Reviewerly/exHarmony huggingface dataset:
An Improved Resampling Scheme for Particle Filtering in Inertial Navigation System

The particle filter provides numerical approximation to the nonlinear filtering problem in inertial navigation system. In the heterogeneous environment, reliable state estimation is the critical issue. The state estimation will increase the positioning error in the overall system. To address such problem, the sequential implementation resampling (SIR) considers cause and environment for every specific resampling task decision in particle filtering. However, by only considering the cause and environment in a specific situation, SIR cannot generate reliable state estimation during their process. This paper proposes an improved resampling scheme to particle filtering for different sample impoverishment environment. Adaptations relating to noise measurement and number of particles need to be made to the resampling scheme to make the resampling more intelligent, reliable and robust. Simulation results show that proposed resampling scheme achieved improved performance in term of positioning error in inertial navigation system In conclusion, the proposed scheme of sequential implementation resampling proves to be valuable solution for different sample impoverishment environment.
Lattice-Based Searchable Encryption With Keywords Revocable and Bounded Trapdoor Exposure Resistance

Searchable encryption plays a vital role in keeping data privacy of current cloud storage. Considering the security threat of trapdoor exposure, Emura et al. introduced a formal definition of keywords revocable public-key encryption with keywords search (KR-PEKS), which satisfies trapdoor exposure resistance (TER) and trapdoor re-generability. However, there is little work on lattice-based searchable encryption schemes to have the above properties, while it was widely accepted that cryptosystem on lattice has the potential to resist quantum computing attack. In this paper, we give a model of KR-PEKS with novel security, called bounded TER, i.e., a bounded number (previously set) of trapdoors for per keyword are exposed, which cannot affect other keywords. Then, we propose a lattice-based KR-PEKS scheme with bounded TER and trapdoor re-generability and show that this scheme is secure under the hardness assumption of learning with errors (LWE) problem. Finally, we conduct some simulation experiments on our scheme and give a comparison, which indicates that our scheme has less computational cost than the prior arts.
Antimode collapse generative adversarial networks

Generative adversarial networks (GANs) are a class of techniques widely applied in image synthesis, recovery, compensation, and other related fields. We propose and introduce a novel, improved conditional model antimode collapse GANs (AMCGANs). Through a newly designed network architecture and optimizing strategy, the function of the class label information is moderately constrained and it no longer directly influences the discriminator, and hence the synthesized images belonging to the same classes will not be excessively concentrated due to the attraction of the same labels. Thus, the mode collapse problem, namely generating homogeneity, which always hinders image synthesization approaches can be effectively restrained. On the other hand, by sharing the feature extraction part and only updating its weights during the training of discriminator, AMCGANs achieves relatively efficient computing performance. In addition, it works well both for supervised and for semisupervised learning circumstances. Extensive experiments have been conducted on the Fashion-MNIST and CIFAR10 data sets to verify the effectiveness of the proposed approach.
Sub-hypergraph matching based on adjacency tensor

Point correspondence problem is an important problem in pattern recognition and computer vision, which can be solved by graph matching. Recently, high order graph matching methods have attracted much attention due to their robustness to geometric transformations. Since high order graph matching usually suffers from high complexity, we previously proposed an adjacency tensor based algorithm, which effectively reduced the complexity of high order graph matching, especially high storage complexity. However, this method can only be applied to equal sized hypergraphs, and it cannot be directly extended to hypergraphs with outliers which are common in real world tasks. Aiming at this problem, in this paper we propose a third order subgraph matching method by extending our previous method to deal with partial point correspondence problem with outliers. Specifically, first a novel objective function focusing on the outlier problem is proposed, by encoding the attributes in a hypergraph with an adjacency tensor, and representing vertex assignments with a partial permutation matrix. Then the objective function is transformed and relaxed to a tractable matrix form and solved by a gradient based optimization algorithm. Consequently, the proposed algorithm can not only tackle the outlier vertices in the hypergraphs, but also involve the same low computational and storage complexities with our previous algorithm. Both synthetic data and real image comparisons with the state-of-the-art methods validate the effectiveness of the proposed method.
Retrieving Relationships from a Knowledge Graph for Question Answering

