Reviewerly/exHarmony huggingface dataset:
Generating face images from fine-grained sketches based on GAN with global-local joint discriminator

This paper explores the face image generating with clear details from fine-grained sketches. Edge maps are usually used as sketches in face image generation tasks. However, there are some problems such as discontinuous lines and a lack of detailed information in the edge map, so the generated face image is not clear enough and lacks details. To address this problem, a face sketch dataset with rich details is made. The discriminator combines a global discriminator and a local discriminator, which ensures that the generated face image has a complete face structure and generates clearer face details. A self-attention mechanism is employed in the generative model to establish long-term dependencies on partial feature information. The model is quantitatively evaluated by using the valuation criteria IS, FID, and KID. Among them, the evaluation scores of the model on IS, FID, and KID are 1.956 ± 0.053, 17.941 ± 0.970, and 0.009 ± 0.001, respectively. The evaluation results show that the model achieves good performance. Furthermore, the visual effects of the model are analyzed by comparing with the pix2pixHD and CycleGAN models on the generated images. The final results show that our model outperforms the other two models and can generate high-quality face images with sharp details.
Exploring Defect Data with Network Visualization

Jira software is agile project management solutions, originally designed as a bug and issue tracker. Searching for issues or defect information can be performed by Jira Query Language (JQL). However, querying a data repository simply returns the specified data and information. In this work, we present an approach of network visualization to uncover relationships and communications among actors such as software features, defects, and staff. The technique of social network analysis is applied for analyzing the defects collected from a banking software project. And Gephi is used as a tool for generating a network of specified actors as nodes and their linkages. The approach of visual network analysis is practical and provides insights in defect analysis essential for the proactive software development process.
An Automatic Staging Method for Early Cervical Cancer on MRI Images: A Pilot Study

Objective: To automatically segment sagittal mag-netic resonance imaging(MRI) images of cervical cancer (CC) patients and calculate the Intersection over Union (IoU) value of segmentation results for staging CC. Methods: This work uses 51 stage IB MRI images and 27 stage IIA MRI images as a dataset, all of which were pathologically confirmed in patients with CC. All labels used for training were manually labeled by radiologists with many years of clinical experience. MRI images and corresponding labels are put into a semantic segmentation network (based on U-net) to segment the tumor, uterus and vagina, respectively. Finally, the IoU values between the tumor and the other two tissues are calculated and the appropriate threshold is selected to classify images. Results: The segmentation of the uterus has good performance with Intersection over Union (IoU) of 0.872 and Pix Accuracy(PA) of 0.923. The classifier uses the best diagnostic threshold for tumor and vagina as the main criteria and the best diagnostic threshold for tumor and uterus as an auxiliary judgment to obtain a final classification accuracy of 0.807, the precision of 0.846, and recall of 0.863. Conclusions: In this study, the segmentation results were used to calculate the IoU values between different tissues which can distinguish the early staging of CC. It can provide some reference for radiologists to determine the tumor staging on MRI images of CC patients.
Enabling Smart Building Applications on the Edge using Artificial Intelligence

Smart Building systems solutions have enabled building managers to track efficient use of space, maximize downtime while ensuring a comfortable and productive environment for its occupants. One facet of this system is to accurately determine the position and number of occupants to suitably tailor HVAC (Heating Ventilation and Cooling) requirements and gather space utilization metrics. There have been several space occupancy systems designed using different types of sensors. We propose a Smart imaging sensor powered by a convolutional neural network (CNN)-based detection algorithm that can monitor and provide accurate metrics of space utilization across different space configurations. We have developed and ported this application onto a legacy BF70x device to run a high accuracy low latency people detection application. This system coupled with a cloud based finetuning setup helps us get good accuracy across different environments.
Formal Verification of Deep Neural Networks in Hardware

Deep neural network (DNN) verification is an emerging field. The problem is more pronounced when they are modelled as hardware logic. Existing formal verification solutions for such hardware designs are fraught with issues like scalability and compute intensive resources. We solve this problem by creating a lightweight verification infrastructure and verifying the DNN logic by way of equivalence check. The light weight formal verification model is created by applying neural network simplification approaches discussed in the paper. The DNN formal model as a consequence is made simpler by removing the unwanted nodes, as a result it is capable of verifying intricate deep neural network designs effectively as compared with simulation based conventional strategies. The approach is applied on a design which is a car collision avoidance system (CARCAS) which is a logic deployed in autonomous car driving paradigm.
Refinement Of Reinforcement Learning Algorithms Guided By Counterexamples

