Reviewerly/exHarmony huggingface dataset:
Utilizing Chinese Dictionary Information in Named Entity Recognition

Supervised methods are widely used in Chinese named entity recognition (NER). To achieve state-of-the-art performance, NER systems often use additional resources like gazetteer which is a large dictionary containing well known named entities. However, previous works do not consider the situation that the corpus coverage rate of gazetteer is low and the gazetteer contains noise. In this situation, the effect of the gazetteer is limited. In this paper, we extend the gazetteer with a dictionary which contains common words and part-of-speech (POS) information of the words. We first obtain the Chinese word segmentation (CWS) and part-of-speech information using the dictionary. Then, a feature is developed to contain the CWS and POS information together. Finally, the feature is added to the LSTM-CRF model. Experimental results show that our method yields a 4.71% improvement over the gazetteer feature method.
An experimental quantum Bernoulli factory

Randomness processing in a Bernoulli factory has been identified as an area where a quantum advantage may exist. We report two quantum photonic implementations of a Bernoulli factory which demonstrate large reductions in resources over their classical counterpart.
On-Line Big-Data Processing for Visual Analytics with Argus-Panoptes

Analyses with data mining and knowledge discovery techniques are not always successful as they occasionally yield no actionable results. This is especially true in the Big-Data context where we routinely deal with complex, heterogeneous, diverse and rapidly changing data. In this context, visual analytics play a key role in helping both experts and users to readily comprehend and better manage analyses carried on data stored in Infrastructure as a Service (IaaS) cloud services. To this end, humans should play a critical role in continually ascertaining the value of the processed information and are invariably deemed to be the instigators of actionable tasks. The latter is facilitated with the assistance of sophisticated tools that let humans interface with the data through vision and interaction. When working with Big-Data problems, both scale and nature of data undoubtedly present a barrier in implementing responsive applications. In this paper, we propose a software architecture that seeks to empower Big-Data analysts with visual analytics tools atop large-scale data stored in and processed by IaaS. Our key goal is to not only yield on-line analytic processing but also provide the facilities for the users to effectively interact with the underlying IaaS machinery. Although we focus on hierarchical and spatiotemporal datasets here, our proposed architecture is general and can be used to a wide number of application domains. The core design principles of our approach are: (a) On-line processing on cloud with Apache Spark. (b) Integration of interactive programming following the notebook paradigm through Apache Zeppelin. (c) Offering robust operation when data and/or schema change on the fly. Through experimentation with a prototype of our suggested architecture, we demonstrate not only the viability of our approach but also we show its value in a use-case involving publicly available crime data from United Kingdom.
An Improvement of the CS Attack to DSC Cipher

Abstract The DECT standard cipher (DSC) is a 64-bit stream cipher, which was used in the digital enhanced cordless telecommunications (DECT) standard to protect the privacy of users. The Coisel–Sanchez (CS) attack proposed by Coisel and Sanchez in CHES 2015 is the most effective cryptanalysis against the DSC cipher up to now. Based on the CS attack, we present an improved attack in which an information collection method with unknown memory is used to improve the utilization of information and the message classification technique is adopted to reduce the computation time of evaluation indexes. Meanwhile, we present an improved pre-sieving technique to avoid the computation of evaluation indexes of a large number of incorrect candidates. Our known plaintext attack requires only 2 min of communication compared to 3 min for the CS attack. By analyzing 213 keystreams, our approach is able to increase the success probability of retrieving secret key from 55–89%. As far as we know, the attack we present in this paper is the most effective one among the existing attacks to the DSC cipher.
Probabilistic Context-Aware Ambients

The calculus of context-aware ambients has been introduced for modeling mobile systems that are context-aware. However, due to the randomness of contexts in real life, the methods to deal with it are necessary. Therefore, based on the aforementioned calculus, we present the calculus of probabilistic context-aware ambients in this paper. We provide the syntax and semantics of our calculus. The new calculus is mainly designed for modeling the processes and contexts both with randomness.
Enhanced PRACH Detection by Wavelet De-noising

Random Access Request is mainly used by mobile systems to request uplink resources from Base Station. However noise and interference significantly degrades the detection performance causing them to falsely detect or miss detect the access requests. Improving the detection performance in noisy channel is seldom touched and always challenging. In this paper, we propose an enhanced Physical Random Access Channel (PRACH) detector with Wavelet de-noising technique which efficiently suppress wide range of noise and interference in the received signal. The proposed algorithm first de-noise then reconstructs preamble from the received signal and efficiently utilizes them to improve the detection performance. Through simulation, we verify that proposed algorithm successfully meets the 3GPP detection performance requirement with gain of 6 dB in low SNR range which is the key advantage.
Application of Allen’s Temporal Logic to Ontological Modeling for Enterprise Interoperability

