Reviewerly/exHarmony huggingface dataset:
Processing Drone Images with the Open Source Giwer Software Package

While various open source and commercial image processing software exist, they often lack high level image processing skills, like the flexibility to assemble custom algorithm workflows, where the experience of the evaluator can be built into the system. We have developed the Giwer (acronym for GeoImage Workflow Editing Resources) open source software package for handling, processing and analysing drone images. Giwer can be used to process large quantities of drone images – both traditional RGB and multispectral recordings. Organized into a database, the images can be searched and collected based on their attribute data. Processing is aided by a rich library of functions such as digital filterbank, classification, clustering, mathematical statistics, raster calculator, image algebra, and more. From the functions available in Giwer, arbitrary processing processes, i.e. workflows can be created, so the user can compile the most suitable processing processes based on his own knowledge and experience, which he can run not only for one image, but also for many images at once.
Reform of Education Services for Adolescents Through Digital Technology

The problem of HIV/AIDS cases affects the number of human resources in education such as teachers, administrators, principals who are reduced due to illness or death. Increasing cases of HIV/AIDS that spread among adolescents make the government pay more attention to the increase in these cases. Empirical facts found during the study show that one of the stakeholders in the world of education stated that learning about HIV/AIDS prevention has not been considered an urgent matter. The research is qualitative research using a descriptive approach based on the perspective of public policy. In this case, researchers have developed a basic health promotion system about HIV/AIDS and have been introduced to sufferers newly infected with HIV/AIDS. The results of studies in the city of Banjarmasin explored policy issues in education for the prevention of HIV/AIDS. Study findings assist policymakers in making decisions regarding HIV/AIDS prevention policies.
Coronavirus Optimization Algorithm for Breast Cancer Classification

Early diagnosis of diseases is necessary and important to assist doctors in order to treat and control diseases such as breast cancer early. A high accuracy machine learning techniques should be available to assist clinician to come out with accurate diagnosis. In this work we tackle this problem using the coronavirus algorithm. The Coronavirus algorithm was used as a feature optimization method, while J48 and PART were used for classification. The proposed approach has been tested on the Wisconsin Diagnosis Breast Cancer (WDBC) dataset located in the UCI Machine Learning Database repository. Experimental results show that our approach is able to generate competitive results when compared with previous available approaches. The proposed approach could obtain an accuracy of 94.01 % for the j48 algorithm and an accuracy of 94.18% for the PART algorithm.
Multilevel Strong Auxiliary Network for Enhancing Feature Representation to Protect Secret Images

Image data play an important role in the network information, however, some images containing sensitive or confidential information are easy to attract the attention of malicious attackers. Based on deep learning and data hiding technology, a novel hiding-revealing network is designed to protect these secret images. The sender uses the hiding network to conceal the secret image into an ordinary cover image and the receiver uses the revealing network to recover the secret image. Symmetrical shortcut connection is designed to improve both the hiding and the revealing performance without adding any parameters. Consider that some secret images may have complex spatial features, a multi-level strong auxiliary module is designed to enhance feature representation and boost the restoration quality of the secret image. Then, a lifeline is proposed to transform the image hiding task into a residual identity mapping, which reduces the difficulty of network learning and obviously improves the hiding performance. In addition, a mixed loss function is designed to further improve the perceptual quality of both the hidden image and the revealed image, which further completely eliminates the secret content in the residual image and ensures the hiding security. Experimental results demonstrate that, compared with the state-of-the-art methods, our proposed method achieves the best performance in both hidden image synthesis and secret image restoration.
A Kalman-based Autoencoder Framework for End-to-End Communication Systems

It is a promising technology to replace traditional transceiver design with neural network (NN)-based autoencoder to realize end-to-end communication. The joint optimization of multiple blocks helps the communication system to achieve better performance. However, this approach needs a differentiable channel modeling to train both NNs at the transceiver. For a non-differentiable channel in a real scenario, gradient backpropagation cannot be performed. In this paper, we propose a Kalman-based autoencoder framework to achieve gradient estimation and backpropagation by adding a control layer at the last layer of the encoder NN, which combines the rich feature representations learned by the NN with the Bayesian filter method based on Extended Kalman Filter (EKF) and Cubature Kalman Filter (CKF). According to simulation results, the proposed framework has a fast convergence rate and strong robustness in both additive white Gaussian noise (AWGN) and Rayleigh block fading (RBF) of perturbation scenarios.
Analysis of Generating Handwriting Based on GAN Model With Different Structures

