Reviewerly/exHarmony huggingface dataset:
Adaptive exploration network policy for effective exploration in reinforcement learning

How to achieve effective exploration is a key issue in the training of Reinforcement learning. The known exploration policy addresses this issue by adding noise to the policy for guiding the agent exploring. However, it has two problems that 1) the exploration scale has low adaptability to the training stability due to the added noise from a fixed distribution and 2) the policy learned after the training may be locally optimal because the exploration is insufficient. Adaptive exploration policy addresses the first problem by adjusting the noise scale according to the training stability. But the learned policy may still be locally optimal. In this paper, we propose an adaptive exploration network policy to address this problem by considering exploration direction. The motivation is that the agent should explore in the direction of increasing the sample diversity to avoid the local optimum caused by insufficient exploration. Firstly, we construct a prediction network to predict the next state after the agent makes a decision at the current state. Secondly, we propose an exploration network to generate the exploration direction. To increase the sample diversity, this network is trained by maximizing the distance between the predicted next state from prediction network and the current state. Then we adjust the exploration scale to adapt to the training stability. Finally, we propose adaptive exploration network policy based on the new noise constructed by the generated exploration direction and the adaptive exploration scale. Experiments illustrate the effectiveness of our method.
Research on the whole process of construction quality monitoring of airport asphalt road surface based on IoT

The traditional monitoring means of airport asphalt runway construction has problems such as irregular construction operation, unreliable process control and unscientific quality assessment. To meet the higher requirements of runway construction quality monitoring process, this paper analyzes the mechanized asphalt pavement construction process and the root causes affecting the construction quality, researches the application of Internet of Things (IoT) technology in asphalt pavement mechanized construction monitoring information system, and also develops the overall structure design of asphalt pavement monitoring system based on IoT technology, completes the development of a remote monitoring system based on the Web through serial communication, network protocol and database design. Finally, the system was analyzed in the test results of the west runway overhaul project of Capital International Airport. The results showed that the system has good hardware seismic resistance, good data integrity and real-time performance, and high reliability. The active and effective use of the airport asphalt runway construction management system can reflect the runway construction process comprehensively, while it is important to promote the traditional construction monitoring to advanced automated real-time process monitoring and management.
Research on target detection method based on attention mechanism and reinforcement learning

The development of intelligent manufacturing promotes the intellectualization of traditional navigation technology. Because actor-critic (AC) algorithm is difficult to converge in the actual application process, this paper uses the optimization algorithm of this method, which is called deep deterministic policy gradient (DDPG). Through the use of experience playback and dual network design, the learning rate can be greatly improved compared with the original algorithm. Because curiosity strategy has more advantages in alleviating sparse reward problem, this paper also takes curiosity mechanism as an internal reward exploration strategy and proposes the DDPG method based on improved curiosity mechanism to solve the problem that robots lack external reward in some complex environments and tasks cannot be completed. The simulation and real experiment results show that the proposed method is more stable when completing the navigation task and performs well in the long-distance autonomous navigation task.
Region of interest image encryption method based on panoramic segmentation and a novel coupled chaotic map

Social media is a platform for people to share their lives and interact with others. Image sharing is an integral component. Privacy information will inevitably be compromised during the process of sharing whole photos. Sometimes, it is not even caused by the person who shares the image but the third party who forwards the image. In the majority of cases, we do not attempt to protect the entirety of the media; rather, we seek to protect the most crucial portion, whether it is the background or specific objects, which decreasing superfluous cryptography procedures. Unfortunately, current research on such field is extremely rare. In this research, we established a novel pixel-level image encryption technique relying on Panoptic FCN panoramic segmentation and chaos for client-intended images on social media sites. Our suggested technology is capable of automated picture encryption on either the whole images or user-selected areas, whether they are rectangular or irregular, which is suited for all region of interest (ROI) encryption. Relying on a novel coupled chaotic map, this universal new encryption method flattens the array of the image ROI into a series of pixels. The module of Panoptic FCN can be replaced by any other panoptic segmentation models which are stronger in performance. Statistical and cryptographic evaluations demonstrate that our technique preserves the high efficiency for practical applications.
Deeplab V3+ based segmentation method for PV panels with aerial orthoimages

