Reviewerly/exHarmony huggingface dataset:
Reform complementarities and growth: Evidence and mechanisms

Abstract When reforms of different policy areas are said to be complementary, the presence of one reformed area bolsters the effectiveness of reform of the other. We use the five areas of the Economic Freedom of the World (EFW) index over 2000–2017 to test for the impact of reform complementarities on real per capita income growth in up to 131 countries. Using a novel index for complementarity (Braga De Macedo &amp; Oliveira‐Martins, 2008, Econ. Transit.), we find robust evidence that pursuing broader reform packages is associated with an increase in annual growth by about 1.2%. Further analysis shows that the effect of complementarities operates largely through its positive impact on domestic investment.
Using Synthetic Images to Evaluate and Improve Object Detection Neural Network Performance on Aerial Image Datasets

Machine learning-based models for object detection heavily rely on large datasets of labeled images. When models trained on these datasets are applied to Unmanned Aerial Vehicle (UAV) imagery, the problem arises that the conditions under which the training images were created (lighting, altitude, angle) may be different to the UAVs applied conditions, leading to misclassifications. This problem becomes even more pressing in safety critical applications where failures can have huge negative impacts and constitute obstacles for certification of cognitive UAV components. Along a case study on car detection in low-altitude aerial imagery, we show that using, both, artificial and real images for model training has a positive effect on the performance of object detection algorithms when the trained model is applied on images from another domain. Additionally, we show that weak points from object detection neural networks trained on real-world images transfer to synthetic images and that synthetic data can be used to evaluate neural networks trained on real-world data. Since simulated images are easy to create and object labels are inherently given, the presented approaches show a promising direction for scenarios where adequate datasets are difficult to obtain, as well as for the targeted exploration of weak points of object detection algorithms.
Research on College English Teaching Evaluation Based on Neural Network

Aiming at the difficulty in adapting the traditional English teaching evaluation model to the new situation of current education development, a research on college English teaching evaluation based on neural network is proposed. The thesis proposes that based on the neural network, from the perspective of teachers and students, while creating a positive and harmonious learning atmosphere, a college English teaching environment with interactive mechanism is established, and a teaching mode of English interactive mechanism is constructed in it. To obtain a better neural network model, to establish the performance evaluation index of college English teaching, to calculate the weight of the performance evaluation index of English teaching, and to realize the evaluation of college English teaching based on neural network, the experimental results show that the use of this method can significantly improve students’ English scores, and the method has high approximation accuracy.
Statically Fused Smooth Variable Structure Filter for Robust Tracking with Doppler Radar

The smooth variable structure filter (SVSF) provides robust state estimation in case of model uncertainty but requires a linear measurement model and a full-rank measurement matrix. Doppler radar reports target position and range rate in the polar or spherical coordinates without the tangential velocity, leading to a nonlinear and underdetermined measurement model, which does not satisfy the requirements of the standard SVSF. In this paper, we propose a robust nonlinear estimator, named as the statically fused smooth variable structure filter (SFSVSF), for robust target state estimation with Doppler radar. First, the converted position measurement (CPM) is calculated in Cartesian coordinates and a converted Doppler measurement (CDM) is constructed from the product of the range and range rate measurements. Accordingly, the nonlinear Doppler radar measurement model is decomposed into a Cartesian-measurement model and a pseudo-measurement model. Both are linear time-invariant and underdetermined, and can be handled by the generalized SVSF. Thus, two generalized SVSFs, referred to as the CPM-SVSF and the CDM-SVSF, are implemented based on these two sub-models for robust estimations of the Cartesian state and the pseudo state, respectively. The Cartesian-state estimation and the pseudo-state estimation are statically fused by an approximate linear minimum mean square error (LMMSE) estimator for the final estimation of target state. Simulation results show the effectiveness of the SFSVSF algorithm in nonlinear estimation and the advantage in robustness over the EKF in case of target motion uncertainty.
Research on vehicle tracking method based on UAV video

In the road video shooting of unmanned aerial vehicle (UAV), the UAV is in an unconstrained state, and the video picture is jitter and unstable. When tracking multiple vehicles in uav video, identity switching of tracking targets is easy to occur. Based on this, an improved DeepSort vehicle target tracking algorithm based on Yolov5s is proposed. When Yolov5s trains the vehicle detection model, DIOU-NMS is used instead of NMS to remove the redundant target position prediction box. Aiming at the fact that DeepSort pre-trained appearance extraction model did not contain the appearance model of the vehicle, the vehicle appearance model was obtained by using the lightweight ShuffleNet V2 network for vehicle rerecognition training on VeRi data. The improved algorithm is used to evaluate the MOT of UAVDT data sets. The experimental results show that compared with the original algorithm, the tracking identity switching (IDSW) of this experimental method is reduced by 49.1%, MOTA is improved by 3.2% and MOTP is improved by 0.3%.
A Survey on Emerging Cyber Crimes and Their Impact Worldwide

