Reviewerly/exHarmony huggingface dataset:
A novel Deep Learning Scheme for Cooperative Task Allocation of Large-scale UAVs

Task allocation of large-scale unmanned aircraft vehicles (UAVs) is an important guarantee for UAVs to complete scheduled missions. In this study, a knowledge-based fast task allocation method is proposed for large-scale UAVs cooperative task allocation problem. Firstly, according to the multi-UAVs mission requirement, the large-scale UAVs task allocation mathematical model is established. Secondly, A knowledge-based fast cooperative task allocation (KB-FCTA) method utilizing deep learning method is presented and the database for KB-FCTA is obtained by discrete particle-GuoTao-simulated annealing (DPSO-GT-SA) algorithm. Finally, the proposed method is evaluated through 10-cross-validation method and simulation results show the rapidity and effectiveness of proposed algorithm.
Learning Rate-Free Latent Factor Analysis via PSO

With the explosive growth of the Internet, data generated by numerous industrial applications grows exponentially. Therefore, it is difficult for people to obtain useful information from big data, which contain a wealth of knowledge and are high-dimensional and sparse (HiDS) [1–5], e.g., node interaction in sensor networks [6–8], user-service invoking in cloud computing [9–15], protein interaction in biological information [16–18], user interactions in social networks service systems [19–21], and user-item preferences in recommender systems [22–25].
Flexible XR Prototyping – A Sports Spectating Example

Extended Reality (XR) prototyping is a useful way that has the potential to assist the AR application development process. It allows for off-site development and evaluation in cases where on-site access is challenging or even impossible. In this work, we summarize our Flexible XR Prototyping framework, showing the different phases and considerations needed for an improved and more effortless XR prototyping experience. We then show how this can be used for the example use case of AR sports spectating in a stadium and provide some examples of the different prototypes developed for an on-site AR sports spectating application. Our goal is to share our own experience in AR prototyping and to spark discussion on the XR prototyping process.
Unified Addressing Algorithm for Tasks in Cloud Control Systems

With the rapid development of cloud computing and control theory, a new paradigm of networked control systems named cloud control systems has been proposed to satisfy the requirements of large-scale and complex applications. It is critical to manage large-scale tasks with personalized needs on the large number of heterogeneous devices. However, current TCP/IP-based addressing approach does not consider the runtime constraints of tasks, resulting highly concurrent service search, and communication between tasks will become the system bottleneck. In this paper, we propose a task property encoding method which takes multi-dimensional properties into consideration. Based on the proposed encoding method, we design a uniform addressing method for tasks in the system. Finally, we experimentally demonstrate the effectiveness of task property encoding, and the unified addressing method for tasks is more effective than other methods in the case of high task concurrency.
Dual-Tuning: Joint Prototype Transfer and Structure Regularization for Compatible Feature Learning

Visual retrieval system faces frequent model update and deployment. It is a heavy workload to re-extract features of the whole database every time. Feature compatibility enables the learned new visual features to be directly compared with the old features stored in the database. In this way, when updating the deployed model, we can bypass the inflexible and time-consuming feature re-extraction process. However, the old feature space that needs to be compatible is not ideal and faces outlier samples. Besides, the new and old models may be supervised by different losses, which will further causes distribution discrepancy problem between these two feature spaces. In this article, we propose a global optimization Dual-Tuning method to obtain feature compatibility against different networks and losses. A feature-level prototype loss is proposed to explicitly align two types of embedding features, by transferring global prototype information. Furthermore, we design a component-level mutual structural regularization to implicitly optimize the feature intrinsic structure. Experiments are conducted on six datasets, including person ReID datasets, face recognition datasets, and million-scale ImageNet and Place365. Experimental results demonstrate that our Dual-Tuning is able to obtain feature compatibility without sacrificing performance.
The Teaching Methods in the Integration of AI with University Computers in the Context of New Engineering Construction

To meet the demands of the “new engineering” construction, the problems of the existing computer science course should be paid attention, such as out of dated teaching methods and content, lack of new technologies applied in the experiments, etc. This paper analyzes the challenges of introducing AI(AI) into university computer courses, from teaching objectives, teaching content and teaching mode to give an empirical evidence. The whole process of teaching is introduced in various aspects. Assessment methods and the final evaluation is explained through questionnaires, which provides a useful reference for the further reform of university computer courses and the training of new engineering talents.
Hardware Implementation of High-Performance Polynomial Multiplication for KEM Saber

