Reviewerly/exHarmony huggingface dataset:
Study on Motion Trajectory Error of Handling Mechanism Based on Hybrid Particle Swarm Optimization Algorithm

Two vertical linear steering mechanisms are widely used in engineering. In order to optimize the structural parameters and reduce the trajectory error of the mechanism, this research, established a plane coordinate model of a loading and unloading four-bar mechanism, designed the kinematic equation and constructed the error function. Hybrid particle swarm optimization was used to optimize the objective function, and the optimized geometric parameters were imported into the MATLAB software for verification. The results show that the average lateral and longitudinal errors of the optimized mechanism are reduced by 54.3% and 57.8%, respectively. Hybrid particle swarm optimization is applied to optimize the geometric parameters of two vertical linear guiding crank and rocker mechanisms, which can effectively reduce the trajectory error of the motion points of the mechanism and provide a reference for the optimal design of the handling mechanism.
WEB2ONTO: Automatic Ontology Construction Approach from Web pages

This paper presents an approach for constructing ontology automatically from web pages thus migrating data from web into ontology. The proposed approach can be applied to web pages in any domain. It is a step towards utilizing the huge amount of data published on the web to automatically build ontology. The proposed approach first extracts the triples from web page. Next it utilizes natural language processing techniques to process the extracted triples. Finally it applies ontology design patterns before inserting triples into ontology. WEB2ONTO is compared with only fully automatic work (as our knowledge) and the results show that WEB2ONTO is close to cover all the information in the documents correctly. WEB2ONTO is better at reducing repeat rate where it doesn't insert the same object twice and doesn't repeat the relation between two concepts. Also WEB2ONTO ontology is simpler representation than other work.
Remote Sensing Image Aircraft Detection Based on Feature Fusion across Deep Learning Framework

The detection of remote sensing image aircraft targets based on deep learning has practical and important significance in the fields of military reconnaissance and disaster rescue. As a typical representative of the two mainstream detection algorithms, YOLOv3 and Faster_R_CNN have good detection effects on remote sensing image aircraft targets. However, for low quality remote sensing images, the two detection algorithms also have the phenomenons of omission and false detection. To deal with this, this paper proposes a target detection algorithm (YF_R_CNN) for "Separate training, joint detection", which realizes the cross-platform detection feature fusion of YOLOv3 based on darknet framework and Faster_R_CNN based on tensorflow framework, effectively alleviating the problems of existing algorithms. The experimental results show that the detection accuracy of YF_R_CNN algorithm reaches 94.8%, which is 3.7% and 3.1% higher than YOLOv3 and Faster_R_CNN respectively. The detection accuracy is obviously improved, and the algorithm has better flexibility and robustness.
Clustering-Based Score Normalization for Speaker Verification

Score normalization can improve speaker verification (SV) performance by adjusting the distribution of test scores to follow a normal distribution. In this paper, all of the imposter scores for the target speakers are first obtained from the normalization cohort; then, these scores are clustered by an unsupervised clustering algorithm, and Gaussian mixture models (GMMs) are used to fit the score distribution. The mean and the standard deviation of the Gaussian component with the maximum mean value is used in the SV score normalization method. Experiments are carried out on the NIST SRE 2016 test set and the VOiCES test set. Compared with conventional score normalization methods, the proposed method can effectively improve SV performance.
Courses of blockchain technology to teach at a career-oriented college

Blockchain technology has evolved from a hype to a technical reality and is quickly becoming a demanded skill. Career-oriented colleges are facing the challenge to incorporate blockchain technology...
Privacy Preserving Feature Selection via Voted Wrapper Method for Horizontally Distributed Medical Data

Feature selection plays a crucial step for data mining algorithms via eliminating the curse of dimensionality. Many feature selection approaches are developed for analyzing centralized data on the same location. In recent years, multi-source biomedical data mining methods have been developed to analyze different distributed databases at different locations such as different hospitals. However, a major concern is privacy of sensitive personal medical records in different hospitals. Therefore, as the needs for new privacy preserving distributed data mining algorithms increase, it is necessary to develop new privacy preserving feature selection algorithms for biomedical data mining. In this paper, a privacy preserving feature selection method named “Privacy Preserving Feature Selection algorithm via Voted Wrapper methods (PPFSVW)” is developed. This method was tested on six benchmark datasets under two testing scenarios. Our experimental results indicate that the proposed algorithm workflow can work effectively to improve the classification performance regarding accuracy via selecting informative features and genes. Besides, the proposed method can make the classifier achieve higher or same level classification accuracy with fewer features compared with those sophisticated methods, such as SVM-RFE, RSVM and SVM-t. More importantly, the individual private information can be protected during the whole feature selection process.
Spectator

