Reviewerly/exHarmony huggingface dataset:
Question Answering Based Assisted Decision for Electric Power Fault Diagnosis

With the development of Information Technology, question answering approaches have been applied in assisted decision, which can assist custom service staffs to automatically answer some basic questions and therefore can greatly reduce the cost of system operation. In this paper, we propose a question answering based approach for electric power fault diagnosis. First, the question answer data is analyzed and preprocessed from the conversation of manually answering the possible causes of given power equipment faults for making sure that one question corresponds to multiple answers. Then, the question and answers at word-level are matched based on a general “compare-aggregate” framework. Based on the matching measure, we can further rank the answers and select the answer with the highest correlation as the most likely fault cause for assisted decision. The relevant experimental results show that the model achieves good results and finds the best answer to the question.
Three-Dimensional Modeling of Mineral/Elemental Compositions for Shale Reservoirs

Summary Mineral compositional analysis of rocks is important for developing shale resources because the relationships between mineral compositions and petrophysical properties are critical for resource evaluation and completion optimization. Elementary properties are now routinely analyzed at wells in evaluating shale reservoirs. However, these properties have not been modeled in the three-dimensional (3D) reservoir. This is because an elemental composition has a physical constraint that is relatively easily adhered to in data analysis for wells but not in 3D modeling of reservoirs. A critical condition of elemental composition is that the sum of its components is equal to 100% to honor the mass-preservation principle. Traditional modeling methods do not satisfy this physical condition, sometimes producing nonphysical values, such as negative porosity values and fluid-saturation values greater than 100%. To date, only the compositional-modeling methods using a log-ratio transform can consistently satisfy this physical constraint. This paper presents modeling methods using additive log-ratio transform for modeling mineral compositions.
A Semantic Segmentation and Edge Detection Model Based on Edge Information Constraint Training

Abstract The purpose of semantic segmentation is to classify the pixels within the target contour. Edge detection is another major basic vision task in machine vision. Today’s most effective semantic segmentation models and contour edge detection models are isolated networks. The edge of the output of the semantic segmentation model is coarse and cannot be directly used. And the output of the edge detection network cannot output the classification information of the pixels inside the contour. In view of the above shortcomings of the existing network, we propose a semantic segmentation model based on edge constraint optimization, so that the output of the semantic segmentation model has more delicate edge information, and the network directly outputs accurate contour edge graphs. The edge information output by the network can be directly used for tasks such as corner detection and center point detection. Experiments show that the mIOU statistics obtained by our model on the validation set of PASCAL VOC2012 can reach 83.9%. At the same time, more detailed edge details can be obtained. This algorithm has high engineering and theoretical research value.
On the visualization of large-order graph distance matrices

The development of effective ways of depicting the structure of finite graphs of large order forms a very active and dynamic area of research. In this article, we explore the use of colour parameterizations of the entries of graph distance matrices to depict the structure of graphs with vertex sets of large cardinality, producing many new works of mathematical art given by the application of colour processing functions on matrices of this form. Computer-generated works of art of this form often reveal interesting patterns concerning the corresponding graphs.
A Scalable Virtual memory system based on decentralization for many-cores

Traditional centralized virtual memory system design encounters severe scalability problems, which impede the multithreaded applications’ performance increment on many-core systems. In this paper, we propose a decentralized system model to scale the VM systems for many-cores. Our model improves system parallelism by avoiding resource sharing and minimizing state coordination. By applying the model, we build a novel scalable virtual memory system called MedusaVM+. MedusaVM+ presents a decentralized system architecture, which avoids resource conflicts or cache line contention among processors or threads. Furthermore, MedusaVM+ provides a scalable address space by incorporating decentralized VM space management and a hybrid page table design. Critical system services and internal system operations, such as TLB coherence, are also fully optimized to maximize the system parallelism. Our prototype system is implemented based on the Linux kernel 4.4.0 and glibc 2.23. Experimental results evaluated on a 72-core machine demonstrate that MedusaVM+ scales much better than the state-of-the-art systems and decreases the memory consumption by up to 27 × compared with current approaches. For microbenchmark experiments, MedusaVM+ achieves nearly linear performance speedup. When evaluated with multithreaded applications, MedusaVM+ also outperforms other systems by up to a factor of 4.5 × .
1300 Pieces of Rubbish

