Reviewerly/exHarmony huggingface dataset:
Identifying Possible Financial Frauds using SQL Row Pattern Recognition

In 2016, the SQL standardization committee finished the work concerning a feature called Row Pattern Recognition.The specification, which was published in the same year, describes the common search for patterns inside the tables' rows as well as the search inside an existing window frame.In this paper we discuss how this feature can be used to identify suspicious transactions in relation to customers' bank accounts.Several examples are usedto show the way how transfers to and from one account can be traced, and how possible irregularities can be detected.This paper has two main goals.First, to show strength and conciseness of the SQL specification of this feature.Second, to show a way how to implement complex problems in a particular business area.
A Double Penalty Model for Ensemble Learning

Modern statistical learning techniques often include learning ensembles, for which the combination of multiple separate prediction procedures (ensemble components) can improve prediction accuracy. Although ensemble approaches are widely used, work remains to improve our understanding of the theoretical underpinnings of aspects such as identifiability and relative convergence rates of the ensemble components. By considering ensemble learning for two learning ensemble components as a double penalty model, we provide a framework to better understand the relative convergence and identifiability of the two components. In addition, with appropriate conditions the framework provides convergence guarantees for a form of residual stacking when iterating between the two components as a cyclic coordinate ascent procedure. We conduct numerical experiments on three synthetic simulations and two real world datasets to illustrate the performance of our approach, and justify our theory.
Corporate Social Responsibility and Customer Satisfaction: Role of Artificial Intelligence

Every corporation for maintaining long-term customer relationships in terms of customer retention and loyalty, certainly considers customer satisfaction as one of its major objective as it not results into retaining the customers who generate the revenues but also supports the business firms to relieve their social obligations towards the customer segment of the society.To remain economically and socially viable, every firm is looking forward to incorporate the customer satisfaction in its Corporate Social Responsibility (CSR) agenda so as to achieve the objective of enhanced customer satisfaction.The rapid advancement in Artificial Intelligence (AI) has opened the new ways to perform CSR in times of competitive environment to bring increased customer satisfaction for several firms.Many companies are using AI strategically to enhance CSR efficiency to widen the scope for improved customer satisfaction.But the question arises here how companies can make use of AI techniques for increasing the customer satisfaction through CSR in this competitive environment?The objective of the present research paper is to address this question by proposing that applicability of AI techniques such as machine learning, neural networks, predictive analysis ,visual searching ,chatbots ,sentiment analysis etc., will help the corporations to deliver the abundant benefits to customers and target increased customer satisfaction.This research throws light on that how AI driven CSR will result into augmented customer satisfaction in this competitive environment.This study is carried out by with the help of secondary sources like conceptual and empirical papers, books and websites to explore the concept.
A Mixed Method Approach to Understand "Engage to Be Engaged" Engaging Peers with Story Telling Nurtures an Engaged Employee

Business success stories are a new and upcoming strategy in the corporate world and overtime have been found to be highly beneficial to both companies and employees alike.The research study aims to analyze the extent to which successful business stories have a positive and progressive influence on employee motivation and engagement.The objective to understand the positive effect of sharing business stories on the story sharer.The methodology adopted is thematic content analysis and factor analysis.The major findings are that although story telling has been used as a knowledge sharing tool it has a remarkable effect on the sharer.As much as the employee who shares successful business stories engages the audience, the employee himself feels a sense of satisfaction resulting in improved morale and encouragement to be a part of an engaged community.
A Cytopathologist Eye Assistant for Cell Screening

Screening of Pap smear images continues to depend upon cytopathologists’ manual scrutiny, and the results are highly influenced by professional experience, leading to varying degrees of cell classification inaccuracies. In order to improve the quality of the Pap smear results, several efforts have been made to create software to automate and standardize the processing of medical images. In this work, we developed the CEA (Cytopathologist Eye Assistant), an easy-to-use tool to aid cytopathologists in performing their daily activities. In addition, the tool was tested by a group of cytopathologists, whose feedback indicates that CEA could be a valuable tool to be integrated into Pap smear image analysis routines. For the construction of the tool, we evaluate different YOLO configurations and classification approaches. The best combination of algorithms uses YOLOv5s as a detection algorithm and an ensemble of EfficientNets as a classification algorithm. This configuration achieved 0.726 precision, 0.906 recall, and 0.805 F1-score when considering individual cells. We also made an analysis to classify the image as a whole, in which case, the best configuration was the YOLOv5s to perform the detection and classification tasks, and it achieved 0.975 precision, 0.992 recall, 0.970 accuracy, and 0.983 F1-score.
LeMo: an assembly kit for musical acoustics education

