Reviewerly/exHarmony huggingface dataset:
Towards continuous value delivery with review meetings in agile methodologies: A structured review

Continuous value delivery (CVD) is the practice to ensure the delivery of user and business value via a fast, reliable, and repeatable process.The change management lies at the core of CVD as the adjustments in value propositions often take place.Review meetings play an important role in change management because the review is a formal assessment with the intention of instituting change, if necessary.A thorough discussion of different agile methods in terms of review meetings as needed.Different papers were studied to extract the data regarding these meetings.It is observed that the sprint review meeting is important to the development team as it is an opportunity for the team to show its work explicitly and get appreciated.Development team spirits are increased by such environments.The sprint review establishes a firm level of sociable comparative competition between scrum teams that keeps everyone focused.It is actually equivalent to a user acceptance test.This paper discusses the importance of review meetings and their impact on CVD in well-known agile methods and using a structured review methodology.It evaluates the processes of review meetings such as planning, adaptability, integration in increment and reevaluation in wellknown agile methods in order to find their effectiveness towards achieving CVD.It is found that Scrumban when strengthened with the EVO's increment inspection process, can help in achieving CVD.
Parents-Partners In Education

This article concerns the nature of relations between kindergarten – family, educator – parents, educator - child. For a good socio-emotional development of preschool children, the teacher’s activity with children should include activities or items that pre-schooler children interact with adults. By attending the kindergarten, the child is faced with new situations, changing his way of life according to the new requirements, here the child will find more complex and better organized educational support. However, the family has a fundamental role in raising and educating the child by finding and choosing the most effective and qualitative education systems that will be molded on their own children. The educator – parent relationship is a primary direct impact on child development. The relationship between parent and educator will influence the pattern of behaviour that the child will admire and imitate. Micro research aimed to capture the way that parents appreciate / do not appreciate the quality in preschool institution. Also looking into the importance of partnership in kindergarten through the goals, objectives and activities to encourage child development. Questionnaire-based survey helped us collect sufficient data for the chosen sample.
A Transfer Learning Method for Ship Target Recognition in Remote Sensing Image

In this paper, an effective approach of ship target recognition is proposed. This method based on the theory of transfer learning aims at using labeled ships with different imaging angles and different resolutions to help identifying unlabeled ships in a fixed angle. Since training ship samples and test ship samples are imaging in different angles, they obey different distributions. However, in traditional machine learning method, training data and test data obey the same distribution. In order to solve this problem, we proposed a method called mapped subspace alignment (MSA) which is different from other domain adaptation methods. While maximizing the difference between different categories, it first uses Isometric Feature Mapping (Isomap) to generate subspace and uses objective functions to spatial alignment and probabilistic adaptation. This paper focuses on the identification of three types of ships which are destroyers, cruisers, and aircraft carriers basing on MSA. The experimental results show that this method is better than several state-of-the-art methods.
CoRg: Commonsense Reasoning Using a Theorem Prover and Machine Learning

Commonsense reasoning is an everyday task that is intuitive for humans but hard to implement for computers. It requires large knowledge bases to get the required data from, although this data is still incomplete or even inconsistent. While machine learning algorithms perform rather well on these tasks, the reasoning process remains a black box. To close this gap, our system CoRg aims to build an explainable and well-performing system, which consists of both an explainable deductive derivation process and a machine learning part. We conduct our experiments on the Copa question-answering benchmark using the ontologies WordNet, Adimen-SUMO, and ConceptNet. The knowledge is fed into the theorem prover Hyper and in the end the conducted models will be analyzed using machine learning algorithms, to derive the most probable answer.
Repurposing Labeled Photographs for Facial Tracking with Alternative Camera Intrinsics

Acquiring manually labeled training data for a specific application is expensive and while such data is often fully available for casual camera imagery, it is not a good fit for novel cameras. To overcome this, we present a repurposing approach that relies on spherical image warping to retarget an existing dataset of landmark labeled casual photography of people's faces with arbitrary poses from regular camera lenses to target cameras with significantly different intrinsics, such as those often attached to the head mounted displays (HMDs) with wide-angle lenses necessary to observe mouth and other features at close proximity and infrared only sensing for eye observations. Our method can predict landmarks of the HMD wearer in facial sub-regions in a divide-and-conquer fashion with particular focus on mouth and eyes. We demonstrate animated avatars in realtime using the face landmarks as input without user-specific nor application-specific dataset.
Document-level Sentiment Analysis Based on Domain-specific Sentiment Words

