Reviewerly/exHarmony huggingface dataset:
TechSoup partners with digital ad firm on Google Ad grants program

Under a new partnership with TechSoup, a provider of technology services and resources for nonprofits, Cause Inspired Media will now provide services for managing Google Ad Grants, which the search engine giant provides to just about any eligible nonprofit organization that asks. According to the two organizations, the new partnership will open up Cause Inspired Media's Google Ad Grant Management services to 1.3 million nonprofit organizations that currently benefit from TechSoup's products and services. The goal of this collaboration is to help nonprofit organizations enhance their social impact and achieve their missions, the company said.
2022 Governance Support Forum

Nonprofit Business AdvisorVolume 2022, Issue 395 p. 4-4 Conferences & Events 2022 Governance Support Forum First published: 06 July 2022 https://doi.org/10.1002/nba.31274Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2022, Issue395August 2022Pages 4-4 RelatedInformation
Neuroscience &amp; HRM: Comparing the Semantics of Vocal Responses in Face-to-Face and VR Job Interviews

This research offers a comparative examination of participant experience when facing a job interview in a physical face-to-face environment and simulated virtual reality (VR). Beyond the conventionally employed neurophysiological techniques, our study acknowledges the realm of organizational neuroscience pertaining to speech and vocal utterances. Using a recruited sample of 31 subjects, we conducted a longitudinal study to capture semantics of participants’ vocal responses in the two interview conditions and analyzed them using sentiment analysis and natural language processing methods. Results revealed that the usage of linguistic dimensions (like nouns, adjectives, achievement-related words) and positive emotion-related dimensions were stronger in the VR condition. Furthermore, the level of presence experienced in a face-to-face job interview was higher than that in VR, but participant anxiety was similar in both interview conditions. The current findings are useful for HR practitioners and job applicants as VR becomes an increasingly popular medium being used in selection practices.
Cross-Modality Image Registration Using a Training-Time Privileged Third Modality

In this work, we consider the task of pairwise cross-modality image registration, which may benefit from exploiting additional images available only at training time from an additional modality that is different to those being registered. As an example, we focus on aligning intra-subject multiparametric Magnetic Resonance (mpMR) images, between T2-weighted (T2w) scans and diffusion-weighted scans with high b-value (DWI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\text {high-b}}$ </tex-math></inline-formula> ). For the application of localising tumours in mpMR images, diffusion scans with zero b-value (DWI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\text {b}={0}}$ </tex-math></inline-formula> ) are considered easier to register to T2w due to the availability of corresponding features. We propose a learning from privileged modality algorithm, using a training-only imaging modality DWI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\text {b}={0}}$ </tex-math></inline-formula> , to support the challenging multi-modality registration problems. We present experimental results based on 369 sets of 3D multiparametric MRI images from 356 prostate cancer patients and report, with statistical significance, a lowered median target registration error of 4.34 mm, when registering the holdout DWI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\text {high-b}}$ </tex-math></inline-formula> and T2w image pairs, compared with that of 7.96 mm before registration. Results also show that the proposed learning-based registration networks enabled efficient registration with comparable or better accuracy, compared with a classical iterative algorithm and other tested learning-based methods with/without the additional modality. These compared algorithms also failed to produce any significantly improved alignment between DWI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$_{\text {high-b}}$ </tex-math></inline-formula> and T2w in this challenging application.
A Distributed Storage System Based on Blockchain Technology

As an emerging business model in the storage industry, distributed storage contains a broad market prospect. Based on blockchain technology, distributed storage not only meets the storage needs in the era of big data by storing huge amount of data evenly in different nodes of the network, but also can reach autonomy under the consensus mechanism. However, due to the complexity of technology integration and design flaws, now distributed storage still faces huge challenges in practical application, specifically in terms of disaster recovery mechanism, hot data storage, and accessible threshold. In this paper, we firstly introduce the background of storage technology and summarize the current status and challenges of distributed storage. To address these challenges, we developed the Practical Distributed File System (PDFS). Compared with other distributed storage systems, the PDFS’ s self-proving algorithm mechanism could quickly achieve finality while guaranteeing the randomness and fairness of generating consensus group. Besides, the disaster recovery mechanism improves the security of data storage. Then we further evaluate the feasibility of PDFS and discussion the application prospects of PDFS. Finally, the paper summarizes the advantages and future challenges of PDFS.
On the Nash equilibrium of moment-matching GANs for stationary Gaussian  processes

