Reviewerly/exHarmony huggingface dataset:
An Attention-Based Model Using Character Composition of Entities in Chinese Relation Extraction

Relation extraction is a vital task in natural language processing. It aims to identify the relationship between two specified entities in a sentence. Besides information contained in the sentence, additional information about the entities is verified to be helpful in relation extraction. Additional information such as entity type getting by NER (Named Entity Recognition) and description provided by knowledge base both have their limitations. Nevertheless, there exists another way to provide additional information which can overcome these limitations in Chinese relation extraction. As Chinese characters usually have explicit meanings and can carry more information than English letters. We suggest that characters that constitute the entities can provide additional information which is helpful for the relation extraction task, especially in large scale datasets. This assumption has never been verified before. The main obstacle is the lack of large-scale Chinese relation datasets. In this paper, first, we generate a large scale Chinese relation extraction dataset based on a Chinese encyclopedia. Second, we propose an attention-based model using the characters that compose the entities. The result on the generated dataset shows that these characters can provide useful information for the Chinese relation extraction task. By using this information, the attention mechanism we used can recognize the crucial part of the sentence that can express the relation. The proposed model outperforms other baseline models on our Chinese relation extraction dataset.
A Three-dimensional Finger Motion Measurement System of a Thumb and an Index Finger Without a Calibration Process

Various wearable systems have been investigated to measure hand motion, but some challenges remain. Many systems require a calibration process to map sensor signals to actual finger joint angles by the principle of measuring the length change of the finger, or bending sensors. Also, few studies have investigated how to measure thumb motion accurately using the wearable systems. This paper proposes an exoskeleton system with linear Hall sensors to measure three-dimensional hand motion without a calibration process. The calibration process is avoided by measuring finger joint angles through an absolute rotation measurement. A new wearing method with lower parts underneath the hand joints and rubber bands is proposed to fix the structure to the hand and adapt it for various hand sizes. As the thumb has a complex biomechanical feature at carpometacarpal (CMC) joint, a new measuring method of the CMC joint is proposed to directly calculate the orientation of the metacarpal. The prototype of the thumb and index finger was manufactured, and the performance was verified experimentally by using an optical motion capture system.
Perceptions about cultural diversity and intercultural communication of future teachers

Intercultural education is a topic of great social and pedagogical interest and significance in the initial training of teachers. Its impact on the university context represents an inescapable impulse to build an open mindset that respects cultural diversity as a key element that will entail a real move towards inclusion. The objective of this study has been to discover the perceptions and profile of a non-probabilistic pilot sample of university students from a Primary Education degree course towards diversity and intercultural communication. A survey design was carried out, for which an ad-hoc questionnaire was built. 62 students participated in the survey, which was completed with a simulated sample of 500 cases using the Monte Carlo procedure for data mining analysis. A descriptive analysis was performed, as well as a cluster and tree forest analysis. Ward's method of minimal variance was applied for its ability to discriminate in determining grouping levels. The results revealed, among other things, the existence of a positive trend towards the recognition of cultural diversity as an enriching aspect in the generation of inclusive attitudes and intercultural communication. Similarly, different profiles were identified among the participants, as well as a precedence of the analysed variables that establish the distinction between profiles. Intercultural education is a topic of great social and pedagogical interest and significance in the initial training of teachers. Its impact on the university context represents an inescapable impulse to build an open mindset that respects cultural diversity as a key element that will entail a real move towards inclusion. The objective of this study has been to discover the perceptions and profile of a non-probabilistic pilot sample of university students from a Primary Education degree course towards diversity and intercultural communication. A survey design was carried out, for which an ad-hoc questionnaire was built. 62 students participated in the survey, which was completed with a simulated sample of 500 cases using the Monte Carlo procedure for data mining analysis. A descriptive analysis was performed, as well as a cluster and tree forest analysis. Ward's method of minimal variance was applied for its ability to discriminate in determining grouping levels. The results revealed, among other things, the existence of a positive trend towards the recognition of cultural diversity as an enriching aspect in the generation of inclusive attitudes and intercultural communication. Similarly, different profiles were identified among the participants, as well as a precedence of the analysed variables that establish the distinction between profiles.
Coreference Resolution: Toward End-to-End and Cross-Lingual Systems

