance of ellipses and their resolution for MT, but also hint toward a possible correlation between the translation of discourse devices like ellipses with the morphological incongruity of the source and target. We also observe that not all ellipses are translated poorly and benefit from reconstruction, advocating for a disparate treatment of different ellipses in MT research.
Mapping Multiple Regions to the Grid with Bounded Hausdorff Distance

We study a problem motivated by digital geometry: given a set of disjoint geometric regions, assign each region $$R_i$$ a set of grid cells $$P_i$$ , so that $$P_i$$ is connected, similar to $$R_i$$ , and does not touch any grid cell assigned to another region. Similarity is measured using the Hausdorff distance. We analyze the achievable Hausdorff distance in terms of the number of input regions, and prove asymptotically tight bounds for several classes of input regions.
A Trigger-Aware Multi-task Learning for Chinese Event Entity Recognition

This paper tackles a new task for event entity recognition (EER). Different from named entity recognizing (NER) task, it only identifies the named entities which are related to a specific event type. Currently, there is no specific model to directly deal with the EER task. Previous named entity recognition methods that combine both relation extraction and argument role classification (named NER+TD+ARC) can be adapted for the task, by utilizing the relation extraction component for event trigger detection (TD). However, these technical alternatives heavily rely on the efficacy of the event trigger detection, which have to require the tedious yet expensive human labeling of the event triggers, especially for languages where triggers contain multiple tokens and have numerous synonymous expressions (such as Chinese). In this paper, a novel trigger-aware multi-task learning framework (TAM), which jointly performs both trigger detection and event entity recognition, is proposed to tackle Chinese EER task. We conduct extensive experiments on a real-world Chinese EER dataset. Compared with the previous methods, TAM outperforms the existing technical alternatives in terms of F1 measure. Besides, TAM can accurately identify the synonymous expressions that are not included in the trigger dictionary. Moreover, TAM can obtain a robust performance when only a few labeled triggers are available.
Non-tactile Thumb Tip Measurement System for Encouraging Rehabilitation After Surgery

The thumb is the only finger that has a high degree of freedom of articulation and can face the other four fingers, and functional training is significant after injury surgery. We propose a system to measure and visualize the range of motion of the thumb during rehabilitation in a non-contact manner using a depth sensor and a deep neural model.
The Copyright Protection and Fair Use of Commercial Data Collections Based on Big Data

Big data is defined as “data collection” with four “V” - “Volume, Variety, Velocity, Veracity” characteristics. Based on big data, whether the commercial data collection protection under Chinese Copyright Law appropriate and whether the rights of “fair use” under Chinese Copyright Law sufficient to promote the data flow and data sharing, is arguable. Therefore, this article aims to clarify the current disputes about commercial data collections as copyright subject matter and discusses the protection provided to commercial data collections under the framework of Chinese Copyright Law on one hand. And on the other hand, this article aims to explore the improvement of the rights of “fair use” of commercial data collections under the framework of Chinese Copyright Law from perspective of big data sharing and big data flow.
James C. Wyant: optical disciple, educator, physician, producer

This author’s unique personal perspective will honor through examples and analysis the contributions that Disciple Wyant has made to the optical sciences and metrology discipline and for the people that work within it. In spring 1974 the author was at the Optical Sciences Center (OSC) as a graduate student when Physician Wyant interviewed and made a diagnosis and improvement to a holographic interferometry configuration of a project the author was working on. “Student” Brooks went on to became one of the first of Dr. Wyant's future graduate students. He also enrolled in Educator Wyant's first Optical Testing OPTI213 class in 1975 where optical metrology and wavefront characterization of electromagnetic radiation was presented, defined and clarified to make the field addictive and irresistible to the author. It would prove to be the author’s strongest career influence for the next 35 years, pointing him to a profession in optical metrology within aerospace laser industrial design, integration and test engineering functions. This presentation will illustrate derivative effects of Producer Wyant's contributions, including examples associated with the presenter's industry experience. Also presented are examples of James Wyant’s generous philanthropic campaign.
STDA-inf: Style Transfer for Data Augmentation Through In-data Training and Fusion Inference

