Reviewerly/exHarmony huggingface dataset:
Trust and Acceptance of E-Banking Technology Effect of Mediation on Customer Relationship Management Performance

The application of E-Banking is a form of bank service to customers that was established in the 1970s and has started in Malaysia. There was a form of innovation that technology gave to electronics in 1981 into the world of the banking industry. The first was the introduction of Automated Teller Machines. On June 1, 2000, the Central Bank of Malaysia was officially established and gave permission to improve the form of service and convenience for customers when conducting online transactions which previously were done offline and could not be done anywhere. Banking in Indonesia has the advantage of instilling harmony in business ideals and values ??that lead to spirituality. A good relationship in building relationships is the standard for choosing a private bank. Where certain conditions banking is able to become mobile on online applications. It has been seen that there is less activity in bank lobbying in conducting transactions between customers and employees, because it has increased customer satisfaction, maintaining relationships with antecedent factors and marketing performance in the field. For this reason, CRM performance has a goal in building and maintaining good relationships, the nature of buyers and customer retention from the responses given. In a traditional commercial society, the physical form that can be seen is trust. The banking world is not far from the name of marketing and management as attracting forms of customer trust by providing products, services and buyer behavior by displaying employee performance. The application of strategies in marketing is a form of reference in attracting customers to become regular customers with the main purpose of this research paper being to analyze CRM performance as a form of trust in customer relationships to E-Banking without meeting bank employees, bringing convenience to customers in using banking e-banking services. accessible online.
Investigating Robustness of Dialog Models to Popular Figurative Language Constructs

Humans often employ figurative language use in communication, including during interactions with dialog systems.Thus, it is important for real-world dialog systems to be able to handle popular figurative language constructs like metaphor and simile.In this work, we analyze the performance of existing dialog models in situations where the input dialog context exhibits use of figurative language.We observe large gaps in handling of figurative language when evaluating the models on two open domain dialog datasets.When faced with dialog contexts consisting of figurative language, some models show very large drops in performance compared to contexts without figurative language.We encourage future research in dialog modeling to separately analyze and report results on figurative language in order to better test model capabilities relevant to real-world use.Finally, we propose lightweight solutions to help existing models become more robust to figurative language by simply using an external resource to translate figurative language to literal (non-figurative) forms while preserving the meaning to the best extent possible.* HJ and VG contributed equally for this paper.Order decided by coin flip.
Use of clinical data to augment healthcare worker contact tracing during the COVID-19 pandemic

This work examined the secondary use of clinical data from the electronic health record (EHR) for screening our healthcare worker (HCW) population for potential exposures to patients with coronavirus disease 2019 (COVID-19).We conducted a cross-sectional study at a free-standing, quaternary care pediatric hospital comparing first-degree, patient-HCW pairs identified by the hospital's COVID-19 contact tracing team (CTT) to those identified using EHR clinical event data (EHR Report). The primary outcome was the number of patient-HCW pairs detected by each process.Among 233 patients with COVID-19, our EHR Report identified 4116 patient-HCW pairs, including 2365 (30.0%) of the 7890 pairs detected by the CTT. The EHR Report also revealed 1751 pairs not identified by the CTT. The highest number of patient-HCW pairs per patient was detected in the inpatient care venue. Nurses comprised the most frequently identified HCW role overall.Automated methods to screen HCWs for potential exposures to patients with COVID-19 using clinical event data from the EHR (1) are likely to improve epidemiological surveillance by contact tracing programs and (2) represent a viable and readily available strategy that should be considered by other institutions.
E-Voting System Using Hyperledger Fabric Blockchain and Smart Contracts

The emergence of the current pandemic has led to a new reality in which bureaucratic formalities have been affected in terms of health security, procedures, resource management, among others. Specifically, in the electoral processes, where the difficulty of fulfilling the social distance and the mobility restrictions reopen the debate on the implementation of other more advanced and modern alternatives, such as electronic voting (e-voting). This article presents the design and implementation of a decentralized e-voting system that has the potential to provide a higher level of transparency, security, and cost-efficiency. Hyperledger Fabric blockchain and smart contracts are used to cast votes, which are then recorded in an immutable way, giving voters anonymity and trust in the fairness of the election process. In addition, promising results of the performance of the e-voting system in terms of latency and transaction load are presented.
IMPROVED XCEPTION WITH DUAL ATTENTION MECHANISM AND FEATURE FUSION FOR FACE FORGERY DETECTION