Recent advances in quantum computing have initiated a new round of cryptosystem innovation as the existing public-key cryptosystems are proven to be vulnerable to quantum attacks. Several types of cryptographic algorithms have been proposed for possible post-quantum cryptography (PQC) candidates and the lattice-based key encapsulation mechanism (KEM) Saber is one of the most promising algorithms. Noticing that the polynomial multiplication over ring is the key arithmetic operation of KEM Saber, in this paper, we propose a novel strategy for efficient implementation of polynomial multiplication on the hardware platform. First of all, we present the proposed mathematical derivation process for polynomial multiplication. Then, the proposed hardware structure is provided. Finally, field-programmable gate array (FPGA) based implementation results are obtained, and it is shown that the proposed design has better performance than the existing ones. The proposed polynomial multiplication can be further deployed to construct efficient hardware cryptoprocessors for KEM Saber.
Documentation-driven GUI development for integration of image processing libraries

A graphical integrated development environment (IDE) for computer vision applications allows developing solutions by composing graphical widgets that represent operators of a computer vision library. A challenge in developing such IDE is the development of a graphical interface for each operator in the library, which is a slow and repetitive task. In this paper, we propose to generate a specific graphical widget editor for the input parameters of each operator, based directly on the library documentation. Our approach allows reducing significantly the development time of an IDE. The only assumption of the proposed approach is that the documentation has a structured format. We validated our approach by integrating the computer vision library Halcon in an IDE, using only its HTML documentation.
Multi-Domain Network Traffic Analysis using Machine Learning and Deep Learning Techniques

Recent heterogeneous computing facilities and data explosion introduce challenges in network traffic analysis and demand intelligence-based approaches to ensure cyber security and the protection of online digital services. Researchers have been proposing various machine and deep learning approaches for network traffic analysis in different problem domains. However, it is also crucial to understand how these algorithms perform across the different domains. Hence in this research work we extend an analysis of diverse machine learning and deep learning techniques across three different problem domains: DDoS attack detection, Malicious URL detection and Tor traffic classification. We employ three publicly available datasets to train eight different machine learning and six deep learning models for both multi-class and binary classification in our comparative study. Our experiments show that Random Forest achieved superior performance compared to other machine learning models with F-measure of 92% for multi-class traffic classification and 100% for binary classification problems. For the deep learning models, Autoencoder with Random Forest achieved superior performance with an F-measure of 89% and 100% for multi-class and binary problems respectively.
Maturité de la stratégie numérique des entreprises, développement financier et inégalités de revenus : étude empirique sur un panel de 149 pays

The purpose of this paper is to study the impact of Digital Business Strategy (DBS) on income inequality. This study investigates how DBS complements financial development in order to influence income inequality in a large panel of 149 countries relying on the GMM estimations for the period 2012 to 2016. Results show that digitalisation helps reduce inequality across countries that belong to the high and the medium DBS maturity levels. This paper highlights the importance of building a mature DBS both for enhancing the positive effects of technology innovation and for promoting financial development as a solution to income inequality.
Generating Synthetic Styled Chu Nom Characters

Images of historical Vietnamese steles allow historians to discover invaluable information regarding the past of the country, especially about the life of people in rural villages. Due to the sheer amount of available stone engravings and their diverseness, manual examination is difficult and time-consuming. Therefore, automatic document analysis methods based on machine learning could immensely facilitate this laborious work. However, creating ground truth for machine learning is also complex and time-consuming for human experts, which is why synthetic training samples greatly support learning while reducing human effort. In particular, they can be used to train deep neural networks for character detection and recognition. In this paper, we present a method for creating synthetic engravings and use it to create a new database composed of 26,901 synthetic Chu Nom characters in 21 different styles. Using a machine learning model for unpaired image-to-image translation, our approach is annotation-free, i.e. there is no need for human experts to label character images. A user study demonstrates that the synthetic engravings look realistic to the human eye.
Key stream cipher based on coloured petri nets