Answering natural language questions posed on a knowledge graph requires traversing an appropriate sequence of relationships starting from the mentioned entities. To answer complex queries, we often need to traverse more than two relationships. Traditional approaches traverse at most two relationships, as well as typically first retrieve candidate sets of relationships using indexing etc., which are then compared via machine-learning. Such approaches rely on the textual labels of the relationships, rather than the structure of the knowledge graph. In this paper, we present a novel approach KG-REP that directly predicts the embeddings of the target relationships against a natural language query, avoiding the candidate retrieval step, using a sequence to sequence neural network. Our model takes into account the knowledge graph structure via novel entity and relationship embeddings. We release a new dataset containing complex queries on a public knowledge graph that typically require traversal of as many as four relationships to answer. We also present a new benchmark result on a public dataset for this problem.
RETRACTED ARTICLE: The sustainable development information management of Winter Olympics based on Internet-based wireless sensor network

Abstract Environment is the foundation for human survival. Sustainable development is the main direction of social progress and the common choice of human progress. The development of the Winter Olympic Games is closely related to the progress of mankind and society. Therefore, it is particularly important to study the sustainable development of the Winter Olympic Games. Based on this, an Internet-based wireless sensor for Winter Olympic environment information acquisition scheme is proposed. The hardware and sensor modules of the wireless sensor are designed, and the adaptive weighting algorithm is used to fuse several wireless sensor nodes. By testing and analyzing the communication ability of a single wireless sensor node and the error rate under different distance, the data is collected for the environment information of a Winter Olympic sports field. The results of the data collection are very close to the actual environmental parameters. It shows that the wireless sensor network system based on the Internet has strong stability and reliability in data collection. It can provide effective data support for the information management research of sustainable development of Winter Olympic Games.
Topic Attentional Neural Network for Abstractive Document Summarization

Abstractive summarization is a renewed and challenging task of document summarization. Recently, neural networks, especially attentional encoder-docoder architecture, have achieved impressive progress in abstractive document summarization. However, the saliency of summary, which is one of the key factors for document summarization, still needs improvement. In this paper, we propose Topic Attentional Neural Network (TANN) which incorporates topic information into neural networks to tackle this issue. Our model is based on attentional sequence-to-sequence structure but has paired encoders and paired attention mechanisms to deal with original document and topic information in parallel. Moreover, we propose a novel selection method called topic selection. This method uses topic information to improve the standard selection method of beam search and chooses a better candidate as the final summary. We conduct experiments on the CNN/Daily Mail dataset. The results show our model obtains higher ROUGE scores and achieves a competitive performance compared with the state-of-the-art abstractive and extractive models. Human evaluation also demonstrates our model is capable of generating summaries with more informativeness and readability.
Predicting HDR Cinema Requirements from HDR Home Master Statistics

The high-dynamic-range (HDR) home video format launched several years ago has been adopted by the industry such that HDR Home Masters are created for most new release titles and released for home video distribution using ultrahigh-definition Blu-ray and digital formats. Mastering titles in HDR Cinema is the next logical evolution toward higher quality content and is thus greatly anticipated. However, mastering for HDR Cinema is still experimental due to the wide variety of potential HDR Cinema display technologies and their unique dynamic range characteristics. Determining the required dynamic range for HDR Cinema Masters is thus of great interest. In this paper, we propose an estimation of two properties of the dynamic range required for HDR Cinema Masters based on two different data sets. The first data set corresponds to the dynamic range statistics and cumulative distribution function of 41 Warner Bros. HDR Home Masters. Analyzing this data set indicates that the maximum light level of a frame (the peak luminance value) is directly related Average Light Level (FALL). In other words, there is a maximum contrast between the intensity of the highlights in an image and the average intensity of the image. The second data set is composed of matching mastered grades for standard dynamic range (SDR) cinema (Digital Cinema Initiative), Enhanced Dynamic Range Cinema (Dolby Cinema theatrical release), and HDR Home formats. Paired matching of content is useful to derive a translation between grades. By performing a cross-analysis between SDR and Enhanced Dynamic Range (EDR) Cinema content, we estimate a trend on the evolution of the FALL statistic when a higher dynamic range is provided. A second cross-analysis between HDR Home and EDR Cinema characterize the translation of midtones between small screens with surround illumination to big screens in a dark-viewing environment. Using the different analysis, we predict two optimized properties of the dynamic range needed to represent most future HDR Cinema mastered content.
Feature Extraction of Normalized Colorectal Cancer Histopathology Images