The problems of creating lifecycle ontologies for enterprise interoperability are addressed in this paper. Enterprise interoperability and enterprise integration are essential components of enterprise engineering (EE). A few definitions and viewpoints on EE are examined in the first hand. An original discipline for EE is considered. A generalized ontological approach to enterprise engineering is developed on the basis of combination of the lifecycle modeling, knowledge management and ontological engineering. It calls for the modeling and coordination of at least three lifecycles: enterprise lifecycle, knowledge lifecycle and product lifecycle. A general representation of lifecycle knowledge graph by a mind map is given. Particular emphasis is put on granular lifecycle upper ontology and meta-ontology. The lifecycle representations being discussed include both visualized and abstract ones. Allen’s logic is used to construct principle temporal relations between stages and phases of lifecycle.
Augmented Reality-Based Simulation of Spring–Mass System

In recent decades, various user interaction technologies have made their presence to visualize the complex behavior patterns of a dynamical system. Out of these, technology Augmented Reality (AR) emerged as a viable choice for studying the interaction between reality and simulation. Augmented reality-based simulation involves computer-based superimposition of an image on another image. In the present work, MATLAB and Unity3D-based simulation techniques have been used for a spring–mass system. Computer-based image processing has been performed in MATLAB. A tracking algorithm and simulation code is developed using MATLAB to get enhanced interaction between the real and the virtual systems. The results of this study shall prove to be quite useful in the rapid prototyping and vibration engineering fields.
Applying of probabilistic methods for managing pricing in a pharmaceutical organization

The problem of price management is considered by an example of pharmaceuticals of the same pharmacotherapeutic group. It is necessary to evaluate the possibility of applying a discount on the price of one or several pharmaceuticals. Two approaches are proposed for solution. The heuristic approach is based on the idea of Ant Colony Optimization algorithm. Another approach is to restore the regression and solve the problem of nonlinear discrete optimization using an adaptive probabilistic algorithm. At the end of the work, the results of an extensive computational experiment are presented.
K-highest Fuzzy Min-Max Network to Classify Histopathological Images

Classification of histopathological images provides great support towards breast cancer diagnosis. Hence, in this work, a Fuzzy Min-Max with a K-highest (Kh-FMM) hyperbox expansion criteria based approach is studied and used to classify the magnification independent breast cancer histopathology images. To categorize the dataset, gray level co-occurrence matrix (GLCM) is utilized to extract features from the images, and are then feed into Kh-FMM neural network. In this paper, BreaKHis dataset is used which contains benign and malignant breast tumor microscopic biopsy images at four different magnifications. Experimental results show that the magnification factor of 200× gives higher accuracy rate than other magnifications.
A novel wavelet thresholding method for implementation of signal compressor design

The paper deals with a work for unique wavelet shrinkage technique is planned and assessed in comparison with the opposite wavelet shrinkage ways like soft and onerous shrinkages. It's evident that wavelet families have distinctive characteristics and details that successively provide them a unique behavior against an identical wave once they were utilized for a particular procedure. As an example, in signal process applications, like wave compression, a comparative analysis in conjugation with analysis study utilizing economical system was needed to accumulate appropriate results. During this work a wavelets analysis tool was utilized to compress an artificial wave. The purpose of this work is to style and execute another wavelets shrinkage strategy, so valuate it against alternative wavelets shrinkage ways, so as to seek out the best wavelet mum operations among the antecedently mentioned families. Conjointly associate applicable decomposition level has been found to utilize the novel shrinkage technique. This new shrinkage technique is employed to shrink the wavelets coefficients and yield a decent compressed pressure signal before transmit it. There are numerous evaluation parameters to make up the evaluation and analysis method for signal compression. But the additional well-known quantitative measures are: N.M.S.E. (normalized mean sq. error) and E.S.N.R. (enhancement of signal to noise ratio). These on top of mentioned performance indices are utilized to urge the comparison results among completely different shrinkage strategies. The planned shrinkage technique outperformed the opposite standard strategies for all elect decomposition levels and completely different threshold choice rules. As an example Hardy-sine has E.S.N.R. =10.26dB and N.M.S.E. =0.02 that is concerning four improvement in E.S.N.R. for onerous-sine as compared with regular hard technique. Later the conclusion is to highlight that; this planned technique may be utilized for wave compression in more studies.
Continuous data stream clustering based on a generalized model of art family neural network