Reinforcement learning (RL) is a technique of learning optimal behaviors with high rewards through repeated interactions with an environment of interest. However, in many real-world applications, along with a high return, it is also required that the agent avoids undesirable states both during training and deployment. Reinforcement learning relies on maximizing a reward function to learn an optimal behaviour. Maximizing a reward does not guarantee safe behaviour. Also, it is important to account for parameter changes in the environment post-deployment which may lead a pre-trained policy to fail. In this paper, we explore a strategy that uses counterexamples to refine RL policies that have been tuned to maximize reward. We show that this method works for a wide variety of RL algorithms. We validate our methodology on Open AI Gym environments.
Operator Assistance for Remote Driving with Augmented Image based on Gaze Information

The authors have been aiming to realize a remote driving system with an HMD(Head Mounted Display) and a 360-degree camera to decrease the task load of the operator. Therefore, we have been tackling an operator assistance method of remote driving applying Augmented Reality(AR). In the previously proposed methods, AR video presented to the operator is generated based on sensing information of 3D LiDAR to deal with the communication delay. In this paper, a new generation method of AR image is proposed, in which Gaze information of the operator is used to create a 3D object to draw his attention to a hazard that might cause a traffic accident. The usefulness of the proposed method has been evaluated based on the results of the experiments using a driving simulator which has a scenario of a person jumping out from a park.
On the Scalability of HeapCheck

In our DSN 2021 paper, we introduced HeapCheck, a combination of hardware and software that provides strong protection against memory-safety vulnerabilities with minimal performance overhead. In this short paper, we discuss how HeapCheck can support programs that use very large numbers of objects, and we also compare HeapCheck to two recently announced industry approaches to memory-safety.
Application of Machine Leaning Methods in Geoscience

This paper presents a systematic overview on the application of different machine learning methods in geoscience. The basic principles of these machine learning methods is contained, along with comparisons of the benefits and drawbacks of different machine learning methods, and the main application fields of different models.
Pipeline cleaning robot path planning research

With the development of society, central air conditioning has been widely used in life. However, due to long-term use and incomplete cleaning, a large number of dust, bacteria and other harmful substances will adhere to the outer wall of the pipeline, which will greatly affect people's normal work and life. In order to solve this problem, an automatic pipeline cleaning robot based on tensioning structure is proposed in this paper. It has unique advantages in the field of mobile robot. At the same time, this paper also introduces the path planning algorithm of robot based on bidirectional fast search random tree (RRT), and puts forward the shortcomings and improvement ideas.
Improvement of Computer Neural Network based on Feedforward Sequence Memory

The existing neural networks mainly involve working memory in the memory module, which can basically meet the needs of a neural network at work. However, for human intelligence, working memory is far from meeting the needs, especially in some advanced activities, such as thinking, language and emotion. Based on the concept driven processing theory, that is, the top-down processing mode of human perception, and the dualprocess theory of attention, that is, automatic processing and control processing based on the cognitive resource theory, this paper puts forward the computer neural network long-term memory storage model and the computer neural network dualprocess model, in order to construct the long-term memory of computer neural network, reduce the loss of computing time and computing resources, and improve the processing rate of information. It provides the possibility for the realization of neural network in automatic processing, subconscious judgment and other activities, as well as high-level activities such as self-control, strategy formulation and organization planning.
An entity recognition method based on curriculum learning for core domain entity mining

Traditional literature analysis mainly focuses on the subject, abstract, keywords, author of the literature, and pays less attention to the main content of the literature. However, in the power domain, the power method and equipment entities contain the core domain knowledge which is of great significance to the analysis of power literature. An entity recognition method based on curriculum learning is proposed. The method formulates two evaluation criteria of sample difficulty related to the power domain from the perspective of complexity and diversity, and guides the model to learn the samples in order from easy to difficult. It focuses on the extraction task of core domain entities such as power method and equipment which are hidden deep in the literature. Analysis shows that the method may overcome the problem that the parameters of the BERT embedded model are large and difficult to train. Experiments show that the proposed method may improve the training efficiency and recognition effect on the power texts. It can reduce the training time by 50%, and also achieved a 2.37% increase in the F1-score.
Security Encryption Scheme under the Double Rules of DNA and Absolute Motion Block Truncated Encoding

