Reviewerly/exHarmony huggingface dataset:
Annotated Guidelines and Building Reference Corpus for Myanmar-English Word Alignment

Reference corpus for word alignment is an important resource for developing and evaluating word alignment methods.For Myanmar-English language pairs, there is no reference corpus to evaluate the word alignment tasks.Therefore, we created the guidelines for Myanmar-English word alignment annotation between two languages over contrastive learning and built the Myanmar-English reference corpus consisting of verified alignments from Myanmar ALT of the Asian Language Treebank (ALT).This reference corpus contains confident labels sure (S) and possible (P) for word alignments which are used to test for the purpose of evaluation of the word alignments tasks.We discuss the most linking ambiguities to define consistent and systematic instructions to align manual words.We evaluated the results of annotators agreement using our reference corpus in terms of alignment error rate (AER) in word alignment tasks and discuss the words relationships in terms of BLEU scores.
An Efficient Approach for Semantic Image Classification using Normalization Method

Nowadays Image changes are programmed with the most luminous glow low-resolution symbolism.There is a significant standoff between these receipts.The research was carried out to aid in curbing crime in the future where the novel receipt is proposed for effective correspondence.The method of automating semantic image classification was adapted for this research as well as with various SVM classifiers.It is notified that for the semantic image classification, a normalized method is the best.The study inculcated weight features to calculate kernel function to generalize it.Adding on, to classify new images Trained SVMs were used.The Experimental results based on the 256 categories of the database showed there are some benefits of using SVM with better performance in the normalized image-catering systems during training and generalization.The identity error rate that was discovered in the study is 97% where there was a decrease of 90% to 97%.The best performance method-filter, combination-identifying individuals is 98.
Channel Capacity of Concurrent Probabilistic Programs

Programs are under continuous attack for disclosing secret information, and defending against these attacks is becoming increasingly vital. An attractive approach for protection is to measure the amount of secret information that might leak to attackers. A fundamental issue in computing information leakage is that given a program and attackers with various knowledge of the secret information, what is the maximum amount of leakage of the program? This is called channel capacity. In this paper, two notions of capacity are defined for concurrent probabilistic programs using information theory. These definitions consider intermediate leakage and the scheduler effect. These capacities are computed by a constrained nonlinear optimization problem. Therefore, an evolutionary algorithm is proposed to compute the capacities. Single preference voting and dining cryptographers protocols are analyzed as case studies to show how the proposed approach can automatically compute the capacities. The results demonstrate that there are attackers who can learn the whole secret of both the single preference protocol and dining cryptographers protocol. The proposed evolutionary algorithm is a general approach for computing any type of capacity in any kind of program.
Eyewear 2019

Computing devices worn on the human body have a long history in academic and industrial research, most importantly in wearable computing, mobile eye tracking, and mobile mixed and augmented reality. As humans receive most of their sensory input via the head, it is a very interesting body location for simultaneous sensing and interaction as well as cognitive assistance. Eyewear Computing devices have recently emerged as commercial products and can provide an research platform for a range of fields, including human-computer interaction, ubiquitous computing, pervasive sensing, psychology and social sciences. The proposed workshop will bring together researchers from a wide range of disciplines, such as mobile and ubiquitous computing, eye tracking, optics, computer vision, human vision and perception, usability, as well as systems research. This year it will also bring in researchers from psychology, with a focus on the social and interpersonal aspects of eyewear technology. The workshop is a continuation from 2016/2018 and will focus on discussing application scenarios as well as focusing on eyewear sensing and supporting social interactions.
Designing at the Intersection of Gamification and Persuasive Technology to Incentivize Energy-Saving

Gamified persuasive system design refers to design solutions at the intersection of gamification and persuasive technology aiming at influencing attitude and behavior change. Although both concepts have been successfully applied in many domains to increase end-user engagement and satisfaction, results are often mixed and highly context specific. Consequently, there is a dearth of knowledge on how to design those solutions and how they are perceived by different types of users within certain application contexts. Thus, this paper investigates the relationship between the HEXAD gamification user types and persuasive principles within the context of energy saving. First results reveal (n = 206) that, three out of six persuasive principles (Reciprocity, Consistency & Commitment, Liking) have been perceived as persuasive by identified HEXAD user types, which highlights the importance of such user types models. Finally, this paper contributes to the present body of gamification literature by providing a human computer interaction (HCI) perspective which highlights guidelines for designing gamified persuasive systems to incentivize energy-saving.
Aplikasi Forecasting Stok Barang Menggunakan Metode Weighted Moving Average (WMA) pada Metrojaya Komputer

