Reviewerly/exHarmony huggingface dataset:
Ambiguity in social ecological system understanding: Advancing modelling of stakeholder perceptions of climate change adaptation in Kenya

Climate change adaptation requires understanding of complex social ecological systems (SESs). One source of uncertainty in complex SESs is ambiguity, defined as the range and variety of existing perceptions in and of an SES, which are considered equally valid, resulting in a lack of a unique or single system understanding. Current modelling practices that acknowledge the presence of ambiguity in SESs focus on finding consensus with stakeholders; however, advanced methods for explicitly representing and aggregating ambiguity in SESs are underdeveloped. Moreover, understanding the influences of ambiguity on SES representation is limited. This paper demonstrates the presence and range of ambiguities in endogenous and exogenous system drivers and internal relationships based on individual fuzzy cognitive maps derived from stakeholder perceptions of climate change adaptation in Kenya and introduces an ambiguity based modelling process. Our results indicate that acknowledging ambiguity fundamentally changes SES representation and more advanced methods are required.
One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework

Deep learning-based video manipulation methods have become widely accessible to the masses. With little to no effort, people can quickly learn how to generate deepfake (DF) videos. While deep learning-based detection methods have been proposed to identify specific types of DFs, their performance suffers for other types of deepfake methods, including real-world deepfakes, on which they are not sufficiently trained. In other words, most of the proposed deep learning-based detection methods lack transferability and generalizability. Beyond detecting a single type of DF from benchmark deepfake datasets, we focus on developing a generalized approach to detect multiple types of DFs, including deepfakes from unknown generation methods such as DeepFake-in-the-Wild (DFW) videos. To better cope with unknown and unseen deepfakes, we introduce a Convolutional LSTM-based Residual Network (CLRNet), which adopts a unique model training strategy and explores spatial as well as the temporal information in a deepfakes. Through extensive experiments, we show that existing defense methods are not ready for real-world deployment. Whereas our defense method (CLRNet) achieves far better generalization when detecting various benchmark deepfake methods (97.57% on average). Furthermore, we evaluate our approach with a high-quality DeepFake-in-the-Wild dataset, collected from the Internet containing numerous videos and having more than 150,000 frames. Our CLRNet model demonstrated that it generalizes well against high-quality DFW videos by achieving 93.86% detection accuracy, outperforming existing state-of-the-art defense methods by a considerable margin.
Arial Image Classification using Deep Neural Networks with Discrete Cosine Transform, TSBTC and Augmentation Techniques

The surfacing of continuous monitoring needs, dramatic increases in computer processing power, enhanced modes of connectivity, processing of images, and capacity to capitalize on information in images are some of the reasons for the emergence of aerial vehicles. The major challenge in this field has been segregation of the aerial images captured by these vehicles. Several attempts have been made for successfully achieving good results for classification of aerial images using K-Nearest Neighbors, decisions trees, image segmentation, Support Vector Machines and k-means clustering. This paper attempts the same using Deep Convolutional Neural Network (DCNN) with an integration of Thepade's Sorted Block Truncation Coding n-ary (TSBTC nary) and Cosine Transform of aerial images from the UC Merced Dataset. A comparison of the performances using these methods is done and it is concluded that TSBTC n-ary on Discrete Cosine Transformed (DCT) images with augmentation gave the best results. The TSBTC 4-ary augmented model gave outstanding results, than most modern methods, with an accuracy of 99.29%.
Using a Multi-view Convolutional Neural Network to monitor solar irradiance

In the last years, there is an increasing interest for enhanced method for assessing and monitoring the level of the global horizontal irradiance (GHI) in photovoltaic (PV) systems, fostered by the massive deployment of this energy. Thermopile or photodiode pyranometers provide point measurements, which may not be adequate in cases when areal information is important (as for PV network or large PV plants monitoring). The use of All Sky Imagers paired convolutional neural networks, a powerful technique for estimation, has been proposed as a plausible alternative. In this work, a convolutional neural network architecture is presented to estimate solar irradiance from sets of ground-level Total Sky Images. This neural network is capable of combining images from three cameras. Results show that this approach is more accurate than using only images from a single camera. It has also been shown to improve the performance of two other approaches: a cloud fraction model and a feature extraction model.
Research and Analysis on Cloud Accounting of Enterprises under the Background of New Economy

