Reviewerly/exHarmony huggingface dataset:
Associating Natural Language Comment and Source Code Entities

Comments are an integral part of software development; they are natural language descriptions associated with source code elements. Understanding explicit associations can be useful in improving code comprehensibility and maintaining the consistency between code and comments. As an initial step towards this larger goal, we address the task of associating entities in Javadoc comments with elements in Java source code. We propose an approach for automatically extracting supervised data using revision histories of open source projects and present a manually annotated evaluation dataset for this task. We develop a binary classifier and a sequence labeling model by crafting a rich feature set which encompasses various aspects of code, comments, and the relationships between them. Experiments show that our systems outperform several baselines learning from the proposed supervision.
PENGEMBANGAN ASPEK NON VERBAL PERILAKU ASERTIF DENGAN ASSERTIVE TRAINING

This study aim at to know that assertive training effective to develop non verbal aspect from assertive behavior.This Quasi experiment’s  pretest-posttest one control group design, Participants were 45 polytechnic students. The members of the experimentsand the control groups were randomly chosen. The treatment assertive training were given to the experiments group 0f 23 students. The 22 students did not experience the treatments. The data were collected  by questionnaires and doing observation. Different tes (t test) was employed to analise the data. It is found that  no difference non verbal aspect assertive behavior  between experiments and control groups in pretest situation. There is difference significantly between pretest and   posttest for non verbal aspect assertive behavior.There is difference significantly for non verbal aspect assertive behavior between eksperiment and control groups.. The finding imply that assertive training effective to develop non verbal aspects assertive behavior
An Intelligent Client-Trusted and Dependable Security Framework to Ease Smartphone Portability on Community Cloud-Computing

Cloud computing is a relatively new technology that is in wide use because of the benefits it offers but is still confronted with security issues. The residence of the client’s sensitive or proprietary data in the cloud service provider’s server and premises expose the data to the possibility of manipulation, modification, inspection, deletion or theft. This possibility creates fears in the mind of the data owner and reduces the user’s trust level in cloud computing. We propose a client trusted security framework to increase users trust level in cloud computing to make it more dependable. The proposed framework includes a user focused software process model for cloud computing security. Formal analysis of the proposed framework shows that it is capable of increasing the trust level of cloud computing by about 67 % when implemented by cloud service providers.
Bitcoin-Altcoin Price Synchronization Hypothesis: Evidence from Recent Data

The study investigates the bitcoin-altcoin price synchronization hypothesis using cointegrating test and VEC Granger Causality/Block Exogeneity Test approaches on the daily data of bitcoin and ten selected alternative coins (altcoins) between August 8, 2015 and December 31, 2018. The data is structurally divided into three distinct periods which are; August 8, 2015 to December 31, 2016; January 1, 2017 to December 31, 2017 and January 1, 2018 to December 31, 2018. The study establishes pure price separation between the altcoin and bitcoin in 2015-2016, price synchronization between bitcoin and each of the selected altcoin in 2017 and dominant altcoin-to-bitcoin price formation in 2018. The study concludes that cryptocurrency buyers are more sensitive in 2018 to the features and quality of project each coin promotes, unlike the indiscriminating choices which dominated cryptocurrency world during the 2017 boom.
FreeLB: Enhanced Adversarial Training for Language Understanding

Adversarial training, which minimizes the maximal risk for label-preserving input perturbations, has proved to be effective for improving the generalization of language models. In this work, we propose a novel adversarial training algorithm - FreeLB, that promotes higher robustness and invariance in the embedding space, by adding adversarial perturbations to word embeddings and minimizing the resultant adversarial risk inside different regions around input samples. To validate the effectiveness of the proposed approach, we apply it to Transformer-based models for natural language understanding and commonsense reasoning tasks. Experiments on the GLUE benchmark show that when applied only to the finetuning stage, it is able to improve the overall test scores of BERT-based model from 78.3 to 79.4, and RoBERTa-large model from 88.5 to 88.8. In addition, the proposed approach achieves state-of-the-art test accuracies of 85.39\% and 67.32\% on ARC-Easy and ARC-Challenge. Experiments on CommonsenseQA benchmark further demonstrate that FreeLB can be generalized and boost the performance of RoBERTa-large model on other tasks as well.
Learning Canonical Representations for Scene Graph to Image Generation