Many information retrieval tasks require viewing documents in some manner, whether this is to view information in context or to provide annotations for some downstream task (e.g., evaluation or system training). Building a high-quality document viewer often exceeds the resources of many researchers and so, in this paper, we describe the design and architecture of our new open-source document viewer, Spectator. In particular, we provide a look into the algorithmic details of how Spectator accomplishes tasks like mapping annotations back to the canonical document. Moreover, we provide a sampling of the use cases that we envision for Spectator, potential future additions depending on community need and support, and highlight situations where Spectator may not be a good fit. Furthermore, we provide a brief description of the sample application that we bundle with Spectator to demonstrate how one might use it within the context of a larger system.
A Statistical-Semantic PSO Model for Customer Reviews-Based Question Answering Systems

Often customers refer to product specifications to know if a product fulfills their requirement, but in case they do not find answer to their queries they turn to the online reviews. A knowledge-based system tries to answer user queries using product specifications alone, but specifications available can be insufficient. An idea is introduced of combining question answer and review datasets to find the most relevant reviews corresponding to the question using semantic and statistical similarity measures and optimizing their weights. This is accomplished by using a large volume of already answered queries to find weights of four different similarity metrics (Cosine, BM25, WordNet and Word Embedding) used to find similarity between question and reviews. Similarity measure weights are optimized using PSO-based weight optimization technique where the fitness function is evaluated in terms of how best the sentiment extracted from top reviews agrees with answer of question in Q/A dataset. Results achieved surpass baseline in three out of four domains.
Automatic Control Point Selection System based Anthropomorphic Animal Face Masking

Anthropomorphic animal face masking is the process where human characteristics are plotted on the animal kinds. The term anthropomorphic derived from anthropomorphism, which means displaying animals, gods, and other objects as if they are human in appearance, character, or behavior. In this research, we are proposing a compact automatic Control Point selection system based morphing process. Our compact system contains the Viola-Jones algorithm based Control Point selection system that detects facial features for the human face and takes the Control Points automatically. In addition, for the animal face Control Points, we made a predefined animal face set containing their corresponding Control Points. The whole process is done by firstly finding the Control Points and secondly, doing triangulation based morphing between the particular human face and the animal face. The simulation results demonstrate that the proposed morphing method produces less deformation and at the same time manages to complete the morphing process without any human assistance.
Results of diagnosing the level of innovative competence for secondary school teachers

One of the higher-priority directions concerning improvement of the institution of general secondary education is the adaptation of teachers’ educational, scientific methodological and organizational activities to the modern challenges. The orientation of the new Ukrainian school to the world educational standards calls for new approaches to the organization of the educational process in institutions of general secondary education, the formation of up-to-date eﬀective innovative competence of teacher-practitioners. In order to diagnose the level of formation of teachers’ innovative competence a summative experiment was conducted as a method to obtain and fix theoretical and empirical data. In the course of the research the criteria for innovative competence evaluation were developed, diagnostic tools were selected, diagnostic procedures and the analysis of the obtained results were conducted. A methodological workshop was held for teachers aimed at defining such basic concepts as «innovative pedagogical activity», «competence», «innovative competence» The level of the formation of teachers’ innovative competence was diagnosed taking into account its components, namely: operational (the formation of intellectual abilities, the quickness of mental operations, ability to solve problem situations), professionalvalue (motivation to obtain innovative competences, professional orientation, ability to self-control and urge for constant professional self- improvement, formation of individual style of pedagogical activity, professionally important personal qualities). Having generalized the results of applying various diagnostic methods (the technique «Six Thinking Hats» by Edward de Bono, the technique of studying the value scales by M. Rokeach, the diagnostic test «Readiness for self-development», «Strategies to form a higher level of basic need for cognition» by Palchevsky, «The methodology of limit meanings» by D. Leontiev) the level of formation of innovative competence of the teacher of the institution of general secondary education was determined.
Computer Architecture