The number of cybercrime cases have increased exponentially in the last couple of years. With each passing year, the cybercrimes become more diverse and cyber criminals become more sophisticated. The explosion of connectivity in addition to growth of the cloud services and internet of things have given cyber criminals more potential targets and discovery of new avenues of attack. Today's cyber criminals target elections, industrial control system, and cause digital wildfires. In the future, they may even target human life. “Global Risks 2018” report reveals that cybercrimes are increasing both in prevalence and disruptive potential and predicts them as one of the top five global risks. This article gives a brief overview of the emerging cybercrimes and their impacts worldwide and solutions to combat this crime.
Classifying Worldwide Standardized Seismograph Network Records Using a Simple Convolution Neural Network

Abstract The U.S. Geological Survey (USGS) maintains an archive of 189,180 digitized scans of analog seismic records from the World-Wide Standardized Seismograph Network (WWSSN). Although these scans have been made public, the archive is too large to manually review, and few researchers have utilized large numbers of these records. To facilitate further research using this historical dataset, we develop a simple convolutional neural network (CNN) that rapidly (∼4.75 s/film chip) classifies scanned film chip images (called “chips,” because they are individually cut segments of 70 mm film) into four categories of “interestingness” to earthquake seismologists based on the presence of earthquakes and other seismic signals in the record: “no interest,” “little interest,” “interest,” and “high interest.” The CNN, dubbed “Seismic Analog Record Network” (SARNet), can identify four types of seismic traces (“no events,” “minor events,” “major events,” and “errors”) in 200 × 200 pixel subcrops with an accuracy of 92% using a confidence threshold of 85%. SARNet then converts 100 random subcrops from each film chip into the overall classification of interestingness. In this task, SARNet performed as well as expert human classifiers in determining the film chip’s overall interest grade. Applying SARNet to 34,000 film chips in the WWSSN archive found that 21% of the images were of “high interest” and had an “indeterminate” rate of only 4%. Thus, the need for the manual review of images was reduced by 79%. Sorting of film chips derived from SARNet will expedite further exploration of the archive of digitized analog seismic records stored at the USGS.
Dehazing Based on Deep Blind Denoising and Dark Channel Prior

Imaging quality is often significantly degraded under hazy weather condition and noise interference. This paper makes an investigation of the dehazing and denoising effects for the single image. The goal is to get high quality image from degraded image by denoising and dehazing. In this paper, we propose an approach to dehazing with noise reduction based on deeply learning. First implement image denoising based on the training data set of the convolutional neural network framework. After obtaining the initial transmittance through the dark channel a priori, soft segmentation of the sky region is performed and the transmittance is optimized. Finally, the smoothing function of the guided filter is used to correct the boundary between the sky area and the non-sky area to restore a haze-free image. Experiments on benchmark images demonstrated that our method achieves superior performance over current state-of-the-art methods, yet keeps efficient and easy to use.
Making Smarter Museums Through New Technologies

Smart technologies have a significant potential to impact people's behaviours thanks to their ability to increase access to different resources and the ability to influence technology-based encounters. The objective of this chapter is to deepen the theme of smart technologies in the museum environment, to describe the elements and the main dimensions capable of impacting both visitors' perception and overall experience. The authors analyze the use of smart technologies to 1) increase people's knowledge and skills in providing intelligent service in cultural heritage and 2) promote the involvement of visitors and improve their experience. Findings show that visitors increasingly lose the role of passive users to play an active role in the co-production of museum experiences. Smart museums lead to cognitive and engaging learning, touching, feeling, and experiencing different emotions, encouraging a return to the museum, inviting to learn, and shaping one's personal experience.
Single Image Video Prediction with Auto-Regressive GANs