Generative Adversarial Networks (GANs) learn an implicit generative model from data samples through a two-player game. In this paper, we study the existence of Nash equilibrium of the game which is consistent as the number of data samples grows to infinity. In a realizable setting where the goal is to estimate the ground-truth generator of a stationary Gaussian process, we show that the existence of consistent Nash equilibrium depends crucially on the choice of the discriminator family. The discriminator defined from second-order statistical moments can result in non-existence of Nash equilibrium, existence of consistent non-Nash equilibrium, or existence and uniqueness of consistent Nash equilibrium, depending on whether symmetry properties of the generator family are respected. We further study the local stability and global convergence of gradient descent-ascent methods towards consistent equilibrium.
Distilling ensemble of explanations for weakly-supervised pre-training of image segmentation models

While fine-tuning pre-trained networks has become a popular way to train image segmentation models, such backbone networks for image segmentation are frequently pre-trained using image classification source datasets, e.g., ImageNet. Though image classification datasets could provide the backbone networks with rich visual features and discriminative ability, they are incapable of fully pre-training the target model (i.e., backbone+segmentation modules) in an end-to-end manner. The segmentation modules are left to random initialization in the fine-tuning process due to the lack of segmentation labels in classification datasets. In our work, we propose a method that leverages Pseudo Semantic Segmentation Labels (PSSL), to enable the end-to-end pre-training for image segmentation models based on classification datasets. PSSL was inspired by the observation that the explanation results of classification models, obtained through explanation algorithms such as CAM, SmoothGrad and LIME, would be close to the pixel clusters of visual objects. Specifically, PSSL is obtained for each image by interpreting the classification results and aggregating an ensemble of explanations queried from multiple classifiers to lower the bias caused by single models. With PSSL for every image of ImageNet, the proposed method leverages a weighted segmentation learning procedure to pre-train the segmentation network en masse. Experiment results show that, with ImageNet accompanied by PSSL as the source dataset, the proposed end-to-end pre-training strategy successfully boosts the performance of various segmentation models, i.e., PSPNet-ResNet50, DeepLabV3-ResNet50, and OCRNet-HRNetW18, on a number of segmentation tasks, such as CamVid, VOC-A, VOC-C, ADE20K, and CityScapes, with significant improvements.
Preprocessing of Nepali News Corpus for Downstream Tasks

Text collected from online resources introduce a lot of errors which results in incorrect learning outcomes in automatic language learning tasks. In this paper, we discuss a Nepali text preprocessing pipeline to generate clean corpus. This pipeline is tested using a language model to observe impact of each steps in learning task. The relevancy of this work lies in systematizing the procedure in the development of standard Nepali corpus.
Image Authentication System Using Image Hashing with Color Filter Array

The availability of sophisticated multimedia editing tools makes image authentication a challenge in the modern age of multimedia communication. We suggested a hashing approach for image authentication in this paper. The proposed system extracts invariant vector distance features form a secondary image which is constructed through ring partitioning of the quaternion image. In parallel, the color filter array (CFA) interpolation coefficients are calculated from the CFA image, which has been used for identification of tampered regions. The concatenation of invariant vector distance and CFA coefficients construct the final hash. The results of experiments indicate that the approach proposed is robust in maintaining content against brightness/contrast adjustment, addition of noise, and arbitrary rotation etc., and identified the tampered regions. Compared to the existing image authentication methods, our approach provides better performance for forgery detection.
Nonconvex Regularization for Convex Image Smoothing

Image smoothing is a fundamental part of many computer vision and graphics. In this paper, to improve the sparsity of the gradient norm of the smoothing image, we use nonconvex penalty functions as regularization terms. A condition on nonconvex regularizers is given to ensure that the objective functions are strictly convex. We apply the powerful majorization-minimization (MM) algorithm for solving the convex image smoothing models. Proof of global convergence for the MM algorithm is provided. The superiority of the proposed method in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM) and visual quality is verified through comprehensive experiments in a variety of applications such as image abstraction, artifact removal, detail enhancement, texture removal, image denoising and high dynamic range (HDR) tone mapping are given to show the effectiveness of the proposed method.
The Level of Organizational Learning and its Impact on Job Performance: A Field Study at the Private Banks in Hodeida City

