Point-Line Visual Stereo SLAM Using EDlines and PL-BoW

Visual Simultaneous Localization and Mapping (SLAM) technologies based on point features achieve high positioning accuracy and complete map construction. However, despite their time efficiency and accuracy, such SLAM systems are prone to instability and even failure in poor texture environments. In this paper, line features are integrated with point features to enhance the robustness and reliability of stereo SLAM systems in poor texture environments. Firstly, method Edge Drawing lines (EDlines) is applied to reduce the line feature detection time. Meanwhile, the proposed method improves the reliability of features by eliminating outliers of line features based on the entropy scale and geometric constraints. Furthermore, this paper proposes a novel Bags of Word (BoW) model combining the point and line features to improve the accuracy and robustness of loop detection used in SLAM. The proposed PL-BoW technique achieves this by taking into account the co-occurrence information and spatial proximity of visual words. Experiments using the KITTI and EuRoC datasets demonstrate that the proposed stereo Point and EDlines SLAM (PEL-SLAM) achieves high accuracy consistently, including in challenging environments difficult to sense accurately. The processing time of the proposed method is reduced by 9.9% and 4.5% when compared to the Point and Line SLAM (PL-SLAM) and Point and stereo Point and Line based Visual Odometry (sPLVO) methods, respectively.

Multi-feature fusion framework for sarcasm identification on twitter data: A machine learning based approach

Sarcasm is the main reason behind the faulty classification of tweets. It brings a challenge in natural language processing (NLP) as it hampers the method of finding people’s actual sentiment. Various feature engineering techniques are being investigated for the automatic detection of sarcasm. However, most related techniques have always concentrated only on the content-based features in sarcastic expression, leaving the contextual information in isolation. This leads to a loss of the semantics of words in the sarcastic expression. Another drawback is the sparsity of the training data. Due to the word limit of microblog, the feature vector’s values for each sample constructed by BoW produces null features. To address the above-named problems, a Multi-feature Fusion Framework is proposed using two classification stages. The first stage classification is constructed with the lexical feature only, extracted using the BoW technique, and trained using five standard classifiers, including SVM, DT, KNN, LR, and RF, to predict the sarcastic tendency. In stage two, the constructed lexical sarcastic tendency feature is fused with eight other proposed features for modelling a context to obtain a final prediction. The effectiveness of the developed framework is tested with various experimental analysis to obtain classifiers’ performance. The evaluation shows that our constructed classification models based on the developed novel feature fusion obtained results with a precision of 0.947 using a Random Forest classifier. Finally, the obtained results were compared with the results of three baseline approaches. The comparison outcome shows the significance of the proposed framework.

Real-Time Sound and Motion Feedback for Violin Bow Technique Learning: A Controlled, Randomized Trial

The production of good sound generation in the violin is a complex task that requires coordination and spatiotemporal control of bowing gestures. The use of motion-capture technologies to improve performance or reduce injury risks in the area of kinesiology is becoming widespread. The combination of motion accuracy and sound quality feedback has the potential of becoming an important aid in violin learning. In this study, we evaluate motion-capture and sound-quality analysis technologies developed inside the context of the TELMI, a technology-enhanced music learning project. We analyzed the sound and bow motion of 50 participants with no prior violin experience while learning to produce a stable sound in the violin. Participants were divided into two groups: the experimental group (N = 24) received real-time visual feedback both on kinematics and sound quality, while participants in the control group (N = 26) practiced without any type of external help. An additional third group of violin experts performed the same task for comparative purposes (N = 15). After the practice session, all groups were evaluated in a transfer phase without feedback. At the practice phase, the experimental group improved their bowing kinematics in comparison to the control group, but this was at the expense of impairing the sound quality of their performance. At the retention phase, the experimental group showed better results in sound quality, especially concerning control of sound dynamics. Besides, we found that the expert group improved the stability of their sound while using the technology. All in all, these results emphasize the importance of feedback technologies in learning complex tasks, such as musical instrument learning.

