Reviewerly/exHarmony huggingface dataset:
A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

One-class support vector machine (OCSVM) is an efficient data-driven mineral prospectivity mapping model. Since the parameters of OCSVM directly affect the performance of the model, it is necessary to optimize the parameters of OCSVM in mineral prospectivity mapping. Trial and error method is usually used to determine the “optimal” parameters of OCSVM. However, it is difficult to find the globally optimal parameters by the trial and error method. By combining OCSVM with the bat algorithm, the intialization parameters of the OCSVM can be automatically optimized. The combined model is called bat-optimized OCSVM. In this model, the area under the curve (AUC) of OCSVM is taken as the fitness value of the objective function optimized by the bat algorithm, the value ranges of the initialization parameters of OCSVM are used to specify the search space of bat population, and the optimal parameters of OCSVM are automatically determined through the iterative search process of the bat algorithm. The bat-optimized OCSVMs were used to map mineral prospectivity of the Helong district, Jilin Province, China, and compared with the OCSVM initialized by the default parameters (i.e., common OCSVM) and the OCSVM optimized by trial and error. The results show that (a) the receiver operating characteristic (ROC) curve of the trial and error-optimized OCSVM is intersected with those of the bat-optimized OCSVMs and (b) the ROC curves of the optimized OCSVMs slightly dominate that of the common OCSVM in the ROC space. The area under the curves (AUCs) of the common and trial and error-optimized OCSVMs (0.8268 and 0.8566) are smaller than those of the bat-optimized ones (0.8649 and 0.8644). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets predicted by the common and trial and error-optimized OCSVMs account for 29.61% and 18.66% of the study area respectively, and contain 93% and 86% of the known mineral deposits. The mineral targets predicted by the bat-optimized OCSVMs account for 19.84% and 14.22% of the study area respectively, and also contain 93% and 86% of the known mineral deposits. Therefore, we have 0.93/0.2961 = 3.1408 &lt; 0.86/0.1866 = 4.6088 &lt; 0.93/0.1984 = 4.6875 &lt; 0.86/0.1422 = 6.0478, indicating that the bat-optimized OCSVMs perform slightly better than the common and trial and error-optimized OCSVMs in mineral prospectivity mapping.
Deeply Learned Pose Invariant Image Analysis with Applications in 3D Face Recognition

Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L 2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.
Reinforced Training Data Selection for Domain Adaptation

Supervised models suffer from the problem of domain shifting where distribution mismatch in the data across domains greatly affect model performance.To solve the problem, training data selection (TDS) has been proven to be a prospective solution for domain adaptation in leveraging appropriate data.However, conventional TDS methods normally requires a predefined threshold which is neither easy to set nor can be applied across tasks, and models are trained separately with the TDS process.To make TDS self-adapted to data and task, and to combine it with model training, in this paper, we propose a reinforcement learning (RL) framework that synchronously searches for training instances relevant to the target domain and learns better representations for them.A selection distribution generator (SDG) is designed to perform the selection and is updated according to the rewards computed from the selected data, where a predictor is included in the framework to ensure a taskspecific model can be trained on the selected data and provides feedback to rewards.Experimental results from part-of-speech tagging, dependency parsing, and sentiment analysis, as well as ablation studies, illustrate that the proposed framework is not only effective in data selection and representation, but also generalized to accommodate different NLP tasks.
An Intelligent Model for Online Recruitment Fraud Detection

This study research attempts to prohibit privacy and loss of money for individuals and organization by creating a reliable model which can detect the fraud exposure in the online recruitment environments.This research presents a major contribution represented in a reliable detection model using ensemble approach based on Random forest classifier to detect Online Recruitment Fraud (ORF).The detection of Online Recruitment Fraud is characterized by other types of electronic fraud detection by its modern and the scarcity of studies on this concept.The researcher proposed the detection model to achieve the objectives of this study.For feature selection, support vector machine method is used and for classification and detection, ensemble classifier using Random Forest is employed.A freely available dataset called Employment Scam Aegean Dataset (EMSCAD) is used to apply the model.Pre-processing step had been applied before the selection and classification adoptions.The results showed an obtained accuracy of 97.41%.Further, the findings presented the main features and important factors in detection purpose include having a company profile feature, having a company logo feature and an industry feature.
A Comprehensive Improved Salp Swarm Algorithm on Redundant Container Deployment Problem

