Reviewerly/exHarmony huggingface dataset:
Latent class model with application to speaker diarization

In this paper, we apply a latent class model (LCM) to the task of speaker diarization. LCM is similar to Patrick Kenny’s variational Bayes (VB) method in that it uses soft information and avoids premature hard decisions in its iterations. In contrast to the VB method, which is based on a generative model, LCM provides a framework allowing both generative and discriminative models. The discriminative property is realized through the use of i-vector (Ivec), probabilistic linear discriminative analysis (PLDA), and a support vector machine (SVM) in this work. Systems denoted as LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid are introduced. In addition, three further improvements are applied to enhance its performance. (1) Adding neighbor windows to extract more speaker information for each short segment. (2) Using a hidden Markov model to avoid frequent speaker change points. (3) Using an agglomerative hierarchical cluster to do initialization and present hard and soft priors, in order to overcome the problem of initial sensitivity. Experiments on the National Institute of Standards and Technology Rich Transcription 2009 speaker diarization database, under the condition of a single distant microphone, show that the diarization error rate (DER) of the proposed methods has substantial relative improvements compared with mainstream systems. Compared to the VB method, the relative improvements of LCM-Ivec-PLDA, LCM-Ivec-SVM, and LCM-Ivec-Hybrid systems are 23.5%, 27.1%, and 43.0%, respectively. Experiments on our collected database, CALLHOME97, CALLHOME00, and SRE08 short2-summed trial conditions also show that the proposed LCM-Ivec-Hybrid system has the best overall performance.
Pitfalls of Jason Concurrency

Jason is a well-known programming language for multiagent systems where fine-grained concurrency primitives allow a highly-concurrent efficient execution. However, typical concurrency errors such as race conditions are hard to avoid. In this chapter, we analyze a number of such potential pitfalls of the Jason concurrency model, and, describe both how such risks can be mitigated in Jason itself, as well as discussing the alternatives implemented in eJason, an experimental extension of Jason with support for distribution and fault tolerance. In some cases, we propose changes in the standard Jason semantics.
Pengaruh Fasilitas Belajar, Minat Belajar, Lingkungan Belajar dan Motivasi Belajar terhadap Hasil Belajar Siswa pada Mata Pelajaran IPS SMK Kabupaten Tanah Datar: Kajian

This research is aimed to see: (1) The influence of learning facilities toward students learning motivation in social science subject of vocational schools in Tanah Datar Regency (2) The influence of learning interest toward students learning motivation in social science subject of vocational schools in Tanah Datar Regency (3) The influence of learning environment toward students learning motivation in social science subject of vocational schools in Tanah Datar Regency (4) The influence of learning facilities toward students learning achievement in social science subject of vocational schools in Tanah Datar Regency (5) The influence of learning interest toward students learning achievement in social science subject of vocational schools in Tanah Datar Regency (6) The influence of learning environment toward students learning achievement in social science subject of vocational schools in Tanah Datar Regency (7) The influence of learning motivation toward students learning achievement in social science subject of vocational schools in Tanah Datar Regency. The kind of this research is causal descriptive. The research population is all students at grade XI of vocational schools in Tanah Datar Regency. Number of the population is 750 students in 2016/2017 academic year. The sample was taken by using the technic of Proportional Random Sampling. The research sampling is 261 students out of the whole population. Primary data in this research is taken from questionnaire given to the respondents, and the secondary data, is related to the research object which is presented by other authorities in the form of students learning achievement in final semester examination (UAS) in social science subject. The technic of data analysis is path analysis with SPSS program 15.0 Version. The research findings are: (1) Learning facilities has significant influence toward students learning motivation in social science subject of vocational schools in Tanah Datar Regency (2) Learning interest has significant influence toward students learning motivation in social science subject of vocational schools in Tanah Datar Regency (3) Learning environment has significant influence toward students learning motivation in social science subject of vocational schools in Tanah Datar Regency (4) Learning facilities have significant influence toward students learning achievement in social science subject of vocational schools in Tanah Datar Regency(5) Learning interest has significant influence toward students learning achievement in social science subject of vocational schools in Tanah Datar Regency(6) Learning environment has significant influence toward students learning achievement in social science subject of vocational schools in Tanah Datar Regency(7) Learning motivation has significant influence toward students learning achievement in social science subject of vocational schools in Tanah Datar Regency.
Adaptive Wide-Lens Distortion Correction Based on Piecewise Polynomial Optimization

