Reviewerly/exHarmony huggingface dataset:
Collision Avoidance Method for UAV Using A* Search Algorithm

In this paper, we proposed a new collision avoidance algorithm that is utilization of A* algorithm. And, we analyzed the existing research such as Search Tree problem, total field sensing, and A* algorithm. In general, the A* algorithm is often used as a path finding algorithm in a static environment in mobile robots. We introduce several approaches to using the A* algorithm in environments with many moving objects. We will explain experimental results with several set-up and heuristic methods in various environments. We discuss the performance of the proposed method, the appropriate conditions for its operation, and what issues can affect performance. Finally, we will analyze the experimental results and identify the limitations of the proposed algorithm.
Optimization of ROI Size for Development of Computer Assisted Framework for Breast Tissue Pattern Characterization Using Digitized Screen Film Mammograms

In this work exhaustive experimentations have been performed for the selection of optimum region of interest (ROI) size for development of a computer assisted framework for breast tissue pattern characterization using digitized screen film mammograms. The classification of breast density or breast tissue pattern depends upon (a) the location and (b) the size of ROI. It has been well-established that the maximum information about the breast density is present at the heart of the breast where the glandular tissues are present in rich amount, thus in this study various experiments have been conducted to check the result of varying ROI size cropped from glandular ducts area for 4-class breast tissue pattern and 2-class breast tissue pattern characterization tasks using various texture feature models. These experiments have been conducted on digital database for screening mammography (DDSM) dataset mammograms. However, for 2-class breast tissue pattern characterization task, images belonging to {BIRADS-I, BIRADS-II} classes have been considered in “fatty” class and images belonging to {BIRADS-III, BIRADS-IV} classes have been considered in “dense” class. The work has been carried out on a total of 480 images, that is, 120 images from each image class (120 × 4 = 480). From each image, ROI of different sizes, that is, 32 × 32, 64 × 64, 128 × 128, and 256 × 256 pixels have been manually extracted from the core region of the breast tissue where glandular tissues are available in prominent amount. Various texture features have been extracted from each ROI using (a) statistical texture feature models, (b) signal processing based texture feature models, and (c) transform domain texture feature models. The computed texture feature vectors are inputted to the support vector machine (SVM) classifier. From the exhaustive experiments conducted in this study, it can be concluded that (a) out of the four different ROI sizes higher prediction rate have been observed using ROI size of 128 × 128 pixels, using all the texture feature models for 4-class breast tissue pattern characterization as well as for 2-class breast tissue pattern characterization, (b) the value of prediction rate of 79.5% has been attained for 4-class breast tissue pattern characterization using GLCM texture features with interpixel distance “d” = 10 using SVM classifier, (c) the highest prediction rate value of 91.2% has been obtained for 2-class breast tissue pattern characterization using Laws’ masks of kernel width 3 with SVM classifier. The results obtained from the study conclude that the ROI size of 128 × 128 pixels manually extracted from the core region of the breast where glandular tissues are available in prominent amount that provides significant amount of clinical information for the differentiations between various breast tissue patterns.
Preliminary Study on Wearable System for Multiple Finger Tracking

Devices that track the human body movement are heavily used in numerous and various fields like medicine, automation and entertainment. The work proposed is focused on the design of a modular device able track the flection of human hand phalanxes. The overall system composed by two parts: a computer program interface and a modular wearable system applied to the finger whose motion is to be monitored. The wearable device is equipped with an Inertial Motion Unit (IMU) with the purpose to detect the first phalanx orientation and a stretch sensor applied between the first and the second phalanx to recognize the flection angle. The configuration is completed with a microcontroller unit (ATmega328P) and a Bluetooth Low Power Module (RN4871) to ensure a reliable and easy to implement communication channel. We conduct two main set of tests to verify the global functionalities. In the first set the device is used to track the full flexion of a single finger while in the second we test the device capability to recognize different grabbed objects starting from the data retrieved from two fingers. The preliminary results open the possibility of a future development focused on a modular device composed by five elements, one for each hand finger and able to detect complex gesture like pinch, spread or tap.
Similarity Measure Optimization for Target Detection

Today, in the era of data and computing, fast and reliable retrieval of information has become of great importance for security and military applications, and continues to be such, as the amount available digital data increases every second. While the search and retrieval of text data has produced mature products and are today being used in search engines everyday by everyone, the retrieval of spoken content still remains a young research, especially for low resource languages where the available data is scarce to train reliable speech recognition systems. This chapter provides a thorough introduction of a speech retrieval task called “keyword search” and presents a novel similarity measure optimization-based approach. The case study was experimented on telephone conversations in three different languages and thousands of keywords randomly selected from each language were searched in the document. The experiments show that the technique introduced in this chapter offers a new methodology to handle the terms that does not even exist in the vocabulary of the speech recognition systems.
Blockchain Technology for Decentralized Data Storage on P2P Network