It is a well-known fact that the expansion of Internet has given rise to many daily activities which are being done online including financial and personal data transactions which has developed the need of security like never before. It has made people realize that the data on the internet must be secured as many types of attacks are increasing day by day with the advancement of technology. So, here the requirement is to ensure that the services are supplied to a legitimate user rather than bots to prevent service abuse. The present article provides such a security from bots with the help of a standard security mechanism called CAPTCHA. A new cognition-based design of CAPTCHA is produced overcoming the limitation of presently available CAPTCHA. Also, a new architecture for producing these CAPTCHA codes randomly is presented in this article so that the differentiation among a legitimate user and Bots can be made. Thus, the major objective of the article is to present an optimum security mechanism for differentiating among humans and Bots.
Sensory Walls: Engineering a Dynamic Therapeutic Solution for Children with Disabilities

Children with learning and developmental disabilities face challenges every day in a regular public-school setting. Research has shown that modification of the classroom setting enhances student's learning experiences and social interactions within that environment. The following manuscript illustrates how medical and technical sciences can work together to create modules with enhanced sensory integration for children with disabilities. Sensory modules were engineered for the Communications and Social Skills (CSS) department of a local High School to increase the children's sensory stimulation, learning, and functional social interactions. Results from this study provides strong conclusive evidence of the positive impact of this technology on the children's intended outcomes.
GAMESPECT: Aspect Oriented Programming for a Video Game Engine using Meta-languages

Video game programming is an area where code duplication is extremely prevalent. Similar types of tasks are performed in multiple places throughout a game engine codebase. For example: game balance, logging, and memory allocation are just a few of the areas which are located across disparate locations of the codebase. Meanwhile, aspect oriented programming (AOP) was created to make use of common themes in a codebase and allow for advice to be given during compilation or runtime. GAMESPECT is a Domain Specific language (DSL) which resides in a higher level language space, above the typical languages used by game engines such as Unreal Engine 4. The language is written with the popular XText language toolset and contains advices in the spirit of aspect oriented programming: point cuts and join points. These advices can be executed at runtime when the programmer wants to finetune values or perform debugging. The advising is made possible through the Clang toolchain. Unique to this approach, GAMESPECT takes a source language and converts into multiple target languages. Depending on the context, GAMESPECT can generate C++, LUA, Blueprints or Skookum Script. This type of source to source translation is made possible through GAMESPECT's “composition specifications”. With GAMESPECT, video game designers can increase their productivity and reduce the required lines of code for certain tasks by a 9-40% reduction. In addition, it provides for an AOP platform which is agnostic to the underlying game code. The applicability of this approach extends beyond game programming into software engineering as a whole.
VSE-fs: Fast Full-Sample Visual Semantic Embedding

The visual semantic embedding (VSE) aims to construct a joint embedding space between visual features and semantic information, whereby classes can be well retrieved for a given image. However, VSE faces the computational challenge due to the large scale image-class data and the constrained system processing power. To speed up model training, many researchers resort to different sampling strategies by involving only a small portion of the classes at each training step. However, these methods are greatly biased especially when the sampling distribution deviates from the true data distribution. In order to retain VSE models fidelity, we adopt the regular full-sample in our algorithm. We also devise two separate optimization strategies to reduce time complexity, and derive more effective updating rules. The experimental results on four real datasets demonstrate that our approach not only converges much faster than the state-of-the-art sampling models, but also generates more accurate class retrieval.
The Integration of Ukrainian Humanities into the International Academic and Information Space: Challenges and Prospects

The study outlines the most important global integration trends of the last decades in the scientific community. It characterizes state formation processes in the context of European integration aspirations and scientific integration of Ukraine. It states a significant increase of the requirements for scientific articles and their components, the rise of the role and number of English-speaking components, the increase in the visualization of the activities of scientists and scientific institutions in recent years. The study presents 2018–2019 statistics on the Ukrainian humanities scientific publications presented in the Scopus and Web of Science, leading global scientometric databases, as well as quantitative indicators of the publication activity in these databases of the members of the editorial boards of Ukrainian cultural editions. It identifies internal and external factors hindering the rapid ‘promotion’ of Ukrainian humanities to the international level. Based on the low level of Ukraine's presence in the global scientific and information space, the author concludes that the main tasks of Ukrainian humanities currently are to overcome the isolation from the European and global science development processes and to actively enter into powerful international scientific internet resources, i.e. Scopus and Web of Science. Implementing these tasks combined with balanced state policy in the humanitarian sphere will strengthen the authority of Ukrainian humanities and open the way to full integration into the global cultural processes.
A hypersensitive breast cancer detector (Conference Presentation)

