Reviewerly/exHarmony huggingface dataset:
A Stochastic Gradient Method with Biased Estimation for Faster Nonconvex Optimization

A number of optimization approaches have been proposed for optimizing nonconvex objectives (e.g. deep learning models), such as batch gradient descent, stochastic gradient descent and stochastic variance reduced gradient descent. Theory shows these optimization methods can converge by using an unbiased gradient estimator. However, in practice biased gradient estimation can allow more efficient convergence to the vicinity since an unbiased approach is computationally more expensive. To produce fast convergence there are two trade-offs of these optimization strategies which are between stochastic/batch, and between biased/unbiased. This paper proposes an integrated approach which can control the nature of the stochastic element in the optimizer and can balance the trade-off of estimator between the biased and unbiased by using a hyper-parameter. It is shown theoretically and experimentally that this hyper-parameter can be configured to provide an effective balance to improve the convergence rate.
Kahaniyan - Designing for Acquisition of Urdu as a Second Language

This paper describes the design of Mobile Assisted Second Language Learning Application (MASLL) - Kahaniyan - created to assist non-native primary school children in learning Urdu. We explore the use of gamification to assist language learning within the context of interactive storytelling. The final design presented in this paper demonstrates how psychological and linguistic aspects coupled with contextual task analysis can be used to create a second language learning tool. The study also reports the results of the user study and the evaluation of the application which was conducted with 32 primary school students. Our results show a positive influence on learning outcomes, with findings that hold great significance for future work on designing MASLL for languages written in Arabic or Persian script.
Abnormal head movement classification using deep neural network DNN

Abnormal head movements play a crucial role in diagnoisis of varity diseases. Moreover, different studies considered with these type of information. In addition, the gestures based mainly on head movement which can be employed in many applications such as using head-nodding or shaking to feedback content-related feedback, detect and interpret the emotion, gaze orientation, focus of attention, driver assistance system and so on. In this paper, a new method proposed to detect and classify the flopping head movements as normal or abnormal based on Convolution Neural Network CNN in the term of special sense interaction and behavioral studies with this movement. The proposed system based on deep learning employing the Convolution Neural Network CNN as most promising approach to deal with lighting condition (illumination change) and distortion and noise. Normal Abnormal Head Movement Dataset (NAHM) static images dataset gathered and used in proposed system implementation. This dataset provided the various image conditions and subjects to prevent the overfitting and under fitting problem that appears with publically datasets. The proposed frame work presents robust learning ability based on accuracy and lose functions which achieved training loss: 0.0106, training accuracy: 0.9980, validation loss: 0.0968 and validation accuracy: 0.9831.
PENANAMAN NILAI KARAKTER GEMAR MEMBACA BERBASIS PEMBIASAAN DAN KETELADANAN TERHADAP KEMAMPUAN BERBAHASA SISWA SEKOLAH DASAR

Advantage the article has the purpose of describing how to instill reading like the charater of an avid reader and habituation which have impact on children's language skills. Based on the literature reviews show that the elementary school children's reading interest is very low. It is influenced by the lack of awareness in reading importance of reading and external support such as the direction and guidance of parents and teachers, the availability of reading material, and the attractiveness of available books. For this reason, there is something that it takes for habituation and exemplary efforts that are made both from the awareness of oneself, family, teachers and the surrounding environment so that children are accustomed to reading books and make children to enjoy reading books which in turn can increase language skills in children. Habitual and exemplary methods are effective ways to instill the character of reading fondness. The more someone likes to read, the more knowledge is gained. To inculcate the reading fondness character values, it takes habituation and example to realize the culture of fond reading. The fond reading character is a form of language skills development. The more skilled a person in language, the more brilliant, and clearer the way he/she thinks because their processes reading there is a process of critical thinking.
Estimating hourly global solar irradiance using artificial neural networks – A case study of Hong Kong

Abstract Solar energy is the most popular resource for power generation among the various available renewable energy alternatives. Solar radiation data are important for solar photovoltaic (PV) systems and passive energy-efficient building designs. Due to the unavailability of measurement in rural locations, solar radiation prediction models are required. In recent years, the Artificial Neural Networks (ANN) were successfully used for predicting solar radiation. However, previous works indicated that the ANN techniques were mainly focusing on prediction of monthly average or daily solar radiation, a few of them were modelled for predicting solar irradiance in hourly basis. In this study, prediction models of global solar irradiance on a horizontal surface will be developed based on neural-network techniques. Hourly meteorological variables between 2012 and 2015 acquired from the measurements made by local meteorological station were used for the study. To consider the effectiveness of individual predictors, different combinations of input variables were analysed using the Levenberg-Marquardt (LM) algorithm. Finally, equations were modelled by regression based on the important predictors. The developed models were used to estimate the global irradiance and assessed against measurement results.
Human action recognition using graph matching

