Reviewerly/exHarmony huggingface dataset:
Is Your Marriage Reliable?

In recent years, global divorce rate is still high. What kind of couple will divorce and what factors lead to divorce are important problems that worth studying. In this paper, we apply three machine learning algorithms (Support Vector Machine (SVM), Random forest (RF) and Natural Gradient Boosting (NGBoost)) on a divorce prediction dataset. The dataset consists of 170 samples, each of which contains 54 questions about the couple's emotional status. We regard the scores of 54 questions as the features of each sample to apply our machine learning algorithms. Compared with SVM and RF, NGBoost has superior performance as NGBoost can achieve 0.9833 accuracy, 0.9769 precision and 0.9828 F1 score. In addition, we also show the most important features in the model of RF and NGBoost to find the most important factors which lead to divorce.
dantro: a Python package for handling, transforming, and visualizing hierarchically structured data

Researchers, especially those relying on computer simulations, frequently generate and analyze large amounts of data.With recent increases in computational capacity, the demand to efficiently and reliably process the generated data is growing accordingly.To address these needs, one powerful approach is to streamline the data handling, transformation, and visualization procedures into a data processing pipeline.Akin to the Continuous Integration pipelines widely used in modern software engineering, a data processing pipeline implements an automated sequence of predefined, yet dynamically configurable operations.Ultimately, using such a data processing pipeline can greatly improve the efficiency, reliability, and reproducibility of the scientific workflow.
Writing-up ethnographic research as a thematic narrative: The excerpt-commentary-unit

With qualitative research apparently threatened by big data, researchers, editors, educators, librarians, and publishers need to understand the mix of research methods used in their field to guide decision making. In response, this study assesses the prevalence and citation impact of academic research between 1996 and 2019 that reports one of four common methods to gather qualitative data: interviews, focus groups, case studies, and ethnography. With minor exceptions, the prevalence of qualitative data has increased, often substantially, since 1996. In addition, all 27 broad fields (as classified by Scopus) now publish some qualitative research, with interviewing being by far the most common approach. The citation impact of interview and focus group research mostly decreased over time, whereas of case study citation impact increased, and ethnography was above average in its two core subject areas. This suggests that methods teachers, researchers, editors, librarians, and publishers should be increasingly open to the value of qualitative data.
Guidelines for cybersecurity visualization design

Cyber security visualization designers can benefit from human factors engineering concepts and principles to resolve key human factors challenges in visual interface design. We survey human factors concepts and principles that have been applied in the past decade of human factors research. We highlight these concepts and relate them to cybersecurity visualization design. We provide guidelines to help cybersecurity visualization designers address some human factors challenges in the context of interface design. We use ecological interface design approach to present human factors-based principles of interface design for visualization. Cyber security visualization designers will benefit from human factors engineering concepts and principles to resolve key human factors challenges in visual interface design.
Using the Smartphone as an Augmented Reality Device in ETO Industry

Industrial Augmented Reality (IAR) has proven its potential in ETO industry by improving productivity and acting as a key technology for consistent digital information flows. In order to leverage these potentials on a significant scale, broad distribution of IAR inside a company should be aimed for. However, most applications have not yet overcome a prototype state and failed to make the step into production. Primary limiting factors are inadequate device availability and acceptance of current IAR solutions, which focus on smart glasses and tablet devices. The aim of this paper is to examine whether smartphones have the potential to serve as a platform for IAR in ETO industry. First the user interface of an existing tablet IAR prototype is adapted to a smartphone in order to later evaluate user experience. Hence a survey to assess worker preferences between tablet and smartphone devices for common IAR supported tasks is developed. The survey design is tested on a group of workers from ETO industry.
Loss Architecture Search for Few-Shot Object Recognition

Few-shot object recognition, which exploits a set of well-labeled data to build a classifier for new classes that have only several samples per class, has received extensive attention from the machine learning community. In this paper, we investigate the problem of designing an optimal loss function for few-shot object recognition and propose a novel few-shot object recognition system that includes the following three steps: (1) generate a loss function architecture using a recurrent neural network (generator); (2) train a base embedding network with the generated loss function on a training set; (3) fine-tune the base embedding network using the few-shot instances from a validation set to obtain the accuracy and use it as a reward signal to update the generator. This procedure is repeated and implemented in the reinforcement learning framework for finding the best loss architecture such that the embedding network yields the highest validation accuracy. Our key insight is to create a search space of the loss function architectures and evaluate the quality of a particular loss function on the dataset of interest. We conduct experiments on three popular datasets for few-shot learning. The results show that the proposed approach achieves better performance than state-of-the-art methods.
Blockchain and Smart Contract Engineering

