Reviewerly/exHarmony huggingface dataset:
Statistical Aspects and Dynamical Entanglement for a Two-Level Atom Moving on Along Cavity Length of x-Direction: Atomic Position Distribution

In this work, the problem of the quantum optical model is considered where an one-mode quantized radiation field interacts with a two-level atom (TLA). Also, the atomic position distribution is taken into account, i.e., the atom passing through the length of the optical cavity. We believe that the atomic position affects the matter-field interaction and can be realized in several multiple experiments, such as ultracold atoms and trapped ions. We take into consideration the atom is moving along the cavity field in the x-direction so that the time-position Schrödinger equation for the atom in the x-direction is obtained. We suppose that the field is initially prepared in a coherent state optical field and the atom is initially prepared in an excited state. Also, the atomic motion along the cavity length vanishes in the cavity wall. By using the Laplace transformation method, an exact analytical solution for the coupled partial differential Schrödinger equation for the wave function is calculated. Some non-classical statistical aspects such as the atomic inversion and the photon distributions are discussed in detail. The collapses-revivals, the Poissonian distributions of the photon by Mandel Q parameter, the degrees of the entanglement, and the fidelity have been investigated. The effect of the atomic position distribution in the x-direction on these phenomena is examined. We observed that the atomic position distribution along the cavity has an effective effect on the quantum statistical properties of these phenomena. Minutely, the influence of the atom location distribution on the amount of quantum entanglement has been obtained.
New TSBuilder: Shifting towards Cognition

The paper reviews a project on the automation of term system construction. TSBuilder (Term System Builder) was developed in 2014 as a multilayer Rosenblatt's perceptron for supervised machine learning, namely 1-3 word terms identification in natural language texts and their rigid categorization. The program is being modified to reduce the rigidity of categorization which will bring text mining more in line with human thinking.We are expanding the range of parameters (semantical, morphological, and syntactical) for categorization, removing the restriction of the term length of three words, using convolution on a continuous sequence of terms, and present the probabilities of a term falling into different categories. The neural network will not assign a single category to a term but give N answers (where N is the number of predefined classes), each of which O ∈ [0, 1] is the probability of the term to belong to a given class.
Object recognition and tracking using Haar-like Features Cascade Classifiers: Application to a quad-rotor UAV

This paper develops a functional Unmanned Aerial Vehicle (UAV) capable of tracking an object using a Haar feature-based cascade classifier. The object in question is a human face; the experiments were conducted in a two-step detection, searching first for the upper body and then searching for the face inside the human body detected area. Once the human face is detected, the quadrotor must follow it automatically. The quadrotor must track the face in a central position and maintain a safe distance. The image processing is made on-board with a high processor single-board computer. Experiments have been conducted showing our mythology's effectiveness; these results are shown in a video.
Introduction of Fingerspelling for Realizing a User Authentication Method Using s-EMG

At the present time, mobile devices such as tablet-type PCs and smart phones have widely penetrated into our daily lives. Therefore, an authentication method that prevents shoulder surfing is needed. We are investigating a new user authentication method for mobile devices that uses surface electromyogram (s-EMG) signals, not screen touching. The s-EMG signals, which are detected over the skin surface, are generated by the electrical activity of muscle fibers during contraction. Muscle movement can be differentiated by analyzing the s-EMG. Taking advantage of the characteristics, we proposed a method that uses a list of gestures as a password in the previous study. In order to realize this method, we have to prepare many gestures that are used to compose passwords. In this paper, we adopted fingerspelling as candidates of such gestures. We measured s-EMG signals of manual kana of The Japanese Sign Language syllabary and examined their ability for the use of passwords of the authentication method.
Novel Interestingness Measures for Mining Significant Association Rules from Imbalanced Data

Associative classification is a rule-based approach that joins Association Rule Mining and Classification to build classifiers that predict class labels for new data. Associative classifiers may generate an overwhelming number of rules which are hard to handle. Delving through these rules to identify the most interesting ones is a challenging task. To overcome this problem, several measures have been proposed. However, for imbalanced datasets, existing measures are no more reliable. In fact, they tend either to favour rules of major classes and consider others as uninteresting or only emphasize on the rules of minor classes and omit other ones. In this respect, we propose five new measures which tend to be fair for both types of classes regardless of their imbalanced distribution. Extensive carried out experiments on real-world datasets show that the new measures are able to efficiently extract significant knowledge from minor classes without decreasing the global predictive accuracy.
Estimation of 3D human hand poses with structured pose prior