Blockchain is a rapidly growing concept that can be used to store a burgeoning list of records in blocks. There is an inundation of data daily where storage becomes a cardinal problem. To maintain the list of records and by not compromising on storage space, blockchain can be adopted by storing data on a peer-to-peer network. Unutilized storage from another peer can be used to store data. Data stored on another device will be subjected to high security by encryption to abstract data from device owner. This paper uses the concept of blockchain to realize the potential of the decentralized Internet. Blockchain makes transaction logs transparent and leaves no room for malpractices. Data distribution over millions of devices will resolve problems such as bandwidth stealing and throttling; additionally, it will lower the latency of data retrieved as the peer will be in proximity to the user. Using blockchain P2P system, it opens a whole new world of possibilities for a new Internet.
Affective computing with eye-tracking data in the study of the visual perception of architectural spaces

In the presented study the usefulness of eye-tracking data for classification of architectural spaces as stressful or relaxing was examined. The eye movements and pupillary response data were collected using the eye-tracker from 202 adult volunteers in the laboratory experiment in a well-controlled environment. Twenty features were extracted from the eye-tracking data and after the selection process the features were used in automated binary classification with a variety of machine learning classifiers including neural networks. The results of the classification using eye-tracking data features yielded 68% accuracy score, which can be considered satisfactory. Moreover, statistical analysis showed statistically significant differences in eye activity patterns between visualisations labelled as stressful or relaxing.
BoostNet: Bootstrapping Detection of Socialbots, and a Case Study from Guatemala

We present a method to reconstruct networks of socialbots given minimal input. Then we use Kernel Density Estimates of Botometer scores from 47,000 social networking accounts to find clusters of automated accounts, discovering over 5,000 socialbots. This statistical and data driven approach allows for inference of thresholds for socialbot detection, as illustrated in a case study we present from Guatemala.
Cross-Lingual Semantic Textual Similarity Modeling Using Neural Networks

Cross-lingual semantic textual similarity is to measure the semantic similarity of sentences in different languages. Previous work pay more attention on leveraging traditional NLP features (e.g., alignment features, syntactic features) to evaluate the semantic similarity of sentences. In this paper, we only use word embedding as basic features without any handcrafted features and build a model which is able to capture local and global semantic information of the sentences to evaluate semantic textual similarity. We test our model on SemEval-2017 and STS benchmark datasets. Our experiments show that our model improves the performance of the semantic textual similarity and achieves the best results compared with the baseline neural-network based methods reported on the two datasets.
Comparison of braking performance between mechanical and dynamic braking for Electric Powered Wheelchair

Braking is the necessary system need to install as the safety feature for the moving transportation. Using the mechanical braking only as primary braking system in Electric Transportation (ET) is insufficient due to some issues such as low strength users hand gripping and abruptly tire locking during braking especially on wet surface condition. In this paper, the performance between mechanical and electrical braking which is by using dynamic braking concept is proposed to enhance the braking performance of Electric Powered Wheelchair (EPW). The experiments were conducted during descending on the slope under wet and dry pavements. From the results of slip ratio, the slipping time between mechanical and dynamic braking in dry pavement is recorded 0.9 seconds and 0.7 seconds respectively. Meanwhile, it is observed that tire is fully locked-up for mechanical braking under the wet surface. However, by using the dynamic braking, the wheel does not lock-up and the slipping time was recorded 1.4 seconds. It can be considered that, mechanical and dynamic braking give their own merit. The high braking torque from mechanical braking is suitable to use under the dry pavement for the short stopping distance. The other sides, braking under the wet pavement, dynamic braking is more efficient compare to the mechanical braking in term of short slipping time and does not cause tire to lock-up while braking.
Sentiment Classification Using Text Embedding for Thai Teaching Evaluation

Sentiment classification gains a lot of attention nowadays. For a university, the knowledge obtained from classifying sentiments of student learning in courses is highly valuable, and can be used to help teachers improve their teaching skills. In this research, sentiment classification based on text embedding is applied to enhance the performance of sentiment classification for Thai teaching evaluation. Text embedding techniques considers both syntactic and semantic elements of sentences that can be used to improve the performance of the classification. This research uses two approaches to apply text embedding for classification. The first approach uses fastText classification. According to the results, fastText provides the best overall performance; its highest F-measure was at 0.8212. The second approach constructs text vectors for classification using traditional classifiers. This approach provides better performance over TF-IDF for k-nearest neighbors and naïve Bayes. For naïve Bayes, the second approach yields the best performance of geometric mean at 0.8961. The performance of TF-IDF is better suited to using decision tree than the second approach. The benefit of this research is that it presents the workflow of using text embedding for Thai teaching evaluation to improve the performance of sentiment classification. By using embedding techniques, similarity and analogy tasks of texts are established along with the classification.
Key Frame Extraction Using Content Relative Thresholding Technique for Video Retrieval

