Reviewerly/exHarmony huggingface dataset:
Supplemental Cultivation Plan of Innovation Quality for the Undergraduates of Building Environment and Energy Engineering in the Applied Technology Universities

One of the most fundamental qualities for science research and technology development is the innovation quality, which covers many aspects such as consciousness, method and spirit. The innovation quality developed in the undergraduate period would be the most basic foundation in their over forty-years working life. A supplemental cultivation plan with a routine consisting of hardware, programming, simulation and optimization was presented in this paper for the undergraduates of Building Environment and Energy Engineering (BEEE). The hardware manufacture of digital devices was trained to promote the freshmen manipulative capability, while software programming with general-purpose languages was introduced to enhance the comprehension of data analysis in the 2nd academic year. During the period when some professional courses were taught, the building simulation modular could be introduced to enhance the understanding for the building thermal process and its corresponding Heating, Ventilating, Air-conditioning and Refrigeration (HVAC & R) system. A building management system (BMS) would be developed step by step and interactive with virtual building platform. That BMS and its virtual cases could be employed as the testbed for optimization of HVAC & R system, such as energy consumption prediction, fault detection and diagnosis. Python and EnergyPlus were used in the custom training program oriented to the employment direction for the system operation and maintenance. Group achievements and individual cases show that the presented supplemental cultivation plan played a very important role in the cultivation of BEEE graduates' innovative quality. The effect for the graduated students need pay more attention to investigate the long-term effects, and more volunteers will participate in this plan to find out their better future.
Omnidirectional Ring Structured Light Noise Filtering Based On DCGAN Network And Autoencoder

The omnidirectional ring structured light depth perception system can project a 360-degree structured light.However, due to the influence of ambient light, refraction and other noise factors, it is easy to cause false detection of structured light. Therefore, denoising plays an important role in the omnidirectional ring structured light depth perception system.Firstly, this paper uses the DCGAN network to generate more data sets due to the lack of data sets. Secondly, based on the above data sets, this paper uses the autoencoder network to denoise the ring structured light. Experiments show that by using a combination of DCGAN and autoencoder network for denoising, the structured light’s noise is lesstherefore more robust to the surrounding environment compared to other methods.
Learning Multi-modal Representations of Narrative Multimedia

This study aims to learn task-agnostic representations of narrative multimedia. The existing studies focused on only stories in the narrative multimedia without considering their physical features. We propose a method for incorporating multi-modal features of the narrative multimedia into a unified vector representation. For narrative features, we embed character networks as with the existing studies. Textual features can be represented using the LSTM (Long-Short Term Memory) autoencoder. We apply the convolutional autoencoder to visual features. The convolutional autoencoder also can be used for the spectrograms of audible features. To combine these features, we propose two methods: early fusion and late fusion. The early fusion method composes representations of features on each scene. Then, we learn representations of a narrative work by predicting time-sequential changes in the features. The late fusion method concatenates feature vectors that are trained for allover the narrative work. Finally, we apply the proposed methods on webtoons (i.e., comics that are serially published through the web). The proposed methods have been evaluated by applying the vector representations to predicting the preferences of users for the webtoons.
A recently discovered hand-coloured geological map of Norfolk and Suffolk attributed to Richard Cowling Taylor (1789–1851)

A recently discovered hand-coloured geological map of Norfolk and Suffolk is probably the work of Richard Cowling Taylor (1789–1851). It was originally published by Laurie &amp; Whittle in 1811 and later hand coloured to show the geological strata of the two English counties. The colouration was based on William Smith's 1815 geological map A Delineation of the Strata of England and Wales, with Part of Scotland, but with significant modifications. It appears to have been hand-coloured between 1816 and 1819 and is a very early example of the adoption of Smith's methodology of using colour to represent the different layers of strata on a geological map.
Hough Transform Voting Scheme for Detection of Parabolas and Open Conics in Images

The Hough transform (HT) is a popular tool for line detection due to its robustness to noise and missing data. Its main use is for transformation of raster images like PNGs, GIFs, and JPEGs to scalable vector graphics. However, the apparent complexities of equations, the difficulties to understand the voting scheme and importance of discretization parameter have prevented HT algorithm developments for generic polynomials. The main concern of this work is to describe the importance of the process of turning discrete the parameters of the equations that describe different curves to be identified in the images. Although it applies to any type of equation, the influence of discretization of open and closed conics, such as parabolas and ellipses, is specifically discussed. The proposed approach identifies the parameters and the possible dependence among them to define the search order of them. The discrete limits and the incremental value of each parameter are commented as well. They are fundamental for the voting scheme, playing a key role on the accuracy of this and promoting a considerable impact on the HT result. Both the number of parameters (that is, the size of the solution space) and the discretization values used are directly proportional to the number of accumulator cells created. Thus, both the computational cost and the processing time may compromise the implementation process. A correct order to calculate the values of the parameters in the algorithm and the appropriate limits for each one of them guarantees a reduction and simplification of operations, as well as an improvement in the efficiency of the results obtained.
Movie Recommender System Using sentiment Analysis