Aiming at the problem that the relationship between non-contiguous words in a sentence cannot be effectively captured, and the existing sentiment lexicon has poor adaptability in the field, a model for document-level sentiment analysis based on domain-specific sentiment words is constructed. We reconstructed word vectors using attention mechanism to capture the relationship between non-contiguous words in word vectors; Words are synthesized using Asymmetric Convolutional Neural Network. Sentences are synthesized by Bidirectional Gated Recurrent Neural Network based on attention mechanism to form document vector features; we used CNN to construct a domain-specific sentiment dictionary to generate emotional vector features; Document vector features and emotional vector features are combined using a linear binding layer to form document features that facilitate document classification. By comparing the performance of this method with other methods through experiments, the results show that there is a big advantage in classification accuracy, and can be widely used in various specific fields such as public health.
Context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning

Both multi-criteria recommendation and context-aware recommendation are well addressed in previous research but separately in most of existing work. In this paper, we aim to contribute to the under-explored research problem which consists in tailoring the multi-criteria rating predictions to users involved in specific contexts. We investigate the application of simultaneous clustering based on the application of a spectral partitioning graph method over situational contexts in the one hand and criteria in the other hand. Besides, we conjecture that even with similar criteria-related ratings, the importance of criteria might differ among users. This idea leads us to use prioritized aggregation operators as means of multi-criteria rating aggregations. Our experimental results on a real-world dataset show the effectiveness of our approach.
Rendering circular depth of field effect with integral image

In this paper, we propose a novel method to achieve efficient rendering of circular depth of field (DOF) effects. Like most of the existing work, we model the task of DOF rendering as the problem of filtering with spatial-varying circular kernels. To avoid direct calculation which is extremely computationally expensive, we approximate the circular kernels with multiple square kernels tilting at different angles. Integral images are then applied to speed up the task of filtering with square kernels. In order to avoid the problem of intensity leakage and edge discontinuity, not only the integral parts, but also the fractional parts of the disparity are considered in calculating the spatial-varying kernel sizes, i.e., the radii of the circles of confusion (COC). Finally, the circular depth of field effect is derived by combining the results of filtering with square kernels at different angles. Experimental results suggest that the proposed method could produce realistic DOF effects with decent running time.
Being more Effective and Interpretable

In this poster, we propose several novel ABR approaches, namely BBA+ and MPC+, which are the fusion of heuristics and AI-based schemes. Results indicate that the proposed methods perform better than recent heuristic ABR methods. Meanwhile, such methods have also become more interpretable compared with AI-based schemes.
Dimensionality Reduction for ECG Signals; Laplacian Eigenmaps and Locality Preserving Projections

Dimensionality reduction using Laplacian Eigenmap and Locality Preserving Projections is investigated for normal and pathological ECG signals. Classification results in the initial domain as well as in manifolds of various dimensions are presented and compared.
Analisis Kebutuhan Pembelajaran Bahasa Inggris pada Mahasiswa Kelas Karyawan

The purpose of this study was to analyze the needs of employee class for learning English to be used as a reference for curriculum development and making syllabus for English for Specific Purposes (ESP). This study identified students’ motivations, constraints faced by the students and the needs for learning English based on four basic language skills. This research uses descriptive analysis method with quantitative approach. Data was obtained through distributing questionnaires to 159 respondents from first semester students of the 2018/2019 academic year at one of the private university in the East Jakarta. They are generally aged 21-29 years with the last education of vocational schools and have jobs as private employees. The identification of their language skill shows that generally the respondents have English basic skill. Analysis of questionnaire data states that students' motivation in learning English is to improve academic grades and be able to speak English. While their main constraints are limited vocabulary and not understanding English grammar. The results of needs analysis concluded that students need English to understand texts and instructions along with to obtain information that is useful for their studies.
A Novel Parallel Framework for Metaheuristic-based Frequent Itemset Mining

