Reviewerly/exHarmony huggingface dataset:
Split Attention Pointer Network for Source Code Language Modeling

There is a growing interest in leveraging Deep Learning (DL) for automating Software Engineering tasks such as program completion. In this paper, we leverage Recurrent Neural Networks (RNNs) for Abstract Syntax Tree (AST)-based code completion. Our approach converts source code into AST nodes and a language model predicts the type and value attributes of next tokens. Our work demonstrates that the attention augmented RNN-based language models are able to understand local context and copy from recent past tokens which have never appeared in the training data set. We observed a drop of performances of both type and value predictions when using a traditional pointer network architecture for out-of-vocabulary (OoV) copying and context understanding, which we call multi-task conflict. To address this challenge, we have devised a new structure of self-attention called Split Attention, where two separate dot-product layers are applied to different parts of the history cache. Based on this structure, we propose a new network called Split Attention Pointer Network (SAPN), which is efficient and flexible in both learning local context and copying OoV tokens from history. The empirical results suggest that our model is superior in syntax-aware generation and OoV token prediction by demonstrating attention behavior similar to human programmers. The results also indicate that our model out performs previous state-of-the-art approaches by more than 6% on widely recognized program completion benchmarks.
Relevance Ranking for Real-Time Tweet Search

Relevance ranking is a key component of many search engines, including the Tweet search engine at Twitter. Users often use Tweet search to discover live discussions and different voices on trending topics or recent events. Tweet search is thus unique due to its focus on real-time content, where both the retrieved content and queries change drastically on an hourly basis. Another important property of Tweet search is that its relevance ranking takes the social endorsements from other users into account, e.g., "likes" and "retweets", which is different from mainly relying on clicks as implicit feedback. The relevance ranking of Tweet search is also subject to strict latency constraints, because every second, a large amount of Tweets are posted and indexed, while tens of thousands of queries are issued to search posted Tweets. Considering the above properties and constraints, we present a relevance ranking system for Tweet search addressing all these challenges at Twitter. We first discuss the formation of the relevance ranking pipeline, which consists of a series of ranking models. We then present the methodology for training the models and the various groups of features we use, including real-time and personalized features. We also investigate approaches of achieving unbiased model training and building up automatic online tuning of system parameters. Experiments using online A/B testing demonstrate the effectiveness of the proposed approaches and we have deployed the proposed relevance ranking system in production for more than three years.
Research status of transportation field based on keyword co-occurrence analysis

Abstract Transportation is the leading and basic industry in the national economy. It is an indispensable and irreplaceable part of social production and living organization system. The macro analysis of the literature in the field of transportation can provide a new perspective for researchers and managers. This paper uses vosviewer software, based on the keyword co-occurrence relationship of literature, to cluster the conference documents published by the Transportation Research Board of the United States from 2015 to 2019, and analyze in detail eight key areas, including aviation, highway, marine transportation, motor carriers, pedestrians and bicyclists, pipelines, public transportation and railroads. The above analysis reveals the research status of the whole and 8 key sub fields in the field of transportation, so as to provide support for science and technology planning and decision-making in the field of transportation.
24-point game based on BP neural network : A smart game designed for children

This paper introduces a 24-point game based on BP (Back Propagation) neural network. This game enables two children to play together and have a contest, which will help the children to strengthen their number recognizing and mathematics ability. The training set consists of 2340 28*28 pixels black-and-white images. Hough transform and morphology are used to preprocess, then we segment the image and extract the feature. We construct a BP neural network by MATLAB neural network toolbox. Input the image processed by the front end into the network to recognize the characters of the poker card. After recognizing the 4 numbers, all the right answers to the 24-point game will be calculated and shown to the players by the system. At the same time, the system can count the time the players spend/cost and give a feedback.
Method of Real Time Calculation of Learning Rate Value to Improve Convergence of Neural Network Training

The scope of this research is a problem of correct initialization and further correction of a neural network learning rate. It is one of the main hyperparameters, which helps to increase a convergence rate of a training process. There are known techniques of time-based decay, step decay and exponential decay, in which the learning rate is initialized manually and then corrected downwards proportionally to some value. In contrast, in this paper, it is proposed to focus on an excitation level of a regressor - an output amplitude of a previous network layer. The formulas, which are based on the recursive least squares method, are derived to calculate the learning rate for each network layer, and their convergence is proved. Using them, the initial learning rate can be chosen arbitrarily, and not only can such rate decrease, but also it is able to increase when the value of the regressor has become lower. Experiments are conducted for a task of image recognition using multilayer networks and the MNIST database. For networks of different structures, the proposed method allows reducing the number of training epochs significantly in comparison with the backpropagation method with a constant learning rate.
Research on Extraction of Human Information Entity Relationship Based on Improved Capsule Network