The health management and maintenance of photovoltaic (PV) plants are inherent problems in the PV industry. The need to establish digital positioning for each PV string and PV module is urgent. This paper provides a complete end-to-end system for digital segmentation and localization of PV strings and modules on the aerial orthophotos. The system includes three main parts: (1) the dataset built from the images captured by Unmanned Aerial Vehicles (UAV) and corresponding image preprocessing techniques. (2) a modified Deeplab V3+ neural network is designed to extract the PV strings in the aerial orthophotos. (3) a PV module extraction algorithm is introduced to get the centroid of every PV module and the sliding window strategy is adopted to avoid the chopped PV strings problem. With the above process, the digital location information of PV panels can be correlated with the actual physical information. We conduct detailed experiments with actual scene data and different models. The extensive results confirm the accuracy and efficiency of the system proposed in this paper with comparative analysis.
Semantic enhancement methods for image captioning

Image captioning, a cross-modal study, aims to generating a description for a given image, which plays an important role in many fields like image retrieval and computer-assisted instruction. Currently, the challenge in image captioning is the limited quality of generated descriptions including insufficient utilization of image feature information and the limited language learning ability of the decoder. In this paper, we address the above problems by constructing a semantic enhancement module and a multi-round decoding mechanism to enhance the decoding ability of the model, which uses the Transformer model as the primary structure. To validate the efficacy of the model, we conducted intensive experiments on the MSCOCO2014 benchmark and evaluated its performance using five evaluation metrics. The experimental results show that the proposed method in this paper has improved to varying degrees on all five-evaluation metrics.
Ability evaluation method of network security talents in power industry based on artificial intelligence

In view of the poor accuracy of the traditional method to evaluate the ability of network security talents in the power industry, this paper proposes a method to evaluate the ability of network security talents in the power industry based on artificial intelligence. Through the establishment of talent ability evaluation system, it uses artificial intelligence algorithm to calculate the index weight value. According to the index corresponding weight to build the evaluation model,it determines the membership range of the index, and calculates the comprehensive score of the assessor's ability. Then, the paper compares the comprehensive score with the ability classification table and determines the ability level of the assessor to realize the evaluation of talent ability. The effectiveness of the designed evaluation method is verified by the demonstration of comparative experiments. The experimental results show that the evaluation results of the design method are consistent with the actual appraisal results in the process of evaluating the attack and defense penetration index of the network security staff. Therefore, it can be proved that the evaluation method of network security talents in power industry based on the artificial intelligence has high accuracy and objectivity, which is more realistic and helpful for the company to form a personnel management pattern with clear talent positioning and full use of talents.
Cyber Security and Cyber Defense: Challenges and Building of Cyber Resilience Conceptual Model

Cyber security encompasses a broad range of practices, tools and concepts related closely to those of information and operational technology security. Cyber security is distinctive in its inclusion of the offensive use of information technology to attack adversaries. Use of the term cyber security as a key challenge and a synonym for information security or IT security misleads customers and security practitioners and obscures critical differences between these disciplines. Recommendation for security leaders is that they should use the term cyber security to designate only security practices related to the defensive actions involving or relying upon information technology and/or operational technology environments and systems. Cyber defense is a computer network defense mechanism which includes response to actions and critical infrastructure protection and information assurance for organizations, government entities and other possible networks [3]. In this paper, we investigate how cyber security and cyber defense may lead to cyber resilience with the novel model of cyber resilience designed and presented. Furthermore, within the same model authors investigate actions for cyber security and cyber defense in conditions of increasing challenge of cyber-attacks and the limited capabilities to respond to this threat describing the process of creation, performance and future of EU Cyber Rapid Response Teams (abbr. CRRT) and Mutual Assistance in Cyber Security, introducing novel approach to cyber security and cyber defense at the EU level.
Few shot text classification using adaptive cross capsule network