Because smart contracts are often small programs, tools to prevent attacks are often based on analyzing the smart contract code to identify vulnerabilities a priori.
A Conceptual Model for Blockchain-based Auditing Information System

Blockchain is viewed as one of the most promising and disruptive inventions and is considered to have the potential to significantly change current auditing profession and reshape the business ecosystem. With the advancement of blockchain, it has been concerned in some studies that auditing could be significantly impacted and eventually replaced. Meanwhile, another viewpoint argues that blockchain technology would push the existing auditing industry to a new direction rather than eliminating the need for auditing in the immediate future. This discussion can hardly be settled without evaluation, however, studies exploring how blockchain technology can be employed in auditing practice or how continuous auditing can be conducted using blockchain technology are limited. This paper analyses the impact of blockchain features on existing audit processes and discusses the possibility of applying blockchain characteristics including immutability, distributed ledger, real-time settlement to the auditing domain. Based on the systematic analysis, this study proposes a conceptual model for blockchain-based auditing information system, which provides solutions to employ blockchain technology in auditing profession, significantly improving the efficiency and effectiveness of auditing and promoting the transformation of the auditing paradigm to real-time, continuous and intelligent auditing.
Clustering Crowdsourced Test Reports of Mobile Applications Using Image Understanding

Crowdsourced testing has been widely used to improve software quality as it can detect various bugs and simulate real usage scenarios. Crowdsourced workers perform tasks on crowdsourcing platforms and present their experiences as test reports, which naturally generates an overwhelming number of test reports. Therefore, inspecting these reports becomes a time-consuming yet inevitable task. In recent years, many text-based prioritization and clustering techniques have been proposed to address this challenge. However, in mobile testing, test reports often consist of only short test descriptions but rich screenshots. Compared with the uncertainty of textual information, well-defined screenshots can often adequately express the mobile application’s activity views. In this paper, by employing image-understanding techniques, we propose an approach for clustering crowdsourced test reports of mobile applications based on both textual and image features to assist the inspection procedure. We employ Spatial Pyramid Matching (SPM) to measure the similarity of the screenshots and use the natural-language-processing techniques to compute the textual distance of test reports. To validate our approach, we conducted an experiment on 6 industrial crowdsourced projects that contain more than 1600 test reports and 1400 screenshots. The results show that our approach is capable of outperforming the baselines by up to 37 percent regarding the APFD metric. Further, we analyze the parameter sensitivity of our approach and discuss the settings for different application scenarios.
Experiments with Maximin Sampling

To apply clustering algorithms to big data, or to build clustering ensembles, it is a standard process to sample the original data set in a way that hopefully spans the original distribution. There are at least six ways to initialize the Maximin (MM) sampling algorithm. This paper contains experiments to determine whether samples produced by the six methods differ significantly; and whether they are superior to simple random sampling. Empirical evidence supports two conclusions. First, there is not enough difference in MM samples generated by the six initializations to support using any but the least costly method: viz., using the first sample in the data as the first MM point. Second, unless the input data have subsets (clusters) that are compact and separated in a well-defined sense, random sampling is demonstrably superior to MM sampling for even small data sets.
Correlations in geometric states

A bstract In this paper we explore the correlations in the geometric states. Here the geometric state means the state in CFTs that can be effectively described by classical geometry in the bulk in the semi-classical limit G → 0. By using the upper bound of Holevo information we show the convex combination of geometric states cannot be a geometric state. To understand the duality between thermofield double state and eternal black hle, we construct several correlated states of two CFTs. In all the examples we show their correlations are too weak to produce the a connected spacetime. Then we review the measure named quantum discord and use it to characterize the classical and quantum correlations in quantum field theories. Finally, we discuss the correlations between two intervals A and B with distance d in the vacuum state of 2D CFTs with large central charge c . The feature is the phase transition of the mutual information I ( ρ AB ). We analyse the quasi-product state of ρ AB for large d . By using the Koashi-Winter relation of tripartite states the quantum and classical correlations between A and B can expressed as Holevo information, which provides a new understanding of the correlations as accessible information.
Efficient and Privacy-Preserving Speaker Verification Scheme for Home Automation Devices

