Reviewerly/exHarmony huggingface dataset:
Traffic Context Aware Data Augmentation for Rare Object Detection in Autonomous Driving

Detection of rare objects (e.g., traffic cones, traffic barrels and traffic warning triangles) is an important perception task to improve the safety of autonomous driving. Training of such models typically requires a large number of annotated data which is expensive and time consuming to obtain. To address the above problem, an emerging approach is to apply data augmentation to automatically generate cost-free training samples. In this work, we propose a systematic study on simple Copy-Paste data augmentation for rare object detection in autonomous driving. Specifically, local adaptive instance-level image transformation is introduced to generate realistic rare object masks from source domain to the target domain. Moreover, traffic scene context is utilized to guide the placement of masks of rare objects. To this end, our data augmentation generates training data with high quality and realistic characteristics by leveraging both local and global consistency. In addition, we build a new dataset named NM10k consisting 10k training images, 4k validation images and the corresponding labels with a diverse range of scenarios in autonomous driving. Experiments on NM10k show that our method achieves promising results on rare object detection. We also present a thorough study to illustrate the effectiveness of our local-adaptive and global constraints based Copy-Paste data augmentation for rare object detection. The data, development kit and more information of NM10k dataset are available online at: https://nullmax-vision.github.io.
Haze Level Evaluation Using Dark and Bright Channel Prior Information

Haze level evaluation is highly desired in outdoor scene monitoring applications. However, there are relatively few approaches available in this area. In this paper, a novel haze level evaluation strategy for real-world outdoor scenes is presented. The idea is inspired by the utilization of dark and bright channel prior (DBCP) for haze removal. The change between hazy and haze-free scenes in bright channels could serve as a haze level indicator, and we have named it DBCP-I. The variation of contrast between dark and bright channels in a single hazy image also contains useful information to reflect haze level. By searching for a segmentation threshold, a metric called DBCP-II is proposed. Combining the strengths of the above two indicators, a hybrid metric named DBCP-III is constructed to achieve better performance. The experiment results on public, real-world benchmark datasets show the advantages of the proposed methods in terms of assessment accuracy with subjective human ratings. The study is first-of-its-kind with preliminary exploration in the field of haze level evaluation for real outdoor scenes, and it has a great potential to promote research in autonomous driving and automatic air quality monitoring. The open-source codes of the proposed algorithms are free to download.
E-commerce fraud risk prediction based on RUSBoost algorithm

In the context of the era of big data, the emergence of e-commerce platforms has brought many opportunities and risks. Due to the COVID-19, e-commerce has achieved unprecedented development, and e-commerce fraud has severely damaged the healthy economic environment. This paper uses the RUSBoost algorithm to build an e-commerce fraud risk prediction model, and verifies the predictive performance of the model through data experiments. The results show that it has a high accuracy rate for identifying e-commerce fraud. If the model is applied to e-commerce, the losses caused by ecommerce fraud could be avoided in time. At present, there are fewer e-commerce fraud risk prediction models and have a wide development prospection.
Algebraic Zero Error Training Method for Neural Networks Achieving Least Upper Bounds on Neurons and Layers

In the domain of artificial neural networks, it is important to know what their representation, classification and generalization capabilities are. There is also a need for time and resource-efficient training algorithms. Here, a new zero-error training method is derived for digital computers and single hidden layer networks. This method is the least upper bound on the number of hidden neurons as well. The bound states that if there are N input vectors expressed as rational numbers, a network having N − 1 neurons in the hidden layer and M neurons at the output represents a bounded function F: RD→RM for all input vectors. Such a network has massively shared weights calculated by 1 + M regular systems of linear equations. Compared to similar approaches, this new method achieves a theoretical least upper bound, is fast, robust, adapted to floating-point data, and uses few free parameters. This is documented by theoretical analyses and comparative tests. In theory, this method provides a new constructional proof of the least upper bound on the number of hidden neurons, extends the classes of supported activation functions, and relaxes conditions for mapping functions. Practically, it is a non-iterative zero-error training algorithm providing a minimum number of neurons and layers.
Abstract Argumentation and Answer Set Programming: Two Faces of Nelson’s Logic

Abstract In this work, we show that both logic programming and abstract argumentation frameworks can be interpreted in terms of Nelson’s constructive logic N4. We do so by formalising, in this logic, two principles that we call noncontradictory inference and strengthened closed world assumption: the first states that no belief can be held based on contradictory evidence while the latter forces both unknown and contradictory evidence to be regarded as false. Using these principles, both logic programming and abstract argumentation frameworks are translated into constructive logic in a modular way and using the object language. Logic programming implication and abstract argumentation supports become, in the translation, a new implication connective following the noncontradictory inference principle. Attacks are then represented by combining this new implication with strong negation. Under consideration in Theory and Practice of Logic Programming (TPLP).
Flexible and Lightweight RGB LED Video Foil for Digital Signage