In order to realize data encryption and transmission security, an information security coding scheme based on double hybrid rules of DNA coding and block truncation coding (BTC) is proposed in this paper. Firstly, compress the original pictures to the BTC. And secondly encode and replace the BTC according to rules to hide additional information. Construct the pseudo genetic codon table, and group different DNA codons. Then, n-nary numerical system is designed by using the double hybrid rules. Finally, the encoded pseudo DNA sequence is replaced to realize steganography. Experiments verify the effectiveness and effectiveness of the scheme.
Design and Optimization of Big Data Distribution System under Cloud Computing Platform

The emergence of big data is firstly caused by the development of computer technology and Internet communication technology. With the continuous development of cloud computing, people have more and more demands for the Internet. How to make full use of existing resources and process complex data has become the focus of adapting to the times. The purpose of this paper to study the system is to grasp the technology of data processing in time and be familiar with the functions of the cloud computing platform. This article mainly uses experimental methods and case analysis to test the performance of the data shunt system. Experimental data shows that it takes about 120s to update the data flow table. The system can correctly identify the data stream, and perform balanced distribution and forwarding according to the identification result.
Design of Cloud Diary System based on Blockchain

This design aims at the overall design, the design of each functional module and the interface design of “cloud diary system based on block chain”. Its purpose is to enable developers to complete the specific implementation, testing and maintenance of each module specified in the outline design on the basis of completing the outline design.
Big Data Classification Model and Algorithm based on Blockchain

Although the 4V properties of big data have been widely discussed, most of them still describe the appearance of big data. Technical characteristics are necessary. Based on the above background, the purpose of this article is to study the big data classification model and algorithm based on blockchain. This article addresses the problem of mutual trust in data classification caused by the lack of a transparent, open, and equal interaction environment in the current centralized big data classification model. This article analyzes the advantages of blockchain-based big data classification in depth and establishes a blockchain-based big data classification model. Through the data connection model of the combination of blockchain and distributed file system, key information in all classification interactions is stored on the chain in an immutable and traceable manner. This article implements a big data classification prototype system based on Ethereum, IPFS, Laravel and other technologies. The experimental results show that the stand-alone running time is 4343ms longer than the three-node parallel running time, and the blockchain-based execution time is 17615ms longer than the serial running time. Based on the blockchain, this article introduces the interstellar file system IPFS and Zigzag coding, capabilities-based access control methods, and publish-subscribe models, and proposes a reliable data connection mechanism based on BIZi network, a blockchain-based data capability access control mechanism, and a zone-based Blockchain data service customization mechanism.
Unsupervised word sense disambiguation method based on word vector

Word Sense Disambiguation (WSD) is an open problem in Natural Language Processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. A disambiguated method was proposed which based on word vector and typical training method such as CBOW and Skip-gram model under sequence training or Negative sampling. The sequence training process and probability of typical models in word2vec are derived. 7 key parameters for training process are discussed about default and optimization. In order to achieve higher accuracy, different corpora are used and the parameters of the training dictionary are adjusted to optimize the results of word disambiguation and improve accuracy and rationality. The results are analyzed and the training model of different parameters and the results of WSD of the corpus are discussed.
KGSMS: Knowledge Graph Sample based Multi-agent Simulation

Recent algorithms on multi-agent problems, including StarCraftII macro-management problems and military strategy games simulations, are suffering from the immense computation burden from joint action spaces and the slow convergence speed from state exploring. Multi-agent reinforcement learning methods and knowledge inference within prior artificial experiences are currently the most ways to solve multi-agent problems. In this paper, we propose a new framework that integrates policies learnt from reinforcement learning methods with empirical actions provided by knowledge graphs. Policies improvements and relations between domains are updated synchronously during the training process. Experiments are carried out on two different scenes of coastal air-defense deployment and asymmetrical military attack missions from Mozi simulation system. Results indicate the success of the integration among reinforcement learning algorithms with knowledge graphs and the acceleration of convergence speed during the training with a large number of agents.
Commonsense Generative Model for Chinese Automatic Knowledge Graph Construction

