Showing papers in "Journal of Intelligent Information Systems in 2019"

Design of a software architecture supporting business-to-government information sharing to improve public safety and security : Combining business rules, Events and blockchain technology

Abstract: To ensure public safety and security, it is vitally important for governments to collect information from businesses and analyse it. Such information can be used to determine whether transported goods might be suspicious and therefore require physical inspection. Although businesses are obliged to report some information, they are reluctant to share additional information for fear of sharing competitively sensitive information, becoming liable and not being compliant with the law. These reasons are often overlooked in the design of software architectures for information sharing. In the present research, we followed a design science approach to develop a software architecture for business-to-government information sharing. Based on literature and a case study, we elicited the requirements an architecture that provides for the sharing of information should meet to make it acceptable to businesses. We then developed the architecture and evaluated it against the requirements. The architecture consists of a blockchain that stores events and rules for information sharing that are controlled by businesses. For each event, two parties use their private keys to encrypt its Merkle root to confirm that they know the data are correct. This makes it easy to check whether information is reliable and whether an event should be accepted. Access control, metadata and context information enable the context-based sharing of information. This is combined with the encryption and decryption of data to provide access to certain data within an organisation.

An overview of recommender systems in the internet of things

Abstract: The Internet Of Things (IoT) is an emerging paradigm that envisions a networked infrastructure enabling different types of devices to be interconnected. It creates different kinds of artifacts (e.g., services and applications) in various application domains such as health monitoring, sports monitoring, animal monitoring, enhanced retail services, and smart homes. Recommendation technologies can help to more easily identify relevant artifacts and thus will become one of the key technologies in future IoT solutions. In this article, we provide an overview of existing applications of recommendation technologies in the IoT context and present new recommendation techniques on the basis of real-world IoT scenarios.

Collaborative filtering recommendation based on trust and emotion

Abstract: With the development of personalized recommendations, information overload has been alleviated However, the sparsity of the user-item rating matrix and the weak transitivity of trust still affect the recommendation accuracy in complex social network environments Additionally, collaborative filtering based on users is vulnerable to shilling attacks due to neighbor preference recommendation With the objective of overcoming these problems, a collaborative filtering recommendation method based on trust and emotion is proposed in this paper First, we employ a method based on explicit and implicit satisfaction to alleviate the sparsity problems Second, we establish trust relationships among users using objective and subjective trust Objective trust is determined by similarity of opinion, including rating similarity and preference similarity Subjective trust is determined by familiarity among users based on six degrees of separation Third, based on the trust relationship, a set of trusted neighbors is obtained for a target user Next, to further exclude malicious users or attackers from the neighbors, the set is screened according to emotional consistency among users, which is mined from implicit user behavior information Finally, based on the ratings of items by the screened trusted neighbors and the trust relationships among the target user and these neighbors, we can obtain a list of recommendations for the target user The experimental results show that the proposed method can improve the recommendation accuracy in the case of data sparsity, effectively resist shilling attacks, and achieve higher recommendation accuracy for cold start users compared to other methods

Word sense disambiguation application in sentiment analysis of news headlines: an applied approach to FOREX market prediction

Abstract: Sentiment analysis of textual content has become a popular approach for market prediction. However, lack of a process for word sense disambiguation makes it questionable whether the sentiment expressed by the context is correctly identified. Meanwhile, many studies in natural language processing have focused on word sense disambiguation. However, there has been a weak link between the two logically relevant fields of study. Therefore, with two motivations, we propose a system for FOREX market prediction that exploits word sense disambiguation in sentiment analysis of news headlines and predicts the directional movement of a currency pair. The first motivation is the implementation of a novel word sense disambiguation that can determine the proper senses of all significant words in a news headline. The main contributions of this work that make the first motivation possible, are the introduction of novel approaches termed Relevant Gloss Retrieval, Similarity Threshold, Verb Nominalization, and also optimization measures to decrease execution time. The second motivation is to prove that determination of proper senses of significant words in textual contents can improve the determination of sentiment, conveyed by the context, and consequently any application based on sentiment analysis. Inclusion of the word sense disambiguation into the proposed system proves the achievement of the second motivation. Carried out tests with the same dataset prove that the proposed system outperforms one of the best systems (to our best knowledge) proposed for market prediction and improves accuracy from 83.33% to 91.67%. The detail for reproduction of the system is amply provided.

