Reviewerly/exHarmony huggingface dataset:
Implementation of Parallel Algorithm Technology for Time Series Data Mining

Abstract With the rapid development of computer technology, Internet technology and artificial intelligence technology, the amount of global data has exploded. However, the single-machine serial mode of traditional data mining cannot be directly transplanted to the cloud platform. Only by parallelizing and improving many classic data mining algorithms can the cloud computing platform and data mining be effectively combined. Therefore, it is of great significance to the research and implementation of parallel algorithm technology for time series data mining. The purpose of this paper is to study the research and implementation of parallel algorithm technology for time series data mining. This paper adopts the method of literature data, mathematical statistics, logic analysis and other research methods to study the parallel algorithm technology research and realization of time series data mining, mainly to make useful explorations of time series data mining and visualization technology. It embodies the design ideas of big data analysis tools, and finally reflects the power and market value of data analysis tools through the display of the platform. Research shows that running in the same data set and the same experimental environment, the improved parallel collaborative filtering algorithm ACF in this paper has higher time running efficiency than the parallel algorithm MCF based on the cooccurrence matrix, and in the case of larger data sets, the more obvious the time difference.
SkeIn: Sketchy-Intensive Reading Comprehension Model for Multi-choice Biomedical Questions

Recent advances in Pre-trained Language Models (PrLMs) have driven general domain multi-choice Machine Reading Comprehension (MRC) to a new level. However, they perform much worse on domain-specific MRC like biomedicine, due to the lack of effective matching networks to capture the relationships among documents, question and candidate options. In this paper, we propose a Sketchy-Intensive (SkeIn) reading comprehension model, which simulates the cognitive thinking process of humans to solve the Chinese multi-choice biomedical MRC questions: (1) obtaining a general impression of content with a sketch reading process; (2) capturing dedicated information and relationships of documents, question and candidate options with an intensive reading process and making the final prediction. Experimental results show that our SkeIn model achieves substantial improvements over competing baseline PrLMs, with average accuracy improvements of +4.03% dev/+3.43% test, +2.69% dev/+3.22% test, and 5.31% dev/5.25% test from directly fine-tuning BERT-Base, BERT-wwm-ext and RoBERTa-wwm-ext-large, respectively, indicating the effectiveness of SkeIn to enhance the general performance of PrLMs on the biomedical MRC tasks.
Salient Object Detection Based on Self-attention Mechanism

Salient object detection can simulate human visual mechanism and extract important information from pictures or videos. Aiming at the problems of imprecise salient region detection and fuzzy edge representation in existing salient object detection algorithms, a self-attention salient object detection algorithm is proposed. On the one hand, the U-shaped network is used to enhance the local and global information of different levels of feature graph to generate fine saliency graph. On the other hand, the self-attention module is used to capture the global information in order to obtain larger perceptual field of vision and context information, and generate different attention weights for salient objects and backgrounds, so as to obtain better performance. Experimental results on four widely used datasets show that the proposed method achieves competitive performance.
Detecting Human Attendance using 1-Dimensional Foot Signal from Laser Range Sensor

Detecting dynamic and static objects is one of the important abilities for a mobile robot, including a healthcare mobile robot. As long as the robot carries out its duties, it will often encounter objects, both dynamic and static. Therefore, recognizing the dynamic and static objects is crucial. In this paper, the existence of human as an example of dynamic objects is obtained using a Laser Range Finder (LRF), since it can work faster than ordinary cameras. To recognize human data obtained from LRF, a sliding window process is applied to get the signal data of human feet which will then be classified using the Support Vector Machine (SVM). Meanwhile, to overcome the difference in the size of the human foot signal caused by the distance changes, a pyramid scanning process is also applied. Based on the experimental results obtained, human can be detected from a range of distances 1 up to 4.25 meter within 73 msec.
LiteTrans: Reconstruct Transformer with Convolution for Medical Image Segmentation

The combination of convolution and Transformer applied to medical image segmentation has achieved great success. However, it still cannot reach extremely accurate segmentation on complex and low-contrast anatomical structures under lower calculation. To solve this problem, we propose a lite Transformer based medical image segmentation framework called LiteTrans, which deeply integrates Transformer and CNN in an Encoder-Decoder-Skip-Connection U-shaped architecture. Inspired by Transformer, a novel multi-branch module with convolution operation and Local-Global Self-Attention (LGSA) is incorporated into LiteTrans to unify local and non-local feature interactions. In particular, LGSA is a global self-attention approximation scheme with lower computational complexity. We evaluate LiteTrans by conducting extensive experiments on synapse multi-organ and ACDC datasets, showing that this approach achieves state-of-the-art performance over other segmentation methods, with fewer parameters and lower FLOPs.
Statistical Analysis of Orbit Determination Techniques on MOTR data