Generating realistic images of complex visual scenes becomes challenging when one wishes to control the structure of the generated images. Previous approaches showed that scenes with few entities can be controlled using scene graphs, but this approach struggles as the complexity of the graph (the number of objects and edges) increases. In this work, we show that one limitation of current methods is their inability to capture semantic equivalence in graphs. We present a novel model that addresses these issues by learning canonical graph representations from the data, resulting in improved image generation for complex visual scenes. Our model demonstrates improved empirical performance on large scene graphs, robustness to noise in the input scene graph, and generalization on semantically equivalent graphs. Finally, we show improved performance of the model on three different benchmarks: Visual Genome, COCO, and CLEVR.
Dimensionality Reduction, Manifold Learning, and Metric Geometry

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Order Learning and Its Application to Age Estimation

We propose order learning to determine the order graph of classes, representing ranks or priorities, and classify an object instance into one of the classes. To this end, we design a pairwise comparator to categorize the relationship between two instances into one of three cases: one instance is `greater than,' `similar to,' or `smaller than' the other. Then, by comparing an input instance with reference instances and maximizing the consistency among the comparison results, the class of the input can be estimated reliably. We apply order learning to develop a facial age estimator, which provides the state-of-the-art performance. Moreover, the performance is further improved when the order graph is divided into disjoint chains using gender and ethnic group information or even in an unsupervised manner.
Round Robin Algorithm With Average Quantum Dynamic Time Based on Multicore Processor

Abstract Modern operating systems that are widely used today, have used a multiprogramming system and use a multicore system to solve multiprogramming. Multicore systems provide greater efficiency than a single processor running on a single chip. The efficiency of the multicore system in completing its tasks is very dependent on the CPU scheduling algorithm used by the operating system. Round Robin algorithm is one of the CPU scheduling that is widely used in multiprogramming. With this algorithm, the CPU will divide the time for each process given the same time until the entire process in the queue is complete. In this study, the researcher improvised the Round Robin algorithm on a multicore processor system to execute the processes contained in a single queue queue that is to give a quantum time based on the average bursttime of the entire process in the queue. In order to minimize waiting time and turnaround time throughout the process.
A history and development of peer-review process

The paper shows the importance of peer review process in the scholarly communication system and discusses both theclosed and the newly emerging open peer review models. It also examines the peer review system at the scholarly academiesor societies in their nomination systems for prizes, rewards, etc. It also discusses the various facets of the newly developedopen peer review models now prevalent in various journals. The paper may help to understand and appreciate the roleplayed by peer review in the scholarly communication system and the efforts being made to make it more transparent.
Study on the Westley Maclean Model and the Co-Orientation Model and their Exemplification in an Industrial Enterprise

Abstract The present paper makes an incursion into the new and complex models of communication that can be found in literature, trying to identify their graphic decoding and their applicability in an industrial enterprise. The authors identified real examples for each model under investigation, so the purpose of the paper is to emphasize the importance of communication and the applicability of the new methods of communication in engineering.
The Impact of Informatics on Nursing Education and Promoting Quality of Health Care

In today's dynamic health systems, technology plays an important role in education and nursing work. So it seems necessary to study the role of nurses and highlight the need for appropriate information technology educational programs to integrate with the ever-increasing pace of technology. A review accompanied by an extensive literature search in databases and a library search focused on the keywords were used. The criteria used for selecting studies primarily focused on nursing informatics and the importance of expertise in the effective use of information technology in all aspects of the nursing profession. In a critical assessment of emerging technologies, the key elements of nursing informatics implementation were considered as healthcare promotion, advanced systems, internet and network. In view of the nature and the development of the information age, it is required to receive necessary IT training for all categories of nurses. Due to the fast development of technology, in order to effectively take advantage of information technology in nursing outcome and quality of health care and to empower nurses; educational arrangement is recommended to set short-term and long-term specialized courses focusing on four target groups: studying, working, graduate, senior undergraduate, and graduate doctoral. The result of this study is expected to assist educational providers with program development.
Unsupervised Cross-Lingual Word Embeddings Learning with Adversarial Training

Recent works have managed to learn cross-lingual word embeddings (CLWEs) in an unsupervised manner. As a prominent unsupervised model, generative adversarial networks (GANs) have been heavily studied for unsupervised CLWEs learning by aligning the embedding spaces of different languages. Due to disturbing the embedding distribution, the embeddings of low-frequency words (LFEs) are usually treated as noises in the alignment process. To alleviate the impact of LFEs, existing GANs based models utilized a heuristic rule to aggressively sample the embeddings of high-frequency words (HFEs). However, such sampling rule lacks of theoretical support. In this paper, we propose a novel GANs based model to learn cross-lingual word embeddings without any parallel resource. To address the noise problem caused by the LFEs, some perturbations are injected into the LFEs for offsetting the distribution disturbance. In addition, a modified framework based on Cramer GAN is designed to train the perturbed LFEs and the HFEs jointly. Empirical evaluation on bilingual lexicon induction demonstrates that the proposed model outperforms the state-of-the-art GANs based model in several language pairs.
Periodic Communication for Distributed Multi-agent Reinforcement Learning under Partially Observable Environment