Persistent entropy for separating topological features from noise in vietoris-rips complexes

Abstract: Persistent homology studies the evolution of k-dimensional holes along a nested sequence of simplicial complexes (called a filtration). The set of bars (i.e. intervals) representing birth and death times of k-dimensional holes along such sequence is called the persistence barcode. k-Dimensional holes with short lifetimes are informally considered to be “topological noise”, and those with long lifetimes are considered to be “topological features” associated to the filtration. Persistent entropy is defined as the Shannon entropy of the persistence barcode of the filtration. In this paper we present new important properties of persistent entropy of Vietoris-Rips filtrations. Later, using these properties, we derive a simple method for separating topological noise from features in Vietoris-Rips filtrations.

EVE: explainable vector based embedding technique using Wikipedia

Abstract: We present an unsupervised explainable vector embedding technique, called EVE, which is built upon the structure of Wikipedia. The proposed model defines the dimensions of a semantic vector representing a concept using human-readable labels, thereby it is readily interpretable. Specifically, each vector is constructed using the Wikipedia category graph structure together with the Wikipedia article link structure. To test the effectiveness of the proposed model, we consider its usefulness in three fundamental tasks: 1) intruder detection—to evaluate its ability to identify a non-coherent vector from a list of coherent vectors, 2) ability to cluster—to evaluate its tendency to group related vectors together while keeping unrelated vectors in separate clusters, and 3) sorting relevant items first—to evaluate its ability to rank vectors (items) relevant to the query in the top order of the result. For each task, we also propose a strategy to generate a task-specific human-interpretable explanation from the model. These demonstrate the overall effectiveness of the explainable embeddings generated by EVE. Finally, we compare EVE with the Word2Vec, FastText, and GloVe embedding techniques across the three tasks, and report improvements over the state-of-the-art.

Deep recurrent convolutional networks for inferring user interests from social media

Abstract: Online social media services, such as Facebook and Twitter, have recently increased in popularity. Although determining the subjects of individual posts is important for extracting users’ interests from social media, this task is nontrivial because posts are highly contextualized, informal, and limited in length. To address this problem, we propose a deep-neural-network-based approach for predicting user interests in social media. In our framework, a word-embedding technique is used to map the words in social media content into vectors. These vectors are used as input to a bidirectional gated recurrent unit (biGRU). Then, the output of the biGRU and the word-embedding vectors are used to construct a sentence matrix. The sentence matrix is then used as input to a convolutional neural network (CNN) model to predict a user’s interests. Experimental results show that our proposed method combining biGRU and CNN models outperforms existing methods for identifying users’ interests from social media. In addition, posts in social media are sensitive to trends and change with time. Here, we collected posts from two different social media platforms at different time intervals, and trained the proposed model with one set of social media data and tested it with another set of social media data. The experimental results showed that our proposed model can predict users’ interests from the independent data set with high accuracies.

A framework for aspect based sentiment analysis on turkish informal texts

Abstract: The web provides a suitable media for users to share opinions on various topics, including consumer products, events or news. In most of such content, authors express different opinions on different features (i.e., aspects) of the topic. It is a common practice to express a positive opinion on one aspect and a negative opinion on another aspect within the same post. Conventional sentiment analysis methods do not capture such details, rather an overall sentiment score is generated. In aspect based sentiment analysis, the opinions expressed for each aspect are extracted separately. To this aim, basically a two-phased approach is used. The first phase is aspect extraction, which is the detection of words that correspond to aspects of the topic. Once aspects are available, the next phase is to match aspects with the sentiment words in the text. In this work, we present a framework for the aspect based sentiment analysis problem on Turkish informal texts. We particularly emphasize the following contributions: for the first phase, improvements for aspect extraction as an unsupervised method, and for the second phase, enhancements for two cases, extracting implicit aspects and detecting sentiment words whose polarity depends on the aspect. Additionally, we present a tool including the implementations of the proposed algorithms, and a GUI to visualize the analysis results. The experiments are conducted on a collection of Turkish informal texts from an online products forum.