Abstract With the in-depth development and popularization of the Internet, all kinds of economic formats have undergone giant changes, information flow and transmission become smoother and faster, the Internet configuration transaction mode is born, which plays a huge role in promoting the development of accounting cloud. At present, China’s accounting cloud-based technology is still in its infancy, and Internet cloud computing also has many disadvantages and its development path is very bumpy. However, this does not affect our exploration of accounting cloud-based development. At present, the application of cloud accounting is more suitable for small and medium-sized enterprises, and the future development has a broad market prospect. By analyzing the correlation between cloud-based accounting and traditional accounting financial processing, this paper obtains the advantages of cloud-based accounting and its potential for future development.
Low-Latency Aerial Images Object Detection for UAV

Visual-based object detection has large applications in security and surveillance for unmanned aerial vehicles (UAVs). Meanwhile, the object detectors are required of low-latency and easy to be deployed on embedded onboard platforms. Aiming to address these problems, we present a PA-YOLOv3 aerial images object detector based on YOLOv3 and PANet algorithms, which can be deployed on embedded platforms. The PA-YOLOv3 model uses the dual-tower structure to improve the feature extraction and expression capabilities in feature fusion stage of the network. Besides, we propose a balanced pruning method to reduce the model size and the imbalance of different feature layers during pruning. After balanced pruning, the latency and size of the model are significantly decreased. Finally, we deploy and quantify the model on the embedded platform with TensorRT technology and apply the model on the UAV system for testing. The comprehensive experiments are executed on VisDrone2018 dataset and real-world scenarios. The experimental results show the inference speed of PA-YOLOv3 boost of about [Formula: see text] model pruning and quantization, while maintaining high detection accuracy.
Significant Improvements over the State of the Art? A Case Study of the MS MARCO Document Ranking Leaderboard

Leaderboards are a ubiquitous part of modern research in applied machine learning. By design, they sort entries into some linear order, where the top-scoring entry is recognized as the "state of the art" (SOTA). Due to the rapid progress being made today, particularly with neural models, the top entry in a leaderboard is replaced with some regularity. These are touted as improvements in the state of the art. Such pronouncements, however, are almost never qualified with significance testing. In the context of the MS MARCO document ranking leaderboard, we pose a specific question: How do we know if a run is significantly better than the current SOTA? Against the backdrop of recent IR debates on scale types, our study proposes an evaluation framework that explicitly treats certain outcomes as distinct and avoids aggregating them into a single-point metric. Empirical analysis of SOTA runs from the MS MARCO document ranking leaderboard reveals insights about how one run can be "significantly better" than another that are obscured by the current official evaluation metric ([email protected]).
A Systematic Mapping Study on Cyber Security Indicator Data

A security indicator is a sign that shows us what something is like or how a situation is changing and can aid us in making informed estimations on cyber risks. There are many different breeds of security indicators, but, unfortunately, they are not always easy to apply due to a lack of available or credible sources of data. This paper undertakes a systematic mapping study on the academic literature related to cyber security indicator data. We identified 117 primary studies from the past five years as relevant to answer our research questions. They were classified according to a set of categories related to research type, domain, data openness, usage, source, type and content. Our results show a linear growth of publications per year, where most indicators are based on free or internal technical data that are domain independent. While these indicators can give valuable information about the contemporary cyber risk, the increasing usage of unconventional data sources and threat intelligence feeds of more strategic and tactical nature represent a more forward-looking trend. In addition, there is a need to take methods and techniques developed by the research community from the conceptual plane and make them practical enough for real-world application.
University Sports Information Management System Based on Big Data

Abstract Student information management is not only the basis of realizing efficient education and teaching management in Colleges and universities, but also an important link. However, in recent years, with the popular development of higher education, the number of students is further expanded, the number of students has increased a lot compared with the past, and the work of student information management has become increasingly heavy, which has brought new challenges to student information management. This requires managers (class teachers, class cadres) to update the concept of student management, innovate the student management mode, and use modern information technology to manage class affairs and student information. This topic is based on the realization of the standardization, digitization and automation of student information management, which can help class teachers and class cadres save manpower, material resources and financial resources, further improve the efficiency and accuracy of class information management in sports colleges and universities, strive to improve the scientific level of student information management, and provide a platform for students to broaden their horizons and actively participate in school activities. In this paper, through the design and development of sports information management system, and through the function and performance test, it is found that the system can meet the needs of colleges and universities, which simulates the results of 200 users’ concurrent number of operating system. The user concurrent number is the largest user concurrent number index of non functional requirements of the system, and the corresponding test items have passed the performance test requirements.
Transfer Learning-Based Classification of Breast Cancer using Ultrasound Images

