Reviewerly/exHarmony huggingface dataset:
Cyber Crisis Management Roles – A Municipality Responsibility Case Study

In this paper we propose a role model that can be applied in societal cyber crisis management to build safety and standard procedures during cyber security crisis. We define societal cyber crisis as the cyber crisis which affect the society in which disaster is or might be the consequence. The process to create our model started by analyzing regulations and responsibilities in Norwegian municipalities, and we used steps of a design science research (DSR) research approach to create our suggested artifact. A combination of conventional crisis management and cyber crisis management is proposed to identify the interrelationships among diverse stakeholders when managing the preparation for and reaction to a cyber crisis incident. We present a cyber incident handling role model (CIHRM) which is usable for visualizing cyber crisis in a diversity of organizations. After our model has been reviewed by the cyber security research community, we plan to implement the model when analyzing crisis management in various organizations to prepare for instructions, training and exercises at our training environment - The Norwegian Cyber Range.
Vision-Based Robotic Traversal of Textureless Smooth Surfaces

This investigation focuses on servoing a robot's tool with respect to a smooth workpiece surface by making use of the surface's characteristic local differential properties. A novel formulation for 6 degree-of-freedom (DoF) textureless visual servoing based on these properties is proposed, which extends an existing 3-DoF scheme. Our approach naturally combines the geometric tools of computer-aided design and machining (CAD/CAM) theory with the manipulator control tools of visual servoing synergistically to achieve full 6-DoF pose control. A novel family of observed feature sets and their associated interaction matrices are presented. A geometric condition on the surface shape is derived under which local asymptotic stability for 6-DoF is guaranteed. Validation of the proposed method is performed in simulation and experiment using an articulated desktop robot equipped with only a monocular camera and 16 laser pointers.
EOG-Based Ocular and Gaze Angle Estimation Using an Extended Kalman Filter

In this work, a novel method to estimate the ocular pose from electrooculography (EOG) signals is proposed. This method is based on an electrical battery model of the eye which relates the EOG potential to the distances between an electrode and the left/right cornea and retina centre points. In this work, this model is used to estimate the ocular angles (OAs), that is the orientation of the two ocular globes separately. Using this approach, an average cross-validated horizontal and vertical OA estimation error of 2.91 ± 0.86° and 2.42 ± 0.58° respectively was obtained. Furthermore, we show how these OA estimates may be used to estimate the gaze angles (GAs) without requiring the distance between the subject’s face-plane and the target-plane, as in previous work. Using the proposed method, a cross-validated horizontal and vertical GA estimation error of 2.13 ± 0.73° and 2.42 ± 0.58° respectively was obtained, which compares well with the previous distance-based GA estimation technique.
A novel CNN model for fine-grained classification with large spatial variants

Abstract Convolutional Neural Networks (CNN) have achieved great performance in many visual tasks. However, CNN models are sensitive to samples with large spatial variants, especially severe in fine-grained classification task. In this paper, we propose a novel CNN model called ST-BCNN to solve these problems. ST-BCNN contains two functional CNN modules: Spatial Transform Network (STN) and Bilinear CNN(BCNN). Firstly, STN module is used to select key region in input samples and get it spatially modified. Since the adoption of STN will cause an information loss phenomenon called boundary loss, we design a brand-new IOU loss method to solve it. We make a theoretical analysis of the IOU loss method. Secondly, to discover discriminative features for fine-grained classification task, BCNN module is applied. BCNN interacts CNN features from different channels to produce more discriminative bilinear features than fully connected features of CNN. ST-BCNN works by reducing irrelevant spatial states and producing fine-grained features. We evaluate our model on 3 public fine-grained classification datasets with large spatial variants: CUB200-2011, Fish100 and UAV43. Experiments show that the IOU loss method can reduce boundary loss and make STN module output spatial transformed image appropriately. Our proposed ST-BCNN model outperforms other advanced CNN models on all three datasets.
The Analysis of Higher Mathematics Teaching Strategy Based on the Innovative Teaching Mode

During the epidemic, China’s Higher Education Vocational Colleges carried out large-scale, full-time online mathematics curriculum teaching. Taking it as an opportunity, this paper selects the higher mathematics course of a vocational college in Western China during the epidemic period as the starting point to build a blended teaching mode of higher mathematics combining online and offline teaching. It analyzes the teaching effect of pure offline teaching of mathematics course in the early stage by means of questionnaire survey, and evaluates the teaching quality of pure online teaching of mathematics course in the near future based on the real-time data of the rain classroom teaching platform. The purpose of this paper is to accelerate the integration of educational information technology and higher mathematics teaching, and promote the innovation of classroom teaching. We should stress the teaching idea of “students as the main part, teachers as the leading role”, rely on the teaching reform, build a strong teaching staff, cultivate the comprehensive quality of students, and provide higher quality technical talents for the society.
Adaptive Flow Control Using Movement Information in Mobile-Assisted Sensor Data Collection

