Reviewerly/exHarmony huggingface dataset:
Vehicle Detection in Aerial Images Based on 3D Depth Maps and Deep Neural Networks

Object detection in aerial images, particularly of vehicles, is highly important in remote sensing applications including traffic management, urban planning, parking space utilization, surveillance, and search and rescue.In this article, we investigate the ability of three-dimensional (3D) feature maps to improve the performance of deep neural network (DNN) for vehicle detection.First, we propose a DNN based on YOLOv3 with various base networks, including SqueezeNet, We assessed the base networks and their performance in combination with YOLOv3 on efficiency, processing time, and the memory that each architecture required.In the second part, 3D depth maps were generated using pairs of aerial images and their parallax displacement.Next, a fully connected neural network (fcNN) was trained on 3D feature maps of trucks, semi-trailers and trailers.A cascade of these networks was then proposed to detect vehicles in aerial images.Upon the DNN detecting a region, coordinates and confidence levels were used to extract the corresponding 3D features.The fcNN used 3D features as the input to improve the DNN performance.The data set used in this work was acquired from numerous flights of an unmanned aerial vehicle (UAV) across two industrial harbors over two years.The experimental results show that 3D features improved the precision of DNNs from 88.23 % to 96.43 % and from 97.10 % to 100 % when using DNN confidence thresholds of 0.01 and 0.05, respectively.Accordingly, the proposed system was able to successfully remove 72.22 % to 100 % of false positives from the DNN outputs.These results indicate the importance of 3D features utilization to improve object detection in aerial images for future research.
Blockchain Governance: What We Can Learn from the Economics of Corporate Governance

Understanding the complexities of blockchain governance is urgent.The aim of this paper is to draw on other theories of governance to provide insight into the design of blockchain governance mechanisms.We define blockchain governance as the process by which stakeholders (those who are affected by and can affect the network) exercise bargaining powers over the network.Major considerations include the definition of stakeholders, how the consensus mechanism distributes endogenous bargaining power between those stakeholders, the interaction of exogenous governance mechanisms and institutional frameworks, and the needs for bootstrapping networks.We propose that on-chain governance models can only be partially utilised because of the existence of implicit contracts that embed expectations of return among diverse stakeholders.
Using TeX Markup Language for 3D and 2D Geological Plotting

Abstract The paper presents technical application of TeX high-level, descriptive markup language for processing geological dataset from soil laboratory. Geotechnical measurements included equivalent soil cohesion, absolute and absolute deformation index, soil compressibility coefficient by time of immersion depth, exposure time to compressive strength to samples and physical and mechanical properties (humidity, density). Dataset was received from laboratory based experimental tests of the physical and mechanical properties of soils. Data were converted to csv table and processed by LaTeX. Methodology is based on LaTeX packages: {tikz}, {tikz-3dplot}, {tikzpicture}, {pgfplot}, {filecontetns}, {spy} for 3D plotting showing correlation in variables and descriptive statistical analysis based on the data array processing. Results demonstrated LaTeX scripts and graphics: 2D and 3D scatterplots, ternaries, bar charts, boxplots, zooming techniques detailing fragment of the plot, flowchart. Research novelty consists in technical approach of TeX language application for geo- logical data processing and graphical visualization. Engineering graphics by TeX was demonstrated with screenshots of the codes used for plotting.
Optimization of PBFT Algorithm Based on Improved C4.5

Aiming at the problems of PBFT algorithm of consortium blockchain, such as high communication overhead, low consensus efficiency, and random selection of leader nodes, an optimized algorithm of PBFT is proposed. Firstly, the algorithm improves C4.5 and introduces weighted average information gain to overcome the mutual influence between conditional attributes and improve the classification accuracy. Then classify the nodes with improved C4.5, and select the ones with a high trust level to form the main consensus group. Finally, the integral voting mechanism is introduced to determine the leader node. Experimental results show that compared with traditional PBFT algorithm, the communication times of the improved PBFT algorithm are reduced greatly, which effectively alleviates the problem that the number of nodes in traditional PBFT algorithm increases and the traffic volume is too large, and significantly reduces the probability of the leader node doing evil and improves the consensus efficiency.
A trusted consensus fusion scheme for decentralized collaborated learning in massive IoT domain