The article considers peculiarities of continuous data stream clustering with the help of ART family neural networks. A generalized model of operation of an arbitrary ART family network is proposed. A methodology for planning a practical data analysis using the proposed generalized model is formulated. Implementation of the model stages for two different ART family networks is considered in detail: ART-2a and Fuzzy ART. A general approach to solving data clustering problems using the generalized model is proposed. Methods for assessment of the effectiveness of the continuous data stream clustering results are considered. Recommendations are developed for configuring the ART networks hyperparameters depending on the nature of the analyzed data.
Serial and Parallel Recurrent Convolutional Neural Networks for Biomedical Named Entity Recognition

Identifying named entities from unstructured biomedical text is an important part of information extraction. The irrelevant words in long biomedical sentences and the complex composition of the entity make LSTM used in the general domain less effective. We find that emphasizing the local connection between words in a biomedical entity can improve performance. Based on the above observation, this paper proposes two novel neural network architectures combining bidirectional LSTM and CNN. In the first architecture S-CLSTM, a CNN structure is built on the top of bidirectional LSTM to keep both long dependencies in a sentence and local connection between words. The second architecture P-CLSTM combines bidirectional LSTM and CNN in parallel with the weighted loss to take advantage of the complementary features of two networks. Experimental results indicate that our architectures achieve significant improvements compared with baselines and other state-of-the-art approaches.
Pattern Generation and Dimensionality Reduction using CNN for Image Restoration

In recent days the image retrieval process is used in many applications are medical images, image classification and image segmentation process.an image retrieval is the process of retrieving images from the database. The retrieved images are classified by numerous classification techniques. In this work, the preprocessing stage, the input images are isolated from background and foreground images. A novel window based pattern generation technique is used to generate the patterns of preprocessed image. These patterns are provide the necessary information which are present in the preprocessed image. From this pattern the effective features are extracted and these features are classified by using a novel dimensionality reduction based classification technique of convolution neural network (CNN). The proposed method performance is high compare than other classification techniques of SVM and KNN.
Novel Color Image Data Hiding Technique Based on DCT and Compressed Sensing Algorithm

Information security and its research are getting significant, and therefore, the steganographic technique is used in high level to send data secretly using a cover image such that its presence cannot be detected. In his paper, an improved RGB image steganographic technique which is a novel approach for hiding the secret image based on compressive sensing algorithm has been proposed. In the proposed approach, the RGB planes of payload image are extracted and compressed; then, the coefficients are reshaped and discrete cosine transform (DCT) is applied over 2 * 2 matrices. The compressed payload image is embedded with chaotically chosen random keys in the segmented RGB planes of cover image. The proposed method results in significant improvements with high PSNR value and payload capacity.
LWT-Based Audio Watermarking Using FWHT and SVD

This chapter introduces an LWT-based audio watermarking scheme using fast Walsh-Hadamard transform (FWHT) and singular value decomposition (SVD) [30]. Conventional wavelet transform provides good results for its multi-resolution characteristics and perfect reconstruction. However, it is mainly calculated by convolution operation, resulting in high computation. In addition, the generated floating numbers increase the storage requirements. As a result, the LWT is designed to increase the efficiency and it is now used in digital watermarking [27]. In our proposed method, watermark information is preprocessed first using a Bernoulli map in order to improve the robustness and enhance the confidentiality of the watermark. Then the original audio is segmented into nonoverlapping frames. Watermark information is embedded into the largest singular value of the FWHT coefficients obtained from the low-frequency LWT coefficients of each frame. A blind watermark detection technique is developed to identify the embedded watermark under various attacks. The main features of the proposed scheme are: (i) it utilizes the LWT, FWHT, and SVD jointly; (ii) it uses Bernoulli map, containing the chaotic characteristic to enhance the confidentiality of the proposed scheme; (iii) watermark extraction process is blind; (iv) subjective and objective evaluations reveal that the proposed scheme maintains high audio quality; and (v) it achieves a good trade-off among imperceptibility, robustness, and data payload. Experimental results indicate that the proposed watermarking scheme is highly robust against various attacks such as noise addition, cropping, re-sampling, re-quantization, and MP3 compression. Moreover, it outperforms state-of-the-art methods [9–10, 14–16, 20, 23, 26, 28] in terms of imperceptibility, robustness, and data payload. The data payload of the proposed scheme is 172.39 bps, which is relatively higher than that of the state-of-the-art methods.
From Fifth Generation Computing to Skill Science

