Reviewerly/exHarmony huggingface dataset:
Internal processes in decision-making (mental, emotional, cultural, ethical and spiritual) of Afghan business leaders

Purpose The purpose of this paper is to understand, which internal processes (mental, emotional, cultural, ethical and spiritual) Afghan business leaders use when making managerial decisions. Design/methodology/approach Primary data were collected through 1-h face-to-face interviews with Afghan business leaders. Interviews were conducted through open-ended questions in a semi-structured format. This method was considered most appropriate to acquire an understanding of senior executives’ interpretation and usage of decision-making processes. The method of analyzing data was thematic analysis where the researchers identified common themes, topics, ideas and patterns of meaning that come up repetitively. The objective of the analysis was to determine the most frequent decision-making processes by business leaders and the reasons for using these processes. Findings Although the usage of internal processes in decision-making are not homogeneous among Afghan business leaders, some of the processes are used more frequently than others such as mental, cultural and ethical processes. During the mental process of decision-making, the majority of leaders use intuitional decision-making, the minority using logic. Regarding the cultural dimension, the majority of leaders stated that they have an open, friendly, caring organization for each employee and horizontal culture in their organization. The minority indicated that they have a friendly culture but they also considered the processes and hierarchy in their organization. Considering the ethical process of decision-making, leaders stated that their priorities are more ethical than getting extra profit. They believe that profit will be generated while considering ethical values. As a leader noted: when you consider ethics and fulfill your obligations, the profit automatically generates. Most leaders use the internal process of emotion in their decision-making, but the usage has not been frequent. The emotional process of decision-making is more involved when the human factor is involved. For instance, one of the participants stated “I did not fire an employee that I had to because he was a needy and poor person.” About the spiritual process of decision-making, although all leaders agreed that they have used spirituality in decision-making, its usage varies. About one-third of the leaders mostly rely on spirituality or on religious teachings during the decision-making process, one-third somehow rely on spirituality or religion, about 50% of the time and one-third rely on spirituality between 25% to 30% of the time. Originality/value This study is pilot research as no previous research was carried out on this topic, therefore, it provides a basis of literature on the usage of internal processes on decision-making in Afghanistan. The findings may differ in other economic and national contexts.
Research on Image Recognition of Convolutional Neural Network under Different Computer Data Set Capacities

Abstract Target detection is chinaafrica growing to a task, computer vision in intelligent video surveillance, automatic monitoring and are widely used in industrial detection, over the years, with the deepening of the study and deepened, target detection algorithm based on depth of convolution neural network is gradually replacing the traditional target detection algorithm, become algorithms, which are frequently used in the field in this article, the focus on the target detection algorithm of commonly used data sets and the performance evaluation index, this paper introduces the development of convolution neural network, two stages of target detection algorithm and the unit target detection algorithm is focused on the content analysis, The future development of target detection algorithm based on deep convolutional neural network is prospected.
Design and Analysis of Educational Resources Recommendation Service System Based on 5G Era

Personalized education resource recommendation system is an intelligent system for better realizing personalized education. As a tool to solve information overload, recommendation system has been widely used in information retrieval and information filtering. In the mobile environment, users may be temporarily interested in something because they are in a certain place. How to capture this temporary interest is an urgent problem to be solved. Based on 5G technology, this paper designs and analyzes the personalized recommendation service system of online educational resources by using Web mining technology in the online teaching system, and designs and develops the functional modules of information acquisition, content aggregation, dynamic publishing and so on. Combining factors such as user preference and project quality into personalized recommendation improves the quality of recommendation.
Graphical viewing at a distance: graphical analytics as a method for the investigation of illustrated books

In this research article, graphical analytics is presented as a method for the investigation of page/layout- and image-based materials. This approach is used to analyse image composition, image constellations and the layout of the examined books, and it facilitates a comparison across several books within a single visualization. The methodology is introduced and the epistemology explained, along with concepts such as framework, simulative and heuristic models, and image constellations. Methodological steps like the establishment of the data set, the collection of specific data, the organization of the information architecture, and the design of the display of these data are explained. The method is illustrated through a case study on nine illustrated books on non-human primates. This material is analysed according to given research questions on the proximity of non-human primates and human beings being depicted in the images of these books, and on the differences between the illustrated books with respect to their target audiences. The findings of our graphical analytics reveal patterns of proximity between humans and non-human primates as well as the iconology of the Book of Genesis in the pictorial and textual materials used by these illustrated books. This contribution presents an introduction to graphical analytics with an exemplary application.
Skin Cancer Prediction and Diagnosis Using Convolutional Neural Network (CNN) Deep Learning Algorithm

