Reviewerly/exHarmony huggingface dataset:
Wide-Frequency Synchrophasor Transfer Method Based on Channel TDM

Wide-frequency synchrophasor is the general term for synchrophasors of harmonics and interharmonics from low frequency to high frequency. Limited by existing transfer standard and transmission channel bandwidth, the current Wide Area Measurement System (WAMS) cannot transfer wide-frequency synchrophasors and can only transfer fundamental synchrophasors. This paper provides a wide-frequency synchrophasor transfer method from substation to master station, and focuses on the channel time division multiplexing (TDM) technology. The compatibility of the transfer method based on this technology with the existing protocol and the network characteristics are analyzed. At last, the feasibility of the method is verified by simulation experiments. Within the channel TDM method, the wide-frequency synchrophasor can be transferred from substation to master station under the existing WAMS architecture and transfer standard, and the transfer method satisfies the requirements of the existing physical channel bandwidth.
Dynamic vehicle target detection and traffic statistics algorithm research

With the continuous development of social economy, the per capita car ownership has increased rapidly, and the traffic congestion problem followed has become increasingly serious. In order to process the video information of intersections effectively, this paper completed the design of vehicle target detection and statistics algorithm based on the characteristics of video images in the green light duration. Firstly, a suitable image preprocessing algorithm is designed. Secondly, the improved mixture Gaussian background modeling algorithm is used to perform background subtraction, and the target vehicles are extracted. At the same time, the circumscribed rectangular frame is used to identify the vehicle. Finally, the total number of vehicles of the entire video is counted by calculating the number of vehicles running in the current frame. The experimental results show that the algorithm has a high detection rate and has certain practicability.
Sequence recognition of natural scene house number based on convolutional neural network

Extracting character information from complex images has always been a research hotspot and a difficult topic in the field of computer vision. Natural scene number is severely distorted due to blurred image, uneven illumination, weak illumination, which makes it difficult to achieve ideal results for character recognition, especially identifying characters of arbitrary length. In this paper, we use the convolutional network to automatically extract the advantages of features, and construct a convolutional neural network that recognizes single digits. In order to highlight important features, we also use grayscale methods to weaken the background information in natural scenes and apply certain Proportional Dropout strategy to prevent overfitting. We use a cyclic network to generate character sequences and construct a deep convolutional neural network that recognizes sequence numbers and without split character characters. We construct a deep convolutional neural network that uses convolutional networks and cyclic network fusion to simultaneously identify multiple digits. We verify on the SVHN data set, we achieve better results in accuracy, we get the recognition rate of single digital house number is 95.72%, better than most algorithms in existing articles and the recognition rate of serial digital house number is 89.14%.
Unified Feature Selection and Hyperparameter Bayesian Optimization for Machine Learning based Regression

In this paper we propose a method that is based on Bayesian optimization that performs feature selection and hyperparameter optimization for training regression models. Without loss of generality, we consider here as regressor a Multilayer Perceptron neural network (MLP) and as feature selection criterion the distance correlation measure. The performances of the proposed method are analyzed when fitting data generated by a custom function with known dependence of variables and some real data with unknown dependence.
A Web Application for Managing and Resolving Complaints of Public

The purpose of the paper is developing a web-site that is designed for Online Complaint Management System. In India there is no direct communication between the government and public in an efficient way for solving the problems. The main purpose is to provide an online way of solving the problems faced by the public by saving time and eradicate corruption and help the common people under the jurisdiction of a municipal corporation to register their grievances about day to day problems in their ward through a website and the municipal authorities to address the problem in a short period of time. Complaints are easier to coordinate and resolve, and to provide company with an effective tool to identify and target problem areas and acts as a management technique for assessing, analyzing and responding to public complaints. This software is used to record resolve and respond to public complaints, requests as well as facilitate any other feedback which provides an interface to register one&amp;rsquo;s complaint and follows it up.
Dynamics of quantum correlation for circularly accelerated atoms immersed in a massless scalar field near a boundary

In this paper, we explore the dynamics of quantum correlation for two circularly accelerated atoms interacting with a bath of fluctuating massless scalar field with a reflecting plane boundary. First, we derive the master equation that governs the system evolution. Then we analyze the behaviors of quantum correlation for various conditions and compare the behaviors of quantum correlation with that of the static atoms immersed in a thermal bath with a boundary. It is found that the dynamics of quantum correlation for circularly accelerated atoms present some features distinct from those of static atoms immersed in a thermal bath.
Multi-phased Task Placement of HPC Applications in the Cloud

