Reviewerly/exHarmony huggingface dataset:
Dynamic Reconfiguration of Smart Sensors: A Semantic Web Based Approach

This paper aims to tackle issues related to the (re-)configuration of Smart Objects. These latter are composed specifically of smart sensors that embed basic sensors, a micro-controller and software snippets as well. In order to make this (re-)configuration feasible at runtime, we propose a semantic Web based approach that relies on a set of ontology modules together with a set of logical rules and reasoning processes to drive the (re-)configuration mechanism while taking into account, at the same time, requirements of the application domain. We validate our approach by means of a prototype that shows relevance of developed ontologies and (re-)configuration mechanism.
A Hybrid Stemmer for the Affix Stacking Language: Marathi

Stemming is the process of term conflation that reduces the morphological variations of the terms to their common stem. It plays a significant role during preprocessing in most of the natural language processing, text mining, and information retrieval applications. The use of stemmers has proven highly effective for the task of information retrieval for many languages like English and Arabic. This paper focuses on the development of automated stemmer for Marathi language. We have adopted a hybrid technique for the development of proposed stemmer. The goal of this work is to overcome the limitations of the existing stemmers available for Marathi and to enhance the accuracy of Marathi stemming. The proposed stemmer is tested on Marathi news articles and the evaluation of the work shows that significant improvement is obtained in the accuracy, due to the proposed hybrid stemmer over the existing rule-based stemmer. We have achieved an average accuracy of 84.82% with the proposed hybrid stemmer for Marathi.
Visual Feedback System for Traditional Chinese Medical Massage Robot

Traditional Chinese medicine physiotherapy is an important part of Chinese medical culture. It can prevent and treat diseases by acupoints massage. However, the number of doctors who are proficient in acupoints is insufficient to meet the demand. It is necessary to use traditional Chinese medicine massage robot to complete this work. The method of teaching or offline programming is widely used in the general massage robot, which greatly reduces the flexibility of robots. Once the human body moves, the location is not accurate, and the real-time detection and recognition of acupoints cannot be performed. Considering the small size and the complex information of acupoints, this paper uses QR code for acupoints identification, which is convenient and laconic. The QR code is printed on A4 paper at 1x1 cm scale, and captured though camera. As for recognition, some methods of open source, such as Zbar, Zxing, opencv4.0, even Wechat are used, but the QR code recognition is inaccurate. We design a new two-dimensional code dedicated to acupoints location, with CRC check function, and a new two-dimensional code detection and recognition algorithm is proposed. In this paper, the camera calibration algorithm and the distance measurement model are designed to convert the detected recognition results to the world coordinate system. In the experiment, the measurement error of depth is controlled within ± 1.7 mm, and the measurement error of horizontal distance is controlled within ± 0.8 mm. The experiment verifies the effectiveness of the algorithm.
Accelerated proximal incremental algorithm schemes for non-strongly convex functions

There have been a number of recent advances in accelerated gradient and proximal schemes for optimization of convex finite sum problems. Defazio introduced a simple accelerated scheme for incremental stochastic proximal algorithms inspired by gradient based methods like SAGA. He was able to prove O(1/k) convergence for non-smooth function but only under the assumption of strong convexity of component terms. We introduce a slight modification of his scheme, called MP-SAGA for which we can prove O(1/k) convergence without strong convexity, but for smooth functions. Numerical results show that our method has better or comparable convergence to Defazio's scheme, even for non-strongly convex functions. As important special cases, we also derive an accelerated schemes for a multi–class formulation of SVM as well as clustering based on the SON regularization. Finally, we introduce a simplification of Point–SAGA, called SP–SAGA for problems such as SON with large number of variables and sparse relation between variables and objective terms.
Clustering and visualization of a high-dimensional diabetes dataset

Data clustering algorithms have proved to be important and widely used methods of artificial intelligence and data mining for discovering unknown yet important patterns in datasets. Nevertheless, one of the additional aspects of data clustering is proper interpretation of the clustering results. In this paper we aim to investigate possibilities of using both data clustering and visualization methods to analyze a sample diabetes dataset. In the first part, we focus on how to cluster a highly-dimensional sample dataset and then, we concentrate on how to properly visually present the clustering results in the most meaningful way to uncover potentially interesting behavioral patterns or features of diabetes patients. In this work we examine two clustering algorithms (DBSCAN, k-Means) along with several different distance measures. We also present sample visualizations of clustering results generated by an application which we have developed and discuss if the proposed way of clustering results visualization can be helpful in understanding the analyzed dataset and lead a viewer to drawing valuable conclusions about it.
Crowd Counting with Segmentation Map Guidance