In a massive IoT systems, large amount of data are collected and stored in clouds, edge devices, and terminals, but the data are mostly isolated. For many new demands of various intelligent applications, self-organized collaborated learning on those data to achieve group decisions has been a new trend. However, in order to reach the goal of group decisions, trust problems on data fusion and model fusion should be solved since the participants may not be trusted. We propose a consistent and trust fusion method with the consortium chain to reach a consensus, and complete the self-organized trusted decentralized collaborated learning. In each consensus process, consensus candidates check others’ trust levels to ensure that they tends to fuse consensus with users with high trust, where the trust levels are evaluated by scores according to their historical behaviors in the past consensus process and stored in the public ledger of blockchain. A trust rewards and punishments method is designed to realize trust incentive consensus, the candidates with higher trust levels have more rights and reputation in the consensus. Simulation results and security analysis show that the method can effectively defend malicious users and data, improve the trust perception performance of the whole federated learning network, and make the federated learning more trusted and stable.
Implementation of Chatbot in Online Commerce, and Open Innovation

This study describes the chatbot journey and focuses on its implementation within the digital marketing strategy in the first part of a company’s sales funnel. The main goal was to apply a chatbot via Facebook Messenger supported by the ManyChat platform to increase the number of leads, comparing the chatbot with the previous strategy used by the company to obtain contact information. This research work takes a step further and shows that implementing a chatbot through the ManyChat platform by a company that markets online has a positive impact on the capturing of leads, as opposed to the results obtained by authors such as Luo et al. and Leung et al. A chatbot platform used with the intention of obtaining leads seems to be an agile and powerful tool; in fact; the main conclusion of this work is that including this method can be one of the main axes of obtaining information about consumers with the aim of performing marketing actions in a two-way communication that facilitates sales by companies.
Compositional Generalization for Neural Semantic Parsing via Span-level Supervised Attention

We describe a span-level supervised attention loss that improves compositional generalization in semantic parsers.Our approach builds on existing losses that encourage attention maps in neural sequence-to-sequence models to imitate the output of classical word alignment algorithms.Where past work has used word-level alignments, we focus on spans; borrowing ideas from phrase-based machine translation, we align subtrees in semantic parses to spans of input sentences, and encourage neural attention mechanisms to mimic these alignments.This method improves the performance of transformers, RNNs, and structured decoders on three benchmarks of compositional generalization.
A Survey on Adversarial Attack in the Age of Artificial Intelligence

With the rapid evolution of the Internet, the application of artificial intelligence fields is more and more extensive, and the era of AI has come. At the same time, adversarial attacks in the AI field are also frequent. Therefore, the research into adversarial attack security is extremely urgent. An increasing number of researchers are working in this field. We provide a comprehensive review of the theories and methods that enable researchers to enter the field of adversarial attack. This article is according to the “Why? → What? → How?” research line for elaboration. Firstly, we explain the significance of adversarial attack. Then, we introduce the concepts, types, and hazards of adversarial attack. Finally, we review the typical attack algorithms and defense techniques in each application area. Facing the increasingly complex neural network model, this paper focuses on the fields of image, text, and malicious code and focuses on the adversarial attack classifications and methods of these three data types, so that researchers can quickly find their own type of study. At the end of this review, we also raised some discussions and open issues and compared them with other similar reviews.
Towards Domain Invariant Single Image Dehazing

Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should localize and recover affected regions while ensuring consistency between recovered and its neighboring regions. However owing to fixed receptive field of convolutional kernels and non uniform haze distribution, assuring consistency between regions is difficult. In this paper, we utilize an encoder-decoder based network architecture to perform the task of dehazing and integrate an spatially aware channel attention mechanism to enhance features of interest beyond the receptive field of traditional conventional kernels. To ensure performance consistency across diverse range of haze densities, we utilize greedy localized data augmentation mechanism. Synthetic datasets are typically used to ensure a large amount of paired training samples, however the methodology to generate such samples introduces a gap between them and real images while accounting for only uniform haze distribution and overlooking more realistic scenario of non-uniform haze distribution resulting in inferior dehazing performance when evaluated on real datasets. Despite this, the abundance of paired samples within synthetic datasets cannot be ignored. Thus to ensure performance consistency across diverse datasets, we train the proposed network within an adversarial prior-guided framework that relies on a generated image along with its low and high frequency components to determine if properties of dehazed images matches those of ground truth. We preform extensive experiments to validate the dehazing and domain invariance performance of proposed framework across diverse domains and report state-of-the-art (SoTA) results. The source code with pretrained models will be available at https://github.com/PS06/DIDH.
Learning from Miscellaneous Other-Class Words for Few-shot Named Entity Recognition