The task of coreference resolution has attracted considerable attention in the literature due to its importance in deep language understanding and its potential as a subtask in a variety of complex natural language processing problems. In this study, we outlined the field’s terminology, describe existing metrics, their differences and shortcomings, as well as the available corpora and external resources. We analyzed existing state-of-the-art models and approaches, and reviewed recent advances and trends in the field, namely end-to-end systems that jointly model different subtasks of coreference resolution, and cross-lingual systems that aim to overcome the challenges of less-resourced languages. Finally, we discussed the main challenges and open issues faced by coreference resolution systems.
Distributed Graph Computation Meets Machine Learning

<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TuX<sup>2</sup></small> is a new distributed graph engine that bridges graph computation and distributed machine learning. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TuX<sup>2</sup></small> inherits the benefits of elegant graph computation model, efficient graph layout, and balanced parallelism to scale to billion-edge graphs, while extended and optimized for distributed machine learning to support heterogeneity in data model, Stale Synchronous Parallel in scheduling, and a new Mini-batch, Exchange, GlobalSync, and Apply ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MEGA</i> ) model for programming. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TuX<sup>2</sup></small> further introduces a hybrid vertex-cut graph optimization and supports various consistency models in fault tolerance for machine learning. We have developed a set of representative distributed machine learning algorithms in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TuX<sup>2</sup></small> , covering both supervised and unsupervised learning. Compared to the implementations on distributed machine learning platforms, writing those algorithms in <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TuX<sup>2</sup></small> takes only about 25 percent of the code: our graph computation model hides the detailed management of data layout, partitioning, and parallelism from developers. The extensive evaluation of <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TuX<sup>2</sup></small> , using large datasets with up to 64 billion of edges, shows that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TuX<sup>2</sup></small> outperforms PowerGraph/PowerLyra, the state-of-the-art distributed graph engines, by an order of magnitude, while beating two state-of-the-art distributed machine learning systems by at least 60 percent.
Distributed fusion Kalman filtering under binary sensors

Summary Binary sensors are special sensors that only transmit one‐bit information at each time and have been widely applied to environmental awareness and medical monitoring. This paper is concerned with the distributed fusion Kalman filtering problem for a class of binary sensor systems. A novel uncertainty approach is proposed to better extract valid information from binary sensors at switching instant. By minimizing a local estimation error covariance, the local robust Kalman estimates are firstly obtained. Then, the distributed fusion Kalman filter is designed by resorting to the covariance intersection fusion criterion. Finally, a blood oxygen content model is employed to show the effectiveness of the proposed methods.
Accounting for bitcoins in light of IFRS and tax aspects

Abstract This essay presents recommendations in regard to accounting for operations that involve bitcoins, in compliance with the International Financial Reporting Standards (IFRS), and analyzes their main tax aspects. There is no specific pronouncement on the part of the International Accounting Standards Board (IASB) or from the Brazilian Accounting Pronouncements Committee (CPC) regarding the accounting treatment to be applied in operations that use these currencies. Bitcoin is of interest to economists as a virtual currency with the potential to disrupt existing payment systems and even monetary systems. This essay offers a contribution for standard-setters and the tax authority (fisco) by providing the basis for possible guidelines to be issued on the accounting treatment of bitcoin operations, as well as by defining the appropriate tax treatment; in addition, it makes a contribution for accounting professionals by suggesting the accounting policy to be adopted in these operations. Here, the analysis of the characteristics of bitcoins is compared with the guidelines and concepts of IFRS, in order to elaborate the recommendation for accounting treatment, and it suggests that the most adequate procedure would be that of foreign currency, which would go against the tax treatment adopted up until now by the Brazilian Internal Revenue Service (Receita Federal) or the Internal Revenue Service (IRS) of the United States of America (USA), which suggest treating virtual currencies as goods and not as currencies. It warrants mentioning that this contradiction may cause tax risks for taxpayers.
On the perceived complexity of literature. A response to Nan Z. Da