This paper presents different types of feature extraction of normalized colorectal cancer histopathology images. These highlights are exceptionally helpful for separating epithelium and stroma in colorectal cancer (CRC) histopathology images. It is also useful for selecting features and its analysis. In this paper, 27 features are extracted in which 5 are the visual texture features and 22 are the other features such as GLCM, run length and intensity-based features to separate epithelium from the stroma of Colorectal Cancer histopathology images. The utilized component has straightforwardly identified with the human recognition which makes it conceivable to distinguish the nearness of tissue based on parameters. The quantity of utilized highlights is little to differentiate the epithelium from the stroma of CRC histopathology images. In the simulation, we use well-defined and verified histopathology images of stroma and epithelium to correctly differentiate epithelium from stroma. The textural features measure provides the excellent result for 16 typical texture patterns. The issue emerges between the human vision, and modernized strategies that are experienced in this examination show the central point in dissecting of the surface. In which, some of them has removed by using better techniques. In conclusion, perception-based features work well in comparison to previously features used. Some modification like colour normalization of epithelium and stroma image and some of the new features are added because the classification of perception-based features is less.
Interactive Genetic Algorithm Joining Recommender System

The recommender systems tend to face the problem of lacking clues about the new user, as a result, it is difficult for the system to provide users with the right recommendation results. In this study, the Interactive genetic algorithm is adapted to join with recommender system, and this new system is used to solve the problem of the traditional recommender system. In addition, this study proposes a double-layer encoding structure that involves the global and area encoding, which will help the optimization of interactive genetic algorithm (IGA) to solve the user fatigue problem. The case study testified that this recommended framework was able to rapidly filter vast amounts of data and to offer personalized recommendations for each film viewer. For future research, further studies on IGA that integrates analysis of screen genes to achieve a higher quality of recommendation and to further solve IGA user fatigue issues. Finally, this new recommender system was used to test film viewers in Taiwan and Hollywood films released in the past 12 years, and the experimental results show evidence that the new system is useful.
<i>La prise de l’IRCAM?</i> Spanish Composers Facing New Technologies

Since opening in 1977, IRCAM has been a prominent meeting point for international composers. Nevertheless, the Spanish presence at the French institution was rather rare during the twentieth century; we need to wait until the twenty-first century to detect a larger number of Spaniards developing their creative projects at IRCAM. After a succinct overview of the history of Spanish electroacoustic music, I will study the evolution of the presence of Spaniards at IRCAM—from attending courses, to commissions and productions, to engagement with the pedagogical team—willing to detect any possible tendencies or fashions over time. In particular, I will focus on two different moments: I will observe José Manuel López López’s work as the first Spanish pupil at the French institute in the early 1990s, and I will showcase some recent projects by young Spanish composers with rising careers. Both sides will demonstrate the rapid evolution of technological novelties ‘made at IRCAM’ within the last 25 years.
Performance Scaling of Cassandra on High-Thread Count Servers

NoSQL databases are commonly used today in cloud deployments due to their ability to "scale-out" and effectively use distributed computing resources in a data center. At the same time, cloud servers are also witnessing rapid growth in CPU core counts, memory bandwidth, and memory capacity. Hence, apart from scaling out effectively, it's important to consider how such workloads "scale-up" within a single system, so that they can make the best use of available resources. In this paper, we describe our experiences studying the performance scaling characteristics of Cassandra, a popular open-source, column-oriented database, on a single high-thread count dual socket server. We demonstrate that using commonly used benchmarking practices, Cassandra does not scale well on such systems. Next, we show how by taking into account specific knowledge of the underlying topology of the server architecture, we can achieve substantial improvements in performance scalability. We report on how, during the course of our work, we uncovered an area for performance improvement in the official open-source implementation of the Java platform with respect to NUMA awareness. We show how optimizing this resulted in 27% throughput gain for Cassandra under studied configurations. As a result of these optimizations, using standard workload generators, we obtained up to 1.44x and 2.55x improvements in Cassandra throughput over baseline single and dual-socket performance measurements respectively. On wider testing across a variety of workloads, we achieved excellent performance scaling, averaging 98% efficiency within a socket and 90% efficiency at the system-level.
Quality, Context, and Information Fusion

Context has received significant attention in recent years within the information fusion community as it can bring several advantages to fusion processes by allowing for refining estimates, explaining observations, constraining computations, and thereby improving the quality of inferences. At the same time, context utilization involves concerns about the quality of the contextual information and its relationship with the quality of information obtained from observations and estimations that may be of low fidelity, contradictory, or redundant. Knowledge of the quality of this information and its effect on the quality of context characterization can improve contextual knowledge. At the same time, knowledge about a current context can improve the quality of observations and fusion results. This chapter discusses the issues associated with context exploitation in information fusion, understanding and evaluating information quality in context and formal context representation, as well as the interrelationships among context, context quality, and quality of information. The chapter also presents examples of utilization of context and information quality in fusion applications.
Effects of weighting the ends of a tubular bell on modular frequencies