Over a one-month period we ran an in-home study exploring the notions of frugality, resources conservation, and sufficiency amongst 40 urban and regional households across Australia. We used a Research-through-Design approach, adopting the design-led method of design research artefacts, which were all created from discarded items of household waste. What followed was an extremely rewarding but overwhelming time, which culminated in the return of 1,300 individual artefacts from our study participants. This pictorial illustrates and discusses our struggles to extract insights from this vast amount of data produced by this exploratory study and justify our findings. As such, this pictorial contains learnings from our experience deploying, receiving, sorting, and then analysing an overwhelming amount of design research artefacts to study everyday resource sufficiency in the home.
A Robust Monocular Depth Estimation Framework Based on Light-Weight ERF-Pspnet for Day-Night Driving Scenes

Abstract With the development of deep learning, various fields of computer vision have made huge progress. Among them, depth estimation is an important part of scene perception, therefore receives much interest and is widely used in daily life with the assistance of GPUs. Besides, the ways to obtain depth maps have also been improved, from using multiple images to a single image to obtain depth, which is called monocular depth estimation task. In this paper, we design a convolutional neural network called ERF-PSPNet to perform the task. We prove that by using unsupervised training, monocular depth estimation’s result learned from large-scale dataset is close to the result of stereo matching. We also show that the monocular depth estimation model proposed in this paper can achieve a satisfying precision while maintaining a certain real-time frame rate for day-night driving scenes, which confirms the practical applicability of our design and result.
Rewriting with generalized nominal unification

Abstract We consider matching, rewriting, critical pairs and the Knuth–Bendix confluence test on rewrite rules in a nominal setting extended by atom-variables. We utilize atom-variables instead of atoms to formulate and rewrite rules on constrained expressions, which is an improvement of expressiveness over previous approaches. Nominal unification and nominal matching are correspondingly extended. Rewriting is performed using nominal matching, and computing critical pairs is done using nominal unification. We determine the complexity of several problems in a quantified freshness logic. In particular we show that nominal matching is $$\prod _2^p$$ -complete. We prove that the adapted Knuth–Bendix confluence test is applicable to a nominal rewrite system with atom-variables, and thus that there is a decidable test whether confluence of the ground instance of the abstract rewrite system holds. We apply the nominal Knuth–Bendix confluence criterion to the theory of monads and compute a convergent nominal rewrite system modulo alpha-equivalence.
Improved BLSTM RNN Based Accent Speech Recognition Using Multi-task Learning and Accent Embeddings

A major challenge in Automatic speech recognition (ASR) systems for Mandarin is to be able to handle speakers with different kinds of accents. ASR systems that are trained using single-task learning underperform due to poor generalization ability when confronted with a new accent. In this paper, we explore how to use accent sentences information that accent embeddings and multi-task learning on the basis of the bidirectional long short term memory (BLSTM) to improve accent speech recognition. Firstly we consider augmenting the speech input with accent information in the form of embeddings extracted by a standalone network. Then we propose multi-task learning architecture that we jointly learn an accent classifier and a multi-accent acoustic model. Experiments with these methods demonstrate that we obtain a 4% average relative improvement in word error rate over a multi-accent baseline system.
Campus Guide Using Augmented Reality Techniques

Nowadays, many students and staff struggle to find specific information about classes or teachers whether in their colleges or any other institution on campus. Therefore, in this research, a mobile application (College Guide) was built to address this need. College Guide was developed as a guide for members and visitors to the College of Computer and Information Technology (CC&IT). The underlying technology upon which the application was based is augmented reality, which was used to enhance the quality and ease of use of the application, as users could get the information easily and contact others effectively. Five steps were carried out to develop the application: determine the problem and write the objectives and solution, analyze the system, gather requirements, design the interfaces, and implement the app. The results of using the system indicated that most users found it easy to use as the complete process included just three steps: open app, scan QR code, and show information. In addition, users found the design of the interfaces to be user-friendly, and no one suffered from using the application. Many users found it fun to use the augmented reality technique. Communication with staff was much faster than in the traditional way. In future work, we intend to extend the system to include all faculties and add more features, like 3D objects and live chat with the administration. We also want to involve the IT department in developing the application so that any faults can be reported and data updated directly.
Robust Automatic Multiple Landmark Detection