Here, the authors present multistage estimation model embedding with structured pose prior (SPP), a novel coarse‐to‐fine framework for real‐time 3D hand estimation from single depth image. Authors’ main contributions can be summarised as follows: (i) The authors proposed SPP to enforce constraints of canonical hand pose instead of original hand pose. (ii) The authors are the first to adopt under‐complete stacked denoising auto‐encoder (SDA) to construct pose prior by mapping canonical hand pose to latent representation. In the case of enforcing constraints of canonical hand pose, the authors empirically validate that under‐complete SDA outperforms over‐complete SDA in improving the hand estimation accuracy. (iii) The authors propose candidate keypoints patches (CKP) as intermediate data to conduct further hand pose refinement. Experimental evaluation on two publically available datasets shows that authors’ model is competitive both in accuracy and computation time. Especially, authors’ method placed first in the location of palm key‐point on both two datasets, and the high accuracy of hand palm key‐point plays an important role in many applications, such as that manipulator can grasp objects to specific coordinates with the guiding of human hand palm.
Image color style migration method for mobile applications based color feature extraction

With the development of the computer image processing and smartphones, image processing on smartphones will be a popular technology. This paper mainly provides an image color style migration method for mobile applications based color feature extraction. Firstly, we use dichotomy to extract color fe ature of the template image. Then we use Quartz2D engine of iOS to draw the target image according to the template image color feature. Finally we use Metal interface of iOS to adjust the target image’s color saturation and sharpen its edge. Through the whole process, the source image’s color style has been migrated to a new image. We have implemented the image color style migration system orient to iOS application. The experiment results show that the system runs stable and reliable.
The prediction of foundation pit based on genetic back propagation neural network

Predicting the deformation of the foundation pit is one of the key issues for the construction safety of the foundation pit. In the traditional construction process often neglects the deformation prediction. It will cause the best time of repairing the pit is often missed. BP neural network has the characteristic of markova chain which is exactly match temporal-series data collected from displacement monitoring. So the BP neural network can understand the data better than SVM and RF. Further, the GA-BP neural network improved the training process based on BP neural network. We proposed a GA-BP neural network to predict the deformation of the foundation pit. To enforce the validation of the performance, we collected the real data from the Zhoushan foundation pit project. Compared with support vector regression and random forest regression, the results showed that GA-BP method has the error basically within -0.05–0.05 mm, the maximum relative error 0.36% and the predicted fitting value IA above 0.9, which are obviously better than other two methods.
Information Retrieval: A Case Study on Contributions of Greimasian Semiotics to Semantic Computing in Agriculture for Knowledge Management

This paper aims to discuss how a linguistic-discursive approach can contribute to semantic computing field, especially to information retrieval process. A brief history of devices and methods used to storage and share knowledge reveals different strategies that aim to optimize users' searches through increasingly fast and accurate answers. The modern data-intensive technologies, the increased computational, and the data storage resources knowledge, that can be shared and accessed instantly, demand new discussions about the contributions of language theories to information retrieval. For this, it is analyzed a document that composes an innovative platform of knowledge management in agriculture, applying concepts offered by Greimas and his group in order to extract relevant information that can support the search engines. This scientific project related to the study of meaning generation associated to knowledge domain, to semantic computing approach become to be an important tool to help in the search of computer science advances, especially for bringing about continuous improvement within organizations and making them more competitive.
Hue rotation (HR) and hue blending (HB): Real‐time image enhancement methods for digital component video signals to support red‐green color‐defective observers

Abstract The mutual understanding of color‐normal observers (CNOs) and color‐defective observers (CDOs) is now essential because personal color information display environments have been widely adopted. However, existing tools for CDOs offer only color discrimination; they fail to support color impression (ie, saturation and contrast). Therefore, we need a novel tool that offers help in distinguishing opponent colors, while preserving color saturation. We introduce two key techniques for realizing this difficult goal. The former is the repeated sequential display of the original and processed images to support the formation of unified correct percepts that provide discrimination of both red‐green and yellow‐blue opponent colors. One image, ie, original, exhibits correct yellow‐blue but distorted red‐green information for CDOs while the other, ie, processed, provides synthesized distinguishable red‐green but confusable yellow‐blue information for CDOs; here, hue rotation (HR) is useful for advanced users whereas hue blending (HB) is suitable for general. The latter is realized by the real‐time video processing available on smartphones; our algorithms support direct processing of the digital component video signal formats (eg, Y , C R , and C B ). Subjective tests suggest that the two above‐mentioned algorithms will, along with embedding a lightweight real‐time dichromatic simulation facility for CNOs, greatly help the mutual understanding of CNOs and CDOs.
Analysis of the Quantization Noise of Linear Time-Invariant Filters for Image Processing