Speaker recognition technology has been widely used in home automation recently, which can provide identification for the user efficiently. However, speaker recognition technology brings security risks as well as convenience, especially the privacy disclosure of the user's voiceprint templates. This paper proposes an efficient and privacy-preserving speaker verification scheme for home automation devices, named SEASON. We modify the high-accuracy deepspeaker framework in terms of privacy, based on residual network. With SEASON, users can access the home automation devices securely. Detailed security analysis shows that SEASON can actually resist various security threats. We implement SEASON with real datasets and 48 real records from four testers. Experiments reveal that SEASON can achieve efficient and accurate verification.
Smart &amp; Integrated Management System - Smart Cities, Epidemiological Control Tool Using Drones

This paper describes the development of a real application using Drones over urban regions to help the authorities at epidemiological control through a disruptive solutions based on a customizable Smart & Integrated Management System (SIGI), devices and software based on the Enterprise Resource Planning (ERP) concept. Compound by management software, Drones and specific IoT devices, both referred to as sensors, the sensors collect the data of the interest areas in real time, creating a specified database. Based on the data collected from the interest areas, SIGI software has the ability to show real-time situational analysis of these areas and allows that the administrator can optimize resources (material and human) improving the efficiency of resource allocation in these areas. In addition to the development of the management software, the development of sensors to collect the information in the field and update these information to the database of the management software, are considered. The sensors will be recognized as IoT devices for the collection of meteorological data, images and command / control Drones. Initially the system will be customized, using an Artificial Intelligence tool, to collect data and identify the outbreaks of the dengue mosquito, zika and Chikungunya, nominee by risk areas. After the definition of the potential risk areas, in a complementary way, a totally customized Drone will be used to map these areas of interest, generating aerial photographs, identifying and geotagging the potential “targets”, which will allow the agents to identify potential mosquito breeding sites. After the identification of breeding areas, the next step will be the effective combat of the vectors, using the Drones to fly over the areas of interest, where biological defenses will be “dropped” over the targets to combat mosquitoes. Due some Drone flight restrictions over the cities, the whole process will be monitored by a situation room, that will be able to control the Drone remotely, access the air space controller, reads the sensors installed in the city (field), that will measure, for example, rainfall through weather stations installed in risk areas and subsequently processed by Intelligent System Integrated Management (SIGI), which will result to the information public official reflecting the situational analysis of the areas, which will enable a better management of available resources, helping the public agent, preventively in the decision making.
A Light-Weight Object Detection Framework with FPA Module for Optical Remote Sensing Imagery

With the development of remote sensing technology, the acquisition of remote sensing images is easier and easier, which provides sufficient data resources for the task of detecting remote sensing objects. However, how to detect objects quickly and accurately from many complex optical remote sensing images is a challenging hot issue. In this paper, we propose an efficient anchor free object detector, CenterFPANet. To pursue speed, we use a lightweight backbone and introduce the asymmetric revolution block. To improve the accuracy, we design the FPA module, which links the feature maps of different levels, and introduces the attention mechanism to dynamically adjust the weights of each level of feature maps, which solves the problem of detection difficulty caused by large size range of remote sensing objects. This strategy can improve the accuracy of remote sensing image object detection without reducing the detection speed. On the DOTA dataset, CenterFPANet mAP is 64.00%, and FPS is 22.2, which is close to the accuracy of the anchor-based methods currently used and much faster than them. Compared with Faster RCNN, mAP is 6.76% lower but 60.87% faster. All in all, CenterFPANet achieves a balance between speed and accuracy in large-scale optical remote sensing object detection.
Can we use software bug reports to identify vulnerability discovery strategies?