With the continuous growth in the space of information sharing over the Internet, the sharing of video information is also growing. The video information is highly appreciable for the online distance education, security, and distance healthcare, interactive communication over the Internet and also in multimedia news systems. The information captured in the video format demands summarization for effective processing and efficient storage. In order to summarize any video information, the best possible strategy is extracting key frames from videos. A number of research attempts are made in order to establish the most efficient key frame extraction framework. Nonetheless, most of the parallel research outcomes are affected by either high or low key frame extractions. Thus, the demand from the modern research is to build an optimal framework to extract key frames from motion videos. The major challenges are identified in this work and addressed in the finest way possible. This work demonstrates the framework for few different cases such as object in motion, camera in motion or both in case of highly colour contrast video sequences. The results of this framework demonstrate lowest time complexity and higher level of information preservation compared to the parallel research outcomes.
Analysis of the spatial patterns in localization of gold mineralization relative to the system of deep faults in the Chortomlyk greenstone structure of the Ukrainian shield

The results of analysisof the patterns of the spatial relationship between hydrothermal gold ore formations and the zones of metasomatites and systems of deep faults within the Chortomlyk greenstone structure of the Middle Pridniprovie megablock in the Ukrainian Shield are given. As a result of studying the localization conditions of gold mineralization, it was established that the gold-bearing mineralization is confined to tectonically fractured zones and is localized among metasomatically altered rocks. The hydrothermal series of metasomatites in the Chortomlyk greenstone structure is represented by greisens, propylites, amphibole-carbonate metasomatites and listvenite-berezites. The mineralization of Au and Mo is associated with metasomatites of the greisen type. The study of spatial relationship between the fields of metasomatites and the gold mineralization and zones of deep faults revealed that the closest relationship is expressed with systems of faults with azimuths of 0° and 270°, 17° and 287°, 77° and 347°. The results of the studies allowed us to develop a newprospecting criterion, which, in turn allows us to state that the gold mineralization within the Chortomlyk greenstone structure is spatially confined to metasomatites related to the schistosity, fracture, millonitization, and cataclase zones with high content of sulphide mineralization. These zones are localized in nodes of intersecting faults of the first order of the system 77° and 347° with discontinuous violations of high orders of azimuths of 0° and 270°, 17°and 287°. The results of the research can be used to develop a set of predictive criteria and the allocation of promising sites of hydrothermal mineralization of gold within the Chortomlyk greenstone structure of the Middle Pridniprovie megablock in the Ukrainian shield.
Immersive Multimedia in Information Revolution

This chapter describes the immersive multimedia's role in our lives, educational activities, and business, and social media benefits from the growth of this emerging reality. Consequently, this chapter analyses the impact and the use for immersive multimedia in different contexts. In the modern world, technological advancement led to the discovery of the powerful application of multimedia. Education and learning systems have significant contribution to improve this field of research. This chapter is going to help expanded the knowledge and information about multimedia in general and immersive multimedia in particular, and its strong influences on education. How technological innovation can be used by external stakeholders to direct and promote innovation in education, how teaching can benefit from the proximity to technology, and how social networks can seize the advantages of an immersive system are some of the answers the authors try to find in this chapter.
Levellings based on spatially adaptive scale spaces using local image features

The authors propose here to overcome lacks of robustness against noise and adaptability to image features for which classical morphological operators suffer from. For doing this, they propose to deal with partial differential equations (PDEs) for generalised Cauchy problems, and they show that the proposed PDEs are equivalent to impose both robustness and adaptability to structuring functions of the corresponding sup-inf operators. This allows them to introduce spatially adaptability in levellings, and it turns out that the proposed approach constitutes a PDE formulation and a generalisation of a larger class of levellings, the so-called extended levellings, for which one of them are characterised by quasi-flat zones. They show the efficiency of the proposed approach on synthetic, grey, and colour images with different types of noises.
Concentration of Some Metals in Water and Soil Samples at Some Locations near the Hotmud Flow at Porong Disaster Area, Sidoarjo, East Java, Indonesia.