Abstract Method reduces cost, weight, regulations, and complicated installation associated with traditional signage.
An Image Encryption Algorithm with an Efficient Quantization Scheme

This paper proposes an image encryption algorithm with novel quantization scheme that fully exploits properties of sequences generated by logistic map. Since the generated iterates are decimal numbers, they need to be quantized prior to operations done during encryption. However, some of the image encryption algorithms do not utilize the whole potential of generated iterates. Approach described in this paper uses a higher amount of quantization states and obtained elements are then split to multiple sequences. The paper also analyzes the statistical properties of proposed solution by means of several numerical parameters. The values of both numerical parameters and encryption speeds are compared with other solutions and they are favorable for the proposed solution.
Enhanced Speaker Embeddings with CNN-based Multi-scaling Features

In recent years, biometrics-based technologies have grown in popularity. With the tremendous technological progress, biometric authentication has become part of our daily lives. These technologies also include the speaker authentication systems based on speaker's voiceprints (embeddings). Nowadays, the use of DNN-based embeddings is a prevailing method to create speaker acoustic models specific to individual speakers. In this work, we proposed a CNN-based speaker embeddings inspired by the concept of x-vector. In addition, we used the Res2Net block, which was first designed in the field of computer vision and substituted it in place of the TDNN in proposed architecture. Experimental results obtained on VoxCelebl show that our design yielded comparable accuracy to other published state-of-the-art methods, while achieving an improvement of 73% (relative change) over the baseline takx-vector embedding.
Maximum Information Fusion of Passive and Active Radars for Air Surveillance

Modern passive radar systems seem promising as cost-effective gap fillers of active radar coverage and also as covert early warning sensors. Integration of passive systems into already operational active radar networks is therefore of great interest to military and civilian air surveillance. We here propose a maximum information fusion approach specifically for solving this issue demonstrating a data association, data fusion and tracking method for bistatic passive radar and monostatic active radar data in air surveillance. Conventionally, separate bistatic and Cartesian trackers in two stages are proposed to provide a combined air picture. Such approaches effectively avoid false plots of the passive radar systems being tracked. However, these approaches also filter out potentially valid plots, which could have proven valuable if fused with active radar plots directly. This is especially of relevance in challenging terrain such as alpine valleys with scarce transmitters of opportunity and active radar clutter. We test our approach using real world passive and active radar data. While the tracking performance of our im-plementation is markedly inferior to existing commercial passive radar systems, our prototypical implementation demonstrates the feasibility and potential of the new approach for passive and active radar data fusion at the plot level.
An improved railway signal safety protocol I based on commercial cryptography 4 for metro train control system

With the development of metro, the information safety of train control system has become a key element in closed transmission. This paper proposes an improved railway signal safety protocol I (RSSP-I) based on commercial cryptography 4 (SM4) for metro train control system. RSSP-I is mainly used for safety-related communication in closed transmission system and cannot defense the disguise threat. This paper improves RSSP-I protocol based on SM4 to defense the disguise threat, and can protect against repeat, loss, insertion, disorder, corruption, delay and disguise threat. The protocol proposed in this paper can strengthen the security of information transmission process.
Evaluation of the effectiveness of developing real-world software projects as a motivational device for bridging theory and practice

While incorporating project-based exercise is a common practice in software engineering education, few studies have been conducted in investigating how real-world project development influences university students’ proactive learning and knowledge transformation. This study aims to evaluate the effectiveness of developing real-world projects with industry engagement in encouraging students to apply knowledge to practice and be more proactive in learning. Using a two-group, post-test quasi-experimental design, the performance between the students taking real-world project development and the students in the control group are compared using descriptive statistics, the independent samples t-test and Welch t-test, accordingly. Both the Spearman’s rank-order and Kendall’s τ-b are used to examine the relationship between students’ practical works and the level of knowledge transformation estimated by the students through online surveys. The results suggest that using real-world projects in the classroom can be an effective motivational device for proactive learning and knowledge transformation. Project-based exercise should be both comprehensive and keeping pace with technology development driven by the software industry evolution to be more effective. The direct interaction with stakeholders, dynamic requirements change, employment of Agile methods, self-organising teams, and using challenging real-world projects, are essential in simulating a real-world software development environment in the classroom.
A Scalable and Adaptive Convolutional Neural Network Accelerator