CBSSD: community-based semantic subgroup discovery

Abstract: Modern data mining algorithms frequently need to address the task of learning from heterogeneous data, including various sources of background knowledge. A data mining task where ontologies are used as background knowledge in data analysis is referred to as semantic data mining. A specific semantic data mining task is semantic subgroup discovery: a rule learning approach enabling ontology terms to be used in subgroup descriptions learned from class labeled data. This paper presents Community-Based Semantic Subgroup Discovery (CBSSD), a novel approach that advances ontology-based subgroup identification by exploiting the structural properties of induced complex networks related to the studied phenomenon. Following the idea of multi-view learning, using different sources of information to obtain better models, the CBSSD approach can leverage different types of nodes of the induced complex network, simultaneously using information from multiple levels of a biological system. The approach was tested on ten data sets consisting of genes related to complex diseases, as well as core metabolic processes. The experimental results demonstrate that the CBSSD approach is scalable, applicable to large complex networks, and that it can be used to identify significant combinations of terms, which can not be uncovered by contemporary term enrichment analysis approaches.

One-pass MapReduce-based clustering method for mixed large scale data

Abstract: Big data is often characterized by a huge volume and a mixed types of attributes namely, numeric and categorical. K-prototypes has been fitted into MapReduce framework and hence it has become a solution for clustering mixed large scale data. However, k-prototypes requires computing all distances between each of the cluster centers and the data points. Many of these distance computations are redundant, because data points usually stay in the same cluster after first few iterations. Also, k-prototypes is not suitable for running within MapReduce framework: the iterative nature of k-prototypes cannot be modeled through MapReduce since at each iteration of k-prototypes, the whole data set must be read and written to disks and this results a high input/output (I/O) operations. To deal with these issues, we propose a new one-pass accelerated MapReduce-based k-prototypes clustering method for mixed large scale data. The proposed method reads and writes data only once which reduces largely the I/O operations compared to existing MapReduce implementation of k-prototypes. Furthermore, the proposed method is based on a pruning strategy to accelerate the clustering process by reducing the redundant distance computations between cluster centers and data points. Experiments performed on simulated and real data sets show that the proposed method is scalable and improves the efficiency of the existing k-prototypes methods.

Measuring network reliability and repairability against cascading failures

Abstract: Cascading failures on techno-socio-economic systems can have dramatic and catastrophic implications in society. A damage caused by a cascading failure, such as a power blackout, is complex to predict, understand, prevent and mitigate as such complex phenomena are usually a result of an interplay between structural and functional non-linear dynamics. Therefore, systematic and generic measurements of network reliability and repairability against cascading failures is of a paramount importance to build a more sustainable and resilient society. This paper contributes a probabilistic framework for measuring network reliability and repairability against cascading failures. In contrast to related work, the framework is designed on the basis that network reliability is multifaceted and therefore a single metric cannot adequately characterize it. The concept of ‘repairability envelope’ is introduced that illustrates trajectories of performance improvement and trade-offs for countermeasures against cascading failures. The framework is illustrated via four model-independent and application-independent metrics that characterize the topological damage, the network spread of the cascading failure, the evolution of its propagation, the correlation of different cascading failure outbreaks and other aspects by using probability density functions and cumulative distribution functions. The applicability of the framework is experimentally evaluated in a theoretical model of damage spread and an empirical one of power cascading failures. It is shown that the reliability and repairability in two systems of a totally different nature undergoing cascading failures can be better understood by the same generic measurements of the proposed framework.