An adaptive piecewise low-order mapping method that automatically extracts control point pairs from single orthogonal grid pattern is proposed to correct serious wide-lens distortion. Firstly, distorted control points (DCPs) are located by oriented searching along crossing lines in thinned grid image, and the relative distribution relationship between DCPs is obtained to estimate ideal control points in normalized grid pattern. Subsequently, a piecewise polynomial optimization problem, which minimizes two cost functions, is built to model whole radial distortion, thus adaptive pieces and corresponding segmented coefficients are optimized. Finally, an inverse mapping table is established to improve correction efficiency. Experimental results illustrate that the proposed method is effective and practical for high resolution wide-angle lens with perfect precision, and completely preserves the undistorted image edge.
Optoelectronic logic gates using the all-or-nothing response in a two-section excitable laser

We theoretically analyze and demonstrate a kind of novel optoelectronic logic gates with the possibility of massive scalability based on the all-or-nothing response in the vertical-cavity surface-emitting laser (VCSEL) with saturable absorber (SA). We show that the designed system exhibits sub-nanosecond response pulses under a single pulse perturbation and analyze the roles of bias pump current and SA injection current. Switch-controlled optoelectronic logic AND, OR and NOT gates are achieved, respectively, which grants the capacity for complex, ultrafast categorization and decision-making tasks.
Deriving Multiple Representation Database: A Model Generalisation Approach

Abstract. The main aim of the research being conducted is to automatically derive multiple representation database through model generalisation. In this regard, this paper presents a detailed methodology of the derivation process of multiple representation database. The topographic information of three different levels of detail (LoDs) viz., LoD1 (1 : 10,000), LoD2 (1 : 25,000) and LoD3 (1 : 250,000) are considered for this research. The abstract LoDs, i.e., LoD2 and LoD3 are derived automatically by model generalisation. The method for linking corresponding features in all these LoDs is shown and thus deriving multiple representation database. The major component of the proposed methodology is the model generalisation process. The design of the model generalisation process which includes generalisation operators, classification hierarchies, geometric constraints, functional hierarchies and topological constraints are discussed in this article. The proposed methodology is currently in the implementation stage.
THE EFFECT OF ANTICIPATION GUIDE STRATEGY ON STUDENTS’ READING COMPREHENSION

This research focuses on the influence of anticipation guide strategy on students' reading comprehension at the twelfth grade of MA KM Muhammadiyah Padang Panjang, 2017/2018 Academic Year. The research design was an experimental research conducted to the Science class as the participants of the research. The instrument of this research was a multiple-choice test distributed as the pre-test and post-test. As the result, the students’ reading comprehension became significantly better after being taught by the anticipation guide strategy. It is found that the pre-test average score was 42.36, and after the treatment, the post-test average score was 75.7. The result shows that the anticipation guide strategy has significant influence on the student’s reading comprehension.
Optimizing Word Embedding for Fine-Grained Sentiment Analysis

Word embeddings have been extensively used for various Natural Language Processing tasks. However, word vectors trained based on corpus context information fail to distinguish words with the same context but different semantics, which may lead to a similar word vector with opposite semantic terms. This will affect some Natural Language Processing tasks, such as fine-grained sentiment analysis tasks. In this paper, a new word vectors optimization model is proposed. This model can be applied to any pre-trained word vectors. Within a certain range, it can make the opposite semantic words away from each other, and the same semantic words are close to each other. The experimental results show that our model can improve the traditional word embedding in the fine-grained emotional analysis task of Chinese Weibo, and the optimized word vector using our model outperforms the unoptimized word vector.
Precursor-induced conditional random fields: connecting separate entities by induction for improved clinical named entity recognition

Abstract Background This paper presents a conditional random fields (CRF) method that enables the capture of specific high-order label transition factors to improve clinical named entity recognition performance. Consecutive clinical entities in a sentence are usually separated from each other, and the textual descriptions in clinical narrative documents frequently indicate causal or posterior relationships that can be used to facilitate clinical named entity recognition. However, the CRF that is generally used for named entity recognition is a first-order model that constrains label transition dependency of adjoining labels under the Markov assumption. Methods Based on the first-order structure, our proposed model utilizes non-entity tokens between separated entities as an information transmission medium by applying a label induction method. The model is referred to as precursor-induced CRF because its non-entity state memorizes precursor entity information, and the model’s structure allows the precursor entity information to propagate forward through the label sequence. Results We compared the proposed model with both first- and second-order CRFs in terms of their F 1 -scores, using two clinical named entity recognition corpora (the i2b2 2012 challenge and the Seoul National University Hospital electronic health record). The proposed model demonstrated better entity recognition performance than both the first- and second-order CRFs and was also more efficient than the higher-order model. Conclusion The proposed precursor-induced CRF which uses non-entity labels as label transition information improves entity recognition F 1 score by exploiting long-distance transition factors without exponentially increasing the computational time. In contrast, a conventional second-order CRF model that uses longer distance transition factors showed even worse results than the first-order model and required the longest computation time. Thus, the proposed model could offer a considerable performance improvement over current clinical named entity recognition methods based on the CRF models.
Does Rebranding As Name Change Rebuild Or Destroy Brand Equity (Customer Loyalty And Brand Reputation) After Brand Crisis?: The Case of Malaysian Airline System (MAS)