Professor Koichi Furukawa, an eminent computer scientist and former Editor-in-Chief of the New Generation Computing journal, passed away on January 31, 2017. His passing was a surprise, and we were all shocked and saddened by the news. To remember the deceased, this article reviews the great career and contributions of Professor Koichi Furukawa, focusing on his research activities on the foundation and application of logic programming. Professor Furukawa had both a deep understanding and broad impact on logic programming, and he was always gentle but persistent in articulating its value across a broad spectrum of computer science and artificial intelligence research. This article introduces his research along with its insightful and unique philosophical framework.
Simultaneous Display of Front and Back Sides of Spherical SOM for Health Data Analysis

We propose to simultaneously display the front and back sides of the spherical SOM so that cluster locations can be expressed in terms of phase relations even if they are on opposite sides of the spherical SOM. The technique is showcased on the medical health care-data. Furthermore, the component map was converted numerically (DIM (Dimensional Interaction Map) mode) for the medical data case and the result compared with that obtained with the front and back sides of the map.
Research in Progress: Holistic Climate Service Prototypes for Farmers in Tambuu, Tanzania

Urgent efforts are needed to address climate change and changing climate variability. Solutions are needed at economic, political, scientific, educational, as well as technological domains. Our previous research in Tambuu, Tanzania, shows that the acute information needs of the community are credible and trustworthy weather information, knowledge in best uses of weather information, and education about best farming practices. Delivering the information for end users in Tambuu is a challenge in itself. Based on these results and following the principles of System Action Design Research, we developed prototypes for future climate services together with local farmers in Tambuu community. These prototypes and our future plans are presented in this research-in-progress paper.
Deep‐network based method for joint image deblocking and super‐resolution

Many pieces of research have been conducted on image-restoration techniques to recover high-quality images from their low-quality versions, but they usually aim to handle a single degraded factor. However, captured images usually suffer from various degradation factors, such as low resolution and compression distortion, in the procedures of image acquisition, compression, and transmission simultaneously. Ignoring the correlation of different degraded factors may result in the limited efficiency of the existing image-restoration methods for captured images. A joint deep-network-based image-restoration algorithm is proposed to establish a restoration framework for image deblocking and super-resolution. The proposed convolutional neural network is made up of two stages. A deblocking network is constructed with two cascade deblocking subnets first, then, super-resolution is performed by a very deep network with skipping links. Cascading these two stages forms a novel deep network. An end-to-end training scheme is developed, which makes the two stages be trained jointly so as to achieve better performance. Intensive evaluations have been conducted to measure the performance of the authors’ method both in general images and face images. Experimental results on several datasets demonstrate that the proposed method outperforms other state-of-the-art methods, in terms of both subjective and objective performances.
An Image Segmentation Model Based on Cascaded Multilevel Features

In deep convolutional networks for segmentation, resolution is significantly reduced by multiple pooling and convolution operations, which makes the prediction accuracy of pixel class reduced. Based on the deep convolutional coding-decoding network, an end-to-end image segmentation model by cascading multi-level features in encoder and decoder is proposed in this paper. Firstly, the last layer convolution feature of the first two convolution stages and all convolution layer features of the last three convolution stages in the encoder are selected, and the features of the latter three stages are added pixel by pixel through skip connection. Secondly, all the convolution layer features of the last three convolution stages in the decoder are selected to fuse pixel by pixel. Finally, the above multi-level features are cascaded in the way of channel splicing, and then sent to the new convolution layer to learn and make category prediction. In this paper, the experiments are carried out on the CUB_200_2011 and ISPRS Vaihingen datasets, and compared with the research results in the literature. The results show that the proposed model does better than the comparative methods, and has achieved good segmentation effect on common images and remote sensing images.
Generalized Bayesian Structure Learning from Noisy Datasets

In recent years, with the open data movement around the world, more and more open data sets are available. But, the quality of the datasets poses issues for learning models. This study focuses on learning the Bayesian network structure from data sets containing noise. A novel approach called GBNL (Generalized Bayesian Structure Learning) is proposed. GBNL first uses a greedy algorithm to obtain an appropriate sliding window size for any dataset, then it leverages a difference array-based method to quickly improve the data quality by locating the noisy data sections and removing them. GBNL can not only evaluate the quality of the data set but also effectively reduce the noise in the data. We conduct experiments to evaluate GBNL on five large datasets, the experiment results validate the accuracy and the generalizability of this novel approach.