Despite great effort, current action recognition systems still do not perform well enough for many applications. Majority of existing methods utilise some (combinations of) image descriptors, which have fundamental limitations that they can vary significantly from person to person and are sensitive to changes of illumination, clothing etc. Such methods thus often require a large number of training examples to cover as much of variation as possible. In this paper, we propose to extract minimal representative information, namely deforming skeleton graphs corresponding to foreground shapes, to effectively represent actions and remove the influence of these typical variations. We propose a novel approach to action recognition based on matching of skeleton graphs, combining static pairwise graph similarity measure using optimal subsequence bijection with dynamic time warping to robustly handle topological and temporal variations. For common periodic actions, we extract a consistent starting frame from each video to temporally align deforming skeleton graphs. We further develop a hierarchical matching strategy to significantly improve matching efficiency while keeping recognition accuracy. Our method outperforms the state-of-the-art methods on standard benchmarks KTH (97.7%), UCF sport (92.3%) and Olympic sports (80.5%) datasets.
The CUED’s Grammatical Error Correction Systems for BEA-2019

We describe two entries from the Cambridge University Engineering Department to the BEA 2019 Shared Task on grammatical error correction.Our submission to the lowresource track is based on prior work on using finite state transducers together with strong neural language models.Our system for the restricted track is a purely neural system consisting of neural language models and neural machine translation models trained with backtranslation and a combination of checkpoint averaging and fine-tuning -without the help of any additional tools like spell checkers.The latter system has been used inside a separate system combination entry in cooperation with the Cambridge University Computer Lab.
NICT’s Supervised Neural Machine Translation Systems for the WMT19 Translation Robustness Task

In this paper we describe our neural machine translation (NMT) systems for Japanese↔English translation which we submitted to the translation robustness task.We focused on leveraging transfer learning via fine tuning to improve translation quality.We used a fairly well established domain adaptation technique called Mixed Fine Tuning (MFT) (Chu et al., 2017) to improve translation quality for Japanese↔English.We also trained bi-directional NMT models instead of uni-directional ones as the former are known to be quite robust, especially in low-resource scenarios.However, given the noisy nature of the in-domain training data, the improvements we obtained are rather modest.
Analysis and Design of Voip Server (Voice Internet Protocol) using Asterisk in Statistics and Statistical Informatics Communication of Banten Province using Ppdioo Method

Abstract Communication is the most important thing in the success of an activity. Communication that occurs in a computer network does not only process data packet transfers between computers, but computer technology is currently able to transfer data in the form of sound frequencies that are processed into data packets and sent between computers on the internet network. The development of internet technology is not only using for getting information, but also that is widely used. One of them is audio call or VoIP (Voice over Internet Protocol), this technology has been used by several messenger service providers, such as Yahoo Messenger, Goggle Talk, Skype, and others. Voice over Internet Protocol is a technology that makes internet media able to conduct voice communication remotely directly. Analog sound signals, like what you hear when communicating on the telephone are converted to digital data and sent over the network in the form of data packets in real time. Asterisk is a softswitch to operate the proxies, with session initiation protocol (SIP) based. The aim is to build an Asterisk-VoIP based server, so that it can be developed in future research as needed. The methodology in outline consists of two lines, namely literature study and PPDIOO (Plan, Prepare, Design, Implement, Operate, Optimize). It has been implemented in the Banten Province Communication and Information Service, which has been previously established by the internet network. So that VoIP here is functioned for maximizing it.
LIDA: Lightweight Interactive Dialogue Annotator

Dialogue systems have the potential to change how people interact with machines but are highly dependent on the quality of the data used to train them.It is therefore important to develop good dialogue annotation tools which can improve the speed and quality of dialogue data annotation.With this in mind, we introduce LIDA, an annotation tool designed specifically for conversation data.As far as we know, LIDA is the first dialogue annotation system that handles the entire dialogue annotation pipeline from raw text, as may be the output of transcription services, to structured conversation data.Furthermore it supports the integration of arbitrary machine learning models as annotation recommenders and also has a dedicated interface to resolve inter-annotator disagreements such as after crowdsourcing annotations for a dataset.LIDA is fully open source, documented and publicly available 1 .
Data Augmentation with Atomic Templates for Spoken Language Understanding