Density-based unsupervised ensemble learning methods for time series forecasting of aggregated or clustered electricity consumption

Abstract: This paper presents a comparison of the impact of various unsupervised ensemble learning methods on electricity load forecasting. The electricity load from consumers is simply aggregated or optimally clustered to more predictable groups by cluster analysis. The clustering approach consists of efficient preprocessing of data obtained from smart meters by a model-based representation and the K-means method. We have implemented two types of unsupervised ensemble learning methods to investigate the performance of forecasting on clustered or simply aggregated load: bootstrap aggregating based and the newly proposed density-clustering based. Three new bootstrapping methods for time series analysis methods were newly proposed in order to handle the noisy behaviour of time series. The smart meter datasets used in our experiments come from Australia, London, and Ireland, where data from residential consumers were available. The achieved results suggest that for extremely fluctuating and noisy time series the forecasting accuracy improvement through the bagging can be a challenging task. However, our experimental evaluation shows that in most of the cases the density-based unsupervised ensemble learning methods are significantly improving forecasting accuracy of aggregated or clustered electricity load.

Pendidikan Sejarah Sebagai Penguat Pendidikan Karakter

Abstract: Pendidikan tidak hanya diartikan sebagai transfer pengetahuan melainkantransfer nilai, terutama nilai nilai yang terkandung dalam 18 nilai karakteryang ditargetkan dalam pendidikan karakter. Pendidikan adalah upayauntuk membentuk karakter siswa sehingga mereka dapat mengetahui danmembedakan antara yang baik dan buruk dalam kehidupanbermasyarakat, berbangsa dan bernegara. Pendidikan karakter saat inisangat diperlukan untuk mengatasi masalah generasi penerus bangsa iniyang semakin sulit dikendalikan. Pembelajaran sejarah sebagai pendukungpendidikan karakter memiliki peran yang sangat sentral karenapembelajaran sejarah memiliki lingkup materi sebagai berikut: (1)mengandung nilai-nilai heroik, teladan, perintis, patriotisme, nasionalisme,dan semangat pantang menyerah yang mendasari proses pembentukankarakter dan kepribadian siswa; (2) berisi repertoar peradaban bangsatermasuk peradaban Indonesia; (3) menanamkan kesadaran persatuan dan persaudaraan dan solidaritasuntuk menjadi bangsa yang bersatu dalam menghadapi ancaman disintegrasi; (4) mengandung ajaran dankebijaksanaan moral yang berguna dalam mengatasi krisis multidimensi yang dihadapi dalam kehidupansehari-hari; (5) menanamkan dan mengembangkan sikap tanggung jawab dalam menjaga keseimbanganlingkungan dan keberlanjutan. Dilihat dari ruang lingkup, sangat tepat jika pembelajaran sejarahdigunakan untuk mendukung pendidikan karakter.

Real-time recommendation with locality sensitive hashing

Abstract: Neighborhood-based collaborative filtering (CF) methods are widely used in recommender systems because they are easy-to-implement and highly effective. One of the significant challenges of these methods is the ability to scale with the increasing amount of data since finding nearest neighbors requires a search over all of the data. Approximate nearest neighbor (ANN) methods eliminate this exhaustive search by only looking at the data points that are likely to be similar. Locality sensitive hashing (LSH) is a well-known technique for ANN search in high dimensional spaces. It is also effective in solving the scalability problem of neighborhood-based CF. In this study, we provide novel improvements to the current LSH based recommender algorithms and make a systematic evaluation of LSH in neighborhood-based CF. Besides, we make extensive experiments on real-life datasets to investigate various parameters of LSH and their effects on multiple metrics used to evaluate recommender systems. Our proposed algorithms have better running time performance than the standard LSH-based applications while preserving the prediction accuracy in reasonable limits. Also, the proposed algorithms have a large positive impact on aggregate diversity which has recently become an important evaluation measure for recommender algorithms.