Recommendation systems (RSs) have accumulated huge enthusiasm for applications in webbased business and advanced media. Conventional methodologies in RSs incorporate, for example,collaborative filtering (CF) and content based (CBF) through these approaches that have certain limitations,for example, the need of earlier client history and propensities for playing out the assignment ofrecommendation. The various techniques, Classifiers and the tools are available for sentiment analysis fromwhich the researchers can achieve good results. In our work we utilized movie tweets and on the basis oftweets we calculated the sentimental score , we used the classifier Naive Bayes with Vardar sentiment theNLP technique and other tools are listed with their use. In this paper different sentiment analysis techniqueapplied to improve the recommendation systems and generate good recommendations for movies.
Avoiding Fakery: Activities to Prepare Students for Effective Searches

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MTNAS: Search Multi-task Networks for Autonomous Driving

Multi-task learning (MTL) aims to learn shared representations from multiple tasks simultaneously, which has yielded outstanding performance in widespread applications of computer vision. However, existing multi-task approaches often demand manual design on network architectures, including shared backbone and individual branches. In this work, we propose MTNAS, a practical and principled neural architecture search algorithm for multi-task learning. We focus on searching for the overall optimized network architecture with task-specific branches and task-shared backbone. Specifically, the MTNAS pipeline consists of two searching stages: branch search and backbone search. For branch search, we separately optimize each branch structure for each target task. For backbone search, we first design a pre-searching procedure t1o pre-optimize the backbone structure on ImageNet. We observe that searching on such auxiliary large-scale data can not only help learn low-/mid-level features but also offer good initialization of backbone structure. After backbone pre-searching, we further optimize the backbone structure for learning task-shared knowledge under the overall multi-task guidance. We apply MTNAS to joint learning of object detection and semantic segmentation for autonomous driving. Extensive experimental results demonstrate that our searched multi-task model achieves superior performance for each task and consumes less computation complexity compared to prior hand-crafted MTL baselines. Code and searched models will be released at https://github.com/RalphLiu/MTNAS.
Research on Sensitivity Audit Scheme of Encrypted Data in Power Business

With the rapid progress of informatization construction in power business, data resource has become the basic strategic resource of the power industry and innovative element in power production. The security protection of data in power business is particularly important in the informatization construction of power business. In order to implement data security protection, transparent encryption is one of the fifteen key technical standards in the Construction Guideline of the Standard Network Data Security System. However, data storage in the encrypted state is bound to affect the security audit of data to a certain extent. Based on this problem, this paper proposes a scheme to audit the sensitivity of the power business data under the protection of encryption to achieve an efficient sensitivity audit of ciphertext data with the premise of not revealing the decryption key or data information. Through a security demonstration, this paper fully proves that this solution is secure under the known plaintext attacks.
Balance-batch: An Optimized Method for Semantic Segmentation Loss Functions

Class-imbalanced data easily generates under-fitting problems in deep neural networks, which seriously limits the performance of the network. Several schemes have been proposed to alleviate the class-imbalance, i.e., data augmentation and network structure optimization. Our work has two main contributions: First, we proposed an optimized method Balance-batch which invests equal consideration for each class in mini-batch and tries to balance the losses of classes. Optimizing loss functions is a simpler and more effective way to tackle the class-imbalance than others. Second, we extend the binary classification Dice loss which is employed in medical images segmentation to multiple classifications. Multi-classifications dice loss reflects the improvement of Balance-batch to unstable loss functions. Our various loss function experiments on Pascal VOC2012 show the effectiveness of Balance-batch, which is beyond the advanced level of these loss function methods.
Multi-label Classification: New Measure to Remove Cyclical Dependencies