Face forgery detection has become a research hotspot in recent years, and many related methods have been proposed until now. For those images with low quality and/or diverse sources, the detection performances of existing methods are still far from satisfactory. In this paper, we propose an improved Xception with dual attention mechanism and feature fusion for face forgery detection. Different from the middle flow in original Xception model, we try to catch different high-semantic features of face images using different levels of convolution, and introduce the convolutional block attention module and feature fusion to refine and reorganize those high-semantic features. In the exit flow, we employ the self-attention mechanism and depthwise separable convolution to learn the global information and local information of the fused features separately to improve the classification ability of the proposed model. Experimental results evaluated on three Deepfake datasets demonstrate that the proposed method outperforms Xception as well as other related methods both in effectiveness and generalization ability.
TransMIA: Membership Inference Attacks Using Transfer Shadow Training

Transfer learning has been widely studied and gained increasing popularity to improve the accuracy of machine learning models by transferring some knowledge acquired in different training. However, no prior work has pointed out that transfer learning can strengthen privacy attacks on machine learning models. In this paper, we propose TransMIA (Transfer learning-based Membership Inference Attacks), which use transfer learning to perform membership inference attacks on the source model when the adversary is able to access the parameters of the transferred model. In particular, we propose a transfer shadow training technique, where an adversary employs the parameters of the transferred model to construct shadow models, to significantly improve the performance of membership inference when a limited amount of shadow training data is available to the adversary. We evaluate our attacks using two real datasets, and show that our attacks outperform the state-of-the-art that does not use our transfer shadow training technique. We also compare four combinations of the learning-based/entropy-based approach and the fine-tuning/freezing approach, all of which employ our transfer shadow training technique. Then we examine the performance of these four approaches based on the distributions of confidence values, and discuss possible countermeasures against our attacks.
Crowdsourcing Evaluation of Saliency-Based XAI Methods

Understanding the reasons behind the predictions made by deep neural networks is critical for gaining human trust in many important applications, which is reflected in the increasing demand for explainability in AI (XAI) in recent years. Saliency-based feature attribution methods, which highlight important parts of images that contribute to decisions by classifiers, are often used as XAI methods, especially in the field of computer vision. In order to compare various saliency-based XAI methods quantitatively, several approaches for automated evaluation schemes have been proposed; however, there is no guarantee that such automated evaluation metrics correctly evaluate explainability, and a high rating by an automated evaluation scheme does not necessarily mean a high explainability for humans. In this study, instead of the automated evaluation, we propose a new human-based evaluation scheme using crowdsourcing to evaluate XAI methods. Our method is inspired by a human computation game, “Peek-a-boom”, and can efficiently compare different XAI methods by exploiting the power of crowds. We evaluate the saliency maps of various XAI methods on two datasets with automated and crowd-based evaluation schemes. Our experiments show that the result of our crowd-based evaluation scheme is different from those of automated evaluation schemes. In addition, we regard the crowd-based evaluation results as ground truths and provide a quantitative performance measure to compare different automated evaluation schemes. We also discuss the impact of crowd workers on the results and show that the varying ability of crowd workers does not significantly impact the results.
Optical inline monitoring of the burnish surface in the punching process

Abstract Punching is a wide-spread production process, applied when massive amounts of the ever-same cheap parts are needed. The punching process is sensitive to a multitude of parameters. Unfortunately, the precise dependencies are often unknown. A prerequisite for optimal, reproducible and transparent process alignment is the knowledge of how exactly parameters influence the quality of a punching part, which in turn requires a quantitative description of the quality of a part. We developed an optical inline monitoring system, which consists of a combined imaging and triangulation sensor as well as subsequent image processing. We show that it is possible to capture images of the cutting surface for every part within production. We automatically derive quality parameters using the example of the burnish height from 2D images. In addition, the 3D parameters are calculated and verified from the triangulation images. As an application, we show that the status of tool wear can be inferred by monitoring the burnish height, with immediate consequences for predictive maintenance. Although limited by slow images processing in our prototype, we conclude that connecting machine and process parameters with quality metrics in real time for every single part enables data-driven process modelling and ultimately the implementation of intelligent punching machines.
Improved Classification of Cancerous Histopathology Images using Color Channel Separation and Deep Learning