Style transfer has been effectively applied to data augmentation. However, previous work requires careful selection of style images out of the concerned datasets, and neglects the impact of style transfer on the inference procedure. In this paper, we propose a novel method STDA-inf for image classification: Style Transfer for Data Augmentation through in-data training and fusion inference. Firstly, we acquire the transferred training data in an adaptive way of in-data, in which style images are extracted from the training data itself. An online end-to-end training strategy is utilized to create an adversarial training effect, thereby alleviating the overfitting on textures when identifying different classes. Moreover, we fuse the outputs of the original and transferred images from the trained network, obtaining a more accurate classification. It is demonstrated by the experiments that our proposed method outperforms the previous style augmentation method with $$7\mathrm{\%}$$ improvement of classification accuracy on STL-10 and $$3\mathrm{\%}$$ on Caltech-256 dataset, respectively. Its superiority is also demonstrated over the other data augmentation methods.
The Improvementof English Speakingskills Throughcollaborativelearning Usingmovieasmedia Abstract (ActionResearchattheEnglishLiteratureStudyProgramofDarmaPersadaUniversity)

The purpose of this study is to determine in detail the process and results of the   Englishspeaking skills improvement through collaborative learning using movie as media. This researchisanactionresearchconductedintheEnglishliteraturestudyprogramofDarmaPersadaUniversity. Data obtained from the results of the English speaking skills test before and after theaction. The results showed that the collaborative learning process using movie as media madestudents' speaking skills increase, they became more active during the learning process. It was indicate dby the level of the irparticipationin learning interactions. There sults of the improvement in English speaking skills have increased quite significantly from the pre-actionstage to the third cycle in the research process. This action research carried out with collaborativelearning using movie as media significantly helps students to improve their skills in speakingEnglish. This learning has also proven to be able to make students work well and shape theirenthusiasm forlearning.
Image Ship Target Detection SAR Complex Environment

The detection of SAR image ship targets in complex scenarios has been a great challenge. In this paper, the high performance algorithm framework of the improved YOLOv3 algorithm is proposed to increase the weight ratio of important feature regions and make full use of peripheral information. Finally, the experimental results show that the algorithm improves the accuracy compared with the original network.
Graph-Based Transfer Learning for Conversational Agents

Graphs have proved to be a promising data structure to solve complex problems in various domains. Graphs store data in an associative manner which is analogous to the manner in which humans store memories in the brain. Generathe chatbots lack the ability to recall details revealed by the user in long conversations. To solve this problem, we have used graph-based memory to recall-related conversations from the past. Thus, providing context feature derived from query systems to generative systems such as OpenAI GPT. Using graphs to detect important details from the past reduces the total amount of processing done by the neural network. As there is no need to keep on passingthe entire history of the conversation. Instead, we pass only the last few pairs of utterances and the related details from the graph. This paper deploys this system and also demonstrates the ability to deploy such systems in real-world applications. Through the effective usage of knowledge graphs, the system is able to reduce the time complexity from O(n) to O(1) as compared to similar non-graph based implementations of transfer learning- based conversational agents.
An Unified Data Analytic Scheme with Enhanced RSA Secutiry Algorithm

With the increased usage of internet, concerns on data security and accuracy have become major issues. Usage of internet and impact of various social networks amplify the security concerns for data storage. Attribute related analytical report generation and very good security system for clients implemented, focused on enabling and ensuring the data security through data analytic integrated framework which uses AES (Advanced Encryption Standard) algorithm with 1024 bit to protect from unauthorized users. In this AES algorithm, symmetric key technique is used in which key transportation is an issue. In the proposed system, the security level can be improvised by using asymmetric key based RSA algorithm up to 2030 bit to secure from intruder. The private key, as transmitted to the receiving system previous of the transmission of plain text message.... This model focuses on data security with embedded technique.
Investigation of Homophonic Encryption on Zodiac Z408 and Z340 Ciphers

This article investigates homophonic encryption on the example of the known cipher Z408 and Z340, and analyzes their problems for further use of the result of the article in an attempt to improve and correct the main shortcomings of this type of encryption. Similar encryption methods and studies of similar problems were also considered. Strengths, and weaknesses were sought. In the practical part, an encryption system similar to Z408 was created and a study of the complexity of using this encryption method for automation was conducted.
Semantic Indexing of 19th-Century Greek Literature Using 21st-Century Linguistic Resources

Manual classification of works of literature with genre/form concepts is a time-consuming task requiring domain expertise. Building automated systems based on language understanding can help humans to achieve this work faster and more consistently. Towards this direction, we present a case study on automatic classification of Greek literature books of the 19th century. The main challenges in this problem are the limited number of literature books and resources of that age and the quality of the source text. We propose an automated classification system based on the Bidirectional Encoder Representations from Transformers (BERT) model trained on books from the 20th and 21st century. We also dealt with BERT’s constraint on the maximum sequence length of the input, leveraging the TextRank algorithm to construct representative sentences or phrases from each book. The results show that BERT trained on recent literature books correctly classifies most of the books of the 19th century despite the disparity between the two collections. Additionally, the TextRank algorithm improves the performance of BERT.