In this paper, we introduce an approach for future frames prediction based on a single input image. Our method is able to generate an entire video sequence based on the information contained in the input frame. We adopt an autoregressive approach in our generation process, i.e., the output from each time step is fed as the input to the next step. Unlike other video prediction methods that use "one shot" generation, our method is able to preserve much more details from the input image, while also capturing the critical pixel-level changes between the frames. We overcome the problem of generation quality degradation by introducing a "complementary mask" module in our architecture, and we show that this allows the model to only focus on the generation of the pixels that need to be changed, and to reuse those that should remain static from its previous frame. We empirically validate our methods against various video prediction models on the UT Dallas Dataset, and show that our approach is able to generate high quality realistic video sequences from one static input image. In addition, we also validate the robustness of our method by testing a pre-trained model on the unseen ADFES facial expression dataset. We also provide qualitative results of our model tested on a human action dataset: The Weizmann Action database.
US Cybersecurity Laws and Regulations

This study presents a historic overview of laws and regulations pertaining to cybersecurity issues in the United States. Cybersecurity is a relatively new field and has presented some unique challenges to law enforcement agencies in the last few decades. The author investigates the current state of legal frameworks to provide decision makers with mitigation steps when moving forward. More specifically, this study presents a possible approach to develop and evaluate policies to combat cybercrime. The theoretical lens used to explain offender motivation integrates rational choice, deterrence, and routine activity theories. The current work identifies trends and deficiencies in the existing cybersecurity laws and regulations. Exploring such problems can be a valuable tool to better understand this complex and rapidly developing field in which cybercriminals often rely on loopholes and lack of resources of law enforcement agencies to avoid prosecution.
Construction of an Infinite Cyclic Group Formed by Artificial Differential Neurons

Infinite cyclic groups created by various objects belong to the class to the class basic algebraic structures. In this paper, we construct the infinite cyclic group of differential neurons which are modifications of artificial neurons in analogy to linear ordinary differential operators of the n-th order. We also describe some of their basic properties.
Face Alignment Using a GAN-based Photorealistic Synthetic Dataset

Face-related technology has matured over the past several decades. However, issues such as insufficient real-world training data and the privacy violations or data abuse caused by face applications have also triggered global controversy. For the question: "Can synthetic data be used to introduce novel variations in the real-world data? ". In this paper, we open a new research direction through synthetic datasets. We try to use synthetic datasets to reduce the dependence of the model on the real-world dataset. However, considering the differences between synthetic and real-world data, our work aims to convert the synthetic face images generated by the Face generating middleware 3D model (FaceGen) into more realistic face images for training face alignment algorithms.
Bayesian Inference on the Bimodality of the Generalized von Mises Distribution

Abstract This article introduces Bayesian inference on the bimodality of the generalized von Mises (GvM) distribution for planar directions (Gatto and Jammalamadaka in Stat Methodol 4(3):341–353, 2007). The GvM distribution is a flexible model that can be axial symmetric or asymmetric, unimodal or bimodal. Two inferential approaches are analysed. The first is the test of null hypothesis of bimodality and Bayes factors are obtained. The second approach provides a two-dimensional highest posterior density (HPD) credible set for two parameters relevant to bimodality. Based on the identification of the two-dimensional parametric region associated with bimodality, the inclusion of the HPD credible set in that region allows us to infer on the bimodality of the underlying GvM distribution. A particular implementation of the Metropolis–Hastings algorithm allows for the computation of the Bayes factors and the HPD credible sets. A Monte Carlo study reveals that, whenever the samples are generated under a bimodal GvM, the Bayes factors and the HPD credible sets do clearly confirm the underlying bimodality.
Bidirectional Heuristic Search for Bayesian Network Structure with Ancestral Partition

Exact algorithms for learning optimal Bayesian networks require much more time and are used in small Bayesian networks. This paper adds ancestral partition constraints into the bidirectional heuristic search algorithm based on the order graph. The ancestral partition can be obtained by extracting strongly connected components from possible parent sets. Experiments show that ancestral partition can significantly improve the efficiency and scalability of bidirectional heuristic search. In addition, with ancestral partition constraints, bidirectional heuristic search has better efficiency and can search larger Bayesian networks than state-of-the-art algorithms.
An Enhanced Term Weighted Question Embedding for Visual Question Answering