Objective: One of the most significant cancers impacting the health of women is breast cancer. This study aimed to provide breast cancer classification (benign and malignant) using the transfer learning method on the ultrasound images.Methods: In the present study, a public imaging dataset was used for the breast cancer classification. Transfer learning technique was implemented for the detection and classification of breast cancer (benign or malignant) based on the ultrasound images. The current research includes data of 150 cases of malignant and 100 normal cases obtained from the Mendeley data. The relevant dataset was partitioned into training (85% of the images) and validation (15% of the images) sets. The present study implemented Teachable Machine (teachablemachine.withgoogle.com) for predicting the benign or malignant of breast cancer tumor based on the ultrasound images.Results: According to the experimental results, accuracy, sensitivity and specificity with 95% confidence intervals were 0.974 (0.923-1.0), 0.957 (0.781-0.999) and 1 (0.782-1.0), respectively.Conclusion: The model proposed in this study gave predictions that could be useful to clinicians in classifying breast cancer based on ultrasound images. Thus, this system can be developed in mobile, web, or alternative environments and offered as a computer-aided system for the use of radiologists, pathologists or other healthcare professionals in hospitals.
Blockchain and Smart-Contract: A Pioneering Approach of Inter-Firms Relationships? The Case of Franchise Networks

This paper is interested in the analysis of Blockchains and Smart-contracts applied to inter-firms relationships, in particular the franchise networks. After defining the Blockchain technology and the Smart-contract as a particular type of contract stored in blockchains, we question the theory of contracts and its conception(s) of transactions, information asymmetries, firm or inter-firm relations. To better understand the challenges of blockchain for franchise networks and identify opportunities for implementation in these networks, we present some relevant applications of this technology. We identify different ways where blockchain technology could improve the network management and therefore their performance: the supply-chain, the brand-name protection, security and transparency in the payment of fees and royalties, access to reliable information via an oracle.
Comparing Industry Frameworks with Deeply Quantized Neural Networks on Microcontrollers

Differently to the common belief, the industry quest for ultra-low-power neural networks is just at the beginning. Examples are the efforts carried by open communities such as TinyML and TinyMLPerf currently focusing on deep learning frameworks for Machine Learning (ML) and associated applications targeting micro-controllers (MCUs). However little attention has been put on deep learning frameworks and applications to enable ultra-low precision ML. These are enabling technologies to target uW hardware implementations. This work aims to compare two Deep Learning frameworks with support to deep quantization, QKeras and Larq, that abstract Tensorflow and Keras frameworks. Currently, Tensorflow is one of the most used deep learning tools by the research and industry communities aimed at deploying ML on the field. Two applications are presented with associated deeply quantized neural networks: Human Activity Recognition (HAR) exploiting a Hybrid Binary Neural Network (HBN) and Anomaly Detection for Industry 4.0 based on a Hybrid Binary AutoEncoder (HBAE). The pros and cons of the frameworks will be discussed during their usage on those applications. Results show an accuracy of up to 98.6% for the HBN and a PSNR up to 111.2 for the HBAE. The inference time has also been measured on different ARM-based architecture.
Deep Learning Enables Individual Xenograft Cell Classification in Histological Images by Analysis of Contextual Features

Patient-Derived Xenografts (PDXs) are the preclinical models which best recapitulate inter- and intra-patient complexity of human breast malignancies, and are also emerging as useful tools to study the normal breast epithelium. However, data analysis generated with such models is often confounded by the presence of host cells and can give rise to data misinterpretation. For instance, it is important to discriminate between xenografted and host cells in histological sections prior to performing immunostainings. We developed Single Cell Classifier (SCC), a data-driven deep learning-based computational tool that provides an innovative approach for automated cell species discrimination based on a multi-step process entailing nuclei segmentation and single cell classification. We show that human and murine cell contextual features, more than cell-intrinsic ones, can be exploited to discriminate between cell species in both normal and malignant tissues, yielding up to 96% classification accuracy. SCC will facilitate the interpretation of H&E- and DAPI-stained histological sections of xenografted human-in-mouse tissues and it is open to new in-house built models for further applications. SCC is released as an open-source plugin in ImageJ/Fiji available at the following link: https://github.com/Biomedical-Imaging-Group/SingleCellClassifier .
LightSys: Lightweight and Efficient CI System for Improving Integration Speed of Software