As a representative of lightweight virtualization, container technology has been widely used in cloud services and edge computing applications.However, in the resource pool scenario composed by multiple intelligent terminal devices, considering the limited resources and poor stability of these devices, it is necessary to split the overall service into multiple microservices and deploy the backups of them in respective containers.Traditional container scheduling policies tend to be less effective in solving such problems.Therefore, the article used a meta-heuristic algorithm to solve this kind of problems.Based on a newly proposed salp swarm algorithm (SSA), the paper presented a comprehensive improved SSA (CISSA).CISSA improved the performance of the original SSA by 4 steps.To verify the performance of CISSA in different kinds of test functions, the algorithm was compared with 7 commonly-used meta-heuristic algorithms in 29 benchmark functions provided by the author of SSA.In addition, the article constructed three container cluster models of different sizes, all these algorithms were used to solve these redundant container deployment problems, the experimental results indicate that the CISSA is superior to other algorithms in such problems of different dimensions.
Effect of air quality and dust deposition on power generation performance of photovoltaic module on building roof

Due to the dust deposition on the surface of photovoltaic modules and the air pollution, the power generation performance of photovoltaic modules will be significantly affected. In order to quantitatively estimate the effect of air quality and dust deposition on the power generation performance of photovoltaic modules, a distributed photovoltaic system on a building roof in Shanghai is studied in this paper. Both artificial and natural dust deposition conditions are tested in terms of the influence of these factors on the power generation efficiency of photovoltaic modules. The variation of solar radiation intensity with PM2.5 concentration in Shanghai and the variation of photovoltaic module power generation capacity with PM2.5 concentration are tested and analyzed. The experimental results show that the surface dust of photovoltaic module reduces the power and efficiency of photovoltaic module. Experimental comparison between the dusty photovoltaic module and clean photovoltaic module shows that the dust on photovoltaic modules can reduce the power and efficiency significantly, where the highest power generation decrease is 35.226% and the highest power generation efficiency decrease is 5.546% as indicated in the experiments. Result shows that the amount of solar radiation is exponentially correlated to the PM2.5 concentration. Practical application: The influence of air quality and dust deposition on the performance of distributed PV modules is of vital importance under severe particulate pollution in Shanghai. This research helps predict the degradation of the photovoltaic system power generation efficiency, and determine whether to clean up the dust on the photovoltaic system according to accumulated PM concentration. This reduces the cost of cleaning of the dust on photovoltaic system. This study provides theoretical and experimental basis for the design of the building roof mounted solar photovoltaic system according to the local haze condition.
Multi-scale Relation Network for Few-Shot Learning Based on Meta-learning

Deep neural networks can learn a huge function space, because they have millions of parameters to fit large amounts of labeled data. However, this advantage is a major obstacle for few-shot learning, because which has to make predictions based on only few samples of each class. In this work, inspired by multi-scale features methods and relation network which uses neural network to learn metrics, we propose a concise and efficient network, multi-scale relation network. The network consists of a feature extractor and a metric learner. Firstly, the feature extractor extracts multi-scale features by combining features from different convolutional layers. Secondly, we generate the relation feature by calculating the absolute value of the difference between multi-scale features. The results on benchmark sets show that our method avoids the over fitting and elongates the period of learning process, providing higher performance with simple design choices.
On Training Deep Neural Networks Using a Streaming Approach

Abstract In recent years, many deep learning methods, allowed for a significant improvement of systems based on artificial intelligence methods. Their effectiveness results from an ability to analyze large labeled datasets. The price for such high accuracy is the long training time, necessary to process such large amounts of data. On the other hand, along with the increase in the number of collected data, the field of data stream analysis was developed. It enables to process data immediately, with no need to store them. In this work, we decided to take advantage of the benefits of data streaming in order to accelerate the training of deep neural networks. The work includes an analysis of two approaches to network learning, presented on the background of traditional stochastic and batch-based methods.
Auditing the Blockchain Oracle Problem

ABSTRACT Blockchain brought about the potential for improved data reliability, but only when those data are the result of transactions that happen completely within a blockchain. The reliability of blockchain data is threatened when information is introduced from the outside world, such as when oracles are used to observe occurrences and provide this information to smart contracts. This specific threat to the reliability of blockchain data is known as the oracle problem. This study offers a working definition of blockchain oracles and argues that they should be viewed as service organizations under the auditing standards from the AICPA and PCAOB. The study then discusses risks associated with the oracle's function of collecting, storing, transforming, and transmitting information, and proposes control objectives for auditors to consider when evaluating the information provided by oracles. Finally, the study closes by discussing open questions the audit profession still needs to address with the oracle problem.
Digital pathology in the time of corona