Visual Question Answering (VQA) is a multi-modal AI-complete task of answering natural language questions about images. Literature solved VQA with a three-phase pipeline: image and question featurisation, multi-modal feature fusion and answer generation or prediction. Most of the works have given attention to the second phase, where multi-modal features get combined ignoring the effect of individual input features. This work investigates VQA’s natural language question embedding phase by proposing a new question featurisation framework based on Supervised Term Weighting (STW) schemes. In addition, two new STW schemes integrating text semantics, qf.cos and tf.rf.sim, have been introduced to boost the framework’s performance. A series of tests on the DAQUAR VQA dataset is used to compare the new system to conventional pre-trained word embedding. Over the past few years, STW schemes have been commonly used in text classification research. In light of this, tests are carried out to verify the effectiveness of the two newly proposed STW schemes in the general text classification task.
An efficient and dynamic data access control framework for IoT

IoT devices collect large amounts of industrial and personal data, which can raise serious privacy and security concerns. Due to storage constraints, IoT devices need to upload the collected data to a third party's cloud server for storage. This situation would severely limit the data owner's ability to manage the data. If the data is uploaded directly, the data owner cannot guarantee that the service provider will not disclose and sell this data, which could lead to a data breach. In this paper, we formalize an encrypted access control framework for large-scale time-series data access control in IoT scenarios based on puncturable attribute-based encryption. The scheme allows data owners to share time-series data collected by IoT devices at a fine-grained level, while ensuring the confidentiality of the data during transmission and storage. The decryption keys can repeatedly be updated to revoke the recipients' ability to decrypt a selected message, or a message created during a specific time period. Through careful protocol design and implementation, the computational efficiency of the proposed scheme is comparable to the state-of-the-art method with the same functionalities. Encryption can be done in about several hundred milli-second, decryption takes only about tens of millisecond and the time required for decryption is constant.
Lightweight blockchain system for resource-constrained IoT devices

Recently, the security and scalability challenges of traditional internet of things systems are emerging as the wide application of IoT in logistics, warehousing, smart home, etc. The traditional IoT systems are built on centralized architecture which is highly vulnerable to the single point of failure and network attacks. Fortunately, the blockchain offers a reliable security solution for IoT systems due to its decentralized architecture, cryptographic schemes, and consensus algorithms. However, there are two main challenges when integrating blockchain technologies into the IoT system: First, IoT devices have limited resource capabilities. Second, an IoT system generally contains massive devices. In this paper, to address the challenges mentioned above, we proposed a lightweight blockchain for resource-constrained IoT devices. Proof of authentication (PoAh) consensus algorithm and DAG-based blockchain architecture were adopted in our proposed system. The performance, power consumption, and resource usage of the proposed blockchain system are verified by numerous experiments. The experimental results show that the proposed system was lightweight and suitable for resource-constrained IoT devices.
TIPTAB: A tangible interactive projection tabletop for virtual experiments

Abstract Virtual experiments are a new way of doing experiments. A visible difference between virtual and real experiments is the interaction mode. Such a difference causes unfamiliarity and induces unnecessary cognitive load and inefficiency during learning. The objective of the research is to reduce the interactive operation difference between virtual and real experiments as much as possible. TIPTAB, a tangible interactive projection tabletop, is proposed for virtual experiments. A depth camera is used to detect tangible objects in the real environment. These objects are printed by three‐dimensional (3D) printers in advance and tracked in real‐time. The tracked 3D poses are then used as inputs for further experiments. Both object textures and scene contents are augmented by projectors, and thus the virtual presentation of digital space is seamlessly registered and fused with physical space. An infrared (IR) camera and an IR transmitter are used to detect finger touch operations on the tabletop. During experimental learning, students can interact without wearing any devices as they do in real experiments, which greatly reduces their interaction burdens. Experimental results show that the proposed method is more efficient compared with other representative interaction methods. Based on the cognitive load theory and the statistical data, TIPTAB has a low cognitive load for students during virtual experiments. Questionnaire survey results also indicate that TIPTAB offers a good learning experience.
Improved Bayesian ridge regression based data missing reconstruction of smart meters

In order to promote the digitalization of the power grid, many smart meters are installed on the distribution network side to monitor the operating status of the power grid and equipment information in an all-round way. Frequent data missing phenomenon will lead to misjudgment in the thematic analysis of abnormal electricity consumption in distribution network. In this paper, a missing data reconstruction method based on Improved Bayesian Ridge Regression (IBRR) is proposed. Regularization methods are added to the posterior distribution estimation of parameters to automatically filter redundant information in massive data, thereby avoiding Overfitting phenomenon of maximum likelihood estimation, and improve model training speed and generalization ability. A mutation processing mechanism is proposed for the abnormal fluctuations in the reconstruction results. The results show that, compared with the traditional method, the proposed method has better reconstruction accuracy, and the reconstruction speed is greatly improved.