Classifying data by linking it to a set of labels with a degree of membership is the objective of the multi-labels classification. These labels or classes can have order relationships between them, which can affect the predictive quality of classifiers. Consider these relationships or ignore them, when building the classifier, each has its drawbacks. The first approach facilitates the spread of learning errors and increases complexity of the task, especially if there are cyclical relationships between classes. While the second approach can give inconsistent predictions. There are several approaches intended for solving multi-label classification tasks, some of them take into consideration dependencies between labels and others consider them independent. A new approach called PSI-MC [1] proposes a novel way to teach classifiers the relations between labels without fixing a predefined structure. We propose an approach that uses the same principle as the PSI-MC, and which improves the way to eliminate cycles. Finally, we will test our new approach by using two datasets. According to the Hamming-Loss measure, our proposed approach is slightly better than binary relevance approach that does not take into account the relation between labels.
A Risk Evaluation of Password Management Strategies

This paper presents how to analyze risks of the password management quantitatively using a mathematical model. Passwords at online services are not always managed properly. There is growing concern about illegal access. We need to spread good security policies and raise awareness for those services users to manage passwords properly. To campaign effectively, we analyze risks of the security policies quantitatively.
Research on Detecting Moving Objects in Football Match Video

Abstract In recent years, detecting moving objects in video has attracted extensive attention in the field of computer vision and pattern recognition. With football becoming one of the most popular sports, analyzing the process of football games is of commercial value and guiding significance. However, the previous research mainly focused on the event detection, and rarely on getting the trajectory of moving objects by directly extracting from the video. This article discussed the method of detecting moving objects in the football match video. We extract the marker lines and build the coordinate system based on LUV color space. And we use the 68 landmarks to recognize the human face and the Kalman filter approach to detect the football. Our research is aimed at helping the audience and football professionals in analyzing the player performance and team strategy.
Sentiment Analysis to Determine Best Seller for the Same Products on the Tokopedia E-Commerce Portal

Tokopedia, as one of the most popular Electronic Commerce portals, has increased people'sinterest in shopping online. Tokopedia has made the growth in the number of sellers and buyers on Tokopediaincrease. This number of sellers has an impact on the number of similar products sold by many differentsellers. Tokopedia creates difficulties for prospective buyers because they are confused about determining thebest seller when buying the desired security. Based on these problems, a system is designed to assist decisionmaking in choosing products from best sellers. The course will generate positive sentiment data on eachproduct at several different sellers. The method used to create this data is the Text Mining method. Thissentiment data is based on user reviews on the same product from several different sellers. Data is retrievedusing the Web Scraping method on the product detail page on the Tokopedia website. The data taken isinformation from a product that has been selected, including user reviews data in it. The data that has beenaccepted will go through the Text Preprocessing stages until finally, the information is ready for sentimentprocessing. The system will process data by classifying positive and negative sentiments from the user reviewdata. This classification process uses the Naive Bayes Classifier algorithm. The result of the system is thesentiment classification and value of positive sentiments on each user review on a product.
End-to-End Autonomous Driving Controller Using Semantic Segmentation and Variational Autoencoder

There is a great interest in developing robust and efficient controllers for autonomous cars. A special issue is related with the high energy consumed by the embedded GPUs for tasks like sensory data processing, feature extraction and decision making. In this paper, the benefits of using Semantic-Segmentation and Variational-Autoencoder methods integrated with a camera-based end-to-end controller are shown. With Semantic Segmentation an agent trained using Deep Deterministic Policy Gradient algorithm exhibits a faster convergence, a better performing policy and increased robustness when compared with the alternative raw RGB input. The Variational-Autoencoder, used for dimensionality reduction, represented a significant decrease in the GPU usage and in the neural network processing time.
An Multi Variant Robust Methodology for Detecting the Phishing Websites

Phishing attacks include the use of techniques to create polymorphic phishing web pages to give the impression of real sites. Other sites rely on the risk of phishing, including money linked, informal organizations, the travel industry, web-based business, and so on. For e.g., phishers are specifically attached to travel-related administrations by mirroring them as excursion experts, aircraft reservations, accommodation bookings, and so on. Be that as it may, concentrating on reputable pages, given all, leaves the web page looking outwardly like the first one. Right now, suggest a methodology that relies on the favicon database to discover the existence of the site and use it to determine the validity of the site. Phishing is a program that tricks individuals into giving touchy data such as usernames and passwords, paying card subtleties, delicate bank data, and so on, by way of e-mail mocking, tweeting, or using counterfeit sites whose look and feel gives the presence of a real site Right now, a technique called parse tree approval is proposed to determine whether a website page is legitimate or phishing. This paper provides an in-depth study of methods accessible to the detection of phishing sites. A similar research was carried out into the use of enemy phishing instruments and their containment was acknowledged. We have broken down the URL-based highlights used in the past to refine their meanings according to the current situation, which is our major commitment. In addition, the step-by-step method of structuring the enemy of the phishing model is talked about developing a competent system that adds to our commitment. The views of this analysis are presented alongside recommendations on the existing frameworks.
Solar Energy Potential Mapping around Yogyakarta Palace As an Effort in Integrating Renewable Energy into Cultural Based Economic Development