Reinforcement learning (RL) has proven to be a powerful tool for automatic single landmark detection in 3D medical images. In this work, we extend RL-based single landmark detection to detect multiple landmarks simultaneously in the presence of missing data in the form of defaced 3D head MR images. Our purposed technique is both time-efficient and robust to missing data. We demonstrate that adding auxiliary landmarks can improve the accuracy and robustness of estimating primary target landmark locations. The multi-agent deep Q-network (DQN) approach described here detects landmarks within 2mm, even in the presence of missing data.
Research on Facial Expression Recognition Based on Deep Learning

With the advent of information and 5G era, contemporary social media platforms increasingly support users to send text, voice, image, video and other multimedia data at the same time, in which images can convey the sender's emotional state very simply and directly. The classification of emotions expressed in images has become the focus of research and attracted more and more attention. Convolutional neural network is often used in image analysis and processing. In this paper, four different models are constructed based on the CNN to realize the analysis and calculation of facial emotions in images, so that pictures can be predicted through the network to convey what kinds of emotions, and finally the performance of the model is judged by comparing the accuracy and loss rates of the four models. The highest accuracy in this paper can reach 63.07%.
A Novel Approach of Object Oriented Image Steganography Using LSB

Image Steganography involve hiding the secret image in the cover image in such a way that it cannot be detected easily. Hiding the secret image in the selective objects of the cover image is a novel approach that enhances the robustness and security. This paper describes, LSB (Least significant bit) steganography method applied only on certain selected skin tone objects of the cover image to embed the given secret image within. In the proposed work more than one cover image is used to enhance the protection of concealed information. For the skin tone detection of the cover image an alternate colour space YCbCr (Yellow, Chromatic Blue, and Chromatic red) is utilized. Embedding the secret image in the skin region of the cover image provide an excellent protective location for information hiding. Before, that Advance encryption standard technique is used to encrypt secret image which is to be embedded in the selected skin tone objects to have secure stego image. The neural network approach is applied to find the largest object among the selected skin tone objects. The performance parameters like Peak signal to noise ratio and hiding capacity are evaluated thereafter.
Look Before You Sweep: Visibility-Aware Motion Planning

This paper addresses the problem of planning for a robot with a directional obstacle-detection sensor that must move through a cluttered environment. The planning objective is to remain safe by finding a path for the complete robot, including sensor, that guarantees that the robot will not move into any part of the workspace before it has been seen by the sensor. Although a great deal of work has addressed a version of this problem in which the “field of view” of the sensor is a sphere around the robot, there is very little work addressing robots with a narrow or occluded field of view. We give a formal definition of the problem, several solution methods with different computational trade-offs, and experimental results in illustrative domains.
The areas of educational studies of the cloud-based learning systems

The article analyzes the current stage of educational studies of the cloud-based learning systems. The relationship between the notions of the cloud-based learning system and the cloud-based learning environment are investigated. It was found that the researchers paid most attention to the design of a cloud-based learning environment. However, in the process of a cloud-based environment design, the researchers consider a cloud-based system as a component within the cloud-based learning environment of as a stage in the process of design. It is shown that in the research literature there is no single interpretation of the concept of a cloud-based system for educational purposes. Still the number of basic approaches to the interpretation of the concept under investigation are revealed. The first approach is based on the understanding of the system, as a set of cloud services or cloud-based technologies. The second approach is to onsider a separate clo d service as a cloud-based learning system. In this case, the cloud service tools should include such components that cover the content, the tools, the forms and the methods of learning. The structure of the cloud-based learning system within the interpretation of the latest works of Ukrainian researchers is considered.
Computer Aided Cancer Regions Detection of Hepatocellular Carcinoma in Whole-slide Pathological Images based on Deep Learning

The emergency of whole slide imaging (WSI) in digital pathology is becoming a routine clinical diagnosis for many cancers. However, manual cancer regions review in WSIs for diagnosis is labor-intensive and error-prone task due to large scale, high-resolution and complexity of tumor heterogeneity. In this paper, we propose a fully automatic cancer region recognition framework for computer-assisted diagnostics in pathology WSIs based on deep convolutional neural network. Our framework leveraged patch-based images with image-level benign/malignant annotation for neural network training to perform a classification task instead of pixel-level segmentation, which could improve computation efficiency and alleviate annotation workload. The evaluation has been conducted on 100 liver digital whole-slide images and experimental results demonstrated our method can achieve the segmentation accuracy of 0.880 and 0.872 at 15x and 20x respectively, which is feasible and fast cancer detection for diagnosis on WSIs.
Imbalanced Classification Based on Over-sampling and Feature Selection