Frequent Itemset Mining (FIM) is an important but very time-consuming data mining task. As a result, traditional FIM algorithms are often not scalable to large databases. To address this issue, several metaheuristics have been developed in recent years to find good approximate solutions to the FIM problem. It was shown that such approaches can be much more efficient than exact algorithms. However, metaheuristics often have long runtimes on massive datasets and the quality of their solutions can be improved. To address this issue, this paper proposes a parallel framework called CFIM (Cluster for Frequent Itemset Mining) for metaheuristic-based FIM. It accelerates FIM by using multiple cluster workers. The proposed approach partitions a transactional database and the set of all items at the level of cluster workers. The itemset generation process is performed by each worker, which then send results to a master node. This latter performs a merging step to only keep high quality itemsets by considering their frequency and diversification. Three metaheuristics (GA, PSO and BSO) are integrated in this framework to yield three novel metaheuristics (CGA, CPSO and CBSO). Extensive experiments show that CPSO outperforms CGA, CBSO, and state-of-the-art high performance computing FIM approaches.
Improving Code Generation From Descriptive Text By Combining Deep Learning and Syntax Rules

Code generation is a model-driven engineering approach that enables developers to generate source code automatically and achieves extremely high development productivity.Specifically, generating code from a descriptive text reduces the time and expense of software development significantly.However, the performance of existing methods is not satisfying, since they are either of low accuracy (lack of specifics of the generated code) or too complicated (lack of efficiency in training).In this work, we proposed three novel methods by combining neural architectures and syntax rules, aiming at explicitly capturing the syntactical characteristics of target code.First, we proposed three models based on the Combination of Deep learning and Syntax rules (CDS models).Then, we evaluated CDS models with BLEU metric by comparing our models with existing methods.The results show that our models outperform existing methods for the challenging code generation task.Finally, we conducted a comparative study between the three CDS models.With further analysis we provided advice on the choice of neural architectures by considering both task accuracy and efficiency.Experimental results show that (1) there is a trade-off between speed and accuracy of the model, and (2) one of our CDS models (i.e., the CDS-POOLING model) outperforms other existing methods for the challenging code generation task.
Collecting Data from Continuous Practices: an Infrastructure to Support Team Development

Through software analytics, raw data with low value originates information that is valuable and able to provide insights, enabling the support of claims that would otherwise not be possible to verify.The software development ecosystem has plenty of sources that can help understanding the quality of processes and products but, to reach that goal, it is necessary to collect and store the data.This paper describes an infrastructure to allow the collection, storage and analysis of data from software repositories.The scope of the research is an industrial case study, which encompasses several specificities: tools and work methodology.The current solution is able to collect information from the continuous delivery & deployment pipeline, which includes data sources such as the source code repository (SVN), the static analysis tool (SonarQube), the continuous integration server (from Jenkins jobs) and the continuous testing tool (an in-house tool called Cerberus).Future work also includes the implementation of components that will allow the collection of unstructured data from the bug-tracking system and incident management tool.As stated in the literature, correlating the history of issues and incidents will allow the team to address, or at least identify, areas of improvement.
Activity Based Students’ Performance Measure Using Moodle Log Files

Blended learning, a thoughtful integration of face-to-face and online learning aims at: improving students" learning outcome and providing personalized learning experience through eLearning technologies, such as Moodle.To ascertain the benefits of blended learning, a study is performed on 150 students of the blended course offered at RGUKT Nuzvid through the Moodle Platform.The activity log files generated by the Moodle are collected at the end of the course.A set of the metrics are derived from the log files and actual grade of the corresponding students are obtained from final exam result.The metrics obtained from the log files are mapped to the final grade using a classification technique (Decision Tree Classification) and the hidden rules are generated to show the relation between the metrics and the final Grade.The results show the significance of the model.
Forecasting of Three Components of Solar irradiation for Building Applications

Solar energy and the concept of passive architecture and Net Zero Energy buildings are being increased. For an optimal management of the building energy, a Model Predictive Control is generally used but requires an accurate building model and weather forecast. For a more reliable modelling, the knowledge of the global solar irradiation is not sufficient; three methods, smart persistence, artificial neural network and random forest, are compared to forecast the three components of solar irradiation measured on the site with a high meteorological variability. Hourly solar irradiations are forecasted for time horizons from h+1 to h+6. The random forest method (RF) is the most efficient and the accuracy of forecasts are in term of nRMSE, from 19.65% for h+1 to 27.78% for h+6 for global horizontal irradiation, from 34.11% for h+1 to 49.08% for h+6 for beam normal irradiation, from 35.08% for h+1 to 49.14% for h+6 for diffuse horizontal irradiation. The improvement brought by the use of RF compared to the two other methods increases with the forecasting horizon. A seasonal study is realized and shows that the forecasting during spring and autumn is less reliable than during winter and summer due to a higher meteorological variability.
Penggunaan Model Waterfall dalam Pembuatan Aplikasi Pemesanan Tiket Bus