Entity relation extraction is to learn the implicit semantic relations among entities from multiple entities of a single sentence. Extracting entity relationships from unstructured text information is a key step in building large-scale knowledge map, optimizing personalized search, machine translation and intelligent Q & A. At present, the more popular depth model of entity relationship extraction has a better effect on the relationship extraction of single entity pair, but the evaluation index data of the model is not high when it is extended to the situation of single sentence multi entity pair and document level complex semantics. In this paper, an improved capsule network model based on dynamic routing rules is introduced, and it is applied to the relationship extraction of multi entity pairs of unstructured human information in the field of literature. The capsule network uses the route iteration method to connect the capsules between different hidden layers, which makes the capsule network establish the position relationship between different features in the routing process. Therefore, the capsule network is more robust to the position and angle changes of the target than other neural networks, so as to avoid the loss of information. In the experiment, we use the improved capsule network model, transformer and CNN model to extract the entity relationship of human information. The experimental results show that the improved capsule network model can achieve high accuracy, recall rate and F1 value in the multi entity pair relation extraction of small language database in the field of literature.
Augmenting Organizational Knowledge Management Using Geographic Information Systems

Many firms are realizing that to thrive in complex and dynamic environments, they must be swift and flexible in their operations. Key decisions are now becoming more dependent on understanding and coding complex information acquired from knowledge management systems. GIS technology has the ability to incorporate this requirement. GIS has developed into an important part of many disciplines and cuts across all areas of the modern information society. It offers a framework for planners to devise things more efficiently. Thus GIS is a computing application capable of creating, storing, manipulating, visualizing, analyzing and interpreting geographical data in many ways that show relationships, patterns, and trends in the form of maps, globes, reports, and charts. Thus, this chapter emphasizes the role of knowledge management for the development of organizations, and therefore, the importance of GIS in organizational knowledge management. The key issues and strategies for effective and efficient organizational knowledge management using GIS are discussed.
An Application of Auxiliary Operation of Power Dispatching based on Semantic Analysis

Power dispatching plays quite an important role in power grid operation in terms of safety and stability. Structured data has been well managed in the real-time power dispatching and controlling system. However, the management of unstructured data such as text data has not received enough attention. In this paper, we accomplish an application of auxiliary operation of power dispatching by doing semantic analysis on operation procedures, which belong to an important category of power dispatching text data for fault disposal. The method that we proposed in our application is achieved based on Natural Language Processing (NLP) technologies. Our contributions are mainly two-fold: 1) we accurately extract key information from text data through our proposed method, and 2) we help power dispatching operators reduce their work and improve efficiency by providing auxiliary operation advice.
Bridging the Gap between Click and Relevance for Learning-to-Rank with Minimal Supervision

Recently, unbiased learning-to-rank models have been widely studied to learn a better ranker by eliminating the biases from click data. Toward this goal, existing work mainly focused on estimating the propensity weight to design a specific bias type from click data. From a different perspective, we propose a simple-yet-effective ranking model, namely wLambdaMART, which estimates the confidence of click data with a few labeled data, instead of learning the propensity weight to reduce the bias from click data. We first train a confidence estimator to bridge the gap between biased click data and unbiased relevance. Then, we infer confidence weights for all click data and apply them to LambdaMART to learn a debiased ranker. Practically, since it is found that learning the confidence estimator only requires a few labeled data, it does not incur high labeling costs. Our experimental results show that wLambdaMART outperforms state-of-the-art click models and unbiased learning-to-rank models on the real-world click datasets collected from a commercial search engine.
Categorical Clustering Applied to the Discovery of Character Builds in TCTD2: the BaT Approach

This article describes an attempt to categorize character configurations of players of Tom Clancy's the Division 2, conducted to highlight behavioral differences in approach to gameplay based on one's character build. Nine distinct character builds were extracted for maximum coherence and minimum variance and each build showed significant differences in separate measures of behavior such as playtime, character health and armor among other attributes. The proposed method was also able to recover builds recognized by social forums as well as discovering new ones. Appropriation of Character builds as categorical text-based data (BaT: Build as Text), provides a unique opportunity for game researchers to use a diverse set of input data which will in turn contribute to the improvement of the process of game design informed by player choices. Longitudinal observations in interconnection of obtained clusters may provide further insight into formation and evolution of gameplay types.
ABMMRS Eradicator: Improving Accuracy in Recommending Move Methods for Web-based MVC Projects and Libraries Using Method’s External Dependencies