This paper explored on rebranding, brand reputation and customer loyalty.The purpose of this research study was to examine rebranding as a tool to rebuild brand reputation and restore customer loyalty after brand crisis in the Malaysia airline.The study utilized quantitative research design method to gather data from 375 respondents from the northern region of Malaysia.Seven Likert scale was used to measure the rebranding, brand reputation, and customer loyalty constructs.Statistical Package for the Social Sciences (SPSS) 20.0 was utilised to analyse the collected data.The finding affirmed that rebranding significantly rebuilds brand reputation after brand crisis.Additionally, the outcomes of this study equally revealed that rebranding significantly restore customer loyalty after brand crisis.This paper presents significant theoretical contribution for the academic knowledge purpose and practical contributions for practitioners, in making clear decisions before embarking on rebranding exercise.
Probabilistic Planning with Reduced Models

Reduced models are simplified versions of a given domain, designed to accelerate the planning process. Interest in reduced models has grown since the surprising success of determinization in the first international probabilistic planning competition, leading to the development of several enhanced determinization techniques. To address the drawbacks of previous determinization methods, we introduce a family of reduced models in which probabilistic outcomes are classified as one of two types: primary and exceptional. In each model that belongs to this family of reductions, primary outcomes can occur an unbounded number of times per trajectory, while exceptions can occur at most a finite number of times, specified by a parameter. Distinct reduced models are characterized by two parameters: the maximum number of primary outcomes per action, and the maximum number of occurrences of exceptions per trajectory. This family of reductions generalizes the well-known most-likely-outcome determinization approach, which includes one primary outcome per action and zero exceptional outcomes per plan. We present a framework to determine the benefits of planning with reduced models, and develop a continual planning approach that handles situations where the number of exceptions exceeds the specified bound during plan execution. Using this framework, we compare the performance of various reduced models and consider the challenge of generating good ones automatically. We show that each one of the dimensions---allowing more than one primary outcome or planning for some limited number of exceptions---could improve performance relative to standard determinization. The results place previous work on determinization in a broader context and lay the foundation for a systematic exploration of the space of model reductions.
Combining Deduplication and String Comparison for Avoiding Redundant Data with Enhanced Authentication Approach on Cloud

Every day activity many number of data and file has been generated.Every organization wants to store and main that information with efficient manner.Efficient denotes, storage space utilization, securely maintaining the organization records, accessing records quickly.On the organization server maintain the files some of them are stored for more than once.It will increase the utilization of memory.It will lead us to insufficient storage space.Sometimes outgoing data in the network connection contain the duplicate files so, network traffic is occurring (or) the transfer speed gets shrunk.So, here we propose a technique called data deduplication and we also use the algorithm of machine learning called string comparison to detect the redundant data and files.To ensure the security we also proposed hybrid authentication approach.
Research on the Design Principles for Intelligent Products

With the rapid development of technology, more and more intelligent products have emerged. They perceive the external environment and internal state, automatically analyze and make decisions based on real-time data to provide people with a more convenient way of life. Compared with traditional products, the main difference is that intelligent products can be combined with technology such as the Internet and artificial intelligence to form a software and hardware service system not just hardware functions or virtual network space. Intelligent products have become one of the focus issues discussed at present. This research explains the definition and classification of intelligent products and discusses their current status and trends from the technical, commercial and user perspectives through quantitative text analysis and literature review. Then expound the current status and trends of intelligence products from the perspective of design. Based on the work above, this research identifies the design principles for intelligent products, which academics can do further investigate on this topic and practitioners may find something helpful in the process of design work. It is foreseeable that the wave of intelligent products is no longer limited to the deepening, generalization or internalization of the mobile Internet, but the terminal form of the service output and information perception in the age of artificial intelligence.
Forensic Analysis of Xbox One

With the development of internet technology, more and more people turn their attention to video game consoles to release pressure. There are several famous video game console manufacturers on the market currently, including Microsoft, Sony and Nintendo. Xbox one is one of those popular game consoles which was published by Microsoft. Nevertheless, seldom researchers focus on analysis of its operation system, file system and data. In this paper we are going to analyze the file system, data on hardware, network data and so on of Xbox one concretely.
Improved Security for Android System based on Multi-Chaotic Maps using a Novel Image Encryption Algorithm