Abstract The Yogyakarta Palace is the main heritage buildings in Yogyakarta Province. It is a main cultural symbol, which is also one of the main tourism objects. This research focuses on The Palace main buildings and surrounding area as an effort to integrate acceleration of renewable energy use into the cultural-based economic development. It considers cross-cutting issues in integrating engineering approach within a complex socio-culture context. The research applied distribution of solar energy potential mapping by using analysis of 3D buildings data in the Open Street Map (OSM), Digital Elevation Model (DEM) and solar intensity data. QGIS software was applied to analyze the 3D buildings data and geospatial aspect. The research also provides a classification of solar energy potential. The mapping shows valuable capacity of The Palace and its surrounding area to produce electricity by the PV technology. Implementing, for instance, the Building Integrated Photovoltaics (BIPV) method in selected buildings, could provide encouraging environment for green tourism in a city of culture melting point as well as increase of renewable energy role and economic development. However, in such delicate issue of PV implementation the mapping and installation works would be far simpler tasks compared to the required intensive socio-culture inclusive process.
Multi-surface phase demodulation based on the selective weighted multi-step algorithms

Abstract The transparent parallel plate plays a practical role in the optical system. The accurate measurement of its surface topography is a great importance of quantifying and evaluating the performance of the transparent plate. Based on the phase shifting interferometry, a measurement method using triple iteration and selective phase demodulation algorithms is presented, which can realize multi-surface interferometry at arbitrary positions. Through the design of the three-dimensional data matrix used to store errors, its element pixels can be read out a wealth of key parameter information, thus guiding the design of the algorithm, realizing the unity of the actual use of the algorithm and the measurement scheme, achieving the multi-surface interference measurement with the minimum residual errors with arbitrary cavity length, and expanding the limitations of the traditional weighted multi-step algorithm.
Sellf-Adaptive Weighted Skip Connections for Image Super-Resolution

Recently, the introduction of deep convolutional neural networks has achieved great performance in low-level vision tasks like single image super-resolution. However, deeper networks tend to have larger number of parameters and be more difficult to be trained. Considering massive low-frequency information in low-resolution inputs, we propose a self-adaptive weighted skip connections (SAWSC) structure to make full use of both the low-level and high-level features in order to better the representation ability of super-resolution networks. In our proposed network, we follow a coarse-to-fine strategy, which reconstructs high-resolution images progressively based on the Laplacian pyramid. At each upscale level, feature maps of each block are connected to subsequent blocks with self-adaptive weights. During each block, several residual channel attention layers are cascaded. Evaluations on five public benchmark datasets show that this algorithm achieves better performance than some other existing methods.
Serverless Computing Using Amazon Web Services

In the last decade cloud computing transformed the way in which we build applications. The boom in cloud computing helped to develop new software design and architecture. Helping the developers to focus more on the business logic than the infrastructure. FaaS (function as a service) compute model it gave developers to concentrate only on the application code and rest of the factors will be taken care by the cloud provider. Here we present a serverless architecture of a web application built using AWS services and provide detail analysis of lambda function and micro service software design implemented using these AWS services.
Convolutional Neural Network Algorithm based on Improved Support Vector Machine

This electronic Aiming at the problem that the traditional Convolutional Neural Network algorithm uses the Softmax function to classify tires with poor accuracy, a convolutional neural network image classification and recognition algorithm based on improved support vector machine is proposed. This algorithm uses the support vector machine instead of the Softmax function to complete the image classification problem in the convolutional neural network algorithm. At the same time, a relaxation variable and a penalty factor are introduced into the traditional support vector machine, thereby changing the support vector machine to be used for classifier for multiple classification problems. This improved algorithm is applied to tire damage image recognition. Through a series of comparison experiments between the improved algorithm and the traditional convolutional neural network algorithm, the classification and recognition effects of the improved algorithm on the classification and recognition of damaged tire images are analyzed. The effectiveness of the proposed improved algorithm is proved.