Spoken Language Understanding (SLU) converts user utterances into structured semantic representations. Data sparsity is one of the main obstacles of SLU due to the high cost of human annotation, especially when domain changes or a new domain comes. In this work, we propose a data augmentation method with atomic templates for SLU, which involves minimum human efforts. The atomic templates produce exemplars for fine-grained constituents of semantic representations. We propose an encoder-decoder model to generate the whole utterance from atomic exemplars. Moreover, the generator could be transferred from source domains to help a new domain which has little data. Experimental results show that our method achieves significant improvements on DSTC 2\&3 dataset which is a domain adaptation setting of SLU.
Proximal Parameter Distribution Optimization

Encouraging the agent to explore has become a hot topic in the field of reinforcement learning (RL). The popular approaches to engage in exploration are mainly by injecting noise into neural network (NN) parameters or by augmenting additional intrinsic motivation term. However, the randomness of injecting noise and the metric for intrinsic reward must be chosen manually may make RL agents deviate from the optimal policy during the learning process. To enhance the exploration ability of agent and simultaneously ensure the stability of parameter learning, we proposed a novel proximal parameter distribution optimization (PPDO) algorithm. On the one hand, PPDO enhances the exploration ability of RL agent by transforming NN parameter from a certain single value to a function distribution. On the other hand, PPDO accelerates the parameter distribution optimization by setting two groups of parameters. The parameter optimization is guided by evaluating the parameter quality change before and after the parameter distribution update. In addition, PPDO reduces the influence of bias and variance on the value function approximation by limiting the amplitude of the two consecutive parameter updates, which can enhance the stability of the parameter distribution optimization. Experiments on the OpenAI Gym, Atari, and MuJoCo platforms indicate that PPDO can improve the exploration ability and learning efficiency of deep RL algorithms, including DQN and A3C.
Data matters: how earth and environmental scientists determine data relevance and reusability

Purpose The purpose of this study is to examine the information needs of earth and environmental scientists regarding how they determine data reusability and relevance. Additionally, this study provides strategies for the development of data collections and recommendations for data management and curation for information professionals working alongside researchers. Design/methodology/approach This study uses a multi-phase mixed-method approach. The test environment is the DataONE data repository. Phase 1 includes a qualitative and quantitative content analysis of deposited data. Phase 2 consists of a quasi-experiment think-aloud study. This paper reports mainly on Phase 2. Findings This study identifies earth and environmental scientists’ information needs to determine data reusability. The findings include a need for information regarding research methods, instruments and data descriptions when determining data reusability, as well as a restructuring of data abstracts. Additional findings include reorganizing of the data record layout and data citation information. Research limitations/implications While this study was limited to earth and environmental science data, the findings provide feedback for scientists in other disciplines, as earth and environmental science is a highly interdisciplinary scientific domain that pulls from many disciplines, including biology, ecology and geology, and additionally there has been a significant increase in interdisciplinary research in many scientific fields. Practical implications The practical implications include concrete feedback to data librarians, data curators and repository managers, as well as other information professionals as to the information needs of scientists reusing data. The suggestions could be implemented to improve consultative practices when working alongside scientists regarding data deposition and data creation. These suggestions could improve policies for data repositories through direct feedback from scientists. These suggestions could be implemented to improve how data repositories are created and what should be considered mandatory information and secondary information to improve the reusability of data. Social implications By examining the information needs of earth and environmental scientists reusing data, this study provides feedback that could change current practices in data deposition, which ultimately could improve the potentiality of data reuse. Originality/value While there has been research conducted on data sharing and reuse, this study provides more detailed granularity regarding what information is needed to determine reusability. This study sets itself apart by not focusing on social motivators and demotivators, but by focusing on information provided in a data record.
Noise and Security Analysis of Trusted Phase Noise Continuous Variable Quantum Key Distribution using a Local Local Oscillator

We study the validity of partially trusting the phase noise associated with estimation uncertainty in a continuous variable quantum key distribution system with a local local oscillator (LLO-CVQKD) and investigate optimal system parameters. We find that the trusted phase noise model enhances the key rate and transmission distance performance but opens a security loophole by manipulating the reference pulse amplitude. Therefore, we propose a “reference pulse attack” on the trusted phase noise LLO-CVQKD system where Eve achieves 100% information hacking without being detected at 23.8km and 32.0km while using lossless and 0.14dB/km loss channels, respectively. Nevertheless, by re-analyzing the attack, countermeasures are also proposed to keep the performance advantages of the trusted phase noise model. As a result, the trusted phase noise LLO-CVQKD can still provide a superior secure key rate (e.g., 9 times higher key rate at 20km) and an increased transmission distance by 45%, compared to that of the untrusted phase noise system.
Effect of bubbles on measurement accuracy of digital image correlation for a stress corrosion test

