bluuebunny/crossref_metadata_2025_split huggingface dataset:
Automatic Identification of Close Languages - Case study: Malay and Indonesian
Authors: Ranaivo-Malancon Bali 
Published: April 2020

Identifying the language of an unknown text is not a new problem but what is new is the task of identifying close languages. Malay and Indonesian as many other languages are very similar, and therefore it is a real difficulty to search, retrieve, classify, and above all translate texts written in one of the two languages. We have built a language identifier todetermine whether the text is written in Malay or Indonesian which could be used in any similar situation. It uses the frequency and rank of trigrams of characters, the lists of exclusive words, and the format of numbers. The trigrams are derived from the most frequent words in each language. The current program contains as language models: Malay/Indonesian (661 trigrams), Dutch (826 trigrams), English (652 trigrams), French (579 trigrams), and German (482 trigrams). The trigrams of an unknown text are searched in each language model. The language of the input text is the language having the highest ratio in “number of shared trigrams / total number of trigrams” and “number of winner trigrams / number of shared trigrams”. If the language found at trigram search level is ’Malay or Indonesian’, the text is then scanned by searching the format of numbers and ofsome exclusive words.
Modeling Human-Like Robustness in an Intelligent Tutoring System
Authors: Kazi Hameedullah , Haddawy Peter , Suebnukarn Siriwan 
Published: April 2020

Intelligent tutoring systems are no different from other knowledge based systems in that they are often plagued by brittleness. Intelligent tutoring systems for problem solving are typically loaded with problem scenarios for which specific solutions are constructed. Solutions presented by students, are compared against these specific solutions, which often leads to a narrow scope of reasoning, where students are confined to reason towards a specific solution. Student solutions that are different from the specific solution entertained by the system are rejected as being incorrect, even though they may be acceptable or close to acceptable. This leads to brittleness in tutoring systems in evaluating student solutions and returning appropriate feedback. In this paper we discuss a few human-like attributes in the context of robustness that are desirable in knowledge based systems. We then present a model of reasoning through which a tutoring system for medical problem-based learning, can begin to exhibit human-like robust behavior in evaluating solutions in a broader context using UMLS, and respond with hints that are mindful of the partial correctness of the student solution.
Test Case Classification using Category-Partition Finite State Machine
Authors: Suphapala Penpicha , Leelanuntakul Umaporn , Ngamsaowaroj Nuchakorn , Sophatsathit Peraphon 
Published: April 2020

Testing is an essential activity in software development process. Testers and developers alike are facing a formidable expectation of delivery bug-free software. Certifying bug-free with exhaustive test is commonly known to be impossible. Numerous efforts have been attempted to arrive at a plausible test scenario wherein thorough coverage can be attained. Conventional approaches usually require large amount of test data (or input domain) to generate necessary test cases at premium expenses which, in many cases, end up to be a recalcitrant test process. This paper proposes a straightforward, yet practical algorithmic method to reduce all relevant test cases. The central idea rests upon identifying the relationships among category partition of input specifications and program constraints that are subsequently employed to construct a finite state machine. As such, all paths connecting the start and end states represent the required test cases. Reduction on the number of generated test frames based on the proposed method in comparison with conventional approaches proves to be quite significant.
Digit Set Conversion from Redundant Number System into Complement Representation
Authors: Charoensiri Veerasit , Surarerks Athasit 
Published: April 2020

Redundant number system was proposed in order to solve the carry-propagation problem. Although it provides a carry-free parallel addition, this representation requires a lot of space to store itself. Many conversions from the redundant number system into another number representation have been introduced to decrease the storage usage. This paper proposes a generic algorithm in order to convert the redundant number representation into the complement number representation. The proposed algorithm can perform the conversion of a number in any integer radix and eliminates the carry chain of the traditional method. The proofs of the proposed algorithm in term of correctness are also included in this paper.
An Improvement of Iris Pattern Identification Using Radon Transform
Authors: Ariyapreechakul Pattraporn , Covavisaruch Nongluk 
Published: April 2020

This research proposes an improvement of iris personal identification system using Radon transform which is used to extract feature stored as templates in a database. In our research, the system has been tested to find the best system performance with 3 distance functions: regional correlation, Euclidean distance and absolute distance. The test images are from CASIA iris database. Our experimental results reveal that the improved approach greatly increases the system performance for all distance functions. Among the 3 distance functions, the absolute distance gives the best results which reduce EER from 18% (previous approach) to 3.695%.
An Application of the Probabilistic Model to the Prediction of Student Graduation Using Bayesian Belief Network
Authors: Yingkuachat Jiraporn , Praneetpolgrang Prasong , Kijsirikul Boonserm 
Published: April 2020

