Reviewerly/exHarmony huggingface dataset:
Attention and Multi-layer Fusion for Real-time Semantic Segmentation

Semantic segmentation is a challenging task in the field of computer vision. One of the significant difficulties is that the pixel loss caused by the downsampling process cannot be restored during the upsampling process. So now the focus of semantic segmentation research is to solve the problem of pixel loss and pixel complementation. For the pixel complementation and prediction output edge smoothing problem, this paper proposes a Gated Multi-layer Fusion Network. The structure enhances the precise restoration of pixels from two aspects: First, the parallel layer before fusion is optimized by the gating structure, so that the parallel layer provides finer pixel semantics, and its parameter can be adjusted to optimize downsampling; Second, the different layers contain different pixel semantics, the pixel semantics of different layers can be combined by multiple layers of fusion, which makes the pixel restoration be more accurate, and the edge smoothing problem of prediction results be solved better. Considering the real-time of the model, this paper adopts the small model as the basic model, and in the fusion process, the paper uses $1 \times1$ convolution to reduce the dimension. For the data set with images of $512 \times512$, the Mean IOU is up to 72.9%, and the speed is 43.1frames per second.
Subsampling-based HOG for Multi-scale real-time Pedestrian Detection

Pedestrian Detection is a key problem in computer vision research. And the feature of Histogram of Oriented Gridients(HOG) is commonly used in this field to describe image local texture. In this paper, the method about a background-based model and a multi-scale sliding window subsampling HOG feature( D-HOG) is proposed, which can be used for pedestrian detection. First, generating a foreground image which is regarded as the input image. Then, a plurality of target regions are obtained on the foreground image by using a multi-scale sliding window. Next, extracting the D-HOG features of each obtained target region. Finally, the detection phase is performed by a support vector machine(SVM). The experimental results demonstrate that the D-HOG feature vector with multi-scale sensitivity can maintain the accuracy of HOG and improve real-time performance, which is suitable for multi-scale and different proportions of pedestrian detection.
Web crawler design, implementation and data analysis about Sina Weibo

In this paper, discussed designed and implemented a crawler base on python language and use this program crawl posts from Sina weibo. The key word is academic library. The time span is from 1/1/2018 to 31/12/2018 and the total number is 39374 and make data clean, cut words and delete all text that no related to academic library. After that, analyzed this data set for knowing the user’s attitude about library. In order to describe the situation better, high frequency words rank, cloud word, semantic network about image of Library service, semantic network been used. Finally, according to the user's attitude, make the the necessary discuss.
Mining optimized Frequent Itemsets Based on Max-miner: A Distributed Approach

Association rule mining is a significant instrument in data mining. There are many approaches for extracting frequent rules hidden in data. However, most of them have the problems of too many redundant rules and high computational complexity, especially in massive data mining task. To solve these problems, an optimized frequent itemset mining algorithm for association rules based on max-miner (OFI-AR) is proposed, where the maxminer could mine the initial optimized frequent itemsets. The OFI-AR need not to set a parameter named Confidence and can still extract valuable association rules. At first, transactions are divided into serval sub-transactions based on a distributed computing platform Spark. Then, a proposed sequential frequent itemset mining algorithm are used to extract frequent itemsets on sub-transactions. After that, these itemsets are aggregated and be filtered by a proposed value comparison method, which combine the approach of max-miner and depth-first search. The rest of frequent itemsets are regarded as optimized ones and can generate valuable rules. The experimental results demonstrate that the OFI-AR could find valuable association rules without the set of confidence and efficiently reduce the runtime.
Using Darts to Improve Mold ID Recognition Model Based on MASK R-CNN

Abstract Mask R-CNN model is an excellent image segmentation model, but cannot perform well on the task of mold ID recognition. To address the aboved problem, this paper improves the classification branch of Mask R-CNN model by applying DARTs, making the improved classification branch more suitable for mold ID recognition task. Firstly, DARTs is employed to obtain network cell structure. Secondly, the network cell structure is used to improve the classification branch. Moreover, we make the model converge under 2GPU days based on transfer learning. Finally, the experimental results show that the improved model can achieve higher accuracy with IOU of 0.5 (increased by 1.02%) and 0.75 (increased by 2.42%).
Review On Sentiment Analysis of Twitter Posts About News Headlines Using Machine Learning Approaches and Naïve Bayes Classifier

In today's world there are so much micro blogging sites, among all twitter is one of the popular site. It has become an important part for all individuals, politicians, companies, celebrities, etc. Almost all the major news outlets have Twitter account where they post news headlines for their followers. People with Twitter accounts can reply or retweet the news headlines. Twitter users who have an account can also post news headlines from any other news outlets. When people post, reply or retweet news posts on Twitter, it is obvious that they are expressing their sentiments through that.
The use of visual multimedia in moto cycle mechanics training for deaf students

