Reviewerly/exHarmony huggingface dataset:
Deploying and Optimizing Convolutional Neural Networks on Heterogeneous Architecture

Deploying convolutional neural networks to hardware platform can accelerate the inference and is critical for the application of artificial intelligence. In this paper, we design an FPGA+CPU heterogeneous platform to accelerate CNNs. Dataflow optimizing, accelerator structure optimization and compute precision optimization are proposed to improve performance of the accelerating platform. Different ResNet and MobileNet networks are successfully deployed on the platform. By applying the proposed dataflow optimization and precision optimization, the performance improvement of inference is 3.25× on ResNet. By applying the accelerator structure optimization and precision optimization, the performance improvement of inference is 3.63× on MobileNet.
Live Software Development Environment Using Virtual Reality: A Prototype and Experiment

Successful software systems tend to grow considerably, ending up suffering from essential complexity, and very hard to understand as a whole. Software visualization techniques have been explored as one approach to ease software understanding. This work presents a novel approach and environment for software development that explores the use of liveness and virtual reality (VR) as a way to shorten the feedback loop between developers and their software systems in an interactive and immersive way. As a proof-of-concept, the authors developed a prototype that uses a visual city metaphor and allows developers to visit and dive into the system, in a live way. To assess the usability and viability of the approach, the authors carried on experiments to evaluate the effectiveness of the approach, and how to best support a live approach for software development.
Smart discovery of periodic-frequent human routines for home automation

In this paper, we present an approach to discover periodic-frequent multi-step human routines in event data from smart devices and sensors deployed at home 1. Based on the discovered routines, our approach is able to suggest rules to automate the control of different aspects of the home environment. We evaluate our approach through an in the lab study, a study based on synthetic data, and an in-the-wild study. Our results show that our approach exhibits a high recall-precision performance, with a recovery rate of around 90% for most of the cases under investigation.
Fusing Multimodal Human-Robot Communication using Deep Learning

To enable humanoid robots to share common house space with humans in a social environment, we need to develop effective and robust human-robot communication technology. In the present paper, we have developed gesture and speech based real time communication methodology with humanoid robot using deep learning based techniques. More specifically, for speech based communication, two architectures have been developed using Long Short Term Memory(LSTM) based Recurrent Neural Networks and Convolutional Neural Networks (CNN) for both Hindi and English speech. For gesture recognition and subsequent communication, a specially configured 3-D CNN architecture has also been developed. Further, a fusion framework has been used for comparing confidence scores of all the input modes so that the robot can take a decision for communication based on maximum confidence score. Rigorous experiments have been conducted with standard data sets as well as indigeneously created data sets. Performance results of different architectures have been presented and useful conclusions have been drawn about the effectiveness of using such multiple modes for the robot for decision reinforcement purposes. For conducting all the experiments the Humanoid Robot NAO has been used for evaluating the above methodology.
Application of multiple linear regression in cement burden

In the process of cement burden, as the changing composition of the four raw materials, the oxide content in the raw meal product fluctuates. When the three-rate value is unqualified, the manual cannot judge the adjustment type and quantity of raw materials. This process has the disadvantage of large amount of calculation and difficult to accurately adjust the ratio of raw materials. Finally, the quality of raw materials fluctuates greatly and the qualified rate is low. So as to improve the pass rate of raw cement, this paper explored the optimal allocation of raw materials, which obtained the oxide data by near-infrared online analyzer, using segmented multiple linear regression to obtain regression parameters. Through the target value of four oxides of raw materials, the regression coefficient is used to solve the adjustment scheme of raw materials. Finally we use a variant of NSGA-II to solve the optimal blending scheme of raw materials.
Latent-Space Data Augmentation for Visually-Grounded Language Understanding

This is an extension from a selected paper from JSAI2019. In this paper, we study data augmentation for visually-grounded language understanding in the context of picking task. A typical picking task consists of predicting a target object specified by an ambiguous instruction ,e.g., “Pick up the yellow toy near the bottle”. We specifically show that existing methods for understanding such an instruction can be improved by data augmentation. More explicitly, MCTM [1] and MTCM-GAN [2] show better results with data augmentation when specifically considering latent space features instead of raw features. Additionally our results show that latent-space data augmentation can improve better a network accuracy than regularization methods.
The Coding Technology of Operation and Management Risk Characteristics Based on the In-Depth Learning