Clustering of semantically enriched short texts

Abstract: The paper is devoted to the issue of clustering small sets of very short texts. Such texts are often incomplete and highly inconclusive, so establishing a notion of proximity between them is a challenging task. In order to cope with polysemy we adapt the SenseSearcher algorithm (SnS), by Kozlowski and Rybinski in Computational Intelligence 33(3): 335–367, 2017b. In addition, we test the possibilities of improving the quality of clustering ultra-short texts by means of enriching them semantically. We present two approaches, one based on neural-based distributional models, and the other based on external knowledge resources. The approaches are tested on SnSRC and other knowledge-poor algorithms.

ISoTrustSeq: a social recommender system based on implicit interest, trust and sequential behaviors of users using matrix factorization

Abstract: Recommender systems try to propose a list of main interests of an on line social network user based on his predicted rating values. In the recent years, several methods are proposed such as Interest Social Recommendation method (ISoRec), and Social Recommendation method based on trust Sequence Matrix Factorization which employs matrix factorization techniques to address the trust propagation and sequential behaviors issues. Main drawback of these works is that they ignore implicit interest of users. Therefore, the main goal of this paper is to solve user-item rating based on the trust, sequential interest and the implicit interest of users, simultaneously. In order to solve this problem, our proposed method combines these parameters as its inputs. This method based on matrix factorization named as ISoTrustSeq. Experimental results show higher accuracy of predicted values in compared to the above-mentioned methods. Our results are also much better than these methods in terms of variation in the number of user-items features.

Business information architecture for successful project implementation based on sentiment analysis in the tourist sector

Abstract: In the today’s market, there is a wide range of failed IT projects in specialized small and medium-sized companies because of poor control in the gap between the business and its vision. In other words, acquired goods are not being sold, a scenario which is very common in tourism retail companies. These companies buy a number of travel packages from big companies and due to lack of demand for these packages, they expire, becoming an expense, rather than an investment. To solve this problem, we propose to detect the problems that limit a company by re-engineering the processes, enabling the implementation of a business architecture based on sentimental analysis, allowing small and medium-sized tourism enterprises (SMEs) to make better decisions and analyze the information that most possess, without knowing how to exploit it. In addition, a case study was applied using a real company, comparing data before and after using the proposed model in order to validate feasibility of the applied model.

Validating data acquired with experimental multimodal biometric system installed in bank branches

Abstract: An experimental system was engineered and implemented in 100 copies inside a real banking environment comprising: dynamic handwritten signature verification, face recognition, bank client voice recognition and hand vein distribution verification. The main purpose of the presented research was to analyze questionnaire responses reflecting user opinions on: comfort, ergonomics, intuitiveness and other aspects of the biometric enrollment process. The analytical studies and experimental work conducted in the course of this work will lead towards methodologies and solutions of the multimodal biometric technology, which is planned for further development. Before this stage is achieved a study on the data usefulness acquired from a variety of biometric sensors and from survey questionnaires filled in by banking tellers and clients was done. The decision-related sets were approximated by the Rough Set method offering efficient algorithms and tools for finding hidden patterns in data. Prediction of evaluated biometric data quality, based on enrollment samples and on user subjective opinions was made employing the developed method. After an introduction to the principles of applied biometric identity verification methods, the knowledge modelling approach is presented together with achieved results and conclusions.

Trust inference using implicit influence and projected user network for item recommendation

Abstract: Trust plays a very important role in many existing e-commerce recommendation applications. Social or trust network among users provides additional information along with ratings for improving user reliability on the recommendation. However, in the real world due to the sparse nature of trust data, many algorithms are built for inferring trust. In this work, we propose a new path based trust inference method utilizing the implicit influence information available in the existing trust network. The proposed approach uses the transitivity property of the trust for trust propagation and scale-free complex network property to limit the propagation length in the network. In this regard, we define a new terminology, degree of trustworthiness for a user, which adds the global influence in the inferred trust along a path and considers the maximum trust gaining path between two users. To reduce the sparsity of the network further, we use the projected user network information from user-item feedback history to reconstruct the inferred trust and introduce two methods of reconstruction from the truster and trustee point of view. The proposed reconstruction process can infer the trusted neighbors for a user who has put no trust on others, so far. We have applied the techniques in two real-world datasets and achieved significant performance improvement from the existing trust-based and neighborhood-based methods.