More than hundred thousands people in the Porong subdistrict have been displaced by the hot mud flowing from a natural gas well being drilled by Lapindo Brantas, an oil well company since late May 2006. The mud was estimated to be flowing at a rate of 125,000 m3 per day and rapidly flooded surrounding areas displacing hundred thousands of people. For about thousand people had to seek medical treatment after exposure to and inhalation of a poisonous gas. This disaster is happen continue until now. The monitoring of mud flow and water quality are performed by taking separate samples in the field and transporting the samples to the laboratory for analysis. The sediment and water samples of hot mud flow at Porong Area, East Java, Indonesia, were taken from several points and analyzed are as follow; concentration of Cr, Cd, Zn, Cu, Pb, Fe in sediment samples in the range of : 0.26 – 0.42; 0.16 – 0.34; 54.80 – 61.10; 21.22 – 24.16; 3.45 – 9.83; 1314 –1526 ppm respectively. And concentration of Cr, Cd, Zn, Cu, Pb, Fe in water sampels in the range of : 0.14 – 0.27; 0.001 – 0.002; 0.19 – 0.42; 0.012 – 0.036; 0.26 – 0.36; 0.20 – 0.58 ppm respectively. The soil and water samples were taken from several locations near the hot mud flow at Porong Area, East Java, Indonesia and the concentration of Cr, Cd, Zn, Cu, Pb, Fe in soil and water samples are not detected.
Cost Sensitive Learning in the Presence of Symmetric Label Noise

In binary classification framework, we are interested in making cost sensitive label predictions in the presence of uniform/symmetric label noise. We first observe that 0–1 Bayes classifiers are not (uniform) noise robust in cost sensitive setting. To circumvent this impossibility result, we present two schemes; unlike the existing methods, our schemes do not require noise rate. The first one uses $$\alpha $$-weighted $$\gamma $$-uneven margin squared loss function, $$l_{\alpha , usq}$$, which can handle cost sensitivity arising due to domain requirement (using user given $$\alpha $$) or class imbalance (by tuning $$\gamma $$) or both. However, we observe that $$l_{\alpha , usq}$$ Bayes classifiers are also not cost sensitive and noise robust. We show that regularized ERM of this loss function over the class of linear classifiers yields a cost sensitive uniform noise robust classifier as a solution of a system of linear equations. We also provide a performance bound for this classifier. The second scheme that we propose is a re-sampling based scheme that exploits the special structure of the uniform noise models and uses in-class probability estimates. Our computational experiments on some UCI datasets with class imbalance show that classifiers of our two schemes are on par with the existing methods and in fact better in some cases w.r.t. Accuracy and Arithmetic Mean, without using/tuning noise rate. We also consider other cost sensitive performance measures viz., F measure and Weighted Cost for evaluation.
Research on Short Text Classification Method Based on Convolution Neural Network

Short text classification is one of the hotspots of research in Natural Language Processing. a new model of text representation is proposed in this paper (N-of-DOC), and in order to solve the problem of sparse representation in Chinese, the word2vec distributed representation is used, finally, it is applied to the improved convolution neural network model (CNN) to extract the high level features from the filter layer, the classification model is obtained by connecting the softmax classifier after the pooling layer. In the experiment, the traditional text representation model and the improved text representation model are used as the input of the original data, respectively. It acts on the model of traditional machine learning (KNN, SVM, logistic regression, naive Bayes) and the improved convolution neural network model. The results show that the proposed method can not only solve the dimension disaster and sparse problem of Chinese text vectors, but also improve the classification accuracy by 10.23% compared with traditional methods.
Hierarchical Clustering of OSS License Statements toward Automatic Generation of License Rules

Reusing open source software (OSS) components for one's own software products has become common in the modern software development. Automated license identification tools have been proposed to help developers identify OSS licenses, since a large number of licenses sometimes must be checked before attempting to reuse. Of the existing tools, Ninka[1] can most correctly identify licenses of each source file by using regular expressions. In case Ninka does not have license identification rules for unknown licenses, Ninka reports these as “unknown licenses” which must be checked by developers manually. Since completely-new or derived OSS licenses appear nearly every year, a license identification tool should be appropriately maintained by adding regular expressions corresponding to the new licenses. The final goal of our study is to construct a method to automatically create candidate license rules to be added to a license identification tool such as Ninka. Toward achieving the goal, files identified as unknown licenses must be classified by license firstly. In this paper, we propose a hierarchical clustering which divides unknown licenses into clusters of files with a single license. We conduct a case study to confirm the usefulness of our clustering method when it is applied for classifying 2,801, 1,230 and 2,446 unknown license statement files for Linux Kernel v4.4.6, FreeBSD v10.3.0 and Debian v7.8.0 respectively. As a result, it is confirmed that our method can create clusters which are suitable as candidates for generating license rules automatically.
On the properties of a Takács distribution

The Takács law – which includes the generalized Dickman law as a special case – arises as a limiting distribution of normalized sums. We provide some properties of this law and a feasible method for computing the probability density and distribution functions of the Takács random variable.