Few-shot Named Entity Recognition (NER) exploits only a handful of annotations to identify and classify named entity mentions.Prototypical network shows superior performance on few-shot NER.However, existing prototypical methods fail to differentiate rich semantics in other-class words, which will aggravate overfitting under few shot scenario.To address the issue, we propose a novel model, Mining Undefined Classes from Other-class (MUCO), that can automatically induce different undefined classes from the other class to improve few-shot NER.With these extra-labeled undefined classes, our method will improve the discriminative ability of NER classifier and enhance the understanding of predefined classes with stand-by semantic knowledge.Experimental results demonstrate that our model outperforms five state-of-the-art models in both 1shot and 5-shots settings on four NER benchmarks.We will release the code upon acceptance.The source code is released on https: //github.com/shuaiwa16/OtherClassNER.git.
Personalized Adaptive Meta Learning for Cold-start User Preference Prediction

A common challenge in personalized user preference prediction is the cold-start problem. Due to the lack of user-item interactions, directly learning from the new users' log data causes serious over-fitting problem. Recently, many existing studies regard the cold-start personalized preference prediction as a few-shot learning problem, where each user is the task and recommended items are the classes, and the gradient-based meta learning method (MAML) is leveraged to address this challenge. However, in real-world application, the users are not uniformly distributed (i.e., different users may have different browsing history, recommended items, and user profiles. We define the major users as the users in the groups with large numbers of users sharing similar user information, and other users are the minor users), existing MAML approaches tend to fit the major users and ignore the minor users. To address the task-overfitting problem, we propose a novel personalized adaptive meta learning approach to consider both the major and the minor users with three key contributions: 1) We are the first to present a personalized adaptive learning rate meta-learning approach to improve the performance of MAML by focusing on both the major and minor users. 2) To provide better personalized learning rates for each user, we introduce a similarity-based method to find similar users as a reference and a tree-based method to store users' features for fast search. 3) To reduce the memory usage, we design a memory agnostic regularizer to further reduce the space complexity to constant while maintain the performance. Experiments on MovieLens, BookCrossing, and real-world production datasets reveal that our method outperforms the state-of-the-art methods dramatically for both the minor and major users.
Knowledge-aware Zero-Shot Learning: Survey and Perspective

Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a.k.a. side information) has been widely investigated. In this paper we present a literature review towards ZSL in the perspective of external knowledge, where we categorize the external knowledge, review their methods and compare different external knowledge. With the literature review, we further discuss and outlook the role of symbolic knowledge in addressing ZSL and other machine learning sample shortage issues.
Bipartite Cooperative Coevolution for Energy-Aware Coverage Path Planning of UAVs

The coverage path planning of unmanned aerial vehicles (UAVs) is a complex optimization problem in practice, especially for those involving multiple target areas. It is challenging to comprehensively plan the inter-area visiting order and the intra-area coverage paths simultaneously. Due to the battery limitation, usually the task can hardly be finished by a single UAV, and instead a fleet of UAVs are required. In this article, we first formulate an energy-aware multi-UAV multi-area coverage path planning (EM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CPP) model, in order to characterize the practical path planning requirements of UAVs in complex conditions. Subsequently, to accomplish the optimization task, we propose a bipartite cooperative coevolution (BiCC) algorithm that coevolves an inter-area path planning and an intra-area path planning components to obtain good solutions. The basic operators in BiCC, such as the initialization and the reproduction operators, are tailored for the task of EM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CPP. Besides, we also develop a fast heuristic algorithm for EM <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^2$</tex-math></inline-formula> CPP, which is able to produce approximate solutions in a short time. Simulations on real-world datasets validate the good performance of the proposed methods.
A Traceable Online Insurance Claims System Based on Blockchain and Smart Contract Technology

In the current medical insurance claims process, there are problems of low efficiency and complex services. When a patient applies for medical insurance claims, he/she must go to the hospital to apply for a diagnosis certificate and receipt and then send the relevant application documents to the insurance company. The patient will not receive compensation until the company completes the verification with the patient’s hospital. However, we can improve the current dilemma through blockchain technology. Blockchain technology can effectively open up the information channels of the insurance industry and medical institutions, promote industry integration, and enhance the ability of insurance companies to obtain information. In this research, we used blockchain and smart contract technology to make the following contributions to the development of Internet insurance. First, blockchain and smart contract technology can effectively solve the problem of online underwriting. Second, it is conducive to improving supervision. Third, it is conducive to solving risk control problems. Fourth, it is conducive to effective anti-money laundering. The proposed scheme fulfills the following security requirements: mutual authentication of identities, non-repudiation between each of two roles, and other major blockchain-based security requirements. In the event of a dispute, we also proposed an arbitration mechanism to divide responsibilities.
Digital competence across boundaries - beyond a common Nordic model of the digitalisation of K-12 schools?