We can derive orbit for a satellite with radar data using several methods. In this paper we mainly analyze two different orbit determination methods realized in Multi Object Tracking Radar, which is a phased array radar with skin mode tracking, Extended Kalman Filter (EKF) and Differential Correction-Least Squared. For a skin mode radar, track duration and track continuity is not assured. Statistical analysis of residuals and convergence performance of Differential Correction and EKF during tracking breaks is discussed. A detailed comparison of EKF and Differential correction based Orbit Determination performance on Multi Object Tracking Radar data is presented.
Global Dynamic Path Planning based on Fusion Improved A-star And Dynamic Window Method

Aiming at the mobile robot can quickly plan out a better global, and can meet the needs of real-time obstacle avoidance, this paper proposes a fusion algorithm based on improved A-star and dynamic window method. Firstly, A-star algorithm is optimized in path search. Although adding heuristic function to A-star algorithm can reduce a large number of useless search nodes, experiments show that there are still many useless search nodes, which increases the search time of the algorithm, Moreover, a weighting factor is introduced into the heuristic function, which makes the improved A-star algorithm further reduce the search useless nodes in the process of global path planning and improve the efficiency of the algorithm. Then aiming at the problem that A-star algorithm is not suitable for real-time obstacle avoidance in dynamic obstacle environment, the dynamic window algorithm is integrated, and the reasonable evaluation function is set according to the actual environment, which increases the flexibility and reliability of the whole system, so that the robot can reach the target point safely and reliably. The simulation results show that: compared with the traditional A-star algorithm, the improved algorithm can plan the optimal global path more quickly, reduce the search time by about 60%, and the path result is smoother and can avoid obstacles in real time. The improved algorithm is tested in the actual environment, and the test results show that it can meet the actual needs.
Mobile Application to Disseminate the History of Historical Buildings

Most of the cities around the world have historical buildings, some of them are usually constituted as tourist places that people can visit; however, many others are not so well known and the inhabitants themselves tend to be unaware of their historical value and significance to the heritage of the city or the country. This paper presents the process of development of a mobile application prototype that pretend to help to disseminate the history of places or buildings with historical value. In this first case, the prototype was designed for an old building that used to be a cinema. The prototype aims to use Augmented Reality to disseminate the history of buildings, and at the same time preserve the history of the city through a mobile application that shows various audiovisual materials, like a timeline and a photo gallery and videos, which allow people to know the historical context of the building when it was in splendor.
Mixed computation

Abstract Proof-theoretic models of grammar are based on the view that an explicit characterization of a language comes in the form of the recursive enumeration of strings in that language. That recursive enumeration is carried out by a procedure which strongly generates a set of structural descriptions Σ and weakly generates a set of strings S; a grammar is thus a function that pairs an element of Σ with elements of S. Structural descriptions are obtained by means of Context-Free phrase structure rules or via recursive combinatorics and structure is assumed to be uniform : binary branching trees all the way down. In this work we will analyse natural language constructions for which such a rigid conception of phrase structure is descriptively inadequate and propose a solution for the problem of phrase structure grammars assigning too much or too little structure to natural language strings: we propose that the grammar can oscillate between levels of computational complexity in local domains, which correspond to elementary trees in a lexicalised Tree Adjoining Grammar.
The Mondrian User Interface Pattern: Inspiring Eco-responsibility in Homes

The increase in energy demand has motivated a large body of research, using a diverse collection of approaches to reduce domestic energy use. One promising approach to this societal problem is the development of systems that cooperate with the residents combined with digital behavior changes interventions. In this chapter, we present the Mondrian User Interface pattern intended to help system developers to structure and populate the interactive components of their system in order to inspire eco-responsibility. The Mondrian UI serves as an ambient decorative display that supports different levels of users’ attention investment while sustaining daily use. Like Mondrian’s Collections, the Mondrian UI pattern divides the screen into rectangles of different sizes painted in primary colors. The rectangles contain information that can be zoomed in and zoomed out to support multilevel representations of the same semantic information while maintaining informational context accessible. In order to minimize attentional effort, the top-level content of a rectangle is intended to communicate information at a glance while the other levels provide analytic details on demand with minimalist one-click interaction. Each rectangle of the Mondrian UI is populated with eco-inspiring content such as spatiotemporal information about energy use, recommendations and explanations, and social incentives, or with utilitarian content, such as the weather forecast, that family members have the habit of consulting frequently. Utilitarian content along with the display located in a socializing place is intended to enforce the daily use of eco-inspiring information. We have applied the Mondrian UI pattern to the design and implementation of the user interface of an e-coach electricity management system.
Research on Static Missile Attitude Measurement Technology Based on Collinear Perspective Model