Musical acoustics is a scientific field essential for an in-depth understanding of musical instruments and sounds. As such, it is relevant to wide audiences involved with music, including musicians, composers and even casual listeners. This work describes a twofold approach for introductory, hands-on education in musical acoustics. First, a concise classification approach for acoustic instruments is described. The approach consists of classifying instruments based on their fundamental acoustic properties of vibration generation, resonance and radiation. Then, an assembly kit of modular instrument components is described. The kit contains stand-alone resonator and radiator modules of various types, allowing the assembly of different fully functioning instrument prototypes. Students and general audiences, guided by educators, may use the kit to learn and experience the acoustic behaviour of musical instruments, as well as specific acoustic phenomena.
Cell2Grid: an efficient, spatial, and convolutional neural network-ready representation of cell segmentation data

Cell segmentation algorithms are commonly used to analyze large histologic images as they facilitate interpretation, but on the other hand they complicate hypothesis-free spatial analysis. Therefore, many applications train convolutional neural networks (CNNs) on high-resolution images that resolve individual cells instead, but their practical application is severely limited by computational resources. In this work, we propose and investigate an alternative spatial data representation based on cell segmentation data for direct training of CNNs.We introduce and analyze the properties of Cell2Grid, an algorithm that generates compact images from cell segmentation data by placing individual cells into a low-resolution grid and resolves possible cell conflicts. For evaluation, we present a case study on colorectal cancer relapse prediction using fluorescent multiplex immunohistochemistry images.We could generate Cell2Grid images at 5-μm resolution that were 100 times smaller than the original ones. Cell features, such as phenotype counts and nearest-neighbor cell distances, remain similar to those of original cell segmentation tables ( p<0.0001 ). These images could be directly fed to a CNN for predicting colon cancer relapse. Our experiments showed that test set error rate was reduced by 25% compared with CNNs trained on images rescaled to 5μm with bilinear interpolation. Compared with images at 1-μm resolution (bilinear rescaling), our method reduced CNN training time by 85%.Cell2Grid is an efficient spatial data representation algorithm that enables the use of conventional CNNs on cell segmentation data. Its cell-based representation additionally opens a door for simplified model interpretation and synthetic image generation.
Practical fully leakage resilient signatures with auxiliary inputs

Digital signatures are essential techniques used in the real world, especially for validating the authenticity of a given message. The security of the digital signatures based on the traditional security definition, however, may be vulnerable to the side-channel attacks. Currently, although several leakage-resilient signature (LR-Sig) schemes have been proposed to model the side-channel attacks, the schemes are highly inefficient. Because either the signature size is quite large or a limited fraction of the secret key can be leaked. In this paper, we present practical LR-Sig schemes that are able to withstand a large scale of leakage. Technically, we build the first generic construction of LR-Sig, which is secure in the auxiliary input model (allowing leakage by any hard-to-invert function), continual memory leakage (updating the secret key periodically while remaining the public key fixed) and fully leakage resilience (admitting leakage of the secret key and the signing randomness). Apart from the strong security model, the sizes of our signature and public key are the same as each underlying standard signature scheme. In particular, the verification algorithm of our LR-Sig is as fast as the original scheme. Thereby, our instantiation LR-ECDSA can be easily adopted in the existing cryptocurrencies since no additional modification is needed for the verifier side. Furthermore, we provide the first comprehensive quantitative analysis for different LR-Sig schemes. The state-of-the-art signature schemes, which are able to leak over 50% of the secret key, are at least 565k bits. Comparatively, our shortest instantiation LR-BLS has a signature size of only 1 group element (382 bits, shorten by 1479 times) regardless of the percentage of leakage. Meanwhile, our LR-BLS is also the first deterministic LR-Sig. Besides, our instantiation LR-Schnorr enjoys the fastest verification.
Proposal of a new fidelity measure between computed image quality and observers quality scores accounting for scores variability

Assessment of the visual quality of colour images is usually a difficult process, validated through hard-to-carry-out psychophysical experiments, used to record observer quality scores. Visual image quality metrics aim to maximise the agreement between computed indexes and observer scores, or opinions. Therefore, in this area, it is of critical importance to have appropriate measures of this agreement (i.e. performance) between the computed image quality metric values and observer’s quality scores, both for the development, as well as for the use of image quality metrics. Among the measures of agreement, the most used one nowadays is the well-known Pearson correlation coefficient, while Spearman rank correlation coefficient is also commonly used. The aim of this paper is two-fold. First, to introduce the Standardised Residual Sum of Squares (STRESS) as an alternative metric for the agreement between computed image quality and observers quality scores and analyse its properties and advantages in front of Pearson, Spearman and Kendall correlation coefficients; Second, to introduce a new version of STRESS (called USTRESS) that takes observers’ scores variability into account. The results on synthetic and real datasets support that STRESS has a series of benefits in front of the classical approaches and that the inclusion of uncertainty in STRESS has an important effect on the results, quantified by statistical significance tests. A free to download MATLAB code version of USTRESS is available at https://viplab.webs.upv.es/resources/
Enhancement method with naturalness preservation and artifact suppression based on an improved Retinex variational model for color retinal images