Lightweight domain modeling for adaptive web-based educational system

Abstract: Support for adaptive learning with respect to increased interaction and collaboration over the educational content in state-of-the-art models of web-based educational systems is limited Explicit formalization of such models is necessary to facilitate extendibility, reusability and interoperability Domain models are the most fundamental parts of adaptive web-based educational systems providing a basis for majority of other functional components such as content recommenders or collaboration widgets and tools We introduce a collaboration-aware lightweight domain modeling for adaptive web-based learning, which provides a suitable representation for learning resources and metadata involved in educational processes beyond individual learning It introduces the concept of user annotations to the domain model, which enrich educational materials and facilitate collaboration Lightweight domain modeling is beneficial from the perspective of automated course semantics creation, while providing support towards automated semantic description of learner-generated content We show that the proposed model can be effectively utilized for intelligent processing of learning resources such as recommendation and can form a basis for interaction and collaboration supporting components of adaptive systems We provide the experimental evidence on successful utilization of lightweight domain model in adaptive educational platform ALEF over the period of five years involving more than 1,000 real-world students

Granular methods in automatic music genre classification: a case study

Abstract: Classification of music files by using the characteristics of the songs based on its genre is a very popular application of machine learning. The focus of this work is on automatic music genre classification based on granular computing methods (fuzzy rough, rough and near sets). We have proposed a modified form of supervised learning algorithm based on tolerance near sets (TCL 2.0) with a goal of exploring the scalability of the learning algorithm to a well researched music database composed of several genres. In the tolerance near set method, tolerance classes are directly induced from the dataset using the tolerance level e and a distance function. We have compared the tolerance-based near set algorithm to a family of nearest neighbour (NN) algorithms based on fuzzy rough methods (FRNN) available in the WEKA platform. In terms of performance, the classification accuracy of TCL 2.0 is identical to the Bayesian Networks (BN) Algorithm, and comparable with the Sequential Minimal Optimization (SMO) Algorithm. However, the average classification accuracy of FRNN algorithms and the classical rough sets algorithm is better than TCL 2.0, BN and SMO algorithms. For this dataset, any accuracy over 90% is considered a very good classification accuracy which is achieved by all tested classifiers in this work.

Strategi kepemimpinan kepala sekolah di era revolusi industri 4.0 berlandaskan pada konsep panca upaya sandhi

Abstract: Leadership is an important process in educational management activities. The right strategy is needed by a school principal in this fast-paced era. The strategy implemented is expected to realize the objectives set. Related to leadership, the Hindu community in Bali has the value of local wisdom which is used as a basis for carrying out leadership called the Panca Upaya Sandhi. If this local wisdom can be maintained and applied in line with the current style of modern leadership, it is expected to be able to optimize the quality of leadership that is carried out and also can maintain the values ​​inherited by the ancestors. The purpose of this research is to find out how the Principal's leadership strategy in the era of the industrial revolution 4.0 is based on the concept of the Panca Upaya Sandhi. The method used in this research is qualitative method. The data were collected through library research, where the information was obtained from the literature books, journals, regulations, research reports, monographs, mass media and written sources, both print and electronic. The results of this study are the Principal's leadership strategy that can be applied in the era of the industrial revolution 4.0 consisting of leadership strategies oriented to improving the quality of Human Resources (HR) and supporting facilities and infrastructures in the field of Information and Communication Technology (ICT), open leadership, leadership ready to face the unexpected, leadership that reacts quickly to changes, results-oriented leadership, leadership with formula 4C that is critical thinking, creativity, communication, collaboration, and leadership that are able to develop an entrepreneurial spirit. This leadership strategy can be optimized based on the philosophy of the Panca Upaya Sandhi consisting of Maya, Upeksa, Indra Jala, Vikrama, and Lokika.