In the enterprise risk identification project, the data has the characteristics of the complex data sources and the diverse risks. The traditional feature extraction methods cannot cope with the multiple complex data sources at the same time. In this study, the risk feature extraction based on the single relationship of thein-depth learning and the feature coding technology of the risk atlas considering the time series can effectively extract the feature vectors of the risk atlas and avoid them at the same time. It avoids the complexity and blindness of the artificial feature extraction. And it is verified by a large-scale organization’s experiment on the enterprise’s overdue risk. Under the same model framework, the characteristics generated by using the risk feature coding technology are better than those given by the traditional experts under the model’s various indicators, which provide the strong support for the follow-up risk identification.
A mobile robot mapping model inspired from the place cells functionality of hippocampus based on dimension reduction technique

In this paper, a new mobile robot mapping algorithm inspired from the functionality of hippocampus cells is presented. Place cells in hippocampus can store a map of the environment. This model fuses odometry and vision data based on dimensionality reduction technique, hierarchically. These two types of data are first fused and then considered as inputs to the place cell model. Place cells do the clustering of places. The proposed Place cell model has two types of inputs: Grid cells input and input from the lateral entorhinal cortex (LEC). The LEC is modelled based on the dimension reduction technique. Therefore, the data that causes locations different to be inserted into the place cell from this layer. Another contribution is proposing a new unsupervised dimension reduction method based on k-means. The method can find perpendicular independent dimensions. Also, the distance of cluster centres found in these dimensions is maximised. The method was compared with LDA and PCA in standard functions. Although LDA is a supervised method, the result showed that the proposed unsupervised method outperformed. To evaluate the place cells model, sequences of images collected by a mobile robot was used and similar results to real place cells achieved.
How to Build Hadoop in the Field of Transportation by Cloud Computing

Since the twenty-first century, traffic big data has gained unprecedented attention as the basis of many intelligent transportation solutions. Cloud platform technology and big data mining technology have changed the restrictions of traditional traffic management administrative areas, established a comprehensive and three-dimensional intelligent transportation system, and provided new technologies and new means for traffic supervision, security early warning, and high-efficiency management and control. This paper analyzes the importance of the combination of intelligent traffic data analysis center and traffic big data and cloud platform technology from the technical and functional levels, and proposes the architecture design and implementation of the cloud big data platform based on cloud computing.
Construction of Environmental Map Based on Lidar Based Tracking System

For many ground robots, the environment is basically unknown, which requires us to build an environment map so that the robot can start from an unknown location in the unknown environment and locate itself by repeatedly observing the map features during the move. And then gradually build a map according to your location, to achieve the purpose of simultaneous positioning and map construction. This paper mainly introduces the selection of environmental map experimental equipment for the construction of laser radar tracking system and its reasons, analyzes the basic principle of laser radar operation, designs the corresponding experimental scheme, conducts preliminary communication experiments, and finally displays the environment diagram drawn. On the computer, this paper basically completed the work of graphically drawing the scanned data and drawing the environmental map, which is helpful for further research on the tracking system of the laser radar.
VRAnat: A Complete Virtual Reality Platform for Academic Training in Anatomy

The study of anatomy has always been reliant on the imagination as images don’t convey enough information and the creation of realistic models can be too expensive. A Virtual Reality (VR) application can be an excellent gap filling solution to this issue. It allows the students to interact with 3D realistic models of any organ. It displays several cases on each organ with minimal cost. In this paper, we propose a fully developed VR platform for academic training in human anatomy. We draw the complete pipeline from organ’s data acquisition to multi-user interface in VR environment. The final software allows full interactivity student-organ and student-tutor or student-student.
Personalized Distribution Recommendation Model of Dormitory Based on Deep Learning

There are a large number of colleges and universities, and the tasks of assigning jobs in the dormitory are large. How to meet the diverse needs of students for the allocation of dormitory is a subject worth studying. In order to meet the individual needs of students, a deep learning model is first constructed, and the collected data is analyzed and transformed in the model. Secondly, the Pearson coefficient is used to determine the similarity between students, and the student recommendation list is constructed. Finally, the experimental data is used. Verification, the proposed model can effectively improve the accuracy of students’ personalized recommendations.
“Play Like A Machine”—Generative Musical Models for Robots

In the previous chapter we discussed a number of approaches that allow Robotic Musicians to “Listen Like a Human” by modeling high level human musical percepts. In this chapter we focus on the second part of our Robotic Musicianship guideline—“Play Like a Machine”. While implementing “Listen Like a Human” modules in Robotic Musicians allow robots to connect to humans in a relatable manner, “Play Like A Machine” modules are aimed at generating novel musical outcomes that are humanly impossible in an effort to surprise and inspire human collaborators.
Review of Sentiment Detection

