Reviewerly/exHarmony huggingface dataset:
Syntax-Based Chinese-Vietnamese Tree-to-Tree Statistical Machine Translation with Bilingual Features

Because of the scarcity of bilingual corpora, current Chinese--Vietnamese machine translation is far from satisfactory. Considering the differences between Chinese and Vietnamese, we investigate whether linguistic differences can be used to supervise machine translation and propose a method of syntax-based Chinese--Vietnamese tree-to-tree statistical machine translation with bilingual features. Analyzing the syntax differences between Chinese and Vietnamese, we define some linguistic difference-based rules, such as attributive position, time adverbial position, and locative adverbial position, and create rewards for similar rules. These rewards are integrated into the extraction of tree-to-tree translation rules, and we optimize the pruning of the search space during the decoding phase. The experiments on Chinese--Vietnamese bilingual sentence translation show that the proposed method performs better than several compared methods. Further, the results show that syntactic difference features, with search pruning, can improve the accuracy of machine translation without degrading the efficiency.
Visual information retrieval from historical document images

Information retrieval from documentary heritage is considered a challenging issue because of the documents’ unique structures and level of degradation. Text characters in printed documents historically are accompanied by typographical objects. Retrieving and pursuing these visual typographical elements, which inform the content of historical manuscripts can help us better understand our documentary cultural heritage. Extracting these visual objects aids us in understanding and conveying more information about different practices of representation in historical documents and their effects on the current trends of publishing. Two important typographical objects related to the history of knowledge and information are footnotes and tables; the former are one of the critical elements that demonstrate authority and link the manuscript to its sources, and the latter summarize information in a compact and organized manner essential to the growth of scientific knowledge. To the best of our knowledge, there is currently no work that considers in depth the automated detection of these two typographical objects from the large collections of historical documents that would allow further historical study. This article focuses on the problem of detecting the presence of these two visual elements from historical printed documents and establishes two frameworks. The footnote detection framework uses a set of layout-based methods to extract some features regarding the font and appearance, and the table detection framework extracts spectral-based features from the images. These frameworks are tested on a large collection of 18th-century printed documents with more than 32 million images, and the results show their effectiveness and generalization power.
A Robust Long-Term Pedestrian Tracking-by-Detection Algorithm Based on Three-Way Decision

Pedestrian Detection Technology has become a hot research topic in target detection field recent years. But how to track the pedestrian target accurately in real time is still a challenge problem. Recently deep learning has got the extensive research and application in both target tracking and target detection. However, the tracking effect based on deep learning needs to be improved in the motion blur and occlusion cases. In this paper, we propose a new model that combines the target tracking and target detection and introduce the idea of granular computing to realize high-precision long-term robust pedestrian tracking. In this model, we use a pre-trained tracking model to track the specified object and use the three-way decision theory to judge the color histogram feature and correct the results by the detector. Compared with the separated tracker, our model invokes the target detector to detect the current frame when the tracking result is wrong and the detection result which is the most similar to the target is selected as the tracking result. Experimental results show that our model can significantly improve the tracking accuracy especially in the complex situations, compared with the separated tracker and the detector.
Beyond the digital Jordan curve: Unconstrained simple pixel-based raster relations

Artificial intelligence and satellites have brought spatial-image interpretation to the forefront. Over the past two decades, the literature has provided three distinct methods for identifying and classifying the boundary of raster objects: the digital Jordan curve, the hyper-raster, and a marginal approach. While each approach has its own merits and drawbacks, there should be methods by which to convert one representation to another. This, however, is currently unattainable as the digital Jordan curve approach and the hyper-raster approach are formulated upon different domains of objects, with the more verbose digital Jordan curve approach serving as a subset of the hyper-raster approach with respect to objects which are simple regions in R2. This paper revisits the approach of the digital Jordan curve by removing the digital Jordan curve restriction, replacing it with the set of pixels that neighbor an exterior pixel, allowing for a host of different relations in pixel space. This increase in domain results in a set of 62 available 9-intersection matrices, a nearly four-fold increase from the original 16 matrices and demonstrates the novelty of this approach with respect to the egg-yolk relations, a model in R2 for relations between regions with broad boundaries.
Involve Convolutional-NN to Generate Item Latent Factor Consider Product Genre to Increase Robustness in Product Sparse Data for E-commerce Recommendation