Improving large-scale hierarchical classification by rewiring: a data-driven filter based approach

Abstract: Hierarchical Classification (HC) is a supervised learning problem where unlabeled instances are classified into a taxonomy of classes. Several methods that utilize the hierarchical structure have been developed to improve the HC performance. However, in most cases apriori defined hierarchical structure by domain experts is inconsistent; as a consequence performance improvement is not noticeable in comparison to flat classification methods. We propose a scalable data-driven filter based rewiring approach to modify an expert-defined hierarchy. Experimental comparisons of top-down hierarchical classification with our modified hierarchy, on a wide range of datasets shows classification performance improvement over the baseline hierarchy (i.e., defined by expert), clustered hierarchy and flattening based hierarchy modification approaches. In comparison to existing rewiring approaches, our developed method (rewHier) is computationally efficient, enabling it to scale to datasets with large numbers of classes, instances and features. We also show that our modified hierarchy leads to improved classification performance for classes with few training samples in comparison to flat and state-of-the-art hierarchical classification approaches. Source Code: https://cs.gmu.edu/~mlbio/TaxMod/

Activity prediction in process mining using the WoMan framework

Abstract: Process Management techniques are useful in domains where the availability of a (formal) process model can be leveraged to monitor, supervise, and control a production process. While their classical application is in the business and industrial fields, other domains may profitably exploit Process Management techniques. Some of these domains (e.g., people’s behavior, General Game Playing) are much more flexible and variable than classical ones, and, thus, raise the problem of predicting which activities will be carried out next, a problem that is not so compelling in classical fields. When the process model is learned automatically from examples of process executions, which is the task of Process Mining, the prediction performance may also provide indirect indications on the correctness and reliability of the learned model. This paper proposes and compares two strategies for activity prediction using the WoMan framework for workflow management. The former proved to be able to handle complex processes, the latter is based on the classic and consolidated Naive Bayes approach. An experimental validation allows us to draw considerations on the pros and cons of each, used both in isolation and in combination.

Model Penerapan Model Pembelajaran Kooperatif Tipe GI (Group Investigation) Untuk Meningkatkan Hasil Belajar IPS/Sejarah Siswa Kelas IXG SMP Negeri 3 Semarapura

Abstract: This research primarily aims to improve the achievement of learning history to the 9th year students of G SMP Negeri 3 Semarapura semester 1 in the year 2018/2019. This is a kind of class action research that involves 27 students of the 9th year of G SMP Negeri 3 Semarapura. The object of this research is the learning achievement of the 9th year students of G SMP Negeri 3 Semarapura semester 1 in the year 2018/2019 with the implementation of cooperative learning model GI (Group Investigation) type. This class action research is implemented into two cycles in which the students are learning achievement collected using a learning achievement test. The data which has been obtained then analyzed descriptively. The research achievement shows that the implementation of cooperative learning model on GI type can improve the students learning progress, as the following cycle I: the average is 7,19 the percentage of comprehending ability is 71,9 %, and classical achievement is 70,4 % cycle II: the standard is 7,93 the percentage of grasping knowledge is 79,3 %. The classical result is 100 %, and both are in a suitable qualification. Copyright © Universitas Pendidikan Ganesha. All rights reserved. * Corresponding author. E-mail addresses: ketut.sukasni@gmail.com A R T I C L E I N F O Article history: Received 14 Desember 2019 Accepted 23 Desember 2019 Available online 31 Desember 2019 Kata Kunci: Model Pembelajaran Kooperatip; GI; Hasil Belajar

Semantic similarity aggregators for very short textual expressions: a case study on landmarks and points of interest

Abstract: Semantic similarity measurement aims to automatically compute the degree of similarity between two textual expressions that use different representations for naming the same concepts. However, very short textual expressions cannot always follow the syntax of a written language and, in general, do not provide enough information to support proper analysis. This means that in some fields, such as the processing of landmarks and points of interest, results are not entirely satisfactory. In order to overcome this situation, we explore the idea of aggregating existing methods by means of two novel aggregation operators aiming to model an appropriate interaction between the similarity measures. As a result, we have been able to improve the results of existing techniques when solving the GeReSiD and the SDTS, two of the most popular benchmark datasets for dealing with geographical information.

