Miracle-dz/UniFAR-FTUs huggingface dataset:
Smurf: Self-Service String Matching Using Random Forests

We argue that more attention should be devoted to developing self-service string matching (SM) solutions, which lay users can easily use. We show that Falcon, a self-service entity matching (EM) solution, can be applied to SM and is more accurate than current self-service SM solutions. However, Falcon often asks lay users to label many string pairs (e.g., 770-1050 in our experiments). This is expensive, can significantly compound labeling mistakes, and takes a long time. We developed Smurf, a self-service SM solution that reduces the labeling effort by 43-76%, yet achieves comparable F 1 accuracy. The key to make Smurf possible is a novel solution to efficiently execute a random forest (that Smurf learns via active learning with the lay user) over two sets of strings. This solution uses RDBMS-style plan optimization to reuse computations across the trees in the forest. As such, Smurf significantly advances self-service SM and raises interesting future directions for self-service EM and scalable random forest execution over structured data.
Harnessing Truth Discovery Algorithms On The Topic Labelling Problem

Topics in topic modelling approaches are represented as a collection of weighted words. The labels for the topics, however, are not clearly defined and must be interpreted manually. Topic labelling proposes to automatically label the topics by leveraging a knowledge base or applying data mining and machine learning algorithms. We propose a naive topic labelling approach where we transform the labeling problem into selecting the best label for each word in the topic. The candidate labels are generated by querying a knowledge base using the top-N words of each topic. We construct a heterogeneous graph of topics, words, articles and candidate labels. To rank the candidate labels, we apply truth discovery algorithms on the graph. The performance evaluation using popular topic modelling datasets shows that the approach receives satisfactory accuracy.
Extracting Japanese Behavioral Facilitation Tweet in Disaster Situations

There are many disasters such as big earthquakes, flood disasters, hurricanes, and typhoons. Especially in Japan, there are so many natural disasters. Nowadays, after the disaster, we help each other not only in the real world but also on the social media. However, there are many rumors on the social media. Especially, twitter spreads many rumors after the disaster, because it is easy to spread information. According to our former study, there are many behavioral facilitation tweets in the rumors tweets. We consider it is important to extract automatically such rumor behavioral facilitation tweets. In this paper, as a first step of extracting rumor behavioral facilitation tweets, we propose the method which extracts behavioral facilitation tweets from disaster tweets. Our proposed method is rule-based. We also conducted the small experiment to measure our proposed method is suitable for extracting behavioral facilitation tweets.
Ship Classification and Detection Based on CNN Using GF-3 SAR Images

Ocean surveillance via high-resolution Synthetic Aperture Radar (SAR) imageries has been a hot issue because SAR is able to work in all-day and all-weather conditions. The launch of Chinese Gaofen-3 (GF-3) satellite has provided a large number of SAR imageries, making it possible to marine targets monitoring. However, it is difficult for traditional methods to extract effective features to classify and detect different types of marine targets in SAR images. This paper proposes a convolutional neutral network (CNN) model for marine target classification at patch level and an overall scheme for marine target detection in large-scale SAR images. First, eight types of marine targets in GF-3 SAR images are labelled based on feature analysis, building the datasets for further experiments. As for the classification task at patch level, a novel CNN model with six convolutional layers, three pooling layers, and two fully connected layers has been designed. With respect to the detection part, a Single Shot Multi-box Detector with a multi-resolution input (MR-SSD) is developed, which can extract more features at different resolution versions. In order to detect different targets in large-scale SAR images, a whole workflow including sea-land segmentation, cropping with overlapping, detection with MR-SSD model, coordinates mapping, and predicted boxes consolidation is developed. Experiments based on the GF-3 dataset demonstrate the merits of the proposed methods for marine target classification and detection.
EduRP: an Educational Resources Platform based on Opinion Mining and Semantic Web