PO. Lorena is a business engaged in transportation between provinces in almost all parts of Indonesia where the presentation of ticket reservations, processing of bus ticket data, administtrative reports and frequent human errors found in the calculation of the remaining empty seats is still done manually whisch is one of the problems.To facilitate prospective passengers in obtaining these services, it is necassary to have a java-based bus ticket booking application.In this study, to bulid the application using a grounded research model based on real data in the field where data collection is done by interviews and observations.The method of development is the Waterfall method.Apllication that is built using the Java programming language and MySQL software as a database server.The test results use Black Box test which shows that the system can function according to the needs of users who can switch to a computerized system so that it can improve the performance of information systems and be an alternative for prospective passengers and employees.
ExpressPass+

short-paper Share on ExpressPass+: ECN-friendly Credit Reservation Congestion Control for Datacenters Authors: Zihao Wei College of Computer, National University of Defense Technology College of Computer, National University of Defense TechnologyView Profile , Dezun Dong College of Computer, National University of Defense Technology College of Computer, National University of Defense TechnologyView Profile , Shan Huang College of Computer, National University of Defense Technology College of Computer, National University of Defense TechnologyView Profile , Liquan Xiao College of Computer, National University of Defense Technology College of Computer, National University of Defense TechnologyView Profile Authors Info & Claims SIGCOMM Posters and Demos '19: Proceedings of the ACM SIGCOMM 2019 Conference Posters and DemosAugust 2019 Pages 169–171https://doi.org/10.1145/3342280.3342348Published:19 August 2019Publication History 0citation283DownloadsMetricsTotal Citations0Total Downloads283Last 12 Months36Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
When Media Owning Sports Club: Republika Editorial Policy in News Coverage about Inter Milan and Satria Muda

This paper aims to know about content of the news and editorial policy in Republika, when the media coverage news about Inter Milan and Satria Muda Pertamina. Both of the sport teams are owned by Erick Thohir, the boss of Republika. Based on literature, the interest of mass media owner and newsroom policy often difference. Because mass media owner have their goals, meanwhile the newsroom should obey the professional journalism standard. At the practice, the newsroom make usually make a compromise.This paper uses qualitative research methods. The data collecting technique are using documentary research and interviews. The documentary research held by collecting and analyzing news about Inter Milan and Satria Muda in Republika daily newspaper (print edition). The time period for Inter Milan news is August 2014 to October 2015 while Satria Muda in January to June 2016. At the time frame, both club did not win any competition that they were participated. So, at that time the club was in the negative period. The interviews held with the representative of the Republika sport section editor to confirm the result from text analysis.As the findings from text analysis, Republika always brings good news to Inter Milan and Satria Muda Pertamina, although both team was not winning the competition, and there were relatively much negative results in the single season. Republika explained that they have direct access to news sources from Inter Milan and Satria Muda Pertamina directly. So Republika will gain more information from both club than the other media. Not only in sporting theme (e.g. match results, preview, review), Republika sometimes deliver the news about the figure of Erick Thohir. Keywords : editorial policy, media ownership, sport journalism, sport communication
Text Detection in Natural Image By Connected Component Labeling

The process of detect the text from the natural image is complex and difficult process because the variance by the devises that take the images and different the texts that found in images in the orientation, size and style. Given the importance the texts in images in the several of application of computer vision. In this paper dependent on the spatial natural images and on the spatial data set for the street sign that include the texts by the different size and different orientation. In this paper detected the texts in images by using robust method by using several algorithms, at the first stage making preprocessing for the image to blur the image and reduce the nose on it by Gaussian blur, second stage making processing that include canny edge detection to detect the edges and dilation, third stage applying connected component to filling all objects in image then applying stroke width transform(SWT) to detect the letter candidate and applying the system on the several images that include different types of texts.