Machine learning has become ubiquitous and penetrated every field of technology, medicine, and finance. Convolutional Neural Network (CNN) is one of the most commonly used class of machine learning algorithms that is being used in video and image processing, big data processing, natural language processing, robotics, and a variety of pattern matching and recognition tasks. Depending on the end application, CNNs are being employed on different scales ranging from tiny motion sensors and smartphones to automobiles and server farms. Although existing CNN accelerators are adaptive for different types of CNN models, they are generally suited for a particular scale of operation. In this paper, we describe a scalable and adaptive CNN accelerator. The same hardware-cum-software stack can be configured by a system-level parameter to be synthesized for different scales of operation. This makes the accelerator highly portable across systems of different scales. Furthermore, one single synthesized hardware can run inference for multiple CNN models because of the flexible software stack and hardware control unit making the system highly adaptive. We demonstrate the working of the system at different scales by implementing it on the Xilinx Virtex 7 FPGA and by running multiple CNN models at each scale.
Pricing European Options with Deep Learning Models

Applications of artificial neural networks for valuing financial options have a long history in academic research. When appropriately trained, these artificial neural networks have proved to obtain better results than traditional parametric models like Black-Scholes. This short paper intends to review the input variables and specific architecture of a multi-layer perceptron, long-short term memory and a convolutional neural network model for the purpose of pricing options. Additionally, it describes the data gathering process for the dataset that will be used to train these models.
Observation points classifier ensemble for high‐dimensional imbalanced classification

Abstract In this paper, an Observation Points Classifier Ensemble (OPCE) algorithm is proposed to deal with High‐Dimensional Imbalanced Classification (HDIC) problems based on data processed using the Multi‐Dimensional Scaling (MDS) feature extraction technique. First, dimensionality of the original imbalanced data is reduced using MDS so that distances between any two different samples are preserved as well as possible. Second, a novel OPCE algorithm is applied to classify imbalanced samples by placing optimised observation points in a low‐dimensional data space. Third, optimization of the observation point mappings is carried out to obtain a reliable assessment of the unknown samples. Exhaustive experiments have been conducted to evaluate the feasibility, rationality, and effectiveness of the proposed OPCE algorithm using seven benchmark HDIC data sets. Experimental results show that (1) the OPCE algorithm can be trained faster on low‐dimensional imbalanced data than on high‐dimensional data; (2) the OPCE algorithm can correctly identify samples as the number of optimised observation points is increased; and (3) statistical analysis reveals that OPCE yields better HDIC performances on the selected data sets in comparison with eight other HDIC algorithms. This demonstrates that OPCE is a viable algorithm to deal with HDIC problems.
HAS THE COVID-19 PANDEMIC AFFECTED THE FLOW OF INFORMATION AMONG CRYPTOCURRENCIES?

In this paper, we investigated the impact of the COVID-19 pandemic on the cryptocurrency market. The direction of information transfer between the 67 digital cryptocurrency markets was evaluated, in particular Bitcoin, Ethereum, Litecoin, and Ripple, and we determined which of them were the most influential in the markets. The comparison of the first half of 2019 (outside the pandemic of COVID-19) against the first semester of 2020 (during the COVID-19 pandemic) was used to analyze the pandemic influence. We found two distinct behaviors: (i) in 2019, Bitcoin, as the primary capitalization bond, presented a more substantial transfer of information than the other cryptocurrencies toward Bitcoin [Formula: see text] Ripple (0.0541), followed by Litecoin [Formula: see text] Ripple (0.0522); (ii) in 2020, the most substantial transfers of information occurred from Ethereum to other cryptocurrencies (Litecoin, Bitcoin, and Ripple, in that order). In this period, the weakest transfers happened from Litecoin [Formula: see text] Ripple and in the opposite direction, with equal value (0.0104). Our results indicate that there was a change in the direction of the information flow between the investigated cryptocurrencies, where ETH became the dominant cryptocurrency during the period of turbulence caused by the COVID-19 pandemic.
3D cumulus cloud scene modelling and shadow analysis method based on ground-based sky images

Cloud scene modelling and shadow analysis in 3D geographical scenes can provide auxiliary support for evaluating photovoltaic (PV) power generation. Thus far, real cloud scene based on measured data has not been constructed in 3D geographical scenes, and shadow with different intensity caused by cumulus cloud thickness has not been considered. In this study, a 3D cumulus cloud scene modelling and shadow analysis method based on ground-based sky images was proposed. First, a voxel-based 3D cumulus cloud scene modelling on GPU was proposed to model in the 3D geographic space and calculate cumulus cloud density. Then, a cumulus cloud density-based shadow analysis method was proposed, which can express shadow intensity. The experimental result showed that our method can accomplish near real-time 3D cumulus cloud modelling and shadow analysis in a 3D geographic space based on ground-based sky images, and the shadow intensity is inversely proportional to the direct solar radiation.
A Classification-Aided Recurrent Neural Network for Tracking in Urban Terrain