In area of the reinforcement learning, an environment is important because when a well-known reinforcement learning technique for an environment is applied to another environment, it does not guarantee whether the technique also works well or not. To apply reinforcement learning techniques to real world environments, a partial observation condition is one of important issues. In addition, a communication is also important for a multi-agent setting under the partial observation condition. In this paper, a periodic communication for distributed multi-agent reinforcement learning is studied. Via the periodic communication, each agent is able to share an auxiliary observation which is a compressed version of an observation. After sharing auxiliary observations, each agent makes a decision based on own observation and other’s shared observation. However, due to the periodicity of a communication, an absence of sharing auxiliary observation occurs for a non-communication phase. Thus, it is necessary to consider how to compensate the absence of shared auxiliary observations within non-communication phase. To this end, several methods are proposed to predict auxiliary observations of other agents. In simulation results, it is shown that an affection of the partial observation condition and performances according to the methods of compensation of auxiliary observations.
Extracting Hierarchical Relations Between the Back-of-the-Book Index Terms

Aiming at solving the problem that the single level back-of-the-book index system is not enough to fully explore the semantics relations between the index terms, a method to extract the hierarchical relations between the index terms based on combination of lexical-syntactic analysis and text structure features is proposed in this paper. It first organizes index terms according to the text structure features, and constructs the indexed term pairs with hierarchical relations step by step. Then based on word vectors, the semantic similarity of paired index terms is calculated to eliminate the misidentified pairs. Finally, the index term pairs with hierarchical relations are optimized in the direct graph to remove redundant and conflict relations, and the hierarchical index system is built at last. Compared with the other results, our method improves precision rate and F value by 11.44% and 5.65% respectively.
Reflex-Augmented Reinforcement Learning for Operating Strategies in Automotive Electrical Energy Management

This paper introduces reflex-augmented reinforcement learning (RARL) for operating strategies in automotive electrical energy management. RARL makes it possible to overcome the limitations of rule-based decision systems (RBDS) and to face the increasing complexity in a vehicle's electrical energy system. We suggest a deep Q-learning-based RARL approach for operating strategies determining the behavior of the electrical energy system. This also provides a general approach to realize reinforcement learning in cybernetic management systems for safety-critical applications. In a simulation-based study of more than 50 hours of driving with an extensive model of a vehicular electrical energy system, we show that RARL-based operating strategies fulfill the major requirements of a real vehicle. Compared to an RBDS, RARL requires less effort to design an operating strategy of this level of performance. Furthermore, we evaluate different variants of the biologically-inspired reflex of RARL enabling the application in safety-critical systems. Finally, we do not only provide an approach to replace the RBDS, but we also suggest that RARL is a key to integrate further sources of information into decision-making to enhance electrical energy management.
Handwritten Optical Character Recognition (OCR): A Comprehensive Systematic Literature Review (SLR)

Given the ubiquity of handwritten documents in human transactions, Optical Character Recognition (OCR) of documents have invaluable practical worth. Optical character recognition is a science that enables to translate various types of documents or images into analyzable, editable and searchable data. During last decade, researchers have used artificial intelligence / machine learning tools to automatically analyze handwritten and printed documents in order to convert them into electronic format. The objective of this review paper is to summarize research that has been conducted on character recognition of handwritten documents and to provide research directions. In this Systematic Literature Review (SLR) we collected, synthesized and analyzed research articles on the topic of handwritten OCR (and closely related topics) which were published between year 2000 to 2018. We followed widely used electronic databases by following pre-defined review protocol. Articles were searched using keywords, forward reference searching and backward reference searching in order to search all the articles related to the topic. After carefully following study selection process 142 articles were selected for this SLR. This review article serves the purpose of presenting state of the art results and techniques on OCR and also provide research directions by highlighting research gaps.
Accelerating SWAT Simulations Using An In-Memory NoSQL Database