Early detection of breast cancer through screening mammography yields a 20-35% increase in survival rate; however, there are not enough radiologists to serve the growing population of women seeking screening mammography. Although commercial computer aided detection (CADe) software has been available to radiologists for decades, it has failed to improve the interpretation of full-field digital mammography (FFDM) images due to its low sensitivity over the spectrum of findings. In this work, we leverage a large set of FFDM images with loose bounding boxes of mammographically significant findings to train a deep learning detector with extreme sensitivity. Building upon work from the Hourglass architecture, we train a model that produces segmentation- like images with high spatial resolution, with the aim of producing 2D Gaussian blobs centered on ground-truth boxes. We replace the pixel-wise L2 norm with a weak-supervision loss designed to achieve high sensitivity, asymmetrically penalizing false positives and false negatives while softening the noise of the loose bounding boxes by permitting a tolerance in misaligned predictions. The resulting system achieves a sensitivity for malignant findings of 0.99 with only 4.8 false positive markers per image. When utilized in a CADe system, this model could enable a novel workflow where radiologists can focus their attention with trust on only the locations proposed by the model, expediting the interpretation process and bringing attention to potential findings that could otherwise have been missed. Due to its nearly perfect sensitivity, the proposed detector can also be used as a high-performance proposal generator in two-stage detection systems.
Introducing PulseAT: A Tool for Analyzing System Utilization in Distributed Systems

For the development and maintenance of distributed systems, it is useful to analyze the system condition and utilization for each hardware component. With pulseAT, a tool has been developed which collects that system utilization systematically with lightweight pulseAT Agents. The hierarchical structure of pulseAT allows having all system utilization data at one place on a pulseAT Manager to show an overall current health condition of the system. A cloud-based pulseAT Analyzer stores the data into a time-based database to support long-term analyses and to process analyzing algorithms, e.g., to forecast future health conditions. This paper describes the structure of pulseAT, main concepts, e.g., how the response time for each component is calculated. Some technical details of the implementation are shown. Finally, it describes how pulseAT has been tested on a mobile robot, the DAEbot.
Direct print technology with Qz Tray for web application transaction

Abstract A web-based application system has been widely used as an application that handles the transaction processes of a company. The problem faced a transaction web applications are cannot directly print the transaction invoice into the cashier’s printer. To print invoices from a web application, the web application must have quite a lot of supporting applications and enough configuration for the print process to be carried out. For web applications that have facilities for transactions, a technology is needed that can help users to speed up the process of transaction results in the form of transaction invoices. The technology called direct print. Direct print technology can be implemented with a supporting application such as Qz Tray. Qz Tray can help in the printing process directly from a web application that requires a print process into the transaction invoice. Configuration from Qz Tray is quite easy because the assets and coding print functions have been provided by Qz Tray directly. Asset and print function coding can be downloaded and used directly on a web application. The results of direct printing by Qz Tray can also be tailored to the needs of users so the display of invoice prints is as expected.
From the Talking Initials of Luigi Vanvitelli to the Graphics of the Alphabet for Language Teaching of XX and XXI Century

In a text, “parlanti” are the illustrated initials decorated with a figurative image whose name begins with the letter itself: the drawing has an illustrative-mnemonic function compared to the letter, but remains independent from text content. The use, although sporadic, of talking initials, dates back to the richly miniated manuscripts of the Middle Ages. With the advent of the press, figurative initials gradually lose their primary role in text composition. However, it is with woodcut, that initials are completely free from the rich decorative apparatus of earlier centuries, and reaches an essential representation, enhanced by the use of graphic engraving technique. One of the last examples of the eighteenth century are the talking initials designed by Luigi Vanvitelli and engraved on copper by Carlo Nolli in Le Antichità di Ercolano esposte. The contribute documents the images and imaginations represented in Vanvitelli’s illustrated letters to arrive at modern and contemporary representations of talking alphabets used in the books for younger learners. The aim is to highlight the ability of graphic design to produce a connection between perceptual, cognitive and executive functions expressed both in XVIII initials and in alphabet books created for language teaching.
Exploring Letter’s Differences between Partial Indonesian Branch Language and English

Differences of letter usage are the most basic differences between different languages, which can reflect the most essential diversity. Many linguists study the letter differences between common languages, but seldom research those between non-common languages. This paper selects three representative languages from the Indonesian branch of the Austronesian language family, namely Malay, Indonesian and Filipino. To study the letter differences between these three languages and English, we concentrate on word length distribution, letter frequency distribution, commonly used letter pairs, commonly used letter trigrams, and ranked letter frequency distribution. The results show that great differences do exist between three Indonesian-branch languages and English, and the differences between Malay and Indonesian are the smallest.