As one of the fundamental vision tasks, crowd counting has attracted a tremendous amount of efforts and achieved significant improvement over the decades. To be applied in real-world applications, the techniques of crowd counting need to be robust and efficient. However, the state-of-the-art works may easily fail in front of an extremely complex background. The reason is that the background is very similar to the small congested heads under some circumstances. To this end, we propose a new deep model to integrate a segmentation map to compensate for the false response under a complex environment. Additionally, we propose a new method to generate segmentation ground truth merely based on the density map instead of manual labeling. Besides, we modify the geometry-adaptive method for density generation employed in the highly congested area. Experiments on ShanghaiTech dataset demonstrate the advantage of the proposed models over the state-of-the-art.
Visual tracking enhancement by trajectory simulation based on hidden semi‐Markov model

In this Letter, the authors present a novel tracking system, in which tracking accuracy can be enhanced by trajectory simulation. They generate synthetic trajectories based on observed trajectories by adopting the hidden semi-Markov model (HSMM). In the course of trajectory simulation, HSMM can encode representative states and speeds of the targets. The simulated trajectories enforce the proposed tracker to focus on the areas where targets will move at following frames. Experimental results demonstrate that it is easy to integrate the proposed trajectory simulation into traditional visual tracking methods and the trajectory simulation can considerably improve the accuracy of visual trackers.
Developing Dementia Personas for User Centered Architectural Design Considerations in Non-specialized Contexts

This paper is concerned with dementia persona development as a research and design tool to help architects and designers to uncover important information towards design processes and decisions in practice. Architects design spaces for specific functions, but do they truly consider integrating these objectives with a focus on creating meaningful spaces for people with dementia while designing and if so, on what grounds. The reason for using dementia personas and not directly approaching people with dementia is due to the fact that it can be very hard to understand the needs of dementia care as people with dementia are dependent on caregivers and family members, in addition to this many designers and architects do not have ethical clearance to work with people living with dementia; as a consequence of their designation. A literature analysis and participatory workshops were used to develop the dementia personas discussed in this paper. The process of developing dementia personas posed many challenges; iterative revisions had to be made to make the personas relatable and concrete enough to be used as a successful design tool. The complex context of the case requires more personas to represent the diversity of persons with dementia in the service provision on different levels and this is the start of the persona development process. The findings are reported herein.
Improving explicit term matching with implicit topic matching for short text conversation

ABSTRACT Short Text Conversation (STC) is the task of retrieving comments made to a post and reuse them as appropriate comments to a later post. Large vocabulary variations and very limited length of posts and comments pose great challenges to traditional retrieval techniques of matching terms. To address these problems, we propose a new technique of re‐ranking comments initially retrieved through explicit term matching by using evidence of implicit topic matching gained from topic models of large collections of posts and comments. The key of our technique lies in (1) combining each post with the comments responding to it into a longer pseudo‐document for topic modeling purposes, and (2) carrying out implicit topic matching by comparing the most probable topics contained in the topic composition of a comment and that of the query post. Experiments using the NTCIR‐13 STC Chinese WeiBo collection showed that with properly tuned parameters, our technique can lead to improved retrieval effectiveness. The paper analyzes and discusses the results and outlines future work.
A Sampling-Based Approach for Discovering Subspace Clusters

Subspace clustering aims to discover clusters in projections of highly dimensional numerical data. In this paper, we focus on discovering small collections of interesting subspace clusters that do not try to cluster all data points, leaving noisy data points unclustered. To this end, we propose a randomised method that first converts the highly dimensional database to a binarised one using projected samples of the original database. This database is then mined for frequent itemsets, which we show can be translated back to subspace clusters. In our extensive experimental analysis, we show on synthetic as well as real world data that our method is capable of discovering highly interesting subspace clusters.
Semi-supervised Auto-encoder Based Event Detection in Constructing Knowledge Graph for Social Good