Retinal images are widely used for the diagnosis of various diseases. However, low-quality retinal images with uneven illumination, low contrast, or blurring may seriously interfere with diagnosis by ophthalmologists. This study proposes an enhancement method for low-quality retinal color images. In this paper, an improved variational Retinex model for color retinal images is first proposed and applied to each channel of the RGB color space to obtain the illuminance and reflectance layers. Subsequently, the Naka-Rushton equation is introduced to correct the illumination layer, and an enhancement operator is constructed to improve the clarity of the reflectance layer. Finally, the corrected illuminance and enhanced reflectance are recombined. Contrast-limited adaptive histogram equalization is introduced to further improve the clarity and contrast. To demonstrate the effectiveness of the proposed method, this method is tested on 527 images from four publicly available datasets and 40 local clinical images from Tianjin Eye Hospital (China). Experimental results show that the proposed method outperforms the other four enhancement methods and has obvious advantages in naturalness preservation and artifact suppression.
Efficient Light Probes for Real-Time Global Illumination

Reproducing physically-based global illumination (GI) effects has been a long-standing demand for many real-time graphical applications. In pursuit of this goal, many recent engines resort to some form of light probes baked in a precomputation stage. Unfortunately, the GI effects stemming from the precomputed probes are rather limited due to the constraints in the probe storage, representation or query. In this paper, we propose a new method for probe-based GI rendering which can generate a wide range of GI effects, including glossy reflection with multiple bounces, in complex scenes. The key contributions behind our work include a gradient-based search algorithm and a neural image reconstruction method. The search algorithm is designed to reproject the probes' contents to any query viewpoint, without introducing parallax errors, and converges fast to the optimal solution. The neural image reconstruction method, based on a dedicated neural network and several G-buffers, tries to recover high-quality images from low-quality inputs due to limited resolution or (potential) low sampling rate of the probes. This neural method makes the generation of light probes efficient. Moreover, a temporal reprojection strategy and a temporal loss are employed to improve temporal stability for animation sequences. The whole pipeline runs in realtime (&gt;30 frames per second) even for high-resolution (1920×1080) outputs, thanks to the fast convergence rate of the gradient-based search algorithm and a light-weight design of the neural network. Extensive experiments on multiple complex scenes have been conducted to show the superiority of our method over the state-of-the-arts.
Partners Who Grow Together: Collaborative Machine Learning in Video Game AI Design

The majority of research at the intersection of AI and video games focuses on developing agents capable of playing games without human input, or developing AI game enemies. The research in this paper explores a counter approach, whereby a player trains an a AI partner during game play and learns to play cooperatively with the agent. We created a 2D video game that allows the player to cooperate with an AI agent manipulated by two underlying algorithms, either with reinforcement learning or a random process. For our reinforcement learning approach we used a Q-learning table, that is updated based on the player. We found that players engaged strongly with the idea of training their own custom AI agent and believe this shows significant potential for future exploration.
Evaluating Human-Artificial Agent Decision Congruence in a Coordinated Action Task

Recommender systems designed to augment human decision-making in multi-agent tasks need to not only recommend actions that align with the task goal, but which also maintain coordinative behaviors between agents. Further, if these systems are to be used for skill training, they need to impart implicit learning to its users. This work compared a recommender system trained using deep reinforcement learning to a heuristic-based system in recommending actions to human participants teaming with an artificial agent during a collaborative problem-solving task. In addition to evaluating task performance and learning, we also evaluate the extent to which the human action are congruent with the recommended actions.
Advantage Mapping: Learning Operation Mapping for User-Preferred Manipulation by Extracting Scenes with Advantage Function

When a user manipulates a system, a user input through an interface, or an operation, is converted to the user’s intended action according to the mapping that links operations and actions, which we call “operation mapping”. Although many operation mappings are created by designers assuming how a typical user would operate the system, the optimal operation mapping may vary from user to user. The designer cannot prepare in advance all possible operation mappings. One approach to solve this problem involves autonomous learning of an operation mapping during the operation. However, existing methods require manual preparation of scenes for learning mappings. We propose advantage mapping, which enables the efficient learning of operation mappings. Working from the idea that scenes in which the user’s desired action is predictable are useful for learning operation mappings, advantage mapping extracts scenes according to the magnitude of entropy in the output of the action value function acquired from reinforcement learning. In our experiment, the user’s ideal operation mapping was more accurately obtained from the scenes selected by advantage mapping than from learning through actual play.
Design and Prototype Conversational Agents for Research Data Collection