Oral cancer is ranked second most diagnosed cancer among Indian population and ranked sixth all around the world.Oral cancer is one of the deadliest cancers with high mortality rate and very less 5-year survival rates even after treatment.It becomes necessary to detect oral malignancies as early as possible so that timely treatment may be given to patient and increase the survival chances.In recent years deep learning based frameworks have been proposed by many researchers that can detect malignancies from medical images.In this paper we have proposed a deep learning-based framework which detects oral cancer from histopathology images very efficiently.We have designed our model to split the color channels and extract deep features from these individual channels rather than single combined channel with the help of Efficient NET B3.These features from different channels are fused by using feature fusion module designed as a layer and placed before dense layers of Efficient NET.The experiments were performed on our own dataset collected from hospitals.We also performed experiments of BreakHis, and ICML datasets to evaluate our model.The results produced by our model are very good as compared to previously reported results.
Gaussian continuous-variable isotropic state

Inspired by the definition of the non-Gaussian two-parametric continuous-variable analog of an isotropic state introduced by Mi\ifmmode \check{s}\else \v{s}\fi{}ta et al. [Phys. Rev. A 65, 062315 (2002)], we propose to take the Gaussian part of this state as an independent state by itself, which yields a simple, but with respect to the correlation structure interesting, example of a two-mode Gaussian analog of an isotropic state. Unlike conventional isotropic states which are defined as a convex combination of a thermal and an entangled density operator, the Gaussian version studied here is defined by a convex combination of the corresponding covariance matrices and can be understood as an entangled pure state with additional Gaussian noise controlled by a mixing probability. Using various entanglement criteria and measures, we study the nonclassical correlations contained in this state. Unlike the previously studied non-Gaussian two-parametric isotropic state, the Gaussian state considered here features a finite threshold in the parameter space where entanglement sets in. In particular, it turns out that it exhibits an analogous phenomenology as the finite-dimensional two-qubit isotropic state.
Constrained Path Planning for Unmanned Aerial Vehicle in 3D Terrain Using Modified Multi-Objective Particle Swarm Optimization

This paper considered the constrained unmanned aerial vehicle (UAV) path planning problem as the multi-objective optimization problem, in which both costs and constraints are treated as the objective functions. A novel multi-objective particle swarm optimization algorithm based on the Gaussian distribution and the Q-Learning technique (GMOPSO-QL) is proposed and applied to determine the feasible and optimal path for UAV. In GMOPSO-QL, the Gaussian distribution based updating operator is adopted to generate new particles, and the exploration and exploitation modes are introduced to enhance population diversity and convergence speed, respectively. Moreover, the Q-Learning based mode selection logic is introduced to balance the global search with the local search in the evolution process. Simulation results indicate that our proposed GMOPSO-QL can deal with the constrained UAV path planning problem and is superior to existing optimization algorithms in terms of efficiency and robustness.
Neural Networks with À La Carte Selection of Activation Functions

Activation functions (AFs), which are pivotal to the success (or failure) of a neural network, have received increased attention in recent years, with researchers seeking to design novel AFs that improve some aspect of network performance. In this paper we take another direction, wherein we combine a slew of known AFs into successful architectures, proposing three methods to do so beneficially: (1) generate AF architectures at random, (2) use Optuna, an automatic hyper-parameter optimization software framework, with a Tree-structured Parzen Estimator (TPE) sampler, and (3) use Optuna with a Covariance Matrix Adaptation Evolution Strategy (CMA-ES) sampler. We show that all methods often produce significantly better results for 25 classification problems when compared with a standard network composed of ReLU hidden units and a softmax output unit. Optuna with the TPE sampler emerged as the best AF architecture-producing method.
Multiparameter quantum metrology with discrete-time quantum walks

We address multiparameter quantum estimation for one-dimensional discrete-time quantum walks and its applications to quantum metrology. We use the quantum walker as a probe for the unknown parameters encoded in its coin degrees of freedom. We find an analytic expression of the quantum Fisher information matrix for the most general coin operator, and show that only two out of the three coin parameters can be accessed. We also prove that the resulting two-parameter coin model is asymptotically classical, i.e., the Uhlmann curvature vanishes. Finally, we apply our findings to relevant case studies, including the simultaneous estimation of charge and mass in the discretized Dirac model.
Ransomware Attacks: Risks, Protection and Prevention Measures

This article is about the current situation of cybercrime activity in the world. Research was planned to seek the possible protection measures taking into account the last events which might create an appropriate background for increasing of ransomware damages and cybercrime attacks. Nowadays, the most spread types of cybercrimes are fishing, theft of personal or payment data, cryptojacking, cyberespionage and ransomware. The last one is the most dangerous. It has ability to spread quickly and causes damages and sufficient financial loses. The major problem of this ransomware type is unpredictability of its behavior. It could be overcome only after the defined ransom was paid. This conditions created an appropriate background for the activation of cyber criminals’ activity even the organization of cyber gangs – professional, well-organized and well-prepared (tactical) group. So, researches conducted in this field have theoretical and practical value in the scientific sphere of research.
Status and future of modeling of musical instruments: Introduction to the JASA special issue