: Educational platforms have become important tools for e-learning; nonetheless, finding the appropriate educational resources to use often represents a tedious task for learners. Opinions in the educational domain are important information for decision making; they allow teachers to improve the teaching process and enable students to decide on the best educational resources. The large amount of data that is daily generated on the Web makes it difficult, however, to analyze opinions manually. Multiple opinion mining approaches are being proposed as a solution to this problem; this research work introduces EduRP, an education platform that integrates opinion mining techniques and ontology-based user profiling techniques. We specifically propose an opinion mining approach for Spanish text which consists of three main steps: 1) collect opinions from the EduRP platform, 2) process the opinions to normalize the text, and 3) obtain the polarity of the opinions using a machine learning approach. We also propose a profile customization approach that uses Semantic Web technologies, specifically ontologies, to integrate socio-demographic data from different social networks and from the platform itself. Finally, we assess the performance of our system under precision, recall, and F-measure metrics, obtaining average values of 81.85%, 81.80% and 81.54, respectively.
Further studies on robust H∞ control for a class of Takagi–Sugeno fuzzy time-delay systems with application to continuously stirred tank reactor problems

This article is concerned with the problem of robust H ∞ control for a class of Takagi–Sugeno fuzzy systems with parameter uncertainties and time-varying delays. By constructing a novel Lyapunov–Krasovskii functional and utilizing some integral inequalities, a new bounded real lemma is proposed in terms of linear matrix inequalities. Also, the robust H ∞ state feedback control design conditions are obtained on the basis of the cone complementarity linearization algorithm. Moreover, The controller guarantees the robust asymptotic stability of the closed-loop fuzzy system and the H ∞ performance of the system from the exogenous signals to the measurement output below a prescribed level. Finally, as an illustrative example, a continuously stirred tank reactor is employed to show the effectiveness of the theoretical results.
Investigating the Consistency of Emoji Sentiment Lexicons Constructed Using Different Languages

Emojis have been widely used in recent text-based communications and can be important features for sentiment analysis of social media posts such as tweets. Our previous work presented a method for automatically constructing an emoji sentiment lexicon based on the co-occurrence frequency between sentiment words and emojis. This paper investigates whether the proposed framework can be valid over different languages. To conduct this study, we constructed two datasets comprising 150,000 tweets written in English and Japanese and applied a sentiment dictionary to each dataset separately. The results demonstrated that the two constructed lexicons were almost consistent, while the sentiments of some emojis were differently interpreted depending on the language. We also showed the reasonable performance of tweet sentiment analysis using each emoji sentiment lexicon.
Design and Implementation of a Machine Learning-Based Authorship Identification Model

In this paper, a novel approach is presented for authorship identification in English and Urdu text using the LDA model with n-grams texts of authors and cosine similarity. The proposed approach uses similarity metrics to identify various learned representations of stylometric features and uses them to identify the writing style of a particular author. The proposed LDA-based approach emphasizes instance-based and profile-based classifications of an author’s text. Here, LDA suitably handles high-dimensional and sparse data by allowing more expressive representation of text. The presented approach is an unsupervised computational methodology that can handle the heterogeneity of the dataset, diversity in writing, and the inherent ambiguity of the Urdu language. A large corpus has been used for performance testing of the presented approach. The results of experiments show superiority of the proposed approach over the state-of-the-art representations and other algorithms used for authorship identification. The contributions of the presented work are the use of cosine similarity with n-gram-based LDA topics to measure similarity in vectors of text documents. Achievement of overall 84.52% accuracy on PAN12 datasets and 93.17% accuracy on Urdu news articles without using any labels for authorship identification task is done.
ShrinkWrap: Efficient SQL Query Processing in Differentially Private Data Federations

A private data federation is a set of autonomous databases that share a unified query interface offering in-situ evaluation of SQL queries over the union of the sensitive data of its members. Owing to privacy concerns, these systems do not have a trusted data collector that can see all their data and their member databases cannot learn about individual records of other engines. Federations currently achieve this goal by evaluating queries obliviously using secure multiparty computation. This hides the intermediate result cardinality of each query operator by exhaustively padding it. With cascades of such operators, this padding accumulates to a blow-up in the output size of each operator and a proportional loss in query performance. Hence, existing private data federations do not scale well to complex SQL queries over large datasets. We introduce Shrinkwrap, a private data federation that offers data owners a differentially private view of the data held by others to improve their performance over oblivious query processing. Shrinkwrap uses computational differential privacy to minimize the padding of intermediate query results, achieving up to a 35X performance improvement over oblivious query processing. When the query needs differentially private output, Shrinkwrap provides a trade-off between result accuracy and query evaluation performance.
A Study on Estimation of High Impact Papers based on Cited Structure in Body Text