The application of the Generative Adversarial Networks (GANs) model has increased rapidly in the past few years. This paper will demonstrate the training results of different GANs models in different epochs. Since GANs is a structure, they can be applied to different neural network layers such as Convolutional Neural Network (CNN) and multilayer perceptron (MLP). Different neural network layers are constructed and trained separately to compare their performance and efficiency in this experiment. To achieve this goal, all models perform the same task, generating handwritten numbers. In the experiment, three GANs models are tested. Two of them are based on MLP. They use the same structure and the same number of units. The only difference is the activation function. One uses the sigmoid function, and the other uses the LeakyRelu function. The last model is based on CNN. Specifically, the GANs model using Convolutional layers is called DCGAN. All models are constructed using TensorFlow, each of them was trained for 10, 20, 40, 80, 120, and 160 epochs. The generated images and loss value are recorded in all cases of the previously mentioned epochs of training. The experimental results demonstrated that Compared with the GANs model based on MLP, DCGAN has higher training efficiency and better image quality. Different activation functions can also affect the efficiency of the GANs model.
Competence Compliance in the Formation of a Professional Portrait of a Future Specialist of the Communication Sphere

The article discusses an urgent problem – the formation of a professional portrait of a university graduate in the field of communications and its compliance with the needs of the modern labour market. The purpose of the article is to present an analysis of the of the modern labour market requirements and the results of compliance control in the formation of a professional portrait of a future communication specialist. The definition of the compliance system and competency-based compliance is given. The challenges and main trends of the labour market have been identified. The requirements of employers for the competencies and skills of hard skills and soft skills are discussed, it has been proved that soft skills play a key role in the employment of specialists today. A foresight study of positions, professions and specializations in the field of communication was carried out and a forecast of the necessary competencies for them was made. The fundamental skills and competencies that determine the professional portrait of a specialist in the field of communication are highlighted. The competence-based compliance control procedure revealed weaknesses, threats, risks and problems that hinder the formation of a professional portrait of a future communication specialist.
Improved Fall Detection Model on GRU Using PoseNet

This paper investigate an improved detection method that estimates the acceleration of the head and shoulder key point position and position change using the skeleton key point information extracted using PoseNet from the image obtained from the low-cost 2D RGB camera, and improves the accuracy of fall judgment. This paper propose a fall detection method based on the post-fall characteristics of the post-fall, the speed of changes in the main point of the human body, and the change in the width and height ratio of the body's bounding box. The public data set was used to extract human skeletal features and train deep learning, GRU, and as a result of experiments, this paper find the following feature extraction methods. High classification accuracy can be achieved, and the proposed method showed a 99.8% fall detection success rate more effectively than the conventional method using raw skeletal data.
Optimization-based Maneuver Planning for a Tractor-Trailer Vehicle in Complex Environments using Safe Travel Corridors

A solution for a tractor-trailer vehicle's generic maneuver planning task can be introduced by an optimal control problem (OCP). However, the curse of dimensionality is excited along with the OCP solution due to the collision-avoidance constraints in a large scale. The collision-avoidance conditions are weakened by simply constructing a corridor along a homotopically guiding route such that the vehicle's maneuvers are safely separated from obstacles. This approach is motivated by the safe flight corridor (SFC) applied for path planning of unmanned aerial vehicle (UAV). But SFC cannot be applied directly to the ground vehicle cases because a tractor-trailer vehicle cannot be modeled as a mass point in a narrow environment. An extension of the SFC is proposed, which requires different bodies of a multi-body vehicle to stay in different safe travel corridors. In this way, a reduced-scale OCP is formulated, and the problem scale becomes irrelevant to the environmental complexity. Simulation results illustrate that near-optimal maneuvers can be derived within less CPU time.
On the Maximum Entropy of a Sum of Independent Discrete Random Variables

Let $ X_1, \ldots, X_n $ be independent random variables taking values in the alphabet $ \{0, 1, \ldots, r\} $, and $ S_n = \sum_{i = 1}^n X_i $. The Shepp--Olkin theorem states that, in the binary case ($ r = 1 $), the Shannon entropy of $ S_n $ is maximized when all the $ X_i $'s are uniformly distributed, i.e., Bernoulli(1/2). In an attempt to generalize this theorem to arbitrary finite alphabets, we obtain a lower bound on the maximum entropy of $ S_n $ and prove that it is tight in several special cases. In addition to these special cases, an argument is presented supporting the conjecture that the bound represents the optimal value for all $ n, r $, i.e., that $ H(S_n) $ is maximized when $ X_1, \ldots, X_{n-1} $ are uniformly distributed over $ \{0, r\} $, while the probability mass function of $ X_n $ is a mixture (with explicitly defined non-zero weights) of the uniform distributions over $ \{0, r\} $ and $ \{1, \ldots, r-1\} $.
Relation Network and Causal Reasoning for Image Captioning