This paper presents a mathematical analysis of a modified tubular bell. Tubular bells are often modeled as a vibrating beam with free boundary conditions. However, most orchestral tubular bells contain a single cap attached to one end of the tube. This cap affects both the strike note as well as the relative frequencies of the overtones. However, the literature contains little information regarding the relationship between the mass of the cap and the modal frequencies. This analysis may be used to determine how the presence of the one or two caps affects both the strike note and the ratio of the modular frequencies of a tubular bell.
Conflict Measures and Importance Weighting for Information Fusion Applied to Industry 4.0

Information sources such as sensors, databases, and human experts serve as sources in order to realise condition monitoring and predictive maintenance in Industry 4.0 scenarios. Complex technical systems create a large amount of data which cannot be analysed manually. Thus, information fusion mechanisms gain increasing importance. Besides the management of large amounts of data, further challenges towards the fusion algorithms arise from epistemic uncertainties (incomplete knowledge) and—mostly overseen—conflicts in the input signals. This contribution describes the multilayered information fusion system MACRO (multilayer attribute-based conflict-reducing observation) employing the BalTLCS (balanced two-layer conflict solving) fusion algorithm to reduce the impact of conflicts on the fusion result by a quality measure which is denoted by importance. Furthermore, we show that the numerical stability in heavy conflicts is a key factor in real-world applications. Different examples end this contribution.
Experimental Setup: Visual and Text Sentiment Analysis Through Hierarchical Deep Learning Networks

The experimental setup consists of performing visual and text sentiment analysis through hierarchical based deep learning networks. A brief discussion on the deep learning networks is presented for the interested readers. The cross-media bag-of-words model (CBM) is used as the baseline method. The basic aspects of the gated feedforward recurrent neural networks (GFRNN) are illustrated. The mathematical abstraction of HGFRNN is vividly explained. The chapter concludes with hierarchical gated feedforward recurrent neural networks for multimodal sentiment analysis.
Predicting the Topic of Your Next Query for Just-In-Time IR

Proactive search technologies aim at modeling the users' information seeking behaviors for a just-in-time information retrieval (JITIR) and to address the information needs of users even before they ask. Modern virtual personal assistants, such as Microsoft Cortana and Google Now, are moving towards utilizing various signals from users' search history to model the users and to identify their short-term as well as long-term future searches. As a result, they are able to recommend relevant pieces of information to the users at just the right time and even before they explicitly ask (e.g., before submitting a query). In this paper, we propose a novel neural model for JITIR which tracks the users' search behavior over time in order to anticipate the future search topics. Such technology can be employed as part of a personal assistant for enabling the proactive retrieval of information. Our experimental results on real-world data from a commercial search engine indicate that our model outperforms several important baselines in terms of predictive power, measuring those topics that will be of interest in the near-future. Moreover, our proposed model is capable of not only predicting the near-future topics of interest but also predicting an approximate time of the day when a user would be interested in a given search topic.
Comparison of Agile, Quasi-Agile and Traditional Methodologies

In this study we were able to gather a substantial quantity of detailed responses from a group of individuals and companies that are broadly quite similar to those found in several of the major world centers of technological innovation. As such, our analysis of the results provides some tantalizing hints to organizational and methodological challenges and practices of a broad range of groups. One intriguing suggestion is that while “traditional” and well defined Agile groups function according to the standards established to support those approaches, Quasi-Agile groups do not. Instead, Quasi-Agile groups seem to pursue the goals of Agile using measures and underlying approaches more similar to traditional methods. One might expect that such a discordance to affect the effectiveness of a group’s efforts.
EFCNN: A Restricted Convolutional Neural Network for Expert Finding

Expert finding, aiming at identifying experts for given topics (queries) from expert-related corpora, has been widely studied in different contexts, but still heavily suffers from low matching quality due to inefficient representations for experts and topics (queries). In this paper, we present an interesting model, referred to as EFCNN, based on restricted convolution to address the problem. Different from traditional models for expert finding, EFCNN offers an end-to-end solution to estimate the similarity score between experts and queries. A similarity matrix is constructed using experts' document and the query. However, such a matrix ignores word specificity, consists of detached areas, and is very sparse. In EFCNN, term weighting is naturally incorporated into the similarity matrix for word specificity and a restricted convolution is proposed to ease the sparsity. We compare EFCNN with a number of baseline models for expert finding including the traditional model and the neural model. Our EFCNN clearly achieves better performance than the comparison methods on three datasets.