Artificial intelligence (AI) has recently surpassed human performance in several areas; and there is great hope that in the medical field, AI can enable a better prevention, detection, diagnosis and treatment of diseases. Cancer today remains the second leading cause of death. Most, if not all, professionals in the medical field, agree that early detection of cancer offers great chances of healing or disease control. Image processing is a used method used for the detection of skin cancer as soon as the affected area appears. This triggered the idea of creating an Artificial Intelligence; based on Convolution Neural Network (CNN) deep learning algorithm; which allows the diagnosis of skin diseases by simply using an image captured by a mobile application. A mobile application and a web ap- plication were created to help access to this diagnosis in the most landlocked places where the presence of dermatologists is almost non-existent. Through the proposed system skin cancer will predict and identify malignant skin lesions. Dataset from the International Skin Imaging Collaboration (ISIC) Dermoscopic Archive; that support several types of skin cancer; is used to train and test the pro- posed model.
Radio-Frequency Interference Location, Detection and Classification Using Deep Neural Networks

Global Navigation Satellite System (GNSS) signals are used in Earth Observation for Radio Occultation and Reflectometry. The increasing effects of Radio-Frequency Interferences (RFI) on the performance of these receivers and navigation have suddenly sparked serious concerns due to their proliferation. Detection and mitigation of RFI heavily relies on the nature and location of the interfering sources. In some cases, null-steering or shielding can be used to mitigate RFI effects. In this work, a system to detect and locate RFI sources is presented, including signal classification and recording for countermeasure-related decision-making.
Low Dimensional Visual Attributes: An Interpretable Image Encoding

Deep convolutional networks (DCNs) as black-boxes make many computer vision models hard to interpret. In this paper, we present an interpretable encoding for images that represents the objects as a composition of parts and the parts themselves as a mixture of learned prototypes. We found that this representation is well suited for low-label image recognition problems such as few-shot learning (FSL), zero-shot learning (ZSL) and domain adaptation (DA). Our image encoding model with simple task predictors performs favorably against state of the art approaches in each of these tasks. Via crowdsourced results, we also show that this image encoding using parts and prototypes is interpretable to humans and agrees with their visual perception.
ExamChain: A Privacy-Preserving Onscreen Marking System based on Consortium Blockchain

Recently, Onscreen Marking (OSM) system based on the traditional cloud platform has been widely used in various large-scale public examinations. However, the mainstream examination marking process is not transparent, and there is the possibility of a black-box operation, which damages the fairness of the examination. Also, the issues related to data security and privacy are still considered to be serious challenges. In this paper, we deal with the above problems by providing secure and private transactions in a distributed OSM assuming the semi-trusted examination center. We have implemented a proof-of-concept for a consortium blockchain-based OSM in a privacy-preserving and auditable manner, enabling markers to anonymously mark to the distributed ledger.
An Accuracy-and-Diversity-based Ensemble Method for Concept Drift and Its application in Fraud Detection

Concept Drift is one of the most challenging issues in various applications such as fraud detection, spam filtering and sensor networks, causing the performance degradation of learning algorithms which are dedicated to processing static data such as Naive Bayes, due to their poor adaptability to changes. Several techniques and algorithms have been proposed to address the concept drift problem, e.g. the ensemble methods that have already achieved great success. However, most of the ensemble methods consider either accuracy or diversity of a trained model when adapting to new concepts, and thus they can hardly deal with different types of drifts. This paper proposes a novel ensemble method that considers both accuracy and diversity. We linearly combine accuracy and diversity with different coefficients, and propose an entropy-based policy to compute these coefficients. Experiments on 10 synthetic datasets and 4 public datasets demonstrate that our method outperforms the state-of-the-art ones on dealing with different concept drifts. Our method is also applied to the electronic transaction fraud detection and achieves an excellent performance.
Coded Computing at Full Speed