Metrojaya Komputer is a business engaged in buying and selling, the purchasing process often has difficulty in determining how much stock of goods must be purchased for the future period, because it has not used forecasting methods. Forecasting is a forecasting that requires the retrieval of historical data to be processed in order to produce a decision on what predictions are needed for the next period. The purpose of this research is to simplify and accelerate forecasting using the weighted moving average (WMA) approach through the development of information technology based systems. The data needed in forecasting is the previous period data, whether the previous month's data or the previous year, then processed using forecasting equation. Forecasting can be processed based on data from the previous three periods or based on a certain period to produce the required stock of goods. The system development method used is the waterfall method which consists of several stages of communication, planning, modeling, construction, and deployment. This study produces a forecasting information system with a weighted moving average (WMA) approach that can be processed using an information technology based system so that it makes it easier and faster in making decisions regarding the stock of goods needed in sales.
Consumer-Driven Usability Test of Mobile Application for Tea Recommendation Service

The rapidly growing interest in healthy lifestyles and the health benefit of foods and the growing tea-consuming population are driving the growth of the tea industry. In particular, the growing preference among Millennials for premium blended tea is leading the growth of the tea market. In this paper, we study the feasibility of recommendation services for blended tea, which has not been addressed well by existing recommender systems. To this end, we design TeaPickTM, a mobile application that suggests a blend of tea suited to the user’s preferences including desired health benefits. To evaluate the application and its recommendations, we conduct not only a usability test, but also a consumer acceptance test with 31 participants. Our user study shows that the participants were positive about the recommendation service provided by our application and were generally satisfied with the recommended tea.
Automatic summarisation: 25 years On

Abstract Automatic text summarisation is a topic that has been receiving attention from the research community from the early days of computational linguistics, but it really took off around 25 years ago. This article presents the main developments from the last 25 years. It starts by defining what a summary is and how its definition changed over time as a result of the interest in processing new types of documents. The article continues with a brief history of the field and highlights the main challenges posed by the evaluation of summaries. The article finishes with some thoughts about the future of the field.
Partnership of University and Schools Within the Preparation of Future Teachers

The article analyzes partnership of university and schools within the preparation of future teachers. It deals with preparation of future teacher in order to master the project competence which is necessary for the implementation of future professional activities. The authors identify organizational, procedural, communicative, reflective components within the structure of project competence. The authors conclude that an important role plays organizing partnerships within the project activities during pedagogical practice of university students at schools.
H2Pregel : A partition-based hybrid hierarchical graph computation approach

A partition-based hybrid hierarchical graph computation approach, called H2Pregel is proposed to address the redundant supersteps and inefficient computation problems due to low access locality. The H2Pregel preprocesses the input graph through a distributed recode algorithm to ensure the continuity and sequence of vertex ids, then employs a hybrid approach to combine the advantages of both synchronous and asynchronous models, and hierarchically computes the high proportion of interior messages generated by high quality partition algorithms. Moreover, H2Pregel leverages configurable parallel threads to accelerate local computation by “sub-supersteps”, and employs an exterior messages stealing optimization to avoid extra communication overheads between tasks. We implemented H2Pregel on Giraph, a classic open source system based on Pregel. The evaluation results on large-scale graphs show that, compared with Pregel in three partition algorithms, H2Pregel can achieve average speedups by 1.12–4.52 times and decrease average communication messages by 23.5%-55.5%, and average supersteps by 15.8%-82.0%.
ScaRR: Scalable Runtime Remote Attestation for Complex Systems.

The introduction of remote attestation (RA) schemes has allowed academia and industry to enhance the security of their systems. The commercial products currently available enable only the validation of static properties, such as applications fingerprint, and do not handle runtime properties, such as control-flow correctness. This limitation pushed researchers towards the identification of new approaches, called runtime RA. However, those mainly work on embedded devices, which share very few common features with complex systems, such as virtual machines in a cloud. A naive deployment of runtime RA schemes for embedded devices on complex systems faces scalability problems, such as the representation of complex control-flows or slow verification phase. In this work, we present ScaRR: the first Scalable Runtime Remote attestation schema for complex systems. Thanks to its novel control-flow model, ScaRR enables the deployment of runtime RA on any application regardless of its complexity, by also achieving good performance. We implemented ScaRR and tested it on the benchmark suite SPEC CPU 2017. We show that ScaRR can validate on average 2M control-flow events per second, definitely outperforming existing solutions.
Iterative machine learning based rotational alignment of brain 3D CT data