Conversational agents have gained increasing interest from researchers as a tool to collect data and administer interventions. They provide a natural user interface through conversations and hence have the potential to reach a wide population in their homes and on the go. Several developer tools and commercial as well as open-source frameworks allow for the deployment of both text-based chatbots and voice assistants. In this 90 min tutorial, participants will learn how to choose an appropriate platform, how to design and deploy their conversational agents, and how to transform traditional surveys through conversation agents.
Smartphone-Based Human Activity and Fall Recognition Using Deep Feature Extraction and Machine-Learning Classifiers

Human activity recognition (HAR) and fall detection using smartphone sensors are currently popular because they can be extended to many useful applications especially when a person needs an urgent treatment such as a fall. Several methods based on machine learning (ML) and deep learning (DL) have been proposed to improve classification performances. In this work, we propose hybrid models of convolutional neural network (CNN) models and ML algorithms to classify human activities and falls using smartphone-sensor data. The CNN model will be used as feature extraction to extract a set of features. Thereafter, the ML algorithm will apply this set of features to predict the corresponding activity and fall. Several combinations of CNN models and ML algorithms are investigated on two public datasets: UniMiB SHAR and UMAFall. Their accuracy scores are compared in order to determine the best hybrid model. On the UniMiB SHAR dataset, the hybrid model based on the AlexN et model and the extra trees algorithm achieves the highest accuracy score of 95.27%. On the UMAFall dataset, the hybrid model based on the Xception model and the support vector machine/k-nearest neighbors/extra trees algorithms offer the highest accuracy score of 82.24 %.
Application of Gaussian process regression as a surrogate modeling method to assess the dynamics of COVID-19 propagation

In this research, we aimed to assess the possibility of using surrogate modeling methods to replace time-consuming calculations related to modeling of COVID-19 dynamics. The Gaussian process regression (GPR) was used as a surrogate to replace detailed simulations by a COVID-19 multiagent model. Experiments were conducted with various kernels, as a result, in accordance with the quality metrics of the models, kernels were identified in which the surrogate gives the most accurate result (Rational Quadratic kernel and Additive kernel). It was demonstrated that by smoothing the dynamics of COVID-19 propagation, it is possible to achieve greater accuracy in GPR training. The obtained results prove the potential possibility of using surrogate modeling methods to conduct an uncertainty quantification of the multiagent model of COVID-19 propagation.
(Retracted) Anas platyrhynchos optimizer with deep belief network-based sarcasm detection and classification model

Sarcasm is a state of speech where the speaker says something that is externally unfriendly to wound or deride the listener. The design of an automated sarcasm detection model in the domain of natural language processing (NLP) is mainly based on the context of the statement/statement and sometimes even human beings cannot identify the intrinsic sarcasm in the statement. The recently developed deep learning (DL) models can accomplish better performance than the other traditional models. With this motivation, this study introduces an efficient Anas Platyrhynchos optimizer (APO) with deep belief network (DBN) based sarcasm detection and classification (APODBN-SDC) technique. The proposed APODBN-SDC model intends to properly identify the existence of sarcasm or not. Primarily, the data preprocessing stage encompasses different subprocesses and the preprocessed data can be transformed into the feature vectors using TF-IDFs technique. Finally, the normalized feature vectors are given as input to the DBN model for the detection and classification of sarcasm. Finally, the hyperparameter tuning of the DBN is optimally tuned by the use of APO technique and shows the novelty of the work. The proposed method is tested against the benchmark dataset and the results reported the enhancements of the proposed model with maximum precision of 98.88% and recall of 98.90%.
Three-dimensional optical image encryption using phase-shifting compressive Fresnel holography

A new method of three-dimensional (3D) image optical encryption based on phase-shifting compressive Fresnel holography (PSCFH) is presented. In the optical encryption step, the 3D object is encrypted with random phase masks using phase-shifting interferometry to record three intensity images with different phase-shifting. In the decryption step, the three-step phase-shifting technique is first used to extract the complex optical field at the recording plane. Finally, the 3D object is reconstructed from the recovered complex optical field using compressive Fresnel holography with the correct key. Numerical experiments demonstrate the feasibility and robustness of our proposed PSCFH method.