The Bow, Creator of Sound

The art of playing the Baroque violin is primarily the art of the bow, for without the bow, the violin is silent. This lesson discusses the intrinsic qualities of the old bow and explains why the modern bow is ill suited to the demands of the Baroque repertoire. Quoting from a number of historical sources ( Georg Muffat, John Playford, Bartolomeo Bismantova, Michel Corrette, Roger North, Francesco Geminiani, Leopold Mozart, and L’Abbé le Fils), it provides information on the ways the bow was held and explains how holding the violin in the way advocated in the previous lesson impacts on essential aspects of bow technique and posture.

Bag-of-words from image to speech: a multi-classifier emotions recognition system

Recently, recognizing the emotional content of speech signals has received considerable research attention. Consequently, systems have been developed to recognize the emotional content of a spoken utterance. Achieving high accuracy in speech emotion recognition remains a challenging problem due to issues related to feature extraction, type, and size. Central to this study is increasing emotion recognition accuracy by porting the bag-of-word (BoW) technique from image to speech for feature processing and clustering. The BoW technique is applied to features extracted from Mel frequency cepstral coefficients (MFCC) which enhances feature quality. The study considers deployment of different classification approaches to examine the performance of the embedded BoW approach. The deployed classifiers include support vector machine (SVM), K-nearest neighbor (KNN), naive Bays (NB), random forest (RF), and extreme gradient boosting (XGBoost). In this study, experiments used the standard RAVDESS audio dataset with eight emotions: angry, calm, happy, surprised, sad, disgusted, fearful and neutral. The maximum accuracy obtained in the angry class using SVM was 85%, while overall accuracy was 80.1 %. The empirical works have proved that using BoW achieves better results in terms of accuracy and processing time compared to other available methods.

HENRYK WIENIAWSKI (1835 – 1880) - THE LAST VIRTUOSE OF VIOLIN

It is a study aiming to reveal the unknown points about Henryk Wieniawski (1835 – 1880), a Polish violin virtuoso and composer who made her mark on the 19th century. His life, the concept of virtuoso, Polish violin virtuosos in the 17th and 18th centuries, 19th Century violin virtuosos were processed His contributions to the violin playing art, the bow technique and works of the violin are mentioned.and the critics about it are given. The reasons for being the last violin virtuoso are examined. Keywords: Henryk Wieniawski, violin, virtuoso

Text Classification Using SVM with Exponential Kernel

Text classification presents difficult challenges due to the high dimensionality and sparsity of text data, and to the complex semantics of the natural language. Typically, in text classification the documents are represented in the vector space using the Bag of words (BoW) technique. Despite its ease of use, BoW representation does not consider the semantic similarity between words. In this paper, we overcome the shortage of the BoW approach by applying the exponential kernel, which models semantic similarity by means of a diffusion process on a graph defined by lexicon and co-occurrence information, to enrich the BoW representation. Combined with the support vector machine (SVM), experimental evaluation on real data sets demonstrates that our approach successfully achieves improved classification accuracy with respect to the BoW approach.

Characterization of a Silicon Nitride Mask Membrane Process

A silicon nitride membrane growth process has been characterized. The films were grown by LPCVD on 100 mm diameter silicon substrates using ammonia and dichlorosilane as reactant gases. The films were grown using a range of gas ratios at three different temperatures. The film composition was determined by elastic recoil detection. The silicon-nitrogen absorption bands were characterized by FTIR spectroscopy. The photon transmission of the films was measured in the visible region from 350 – 850 nm, and in the x-ray region for photon energies from 1000 to 3000 eV. The film stresses were determined using the wafer bow technique. An increase in the silicon content of the films was observed for increased dichlorosilane gas flow and for increasing growth temperatures. The increased silicon content of the films is correlated to a decrease in the tensile stress and a decrease in the optical transmission of the films.