Many high-performance computing applications present different phases during their execution. Nevertheless, thread and process placement techniques usually provide static-only methods to improve the data and thread locality. Similarly, cloud computing datacenters may present variations in terms of latency over the execution time of applications. To overcome these two problems, in this paper we analyze scientific applications that have different communication patterns along with its execution. For such applications, we evaluate the performance variation of traditional static placement techniques to our new approach that uses code annotations to perform the new placement of tasks, matching also the variations on network performance of Virtual Machines (VMs) during the run time. For our experiments, we use applications from the NAS parallel benchmark suite, running them on two VM sizes with 32 and 64 cores respectively, from the same family of instance types at the West US datacenter from Azure. Results show that compared to traditional static process mapping, our multi-phased placement mechanism achieves average performance gains of 13.57% up to 28.32% on the evaluated scenarios. These results show that there is an opportunity to improve performance by correctly identifying the network variations and reacting by generating a new task-to-instance mapping.
Research on Data Enhanced Ancient Pictogram Recognition Method Based on Convolutional Neural Network

As the carrier of national culture, words and pictograms record the unique culture and history of each nation, but the number of existing ancient pictogram is very small, and it is difficult to collect them, which makes it difficult for the academic research of ancient pictogram and the recognition by deep learning. In addition, due to the preservation environment and their own particularities, the traditional data enhancement methods will cause problems such as wrong data label, inability to simulate real scenes, etc. So, it can't effectively expand the large-scale data. To solve these problems, this paper proposes a set of data enhancement methods for small data sets and natural scenes. For the small data set enhancement method, firstly, we use artificial data enhancement to enhance original data, and then a limited random affine transform is used to limit the extent and extent of the enhancement. For natural scenes, we use the DCGAN to fuse the natural scene image and the ancient pictogram to simulate the natural environment. Finally, the paper designs a neural network model to recognize the ancient pictogram. It is proved that the data enhancement method can solve the problem of insufficient data, and finally achieve 99% accuracy.
Classification of nutritional status of toddlers using fuzzy k-nearest neighbor in every class (FK-NNC)

Abstract The purpose of this research is classify nutritional status of toddlers with algorithm fuzzy k-nearest in every class (FK-NNC). The FK-NNC algorithm is a modification concept of the nearest K neighbor for each class. The largest of the class on membership value will be selected as prediction classes. The optimal K value in FK-NNC algorithm uses k-fold cross validation. The optimal K values are searched by experimenting 1-fold cross validation, 4-fold cross validation and 10-fold cross validation. Accuracy rate of classify nutritional status of toddlers at the Wonorejo Health Center using the FK-NNC algorithm with an optimal K value. The results of this research obtained the optimal K value used in the FK-NNC algorithm at the Wonorejo Health Center is K=8 with 1-fold cross validation experiment. The value of K=8 was applied to the FK-NNC algorithm with a 1-fold cross validation experiment to predict class of nutritional status of toddlers in the Wonorejo Health Center. The percentage of accuracy produced as much as 100% with data proportion 90:10.
Memorizing and Retrieving of Text Using Recurrent Neural Network—A Case Study on Gitanjali Dataset

This paper presents an application of Recurrent Neural Network for retrieving text from a given sequence of words of Gitanjali dataset. A Recurrent Neural Network (RNN) is trained to predict the poem corresponding to which the sequence of words are given. We demonstrate the experiment with two major RNN architectures and state the results to show which hyper-parameters like RNN size, sequence length, number of stacked layers affect the RNN most while completelyBhowmick, Rajat Subhra memorizing the content of the poem. We also state the challenges to train the model in both forward and backward ways. We largely emphasisSil, Jaya on the memorizing capability of RNN and put forward an application which depends on it.
Constructing Uyghur Commonsense Knowledge Base by Knowledge Projection

Although considerable effort has been devoted to building commonsense knowledge bases (CKB), it is still not available for many low-resource languages such as Uyghur because of expensive construction cost. Focusing on this issue, we proposed a cross-lingual knowledge-projection method to construct an Uyghur CKB by projecting ConceptNet’s Chinese facts into Uyghur. We used a Chinese–Uyghur bilingual dictionary to get high-quality entity translation in facts and employed a back-translation method to eliminate the entity-translation ambiguity. Moreover, to tackle the inner relation ambiguity in translated facts, we made a hand-crafted rule to convert the structured facts into natural-language phrases and built the Chinese–Uyghur lingual phrases based on the similarity of phrases that corresponded to the bilingual semantic similarity scoring model. Experimental results show that the accuracy of our semantic similarity scoring model reached 94.75% for our task, and they successfully project 55,872 Chinese facts into Uyghur as well as obtain 67,375 Uyghur facts within a very short period.
Teachers’ evaluation of MotionAR: An augmented reality-based motion graphing application