Knowledge graphs have recently been extensively applied in many different areas (e.g., disaster management and relief, disease diagnosis). For example, event-centric knowledge graphs have been developed to improve decision making in disaster management and relief. This paper focuses on the task of event detection, which is the precondition of event extraction for constructing event-centric knowledge graphs. Event detection identifies trigger words of events in the sentences of a document and further classifies the types of events. It is straightforward that context information is useful for event detection. Therefore, the feature-based methods adopt cross-sentence information. However, they suffer from the complication of human-designed features. On the other hand, the representation-based methods learn document-level embeddings, which, however, contain much noise caused by unsupervised learning. To overcome these problems, in this paper we propose a new model based on Semi-supervised Auto-Encoder, which learns Context information to Enhance Event Detection, thus called SAE-CEED. This model first applies large-scale unlabeled texts to pre-train an auto-encoder, so that the embeddings of segments learned by the encoder contain the semantic and order information of the original text. It then uses the decoder to extract the context embeddings and fine-tunes them to enhance a bidirectional neural network model to identify event triggers and their types in sentences. Through experiments on the benchmark ACE-2005 dataset, we demonstrate the effectiveness of the proposed SAE-CEED model. In addition, we systematically conduct a series of experiments to verify the impact of different lengths of text segments in the pre-training of the auto-encoder on event detection.
Climate Variability and Renewable Energy Planning

Abstract The impact of climate change is a global event. Its significant role in the climate system altered meteorological parameters over geographical locations. MISR satellite data have shown that climatic variability has impact on rainfall and surface vegetation; and increased aerosol loading and solar radiation. This research comparatively compared the meteorological parameters as well as its potentially accruable energy. The dataset was obtained from the automatic weather station (AWS). The location of research is Ota, southwest region of Nigeria. The research recommends that a more sophisticated technology may be required to boost wind and solar energy generation in the area.
A Visualization Method of Facial Expression Deformation Based on Pore-Scale Facial Feature Matching

Facial expression is the main research content of acquiring human psychology and developing human-computer interaction. The expression is made up of deformation and movement of each facial muscle, especially the un-posed micro expression is difficult to study because of the small degree of deformation. How to find an intuitive and interpretable method to visualize the deformation of facial expressions is an important and neglected task. In this paper, we present a new method of facial expression deformation visualization based on pore-scale facial feature matching. We use PSIFT (Pore Scale-Invariant Feature Transform) to extract a large number of facial pore-scale keypoints. Then GMS (Grid-based Motion Statistics) and VFC (Vector Field Consensus) are used to get robust matching pairs of facial feature points, so that to obtain multi-frame pore-scale keypoints tracking trajectories with time. Based on Munsell Color System, we transform matching groups into displacement vector field, Gaussian filter kernel and grid-based principal direction vector are computed to visualize the amplitude and direction of facial muscle movement. Compared with other methods of displacement field visualization (such as Horn-Schunck Optical Flow, SIFT Flow and Line Integral Convolution), our method achieves the best visualization effect in the field of facial expressions deformation on BP4D database and the collected high resolution facial expression video, the research on this subject conducive to the further development of others, such as facial expression recognition, expression modeling, expression generation and so on.
The Merits of Bitset Compression Techniques for Mining Association Rules from Big Data

The massive amounts of data being generated in human’s world today may not be harnessed unless efficient and high-performance processing techniques are employed. As a result, continuous improvement in data mining algorithms and their efficient implementations is actively pursued by researchers. One of the widely applied big data mining tasks is the extraction of association rules from transactional datasets. ECLAT is an algorithm that can mine frequent itemsets as a basis for finding such rules. Since this algorithm operates on vertical representation of a dataset, its implementation may be significantly enhanced by employing sparse bitset compression. This paper studies the performance of four different bitset compression techniques proposed by researchers, using both real-world and synthetic big datasets. The effect of input data characteristics is analyzed for these compression methods in terms of energy consumption, performance, and memory usage behavior. Experimental results can guide the implementations to choose the proper compression method that best fits the problem requirements. The source code of this study is made available at https://github.com/fadishei/biteclat .
An Improved Motion Capture System for Multiple Wheeled Mobile Robots Based on KCF and GMM