Academic papers cite other literature for various purposes: such as sharing research motivations, comparing with previous methods or achieved accuracies, and so on. At some annual conference, high impact papers have been selected and honored after about 10 years from the publication to demonstrate both the emphasis research field and great contributions. This paper has proposed the estimation method of high impact papers using characteristics of those citing purposes. To extract the cited structure of scientific literature, we analyze the paper structure based on purpose and identify the citation points in the paper. For evaluating the proposed method, 17 years long proceedings on VLDB (Very Large Data Bases) Conference and "10 Year Award" papers are applied. We believe that the research becomes a foothold for estimating papers that may have a significant impact on the academic field in the future.
A CPU-GPU Hybrid System of Environment Perception and 3D Terrain Reconstruction for Unmanned Ground Vehicle

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.
Enabling System Artefact Exchange and Selection through a Linked Data Layer

: The use of different techniques and tools is a common practice to cover all stages in the systems development lifecycle, generating a very good number of system artefacts. Moreover, these artefacts are commonly encoded in different formats and can only be accessed, in most cases, through proprietary and non-standard protocols. This scenario can be considered a real nightmare for software or systems reuse. Possible solutions imply the creation of a real collaborative development environment where tools can exchange and share data, information and knowledge. In this context, the OSLC (Open Services for Lifecycle Collaboration) initiative pursues the creation of public specifications (data shapes) to exchange any artefact generated during the development lifecycle, by applying the principles of the Linked Data initiative. In this paper, the authors present a solution to provide a real multi-format system artefact reuse by means of an OSLC-based specification to share and exchange any artefact under the principles of the Linked Data initiative. Finally, two experiments are conducted to demonstrate the advantages of enabling an input/output interface based on an OSLC implementation on top of an existing commercial tool (the Knowledge Manager). Thus, it is possible to enhance the representation and retrieval capabilities of system artefacts by considering the whole underlying knowledge graph generated by the different system artefacts and their relationships. After performing 45 different queries over logical and physical models stored in Papyrus, IBM Rhapsody and Simulink, results of precision and recall are promising showing average values between 70-80%.
A Hybrid Model for Social Media Sentiment Analysis for Indonesian Text

Sentiment analysis for Indonesian social media text is very important because the text content in social media is very diverse and requires an accurate method that can produce an analysis describing the state of the actual data. The main problem in sentiment analysis for Indonesian text on social media is unstructured text data and the use of non-standard languages such that sentiment analysis often produces errors. This paper focuses on sentiment analysis using a hybrid model that combines lexicon based and maximum entropy methods to classify the sentiments of Indonesian public opinion on government. The method consists of extracting datasets, preprocessing, lexicon-based classification, machine learning training, machine learning classification, and result interpretation. The results of the study produce 91 classifications of neutral sentiment, 51 document negative sentiments, 39 document positive sentiments and 152 document of mix sentiments. Based on the evaluation results, the hybrid sentiment model for Indonesian Language sentiment analysis on social media produced a pretty good accuracy score of 84.31% compared to previous studies. The implication of this study is to produce a sentiment analysis system with a hybrid method for Indonesian text on social media.
Big Data Capabilities and Firm's Performance: A Mediating Role of Competitive Advantage

Big data (BD) capabilities have now grabbed the attention of strategic management researchers because of their significant role in firm’s performance and success. Nonetheless, very little empirical evidence has so far been put forward about the contribution of BD capabilities towards a firm performance and Competitive Advantage (CA). This research examines the impact of Big Data Technological Capabilities (BDTC) and Big Data Personal Capabilities (BDPC) on firms’ performance with a mediating role of CA among the firms operating in the world’s top emerging economy such as China. Data were collected through structured questionnaires using a sample size of 312 firms. The hypotheses were tested in AMOS.21 using Structural Equation Modelling (SEM). The results indicate that BDTC and BDPC have significant positive impact on firm performance and CA and there is also significant positive relationship between CA and firm performance. The study highlights that CA partially mediates the relationship between BDTC and firm performance as well as between BDPC and firm performance. Hence, BD-oriented firms achieve CA which in turn leads to superior performance in dynamic markets. This study discussed implications for managerial practices.
Set Labelling using Multi-label Classification