The imbalanced classification is an important problem in the field of machine learning. In the imbalanced classification problem, correct classification for the minority classes is usually hard. This paper presents an improved approach, named AdaBoost-SVM-OBFS. Firstly, the ReliefF algorithm is used to select more classification-related features; Secondly, we augment the minority samples using Boosting algorithm and the over-sampling technique; Finally the new generated samples are added to the original training set to retrain the SVM. Under the evaluation indexes of AUC, F-Measure and FP Rate, the AdaBoost-SVM-OBFS approach is compared with the AdaBoost-SVM on six imbalanced data sets. The experimental results demonstrate that the proposed approach is valid for the classification of imbalanced data.
A Regional Indicator to Describe Potential Artificial Intelligence

Artificial Intelligence (from now on AI) is synonymous with innovation. In the laboratories of companies and universities, we start to use more innovative technologies to answer specific questions, but in Italy IA is not very widespread. As Gerhard Friedrichn, professor of computer science, dean of the Faculty of Technical Sciences of the University of Klagenfurt, one of the pioneers of research about AI in Europe, says, “artificial intelligence will not come in the coming years, but it will eventually come. There is nothing that can prevent it.” Therefore, for the purpose of a better understanding of the phenomenon to date, this study analyzes the potential use of artificial intelligence in the Italian regions through a composite index (using the methodology of Fair and Sustainable Wellness) that measures the territorial gaps and their predisposition or not to innovation which is an essential prerequisite for the dissemination of AI. For this purpose, the present contribution unfolds with the following structure:
Towards Cloud Computing and Blockchain Integrated Applications

Cloud Computing is a technology widely used in academia and industry, providing varied services on demand. Blockchain technology was developed initially for the creation of a crypto-currency and nowadays is being exploited for several other applications, such as health, agriculture, IoT and education. Some work initiatives are already taking place with the integration of these two technologies, either for research or for cloud service provision. This article aims to present a preliminary discussion on some aspects of integration between blockchain and cloud computing. Contributions of this paper include: (i) presentation of two integrated commercial cloud computing and blockchain environments; and (ii) some research opportunities on the use of both environments.
Variance Counterbalancing for Stochastic Large-scale Learning

Stochastic Gradient Descent (SGD) is perhaps the most frequently used method for large scale training. A common example is training a neural network over a large data set, which amounts to minimizing the corresponding mean squared error (MSE). Since the convergence of SGD is rather slow, acceleration techniques based on the notion of “Mini-Batches” have been developed. All of them however, mimicking SGD, impose diminishing step-sizes as a means to inhibit large variations in the MSE objective. In this article, we introduce random sets of mini-batches instead of individual mini-batches. We employ an objective function that minimizes the average MSE and its variance over these sets, eliminating so the need for the systematic step size reduction. This approach permits the use of state-of-the-art optimization methods, far more efficient than the gradient descent, and yields a significant performance enhancement.
Detecting Anomalies in Application Performance Management System with Machine Learning Algorihms

Application performance management which is short for APM is a real-time monitoring of enterprise systems to implement a systematic solution to application performance management and fault management. APM is a relatively new network management direction. It mainly refers to monitoring and optimizing key business applications of enterprises, improving the reliability and quality of enterprise applications, ensuring users to get good services and reducing total cost of ownership (TCO). If a company's business-critical applications are powerful, they can increase competitiveness and achieve business success. Therefore, enhancing application performance management (APM) can generate huge business benefits. This paper explores the application of machine learning-based anomaly detection algorithm in APM system by analyzing continuous time series data from actual network systems.
Hypertext preprocessor framework in the development of web applications

Abstract This writing paper aims to determine the Hypertext Preprocessor (PHP) framework that is widely used and analyze the benefits of the framework in making a web application. A review systematically through a review article about a PHP framework. Article search is accessed from an internet database search. articles are taken from 2014 to 2019. The PHP framework can simplify the process of developing web applications because the concept has already been determined. Whereas if you use regular PHP, a programmer makes coding, folder structure or configuration is made as he wishes. Usually, the PHP framework uses the concept of Model-View-Controller (MVC), its function model is for database queries, its View function is for displaying HTML (Hypertext Markup Language), the controller functions for calculations or logic. The result is that PHP Laravel and CodeIgniter frameworks are the most widely used in building a web application. PHP Framework makes the development of applications web becomes much faster and more stable. The selection of a PHP framework that is appropriate for the project of making the application web also can affect the outcome performance was obtained after the application of the web was completed.