Aimed at the unsatisfactory effect of security in mobile cloud computing, we propose a novel image encryption algorithm by using the Improved Fruit Fly Optimization Algorithm --Artificial Bee Colony (hereafter referred to as IFOA-ABC).First, the feature of task scheduling function in mobile cloud computing is described to set up the task scheduling function; second, the population of fruit fly optimization algorithm is initialized by using orthogonal array and quantitative techniques, and the boundary of fruit fly optimization algorithm is processed, the step size in search is adjusted dynamically, and the artificial bee colony algorithm is used to help fruit fly optimization algorithm develop the global optimal solution; in the simulation experiment, the IFOA-ABC algorithm has obvious advantages over other intelligent algorithms in the comparison of four indicators of mobile cloud task scheduling, which means that the proposed algorithm can effectively improve the privacy and efficiency of cloud computing scheduling.
The Inﬂuence of Quantum Teaching Method and Cooperative Integrated Reading and Composition Method on Descriptive Writing Skill of Elementary Student

Thisresearchaimedatﬁndingouttheinﬂuenceofquantumteaching(QT)methodand cooperative integrated reading composition (CIRC) method on elementary students’ descriptive writing skill. This research used quantitative method with quasi experiment technique. This research used non-equivalent control group pretest-postest design. The population in this research was all the students at grade 5 in SD Muhammadiyah 1 Alternatif Kota Magelang. The data analysis techniques used in this research were paired sample t-test and analysis of variance (anova). The result showed that: (1) there was positive inﬂuence on the use of quantum teaching (QT) method on descriptive writing skill of grade 5 students in SD Muhammadiyah 1 Alternatif Kota Magelang, (2) there was positive inﬂuence on the use of cooperative integrated reading composition (CIRC) method on descriptive writing skill at grade 5 students in SD Muhammadiyah 1 Alternatif Kota Magelang, and (3) there was different inﬂuence on the use of quantum teaching(QT)methodandcooperativeintegratedreading(CIRC)methodondescriptive writing skill at grade 5 students in SD Muhammadiyah 1 Alternatif Kota Magelang.
School Atlas with Augmented Reality

Abstract. The aim of the paper is to describe the technology of creating and adding Augmented Reality to school atlas. No new atlas was created and there is no need of creating one. A current school atlas is used as a base for adding Augmented Reality. Different software, applications and tools for creating AR are presented for Windows, Android and iOS. All of them do not require previous knowledge of programming and are generally easier to use. The steps which a cartographer has to go through are presented, as well as the problems he/she meets and needs to solve in creating an atlas with AR. A classification of Augmented Reality devices is presented. A description of how the use of such technologies act as learning resources for students and its capability to effect learning outcomes is also presented.
Perancangan Sistem Informasi Akuntansi Laporan Keuangan Laba Rugi pada Restoran Eatboss Dengan Menggunakan PHP dan MySQL

This study aims to design a financial statement accounting information system application for Eatboss restaurant using the PHP and MySQL programming languages. The design of the financial statement accounting information system application in Eatboss restaurant using PHP and MySQL is done using library research and survey methods. At this time, it is necessary to prepare financial statements of profit and loss for each company, including restaurants, to find out the profit or loss gained during the specified period of the company. So that the company can determine the next decision for the continuity of better company activities.The design of fiinancial statement accounting information system in Eatboss reataurant using PHP and MySQL can facilitate omapnies in managing financial reports. With the maximum use of the financial statement accounting information system design at Eatboss restaurant using PHP and MySQL, it is expected that the company can more quickly and easily manage its financial statements based on accounting standards.
SCTM: A Multi-View Detecting Approach Against Industrial Control Systems Attacks

Off-the-shelf machine learning based intrusion detection systems (IDS) have proved not suitable for protecting industrial control systems (ICS), as they do not consider cooperative regularities between controllers of control loops, and the serious shortage of attacking training sets. We study the consensus and complementary (2C) features which are widely observed in control loops. Subsequently, a multi-view learning framework is proposed to boost the effectiveness of detecting attacks on ICS by using a large number of unlabeled examples with 2C features. Comprehensive attacks of ICS are designed and implemented on a physical testbed, and the experimental data are collected from the historical sequences and IDS alerts. The experimental results demonstrate that the framework is highly adaptive, and it can rapidly match the dynamics of ICS operating environment. Meanwhile, the effectiveness of the method is discussed when parameters take different values, and it exhibits low false-positive rates but high precision. In addition, the case of error propagation of the framework is analyzed.