The optimal rotational alignment of brain Computed Tomography (CT) images to a required standard position has a crucial importance for both automatic and manual diagnostic analysis. In this contribution, we present a novel two-step iterative approach for the automatic 3D rotational alignment of brain CT data. The angles of axial and coronal rotations are determined by an unsupervised by localisation of the Midsagittal Plane (MSP) method. This includes detection and pairing of medially symmetrical feature points. The sagittal rotation angle is subsequently estimated by regression convolutional neural network (CNN). The proposed methodology has been evaluated on a dataset of CT data manually aligned by radiologists. It has been shown that the algorithm achieved the low error of estimated rotations (≈1 degree) and in a significantly shorter time than the experts (≈2 minutes per case).
Estimating the Parameters for Linking Unstandardized References with the Matrix Comparator

This paper discusses recent research on methods for estimating configuration parameters for the Matrix Comparator used for linking unstandardized or heterogeneously standardized references. The matrix comparator computes the aggregate similarity between the tokens (words) in a pair of references. The two most critical parameters for the matrix comparator for obtaining the best linking results are the value of the similarity threshold and the list of stop words to exclude from the comparison. Earlier research has shown that the standard deviation of the token frequency distribution is strongly predictive of how useful stop words will be in improving linking performance. The research results presented here demonstrate a method for using statistics from token frequency distribution to estimate the threshold value and stop word selection likely to give the best linking results. The model was made using linear regression and validated with independent datasets.
MiNgMatch—A Fast N-gram Model for Word Segmentation of the Ainu Language

Word segmentation is an essential task in automatic language processing for languages where there are no explicit word boundary markers, or where space-delimited orthographic words are too coarse-grained. In this paper we introduce the MiNgMatch Segmenter—a fast word segmentation algorithm, which reduces the problem of identifying word boundaries to finding the shortest sequence of lexical n-grams matching the input text. In order to validate our method in a low-resource scenario involving extremely sparse data, we tested it with a small corpus of text in the critically endangered language of the Ainu people living in northern parts of Japan. Furthermore, we performed a series of experiments comparing our algorithm with systems utilizing state-of-the-art lexical n-gram-based language modelling techniques (namely, Stupid Backoff model and a model with modified Kneser-Ney smoothing), as well as a neural model performing word segmentation as character sequence labelling. The experimental results we obtained demonstrate the high performance of our algorithm, comparable with the other best-performing models. Given its low computational cost and competitive results, we believe that the proposed approach could be extended to other languages, and possibly also to other Natural Language Processing tasks, such as speech recognition.
School Library Utilization Technology Model to Improve Reading Interest and Reading Ability in Elementary Education

This study aims to determine the feasibility of the utilization model in school to increase students' reading interest and measure the effectiveness of the school library utilization model.This study uses a research and development approach by the theory of Borg and Gall.The research subjects were Library in Bubutan IV Elementary School Surabaya, Indonesia, with the research respondents being all students.The results showed that the library utilization model was very effective in increasing students' reading interest and reading ability.The results of measuring the Reading Text Level (RTL) of students showed that out of a total of 872 students, there were only 3.1% of students whose RTL level was still below the minimum standard.Thus, the model of school library utilization is significantly able to increase students' reading interest and reading ability.We recommended that the results of this study provide benefits for the practitioner to improve synergy in all relevant agencies to increase students' reading interest and reading ability since elementary level.
Transitioning from Foreign Aid: Is the Next Cohort of Graduating Countries Ready?

In the coming years, over a dozen middle-income countries (MICs) are likely to transition from multilateral concessional assistance, including assistance from the International Development Association (IDA) and Gavi, the Vaccine Alliance (Gavi). The cohort of upcoming graduates, which includes Nigeria and Pakistan, may find the transition more challenging when compared with the experiences of previous countries that have already graduated. Many upcoming graduates, for example, still have high rates of child and maternal mortality and large proportions of the population living in poverty.Is the upcoming cohort more vulnerable and less “ready” to transition than those countries that previously graduated? If it is, do multilateral agencies need to adjust their transition policies? To help answer these questions, in this working paper we compared two cohorts of countries: a “previous cohort” that graduated from IDA between 2010 and 2015, and an “upcoming cohort” that is anticipated to graduate from IDA, Gavi, or both in coming years. We compared the two cohorts across five categories of indicators: macroeconomic conditions, health financing, health performance, governance, and overall levels of poverty and inequality.Overall, our findings suggest that, on average, the countries that graduated from IDA in the previous 2010-15 period had stronger capacity to manage the donor transition than that of upcoming graduates. The upcoming cohort seems to have, on average, lower per capita income, greater indebtedness, weaker capacity to efficiently use public resources, more limited and less effective health systems, weaker governance and public institutions, and greater inequality.
Hybrid Quantum Salp Swarm Algorithm for Contrast Enhancement of Natural Images