Distributed computing is the mainstream for large-scale machine learning and big data analytics, but its performance usually suffers from unpredictable stragglers, i.e., very slow nodes. Recently, coded computing has emerged as a new distributed computing paradigm that uses coding-theoretical approaches to mitigate the effect of stragglers. Most existing coding schemes only use the results from a certain number of fastest worker nodes to recover the output and completely ignore the partial work done by other worker nodes, leading to inferior performance. In this paper, for scenarios where each worker node transmits its local result to the master node only after it has finished the whole local computation, we introduce communication at full speed to characterize the full utilization of all the communication links between each worker node that has finished its local computation and the master node, and for scenarios where each worker node computes out the local result piece by piece and can forward each piece once available, we introduce computation at full speed to characterize the full utilization of the work done entirely or partially by all the worker nodes. Considering a general polynomial-based coding framework which encapsulates many advanced coding schemes for a variety of fundamental computing tasks, we propose a randomized approach where each worker node partitions its local result into pieces, generates and forwards random linear combinations of these pieces to the master node sequentially, and theoretically demonstrate that it can lead the coding framework to achieve communication at full speed. For some typical task scenarios, we further show that computation at full speed can be achieved by mapping the encoding operations in the randomized approach into a part of encoding on the input dataset. Experiments conducted on Alibaba Cloud as well as simulations show that our approaches can reduce the total runtime significantly.
Multiscale evaluation of microscopic stitching with tilt compensation based on metal engineering surfaces

In this paper, a topographic data stitching method is firstly introduced for compensating and optimizing the tilt errors derived from the lateral guide of the stage of a confocal laser scanning microscope. Afterwards, a novel evaluation process, including both simulated verification and experimental verification, is proposed to analyze the stitching precision and its applicability. At last, the evaluation is conducted with multiscale measured datasets, using five typical engineering surfaces with different surface textures. The results show that the developed method can compensate for the tilt uncertainty without the help of the precision apparatus and exhibits improved performance in comparison with conventional methods. In addition, the results of the comprehensive verification show the stitching precision is mainly influenced by concrete surface textures, surface roughness, acquired data resolution as well as the magnitude of tilt uncertainties.
An Image Dataset of Vehicles Front Views and Parts for Vehicle Detection, Localization and Alignment Applications

This paper introduces a dataset of front view car images collected from traffic scenes. Each image contains the bounding box of a vehicle which has cropped manually and accurately. The database also includes the parts corresponding to each frontal view car, i.e. left and right lights, left and right mirrors, grill, license plate, and bumper. In addition to vision-based applications such as object detection, verification, and part-based analysis, our database is specialized for testing and developing of trainable bounding box location prediction algorithms. We used a car localization algorithm for performance evaluation, which resulted in a localization accuracy of 87%. Also, we tested our dataset on Viola-Jones object detector and measured its performance in terms of false positive and false negative rates for images of whole-car as well as each individual part.
High‐accuracy vehicle lamp detection for real‐time night‐time traffic surveillance

Vehicle lamps are an important image feature of the night-time vehicle detection algorithm. This study proposes a real-time night-time vehicle detection algorithm based on light attenuation characteristic analysis, which consists of vehicle lamp detection and pairing. For the detection phase, this study proposes an automatic dual-threshold method to quickly extract the attenuation regions around bright objects. This method is highly adaptable and can accurately extract attenuation regions to identify vehicle lamps and strong reflections. For the pairing phase, this study uses the moving trajectories of vehicle lamps to complete preliminary pairing. Then, the vehicle lamp pairing is optimised using the relative vertical and horizontal lamp positions in continuous images. Pixel- and object-based methods were used to verify the experimental vehicle lamp detection results. The results of both verification methods indicate that the proposed algorithm without morphological operation is superior to other algorithms. The accuracy of the pairing phase was >94.6% in scenes with multiple lamps, high-driving speed, high traffic flow, and rain. Finally, the proposed night-time vehicle detection algorithm can achieve a real-time execution speed for images 960 × 720 pixels in size.
Lessons Learned in Leveraging Existing Simulations for Cybersecurity Training, Evaluation, and Research

We provide lessons learned in leveraging existing simulations to conduct human-subjects cybersecurity experiments and develop training for cybersecurity professionals. First, we provide criteria for the evaluation and categorization of existing simulation tools into four categories (competitions, testbeds, tabletop exercises, and simulations used in published research). Following this, eight criteria are offered to evaluate simulations on their suitability for use in experiments. We evaluated one representative product in each category. This paper serves as a resource for practitioners who use simulation as a method of training or evaluation. Further, this work is a starting point for researchers to efficiently find and leverage simulations to conduct cybersecurity research.
Election Data Transparency: Obtaining Precinct-Level Election Returns