Move Method Refactoring (MMR) is used to place highly coupled methods in appropriate classes for making source code more cohesive. Like other refactoring techniques, it is mandatory that applying MMR will preserve applications’ behaviors. However, traditional MMR techniques failed to meet this essential precondition for Action methods in web-based application and API methods in libraries projects. The reason is that applying MMR on these methods changes the behaviors of the projects by raising Application-breaking issues, for instance, failure of browser requests and compilation errors in client projects. To resolve this problem, developers are suggested to manually check Action and API methods while applying MMR. However, manually inspecting thousands of lines of code for these issues is a time-consuming and hectic task. In this paper, an advanced MMR technique is proposed which automatically identifies Application-breaking MMR suggestions. This technique first takes the initial move method suggestions from the existing prominent MMR techniques e.g. JDeodorant. For each of the suggestions, it parses the source code and construct Abstract Syntax Tree to examine two types of usage. One is whether a suggestion has not been used in any unit test and Regular Class, and another is whether the suggestion has been used in unit test classes only. If any MMR suggestion is found having one of these two types of usage or both, the respective suggestion is marked as Application-breaking. In order to evaluate the proposed technique, several experiments have been conducted on open source projects. The experimental results show that the proposed technique achieved 96.4% Precision, 90% Recall and 93.1% F-score in detecting Application-breaking MMR suggestions, because of considering external dependencies of the MMR suggestions.
Benchmarking Distributed Stream Processing Frameworks for Real Time Classical Machine Learning Applications

As the volume of data generated is growing at an unprecedented rate, it becomes important to analyze this data in real-time. To handle the huge volume of data streaming at a high velocity, we not only require powerful machines but also means to distribute the computation involved on the multiple machines. There are several open-source distributed stream processing frameworks such as Apache {Storm, Flink, Spark} and Confluent Kafka for building real-time machine learning applications. Prior works benchmarked some of these platforms using low-level operations like filters, joins, windowed computations etc. Our work includes benchmarking these popular frameworks for their applicability to classical machine learning models: Online K-Means, Online Linear Regression and Online Logistic Regression. We study the following quantitative metrics of evaluation: throughput, latency, CPU, and memory usage. The experiments were conducted in both standalone and clusters setups to determine the scalability of the models. This study will help system designers choose the right model and the right framework, given a specific configuration on streaming data.
Short-term photovoltaic power generation prediction model based on fuzzy clustering-Elman neural network

Photovoltaic power generation forecasting is the basis of safe and stable operation in power grids. This paper proposes a short-term photovoltaic power generation prediction model based on Isolation Forest, Fuzzy C Means and Elman. Firstly, similar daily datas are selected according to the forecast date and classified according to the weather. Secondly, the abnormal parts in the Isolation Forest cleaning training samples are adopted. Thirdly, Fuzzy C Means clustering method is used to cluster the meteorological data of similar and the forecasting days. Finally, combined with the Elman neural network algorithm, a fuzzy clustering-Elman neural network prediction model with isolated forest data cleaning is formed. The experimental simulation is carried out according to the actual measured data of a certain city in Anhui Province. The prediction results are respectively compared with the traditional Elman and BP model. It is demonstrated that higher prediction accuracy can be obtained.
Multi-owner Encrypted Ranked Keyword Search Using Machine Learning Techniques

In the present era, the prevalence of network technology and cloud computing capabilities for multiple owner model has drawn much attention. Data owners outsource their data to the cloud and enjoy convenient services. However, extending single owner model into multiple owner model still have some issues, such as dimension reduction issue, high communication overhead, and efficient search in Searchable Symmetric Encryption (SSE) remain a challenging task. Integration of machine learning methods with the framework of searchable Encryption (SE) is a diverse way to solve these problems. In this paper, we developed Multi-owner encrypted ranked keyword search using Machine learning techniques(MERS-ML). Our Scheme utilized principal component Analysis (PCA) model to reduce high-dimensional data into low-dimensional codes and enabled low-overhead system maintenance. K-means clustering approach is used to solve the problem of the quality of different document of multiple owner model. To achieve fast query and efficient search relatively close to O(log N), we designed search balanced index tree. Besides, attribute-based encryption is used to achieve convenient key management as well as authorized access control. Compared with previous work, our scheme provide adequate privacy protection, improved search efficiency, and introduce low overhead on computation and storage.
Data Computing in Covert Domain