This paper proposes a new hybrid approach based on quantum computing and SSA to boost the performance of SSA through finding the optimal trade-off between exploitation and exploration.QSSA relies on embedding the integration of the quantum operators and interference in the optimization process of SSA and the quantum representation of the search space.QSSA is tested on 29 global benchmark functions of CEC2017 and 8 benchmark images.The simulation results showed that employing the principles of quantum can significantly boost the performance of SSA.The performance of QSSA is compared with SSA and other recent and well-known optimization algorithms.The results on CEC2017 demonstrated the capability of QSSA to find the global optima for most of unimodal and multimodal benchmark functions, especially with complex search space.Moreover, the results revealed that the proposed image enhancement based QSSA is robust and can distinctly enhance the contrast of the color images.
Study of English Teaching Method by Convergence of Project-based Learning and Problem-based Learning for English Communication

This study examines the effects of student-centered project-based learning for the development of creative problem-solving skills, communication skills, critical thinking skills, and cooperation. A college students’ creative personality test was used and pre-and post-test were performed. and TOEIC Speaking practice test by Educational Testing Service were selected to measure the English communication skills. The SPSS 18.0 was used and validated at a significance level of 5%. The result of this study shows that in the case of 'independence', the post-test average of the experimental group was statistically significant at the significant level (p<.01), which also showed statistically significant difference. There was statistically significant difference between the control group (M=127±08.2) and in the experimental group (M=132±18.7) applying project-based and problem-based convergent learning to English class were positively changed.
The Effect of Mindfulness and Wisdom as Intervention to Deal with Collage Students' Internet Addiction

sudden death [13,12].Internet addiction has also brought about serious social dysfunctions, especially with regards to juvenile delinquency [14].What's more, the Internet addiction may also result in truancy, exhaustion with study, dropout, and social abilities [15].Researches have shown that eighty percent of college dropouts are caused by the Internet addiction [16].People with the Internet addiction have problems with self-control and self-management [17].Compared with those who do not have non-problematic Internet use, people who have a self-reported problematic use of the Internet have lower life satisfaction and negative self-esteem, and they are lonelier with more social anxiety [11].Monaghan [18] found that during gambling, which can easily cause addiction, the lack of self-awareness may make players addicted to the game and spend lots of money and time on gambling.Therefore, the capability of self-awareness and self-control plays an important role in solving the problem of addiction.To control the Internet addiction, strengthening network management is usually proposed at the macro level.It was mentioned in Bai's study [14] that management can solve problems from the root, but it has limited effect.Furthermore, Cognitive-Behavioral Therapy has been commonly used to treat the Internet addiction [19,20].However, currently there are scarce literatures that use the mindfulness approach to treat the Internet addiction.In the most recent decade, the concept of mindfulness has been widely adopted in Western psychology and psychological treatment.
BREAST CANCER DETECTION USING MACHINE LEARNING TECHNIQUES

Breast cancer is one of the most common kind of cancer, as well as the leading cause of mortality among women. Radiologists use mammograms pictures of the breast, to look for signs of possible tumor formation such as breast masses, lumps of tissue that could be formed by cancer cells, and micro-calciﬁcations, small calcium deposits that cluster around abnormal tissue. Machine learning algorithms used for classification of benign and malignant tumor wherein machine is learned from the past data and can predict the category of new input. In this study, a performance comparison between different machine learning algorithm of support vector machine (SVM), artificial neural network (ANN), Bayesian Network (NB) and k-nearest neighbor (K-NN) on Wisconsin Breast Cancer datasets from the UCI Machine Learning Repository data sets is conducted. The experimental results have shown that support vector machine methods achieved higher accuracy on the task of breast cancer detection, achieved accuracy within the range of 97.6% to 98.8% which is an impressive accuracy for supervised pattern classification
Navigation Fuzzy Cognitive Maps Adjusted by PSO

Fuzzy Cognitive Maps (FCMs) have found their use in many application areas. Especially, if there are complex relations among notions and a simple rule-based description of knowledge is unsatisfactory. However, the complexity of the problem needs automatic adaptation of FCM parameters because it exceeds human's capacity. For tasks as control and decision-making evolutionary algorithms proved their efficacy. In this paper, the paradigm of the so-called Particle Swarm Optimisation (PSO) is utilised for adaptation of FCM parameters. The problem is solved on a FCM for navigation, which is able to communicate with means of Internet of Things (IoT). Although PSO approach shows good adaptation quality but it depends strongly on its initialisation. In the paper, several initialisation methods are compared and a new method to overcome drawbacks of others is presented.