A large volume of user-generated data is evolving on a day-to-day basis, especially on social media platforms like Twitter, where people express their opinions and emotions regarding certain individuals or entities. This user-generated content becomes very difficult to analyze manually and therefore requires a need for an intelligent automated system which mines the opinions and organizes them using polarity metrics, representing the process of sentiment analysis. The motive of this review is to study the concept of sentiment analysis and discuss the comparative analysis of its techniques along with the challenges in this field to be considered for future enhancement.
Positional Embedding and Context Representation for Indonesian Targeted Aspect-based Sentiment Analysis

Inspired by some research on entity level and aspect level sentiment analysis, we build a two-step classification system to tackle the challenges of targeted aspect-based sentiment analysis (TABSA). The system consists of aspect categorization and sentiment classification. We employ a Bi-LSTM network, position features i.e. context representation and positional embedding on both classifiers, and one-hot aspect vectors in sentiment classifier to give additional information. We conducted experiments using 900 Indonesian reviews related to automotive. Despite not using complex feature because of limitation in low-resource language, the system can still make decent prediction. We found that the use of positional embedding and context representation in our model is proven to be able to capture the relation between entity and aspect-sentiment. The use of one-hot aspect vectors was also found to be slightly helpful for sentiment classifier to capture polarity related to a specific aspect. Experiment results showed that our approach outperformed the self-training baseline in terms of F1 and accuracy scores.
A Rigid Visual Servoing Approach for Elastic Objects Using Weighted Primitives

Visual servoing on deformable objects is a recent and open problem. Many approaches propose to use continuous mechanical models embedded into the interaction matrix and compute corresponding camera visual servoing. These methods are highly parametric and are fine-tuned for every specific type of elastic objects. In this paper, we propose a generic approach that enables rigid camera visual servoing to fit as much as possible visual features of objects subjected to non-rigid deformation. The proposed method uses a triangular mesh representation of the visualized object. It computes a relative camera rigid repositioning for every triangular primitive. A specific proportional gain is used for every triangle. The proportional gain represents the relevance of every triangle in the global repositioning of the camera. This gain depends on the variation of the area of every triangle. This has the advantage of making the gain variation continuous which provides smooth camera repositioning. A global rigid correction of camera pose is computed as the scaled mean of all relative per-triangle rigid repositioning. The scale is taken to be the sum of all weights. We provide simulation results to show that with the weighted triangular primitives we are able to better fit the reference visual cue than if we used a naive approach of global uniform averaging of the per-triangle camera repositioning.
Recurrent Enhanced Memory Network For Fault Prediction

The fault diagnosis of the torpedo tank is mostly human judgment, which is low efficiency, low accuracy, high cost and easy to cause safety problems.The traditional method has poor ability to classify high-dimensional datasets. In order to solve above problems, this paper proposes Recurrent Enhanced Memory Network(REMN), a deep learning architecture to generate the vehicle status representation output from the hidden layer and store historical fault characteristics. Motivated by the success of Memory Enhanced Module, the attention mechanism achieves fault prediction of torpedo system by retrieving the historical records most similar to the current vehicle state representation. The experimental results show that compared with other advanced deep learning methods, the prediction accuracy is higher than other methods, which proves its effectiveness.
Integrating Nagari Information in West Sumatera with Laravel Framework

Nagari is another term for the village, located in West Sumatra. There are 648 number of villages. Only less than 7% of the Nagari website can be found through search engines. This information system using the waterfall method. Some such research, have not yet integrated the existing data on several Nagari in one system. There are still many Nagari that do not have information system and website. most nagari websites currently use a Content Management System (CMS) and few are native web based. Because of the considerable cost to create and manage the website, limited number of human resources and abilities. Search information about something contained in the Nagari today still have to open one by one existing Nagari site because the system has not been integrated. The integrated information system about Nagari is called E-Nagari. These E-Nagari can be used together and facilitate in the search for information needed for Government to Government (G2G), Government to Business (G2B), Government for Citizens (G2C).
An Adaptive Clustering Algorithm of Gaussian Curve Fitting the Histogram Based on K-Means