Abstract Online shopping also popular named e-commerce business need a computer machine to provide product information for customer or buyer candidate. Relevant information served by ecommerce system engine famous dubbed recommender system. It will impact seriously in increasing of marketing target achievement. The character of information in ecommerce have to be specific, personalized, relevant and fit according to customer profiling. There are four kind of recommender system to provide ecommerce recommender system, however only one model that most successful to applied in real ecommerce industry that namely collaborative filtering. These approaches rely on rating as basic calculation to generate product recommendation. However, just a little number of rating that given by customer reference to several convince datasets. The problem causes of sparse product rating, it will bring the impact to product recommendation accuracy. Sometime, in extreme condition impossible to generate product recommendation. Some work efforts have been developing to tackle lack of rating, one of them is considering to involving text sentences document such as text genre, product review, abstract, product description, synopsis, etc. All of material text sentences useful as raw component to predict the product rating. Reference previous work method aims extract text sentences document such as product review to become rating value based on bag of word and word order, sometimes they have got the mis in deeper understanding of text sentences description about product. Therefore, it influenced the result of predict the rating was inaccurate. In this research, author proposed novel method enhance variant of convolutional neural network dubbed dynamic convolutional neural network to improve scalability and increase deeper understanding to increase accuracy level. Based on our experiment, our model outperforms over existing state of the art in previous work in extracting text sentences product description based on evaluation approach uses RMSE.
User Interaction and Scenario-Based Experience Design for New Media Technology in Museum Spatial Experience

This article aims to study the interaction and scenario-based experience design of new media technology in the museum, improve people’s sensibility to museum-theme space information, and facilitate propaganda and education. It mainly: (1) summarizes the expression of new media human-computer interaction techniques in the museum; (2) discusses the relationship between museum-them space shaping based on new media technology and humans’ visiting experience; (3) summarizes the experience in human and museum space storyline with new media intervention. The article focuses on theoretical analysis and practical significance study from the 3 aspects. Meanwhile, it outlines the influence humans perceive in museum spatial experience.
Scale-Space Theory, F-transform Kernels and CNN Realization

We present scale-space and F-transform inspired modification to convolutional neural networks. The proposed modification improves network classification accuracy using multi-scale image representation and F-transform kernels pre-training. We evaluate our model on two databases and show better performance than networks without F-transform pre-training.
On Possibilities of Human Head Detection for Person Flow Monitoring System

Along with the development of human society, economy, industry and engineering, as well as with growing population in the world’s biggest cities, various approaches to person detection have become the subject of great interest. One approach to developing a person detection system is proposed in this paper. A high-angle video sequence is considered as the input to the system. Then, three classification algorithms are considered: support vector machines, pattern recognition neural networks and convolutional neural networks. The results showed very little difference between the classifiers, with the overall accuracy more than 95% over a testing set.
Integrating the Escaping Technique in Preventing Cross Site Scripting in an Online Inventory System

This paper discusses the implementation of the Escaping Technique in an Online Inventory System to prevent the Cross Site Scripting (XSS) attack. It also covers discussion about XSS described as a kind of injection attack that injects malicious scripts in the web application. This study also discusses the different types of XSS attacks citing some related studies which provide additional necessary information. Furthermore, a discussion of preventive measures to address the vulnerabilities of XSS attack is also covered in this study. Likewise, the escaping process is able to secure the developed online inventory system against XSS attack. Stripping out unwanted data, like malformed HTML or script tags prevents this data from being seen as code. The implementation of this technique is to be seen in an online inventory system. All of the possible situations were analyzed and imbedded on the developed online inventory system. Thus, it is very vital to secure the confidentiality, availability and integrity of the data inside the system and to mitigate the vulnerability to spread widely.
Optimization Substitution Cipher And Hidden Plaintext In Image Data Using LSB Method

Abstract Cryptography is a way to protect certain messages that have confidential properties or have properties that must be protected from unrelated parties. Substitution techniques have several algorithms such as hill cipher, vigenere cipher, one-time pad and others. This research is a development of previous research that has been carried out many combine substitution techniques. This research combines 4 substitution techniques that have been modified and stored messages that have been hidden into an image with the Least Significant Bit (LSB) algorithm. This research provides additional before the process of standard substitution techniques so that it will provide a more level of security even though the encryption process uses simple techniques and inserts messages into an image with PNG extension using LSB to improve security in messages. This is evidenced by the absence of color differences in the image so that it will not arouse suspicion. However, hex and the size of the image will change. besides this research found that images can only be implemented on social media such as Google+, Telegram, and Google Drive. Because social media like Facebook, WhatsApp, Instagram, and Pinterest will automatically convert, resizing to be smaller than the actual size.
Word Sense Disambiguation based on Maximum Entropy Classifier