This study aimed to identify the level of organizational learning and its impact on job performance at the private banks in Hodeida City. The analytical descriptive method was used and a questionnaire was administered to all employees at 8 private banks, selected by the complete census method. A hundred and sixty-four questionnaires were distributed, but only 135 questionnaires were returned and analyzed. Major findings revealed that there was a high positive impact for the dimensions of organizational learning on the level of job performance in the private banks in Hodeida. Also, the level of practicing organizational learning as well as job performance of the staff was very high. Further, there were no significant differences in the level of organizational learning and performance due to the demographic variables of the study population. It was recommended that banks should promote coordination with other banks and financial institutions, and support researches that would generate new knowledge and create an appropriate environment for creative decision makers.
Privacy, Personal Identity, and Legal Issues

When individuals sign on to a DNA ancestry test, they understand that the company will undertake an analysis of certain segments of their genome, called ancestry information markers (AIMs). These segments can, under proper analysis, reveal their genetic descent from certain regions of the world.
Bias in Face Image Classification Machine Learning Models: The Impact of Annotator’s Gender and Race

An important factor that ensures the correct operation of Machine Learning models is the quality of data used during the model training process. Quite often, training data is annotated by humans, and as a result, annotation bias may be introduced. In this study, we focus on face image classification and aim to quantify the effect of annotation bias introduced by different groups of annotators, allowing in that way the understanding of the problems that arise due to annotation bias. The results of the experiments indicate that the performance of Machine Learning models in several face image interpretation tasks is correlated to the self-reported demographic characteristics of the annotators. In particular, we found significant correlation to annotator race, while correlation to gender is less profound. Furthermore, experimental results show that it is possible to determine the group of annotators involved in the annotation process by considering the annotation data provided by previously unseen annotators. The results emphasize the risks of annotation bias in Machine Learning models.
Twitter Sentiment Analysis Using NLTK and Machine Learning

Abstract: In today’s online social networks like twitter all people choose to express their opinions on social networking sites about the products or organizations, if any of the user has a good experience with any of the product or company, he/she will express their views which can be good reviews/opinion by seeing these opinion other users can know the quality of the product. On Internet, opinion mining which can be on sentiments or topic helps users to know the quality of any of the organizations or products, while developing new techniques to detect the sentiments from these opinions, all existing techniques that are used to discover are either Positive or Negative or Neutral sentiments from topics but this paper proposes 5 levels of sentiments detection such as High Positive, Moderate Positive, Neutral, High Negative and Moderate Negative. To detect sentiments, we are using four Ordinal Regression machine learning algorithms such as SoftMax, Decision Tree, Random Forest and also Support Vector Regression. For classification of tweets, we used NLTK, which cleans the tweets by removing special symbols, removing stop words, word stemming, etc. In this paper the authors have discussed how these algorithms are implemented on tweets and detect the sentiments
Mixed‐attention‐based regional soft partition network for vehicle reidentification

The purpose of vehicle reidentification is to distinguish the same vehicle from different vehicles using different cameras. The main challenge of this task is the significant intra-instance discrepancy of the same vehicle from different views and the subtle inter-instance differences of similar vehicles from the same views. To address this problem, researchers have attempted to align features from different views, such as using additional metadata (colour, type, key points, mask, etc.) to improve performance. Although these attempts improve the performance of vehicle reidentification, considerable efforts are required to create additional precise annotations to the data before using these methods. This results in expensive data preparation costs, rendering these methods less convenient to use. Therefore, we propose a novel deep learning network, called a mixed-attention-based regional soft partition network, to address this problem. This network does not require additional metadata annotations; it only trains the identity label as a supervision signal and uses soft partition attention to identify specific vehicle regions. Experiments showed that the performance of the proposed method was comparable to that of the state-of-the-art method with additional annotations on the VeRri-776 and VERI-Wild datasets.
Implementation of quantum hitting times of cubelike graphs on IBM’s Qiskit platform