The problem of quantization of the coefficients of arbitrary linear time-invariant filters in order to minimize the noise of this phenomenon is investigated in this paper. The implementation of the method of linear time-invariant filtration is proposed, which makes it possible to reduce the bit-width of the filter coefficients and simplify the performance of rounding operations. The analysis of the effect of the bit-width of the filter coefficients on the magnitude of the quantization noise and the resulting error of the filtration method being investigated is carried out. The formulas for determining the minimum bit-width of the filter coefficients, providing high quality of image processing, are derived, depending on the filter used. It is concluded that when using these formulas, the quantization noise that occurs when the filter coefficients are rounded off does not have a significant effect on the image processing result.
Mining Cross-Task Artifact Dependencies from Developer Interactions

Implementing a change is a challenging task in complex, safety-critical, or long-living software systems. Developers need to identify which artifacts are affected to correctly and completely implement a change. Changes often require editing artifacts across the software system to the extent that several developers need to be involved. Crucially, a developer needs to know which artifacts under someone else's control have impact on her work task and, in turn, how her changes cascade to other artifacts, again, under someone else's control. These cross-task dependencies are especially important as they are a common cause of incomplete and incorrect change propagation and require explicit coordination. Along these lines the core research question in this paper is: how can we automatically detect cross-task dependencies and use them to assist the developer? We introduce an approach for mining such dependencies from past developer interactions with engineering artifacts as the basis for live recommending artifacts during change implementation. We show that our approach lists 67% of the correctly recommended artifacts within the top-10 results with real interaction data and tasks from the Mylyn project. The results demonstrate we are able to successfully find not only cross-task dependencies but also provide them to developers in a useful manner.
A Rank-biased Neural Network Model for Click Modeling

Query logs contain rich feedback information from a large number of users interacting with search engines. Various click models have been developed to decode users' search behavior and to extract useful knowledge from query logs. Although the state-of-the-art neural click models have been shown to be very effective in click modeling, the input representations of queries and documents rely on either manually crafted features or on automatic methods suffering from the high-dimensionality issue. Moreover, these neural click models are still rather restrictive when coping with commonly biased user clicks. In this paper, we investigate how to effectively deploy a neural network model for decoding users' click behavior. First, we present two novel rank-biased neural network models ($RBNN$ and $RBNN^* $) for click modeling. The key idea is to deploy different weight matrices across different rank positions. Second, we introduce a new method ($QD\mymathhyphen DCCA$) for automatically learning the vector representations for both queries and documents within the same low-dimensional space, which provides high-quality inputs for $RBNN$ and $RBNN^* $. Finally, a series of experiments are conducted on two different real query logs to validate the effectiveness and efficiency of the proposed neural click models. The experiments demonstrate that: (1) The proposed models can achieve substantially improved performance over the state-of-the-art baseline on two datasets across multiple metrics. By incorporating rank-specific weight matrices, $RBNN$ and $RBNN^* $ are more capable of dealing with the position-bias problem. (2) The input representations of queries, documents and context information significantly affect the performance of neural click models. Thanks to the application of $QD\mymathhyphen DCCA$, not only $RBNN$ and $RBNN^* $ but also the baseline method exhibit enhanced performance. Furthermore, the training cost under the proposed models is greatly reduced.
Integrating Electronic Components into Deformable Objects Based on User Interaction Data

With an increased interest in Organic User Interface, it becomes more and more important to help designers create deformable devices. One current challenge is the integration of rigid and soft electronic components with the device. In this paper, we propose to place electronic components based on how the user is interacting with the device, i.e., in which way the device is being deformed. For this, we developed a prototyping tool that takes as input a set of captured user gestures and a 3D model of the deformable device, and then visualizes the stress distribution resulting from the deformation during interaction. Our tool finally suggests where not to place components because the location is highly deformed when users interact (e.g., where not to place a rigid battery that would constraint interaction); or alternatively where to place components to sense deformation more accurately and efficiently (e.g., a bend sensor to detect a specific gesture, an energy harvesting component). We evaluated our approach by collecting interaction data from 12 users across 3 deformable devices (a watch, camera, and mouse) and applied the resulting stress distributions to the placement of selected electronic components.
Generalization of tumor identification algorithms