Internet of Things (IoT) is gaining great momentum for remote data collection in various smart applications. Recently, the proliferation of wireless sensors and the explosive increase in the number of mobile devices enable Mobile-Assisted Sensing (MAS) in which the mobile gateways collect data from the distributed sensors. Therefore, MAS collects data without additional overheads for building a static network infrastructure. Meanwhile, the connection between a sensor and a mobile gateway is generally considered unreliable due to the arbitrary mobility of mobile gateways. That is, the connection is unexpectedly terminated when the mobile gateway leaves the communication range of the sensor. Therefore, the existing studies have used frequent control messages for suppressing invalid data transmission after the end of the connection. Thus, the conservative flow control degrades data throughput because the control messages occupy a large part of the connection. However, the connection can be reliable according to the condition of the mobile gateway. In this paper, an adaptive flow control (AFC) scheme is proposed for enhancing data throughput using the movement information of mobile gateways. AFC exploits the movement information for estimating the reliability of the connection. Estimating that the connection is reliable, the control messages are hardly emitted during data transmission thereby the connection contains more data. Consequently, AFC enhances the data throughput by reducing control overheads without degrading reliability.
Application of Correlation Analysis for Impact Assessment of Air Quality on the Possibility of Using Chosen Source of Renewable Energy

This publication presents the application of correlation analysis to the study on the possibilities of solar energy use. It is shown the assessment of the impact of air quality on the possibility of using chosen source of renewable energy. The most intense effects of poor air quality can be observed in urban areas where human activities generate pollution in various forms. The aim of the analysis was to investigate the relationship between the amount of solar radiation reaching solar panels and atmospheric pollution caused by climatic and anthropogenic factors in the designated location. Dependency analysis between the experimental data and the amount of light intensity were conducted by selected statistical correlation tests. The Matlab / Simulink software was used for calculations.
A Berry–Esseen theorem for Pitman’s $\alpha $-diversity

This paper is concerned with the study of the random variable $K_n$ denoting the number of distinct elements in a random sample $(X_1, \dots, X_n)$ of exchangeable random variables driven by the two parameter Poisson-Dirichlet distribution, $PD(\alpha,\theta)$. For $\alpha\in(0,1)$, Theorem 3.8 in \cite{Pit(06)} shows that $\frac{K_n}{n^{\alpha}}\stackrel{\text{a.s.}}{\longrightarrow} S_{\alpha,\theta}$ as $n\rightarrow+\infty$. Here, $S_{\alpha,\theta}$ is a random variable distributed according to the so-called scaled Mittag-Leffler distribution. Our main result states that $$ \sup_{x \geq 0} \Big| \ppsf\Big[\frac{K_n}{n^{\alpha}} \leq x \Big] - \ppsf[S_{\alpha,\theta} \leq x] \Big| \leq \frac{C(\alpha, \theta)}{n^{\alpha}} $$ holds with an explicit constant $C(\alpha, \theta)$. The key ingredients of the proof are a novel probabilistic representation of $K_n$ as compound distribution and new, refined versions of certain quantitative bounds for the Poisson approximation and the compound Poisson distribution.
Comparison of Different Tracking Approaches on Pre-Fused Data for Automotive Perception System

The development of autonomous vehicles requires different sensor types for constructing an accurate local environment model. The evolution of sensor technology is challenging the development of sensor data fusion. Although, the sensors are getting more robust and reliable. However, because of the development of sensors the data fusion should be able to handle diversified sensor clusters. Thus, the high-level sensor fusion algorithms, like track to track method, are getting more important. This paper introduces different tracking approaches, which are intended to support a track-to-track fusion method of an autonomous vehicle in highway situations.
Real-time detection of UAV detection image of power line insulator bursting based on YOLOV3

Abstract The insulator fault of high voltage line is the main factor of transmission accidents, so positioning and detection of burst insulator of power line has become an important part of routine detection. Traditional UAV detection is still mainly to evaluate the image transmitted by UAV manually, which is not only time-consuming, but also not accurate. In this paper, UAV aerial images are used to make datasets, the problem of insulator class imbalance (normal insulator and burst insulator) in the model training process is solved by using data augmentation, an improved depth learning algorithm is proposed based on YOLOv3, which provides rich semantic information for prediction layer by adding feature mapping module. At the same time, the residual network is introduced into the feature extraction, which improves the detection accuracy of small objects, more effectively extracts the object features of burst insulator, carries out real-time object detection and positioning, and completes the daily detection of insulator state of power transmission lines. The experimental results show that the detection accuracy (mAP) of the improved YOLOv3 algorithm is 91.22%, and the detection speed is 28 frames/s. The recall rate and Intersection Over Union (IOU) are also improved.
Dual Watermarking for Protection of Medical Images based on Watermarking of Frequency Domain and Genetic Programming