This paper designs a static attitude measurement system of missile body. Based on Bouguet calibration method, the orientation elements in the measurement camera are calibrated with high accuracy. The “N-point perspective problem” model (PNP algorithm) is used to analyze the spatial coordinates of the points to be measured, and the spatial coordinates in the measurement coordinate system are obtained. Finally, genetic algorithm is used to fit the cylindrical surface, the normal vector of cylinder is obtained and used to measure the attitude of static missile, such as pitch and azimuth. The measurement accuracy meets the actual requirements. It has the advantages of fast measurement speed, no need to look at the measurement target, less space constraints, and flexible station setting.
The Effect of Avatar Embodiment on Self-presence and User Experience for Sensory Control Virtual Reality System

The aim of this study is to explore the factors of virtual character embodiment, self-presence, user experience for the somatosensory control virtual reality (VR) system. We would like to understand the influences of avatar embodiment on self-presence and user experience. According to literature review, we constructed the hypotheses based on a systematic view of thinking from the emotional design theory. In order to efficiently realize the extent to which avatar embodiment, we make use of the quenstionaire of Gonzalez-Franco and Peck (Gonzalez-Franco, M., and Peck, T.C. Avatar Embodiment. Towards a Standardized Questionnaire. Front. Robot. AI, 2018, 5:74. https://doi.org/10.3389/frobt.2018.00074) to assess the embodiment level of subjects. Experimental design shows two kind of task types to understand the effect of avatar embodiment on self-presence and user experience. One is non-verbal behaviour experimental design such as dance practicing, while the other is verbal behaviour experimental design like sing practicing.
A Comprehensive Analysis of Subword Contextual Embeddings for Languages with Rich Morphology

Akdemir, Arda Shibuya, Tetsuo Güngör, Tunga Deep language models such as BERT pretrained on large-scale datasets have enabled remarkable progress in a wide range of NLP tasks and became the standard approach for many languages. However, an in-depth understanding of the effect of using these models is still missing for less spoken languages. This study gives a comprehensive analysis of using the BERT model for languages with rich morphology. We experimented with cross-lingual, multilingual, and monolingual BERT models and three non-BERT-based models. We evaluated these models on five morphologically rich languages (Finnish, Czech, Hungarian, Turkish, Japanese) and the English language. Evaluated on Dependency Parsing (DEP) and Named Entity Recognition (NER) tasks, which benefit highly from morphological information, BERT-based models consistently outperformed other approaches. Results revealed that the effects of using BERT-based models significantly differ across languages. Moreover, our analysis provided various critical findings of multi-task learning (MTL), transfer learning, and external features in different settings. We further verified these findings on noisy datasets for the Sentiment Analysis task as a case study. Finally, the proposed BERT-based model achieved new state-of-the-art results on both DEP and NER tasks for the Turkish language.
A Nonlinear Support Vector Machine Analysis Using Kernel Functions for Nature and Medicine

After the emergence of Artificial Intelligence (AI), great developments have taken place in the fields of science, economics, medicine and all other fields that use computer science. Along with the resulting developments in these fields, artificial intelligence has also solved many intractable problems, such as predicting specific serious diseases, determining future product sales, as well as analyzing and studying big data in the shortest possible time … SVM is one of the most important technologies in this field of artificial intelligence that goes into supervised methods, and which every machine learning expert should have in his/her arena. For this reason, in this article, we studied this technique and determined its advantages and disadvantages as well as its fields of application. Next, we applied this technique to three different databases, using four basis change functions, and we compared the results obtained to determine the best way to use the basis change functions.
Medical CT Image Amplification And Reconstruction System

To be able to better the leaflet known to display the details of the CT medical images, this paper designed a set of medical image processing software interface system, outstanding help doctors diagnose in the medical image area, strengthen the details of the original low-resolution image recognition ability, reduce the information content of a single pixel, to improve the physician diagnosis effect. The medical image with higher resolution than the original image is obtained by using the super-resolution reconstruction algorithm based on interpolation, which mainly includes three algorithms: adjacent interpolation, bilinear interpolation, and bicubic interpolation. This article in to protect the image detail better double interpolation method based on the three times in a child function before the @ jit in real-time, and make the operation speed of nearly 600 times, improves the resolution, increases the pixel, and make the image more clear, and you can also realize the image of the shear, to enlarge beneficial to physicians discriminant disease, 16 times, 64 times the level of CT images can be achieved relatively clear amplification effect and can help physicians discriminant disease medical CT images effectively.
Oriented Object Detection with Fine-Grained Enhancement and Angle Constraint