The complexity and size increase of software has extended the delay for developers as they wait for code analysis and code merge. With the larger and more complex software, more developers nowadays are developing software with large source code repositories. The tendency for software platforms to immediately update software packages with feature updates and bug-fixes is a significant obstacle. Continuous integration systems may help prevent software flaws during the active development of software packages, even when they are deployed and updated frequently. Herein, we present a portable and modular code review automation system that inspects incoming code changes such as code format and style, performance regression, static analysis, build and deployment tests, and dynamic analysis before merging and changing code. The proposed mechanisms are sufficiently lightweight to be hosted on a regular desktop computer even for numerous developers. The resulting reduced costs allow developers to apply the proposed mechanism to many source code repositories. Experimental results demonstrate that the proposed mechanism drastically reduces overheads and improves usability compared with conventional mechanisms: execution time (6x faster), CPU usage (40% lower), memory consumption (1/180), and no out-of-memory occurrence.
Quantum error reduction with deep neural network applied at the post-processing stage

Deep neural networks (DNN) can be applied at the post-processing stage for the improvement of the results of quantum computations on noisy intermediate-scale quantum (NISQ) processors. Here, we propose a method based on this idea, which is most suitable for digital quantum simulation characterized by the periodic structure of quantum circuits consisting of Trotter steps. A key ingredient of our approach is that it does not require any data from a classical simulator at the training stage. The network is trained to transform data obtained from quantum hardware with artificially increased Trotter steps number (noise level) toward the data obtained without such an increase. The additional Trotter steps are fictitious, i.e., they contain negligibly small rotations and, in the absence of hardware imperfections, reduce essentially to the identity gates. This preserves, at the training stage, information about relevant quantum circuit features. Two particular examples are considered that are the dynamics of the transverse-field Ising chain and XY spin chain, which were implemented on two real five-qubit IBM Q processors. A significant error reduction is demonstrated as a result of the DNN application that allows us to effectively increase quantum circuit depth in terms of Trotter steps.
Wikidata Support in the Creation of Rich Semantic Metadata for Historical Archives

The research question this paper aims at answering is the following: In an ontology-driven annotation system, can the information extracted from external resources (namely, Wikidata) provide users with useful suggestions in the characterization of entities used for the annotation of documents from historical archives? The context of the research is the PRiSMHA project, in which the main goal is the development of a proof-of-concept prototype ontology-driven system for semantic metadata generation. The assumption behind this effort is that an effective access to historical archives needs a rich semantic knowledge, relying on a domain ontology, that describes the content of archival resources. In the paper, we present a new feature of the annotation system: when characterizing a new entity (e.g., a person), some properties describing it are automatically pre-filled in, and more complex semantic representations (e.g., events the entity is involved in) are suggested; both kinds of suggestions are based on information retrieved from Wikidata. In the paper, we describe the automatic algorithm devised to support the definition of the mappings between the Wikidata semantic model and the PRiSMHA ontology, as well as the process used to extract information from Wikidata and to generate suggestions based on the defined mappings. Finally, we discuss the results of a qualitative evaluation of the suggestions, which provides a positive answer to the initial research question and indicates possible improvements.
Leveraging Multiple Relations for Fashion Trend Forecasting Based on Social Media

Fashion trend forecasting is of great research significance in providing useful suggestions for both fashion companies and fashion lovers. Although various studies have been devoted to tackling this challenging task, they only studied limited fashion elements with highly seasonal or simple patterns, which could hardly reveal the real complex fashion trends. Moreover, the mainstream solutions for this task are still statistical-based and solely focus on time-series data modeling, which limit the forecast accuracy. Towards insightful fashion trend forecasting, previous work <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> proposed to analyze more fine-grained fashion elements which can informatively reveal fashion trends. Specifically, it focused on detailed fashion element trend forecasting for specific user groups based on social media data. In addition, it proposed a neural network-based method, namely KERN, to address the problem of fashion trend modeling and forecasting. In this work, to extend the previous work <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> , we propose an improved model named Relation Enhanced Attention Recurrent (REAR) network. Compared to KERN, the REAR model leverages not only the relations among fashion elements, but also those among user groups, thus capturing more types of correlations among various fashion trends. To further improve the performance of long-range trend forecasting, the REAR method devises a sliding temporal attention mechanism, which is able to capture temporal patterns on future horizons better. Extensive experiments and more analysis have been conducted on the FIT <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> and GeoStyle <xref ref-type="bibr" rid="ref2" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[2]</xref> datasets to evaluate the performance of REAR. Experimental and analytical results demonstrate the effectiveness of the proposed REAR model in fashion trend forecasting, which also show the improvement of REAR compared to the KERN.