The 2020 COVID-19 crisis has had and will have many implications for healthcare, including pathology. Rising number of infections create staffing shortages and other hospital departments might require pathology employees to fill more urgent positions. Furthermore, lockdown measures and social distancing cause many people to work from home. During this crisis, it became clearer than ever what an asset digital diagnostics is to keep pathologists, residents, molecular biologists and pathology assistants engaged in the diagnostic process, allowing social distancing and a ‘need to be there’ on-the-premises policy, while working effectively from home. This paper provides an overview of our way of working during the 2020 COVID-19 crisis with emphasis on the virtues of digital pathology.
Challenges in re-designing operations and jobs to embody AI and robotics in services. Findings from a case in the hospitality industry

Abstract The adoption of artificial intelligence (AI) and service robots in the tourism industry for frontline service automation is generating a growing interest. Although there is a fairly large body of literature about this research field, the impacts on the service encounter need to be further investigated. The paper presents an action research project that led to employ the humanoid robot “Pepper”, equipped with a supervised machine-learning AI system, at the reception of an Italian hotel to provide information to clients. This allowed to explore the role played by this agent and the effects on the changing role taken by frontline employees (FLE) and customers. Findings show that this technology can act as an augmentation force and that FLEs’ role can evolve mainly into that of enabler - of the customers and of technology -, innovator and coordinator, while customers may take above all the role of enabler of the technology. The study also contributes to introduce the new role of “AI supervisor” among FLEs.
Scribble-Based Domain Adaptation via Co-segmentation

Although deep convolutional networks have reached state-of-the-art performance in many medical image segmentation tasks, they have typically demonstrated poor generalisation capability. To be able to generalise from one domain (e.g. one imaging modality) to another, domain adaptation has to be performed. While supervised methods may lead to good performance, they require to fully annotate additional data which may not be an option in practice. In contrast, unsupervised methods don't need additional annotations but are usually unstable and hard to train. In this work, we propose a novel weakly-supervised method. Instead of requiring detailed but time-consuming annotations, scribbles on the target domain are used to perform domain adaptation. This paper introduces a new formulation of domain adaptation based on structured learning and co-segmentation. Our method is easy to train, thanks to the introduction of a regularised loss. The framework is validated on Vestibular Schwannoma segmentation (T1 to T2 scans). Our proposed method outperforms unsupervised approaches and achieves comparable performance to a fully-supervised approach.
Token-level Adaptive Training for Neural Machine Translation

There exists a token imbalance phenomenon in natural language as different tokens appear with different frequencies, which leads to different learning difficulties for tokens in Neural Machine Translation (NMT).The vanilla NMT model usually adopts trivial equal-weighted objectives for target tokens with different frequencies and tends to generate more high-frequency tokens and less lowfrequency tokens compared with the golden token distribution.However, low-frequency tokens may carry critical semantic information that will affect the translation quality once they are neglected.In this paper, we explored target token-level adaptive objectives based on token frequencies to assign appropriate weights for each target token during training.We aimed that those meaningful but relatively low-frequency words could be assigned with larger weights in objectives to encourage the model to pay more attention to these tokens.Our method yields consistent improvements in translation quality on ZH-EN, EN-RO, and EN-DE translation tasks, especially on sentences that contain more low-frequency tokens where we can get 1.68, 1.02, and 0.52 BLEU increases compared with baseline, respectively.Further analyses show that our method can also improve the lexical diversity of translation.
Perceptions of Gender Diversity's Impact on Mood in Software Development Teams

Recent studies show that gender diversity in IT teams has a positive impact on the software development process. However, there is still a great gender inequality. The aim of our study was to examine how the working atmosphere depends on the gender differentiation of IT teams. The analysis of the results of the interviews and questionnaires showed that the atmosphere in gender-differentiated teams is more pleasant compared to purely male ones. The paper also discusses the problem of gender discrimination, which, according to the results of the study, unfortunately still exists and affects the working atmosphere. Finally, we looked at ways to reduce the gender inequity, where it turned out that soft approaches such as dedicated training, workshops to show the human face of the IT industry are preferred.
Zigzag approach to higher key rate of sending-or-not-sending twin field quantum key distribution with finite-key effects