With the development of aerial imaging technology, object detection in aerial images has attracted increasing attention. Although significant progress has been achieved, how to effectively perform feature extraction and boundary regression for oriented object detection remains a hard challenge. In this paper, a fine-grained enhanced detection network (FEDet) is proposed for oriented object detection in aerial images. Specifically, the bottom-up fine-grained enhancement module is designed to enrich the fine-grained information of features and optimize the detection performance. Furthermore, in order to guide the network to learn the angle information and achieve more accurate rotation angle prediction, the oriented-sensitive loss function is proposed with the embedded angle constraint. Extensive experiments on the widely used benchmarks (i.e., DOTA and HRSC2016) demonstrate that the proposed method achieves promising performance.
Early Breast Cancer Diagnosis and Risk Prediction based on Machine Learning

Breast cancer is a disease in which life-threatening (malignant) cells in the breast multiply out of hand, making it the second most fatal type of cancer in women widely. Hence, to diminish the mortality rate and increasing the chances of survival, it is crucial to uncover it as early as attainable. This paper focused on comparing the different classifiers which are support vector machine, naïve Bayes, and K-nearest neighbor algorithms using the DDSM dataset. The target of this computer-aided system is to combine these classification techniques with image pre-processing methods so to compare their performance to find out the most satisfactory approach. The crux is to use the advantages of these techniques to obtain maximum optimal performance. For the comparative study, the digital mammogram of the breast is passed to histogram equalization for image pre-processing which enhances the necessary feature while removing noise that is present in the mammogram, the refined mammograph is then passed to wavelet transformation to extract all the important features for the classification.
Photonic Synapse by Non-Linear Gain in a Vertical Cavity Semiconductor Optical Amplifier

Vertical Cavity Semiconductor Optical Amplifiers (VCSOAs) are proposed as photonic synaptic elements. We exploit the VCSOA's nonlinear gain to controllably tune the strength of incoming optical pulses. This technique offers a hardware-friendly, low-energy photonic weighting mechanism with high potentials for use in neuromorphic photonic hardware.
Using examples to increase recall in self-administered questionnaires

Self-administered questionnaires often use examples or lists of examples to aid respondent recall. We report on the results of a field experiment examining how such examples in survey questions affect the episodic recall of events. Building on part-set cuing theory, the authors propose that examples increase recall when they cue low-accessibility subcategories of events, but may decrease recall when they cue high-accessibility subcategories. Further, cuing with examples rather than subcategory names may in some situations clarify questions and reduce non-useable open-ended responses. Findings from a survey of 2137 adult Medicaid recipients are generally consistent with these predictions.
Teacher Profesionalism in Improving Children’s Speech Ability Speech Delay

The purpose of this study is to describe the professionalism of teachers in improving children's in delay. This research was conducted at KB Ceria Timoho Yogyakarta. This research uses a descriptive qualitative approach. The results showed that the professionalism of teachers in dealing with child at KB Ceria Timoho Yogyakarta through training in early detection of child growth and development, improvement of facilities and infrastructure that support the development of children's language, care and additional facilities needed to support children's language development such as laptop games music, song beetles, tools for watching movies, story books, puppets for stories, presenting people from the education office for information about the curriculum and accreditation and enhancing learning programs by always innovating. The teacher's steps in improving children's ability speech delay at KB Ceria Timoho Yogyakarta is most important by knowing the cause of children talking too late because by knowing the cause the teacher is easy to handle it, not following the child's patterns is wrong, involving children talking at any circumstances, the teacher always makes eye contact when talking with children, the use of APE that supports children's vocabulary, holding parenting by presenting a pediatrician or child psychologist or can also consult with the teacher at any time especially when distributing child report cards
Spatial Dependency Analysis to Extract Information from Side-Channel Mixtures

Practical side-channel attacks on recent devices may be challenging due to the poor quality of acquired signals. It can originate from different factors, such as the growing architecture complexity, especially in System-on-Chips, creating unpredictable and concurrent operation of multiple signal sources in the device.
An Implementation of Web Based GIS for Distribution of Freshwater Fish Ponds

Geographical Information System is a term in the field of mapping which has a scope of how a system can unify geographic objects and their information. Freshwater fishery is the business of the majority of residents of South Bengkulu Regency covering tilapia and catfish. The solution is to create a web-based Freshwater Fish Pond GIS application using the PHP programming language. Making a database using MySQL, and for geolocation storage using Google Fusion Tables. The web that will be created will later display the GIS Web-based Freshwater Fish Pond Distribution Mapping in South Bengkulu Regency. Each pond will be accompanied by information such as the name of the owner, type of fish, type of pond, and location of the pond. It is known that the number of ponds in 4 villages in 2 districts in South Bengkulu Regency has reached 100 ponds consisting of 80 tilapia fish ponds, 20 catfish ponds.