Election data transparency, accessibility, and usability constitute key dimensions of electoral integrity, as they allow for an additional accountability check on how states and localities run elections. Yet, it is an understudied topic, because of challenges in systematic data collection. Lack of data transparency has important implications for a democratic election process: It can reflect non-compliance with federal and state election laws, discriminatory practices, or lack of capacity in modernizing the conduct of elections. Thus far, there is no standard way for states to report election data, and there is no standard way to request, collect, and disseminate them. This paper presents the various ways state and local governments make official precinct-level election results publicly available, based on an OpenElections collection effort that covered primary and general elections in 2016 and 2018 in every U.S. state. We describe methods for obtaining official precinct results, ranging from formal records requests under state law, photographing pages in-person, to results sent by fax, and the benefits and costs of those methods. Using this information, local and state officials could adopt processes and policies to promote public access to official precinct-level election results.
Bounded Sub-optimal Multi-Robot Path Planning Using Satisfiability Modulo Theory (SMT) Approach

Multi-robot path planning (MRPP) is a task of planning collision free paths for a group of robots in a graph. Each robot starts in its individual starting vertex and its task is to reach a given goal vertex. Existing techniques for solving MRPP optimally under various objectives include search-based and compilation-based approaches. Often however finding an optimal solution is too difficult hence sub-optimal algorithms that trade-off the quality of solutions and the runtime have been devised. We suggest eSMT-CBS, a new bounded sub-optimal algorithm built on top of recent compilation-based method for optimal MRPP based on satisfiability modulo theories (SMT). We compare eSMT-CBS with ECBS, a major representative of bounded sub-optimal search-based algorithms. The experimental evaluation shows significant advantage of eSMT-CBS across variety of scenarios.
Toward secure refactoring of object-oriented programs

Abstract Security is a significant issue in software systems. The initially secure design of a software system could be at risk of a refactoring transformation. Consequently, via a refactoring transformation, the software could be considered vulnerable, and the refactoring process can undergo security-aware refactoring. This work deals with three security approaches to assist programmers in refactoring their software systems while simultaneously sustaining or possibly improving the security of their software system. In addition, this work deals with security-aware refactoring at the design stage rather than refactoring that requires a full source code. The main aim of this work is to provide programmers with knowledge about secure refactoring processes to improve the long-term maintainability of the software system in a secure way while sustaining the external behavior of the system.
Vulnerability of Privacy Visor Used to Disrupt Unauthorized Face Recognition

This work studies a vulnerability in privacy visors, new wearable devices that aim to prevent unauthorized face recognition from being performed. Although the use of a privacy visor assumes that the detectors' targets are uncovered bare faces, it is not hard to detect the privacy visor itself. To quantify the effects of the disruption and the vulnerability, we conducted experiments involving two major face-recognition algorithms, namely a method based on convolutional neural networks and a method that aims to identify coordinates of facial landscapes. Our experiments were able to demonstrate that using a privacy visor can reduce the mean face-recognition rates for both algorithms. However, they are less effective if faces with privacy visors are used in training. Faces with privacy visors is detected at a rate of 42.28 % on average.
Rescue Dog Action Recognition by Integrating Ego-Centric Video, Sound and Sensor Information

A dog which assists rescue activity in the scene of disasters such as earthquakes and landslides is called a “disaster rescue dog” or just a “rescue dog”. In Japan where earthquakes happen frequently, a research project on “Cyber-Rescue” is being organized for more efficient rescue activities. In the project, to analyze the activities of rescue dogs in the scene of disasters, “Cyber Dog Suits” equipped with sensors, a camera and a GPS were developed. In this work, we recognize dog activities in the ego-centric dog videos taken by the camera mounted on the cyber-dog suits. To do that, we propose an image/sound/sensor-based four-stream CNN for dog activity recognition which integrates sound and sensor signals as well as motion and appearance. We conducted some experiments for multi-class activity categorization using the proposed method. As a result, the proposed method which integrates appearance, motion, sound and sensor information achieved the highest accuracy, 48.05%. This result is relatively high as a recognition result of ego-centric videos.