Considerable errors are encountered when the digital image correlation (DIC) method is used in a stress corrosion test. In this paper, the stress corrosion test system was built first. The applied maximum strain was accurately measured by DIC. Then, the effect of bubbles on DIC measurement results was analyzed. The bubbles induced additional errors in the measurement results, leading to local concentrations in measured displacement and strain fields. Finally, in order to reduce the displacement and strain errors caused by the bubbles, a twice subtraction method (TSM) was developed. The results of the application example show that TSM is an effective method to reduce the effect of bubbles on measurement accuracy of DIC for a stress corrosion test.
Multi-Objective Automatic Machine Learning with AutoxgboostMC

AutoML systems are currently rising in popularity, as they can build powerful models without human oversight. They often combine techniques from many different sub-fields of machine learning in order to find a model or set of models that optimize a user-supplied criterion, such as predictive performance. The ultimate goal of such systems is to reduce the amount of time spent on menial tasks, or tasks that can be solved better by algorithms while leaving decisions that require human intelligence to the end-user. In recent years, the importance of other criteria, such as fairness and interpretability, and many others have become more and more apparent. Current AutoML frameworks either do not allow to optimize such secondary criteria or only do so by limiting the system's choice of models and preprocessing steps. We propose to optimize additional criteria defined by the user directly to guide the search towards an optimal machine learning pipeline. In order to demonstrate the need and usefulness of our approach, we provide a simple multi-criteria AutoML system and showcase an exemplary application.
A Knowledge Representation Based User-Driven Ontology Summarization Method

As the superstructure of knowledge graph, ontology has been widely applied in knowledge engineering. However, it becomes increasingly difficult to be practiced and comprehended due to the growing data size and complexity of schemas. Hence, ontology summarization surfaced to enhance the comprehension and application of ontology. Existing summarization methods mainly focus on ontology's topology without taking semantic information into consideration, while human understand information based on semantics. Thus, we proposed a novel algorithm to integrate semantic information and topological information, which enables ontology to be more understandable. In our work, semantic and topological information are represented by concept vectors, a set of high-dimensional vectors. Distances between concept vectors represent concepts' similarity and we selected important concepts following these two criteria: 1) the distances from important concepts to normal concepts should be as short as possible, which indicates that important concepts could summarize normal concepts well; 2) the distances from an important concept to the others should be as long as possible which ensures that important concepts are not similar to each other. K-means++ is adopted to select important concepts. Lastly, we performed extensive evaluations to compare our algorithm with existing ones. The evaluations prove that our approach performs better than the others in most of the cases.
Small and large earthquakes can have similar starts

A long-standing question in seismology is whether small and large earthquakes have similar or different onsets. An analysis of earthquakes around Japan shows that, in some cases, these onsets are almost identical. An analysis of earthquakes along the Japan Trench.
Ebook Rate of Use in OhioLINK: A Ten-Year Study of Local and Consortial Use of Publisher Packages in Ohio

This paper examines publisher ebook package use in the OhioLINK academic library consortium between 2007 and 2017 alongside use of the same titles at individual institutions. With nearly 100,000 titles acquired over 10+ years from three publishers and available to users at more than 90 institutions, the picture of ebook use this study presents is unique in its breadth and scope. The data show that, consortiumwide, close to 100 percent of titles were used, with their initial use overwhelmingly taking place within one year of their publication date. At individual institutions, the rate of use was far lower and never exceeded the rate of use of print books at the author's own institution. These findings have important implications for how institutions approach ebook acquisition to maximize rate of use of ebook collections.
Improving the Performance of Thinning Algorithms with Directed Rooted Acyclic Graphs

In this paper we propose a strategy to optimize the performance of thinning algorithms. This solution is obtained by combining three proven strategies for binary images neighborhood exploration, namely modeling the problem with an optimal decision tree, reusing pixels from the previous step of the algorithm, and reducing the code footprint by means of Directed Rooted Acyclic Graphs. A complete and open-source benchmarking suite is also provided. Experimental results confirm that the proposed algorithms clearly outperform classical implementations.