Word sense disambiguation (WSD) is one of the most important research issues in the field of natural language processing.In this paper, a new method of word sense disambiguation is proposed, in which words and parts of speech (POS) are extracted as discriminative features.At the same time, a maximum entropy classifier is adopted to determine ambiguous words' semantic categories.Training data of SemEval-2007: Task#5 is used to optimize the maximum entropy model.A test corpus is applied to test the performance of the WSD classifier.Experimental results show that the performance of word sense disambiguation is improved after the proposed approach is used.
Ontology Modelling on Legal Document: Case Study : Legal Document of Indonesian Republic

Abstract Regulation representation and reasoning process of the regulation representation is an important thing for finding inconsistency in Indonesian Republic Regulation Document. Indonesia is a legal nation. The mean of Legal Nation is that nation using law to regulate the life of society, state, and the government. Indonesia has been using law to maintain it’s stability since it’s founded. An inconsistence in law has been found in Indonesian regulation document. In this paper,we proposed a method to represent a Indonesian Republic regulation in an ontology form for reasoning process. Ontology language that used in this paper is OWL. The purpose of this representation is to be able to do a reasoning process to find inconsistent regulations and do the process of finding concepts in regulations. After transforming Indonesian Republic regulation document into OWL ontology form and reasoning process of Indonesian Regulation ontology inconsistency has been found.
Concepts Approximation Through Dialogue with User

This paper is an attempt to show how dialogue between a system and a user is important to design a robust system which can learn a user's perspective and revise its knowledge base through interactions. The dialogue is crucial in order to better respond to a given query of a user. The problems, where dialogues can play a role, are discussed from two aspects. One is the aspect in which the system becomes able to learn the perspectives of the user(s) and improve its quality of classifications. The other is the aspect where the system can help a user to get answers to its queries. We have, in particular, considered the problems of (i) learning a user's ontology of concepts, (ii) explaining the system's own classification for a cluster to the user in order to get feedback, and (iii) generating a global description for a cluster, in a user-friendly language, based on a sample of objects available to the system.
Channel-wise barcodes for color display applications

We present a channel-wise (CW) framework for data embedding in display images using barcodes. Data are embedded in one or more channels of the color image encoded as a monochrome barcode pattern and extracted from the corresponding channels by the decoder. We highlight two advantageous schemes, realized in the proposed framework: red, green, blue CW barcodes (RGB-CWBs), and blue channel image-barcodes (BC-IBs). Compared with a conventional monochrome black and white barcode BW-B with the same spatial footprint, RGB-CWBs utilize the three channels of color image more effectively and enable a three-fold increase in data rate, whereas BC-IBs significantly reduce the embedding distortion by exploiting the reduced sensitivity of the visual system to spatial detail in blue colors. For RGB-CWBs, we develop an effective framework for the cancellation of cross-channel interference between the display and capture channels using a model for the display and capture processes and for the ambient illumination. Model parameters required for interference cancellation (IC) are learned on-the-fly using either expectation-maximization or through suitable design of the synchronization patterns as “pilot-blocks.” Extensive experiments over multiple display and capture devices validate the advantages of the framework: RGB-CWBs (with IC) and the BC-IBs offer bit error rates comparable to their monochrome counterparts while providing either a three-fold increase in data rate or a significant reduction in distortion, respectively.
Security Testing for Multi-Agent Systems

A combination of formal methods and security testing for communication layer of multi-agent systems is proposed and studied. We start with security property called bisimulation process opacity. Unfortunately, this property is undecidable in general so we propose its more realistic variant based on simulation and tests and testing. A test represents an attacker’s (i.e. possibly one of the agents) scenario to obtain some confidential information on systems. Here we consider system to be secure if it cannot be compromised by a given test or set of tests. By test we can express also capabilities of an attacker related to time properties such as time measurement accuracy, duration of tests (attacks), complete lack of attacker’s time information and so on. At the end we state a decidability result for testing.
Research on lane identification based on deep learning