Image captioning is a cross-modal problem combining computer vision and natural language processing. A typical image captioning model uses a convolutional neural network to extract the features of an image and then uses an Long Short-Term Memory network to transform the representations of the features. However, this method has problems such as not including high-level semantics in the visual network and exposure bias in the language network. To overcome these problems, this paper proposes a novel image captioning model that combines relationship-aware and reinforcement learning. First, we design a relational awareness network as the visual network to mine the latent relationships between objects in an image. Then, a context semantic relational network is proposed to improve the accuracy of image captioning. The context semantic network can generate feature representations for arbitrary pixel positions in an image without association with any specific visual concepts. Subsequently, the high-level context semantics are used as external knowledge to guide the language network in generating sentences. Finally, a policy gradient training algorithm is designed to simplify the state value function in reinforcement learning. We have verified the effectiveness of the model on the MS-COCO and Flickr 30K datasets. The experimental results show that the model proposed in this paper achieves state-of-the-art results.
Research on the Typical Application of Knowledge Graph in Power Marketing

On the premise of introducing the knowledge graph and its application in the daily scenarios of power grids, and building and displaying the knowledge graph for specific scenarios, this study intuitively shows the correlation of each link in each scenario and discovers the corresponding problems. Reasonable suggestions are accordingly made to improve the daily work patterns, enhance the service quality of each link, and boost the refinement of the management of daily affairs. Through the application of knowledge graph in the typical daily scenarios in power marketing, this paper aims to form a panoramic knowledge graph of the power grids on the basis of smart brain among all businesses and organizations in the near future to promote the intelligent operation of the power grids [1].
Analysis of Neural Style Transfer Based on Generative Adversarial Network

The goal of neural style transfer is to transform images by the deep learning method, such as changing oil paintings into sketch-style images. The Generative Adversarial Network (GAN) has made remarkable achievements in neural style transfer in recent years. At first, this paper introduces three typical neural style transfer methods, including StyleGAN, StarGAN, and Transparent Latent GAN (TL-GAN). Then, we discuss the advantages and disadvantages of these models, including the quality of the feature axis, the scale, and the model's interpretability. In addition, as the core of this paper, we put forward innovative improvements to the above models, including how to fully exploit the advantages of the above three models to derive a better style conversion model.
Language-guided Multi-Modal Fusion for Video Action Recognition

A recent study [30] has found that training a multi-modal network often produces a network that has not learned the proper parameters for video action recognition. These multi-modal network models perform normally during training but fall short to its single modality counter-part when testing. The main cause for this performance drop could be two-fold. First, conventional methods use a poor fusion mechanism, where each modality is trained separately and then simply combine together (e.g., late feature fusion). Second, collecting videos is much more expensive than images. The insufficient video data can hardly provide support for training a multi-modal network that has a larger and more complex weight space.In this paper, we proposed the Language-guided Multi-Modal Fusion to address the above poor fusion problem. A sophisticatedly designed bi-modal video encoder is used to fuse audio and visual signal to generate a finer video representation. To ensure the over-fitting can be avoid, we use a language-guided contrastive learning to largely augment the video data to support the learning of multi-modal network. On a large-scale benchmark video dataset, the proposed method successfully elevates the accuracy of video action recognition.
Research on Teaching Methods of Teachers’ Ideological Education Based on Large Data

The ideal form of modern education is one that is personalized toward the receiving student’s aptitude, as derived from a large body of data. The most typical feature of large data is the complexity of data and the huge amount of data space. This paper analyses the data mining technology used to help optimize and execute the use of personalized education online to obtain useful information as well as gather data from real life situations. Based on the research of data set segmentation, the optimization of the key value for simplification is used to improve the efficiency of parallel computing. This paper analyses the characteristics of large data sets derived from digital personalized education, and data set processing for information obtained from user course frequency data, to improve the accuracy of mining association rules.
Narrative Review of Game AI 2000 Onwards and Future Research Directions

The purpose of the chapter is to structure and synthesize the research findings in Gaming AI till date by reviewing the research articles published in high citation, indexed, and peer reviewed journals. Eighteen articles were extracted through systematic process of search and exclusion and inclusion criteria adopted for the study. The selected papers were categorized according to the simulation for AI and AI-based simulation model, and AI applications from literature was reviewed, and conclusions were drawn. Research in Gaming AI is fragmented and unrelated as regards geographical, methodological, and disciplinary aspects. Gaming AI is an emerging research area which is gaining interest in recent years though very little quantitative or qualitative research has been undertaken till date. Recommendations on future research directions were made.
Aspect Level Sentiment Classification Based on Viewpoint Information Unit