Abstract A common problem in this study is that deaf students have communication problems in learning and students experience problems how to communicate with teachers. The purpose of this study is expected to be able to find out that the use of visual media in motorbike mechanics training can help the development, abilities and knowledge of deaf students. The procedure carried out was to analyze the communication skills of deaf students by interviewing deaf students by one researcher and one linguist. Five deaf students participated in this study. They are Deaf students with the classification of Profound Hearing Loss. Stage one, the Instructor provides learning without using visual multimedia. The second stage, the instructor gives an evaluation of learning to students. The third stage, is taking interview data related to how their perceptions of the learning process, namely what media should be used for deaf students. The results show that the students interviewed stated that visual multimedia with full language is very useful if used at every opportunity, be it during motorcycle training or other learning.
Unique Dragonfly Optimization Algorithm for Harvesting and Clustering the Key Features

In many applications, the feature selection plays an important role, as best feature can bring out the accurate results. The features selected must represent the entire dataset. Here we have considered “Sequential Forward Selection” for feature extraction and used refined dragonfly algorithm to approach and to migrate from the best and worst features respectively. We improvised the conventional dragonfly algorithm by adding the convergence and fitness functions. To access the accuracy of the algorithm we introduced the fitness function. This paper has discussed about the general hunting behaviour of the dragonfly and dragonfly algorithm (DA) with convergence and fitness functions. A comparative study was shown for the best search agent position between modified DA and traditional DA, at the same time test function values of refined dragonfly algorithm (RDA) is compared with whale optimization algorithm (WOA) and Tornadogenesis Optimization algorithm (TOA). We have evaluated refined DA on the 23 benchmark function corresponding values are shown in experiment.
The quality classification of professional teacher using fuzzy-analytical hierarchy process

Abstract Teachers are required to carry out their duties and responsibilities based on the four fundamental teacher competencies. Teacher competencies are pedagogic, personality, social, and professional. The quality of professional teacher determines based on an assessment of the four fundamental competencies of teachers above. The focus of this study is the classification of professional teacher quality assessment results based on teacher competencies. The quality classification of professional teachers uses a fuzzy rule-based method. The purpose of this study is to provide better measurement and classification of professional teacher quality; it easier to make policy decision-making processes for improving the quality performance of professional teachers. The results of this study show the quality of the professional teacher 66.7% in medium class and 33.3% in high class with no professional teacher in low class.
Analysis development of augmented reality in android-based computer learning in vocational schools

Abstract The making of this paper aims to analyze the latest technology used in Augmented Reality in vocational education. Augmented Reality is a merger between the virtual world and the real world which is projected in 2D or 3D form that is used for Entertainment, Engineering Design or as a learning medium. Augmented Reality is expected to be able to attract students’ interest compared to conventional learning methods. The method of making paper uses a literature review that is with 30 papers related to Augmented Reality starting in 2014-2019. Augmented Reality is built with a Blender 2.80 application, software Vuforia SDK, Corel Draw X9, Android Studio, and Unity 3D. Most of the research or development of Augmented Reality in 2014-2019 uses Blender 2.80, Vuforia and Unity 3D applications.
Pattern Recognition and Its Application in Image Processing

Abstract With the continuous improvement of China’s national economy and the unprecedented innovation and development of modern science and technology, artificial intelligence technology has been widely used in China’s industrial production, power transportation, aerospace and other fields. As an extension of modern artificial intelligence technology, pattern recognition technology plays an important role in modern computer image processing by virtue of its own intelligence and automation. It fundamentally guarantees the accuracy of image processing and greatly improves computer image recognition and information processing. Overall work efficiency. This paper mainly analyses the main characteristics of pattern recognition technology, and further studies the practical application of pattern recognition technology in image processing, hoping to provide corresponding reference for the future application and development of pattern recognition technology in image processing.
Fault-tolerant Quantum Implementation of 1-bit and 4-bit Comparator Circuit using Clifford+T-group