We propose the task of set labelling. Starting from some examples members of a set, set labelling tries to infer the most appropriate labels for the given set. For this work, we consider sets of words. We illustrate the task and a possible solution with an application to the classification of cosmetic products and hotels. The novel solution proposed in this research is to incorporate a multi-label classifier trained from the labeled datasets. We use vectorization of the description of the seeds as input to the classifier as well as labels assigned to it. Given a previously unseen data, the trained classifier returns a ranked list of candidate labels (i.e., additional seeds) for the set. These results could then be used to infer the labels for the set. We implement our proposed solution to the classification of cosmetic products and hotels. We show that the solution is effective and efficient.
An Approach for Unknown Word Processing based on Japanese Phonological Rules

A morphological analyzer is one of the essential tools for natural language processing in studying Asian languages. One of the errors in morphological analysis is the existence of unknown words. Unknown words are not contained in dictionaries of the morphological analyzer, Consequently, many researchers usually construct rules for detecting morphemes from the unknown words based on knowledge obtained from error analyses. However, the constructed rules in previous researches might be insufficient in both quality and quantity, because these rules were usually found by computationally or manually visual recognition. For this reason, we propose a method for constructing rules based on phonology concerned with the systematic organization of sounds in languages. Phonology covers every linguistic analysis at all levels of language where sound is considered to be structured for conveying linguistic meaning. We deal with obvious rules in phonology such as vowel coalescence, haplology, prosodic shortening, prosodic lengthening, and the generated pattern of onomatopoeia. We evaluate if a morphological analyzer detect known words which are converted into unknown words accurately by our method.
Growth Analysis of Purchase History Graph Based on Relative Value of Edge Multiplicity

We propose a Purchase History Graph (PHG) growth analysis method based on the relative value of edge multiplicity to extract structure that contributes to PHG growth. In a PHG, popular products promote the appearance of various new items and purchasing behavior, leading to the growth of PHG. Therefore, extracting items that contribute to growth is critical. However, when items are extracted based on the multiplicity of edges such as the number of purchases, it is difficult to extract items that fuel growth in its early stages. Here, by extracting items based on the relative value (Z-score) with a multiplicity of peripheral edges, we can extract items regardless of the stage of growth. By observing the transition of the Z-score of a sufficiently grown item, we can obtain conclusions from which we can derive suggestions about how an edge will increase before growth. From the results of an evaluation experiment on the data of an EC site, we concluded: 1) structure was extracted that expands the peripheral portion of the graph, and 2) the transition of the Z-score depends on the purchasing behavior at the early stage of growth.
ICT as an Enabler for a Society where No One is Left Behind

The United Nations Agenda for Sustainable Development was unanimously adopted by all its 193 heads of member states or their representatives. "No-one should be left behind" is the ethical imperative of this development agenda. The agenda consists of seventeen Sustainable Development Goals (SDGs) with 169 targets. Unfortunately not even one of the (SDGs) is dedicated to ICT or Digitalization. Prima facie, the potential impact of ICT and digitalization is highly underestimated in the text of the 2030 Sustainable Development Agenda. This paper is aiming at pointing out the indispensable role of ICT for reaching the SDGs by 2030.
Adaptive-state feedback control for Lipschitz nonlinear systems in reciprocal-state space: Design and experimental results

This article proposes a design of an adaptive control for nonlinear systems which satisfy the Lipschitz condition. The objective is the use of the new reformulation of reciprocal-state space form in adaptive control designs. The presented controller is composed of a state-derivative feedback approach with adaptive gains based on the Lyapunov stability theorem. The first control approach deals with the case of system stabilization. The second is an extension to the tracking problem. High performances are shown through real-time implementation with digital signal processing device (ARDUINO UNO R3 and MEGA 2560).
Fast Algorithm for Integrating Clustering with Ranking on Heterogeneous Graphs

The demands for graph data analysis methods, e.g., clustering and ranking, are increasing. RankClus is a framework to extract good clusters by integrating clustering and ranking on heterogeneous graphs; it enhances the clustering results by alternately updates the results of clustering and ranking for the better understanding of the clusters. However, RankClus is computationally expensive if a graph is large since it needs to iterate both clustering and ranking for all nodes. In this paper, to address this problem, we propose a novel fast RankClus algorithm for heterogeneous graphs. To speed up the entire procedure of RankClus, our proposed algorithm reduces the computational cost of the ranking process in each iteration. Our proposal measures how each node affects the clustering result; if it is not significant, we prune the node. Our extensive evaluations showed that our proposal is much faster than the original method.