Abstract Odd-parity error rejection (OPER), in particular the method of actively odd parity pairing (AOPP), can drastically improve the asymptotic key rate of sending-or-not-sending twin-field (SNS-TF) quantum key distribution (QKD). However, in practice, the finite-key effects have to be considered for the security. Here, we propose a zigzag approach to verify the phase-flip error of the survived bits after OPER or AOPP. Based on this, we can take all the finite-key effects efficiently in calculating the non-asymptotic key rate. Numerical simulation shows that our approach here produces the highest key rate over all distances among all existing methods, improving the key rate by more than 100% to 3000% in comparison with different prior art methods with typical experimental setting. These verify the advantages of the AOPP method with finite data size. Also, with our zigzag approach here, the non-asymptotic key rate of SNS-TF QKD can by far break the absolute bound of repeater-less key rate with whatever detection efficiency. We can even reach a non-asymptotic key rate more than 40 times of the practical bound and 13 times of the absolute bound with 10 12 pulses.
Multi-Scale Thermal to Visible Face Verification via Attribute Guided Synthesis

Thermal-to-visible face verification is a challenging problem due to the large domain discrepancy between the modalities. Existing approaches either attempt to synthesize visible faces from thermal faces or learn domain-invariant robust features from these modalities for cross-modal matching. In this paper, we use attributes extracted from visible images to synthesize attribute-preserved visible images from thermal imagery for cross-modal matching. A pre-trained attribute predictor network is used to extract the attributes from the visible image. Then, a novel multi-scale generator is proposed to synthesize the visible image from the thermal image guided by the extracted attributes. Finally, a pre-trained VGG-Face network is leveraged to extract features from the synthesized image and the input visible image for verification. Extensive experiments evaluated on three datasets (ARL Face Database, Visible and Thermal Paired Face Database, and Tufts Face Database) demonstrate that the proposed method achieves state-of-the-art performance. In particular, it achieves around 2.41\%, 2.85\% and 1.77\% improvements in Equal Error Rate (EER) over the state-of-the-art methods on the ARL Face Database, Visible and Thermal Paired Face Database, and Tufts Face Database, respectively. An extended dataset (ARL Face Dataset volume III) consisting of polarimetric thermal faces of 121 subjects is also introduced in this paper. Furthermore, an ablation study is conducted to demonstrate the effectiveness of different modules in the proposed method.
Underwater Image Enhancement Based on Local Contrast Correction and Multi-Scale Fusion

In this study, an underwater image enhancement method based on local contrast correction (LCC) and multi-scale fusion is proposed to resolve low contrast and color distortion of underwater images. First, the original image is compensated using the red channel, and the compensated image is processed with a white balance. Second, LCC and image sharpening are carried out to generate two different image versions. Finally, the local contrast corrected images are fused with sharpened images by the multi-scale fusion method. The results show that the proposed method can be applied to water degradation images in different environments without resorting to an image formation model. It can effectively solve color distortion, low contrast, and unobvious details of underwater images.
Closing the knowledge‐action gap in conservation with open science

Abstract The knowledge‐action gap in conservation science and practice occurs when research outputs do not result in actions to protect or restore biodiversity. Among the diverse and complex reasons for this gap, three barriers are fundamental: knowledge is often unavailable to practitioners and challenging to interpret or difficult to use or both. Problems of availability, interpretability, and useability are solvable with open science practices. We considered the benefits and challenges of three open science practices for use by conservation scientists and practitioners. First, open access publishing makes the scientific literature available to all. Second, open materials (detailed methods, data, code, and software) increase the transparency and use of research findings. Third, open education resources allow conservation scientists and practitioners to acquire the skills needed to use research outputs. The long‐term adoption of open science practices would help researchers and practitioners achieve conservation goals more quickly and efficiently and reduce inequities in information sharing. However, short‐term costs for individual researchers (insufficient institutional incentives to engage in open science and knowledge mobilization) remain a challenge. We caution against a passive approach to sharing that simply involves making information available. We advocate a proactive stance toward transparency, communication, collaboration, and capacity building that involves seeking out and engaging with potential users to maximize the environmental and societal impact of conservation science.