Commonsense knowledge graph support applications in commonsense reasoning, question answering, and so on. However, automatic knowledge graph construction is still a continuing goal for AI researchers due to the difficulty of obtaining tractable and objective commonsense information. Besides, the relative researches have so far been mainly limited to English, making it slow to develop the research of commonsense knowledge in other languages. Previous studies constructed the knowledge bases as the relational schemas which use the expert knowledge, semi-structured text extraction and unstructured text extraction. However, with the way of extraction, these methods can only capture the explicit knowledge mentioned in the text, while the commonsense knowledge in the text is usually implicit. In this paper, we propose a commonsense generative model with a novel attention mechanism and discuss whether pre-trained language models can effectively learn and generate novel knowledge. The empirical results show that our model could generate correct commonsense knowledge with high scores which up to 50.10% precision on ATOMIC dataset humans given.
An image encryption system based on chaos principle

An image encryption scheme based on chaotic sequences and genetic algorithms is proposed to improve the security of the image encryption system. The proposed scheme uses chaotic sequence as the key of the entire encryption system, which improves the disorder of the encrypted image. Using the optimal key selection function of the genetic algorithm, the encrypted genetic process and some genetic parameter data can be optimized and encrypted. These features enhance security against various attacks such as jigsaw puzzle solver and brute-force attacks. Regardless of the size of the image, the encryption system has good security, and the correlation between adjacent pixels of the encrypted image is close to zero. In an experiment, the performance of the encryption scheme and the traditional image encryption scheme is evaluated, and various parameters of encryption performance are measured in the experiment. As a result, the scheme improves the security of the image encryption system.
Data Storage Security System based on Cloud Computing

Cloud computing is an innovation of the computer model. Although it has unique advantages, it also brings various challenges to people, the most important of which is security. Due to the particularity of certain fields, some businesses have very high requirements for data security. If these data are lost or damaged, the person in charge will have to bear serious consequences. Only by ensuring that the data files are accurate and reliable can data transmission be made more secure and thus a greater reputation can be obtained. In addition, the issue of cloud computing is very important. Only through effective and standardized control can data information be operated in a healthy and normal manner. Therefore, this paper introduces encryption technology when designing the data storage security system to ensure the security of the information on the cloud platform, and the system uses the powerful computing functions of cloud computing to greatly improve the processing efficiency when processing data, and then test the system performance and the functions of each module. After being well and complete, the system can be applied to the information management business.
Cloud-Based Mixed Reality System in Machinery Vocational School: ICT Blueprint

Education is always the most important part of our lives. To make education make a significant impact on today&#x0027;s learners, teaching and learning must evolve to match with today&#x0027;s learner&#x0027;s profile. Any education including vocational education has an important role in economic development and one of the ways to maximize the contribution of vocational educations is strengthening the link and match with the world of work [1]. As the technology also keeps evolving and it is getting more and more advanced, one technology that has significant traction lately is the mixed reality system and is often used for education purposes. Most big ICT companies like Microsoft, and HTC are racing to develop mixed reality systems to help the teaching and learning process to be more effective and efficient. Where everything is cloud-based, having a cloud-based mixed reality system is just makes sense but will have a great challenge and considered as a big ICT project. One of the ways to make the ICT project successful is to make a blueprint before it is started. The objective of this paper is how to develop an ICT Blueprint for implementing a cloud-based mixed reality system for machinery vocational schools to make the project have a higher success rate.
Robust Adaptive Scaffolding with Inverse Reinforcement Learning-Based Reward Design

Reinforcement learning (RL) has shown significant potential for inducing data-driven scaffolding policies but designing reward functions that lead to effective policies is challenging. A promising solution is to use inverse RL to learn a reward function from effective demonstrations. This paper presents an inverse reward deep RL framework for inducing scaffolding policies in an adaptive learning environment. The framework centers on generating a data-driven model of immediate rewards by sampling high learning-gain episodes from previous student interactions and applying inverse RL. The resulting reward model is used to induce an adaptive scaffolding policy using batch constrained deep Q-learning. We evaluate this framework on data from 487 learners who completed an adaptive trianing course that provided direct instruction on principles of leading stability operations. Results show that the framework yields significantly better scaffolding policies more quickly compared to several RL baselines.