In this paper, we presents two watermarking schemes namely: Scheme I present an hybrid of discrete cosine transform (DCT) and discrete wavelet transform (DWT) watermarking technique while Scheme II presents a genetic programming (GP) based technique. Both of these schemes were experimented with 512x512 and 256x256 medical images and other standard images such as Lena, Barbara, Baboon, Flower, Brain etc. against state-of-the-art watermarking techniques. According to our results, shows better performance of the two main criteria use to check for any watermarking techniques (Imperceptivity and Robustness). The experimental results reveals that for imperceptivity, peak-signal-to-noise-ratio (PSNR) and structural similarity index (SSIM) shows better performance when compared against Arsalan's and Hurrah's approaches. Similarly, for robustness the parameters used are normalized correlation (NCC) and bit error rate (BER). According to results obtained, both schemes' parameters show high performance except for Scheme I BER with 0.0038. Finally, in terms of fragility both schemes provided better resistance to any signal processing attacks.
Build and design knowledge management system for sharing material teacher

Abstract Human resources in education become a force for institution itself because it can be an asset and capital owned in order to compete with other instituions. The design of knowledge management system aims to facilitate the educators in sharing knowledge both in each school and between the two schools. In this case the knowledge management system can serve to help educators in collecting knowledge and share material of teaching materials owned, so that tacit and explicit knowledge of each teacher can be well documented. This research uses RAD (Rapid Application Development) method with UML (Unified Modeling Language) as its development tools. While making its own application using PHP 7 and MySQL programming language for database processing. This research produces a web based knowledge management system that can collect, manage, store, and disseminate existing knowledge from teacher.
OmniView

The possibility of providing humans with a 360 field of view (FOV) is an area that has fascinated researchers for some time. We introduce OmniView, an exploratory study to determine an optimized 360 FOV vision using dynamic distortion methods for reducing distortion and enlarging the area of the direction of interest. We developed three variations of OmniView to trigger distortions: eye gaze selection, pointing selection, and automatic selection. Our first study (n=16) compares these methods with no dynamic distortion (equirectangular view) in terms of spatial perception change and reaction time. The second study (n=16) evaluated subjective simulator sickness while performing simple everyday tasks. There is no significant change between OmniView and a no distortion baseline regarding spatial perception and reaction time. However, there is a statistically significant rise in disorientation from before the study to after using Pointing OmniView and Automatic OmniView. Most users (n=13) have a strong preference towards Gaze OmniView. From the results, we provide design guidelines towards further optimizing 360 vision and preserving our spatial sense and reaction time while keeping simulator sickness to a minimum.
Random Forest Algorithm Based on GAN for Imbalanced Data Classification

Abstract Data imbalance increases the difficulty of knowledge mining from data. Aiming at the problem of data imbalanced classification, a random forest algorithm based on GAN is proposed, which can effectively classify imbalanced data sets. The GAN model is used to generate a few class samples, which are mixed with the original data samples to form a new data set to reduce the imbalance of data. Then the new data set is divided into several data subsets with a balanced sample distribution. Finally, all the decision trees are collected to form the forest and the classification results are obtained. The random forest algorithm based on GAN improves the classification effect on minority classes, which makes the equilibrium rate of the dataset reach 50%. The algorithm in this paper has performed relevant experiments on multiple imbalanced datasets, the results show that the algorithm has good performance in data imbalance classification. The optimized algorithm after parallelization improves the running speed.
It’s Not Just Black and White: Classifying Defendant Mugshots Based on the Multidimensionality of Race and Ethnicity