In order to meet the real-time and robustness requirement of driverless cars driving on highway, this paper proposed a lane line identification method based on the Deep Learning. This method first built a lane line image library and then input the pictures of the lane line image for denoising and n ormalized processing. Secondly, the Lenet – 5 network model was used for classification and recognition, with a recognition rate of 99.4%, and the lane line type was displayed through GUI interface. Finally, this method was compared with the support vector machine and BP neural network, and the results effectively verified that the method can satisfy the requirement of real-time and accuracy of lane line identification.
Bringing Assistive Technology Innovation and Material Science Together Through Design

The MATUROLIFE project seeks to develop assistive technology (AT) to support the independence of older adults by embedding ‘smart’ metallized textiles in a suite of innovative products. With an ageing European population, there is a growing need for products that support independence and reduce the health and social care burden. AT is often unsightly and stigmatizes the user resulting in high abandonment rates and too often is developed without the direct involvement of the end user. Innovations in material science provide the opportunity to develop more discrete forms of assistive technology that are embedded in everyday garments and products. This paper outlines the design approach being adopted in this complex, multidisciplinary, European project to bring together innovations in material science with design. It describes the status of the research, 12 months into the 3-year project, with an emphasis on the development of design concepts and the design approaches being adopted.
About Filter Criteria for Feature Selection in Regression

Selecting the best group of features from high-dimensional datasets is an important challenge in machine learning. Indeed problems with hundreds of features have now become usual. In the context of filter methods, the selected relevance criterion used for filtering is the key factor of a feature selection method. To select an appropriate criterion among the numerous existing ones, this paper proposes a list of six necessary properties. This paper describes then three relevance criteria, the mutual information, the noise variance and the adjusted R-squared, and compares them in the view of the aforementioned properties. Any new, or popular, criterion could be analysed in the light of these properties.
Implementation of sequential circuit using feynman and fredkin reversible logic gates

Abstract At present, one of the most vital issue is reversible logic and this reversible logic has various applications in different areas such as low power CMOS, quantum computing, nanotechnology, cryptography, Digital Signal Processing(DSP), quantum dot cellular automata. A unique one-to-one mapping technique is used in reversible logic gates. To create a helpful capacity of gates, the reversible gates require some supported auxiliary data sources known as ancilla inputs. Likewise to keep up the reversibility of the circuits some extra unutilized yields are requiring that are referenced to as the trash yields. The quantity of garbage outputs, the quantity of ancilla sources of info and quantum cost assumes a noteworthy job in the reversible circuit’s assessment. Subsequently these parameters are most enormous for structuring reversible circuit’s effectively. In this paper implementing the sequential circuit using Feynman and Fredkin reversible logic gates. The proposed sequential circuit is simulated using Tanner Tool. The result shows that overhead delay and total delay are reduced by 33.69% and 20.42% respectively.
QoE Assessment of Multi-view Video and Audio IP Transmission Methods from Multipoint

This paper assesses the effect of transmission methods on QoE at Multi-View Video and Audio (MVV-A) IP transmission in which multiple servers transmit the data to a user. We deal with three transmission methods. They are the all viewpoints at all the servers transmission method, the all viewpoints at the requested server transmission method, and the requested viewpoint transmission method. A subjective experiment assesses QoE of the system, and we compare the three transmission methods by means of multidimensional QoE from eight adjective pairs. As a result, we find that the requested viewpoint transmission method and the all viewpoints at the requested server transmission method achieve higher QoE than the all viewpoints at all the servers transmission method.
Analysis of clustered failure time data with cure fraction using copula

Clustered survival data in the presence of cure has received increasing attention. In this paper, we consider a semiparametric mixture cure model which incorporates a logistic regression model for the cure fraction and a semiparametric regression model for the failure time. We utilize Archimedean copula (AC) models to assess the strength of association for both susceptibility and failure times between susceptible individuals in the same cluster. Instead of using the full likelihood approach, we consider a composite likelihood function and a two‐stage estimation procedure for both marginal and association parameters. A Jackknife procedure that takes out one cluster at a time is proposed for the variance estimation of the estimators. Akaike information criterion is applied to select the best model among ACs. Simulation studies are performed to validate our estimating procedures, and two real data sets are analyzed to demonstrate the practical use of our proposed method.