(2021). Digital competence across boundaries - beyond a common Nordic model of the digitalisation of K-12 schools? Education Inquiry: Vol. 12, Teaching and Teacher Education in the Light of the Digitalized K-12 school - a Nordic Perspective, pp. 317-328.
Feature Importance in Gradient Boosting Trees with Cross-Validation Feature Selection

Gradient Boosting Machines (GBM) are among the go-to algorithms on tabular data, which produce state-of-the-art results in many prediction tasks. Despite its popularity, the GBM framework suffers from a fundamental flaw in its base learners. Specifically, most implementations utilize decision trees that are typically biased towards categorical variables with large cardinalities. The effect of this bias was extensively studied over the years, mostly in terms of predictive performance. In this work, we extend the scope and study the effect of biased base learners on GBM feature importance (FI) measures. We demonstrate that although these implementation demonstrate highly competitive predictive performance, they still, surprisingly, suffer from bias in FI. By utilizing cross-validated (CV) unbiased base learners, we fix this flaw at a relatively low computational cost. We demonstrate the suggested framework in a variety of synthetic and real-world setups, showing a significant improvement in all GBM FI measures while maintaining relatively the same level of prediction accuracy.
CEM: Commonsense-Aware Empathetic Response Generation

A key trait of daily conversations between individuals is the ability to express empathy towards others, and exploring ways to implement empathy is a crucial step towards human-like dialogue systems. Previous approaches on this topic mainly focus on detecting and utilizing the user’s emotion for generating empathetic responses. However, since empathy includes both aspects of affection and cognition, we argue that in addition to identifying the user’s emotion, cognitive understanding of the user’s situation should also be considered. To this end, we propose a novel approach for empathetic response generation, which leverages commonsense to draw more information about the user’s situation and uses this additional information to further enhance the empathy expression in generated responses. We evaluate our approach on EMPATHETICDIALOGUES, which is a widely-used benchmark dataset for empathetic response generation. Empirical results demonstrate that our approach outperforms the baseline models in both automatic and human evaluations and can generate more informative and empathetic responses. Our code is available at https://github.com/Sahandfer/CEM.
CALL-Enhanced L2 Vocabulary Learning: Using Spaced Exposure through CALL to Enhance L2 Vocabulary Retention

CALL- and MALL-enhanced learning applications have dominated the field of second language (L2) learning recently. This study aims to investigate the effect of applying a CALL-enhanced L2 vocabulary learning software program on the L2 vocabulary development of English as Foreign Language (EFL) students. 76 preintermediate EFL students registered at a foreign language school were chosen from a total of 156 students after running an Oxford Quick Placement Test (OQPT). The participants were randomly assigned to two groups: the experimental group (EG = 38) and the control group (CG = 38). A vocabulary test as pretest was administered to all the participants before the treatment. During the treatment, the EG learners were requested to utilize a computer-enhanced flashcard software program on their laptops, mobile phones, or other mobile devices at their discretion. By using the program, they could access and utilize a variety of flashcards on many subject matters such as languages, geography, math, and science as well as construct their own flashcards for multiple practices. The CG, on the other hand, was taught through traditional teaching without any CALL tools available. At the termination of the intervention, the vocabulary test was employed as a posttest to both groups to assess the learners’ vocabulary enhancement. The EG outperformed the CG. Findings have led to the reasonable interpretation that L2 vocabulary learning was more productive when the CALL-enhanced flashcard program was utilized for the learning processes.
Evaluating Data Mining Classification Methods Performance in Internet of Things Applications

The world is passing through the stage of the superiority of science and technology. The impact of this superiority in human life cannot be hidden. There is no doubt that the societies that have acquired information and knowledge are now the ones who rule the world and lead the scene of developed and modern countries. With the development of advanced applications of the concept of the Internet of Things (IOT) in addition to the development of information and communication technologies, it can be used in public life, since IOT technologies have the ability to link physical entities and support interaction with the human element. The data that are generated by IoT is huge data that has a high commercial value also mining of data algorithms can be applied on the IOT to getting the hid data. In this paper, a systematic method is presented to a review the extraction of defined data classification. The latest algorithms of classification must be analyzed to apply on the big data. These algorithms are reviewed and the challenges you will face are discussed.