In this paper, we give a procedure to construct quantum circuits for implementing discrete-time quantum walks on a family of Cayley graphs called cubelike graphs. We construct these circuits on IBM’s Qiskit platform and demonstrate an implementation of the quantum hitting times on cubelike graphs. Based on our numerical study, we conjecture that for all families of cubelike graphs there is a linear relationship between the degree of a cubelike graph and its hitting time which holds asymptotically. This conjecture, if proved, will generalize the result of hitting times of discrete-time quantum walks on hypercubes to general family of cubelike graphs.
A System-Level Analysis of Conference Peer Review

The conference peer review process involves three constituencies with different objectives: authors want their papers accepted at prestigious venues (and quickly), conferences want to present a program with many high-quality and few low-quality papers, and reviewers want to avoid being overburdened by reviews. These objectives are far from aligned, primarily because the evaluation of a submission is inherently noisy. Over the years, conferences have experimented with numerous policies to navigate the tradeoffs. These experiments include setting various bars for acceptance, varying the number of reviews per submission, requiring prior reviews to be included with resubmissions, and others. In this work, we investigate, both analytically and empirically, how well various policies work, and more importantly, why they do or do not work. We model the conference-author interactions as a Stackelberg game in which a prestigious conference commits to an acceptance policy; the authors best-respond by (re)submitting or not (re)submitting to the conference in each round of review, the alternative being a "sure accept" (such as a lightly refereed venue). Our main results include the following observations: 1) the conference should typically set a higher acceptance threshold than the actual desired quality; we call this the "resubmission gap". 2) the reviewing load is heavily driven by resubmissions of borderline papers - therefore, a judicious choice of acceptance threshold may lead to fewer reviews while incurring an acceptable loss in conference quality. 3) conference prestige, reviewer inaccuracy, and author patience increase the resubmission gap, and thus increase the review load for a fixed level of conference quality. For robustness, we further consider different models of paper quality and compare our theoretical results to simulations based on plausible parameters estimated from real data.
Development of Programming Log Collection System Requirements Using Interface Requirement Analysis Techniques

As software affects each industry, companies are increasingly trying to recruit talent. Despite the interest and investment of companies, it is difficult to find talents with technical expertise and specialization in a specific field at the same time. As a result, companies have begun to discover talents who can overcome their lack of expertise through talents with high problem-solving skills. Countries worldwide that felt the need to discover competitive talents began to show changes in education for nurturing talents. In particular, the results of the expansion and increase of programming education that cultivate problem-solving ability have begun to be seen. However, programming education is different from existing education, and many learners have difficulties with the introductory process due to the difficult debugging process. In order to analyze the difficulties of introductory learners and support their learning, a system that can collect data from the programming process and analyze behavior types is required. There are several methods for deriving the system requirements, but the interface requirements analysis method was selected in this study. We approached how to process data in the system by deriving the type of data that the system administrator wants to collect. This study laid the foundation for a system that can analyze the programming process of introductory learners by deriving the functional and non-functional requirements required by the data collection system through interface requirements analysis.
Data-Centric Learning Method for Synthetic Data Augmentation and Object Detection

This paper proposes a deep learning framework for decreasing large-scale domain shift problems in object detection using domain adaptation techniques. We have approached data-centric domain adaptation with Image-to-Image translation models for this problem. It is one of the methodologies that changes source data to target domain's style by reducing domain shift. However, the method cannot be applied directly to the domain adaptation task because the existing Image-to-Image model focuses on style translation. We solved this problem using the data-centric approach simply by reordering the training sequence of the domain adaptation model. We defined the features to be content and style. We hypothesized that object-specific information in images was more closely tied to the content than the style and thus experimented with methods to preserve content information before style was learned. We trained the model separately only by altering the training data. Our experiments confirmed that the proposed method improves the performance of the domain adaptation model and increases the effectiveness of using the generated synthetic data for training object detection models. We compared our approach with the existing single-stage method where content and style were trained simultaneously. We argue that our proposed method is more practical for training object detection models than others. The emphasis in this study is to preserve image content while changing the style of the image. In the future, we plan to conduct additional experiments to apply synthetic data generation technology to various other application areas like indoor scenes and bin picking.