In our research, we present a method to build a private key cryptosystem, this method is based on the dynamic priority coloured petri net to produced complex long key sequence. In our research, there are two parts, the first one produced random number based on four colored petri nets which have the same design and one ordinary petri net , all petri nets is combined in such a way that generate long nonlinear random key that can be used in encryption process. The same petri nets model is used in both encryption/decryption algorithm and received the same input vector to produce the same output, this input vector must be shared by secured channel between sender and receiver. The key generated is more complex and tested by cryptography randomness tests. In the second part, the plaintext has been converted to gray code and permutated in such a way to increase the complexity of attack it. Finally, the permutated plaintext is combined with the key by exclusive or operation to produce cipher text.
Psitrum and Universal Simulation of Quantum Computers

Quantum computation is a radical new candidate for a technology that is capable to make a paradigm shift in information processing. However, current promising devices are limited in performance due to many decoherence mechanisms. Quantum computer simulators are thus important for understanding and solving existing problems of the current noisy intermediate-scale quantum (NISQ) processors. In this work, we present Psitrum – a universal gate-model based quantum computer simulator implemented on classical hardware. The simulator allows to emulate and debug quantum algorithms in form of quantum circuits for many applications with the choice of adding quantum noise that limit coherence of quantum circuits. Psitrum allows to keep track of quantum operations and density matrices, and provides variety of unique visualisation tools. The simulator allows to trace out all possible quantum states at each stage M of an N-qubit implemented quantum circuit. The design of Psitrum is flexible and allows users later to add their special quantum gates and variety of noise modules.
Analytics of Emotions

Emotion analytics helps the instructor analyze the learner’s responses to their emotional and cognitive states with automated eye tracking and face recognition methods. It helps us identify students’ knowledge and how they answer the questions. If a student is frustrated, confused, or distracted includes non-cognitive aspects. Here teachers who are experts in responding to the learners based on their emotions and dispositions with the computer-based cognitive tutoring systems in classroom teaching. This chapter discusses the ubiquity and importance of emotions in learning.
An Intelligent Method for Determining the Mental Health of University Students Based on IoT Technology

The mental health of university students not only affects their own development, but also affects campus stability and even social harmony, so the mental health of university students has attracted widespread attention in the field of education, and universities are constantly working to improve the mental health education of university students. This is why psychological intervention models for university students have been studied in depth and in detail. The development of modern Internet of Things technology has made the process of information analysis more scientific and regular, so this study will also use data mining technology as an entry point to analyse the intelligent method of determining the mental health of university students.
Early Prediction of Health Disease for Long - Short Term Memory using AdaGrad Model

Diagnoses codes can be advantageous to decision support systems, which try to help coders by recommending a pertinent subset of probable codes to choose from. These systems are employed as a billing mechanism in the Electronic Health Record. Automatic International Classification of Diseases (ICD) code assignment is regarded as a challenging task in the field of multi-label classification due to the broad range of potential labels and length of patient records. The baseline for autonomous ICD code assignment presented in this research uses Long Short Term Memory (LSTM) in conjunction with an AdaGrad model for gradient-based optimization. As the parameters are linked to often occurring features, the model adjusts the learning rate to the parameters that execute smaller updates with low learning rates. With high learning rates for parameters connected to infrequent features, the larger updates improve. The proposed method obtained precision of 99.0% and recall of 90.8 % that was better compared to existing ANN models that obtained precision of 71.2% and recall of 66.8%.
Concurrent Credit Assignment for Data-efficient Reinforcement Learning

The capacity to widely sample the state and action spaces is a key ingredient toward building effective reinforcement learning algorithms. The method presented in this paper relies on an occupancy model, that is the empirical distribution of the states encountered by the agent under a given policy, that is its “domain of operation”. Then, under a uniform occupancy prior assumption, an evidence lower bound on the parameters of the policy provides a way to express a balance between two concurrent tendencies, namely the widening of the occupancy space and the maximization of the rewards, reminding of the classical exploration/exploitation trade-off. During training, both the policy and the occupancy model are updated as the exploration progresses, and that new states are undisclosed during the course of the training. Implemented on an actor-critic off-policy on classic continuous action benchmarks, this approach is shown to provide significant increase in the sampling efficacy, that is reflected in a reduced training time and higher returns, in both the dense and the sparse rewards cases.
Research and Application of Intelligent Community Based on Naive Bayes