Aspect level sentiment classification is the process of discriminating the sentiment orientation of different aspects expressed in the text. Due to the non-standardization of Chinese comments, there are different levels of noise in the text, which affects the classification results of aspect level sentiment analysis. In this paper, an Aspect level sentiment classification method based on evaluation unit is proposed. Firstly, based on the results of part of speech analysis, dependency syntax analysis and semantic dependency analysis are used to obtain the relationships between words, so as to make relevant rules for viewpoint information recognition. Then, the redundant text is deleted by filtering the hypothetical and declarative statements of the key sentences, and the method of constructing the evaluation collocation table combined with the structure diagram of the comment object is completed to identify the implicit evaluation units. Finally, aspect - level sentiment classification is achieved based on sentiment dictionary.
Digital Transformation Impact in Security and Privacy

Emerging technologies are changing the way people live and how businesses work. There is an exponential growth of data that is leading to the rise of new developments that allow companies to get actionable insights and improve their business model. This is what digital transformation is about. It refers to the process of involving new technologies to create new business processes or change existing ones. Digital transformation can be very beneficial for a company as one of its primary goals is to enhance customer engagement. Nowadays, consumers are expecting personalized experiences from companies. Therefore, it is essential for companies to use technology to innovate their processes and analyze the vast amount of data that is generated daily so they can meet customers’ expectations. However, as more data is generated and new technologies are evolved, risks and threads are becoming a concern for both businesses and consumers. Security and privacy are key factors companies need to consider when implementing technologies that could potentially affect the consumer. In order to succeed and avoid big cost implications, measures that ensure the security of data are necessary. This paper addresses the benefits and drawbacks of digital transformation in business, primarily focusing on the risks that come with it, such as privacy and security. It also explains how new technology is able to solve these problem and new trends that would be interesting to do research on in future.
Question Rewriting for Open-Domain Conversational QA

Open-domain conversational QA (ODCQA) calls for effective question rewriting (QR), as the questions in a conversation typically lack proper context for the QA model to interpret. In this paper, we compare two types of QR approaches, generative and expansive QR, in end-to-end ODCQA systems with recently released QReCC and OR-QuAC benchmarks. While it is common practice to apply the same QR approach for both the retriever and the reader in the QA system, our results show such strategy is generally suboptimal and suggest expansive QR is better for the sparse retriever and generative QR is better for the reader. Furthermore, while conversation history modeling with dense representations outperforms QR, we show the advantages to apply both jointly, as QR boosts the performance especially when limited history turns are considered.
An accurate foreground moving object detection based on segmentation techniques and optimal classifier

Abstract In video surveillance schemes, the motion object detection plays a significant role. To subtract the object background, a segmentation technique based on feature extraction is utilized in which the change in the training rate makes an alteration in the background. Thereafter, the extracted features are trained by using the self‐organizing map (SOM) network in which the weight parameters in the network is optimized with the help of artificial bee colony (ABC) optimization algorithm, so, the proposed methodology is named as HSOM‐ABC technique. This methodology is carried out to perform the classification process in this research. Initially, the whole dataset is preprocessed with the help of grayscale conversion method which converts the original image into grayscale color. After this, fuzzy c‐means clustering is applied to perform the segmentation process and this method divides the foreground and background parts efficiently. Then, feature extraction is done with the help of local binary pattern method which extract the relevant features from the segmented image. Finally, HSOM‐ABC method is proposed to accurate classification process. Hence, the moving objects are identified by categorizing the background and foreground images. MatLab platform is chosen for the proposed work simulation and the performance is evaluated by means of different parameters and it is compared with new existing approaches. Experimental outcomes show that the proposed strategy achieves higher precision value than any other existing methods.
From Community Search to Community Understanding

In this demo, we present an online multi-modal community query engine (MQE) on Alibaba's billion-scale heterogeneous network. MQE has two distinct features in comparison with existing community query engines. Firstly, MQE supports multimodal community search on heterogeneous graphs with keyword and image queries. Secondly, to facilitate community understanding in real business scenarios, MQE generates natural language descriptions for the retrieved community in combination with other useful demographic information. The distinct features of MQE benefit many downstream applications in Alibaba's e-commerce platform like recommendation. Our experiments confirm the effectiveness and efficiency of MQE on graphs with billions of edges.