At the center of Nan Z. Da’s article is the claim that quantitative methods cannot produce any useful insights with respect to literary texts: “CLS’s methodology and premises are similar to those used in professional sectors (if more primitive), but they are missing economic or mathematical justification for their drastic reduction of literary, literary-historical, and linguistic complexity. In these other sectors where we are truly dealing with large data sets, the purposeful reduction of features like nuance, lexical variance, and grammatical complexity is desirable (for that industry’s standards and goals). In literary studies, there is no rationale for such reductionism; in fact, the discipline is about reducing reductionism.”
An intelligent load management application for solar boiler system

A load management application is developed to maximize the utilization of solar boiler systems through the scheduling of household activities. This application uses an hourly and daily Global Solar Radiation prediction model and is fed live weather forecast data directly from a weather application program interface. Unlike Time-of-Use Price-scheduling, the proposed scheduling system is based on the amount of predicted generated solar energy and the behavior of the load. In the first phase of operation, live weather data are collected using a weather application program interface. In the second phase, an Artificial Neural Network (ANN) model is trained using five-year weather-solar radiation data recorded for a North American city (2000–2004). Then, the Global Solar Radiation is predicted for ten days and compared with actual values for 2005. The cumulative hourly and daily energy and optimal days in the ten days are also obtained. In the final phase, household activities that consume hot water, such as washing dishes, using the washing machine, and taking a shower or bath are rescheduled to utilize the generated energy, which maximizes savings and minimizes emissions.
Smartphone-Based Application â€œquizizzâ€ as a Learning Media

This study aims to find out the application and contribution of smartphone-based application â€œâ€œquizizzâ€â€ as learning media in the University of PGRI Madiun. The method used in this research is descriptive qualitative. Data analysis technique used is an interactive method, including the process of collecting data, reducing data (compiling data in patterns, categories and specific issues), presenting data (compiling data in the form of matrices, graphs, networks, certain charts) and drawing conclusions. This research was conducted at the University of PGRI Madiun. The subjects of this study were students in Accounting and Economic Education. The results showed that the usage of smartphone-based application â€œquizizzâ€ as learning media can make students enthusiastic in participating in learning, so students can focus and maximize the use of smartphones as a fun learning media. The concept of maximum learning will have an impact on improving student learning outcomes.
Glottal Flow Synthesis for Whisper-to-Speech Conversion

Whisper-to-speech conversion is motivated by laryngeal disorders, in which malfunction of the vocal folds leads to loss of voicing. Many patients with laryngeal disorders can still produce functional whispers, since these are characterised by the absence of vocal fold vibration. Whispers therefore constitute a common ground for speech rehabilitation across many kinds of laryngeal disorder. Whisper-to-speech conversion involves recreating natural-sounding speech from recorded whispers, and is a non-invasive and non-surgical rehabilitation that can maintain a natural method of speaking, unlike the existing methods of rehabilitation. This article proposes a new rule-based method for whisper-to-speech conversion that replaces the noisy whisper sound source with a synthesised speech-like harmonic source, while maintaining the vocal tract component unaltered. In particular, a novel glottal source generator is developed in which whisper information is used to parameterise the excitation through a high-quality glottis model. Evaluation of the system against the standard pulse train excitation method reveals significantly improved performance. Since our method is glottis-based, it is potentially compatible with the many existing vocal tract component adaptation systems.
Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.
The Care2Report System: Automated Medical Reporting as an Integrated Solution to Reduce Administrative Burden in Healthcare

Documenting patient medical information in the electronic medical record is a time-consuming task at the expense of direct patient care.We propose an integrated solution to automate the process of medical reporting.This vision is enabled through the integration of speech and action recognition technology with semantic interpretation based on knowledge graphs.This paper presents our dialogue summarization pipeline that transforms speech into a medical report via transcription and formal representation.We discuss the functional and technical architecture of our Care2Report system along with an initial system evaluation with data of real consultation sessions.
Defeating Lattice-Based Data Hiding Code Via Decoding Security Hole