In recent years, meta-learning has become a mainstream technique for few-shot learning, and it has been widely used and achieved good results in computer vision and image processing. Based on this powerful empirical performance, we are interested in using Meta-learning frameworks in NLP to deal with the task of few-shot learning (FSL). However, due to the sparse sample size, sample-level comparisons based on other expressions are highly susceptible to interference, leading to serious overfitting problems. To achieve classification tasks, we suggest a novel Adaptive Cross-Capsule Network (ACCN) for learning generalized representations. A dynamic routing technique is utilized with the concept of a prototype network to train the support set to generalize the generalized representations of each category. The support set and the query set can fully interact dynamically to capture the essential semantic aspects of the query set following a successful non-parametric cross-attention method. Experimental results show that ACCN proposed in this paper is well adaptive to the intention classification task under additional categories, which obtain SOTA results on FewRel Datasets, which also can perform significantly better than the original classification system on Huffpost Datasets. This provides a crucial foundation for this study.
Developing Digital Natural Laboratory Based on Edutourism for Environmental Geography

This study aimed to develop products and test the utility of the natural laboratory based on eco-spatial edutourism. This research was developed in three stages: 1) product development using five steps in the ADDIE model: Analysis, design, development, implementation, and evaluation, 2) conducting validation tests from learning experts, and 3) conducting limited trials in small groups. The research subjects were students of Geography Education study program, State University of Malang, in the environmental course in the academic year 2019/2020. The data were analyzed using the percentage table. The results showed: 1) the digital natural laboratory based on eco-spatial edutourism was valid by the expert validators, 2) the digital natural laboratory based on eco-spatial edutourism qualified for use in higher education learning.
Multi-Criteria Decision Making Analysis of Optimal Service Delivery Technique Using AHP

In this paper, we evaluate the most optimal service delivery method that can be implemented by both governmental and private sectors to an effective delivery service to customers. They include conventional service delivery, E-services (online), M-services (smart phones), and robotic process automation (RPA). Benefits and costs were considered in the evaluation process. Benefits were technical feasibility, efficiency, security, reliability, and innovation. The costs included complexity, human resources, facilities management, and maintenance costs. AHP was used to facilitate the multi-criteria decision-making process. Results show that RPA is the least expensive and most beneficial method to delivery services in UAE.
Application of virtual reality technology in the treatment of bulimia nervosa and binge eating disorder

With the continuous development of computer technology and human-computer interaction system, virtual reality technology has been widely used in all aspects of social life.The application of virtual reality technology in psychotherapy provides a new idea for the intervention of eating disorders whose main symptoms are bulimia nervosa and binge eating disorder.In this paper, the application of virtual reality technology in the treatment of bulimia nervosa and binge eating disorder was discussed by reviewing relevant studies at home and abroad, and the future research was prospected.
Multi-channel face liveness detection based on multi-scale feature fusion

A multi-channel face liveness detection method based on multi-scale feature fusion is proposed to solve the problems of poor stability, poor generalization, and poor robustness against unknown attacks of existing face liveness detection models. Firstly, the method uses a multichannel residual network and introduces the center differential convolution and SimAM attention module in the residual block to improve the feature extraction ability and stability of the model. Secondly, the information contained in the feature map at different scales is further mined by multiscale feature fusion at the end of each channel. Finally, the network is supervised by using cross modal focal loss as an aid to binary cross entropy loss. Extensive evaluations in two publicly available datasets demonstrate the effectiveness, generalization, and robustness of the proposed method against unknown attacks.
Research on manifold nonnegative matrix decomposition algorithm for weakly supervised text classification

Traditional text classifiers that rely on supervised learning methods always require a large number of labeled documents. Labeling the documents often requires a certain amount of expertise to ensure the accuracy, which is time-consuming and costly. Therefore, a dataless text classifcation method around a small number of easily accessible label descriptions, ie, seed words,rather than surrounding the labeled documents to provide the supervision information for the classification task, shows a good development prospect. However, since the size of the seed word set is much smaller than the word set contained in the document , many documents do not contain any seed words or even contain some irrelevant seed words, which limits the effect of the seed word supervision. The manifold assumption suggests that highly similar texts tend to belong to the same category, so we maintain a local neighborhood structure for each document and construct a manifold regularizer to spread limited the supervised information between similar documents. We propose a Laplacian Nonnegative Matrix Factorization (LapNMF) method，adding the seed word prior information and document manifold into the framework of non-negative matrix factorization. And use the block corrdinate desent method to solve the problem. Experiments show that in most cases, our LapNMF performs better than the current weakly supervised classification methods, showing certain competitiveness.
Detection of secondary school circuit experiment equipment based on improved YOLOX