Aiming to design a complete Quantum Information Processor (QIP), in this work, we are showing an efficient implementation of fault tolerant quantum comparator circuit. To ensure this fault tolerant property in the design, we have used Clifford+ T-group and also have executed circuit optimization algorithm to contain the cost metrics. In the design phase, initially an optimal 1-bit circuit is formulated and then by integrating 1-bit modules, 4-bit comparator is prepared. Though we have shown only 1-bit and 4-bit circuits but it can be generalized for n-bit design as well. Our proposed design is quite different from existing fault-tolerant comparator models as our circuit is magnitude driven design and can compare magnitude of numbers by encoding the input vector into equivalent binary numbers as well. Towards ensuring improved features in our proposed model, the design is transformed to minimum T - depth based design, which in turn ensures minimum CPU cycles to obtain the first output from the design as well reduces the effect of decoherence. In addition to this, the proposed model has optimal T - count metric to control the circuit overhead. The functional correctness of our design also has been checked using several test patterns.
Investigating Deep Recurrent Connections and Recurrent Memory Cells Using Neuro-Evolution

Neural architecture search poses one of the most difficult problems for statistical learning, given the incredibly vast architectural search space. This problem is further compounded for recurrent neural networks (RNNs), where every node in an architecture can be connected to any other node via recurrent connections which pass information from previous passes through the RNN via a weighted connection. Most modern-day RNNs focus on recurrent connections which pass information from the immediately preceding pass by utilizing gated constructs known as memory cells; however, connections farther back in time, or deep recurrent connections, are also possible. A novel neuro-evolutionary metaheuristic called EXAMM is utilized to conduct extensive experiments evolving RNNs consisting of a suite of memory cells and simple neurons, with and without deep recurrent connections. These experiments evolved and trained 10.56 million RNNs, with results showing that networks with deep recurrent connections perform significantly better than those without, and in some cases the best evolved RNNs consist of only simple neurons and deep recurrent connections. These results strongly suggest that utilizing complex recurrent connectivity patterns in RNNs deserves further study and also showcases the strong potential for using neuro-evolutionary metaheuristic algorithms as tools for understanding and training effective RNNs.
Path Planning of Restaurant Delivery Robot Based on Improved Ant Colony Algorithm

The path planning problem of dining robot is studied, and a path optimization method based on improved ant colony algorithm is proposed. Firstly, according to the working environment of dining room delivery robot, the working environment model of dining room delivery robot is established by grid method. In this paper, simple and complex two kinds of environmental models are given. Secondly, the improved ant colony algorithm adopts adaptive heuristic function, and on this basis, the state transition probability is improved to increase the attractiveness of the target point; and the optimization ant is modified by reward system. Finally, the global path planning simulation experiments of dining robot working in different environments are carried out. The results of the research verify the feasibility of the algorithm.
Dilated Recurrent Neural Network for Epidemiological Predictions

Hand, foot and mouth disease (HFMD) is an acute febrile and exudative infectious disease caused by enterovirus, which children are susceptible to. It spreads among children mainly through the digestive tract, respiratory tract and close contact, etc. Currently, since, lack of effective therapeutic drugs, symptomatic treatment is widely adopted. Therefore, accurate prediction of the number of HFMD patients is very important for the prevention and control of HFMD. In this paper, a predicting model, which is based on the Dilated Recurrent Neural Network (DRNN), is proposed to predict the future number of HFMD patients for different prediction horizons. Experimental results, based upon recorded time series data about the number of hand, foot and mouth disease cases in Xiamen, demonstrate that our model improves the forecast well compared to a set of widely-used methods for time series prediction.
Performance Validation of Jaya Algorithm to The Most Well-known Testbench Problem

Soft computing algorithms are population-based, probabilistic, that have the same common controlling parameters such as population size, number of generations, elite size. In addition to the regular control parameters, the different algorithms need specific control parameters for their algorithm. A good tuning of different algorithm parameters is an integral factor that affects the efficacy of the algorithm. The inappropriate tuning of the algorithm parameters increases the computational effort or adheres to local limits. Teaching Learning-based Optimization (TLBO) algorithms are algorithms that need no algorithm-specific parameter. Jaya is a kind of TLBO algorithm but has only one-step and is user-friendly. The main aims of this paper are to present the Jaya algorithm and its application to the most prominent engineering problem. The validation and monitoring of existing technology in the presented Jaya include case studies, ranging from the recent CEC 2016 workbench to the common engineering challenges for the gear train, welded beam, three-bar truss system. The results achieved reflect the significance of the algorithms in comparison with most popular modern algorithms.
End-to-End Stereo Matching Network with Local Adaptive Awareness

End-to-end training of deep neural networks have been extensively utilized for the problem of stereo matching. Recent efforts mainly focus on the matching cost computation and the cost aggregation with additional guidance. In this paper, we propose a novel end-to-end stereo matching network that explores local adaptive awareness in the disparity upsampling stage and the loss function. An improved hierarchical upsampling module is presented to adaptively learns a disparity refinement function. A shared convolution structure is adopted in the upsampling module to adaptively discover local semantic correlations between image intensity pixels and disparity pixels. The same shared convolution structure is utilized in the proposed loss function to explicitly exploit local patterns among disparity pixels. Experiments on several recent large scale stereo matching benchmarks validate the effectiveness of our proposed network architecture. Comparisons with state-of-the-art networks also show that our network has a comparable accuracy and promising running speed.
A Programming Language for Data Privacy with Accuracy Estimations