With the vigorous development of multi-agent collaborative technology, autonomous position for multiple wheeled mobile robots is becoming more and more important. Motion capture system is a kind of available high-precision position measurement system, where the objects are visually tracked based on kernelized correlation filter (KCF) and then navigated. However, KCF usually behaves badly in the presence of multi-scale and it cannot correct errors itself during the tracking process. To overcome this disadvantage, this paper proposes an improved motion capture system for multiple wheeled mobile robots based on KCF and Gaussian mixture model (GMM). The GMM is used to re-detect and correct the tracking model in KCF tracking process, which enables the tracking algorithm robust to the scale variation of tracking objects. Experiments show that the proposed method has better long-term tracking performance in the case of scale variation for motion capture system.
Robots in challenging environments

The work describes the research advances by the Robotics, Perception and Real-Time Group of University of Zaragoza (Spain) in the field of ground robotics in confined environments. This type of environment presents several special characteristics that often make well-established techniques fail. The aim is to present the main challenges, the developed solutions, and the lessons learned.
Motor unit number estimation via sequential Monte Carlo

A change in the number of motor units that operate a particular muscle is an important indicator for the progress of a neuromuscular disease and the efficacy of a therapy. Inference for realistic statistical models of the typical data produced when testing muscle function is difficult, and estimating the number of motor units is an ongoing statistical challenge. We consider a set of models for the data, each with a different number of working motor units, and present a novel method for Bayesian inference based on sequential Monte Carlo. This provides estimates of the marginal likelihood and, hence, a posterior probability for each model. Implementing this approach in practice requires a sequential Monte Carlo method that has excellent computational and Monte Carlo properties. We achieve this by benefiting from the model’s conditional independence structure, where, given knowledge of which motor units fired as a result of a particular stimulus, parameters that specify the size of each unit’s response are independent of the parameters defining the probability that a unit will respond at all. The scalability of our methodology relies on the natural conjugacy structure that we create for the former and an enforced, approximate, conjugate structure for the latter. A simulation study demonstrates the accuracy of our method, and inferences are consistent across two different datasets arising from the same rat tibial muscle.
The method of multidimensional approach to text summarization

Nowadays, the amount of different type of data for analysis is growing at an alarming rate. Moreover, the size and quantity of textual materials make the extraction of specific information from them more and more complicated and sometimes impossible. The use of external data sources, Linked Data and Online Analytical Processing (OLAP) presents a powerful solution for this task. This study discusses the approach of creating an OLAP interface based on Linked Data and SPARQL and linking them to a summarized text document, which will increase the number of easy accessible information from text.
An Improved Neighborhood-Based Recommendation Algorithm Optimization with Clustering Analysis and Latent Factor Model

With the development of Internet, information overload about products is pervasive. It is important for commercial platforms to predict users' preferences and recommend information. Neighborhood-based recommendation algorithm is one of the most popular methods, which are used to predict the rating of items that have not yet been rated. However, neighborhood-based algorithm suffers from data sparsity in practice, causing low accuracy. A recommendation algorithm is proposed in this paper, in which auxiliary information of users and items are utilized. Specifically, clustering method is introduced to divide users into categories, which reduces the time complexity of the algorithm, and latent factor model approach is applied to predict user-item matrix, which improves the accuracy of neighborhood-based algorithm. Experiments on real-world dataset demonstrate that the proposed KC-LFMCF approach is superior to the conventional neighborhood-based algorithm in terms of recommendation accuracy and time complexity.
Robotics Against the Evidence

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Decomposition-based Multi-objective Backtracking Search Algorithm for Personalized Recommendation

In this paper, a decomposition-based multi-objective backtracking search algorithm (DMOBSA) is proposed for personalized recommendation (PR) problems. In the proposed DMOBSA-PR, an effective individual representation is designed based on the characteristics of PR problems. Moreover, the new crossover and mutation operation in the framework of the BSA algorithm are introduced to update individuals. Furthermore, a decomposition-based multi-objective framework is utilized to balance accuracy and diversity metrics. Finally, the simulation results on Movielens 100K dataset show that DMOBSA-PR is effective compared with other algorithms.