This paper proposes an alternative to the prediction of education accomplishment. It employs a data mining technique, the Bayesian belief network (Bayes net). The technique is used to analyze the independent variables that affect the education accomplishment result of vocational students, undergraduate students and graduate students. The machine learning tool, WEKA, is used to construct the prediction model that is accurate for the prediction based on k-fold cross-validation. The experimental result shows that the Bayes net technique is able to determine important variables for the prediction of the result of education accomplishment and this technique provides high prediction accuracy. From the models constructed by WEKA, we find that the important variables that affect the education accomplishment are the previous GPA., mother and or fathers career, the total income of the family and the grade point average when they enter the first year in bachelor study. The obtained result is consistent with the result analyzed by multiple regression analysis.
Image Compression using Modified Haar Wavelet-Base Vector Quantization
Authors: Jitkam Anusorn , Wongthanavasu Satra 
Published: April 2020

This research presents an image compression algorithm using modified Haar wavelet and vector quantization. For comparison purposes, a standard Haar wavelet with vector quantization and SPIHT, which is used in JPEG2000, are compared with the proposed method using Peak Signal-to-Noise Ratio (PSNR). The proposed method shows better results on average over the compared methods.
An Analysis of a Close-Type Queuing System with General Service-Time Distribution
Authors: Yoshioka Yoshio , Nagase Tomoyuki 
Published: April 2020

This paper presents an innovative approach to solve probability distributions of a close feed back loop type queuing system with general service time distribution. This model is applied to a multiprocessor system where some of its nodes are performed a repair procedure during a nodes malfunction condition. Our model is appropriate for a multiprocessor system that employs a common bus or for a multi-node system in computer networks. A meticulous analysis of the systems model has been conducted and numerical results have been obtained to scrutinize the proposed model.
A Ubiquitous Processor Built-in a Waved Multifunctional Unit
Authors: Fukase Masa-aki , Sato Tomoaki 
Published: April 2020

In developing cutting edge VLSI processors, parallelism is one of the most important global standard strategies to achieve power conscious high performance. These features are more critical for ubiquitous systems with great demands for multimedia mobile processing. Then, one of most important issues for ubiquitous systems is instruction scheduling, because floating point units indispensable for multimedia mobile applications take longer latency than integer units. Although software parallelism has been inevitable to fully utilize hardware parallelism between regular scalar units, it has been really awkward. Thus, we describe in this article a double scheme to achieve instruction scheduling free ILP (instruction level parallelism) and apply the double scheme to a ubiquitous processor HCgorilla we have so far developed. The double scheme is the multifunctionalization of scalar units and making a resultant multifunctional unit (MFU) wave-pipeline. The multifunctionalization frees the instruction scheduling, and the wave-pipelining recovers the reduction of clock speed to be caused by the scale up of a multifunctional circuit. HCgorilla built-in the waved MFU is promising for wide-range dynamic ILP at a rate higher than regular processors.
State-Space Estimation for OFDM and SC-FDE Schemes with Strongly Varying Carrier Frequency Offset
Authors: Pedrosa Pedro , Dinis Rui , Nunes Fernando 
Published: April 2020

Parametric algorithms for the estimation of rapidly-varying Carrier Frequency Offset (CFO) usually employ pilot symbols multiplexed with the data transmission. As the CFO variation rate increases so has to increase the density of pilot symbols transmitted, thus impairing the bandwidth efficiency. In order to reduce the number of pilot symbols used in the estimation of rapidly-varying CFO it was proposed to use a truncated Taylors series to predict the CFO, where the derivatives up to order d-1 are recursively estimated with a d-order Kalman filter (KF).We propose to compare the performance of a fourth-order KF predictor in the most popular block transmission systems: Orthogonal Frequency Division Multiplexing (OFDM) and Single Carrier Frequency-Domain Equalization (SC-FDE). Simulating different transmission scenarios, e.g., channel coding and spatial diversity, our results show that for static multipath fading channels the proposed receiver for the SC-FDE scheme exhibits better Bit Error Rate (BER) performance than that of OFDM.
Efficient Subsequence Search on Streaming Data Based on Time Warping Distance
Authors: Rodpongpun Sura , Niennattrakul Vit , Ratanamahatana Chotirat Ann 
Published: April 2020

Many algorithms have been proposed to deal with subsequence similarity search problem in time series data stream. Dynamic Time Warping (DTW), which has been accepted as the best distance measure in time series similarity search, has been used in many research works. SPRING and its variance were proposed to solve such problem by mitigating the complexity of DTW. Unfortunately, these algorithms produce meaningless result since no normalization is taken into account before the distance calculation. Recently, GPUs and FPGAs were used in similarity search supporting subsequence normalization to reduce the computation complexity, but it is still far from practical use. In this work, we propose a novel Meaningful Subsequence Matching (MSM) algorithm which produces meaningful result in subsequence matching by considering global constraint, uniform scaling, and normalization. Our method significantly outperforms the existing algorithms in terms of both computational cost and accuracy.
Client Driven System of Depth Image Based Rendering
Authors: Fukushima Norishige , Ishibashi Yutaka 
Published: April 2020