Daily horror stories related to software vulnerabilities necessitates the understanding of how vulnerabilities are discovered. Identification of data sources that can be leveraged to understand how vulnerabilities are discovered could aid cybersecurity researchers to characterize exploitation of vulnerabilities. The goal of the paper is to help cybersecurity researchers in characterizing vulnerabilities by conducting an empirical study of software bug reports. We apply qualitative analysis on 729, 908, and 5336 open source software (OSS) bug reports respectively, collected from Gentoo, LibreOffice, and Mozilla to investigate if bug reports include vulnerability discovery strategies i.e. sequences of computation and/or cognitive activities that an attacker performs to discover vulnerabilities, where the vulnerability is indexed by a credible source, such as the National Vulnerability Database (NVD). We evaluate two approaches namely, text feature-based approach and regular expression-based approach to automatically identify bug reports that include vulnerability discovery strategies.
Remote Sensing Roadmap for Mineral Mapping using Satellite Imagery

The application of data mining in remote sensing has been expanding encompassing various extents and fields of earth observations. The Earth is facing unparalleled changes, which require global-scale observation and monitoring. The information about an object, an area of a phenomenon can be obtained in remote sensing by analyzing the data acquired by a device that is not in contact with the investigation entity. Terabytes of earth observation data are generated in terms of volume, variety, and velocity and are seemingly increasing beyond numbers. They are collected in the form of satellite imagery using spectral signatures and electromagnetic radiations. Data Mining, machine learning, and artificial intelligence are the emerging fields of study that have emanated to pursue solutions to automation of extracting data from Earth observations and deduce meaningful patterns for efficient tracking and control mechanisms. The paper attempts to review the work carried out in the field of earth observation data science and explore the intricacies of one of the challenging application areas of remote sensing akin to the field of lithology with mineral mapping considering little to barren vegetation areas subjected to mineral exploration accustomed to pattern recognition and fracture detection employing Machine learning and thereby earth space data science essentials.
Copy-Move Forgery Detection and Performance Analysis of Feature Detectors

Now a days, the digital image integrity are remarkably important for the exchange of data which are generally utilized for different applications like fraud detection, therapeutic imaging, reporting, and advanced crime investigation. Digital images can easily be forged with the advancement of image manipulation tools and information technology. The commonly used image forgery technique in digital forensic filed is Copy-move forgery. The two fundamental classifications for identifying copy-move forged images are keypoint-based and block- based method. Block-based strategies have the burden of high computational expense because of the enormous number of image blocks and it fails to deal with different geometric transformations. On the contrary, keypoint-based methodologies can overwhelmed these two draw-backs however are discovered hard to manage smooth locales. As a result, these two methodologies are combined and proposed a effective copy-move forgery detection. Also, we accomplish a comparative study between different keypoint detectors and feature matching algorithms used to determine computational complexity of each.
Design of Face Recognition Attendance

In recent years, face recognition technology has developed rapidly, and its application range has become more and more extensive. It is one of the most important application fields in computer vision technology. However, there are still many technical factors that restrict the application and promotion of face recognition technology. For example: shadows, occlusions, light and dark areas, dark light, highlights and other factors will make the face recognition rate drop sharply. Therefore, face recognition has extremely high research and application value. We use the Local Binary Patterns (LBP) algorithms with histogram equalization to obtain high-resolution images and improve the recognition rate in different scenarios, and try to apply face recognition to attendance.
Reversible Data Hiding for Encrypted 3D Model Based on Prediction Error Expansion

Since 3D models can intuitively display real-world information, there are potential scenarios in many application fields, such as architectural models and medical organ models. However, a 3D model shared through the internet can be easily obtained by an unauthorized user. In order to solve the security problem of 3D model in the cloud, a reversible data hiding method for encrypted 3D model based on prediction error expansion is proposed. In this method, the original 3D model is preprocessed, and the vertex of 3D model is encrypted by using the Paillier cryptosystem. In the cloud, in order to improve accuracy of data extraction, the dyeing method is designed to classify all vertices into the embedded set and the referenced set. After that, secret data is embedded by expanding direction of prediction error with direction vector. The prediction error of the vertex in the embedded set is computed by using the referenced set, and the direction vector is obtained according to the mapping table, which is designed to map several bits to a direction vector. Secret data can be extracted by comparing the angle between the direction of prediction error and direction vector, and the original model can be restored using the referenced set. Experiment results show that compared with the existing data hiding method for encrypted 3D model, the proposed method has higher data hiding capacity, and the accuracy of data extraction have improved. Moreover, the directly decrypted model has less distortion.