This paper proposes an idea of data computing in the covert domain (DCCD). We show that with information hiding some data computing tasks can be executed beneath the covers like images, audios, random data, etc. In the proposed framework, a sender hides his source data into two covers and uploads them onto a server. The server executes computation within the stego and returns the covert computing result to a receiver. With the covert result, the receiver can extract the computing result of the source data. During the process, it is imperceptible for the server and the adversaries to obtain the source data as they are hidden in the cover. The transmission can be done over public channels. Meanwhile, since the computation is realized in the covert domain, the cloud cannot obtain the knowledge of the computing result. Therefore, the proposed idea is useful for secure computing.
Age Estimation Based on Indonesian Face Recognition using Convolutional Neural Networks

In Indonesia, age identity plays an important role in deciding many things, for example, to determine the level of education, medical treatment, and health, to determine the age allowed to get married, to get a job, etc. An effective way to overcome age counterfeiting is to recognize facial images that have unique biometric features. Age development is generally indicated by skin texture and facial structure, this makes it quite difficult to estimate age. Therefore, we need automatic age identification that can be convinced, can be accounted for in the public interest, and has a high closeness. This paper proposed the age estimation of Indonesian face using Deep Convolutional Neural Networks (CNN) DenseNet-161 model architecture approach. The dataset is collected with a range of 7-22 years old of 2300 face image of Indonesian. We compare the prediction result with the custom architecture of CNN with 3 convolution layer and 3 fully connected. The prediction results of the DenseNet-161 model achieved very good prediction results (MAE = 3.02, Accuracy = 67.93%, and R-Squared = 0.99) than the custom model (MAE = 3.17, Accuracy = 64.47%, and R-Squared = 0.97).
Applying Web Mining and Sentiment Analysis to Assess Tourists Review on Batu City Tourist Destination

Tourist attractions are one of the destinations for the community to eliminate fatigue and to function as a self-entertainment. Lots of favorite cities chosen by the community as a tourist destination, one of which is Batu City, East Java. This city has a variety of tourist sites, which can be categorized into two types, namely natural and artificial. Thousands of people come every year to spend their holidays in the city of Batu. For this reason, this study aims to apply web mining and sentiment analysis to determine people's sentiment or perspective on tourist attractions in Batu through the reviews found on the website. The web mining method is used to retrieve information from the travel website. Then, the retrieve information is analyzed by using python programming language. There are eight tourist sites whose reviews were taken, with the total number of reviews are 4887 reviews. The results show that the tourists sites have a polarity value with a range between 0 to 1, which indicates that all the tourists' site gets a positive sentiment. In addition, the resulting subjectivity value is in the range of 0.4 to 0.6, which indicates that all reviews given by visitors are opinions or personal opinions. It implies that each tourist site has sufficiently met the needs of visitors, regarding the facilities and infrastructure that are provided.
IDJ at my side

Abstract David Sless’s journey to information designer coincides with the history of IDJ. David’s first paper was published in the second issue (1979). David’s journey alongside IDJ included the generosity of Peter Simlinger of IIID (1980s) and the encouragement of Paul Stiff of Reading University (1990s). IDJ is still a vital part of David’s life.
Bridging the gap between school and out-of-school science: A Making pedagogical approach

Making provides a beneficial learning environment that requires skills and knowledge from the areas of science, technology, engineering, and mathematics to design and construct a product or an artefact.In this paper the maker approach reflects on the pedagogical potential of learning through the design and deployment of an automated system that monitors and records environmental parameters in lakes and rivers.IoT technologies are used to connect schools with natural ecosystems, providing the opportunity to students to be actively involved in designing and developing technology artefacts to experiment with, and further, in the formulation of research questions, and in the processing and interpretation of research results and measurements.The study contributes to the research literature on bridging the gap between the school and out-of-school science.
Attention Network for Group Recommendation

Group activities have become important in the current social networks. Recommending activities for a group has become an important task in many information systems. Group recommendation not only needs to aggregate the preferences of group members, but also needs to properly understand the group decision-making process. In addition to integrating the individual preferences of each member of the group, we also need to consider the interactive information among users, groups and activities, so as to assign a higher influence weight to the experts or leaders in the group. Based on the attention mechanism and the latest development of NCF, we contribute a novel solution, namely NCF-AGREE, which can learn aggregation strategies according to historical data, so as to better solve the problem of group preference aggregation. In order to adapt to group recommendation, different members of the group are assigned weights. Experiments on real data sets show that NCF-AGREE is better than baseline method in group recommendation.