Abstract This research presents the results of the initial evaluation of MotionAR: an augmented reality (AR) based motion graphing application for physics classes. MotionAR uses AR trackers to measure relative motion between objects, hence producing a motion graph. The subject of the evaluation is an improved version of our application that addresses recommendations from the first prototype. Among these recommendations were: the use of words instead of arrows to identify targets, the use of the Cartesian coordinate system, and previews of the displacement vs. time, velocity vs. time and acceleration vs. time graphs. In-service teachers used a 10-item questionnaire for evaluation. The teacher-evaluators come from diverse educational backgrounds (rural and urban schools, 0 to 24 years in teaching, no e-learning model in school vs 1:1 student-to-tablet PC ratio, science vs. non-science majors) which makes the initial evaluation a good baseline data. This ongoing research will present the lessons learned from the evaluation, including insights in developing AR applications for physics classes, and recommendations for teaching motion graphs utilizing AR.
Enhancement of Identification Accuracy by Handling Outlier Feature Values Within a Signature Case Base

This is a study of applying Case Based Reasoning techniques to identify a person using offline signature images. Classification related to proper identification is achieved by comparing distance measures of new test cases with existing cases within the base. The patterns pertaining to the images are captured by fusing some standard global features with some indigenously developed local feature sets. Outlier values in both these sets are handled to maintain statistical tolerable limits. The effect of outlier handling within feature values is found to enhance identification accuracy for two standards and one indigenously collected offline signature sets utilized in the experimental phase.
Enhanced Water Cycle Algorithm with Active Learning and Return Strategy

In order to improve the performance of Water Cycle Algorithm (WCA), an alternative adaptation approach for enhancing the global searching ability is proposed. The proposed algorithm, named WCA-ALR, uses a new diversity enhancement approach to effectively improve the exploration capability of the WCA. The proposed approach consists of two major modifications: (1) an active selection method for choosing learning targets; (2) a promising position sifting and returning strategy. The benefits prove that actively selecting a learning target performs better than that of learning from a fixed one. A promising position sifting and returning strategy can also enhance the exploration ability. In order to verify the performance, numerical experiments on five basic benchmark problems are conducted. Then, a set of benchmark problems from the CEC2017 on 10 and 30 dimensions are used to prove the effectiveness of WCA-ALR. Experimental results affirm that the proposed approach can obtain better results, compared to the original WCA.
Investigating algorithmic variations of an RS Graph-based collaborative filtering approach

A personalized recommendation system learns users specific profiles from users feedback and content in order to deliver information tailored to each individual user's interest. Although great effort has been devoted to the proposal, implementation and study of recommendation systems approaches, there is still a lot of room for improvement. Currently, much research on recommender systems focuses on improving the accuracy of their algorithms. In a recent work, we proposed a novel Graph-based Collaborative Filtering approach for recommendation systems based on five steps: (1)-Creating a Homophily network using similarity measures, (2)- Identifying communities in the Homophily network, (3)- Identifying key nodes per community, (4)- Profiling key nodes per community and (5)-Computing recommendations for community users based on the resulting profiles. This paper delves deeper into the first two steps of our approach. In fact, the wide range of algorithms for community detection compelled us to create variations of our approach and to conduct a comparative analysis of their accuracy and time of execution. Results from testing our variations on two open datasets present comparable results. While the Louvain algorithm has the merit of simplicity and straightforwardness, AHC and K-means algorithms render better results when dealing with a larger datasets.
Better Accessibility Research for the Future (Keynote)

Welcome to my insecurities, and the lonely questions I ask myself at 03:00 in the morning. I'll present five 'Notes to My Past Self' which outline the things I should have considered -- and am now considering -- which would have made my accessibility work better. I'll take a look at: Impact and Public Dissemination; Reproducibility; I'll ask 'What is the Web?'; and 'What is Accessibility'; finally, I'll look at an example case focused on Type 1 Diabetes and the #WeAreNotWaiting movement. The talk will be more of a chat in which I hope you'll participate.
Facilitating the Development of Software Product Lines in Small and Medium-Sized Enterprises