Differential privacy offers a formal framework for reasoning about the privacy and accuracy of computations on private data. It also offers a rich set of building blocks for constructing private data analyses. When carefully calibrated, these analyses simultaneously guarantee the privacy of the individuals contributing their data, and the accuracy of the data analysis results, inferring useful properties about the population. The compositional nature of differential privacy has motivated the design and implementation of several programming languages to ease the implementation of differentially private analyses. Even though these programming languages provide support for reasoning about privacy, most of them disregard reasoning about the accuracy of data analyses. To overcome this limitation, we present DPella, a programming framework providing data analysts with support for reasoning about privacy, accuracy, and their trade-offs. The distinguishing feature of DPella is a novel component that statically tracks the accuracy of different data analyses. To provide tight accuracy estimations, this component leverages taint analysis for automatically inferring statistical independence of the different noise quantities added for guaranteeing privacy. We evaluate our approach by implementing several classical queries from the literature and showing how data analysts can calibrate the privacy parameters to meet the accuracy requirements, and vice versa.
Automatically Created Statistical Models Applied to Network Anomaly Detection

In this article we present the use of automatically created exponential smoothing models for anomaly detection in networks. We propose the method of parameters estimation and selection by means of model’s order obtained by Hyndman-Khandakar algorithm. Optimal values of the model parameters are chosen on the basis of information criteria reflecting a compromise between the consistency model and the size of its estimation error. In the proposed method, we use statistical relationships between the forecasted and real network traffic to determine whether the tested trace is normal or attacked. Efficiency of our method is examined with the use of large set of real network traffic test traces. The experimental results prove resilience and effectiveness of the suggested solutions.
End-to-End Optical Character Recognition Using Sythetic Dataset Generator for Noisy Conditions

Optical Character Recognition is one of the most prevailing research fields since 1970s. Numerous research work has been conducted on Optical Character Recognition. The problem of Optical Character Recognition is to convert images of texts into editable texts. Recent advances in deep learning have accelerated the improvements in this field, particularly with languages with large annotated datasets. Bangla, a language with a large number of character classes and complex cursive alphabet shapes, is unfortunately not included in these advancements due to the lack of a large annotated dataset. This work concentrates on attempting to perform OCR in noisy conditions for Bangla text. We have created a dataset of 5000 noisy Bangla text samples. To augment this small collection, we use a strategy to pre-train our proposed end-to-end model on synthetically generated data and then optionally fine-tune on a part of the collected dataset. Our results indicate that attempting to perform noisy OCR is an extremely challenging task, and the best results are obtained when models trained on synthetic data are fine-tuned with some real-world data.
Continuous QoE Prediction Based on WaveNet

Continuous QoE prediction is crucial in the purpose of maximizing viewer satisfaction, by which video service providers could improve the revenue. Continuously predicting QoE is challenging since it requires QoE models that are capable of capturing the complex dependencies among QoE influence factors. The existing approaches that utilize Long-Short-Term-Memory (LSTM) network successfully model such long-term dependencies, providing the superior QoE prediction performance. However, the inherent drawback of sequential computing of LSTM will result in high computational cost in training and prediction tasks. Recently, WaveNet, a deep neural network for generating raw audio waveform, has been introduced. Immediately, it gains a great attention since it successfully leverages the characteristic of parallel computing of causal convolution and dilated convolution to deal with time-series data (e.g., audio signal). Being inspired by the success of WaveNet, in this paper, we propose WaveNet-based QoE model for continuous QoE prediction in video streaming services. The model is trained and tested upon on two publicly available databases, namely, LFOVIA Video QoE and LIVE Mobile Stall Video II. The experimental results demonstrate that the proposed model outperforms the baselines models in terms of processing time, while maintaining sufficient accuracy.
A Bilingual Word Alignment Method of Chinese-English based on Recurrent Neural Network

Word alignment is an important step in statistical machine translation. Chinese-English bilingual language has a large difference in language characteristics, which may lead to some inconsistent results in word alignment. In this paper, a word alignment method based on recurrent neural network (RNN) is proposed. Firstly, Chinese-English bilingual words are transformed into word embedding, which are input to RNN model and incorporate context information. RNN uses internal memory to process input sequences of arbitrary time series. The experimental results show that compared with DNN and IBM4 models, this method improves the accuracy of word alignment and the quality of machine translation.