Aiming at the problem that it is difficult for the existing target detection algorithms to detect high-precision circuit experimental equipment in middle school, an improved YOLOX detection network model is proposed. Based on the YOLOX network model. Firstly, the ECA attention module is added to the feature extraction network to enhance the model's ability to perceive electrical experimental equipment; Secondly, the feature enhancement structure is added to enhance the semantic information of the obtained feature map and improve the detection ability of the target; Finally, EIoU is selected as the loss function to achieve high-precision positioning. The experimental results show that the improved network model mAP reaches 91.9%, which is 1.5% higher than the original network model, which proves that the improvement is effective and feasible.
Skeleton-based human motion prediction via spatio and position encoding transformer network

Many transformer modules, have been applied to computer vision. However, the transformer can extract the distal connections of human skeleton points and apply the attention mechanism to the problem of predicting human motion pose. We introduce a transformer module in the joint dimension. In addition, the Encoder module of the transformer is improved. Finally, our method achieves impressive results on benchmark datasets, including short- and long-term predictions of FNTU, confirming its effectiveness and efficiency.
Research on vehicle target detection method based on YOLOv5

Vehicle target detection is a key research hotspot in the field of computer vision. At present, with the continuous development of deep learning and artificial intelligence, some excellent vehicle target detection algorithms such as YOLOv5, YOLOv4 and YOLOv3 have emerged. Therefore, in order to solve the problem of low accuracy of vehicle target detection, ensure the safety of vehicles on the road and achieve target detection more accurately. This paper provides a YoloV5-based method for detecting car objects and an improved algorithm that uses large-scale internal fusion techniques. Finally, the vehicle target detection accuracy of the improved YOLOv5 algorithm is effectively improved through experimental comparison and analysis. This is of great practical significance for promoting the development of target detection algorithms.
Harnessing transfer learning for Alzheimer's disease prediction

Nowadays, Alzheimer's Disease (AD) has become a massive problem for middle-aged and older adults. Although due to its long incubation period and early mild symptoms, patients have a more extended period and more possibilities to check out, it is still hard for patients and doctors to diagnose in early routine examinations. This article provides a new method to help the doctor to diagnose Alzheimer's Disease in the early phase. We use transfer learning in deep learning to help diagnose Alzheimer's Disease early in developing Computed Tomography (CT) brain images. Using three pre-trained models, ShuffleNet, DenseNet, and NASNet-mobile as the transfer learning training model and convolution neural networks. We made some improvements to make it more relevant to the actual situation. DenseNet has best performance (87.36%) among the three models. We set the output into four classes: the four stages of Alzheimer's are widely recognized (Mild Demented, Moderate Demented, Very Mild Demented).
The Development of a Laundry Location Tracking Application Model Based on Artificial Intelligence and Mobile Computing

There is a lack of information systems that can facilitate the laundry business in reaching the market, so an application is needed to provide information to be known by many people in the city of Banda Aceh. One is by delivering information using the nearest laundry location search application. With the help of the internet and smartphones, it can make it easier for people to find the nearest laundry. The researcher is interested in designing a smartphone application to provide information on the nearest laundry. This study aims to create information media to develop an artificial intelligence-based laundry location search application model and a laundry location search application model with mobile Computing in Banda Aceh. The stages of this research are field observation, application design and design, application trials, research implementation permits, research data collection, and analysis of research results. The research implementation is located in the city of Banda Aceh. This research method is included in the Research and Development (RD) development category. The data collection used is as follows: notes on the results of interviews, field observations, and data related to informants. The results of this study are in the form of an application that provides information on the location of the nearest laundry from a distance of the user automatically when the application is run, and the user can search for the lowest price, 4+ star branches, and laundries that provide Pickup services.