This paper addresses the challenge of tracking small unmanned aerial systems in urban terrain. Tracking is made up of two parts, state estimation and data association. For low, slow, small targets in the presence of other non-target movers with similar dynamics, data association is arguably the greatest challenge. In traditional trackers, measurements are associated with existing tracks based on hand-crafted dynamical models. In this paper, we present recurrent neural network (RNN) based tracker which uses both learned target dynamic models and classification scores to associate measurements with target tracks. Classification scores for each radar measurement are generated by a neural-network trained using in situ &#x201D;pattern-of-life&#x201D; radar data. These scores together with location estimates are then passed to RNN trackers with long-short-term-memory (LSTM) trained for each object class. In this paper, we present simulated and real data results indicating that classification scores significantly improves small UAS track holding times in urban environments.
Increasing State Estimation Accuracy in the Inference Algorithm on a Hybrid Factor Graph Model

We consider an intelligent agent that receives a continuous input from a signal-generating system and aims for estimating the discrete latent state of that system. The agent includes a switching linear dynamical system modeled as a hybrid factor graph for performing state estimation by determining the inference algorithm. We investigate the agent’s performance in relation to two Gaussian mixture reduction methods restricting Gaussian mixture growing while message passing, namely, the naive pruning implemented in the past and the realization of the Kullback-Leibler (KL) discrimination based approach defined by Runnalls (2007). For evaluation, we use simulated data provided with various signal-to-noise ratios. Reviewing the metrics, the agent reaches an improvement in state estimation accuracy by using the KL discrimination based method with minimally increasing computational effort. The findings of our work let us take one step towards establishing intelligent systems for modeling real-world systems with switching behavior capable to analyze noisy data sets.
Cyber Security Risk Management: E-Learning System

Institutes, professors, and students from all around the world are using e-learning as a regular educational tool. It facilitates communication between students and teachers while also assisting them in their lectures by offering a platform for them to share documents, notes, quizzes, and grades. E-learning also gives knowledge seekers, who aren't necessarily students but want to educate themselves on a certain subject, access to an education via a computer. Furthermore, it enables people living in tough circumstances to obtain an education and earn a degree online, on their own time. E-learning systems primarily separated in two purposes 1) functionalities of the hardware and software system support 2) education and transmitting knowledge. In e-Learning, data manipulation, fraudulent user authentication, and confidentiality threats are all major security concerns. Meanwhile, e-Learning trends demand that applications, learning environments, and heterogeneous systems be more interoperable. This paper discussed on risk management of e-learning, risks, threats, and vulnerabilities associated with E-learning and recommendation of risk control.
Multicriteria Optimization for Dynamic Demers Cartograms

Cartograms are popular for visualizing numerical data for administrative regions in thematic maps. When there are multiple data values per region (over time or from different datasets) shown as animated or juxtaposed cartograms, preserving the viewer's mental map in terms of stability between multiple cartograms is another important criterion alongside traditional cartogram criteria such as maintaining adjacencies. We present a method to compute stable stable Demers cartograms, where each region is shown as a square scaled proportionally to the given numerical data and similar data yield similar cartograms. We enforce orthogonal separation constraints using linear programming, and measure quality in terms of keeping adjacent regions close (cartogram quality) and using similar positions for a region between the different data values (stability). Our method guarantees the ability to connect most lost adjacencies with minimal-length planar orthogonal polylines. Experiments show that our method yields good quality and stability on multiple quality criteria.
An Integrated Spectrogram Classifier-based Hand-Gesture Recognition SoC for an FMCW Radar Sensor

This work presents a system-on-chip (SoC) for FMCW radar signal processing to implement hand gesture recognition. For smart IoT device applications, a radar signal processing unit with a lightweight convolutional neural network (CNN) is integrated so that a spectrogram classifier can be implemented at the chip level. The spectrogram classifier is integrated with preprocessing blocks of FFTs and an MTI filter in 40nm CMOS process. The network model size of the CNN was set to about 60.1 KiB to make the system suitable for small IoT applications. To handle variations resulting from different hand sizes and gestures, 10,800 training data from 72 participants were utilized with the customized preprocessing. The performance evaluation results showed that the gesture recognition accuracy for the five gestures was measured to be 91.5 &#x0025; in a user-independent manner.