Over the last decades, physics-based modeling of musical instruments has seen increased attention. In 2020 and 2021, the Journal of the Acoustical Society of America accepted submissions for a special issue on the modeling of musical instruments. This article is intended as an introduction to the special issue. Our purpose is to discuss the role that modeling plays in the study of musical instruments, the kinds of things one hopes to learn from modeling studies, and how that work informs traditional experimental and theoretical studies of specific instruments. We also describe recent trends in modeling and make some observations about where we think the field is heading. Overall, our goal is to place the articles in the special issue into a context that helps the reader to better understand and appreciate the field.
Unravel: A Fluent Code Explorer for Data Wrangling

Data scientists have adopted a popular design pattern in programming called the fluent interface for composing data wrangling code. The fluent interface works by combining multiple transformations on a data table—or dataframes—with a single chain of expressions, which produces an output. Although fluent code promotes legibility, the intermediate dataframes are lost, forcing data scientists to unravel the chain through tedious code edits and re-execution. Existing tools for data scientists do not allow easy exploration or support understanding of fluent code. To address this gap, we designed a tool called Unravel that enables structural edits via drag-and-drop and toggle switch interactions to help data scientists explore and understand fluent code. Data scientists can apply simple structural edits via drag-and-drop and toggle switch interactions to reorder and (un)comment lines. To help data scientists understand fluent code, Unravel provides function summaries and always-on visualizations highlighting important changes to a dataframe. We discuss the design motivations behind Unravel and how it helps understand and explore fluent code. In a first-use study with 14 data scientists, we found that Unravel facilitated diverse activities such as validating assumptions about the code or data, exploring alternatives, and revealing function behavior.
Supervised Contrastive Pre-training for Mammographic Triage Screening Models

Inspired by the recent success of self-supervised contrastive pre-training on ImageNet, this paper presents a novel framework of Supervised Contrastive Pre-training (SCP) followed by Supervised Fine-tuning (SF) to improve mammographic triage screening models. Our experiments on a large-scale dataset show that the SCP step can effectively learn a better embedding and subsequently improve the final model performance in comparison with the direct supervised training approach. Superior results of AUC and specificity/sensitivity have been achieved for our mammographic screening task compared to previously reported SOTA approaches.
Stackelberg Actor-Critic: Game-Theoretic Reinforcement Learning Algorithms

The hierarchical interaction between the actor and critic in actor-critic based reinforcement learning algorithms naturally lends itself to a game-theoretic interpretation. We adopt this viewpoint and model the actor and critic interaction as a two-player general-sum game with a leader-follower structure known as a Stackelberg game. Given this abstraction, we propose a meta-framework for Stackelberg actor-critic algorithms where the leader player follows the total derivative of its objective instead of the usual individual gradient. From a theoretical standpoint, we develop a policy gradient theorem for the refined update and provide a local convergence guarantee for the Stackelberg actor-critic algorithms to a local Stackelberg equilibrium. From an empirical standpoint, we demonstrate via simple examples that the learning dynamics we study mitigate cycling and accelerate convergence compared to the usual gradient dynamics given cost structures induced by actor-critic formulations. Finally, extensive experiments on OpenAI gym environments show that Stackelberg actor-critic algorithms always perform at least as well and often significantly outperform the standard actor-critic algorithm counterparts.
Teknologi dalam pengelolaan administrasi keuangan komite sekolah untuk meningkatkan transparansi keuangan

Sukoharjo State Vocational High School is one of the SMK in Pringsewu Regency. This vocational school is located on Jalan Wiyata No. 107 Sukoharjo III, Sukoharjo District, Pringsewu Regency. School committee finances are obtained based on the results of committee member meetings and determine the amount to be paid by students in 1 year, in the committee member meeting it is also explained that the receipt of payments made by students can be paid in monthly installments or paid at once by students and their parents. The school committee's financial activities are carried out through the process of planning, organizing, coordinating, supervising, or controlling. Some of the financial management activities are obtaining and determining funding sources, utilization of funds, reporting, inspection, and accountability. The school committee financial information system that has been used can perform financial management in a transparent and real-time manner, so that committee members can find out the school committee's finances by utilizing information technology without having to make a financial recapitulation of the school committee to be reported to committee members or administrators.