The K-mean algorithm is simple and efficient in vascular segmentation. But in a K-means clustering algorithm, the number of clustering centers must be determined in advance and the random allocation of clustering centers seriously affects the efficiency of the algorithm. In order to solve this problem, an adaptive clustering algorithm based on K-means with the Gaussian curve fitting histogram is proposed, which is used to segment circular-like blood vessels. Firstly, the histogram outline is fitted into the Gaussian curve by the least square method and the maximum value of the curve is obtained. The number of maximal values is counted as the number of clustering centers and the maximum points are used as the central points of clustering. Also, the K-means algorithm is improved by combining the intensity and position information of pixels. The proposed method has been tested by 6 groups of medical images. The algorithm has the advantages of less iterations, fast convergence speed and getting more than 95% Dice Similarity Measure value. The algorithm can effectively solve the determination of multi-seed points and vascular segmentation in medical image segmentation.
Electronic Image Stabilization based on Laplacian Bit Plane and Matching Block Classification

In this paper, an electronic image stabilization method based on Laplacian bit plane and matching block classification is proposed. The method uses Laplacian to extract the edge information of the image to construct bit plane. Weight is used to measure the feasibility of the vector becoming local motion vector and global motion vector. The matching result of each matching block and the global motion vector of the previous frames are combined to obtain the global motion of the current frame, which is used to compensate for frame motion to obtain a stable image. Finally, it is proved by experiment that the algorithm proposed in this paper requires a small amount of calculation and can eliminate the interference of small objects in the field of view. For CIF images, the algorithm can achieve real-time image processing, removing and reducing the jitter of the image. Compared with traditional bit plane matching electronic image stabilization methods, this algorithm lays stress on the full utilization of gray information and the adaptability of the algorithm when there is a small foreground object in the field of view.
A unified model of video-based human action categorization using Chaotic Quantum Swarm Intelligence on Intuitionistic fuzzy 3D Convolution Neural Network

In the contemporary surveillance schemes of Computer Vision, videos concerning human action categorization have become a predominant zone, involving Pattern Recognition tasks. Factually, most of the human actions comprise complex temporal information, and it is quite difficult to discover the diver se activities of humans precisely, in an unpredictable variety of environmental circumstances. A Deep Learning paradigm can tackle this issue, by providing additional capabilities to vision-based human action recognition. However, there are more complex challenges in extracting the spatio-temporal features, for instance, the presence of noise in videos and the highly vague feature points. This paper proposes a hybrid intelligent Intuitionistic Fuzzy 3D Convolution Neural Network that uses Chaotic Quantum Swarm Intelligence (CQSI-IFCNN), to optimize video-based human action categorization. Vagueness and ambiguity of input video frames are inherited by Intuitionistic Fuzzy networks in terms of membership, hesitation and non-membership components. By applying Chaotic Quantum Swarm Intelligence (CQSI), the learning parameters and error rates that occur in standard convolutional neural network are considerably reduced. The chaotic searching scheme is applied to overcome premature local optima in Quantum Swarm Intelligence. Therefore, this model produces optimized outcomes in Intuitionistic fuzzy 3D Convolutional Neural Networks, thus improving the categorization of human actions in videos. The Performance of CQSI-IFCNN is assessed by using the KTH and UCF Sports Action datasets. From the simulation outcomes, it is observed that CQSI-IFCNN has attained a higher rate of action categorization accuracy than standard CNN and PSO-CNN.
Multi-level approach for controlling architecture quality with Alloy

The software evolution process means the continuous control of functional and non -functional software characteristics by the change of its code without changing the properties of its quality neither the quality of its architecture. Therefore, the most important problem to be treated is how to limit the progressive shifting between the planned and the implemented architecture during the software evolution process. This phenomenon called the erosion of software architecture, often appears unexpectedly and must be controlled. In this paper, we have proposed an approach to detect erosion problems by representing tactical, strategic, and operational architectural constraints at different abstraction levels. These constraints are specified by the use of the Alloy constraint language in order to restore the deviations that may affect the description of the software architecture during the development steps. Our goal is to control architecture quality by limiting the occurrence and impact of erosion, which means the control of architecture conformance during system development and maintenance activities.
Detection of Tunnel Deformation Based on KNN Algorithm

In view of the long-term operation the subway, the subway tunnel is easily affected the pressure of the vehicle load and the surrounding construction, which can cause the deformation the tunnel structure and other problems. In paper, the laser radar installed on the inspection vehicle is used to obtain tunnel data. After filtering the noise, the KNN algorithm is used to judge the tunnel deformation. The results show that the tunnel deformation detection model based on KNN algorithm has good classification effect, and the accuracy rate reaches 99.5%. It provides new reference method for the deformation detection of subway tunnels in operation.