Lattice code has been widely used for data hiding. It can provide security for data hiding by randomly translating its codebook with a secret dither. However, besides the secret dither, there are an infinite amount of points that are near the secret dither and can also be used for perfect decoding in the noiseless scenario. This means that lattice-based data hiding has a serious security hole, named decoding security hole (DSH) in this paper. After a theoretical analysis of DSH, we find that these points form a convex polytope and the centroid of this convex polytope is the secret dither. Based on this finding, a simple yet effective attack method is presented to estimate the secret dither of lattice-based data hiding. Extensive experimental results show that the proposed method significantly outperforms state-of-the-art attack methods, especially when the number of observations is small or the document-to-watermark ratio changes over a wide range.
Performance evaluation of low-cost IoT based chlorophyll meter

Nutrient deficiencies in plants can be identified using a chlorophyll meter. However, current chlorophyll meters are still expensive and have many disadvantages. In this paper, a low-cost IoT-based chlorophyll meter has been developed. The performance of a low-cost IoT-based chlorophyll meter has been compared with the performance of a spectrophotometer (SP-3000nano) and a commercial chlorophyll meter (SPAD-502). A low-cost IoT-based chlorophyll meter has been functioning properly which is able to measure the chlorophyll content of plants in the field, get positions based on GPS satellites, store data in a memory module, and send data to the service system platform. The test results showed the coefficient of determination (R2) between SPAD-502 values and low-cost IoT-based chlorophyll meter values is 0.9705, this shows a significant correlation. An IoT-based chlorophyll meter can be used as a cheap alternative to the SPAD-502 chlorophyll meter.
GTransE: Generalizing Translation-Based Model on Uncertain Knowledge Graph Embedding

This is an extension from a selected paper from JSAI2019. Knowledge graphs are useful for many AI applications. Many recent studies have been focused on learning numerical representations of a knowledge graph in a low-dimensional vector space. Learning representations benefits the deep learning framework for encoding real-world knowledge. However, most of the studies do not consider uncertain knowledge graphs. Uncertain knowledge graphs, e.g., NELL, are valuable because they can express the likelihood of triples. In this study, we proposed a novel loss function for translation-based models, GTransE, to deal with uncertainty on knowledge graphs. Experimental results show that GTransE can robustly learn representations on uncertain knowledge graphs.
ALGORITMA KLASIFIKASI NAIVE BAYES DAN SUPPORT VECTOR MACHINE DALAM LAYANAN KOMPLAIN MAHASISWA

Service in the world of education is an important element for the creation of an academic atmosphere that is conducive to the implementation of a successful teaching and learning process. The process of service to students there is a tendency to be implemented not following the minimum service standards that must be provided to students so that students tend to complain about the services provided. Submission of criticism, complaints, input, or suggestions for dissatisfaction and problems that exist in the university environment is still very limited. Complaints can be constructive if submitted to the right place and party. In this research the data processing of email complaints from students conducted at the academic student body (students.bsi.ac.id). Student complaint data that will be processed is data in the form of * .xls complaint file. Before text data is analyzed using text mining methods, the pre-processing text needs to be done including tokenizing, case folding, stopwords, and stemming. After pre-processing, the classification method is then performed in classifying each complaint category and dividing the status into two parts, namely complaint and not complaint so that the status becomes a normal condition in text mining research. The purpose of this study is to obtain the most accurate algorithm in the classification of student complaints and can find out the results of the classification of the Naïve Bayes algorithm method and Support vector Machine used and compared. In this study, the results of testing by measuring the performance of these two algorithms using Cross-Validation, Confusion Matrix, and ROC Curves. The obtained Support vector Machine algorithm has the highest accuracy value compared to Naïve Bayes. AUC value = 0.922. for the Support vector machine method using the student academic data collection dataset (students.bsi.ac.id) has 84.45%, from the Naïve Bayes algorithm has an accuracy rate of about 69.75% and AUC value = 0.679.
Digital Transformation: The Interplay of Explorative and Exploitative Capability Development

This research focuses on the firms’ capability development in the era of digital transformation. We empirically investigate how firms operating in heavy industry extend their capabilities through explorative and exploitative capability development. Our study uncovers that firms in the industry tend to begin with exploitation and gradually extend to exploration. Our findings highlight the importance and necessity of adopting network capabilities both for execution of transformation activities, and for developing internal capabilities. The empirical evidence indicates that inter-organizational ambidexterity is essential for unleashing the full potential of digital transformation, and that ambidexterity can be achieved through a combination of internal and external capabilities.