In this paper, we propose systems which can render free viewpoint images by using depth image based rendering for live video communications. Experimental results show that an image and depth transmission system is more suitable than an all multi view transmission and an only free viewpoint image transmission system. Especially in the image and depth transmission system, transmitting two images and one depth map, and then predicting the opposite depth map is the best for live communication.
Moving Objects Segmentation at a Traffic Junction from Vehicular Vision
Authors: Tan Joo Kooi , Ishikawa Seiji , Sonoda Shinichiro , Miyoshi Makoto , Morie Takashi 
Published: April 2020

Automatic extraction/segmentation and the recognition of moving objects on a road environment is often problematic. This is especially the case when cameras are mounted on a moving vehicle (for vehicular vision), yet this remains a critical task in vision based safety transportation. The essential problem is twofold: extracting the foreground from the movingbackground, and separating and recognizing pedestrians from other moving objects such as cars that appear in the foreground.The challenge of our proposed technique is to use a single mobile camera for separating the foreground from the background, and to recognize pedestrians and other objects from vehicular vision in order to achieve a low cost and intelligent driver assistance system. In this paper, the normal distribution is employed for modelling pixel gray values. The proposed technique separates the foreground from the background by comparing the pixel gray values of an input image with the normal distribution model of the pixel. The model is renewed after the separation to give a new background model for the next image. The renewal strategy changes depending on if the concerned pixel is in the background or on the foreground. Performance of the present technique was examined by real world vehicle videos captured at a junction when a car turns left or right and satisfactory results were obtained.
Storage of Usage Information on Research Resource Metadata Database: Corpus Construction Using Academic Articles
Authors: Kozawa Shunsuke , Tohyama Hitomi , Uchimoto Kiyotaka , Matsubara Shigeki 
Published: April 2020

Recently, language resources have become indispensable for linguistic researches. However, existing language resources are seldom fully utilized because their variety of usage is not well known, indicating that their intrinsic value is not recognized very well either. Regarding this issue, lists of usage information might improve language resource searches and lead to their efficient use. In this research, therefore, we collect a list of usage information for each language resource from academic articles to promote the efficient utilization of language resources. This paper describes the construction of a text corpus annotated with usage information (UI corpus). In particular, we automatically extract sentences containing language resource names from academic articles. Then, the extracted sentences are annotated with usage information by two annotators in a cascaded manner. We will show that the UI corpus contributes to efficient language resource searches, by combining the UI corpus with a metadata database of language resources and comparing the number of language resources retrieved with and without the UI corpus.
A Fuzzy Backstepping Control of a Mobile Manipulator with Torque Limits Avoidance
Authors: Boukattaya Mohamed , Damak Tarak , Jallouli Mohamed 
Published: April 2020

In this paper, we present a dynamic redundancy resolution technique for mobile manipulator subject to joint torque limits. First, the dynamic model of the mobile manipulator in feasible motion space is given. Next, a control algorithm is proposed which completely decouples the motion of the system into the end-effector motion in the task space and an internal motion in the null space and controls them in prioritized basis with priority given to the primary task space and enables the selection of characteristics in both subspaces separately. A special attention is given to the joints torque limits avoidance where a new weighted pseudo-inverse of the Jacobian that accounts for both inertia and torque limits is proposed to solve problems inherent to torque limits of the system. Simulation results are given to illustrate the coordination of two subsystems in executing the desired trajectory without violating the joint torque limits.
Neural Mechanisms of Bias and Sensitivity in Animal Models of Decision Making
Authors: Nishida Hiroshi , Takahashi Muneyoshi , Bird Gary D. , Lauwereyns Jan 
Published: April 2020

Animals perceive stimuli in their environment and are required to make motor responses according to this perception. The perception-to-action mechanisms rely on the accumulation of neural activity in specific areas of the brain that need to reach a threshold in order for the action to be initiated. These mechanisms can be influenced by various types of information and prospective outcomes; that is, contextual factors can speed up or slow down the processes. Here we describe how behavioral paradigms coupled with neural recordings can illustrate two basic features of the speeding up (or slowing down) of the perception-to-action mechanisms. The features are bias (a general increase in decision activity prior to stimulus onset) and sensitivity (a change in the cumulative firing rate up to a decision point). We thenpropose the direction for future research emphasizing the need to examine bias and sensitivity with a focus on dynamics.
Classication Models Based-on Incremental Learning Algorithm and Feature Selection on Gene Expression Data
Authors: Meesad Phayung , Wichian Sageemas Na , Herwig Unger , Saengsiri Patharawut 
Published: April 2020

