English Article Style Recognition and Matching by Using Web Semantics

With the explosion of internet information, people feel helpless and difficult to choose in the face of massive information. However, the traditional method to organize a huge set of original documents is not only time-consuming and laborious, but also not ideal. The automatic text classification can liberate users from the tedious document processing work, recognize and distinguish different document contents more conveniently, make a large number of complicated documents institutionalized and systematized, and greatly improve the utilization rate of information. This paper adopts termed-based model to extract the features in web semantics to represent document. The extracted web semantics features are used to learn a reduced support vector machine. The experimental results show that the proposed method can correctly identify most of the writing styles.

Research on Multi-label Text Classification Method Based on tALBERT-CNN

Single-label classification technology has difficulty meeting the needs of text classification, and multi-label text classification has become an important research issue in natural language processing (NLP). Extracting semantic features from different levels and granularities of text is a basic and key task in multi-label text classification research. A topic model is an effective method for the automatic organization and induction of text information. It can reveal the latent semantics of documents and analyze the topics contained in massive information. Therefore, this paper proposes a multi-label text classification method based on tALBERT-CNN: an LDA topic model and ALBERT model are used to obtain the topic vector and semantic context vector of each word (document), a certain fusion mechanism is adopted to obtain in-depth topic and semantic representations of the document, and the multi-label features of the text are extracted through the TextCNN model to train a multi-label classifier. The experimental results obtained on standard datasets show that the proposed method can extract multi-label features from documents, and its performance is better than that of the existing state-of-the-art multi-label text classification algorithms.

Diskursus Khilafah dalam Kajian Fiqh Siyasah Klasik dan Kontemporer

Until now, the Muslim community in Indonesia is flooded with massive information about the discourse of the obligation to establish a caliphate. Some argue that establishing a caliphate is an obligation for all Muslims. This article discusses the caliphate discourse in the study of fiqh al-siyasa in the classical era and the contemporary era. The discourse on the caliphate in the contemporary era has warmed up after the collapse of the Ottoman Turks in 1924, the debate on theme of the caliphate in the contemporary era is about whether there is a standard form of government and Islam, the legal status of establishing a caliphate, and so on. This theme is different from the themes of the discussion of the caliphate in the classical era, in this era, themes the caliphate revolved around aspects of Islamic leadership values, such as procedures for choosing a caliph/leader, political ethics that must be held by leaders and other state officials.

Expanding the Current Boundaries of Nature-based Modeling and Computing: Chem-Inspiration for Meta-Heuristics

High-throughput methods in science have created a trend to generate massive amount of data that challenge our ability to mine and search through massive information spaces. Thus more efficient and effective solutions for data analysis and optimization are required continuously. The best solutions for many problems-solving approaches in science could have many sources of inspiration coming from diverse natural phenomena. In this context, most Artificial Intelligence (AI) approaches benefit from emulation natural processes for their information processing strategy. Among the AI protocols, meta-heuristic algorithms for learning model and optimization have exploited a number of biological phenomena leading to highly effective search and learning engines. Examples of these processes are the ant colony organization, brain function and genetics among others. The evolution has turned all these biological events in highly efficient procedures, whose basics principles have then provided an excellent ground of new computational algorithms The aim of this report is pave the way to a new class of nature-based meta-heuristic methods which shall be based on diverse chemical and biomolecular systems. We present five examples from different subjects of Chemistry like Organic Chemistry, Chemical Physics and Biomolecules; and introduce how computational models could be inferred from them. Besides, we develop one of these models, in detail, which is based on protein evolution and folding principles. We consider that the wealth of systems and processes related to Chemistry, as those described in the present communication, might boost the development of relevant meta-heuristic and classification algorithms in upcoming years.

Multimedia Filtering Analysis of Massive Information Combined with Data Mining Algorithms

With the advent of the big data era, information presentation has exploded. For example, rich methods such as audio and video have integrated more information, but with it, a lot of bad information has been brought. In view of this situation, this paper relies on data mining algorithms, builds a multimedia filtering system model for massive information, and integrates content recognition, packet filtering, and other technologies to match the two to ensure the integrity and real time of filtering. Practice results prove that the method is effective.