Due to its versatility, the Soil and Water Assessment Tool (SWAT) has been widely applied to investigate the effects of management activities and climate change on water availability and quality. However, the use of high spatial resolution data and the advantages of SWAT itself have significantly increased the input/output (I/O) demand and thus the runtime of modeling routines that require a large number of iterative simulations. In this study, we proposed a generic scheme to reduce the SWAT runtime by caching the model inputs using the in-memory NoSQL database Redis. Then the SWAT source codes (rev 488) was modified according to this proposed scheme to develop the MA-SWAT (memory accelerated SWAT) model by incorporating a new subroutine known as Fortran_calls_c to retrieve the cached inputs. We then evaluated MA-SWAT with four synthetic hydrological models and five different parallel schemes in a quad-core commodity laptop. The test results showed that when applied with a parallel simulation program, MA-SWAT could achieve a speedup by a factor of 8.4 ~ 10.9 depending on model complexity. Compared with the original SWAT, MA-SWAT significantly improved the computation speed, indicating that the proposed scheme is a desirable method for solving high computational demand problems such as calibration, sensitivity and uncertainty analysis. Moreover, the proposed concept of linking the SWAT model with Redis via the minimalistic C client driver of Redis is a generic method, and it is possible to apply this method to other Fortran-implemented environmental model to alleviate I/O demands.
Laser rods characterization by Fourier transform phase-shifting interferometry

Solid-state lasers are broadly used in various applications. The most common gain medium is neodymium-doped yttrium aluminum garnet (Nd:YAG). Very important parameters are angle between polished face surfaces and result wave-front deformation. These parameters are usually measured with two different methods. The paper presents a procedure for measurements of the parameters in a single measurement sequence using Fourier transform phase-shifting interferometry. The results are figures of both optical surfaces, and their mutual position, wave-front deformation, and homogeneity of refractive index. Based on the knowledge of these parameters can be done precise correction of resulted wave-front and of the wedge by polishing of face surfaces. The presented method reduces manipulation with the elements is non-sensitive to the operator and allows more precise wavefront correction thanks to the knowledge of inhomogeneity of material.
Analysis of Cyber Incident Categories Based on Losses

The fact that is indeed a collective term for various distinct risks creates great difficulty in communications. For example, policyholders of contracts often have a limited or inaccurate understanding about the coverage that they have. To address this issue, we propose a cyber risk categorization method using clustering techniques. This method classifies cyber incidents based on their consequential losses for insurance and risk management purposes. As a result, it also reveals the relationship between the causes and the outcomes of incidents. Our results show that similar cyber incidents, which are often not properly distinguished, can lead to very different losses. We hope that our work can clarify the differences between cyber risks and provide a set of risk categories that is feasible in practice and for future studies.
A successful approach to earthquake early warning systems

The goal of an earthquake early warning system (EEW) is to identify where and when an earthquake has occurred and then warn those in danger. We think of earthquakes as happening instantaneously but from the detection of the initial event there is time until the effects are noticed. An early warning of even 6 to 19 seconds could allow sensitive infrastructure like factories or power plants to enact automated precautions, reducing damage and allowing for quicker recovery. Warnings of 30 seconds could prevent 95 per cent of potential mortalities in some large cities. It is exactly these crucial seconds that Lin is trying to provide. His team's work centres on developing an integrated EEW for Taiwan which, once proven effective, to help bring this system to other countries who live with the threat of seismic activity.&lt;br/&gt; The EEW developed by Lin works through detecting the seismic waves that earthquakes produce. When an earthquake strikes the ground shakes, this creates seismic waves that move through the earth. These waves, exactly like a ripple in a pond, move out from the epicentre. The first wave, known as the primary wave is low intensity and will not affect structures significantly. The secondary wave however arrives a bit later and causes the intense shaking, damage and casualties. "The goal of the system is to deploy onsite Earthquake Early Waring System(EEWS) to detect the seismic waves and provide warning for the neighborhoods," explains Lin. The regional detectors and EEWS can be deployed to monitor a whole country.&lt;br/&gt; For traditional regional EEWS,like Japan,once a wave is detected the information is sent to a central server which determines the epicentre of the quake, the magnitude of the earthquake and the peak ground acceleration, an important measure of earthquake intensity. "With these measurements the server can then predict where peak ground acceleration will exceed thresholds likely to cause damage and send the alarm," says Lin. The whole process takes only about 15 seconds, which is extremely valuable time for those further away from the epicentre, but because the wave can move up to 90km within these 15 seconds there is a blind spot of about 90km in the regional Earthquake Early Waring System. Lin points out that a blind spot of this size may be fine for some regions. "In Japan the epicentre of the typical hazardous earthquake is in the east sea bed about 100km from the shore but for Taiwan the epicentres are typically below a city. Therefore Lin has developed an integrated approach combining the regional EEW with onsite EEW. The onsite EEWS are installed in cities and monitor local seismic activity. They can predict peak ground acceleration for local area from the primary wave within 1 to 3 seconds and reduce the blind spot to 20 or 30km. Each system has its advantages and disadvantages but integration provides maximum coverage.