Analyses of existing public face datasets have shown that deep learning models (DLMs) grapple with racial and gender biases, raising concerns about algorithmic fairness in facial processing technologies (FPTs). Because these datasets are often comprised of celebrities, politicians, and mainly white faces, increased reliance on more diverse face databases has been proposed. However, techniques for generating more representative datasets are underdeveloped. To address this gap, we use the case of defendant mugshots from Miami–Dade County’s (Florida, U.S.) criminal justice system to develop a novel technique for generating multidimensional race–ethnicity classifications for four groups: Black Hispanic, White Hispanic, Black non-Hispanic, and White non-Hispanic. We perform a series of experiments by fine–tuning seven DLMs using a full sample of mugshots (194,393) with race-ethnicity annotations from court records and a random stratified subsample of mugshots (13,927) annotated by a group of research assistants. Our methodology considers race as a multidimensional feature particularly for a more diverse face dataset and uses an averaged (consensus–based) approach to achieve a 74.84% accuracy rate based on annotated data representing only 2% of the full dataset. Our approach can be used to make DLM based FPTs more inclusive of the various subcategories of race and ethnicity as they are being increasingly adopted by various organizations including the criminal justice system.
Lessons Learned: A Case Study in Creating a Data Pipeline using Twitter’s API

With over 300 million users, including frequent postings by elites across the political and entertainment fields, Twitter has become a rich field for mining and analyzing data. Despite its prominence within social science research, relatively little attention has been paid to the process of data acquisition. To that end, our research uses a case study to illustrate the process of acquiring and storing tweets. To construct our data pipeline, we first applied for and created Twitter developer accounts and used the Tweepy app in Python to interact with Twitter’s API. We created a program that uses a producer-consumer multithreading model to request tweets from the API, then cleans the data and pushes it to a MySQL database with four tables: one for tweets, one for user information, one for retweets, and one for special entities (e.g., hashtags).With our pipeline operational, we explore how candidate gender affects Twitter discourse in the 2020 Democratic presidential primary. Specifically, we use unsupervised text analysis methods to examine differences in word frequencies, sentiment, and emotional dimensions. We find that gender is central to the discourse surrounding female candidates, but peripheral for male candidates. The discourse surrounding female candidates in our dataset is also more joyful and positive. Finally, with our case study concluded, we offer lessons to future researchers who wish to acquire and utilize Twitter data for social science research.
Eye Caramba: Gaze-based Assistance for Virtual Reality Aiming and Throwing Tasks in Games

Gaze-based interaction in Virtual Reality (VR) has been attracting attention recently due to rapid advances in eye tracking technology in head-mounted displays. Since gazes are a natural and intuitive interaction modality for human beings, gaze-based interaction could enhance player experience in immersive VR games. Aiming assistance is a common feature in games to balance difficulty for different player skills. Previous work has investigated different aim assistance approaches and identified various shortcomings. We hypothesize that “bullet magnetism” is a promising technique for VR and could be enhanced by extending its functionality through players’ gazes. In this paper, we present a gaze-based aiming assistance approach and propose a study design to evaluate its performance and player experience in a “Mexican-style” VR first-person shooter game.
Towards Blockchain Services For Mobile Money Traceability And Federation

This paper discusses a new approach to mobile money, a money transfer solution via mobile phone, which uses accounts associated with SIM (Subscriber Identity Module) identifiers, and which are managed by the Mobile Network Operators (MNOs). The first solution launched in Kenya in 2007, M-PESA is one of the world major mobile money services. Mobile money becomes the new monopoly of MMOs in an economic system where the rate of unbanked adults is very high. Currently in 2019 Orange Money makes nearly 1.5 million transactions per day, which corresponds to a monetary value of nearly 15 billion CFA francs per day. The USSD is the technology at the heart of the Mobile-money transactions. Blockchain, which is the underlying technology of the first crypto currency Bitcoin, has drawn a lot of global attention in recent years. Blockchain does not stop seeing its field of action widen. The field of action of the Blockchain has not yet completed its enlargement. The rate of unbanked is still very high in developing countries; this rate is a factor that does not favor the emergence of the economy. It is necessary to count on the mobile money to skip this obstacle (according to the World Bank) we make here the state of the art on the mobile money and its underlying technologies in the sub-Saharan countries and in Burkina-Faso. We propose a mobile-money solution based on the Blockchain, in order to increase security, trust, and move towards a federation of mobile-money platforms.
Extremely Low-Resource Text Simplification with Pre-trained Transformer Language Model

Inspired by machine translation task, recent text simplification approaches regard a task as a monolingual text-to-text generation, and neural machine translation models have significantly improved the performance of simplification tasks. Although such models require a large-scale parallel corpus, such corpora for text simplification are very few in number and smaller in size compared to machine translation task. Therefore, we have attempted to facilitate the training of simplification rewritings using pre-training from a large-scale monolingual corpus such as Wikipedia articles. In addition, we propose a translation language model to seamlessly conduct a fine-tuning of text simplification from the pre-training of the language model. The experimental results show that the translation language model substantially outperforms a state-of-the-art model under a low-resource setting. In addition, a pre-trained translation language model with only 3000 supervised examples can achieve a performance comparable to that of the state-of-the-art model using 30,000 supervised examples.