Examining Users’ Concerns while Using Mobile Learning Apps

<p class="0abstract">Mobile learning applications (apps) are increasingly and widely adopted for learning purposes and educational content delivery globally, especially with the massive means of accessing the internet done majorly on mobile handheld devices. Users often submit their feedback on use, experience and general satisfaction via the reviews and ratings given in the digital distribution platforms. With this massive information given through the reviews, it presents an opportunity to derives valuable insights which can be utilized for various reasons and by different stakeholders of these mobile learning apps. This large volume of online reviews creates significant information overload which presents a time-consuming task to read through all reviews. By combining text mining techniques of topic modeling using Latent Dirichlet Algorithm (LDA) and sentiment analysis using Linguistic Inquiry Word Count (LIWC), we analyze these user reviews. These techniques identify inherent topics in the reviews and identifies variables of user satisfaction of mobile learning apps. The thematic analysis done reveals different keywords which guide classification into the topics identified. Conclusively, the topics derived are important to app stakeholders for further modifications and evolution tasks.</p>

The promotion of Aisyiyah Bustanul Athfal (ABA) Kalangan Kindergarten in Bantul, using outdoor advertising and digital media

Aisyiyah Bustanul Athfal (ABA) Kalangan Kindergarten is situated in Baturetno village, Banguntapan, Bantul, Yogyakarta. This strategic location rests on the border between Bantul and Sleman regencies. However, the student population has continuously declined over the years due to poor communication, both within the environment and online. Modern competition requires massive information in the form of progressive promotions, either using outdoor advertising or digital media. This is because parents tend to become more aware of the school’s existence by these efforts. Apart from the two concerns, this program is also aimed at increasing the school’s identity with outdoor and digital media advertising using the FGD method, followed by content development and update activities. The result of this campaign is the installation of posters outside the school premises and the increase in related publications in the digital space.

Applications of Advanced Analysis Technologies in Precise Governance of Social Media Rumors

Social media rumor precise governance is conducive to better coping with the difficulties of rumor monitoring within massive information and improving rumor governance effectiveness. This paper proposes a conceptual framework of social media rumor precise governance system based on literature mining. Accordingly, insightful directions for achieving social media rumor precise governance are introduced, which includes (1) rational understanding of social media rumors, especially large-scale spreading false rumors and recurring false rumors; (2) clear classification of rumor spreaders/believers/refuters/unbelievers; (3) scientific evaluation of rumor governance effectiveness and capabilities. For the above three directions, advanced analysis technologies applications are then summarized. This paper is beneficial to clarify and promote the promising thought of social media rumor precise governance and create impacts on the technologies’ applications in this area.

PARTISIPASI MASYARAKAT DALAM PEMILU SERENTAK TAHUN 2019 DI KABUPATEN BULELENG

BSTRACTThe 2019 election is the first simultaneous election in Indonesia. In addition to electing the presidentand vice president, the 2019 election also elects members of the DPR RI, DPD, Provincial DPR, andCity Regency DPRD. The 2019 simultaneous elections were implemented after previously there was alawsuit against Law No. 42 of 2008. One of the most important things in a democratic climate is publicparticipation in the policy-making process and political process. One form of public participation inthe policy-making process and political process is the involvement of citizens in the election process.This study aims to determine the extent of the participation of the Buleleng people in the 2019simultaneous elections. To obtain the results to be achieved as mentioned above, this study uses aqualitative method by collecting data from interviews to document studies. The results of this studyindicate that the number of users of the right to vote in Buleleng Regency is 439,787 people. Inpercentage terms, public participation in simultaneous elections in Buleleng Regency is 73.91%. In the2019 simultaneous elections, there were 596,589 voters in Buleleng. Meanwhile, the number of votingrights users reached 439,787 people who were divided into 2,146 polling stations. The highparticipation rate is due to the success of KPU socialization, massive information in the media. Of allthe regencies in Bali, Buleleng was recorded as one of the districts with a fairly high invalid vote rateof 37,449 while the number of valid votes was 401,818. The high number of invalid votes is due totechnical difficulties, especially for the elderly. In Buleleng Regency itself, the elderly voter rate is thehighest compared to other districts and cities with a total of 92,101 with the number of male voters is42,170 and the number of female voters is 49,931. Keywords: Buleleng Regency, Public Participation, Election, Political Communication

Detecting the public’s information behaviour preferences in multiple emergency events

Due to the frequent occurrence of public emergency events and the extensive use of social media in recent years, the public is more and more involved in the communication of public emergencies. As important members of the public, the rapid and massive information sharing of social media users makes them play an increasingly crucial role in the emergency processing. Hence, it is necessary to analyse information behaviours of social media users in emergency events. This article mined the information behaviours of users in multiple events to reflect the public’s behaviour preferences, aiming to provide information support for emergency handling. Specifically, we collected the user-generated contents related to emergency events, and then analysed the user-generated contents from multiple dimensions to obtain the corresponding information behaviours. Finally, based on the comparative analysis of four events, the information behaviour preferences of the public during emergencies were obtained. The experimental results indicate that the public’s behaviours in emergencies are related to their own interests and economic status, and curiosity about the details of events is the consistent appeal of the public.