Software Product Lines (SPLs) are Software Engineering methodologies that manage the development and evolution of families of product variants. They aim at handling the commonality and variability of these products. SPLs reduce the development cost, time-to-market, and increase overall quality of the product variants.
Tackling challenges of neural purchase stage identification from imbalanced twitter data

Abstract Twitter and other social media platforms are often used for sharing interest in products. The identification of purchase decision stages, such as in the AIDA model (Awareness, Interest, Desire, and Action), can enable more personalized e-commerce services and a finer-grained targeting of advertisements than predicting purchase intent only. In this paper, we propose and analyze neural models for identifying the purchase stage of single tweets in a user’s tweet sequence. In particular, we identify three challenges of purchase stage identification: imbalanced label distribution with a high number of non-purchase-stage instances, limited amount of training data, and domain adaptation with no or only little target domain data. Our experiments reveal that the imbalanced label distribution is the main challenge for our models. We address it with ranking loss and perform detailed investigations of the performance of our models on the different output classes. In order to improve the generalization of the models and augment the limited amount of training data, we examine the use of sentiment analysis as a complementary, secondary task in a multitask framework. For applying our models to tweets from another product domain, we consider two scenarios: for the first scenario without any labeled data in the target product domain, we show that learning domain-invariant representations with adversarial training is most promising, while for the second scenario with a small number of labeled target examples, fine-tuning the source model weights performs best. Finally, we conduct several analyses, including extracting attention weights and representative phrases for the different purchase stages. The results suggest that the model is learning features indicative of purchase stages and that the confusion errors are sensible.
Simulated Reference Frame Effects on Steering, Jumping and Sliding

In this paper, we investigated the impact of an egocentric simulated frame of reference, the so-called simulated CAVE, on three type of travel techniques: Steering, Jumping and Sliding. Contrary to suggestions from previous work, no significant differences were found regarding spatial awareness between all techniques with or without the simulated CAVE. Our first results also showed a negative effect of the simulated CAVE on participants' motion sickness for every technique, while confirming that the Jumping is eliciting less motion sickness with or without it.
VersionClimber: Version Upgrades Without Tears

VersionClimber is an automated system to help update the package and data infrastructure of a software application based on priorities that the user has indicated (e.g., I care more about having a recent version of this package than that one). The system does a systematic and heuristically efficient exploration (using bounded upward compatibility) of a version search space in a sandbox environment (Virtual Env or conda env), finally delivering a lexicographically maximum configuration based on the user-specified priority order. It works for Linux and Mac OS on the cloud.
Trendingtags—Classification &amp; Prediction of Hashtag Popularity Using Twitter Features in Machine Learning Approach Proceedings

Most of the tweets are associated with unique hash tags. Some hash tags become trending in a very short time. Predicting which hash tags will become trending in the near future is of significant importance for taking proper decisions in news media, marketing and social media advertising. This research work is aimed at predicting the popularity and tagging the hash tags using machine learning algorithms. It categorizes the popularity under five classes namely not popular, marginally popular, popular, very popular and extremely popular using content and contextual features. The content features can be extracted from both hashtag string and content of the tweets, whereas the contextual features can be extracted from the social network graph of the users. The features are evaluated based on the metrics such as micro-F1 score and macro-F1 score. The result shows that contextual features are more effective than content features as it has the highest prediction accuracy of 94.4%.
Several Methods for Constructing White-box Solutions

Abstract White-box cryptography is an important countermeasure for providing software security in untrusted environments. While almost all public white-box solutions have been broken, the methods for constructing such solutions are worth learning. In this paper, we introduce several classic methods for constructing white-box solutions. The methods are divided into two categories: those that make the white-box solutions functionally equivalent to existing ciphers, and those that do not. In each category, we explain each method using steps and figures. Our introduction covers almost all the general methods for constructing white-box solutions and aims at presenting a full overview of white-box designs.
Gigapixel-Level Image Crowd Counting using Csrnet

In this paper, we introduced our work in gigapixel crowd counting, the main challenges of this task are about three aspects. First, there is no model or device permitting us to train a network using gigapixel image or do detecting straightly on them. So we segment these images into small pieces in size of 19201200 to train our network. The second problem is that all the images contain thousands of people which form extremely congested scenes. Our solution is using Dilated Convolution Neural Network which showed good performance on understanding highly congested scenes. The third thing is about accuracy of counting. We will do three times of counting in different scales(origin, 16 times small, 256 times small) and calculate the weighted mean of them as final result.