The morphological features that pathologists use to differentiate neoplasms from normal tissue are nonspecific to tissue type. For example, if given a Ki67 stained biopsy of neuroendocrine or breast tumor, a pathologist would be able to correctly identify morphologically abnormal cells in both samples but may struggle to identify the origin of both samples. This is also true for other pathological malignancies such as carcinomas, sarcomas, and leukemia. This implies that computer algorithms trained to recognize tumor from one site should be able to identify tumor from other sites with similar tumor subtypes. Here, we present the results of an experiment that supports this hypothesis. We train a deep learning system to distinguish tumor from non-tumor regions in Ki67 stained neuroendocrine tumor digital slides. Then, we test the same, unmodified, deep learning model to distinguish breast cancer from non-cancer regions. When applied to a sample of 96 high power fields, our system achieved a cumulative pixel-wise accuracy of 86% across these high-power fields. To our knowledge, our results are the first to formally demonstrate generalized segmentation of tumors from different sites of origin through image analysis. This paradigm has the potential to help with the design of tumor identification algorithms as well as the composition of the datasets they draw from.
Supporting Feature Model Evolution by Lifting Code-Level Dependencies: A Research Preview

[Context and Motivation] Organizations pursuing software product line engineering often use feature models to define the commonalities and variability of software-intensive systems. Frequently, requirements-level features are mapped to development artifacts to ensure traceability and to facilitate the automated generation of downstream artifacts. [Question/Problem] Due to the continuous evolution of product lines and the complexity of the artifact dependencies, it is challenging to keep feature models consistent with their underlying implementation. [Principal Ideas/Results] In this paper, we outline an approach combining feature-to-artifact mappings and artifact dependency analysis to inform domain engineers about possible inconsistencies. In particular, our approach uses static code analysis and a variation control system to lift complex code-level dependencies to feature models. [Contributions] We demonstrate the feasibility of our approach using a Pick-and-Place Unit system and outline our further research plans.
Zero-Shot Learning Via Recurrent Knowledge Transfer

Zero-shot learning (ZSL) which aims to learn new concepts without any labeled training data is a promising solution to large-scale concept learning. Recently, many works implement zero-shot learning by transferring structural knowledge from the semantic embedding space to the image feature space. However, we observe that such direct knowledge transfer may suffer from the space shift problem in the form of the inconsistency of geometric structures in the training and testing spaces. To alleviate this problem, we propose a novel method which actualizes recurrent knowledge transfer (RecKT) between the two spaces. Specifically, we unite the two spaces into the joint embedding space in which unseen image data are missing. The proposed method provides a synthesis-refinement mechanism to learn the shared subspace structure (SSS) and synthesize missing data simultaneously in the joint embedding space. The synthesized unseen image data are utilized to construct the classifier for unseen classes. Experimental results show that our method outperforms the state-of-the-art on three popular datasets. The ablation experiment and visualization of the learning process illustrate how our method can alleviate the space shift problem. By product, our method provides a perspective to interpret the ZSL performance by implementing subspace clustering on the learned SSS.
The Research on Construction Mode of Business Information System Based on Blockchain Technology

In order to explore the application of blockchain technology in business information system, the category feature and core technology of blockchain technology has been analyzed in this paper. Especially, the research has contained the data sharing interaction, tamper resistance, access rights and timeliness via combining the characteristics of business information system. The deployment application mode and miner distribution method were carried out after a lot of explored research, and some difficult problems that need to be solved were put forward too. Gratefully, the multiple advantages of blockchain technology have brought significant opportunities for business information systems to achieve the goal of acceleration and increasing efficiency.
Reality-Creating Technologies as a Global Phenomenon

Digital reality technologies have become a global phenomenon that attracts huge attention from researchers and practitioners around the world. ResearchandMarkets.com predicts that the global revenue for both augmented reality (AR) and virtual reality (VR) applications will reach $94.4 billion by 2023. As an introductory chapter to the edited book volume on the global impacts of digital reality technologies, this chapter examines the current state of digital reality technologies around the world. Global, regional, and country statistics are presented to shed light on the diffusion of a variety of digital reality technologies such as augmented reality, mixed reality, and virtual reality. Potential and existing digital realty technologies around the world will be examined in greater detail to provide readers with contextual information for the remaining chapters of the book.