Efficient question classification and retrieval using category information and word embedding on cQA services

Abstract: Classifying the task of automatically assigning unlabeled questions into predefined categories (or topics) and effectively retrieving a similar question are crucial aspects of an effective cQA service. We first address the problems associated with estimating and utilizing the distribution of words in each category of word weights. We then apply an automatic expansion word generation technique that is based on our proposed weighting method and the pseudo relevance feedback to question classification. Secondly to address the lexical gap problem in question retrieval, the case frame of the sentence is first defined using the extracted components of a sentence, and a similarity measure based on the case frame and the word embedding is then derived to determine the similarities between two sentences. These similarities are then used to reorder the results of the first retrieval model. Consequently, the proposed methods significantly improve the performance of question classification and retrieval.

Design of a clinician dashboard to facilitate co-decision making in the management of non-specific low back pain

Abstract: This paper presents the design of a Clinician Dashboard to promote co-decision making between patients and clinicians. Targeted patients are those with non-specific low back pain, a leading cause of discomfort, disability and absence from work throughout the world. Targeted clinicians are those in primary care, including general practitioners, physiotherapists, and chiropractors. Here, the functional specifications for the Clinical Dashboard are delineated, and wireframes illustrating the system interface and flow of control are shown. Representative scenarios are presented to exemplify how the system could be used for co-decision making by a patient and clinician. Also included are a discussion of potential barriers to implementation and use in clinical practice and a look ahead to future work. This work has been conducted as part of the Horizon 2020 selfBACK project, which is funded by the European Commission.

Pembelajaran PPKn Dalam Resonansi Kebangsaan dan Globalisasi

Abstract: Globalisasi menghadirkan tatanan baru dunia yang lebih terbuka akaninformasi dan modernisasi. Globalisasi tidak hanya memberikan nilaipositif bagi kehidupan manusia, tetapi juga tidak lepas dari pengaruhnegatif yang dibawanya dalam berbagai sisi kehidupan manusia.Dihadapkan pada persoalan globalisasi, tulisan ini memberikan potretbagaimana implementasi. Pancasila sebagai sumber nilai bagi adanyahukum dan kepribadian bangsa Indonesia di tengah-tengah pusaranglobalisasi. Pancasila dalam pusaran globalisasi harus tetap menjadiprinsip dan ideologi kebangsaan yang mampu membangkitkankeyakinan dan rasa percaya diri bahwa kita adalah bangsa yang terhormatdi dunia bukan sebaliknya. PPKn dalam tujuannya bagaimana mampumenghasilkan sifat dan perilaku warga yang baik dan bertanggung jawabserta bermanfaat bagi masyarakat, bangsa dan Negara. Salah satu caradalam mencapai tujuan ini adalah dengan memanfaatkan suatu identitasyang ada dalam suatu masyarakat dalam pembelajaran. Keberadaan suatu identitas bangsa akan ditopangoleh kebudayaan daerahnya masing-masing, dan kebudayaan adalah salah satu pembentuk karaktermasyarakat.

Inferring preferences in ontology-based recommender systems using WOWA

Abstract: In content-based semantic recommender systems the items to be considered are defined in terms of a set of semantic attributes, which may take as values the concepts of a domain ontology. The aim of these systems is to suggest to the user the items that fit better with his/her preferences, stored in the user profile. When large ontologies are considered it is unrealistic to expect to have complete information about the user preference on each concept. In this work, we explain how the Weighted Ordered Weighted Averaging operator may be used to deduce the user preferences on all concepts, given the structure of the ontology and some partial preferential information. The parameters of the WOWA operator enable to establish the desired aggregation policy, which ranges from a full conjunction to a full disjunction. Different aggregation policies have been analyzed in a case study involving the recommendation of touristic activities in the city of Tarragona. Several profiles have been compared and the results indicate that different aggregation policies should be used depending on the type of user. The amount of information available in the ontology must be also taken into account in order to establish the parameters of the proposed algorithm.