Community service has become an indispensable source of service in people's life, but the traditional way of service can't meet people's fast pace of life. Therefore, smart community (smart city) appears in people's vision, which can greatly improve the quality of our life and reduce the inconvenient factors that life may bring. The smart community designed and studied in this paper is divided into five functional modules: user login and registration function, function selection, personnel management, vehicle management, health management module. Among them, the login function, function selection, personnel management module is to interact with the database, to achieve the transmission of information. The health management module is based on the naive Bayes algorithm to classify the safety level of COVID-19, a currently popular influenza virus, to realize the health monitoring of community personnel and prevent the continuous spread of Novel Coronavirus. The intelligent community can ensure the integrity of data and information, simple operation, perfect function and superior performance. At the same time, the intelligent community combined with the naive Bayes algorithm for health management design, embodies the development principle of intelligent community, people-oriented.
Adaptive Similarity-Aware Hyperparameter Tuners for Classification Tasks

With the success of deep learning in recent years, lots of different AI models have been applied to the real world. At the same time, how to train a model with good performance becomes a problem people have to face. One of the most important things is hyperparameter tuning since it determines the setting of training flow. However, most of the conventional hyperparameter algorithms are inefficient, because they usually search from scratch for each new task, and that’s why they require large search trials to find a good combination of hyperparameters. In this paper, we present a systematic hyperparameter tuning framework which utilizes prior knowledge with a suggestion algorithm and an adaptive controller to improve its efficiency rather than search from scratch for each task. In this way, our proposed method can achieve a better performance with the same budget. In the experiments, we applied our methods to tens of popular datasets, and the results show that our proposed methods can outperform than other approaches.
Tawa Pukllay Proof: New Method for Solving Arithmetic Operations with The Inca Yupana Using Pattern Recognition and Parallelism

Yupana is an Inca device used for arithmetic operations. This article describes a new arithmetical system: Tawa Pukllay (TP), where arithmetic operations do not require mental calculations: no carries, no borrows, no memorization of multiplication tables, nor trial and error procedures for divisions. Instead, user recognizes patterns and makes predefined movements to perform the four basic arithmetic operations very quickly; moreover, the result of the operation can be reached by multiple paths and in parallel, allowing each user to create his own strategies. This paper proves with mathematical rigor that TP produces correct numerical results.
Mobile Sensors in Healthcare: Technical, Ethical, and Medical Aspects

Mobile sensors play an increasing role in healthcare, although it is not always clear yet what they can reliably be used for. While some specialties in medicine like cardiology have already integrated apps and smartwatches in their international guidelines, others are still struggling to find infrastructures that are compliant with healthcare standards. The main concerns are data quality and data privacy, which are both handled with different requirements in the consumer market than they are in healthcare. Specific adjustments will make mobile sensors an important part of future healthcare.
Geometric lexical representative perspectives: The impact of threshold values through #LancsBox software

This paper is an investigation of the impact of threshold values in geometric representations of lexical plots. The study uses MI-scores to test lexical representations in the Household Building Corpus (KPMR) data. MI-scores are a form of collocation of the assessment of lexical co-occurrence with values of > 3.0. However, changes in threshold values can provide an additional representative value to the nodes involved. The findings show that the threshold effect can contribute to lexical presence such as hapaxes as lexical density. Rational lexical expressions can be evaluated and viewed through the impact of its lexical wealth, in addition to examining the capabilities of #LancsBox 4.5 as a machine learning processer.
Monocular Depth Estimation Using Cues Inspired by Biological Vision Systems

Monocular depth estimation (MDE) aims to transform an RGB image of a scene into a pixelwise depth map from the same camera view. It is fundamentally ill-posed due to missing information: any single image can have been taken from many possible 3D scenes. Part of the MDE task is, therefore, to learn which visual cues in the image can be used for depth estimation, and how. With training data limited by cost of annotation or network capacity limited by computational power, this is challenging.In this work we demonstrate that explicitly injecting visual cue information into the model is beneficial for depth estimation. Following research into biological vision systems, we focus on semantic information and prior knowledge of object sizes and their relations, to emulate the biological cues of relative size, familiar size, and absolute size. We use state-of-the-art semantic and instance segmentation models to provide external information, and exploit language embeddings to encode relational information between classes. We also provide a prior on the average real-world size of objects. This external information overcomes the limitation in data availability, and ensures that the limited capacity of a given network is focused on known-helpful cues, therefore improving performance. We experimentally validate our hypothesis and evaluate the proposed model on the widely used NYUD2 indoor depth estimation benchmark. The results show improvements in depth prediction when the semantic information, size prior and instance size are explicitly provided along with the RGB images, and our method can be easily adapted to any depth estimation system.