This Gene expression data illustrates levels of genes that DNA encodes into the protein such as muscle or brain cells. However, some abnormal cells may evolve from unnatural expression levels. Therefore, finding a subset of informative gene would be beneficial to biologists because it can identify discriminative genes. Unfortunately, genes grow up rapidlyinto the tens of thousands gene which make it difficult for classifying processes such as curse of dimensionality and misclassification problems. This paper proposed classification model based-on incremental learning algorithm and feature selection on gene expression data. Three feature selection methods: Correlation based Feature Selection (Cfs), Gain Ratio (GR), and Information Gain (Info) combined with Incremental Learning Algorithm based-on Mahalanobis Distance (ILM). Result of the experiment represented proposed models CfsILM, GRILM and InfoILM not only to reduce many dimensions from 2001, 7130 and 4026 into 26, 135, and 135 that save time-resource but also to improve accuracy rate 64.52%, 34.29%, and 8.33% into 90%, 97.14%, and 83.33% respectively. Particularly, CfsILM is more outstanding than other models on three public gene expression datasets.
Interference Study of Medical Body Area Network for Proactive Performance Improvement
Authors: Fang Gengfa , Dutkiewicz Eryk , Vesilos Rein 
Published: April 2020

Medical Body Area Network (MBAN) is a new wireless communications technology designed to sense human’s vital signals through tiny nodes in, on and around the human body wirelessly. MBAN will play an important role in enabling ubiquitous and noninvasive telemetry and healthcare systems in the future. In this paper, we analyze the interference in MBAN both from legacy wireless networks and neighbouring MBANs. We then study the possible solutions to fight against these interferences in MBAN including the channel sharing scheme to solve the inter-network interference. Experiments are carried out based on MQWIN400 radio platform to studythe wireless channel in 400MHz band. We found the receiver’s mobility makes the channel quite dynamic because of multipath effect.
Isolated Word Recognition Based on Combination of Multiple Noise-Robust Techniques
Authors: Hayasaka Noboru 
Published: April 2020

Although many noise-robust techniques have been presented, the improvement under low SNR condition is still insufficient. The purpose of this paper is to achieve the high recognition accuracy under low SNR condition with low calculation costs. Therefore, this paper proposes a novel noise-robust speech recognition system that makes full use of spectral subtraction (SS), mean variance normalization (MVN), temporal filtering (TF), and multi-condition HMMs (MC-HMMs). First, from the results of SS with clean HMMs, we obtained the improvement from 46.61% to 65.71% under 0 dB SNR condition. Then, SS+ MVN+TF with clean HMMs improved the recognition accuracy from 65.71% to 80.97% under the same SNR condition. Finally, we achieved the further improvement from 80.97% to 92.23% by employing SS+MVN+TF with MC-HMMs.
Proposal of QAM-OFDM System with IDAR Method Designed for Satellite Channel
Authors: Boonsrimuang Pornpawit , Boonsrimuang Pisit , Paungma Tawil , Kobayashi Hideo 
Published: April 2020

The future satellite communication systems are required to support the higher transmission data rate for providing multimedia services by employing the efficient modulation method such as multilevel QAM. However, the employment of conventional single carrier transmission method with multi-level QAM which has larger PAPR (Peak to Averaged Power Ratio) would cause the fatal degradation of signal quality due to the non-linear amplifiers located at the earth station and satellite. To overcome this problem, this paper proposes broadband satellite communication systems by using the multilevel QAM-OFDM technique with IDAR (Improved Decision Aided Reconstruction) method, which is designed for satellite channel. In the IDAR method, the characteristics of non-linear amplifiers are required to be known at the receiver for mitigating the intermodulation noise. This paper also proposes the estimation method for AM-AM and AM-PM conversion characteristics of non-linear amplifiers by using low PAPR preamble symbols. The various computer simulations are conducted in this paper to verify the effectiveness of proposed system in the non-linear satellite channel.
On the improvement of search enging using semantic web: case study of Hua Hin tourism information system
Authors: Choksuchat Chidchanok , Chantrapornchai Chantana 
Published: April 2020

This paper describes how to improve the search engine using the ontology for the semantic web technology, the case study on a prototype Hua Hin tourism web page. The methodology contains two parts. First, we describe a way to compute query complexity between the relational database (RDB) and web ontology language (OWL). We present the original RDB, convert to the OWL, and consider the query complexity with the searching time of the prototype system. A number of facts in the ontology, and a number of conjuncts in the conjunctive queries are considered as parameters. The goal is to improve the search engine by reducing the query complexity. Second, the SPARQL queries in web application are considered from the classification and realization of the reasoner. We study from the existing ontology, increasing OWL DL rules, and consider the expressivity. We then compare the searching time between top-down and bottom-up approaches. The advantages are to select the appropriate inference problems for query response through the web application. The prototype Hua Hin semantic web is used to present our methodology throughout the paper.