Reviewerly/exHarmony huggingface dataset:
Program Search for Machine Learning Pipelines Leveraging Symbolic Planning and Reinforcement Learning

In this paper we investigate an alternative knowledge representation and learning strategy for the automated machine learning (AutoML) task. Our approach combines a symbolic planner with reinforcement learning to evolve programs that process data and train machine learning classifiers. The planner, which generates all feasible plans from the initial state to the goal state, gives preference first to shortest programs and then later to ones that maximize rewards. The results demonstrate the efficacy of the approach for finding good machine learning pipelines, while at the same time showing that the representation can be used to infer new knowledge relevant for the problem instances being solved. These insights can be useful for other automatic programming approaches, like genetic programming (GP) and Bayesian optimization pipeline learning, with respect to representation and learning strategies.
Multi-strategy different dimensional mutation differential evolution algorithm

Aiming at the characteristics of differential evolution (DE) algorithm, such as premature convergence and low convergence accuracy, we propose a multi-strategy different dimensional mutation differential evolution (MDMDE) algorithm. On one hand, the algorithm starts from the mutation dimension, and then proposes a different dimensional strategy. On the other hand, multi-strategy mutation is used in the whole generation cycle of the algorithm. In addition, a modified crossover rate and a dynamic mutation factor are proposed to jump out of the local maximum. Finally, MDMDE is applied to 8 kinds of test functions and compared with other two algorithms. The results show that MDMDE can obtain better optimization results and it exhibits more desirable performance than the other algorithms.
Ensemble Dictionary Learning for Single Image Deblurring via Low-Rank Regularization

Sparse representation is a powerful statistical technique that has been widely utilized in image restoration applications. In this paper, an improved sparse representation model regularized by a low-rank constraint is proposed for single image deblurring. The key motivation for the proposed model lies in the observation that natural images are full of self-repetitive structures and they can be represented by similar patterns. However, as input images contain noise, blur, and other visual artifacts, extracting nonlocal similarities only with patch clustering algorithms is insufficient. In this paper, we first propose an ensemble dictionary learning method to represent different similar patterns. Then, low-rank embedded regularization is directly imposed on inputs to regularize the desired solution space which favors natural and sharp structures. The proposed method can be optimized by alternatively solving nuclear norm minimization and l 1 norm minimization problems to achieve higher restoration quality. Experimental comparisons validate the superior results of the proposed method compared with other deblurring algorithms in terms of visual quality and quantitative metrics.
Named Entity Recognition for Electronic Health Records: A Comparison of Rule-based and Machine Learning Approaches

This work investigates multiple approaches to Named Entity Recognition (NER) for text in Electronic Health Record (EHR) data. In particular, we look into the application of (i) rule-based, (ii) deep learning and (iii) transfer learning systems for the task of NER on brain imaging reports with a focus on records from patients with stroke. We explore the strengths and weaknesses of each approach, develop rules and train on a common dataset, and evaluate each system's performance on common test sets of Scottish radiology reports from two sources (brain imaging reports in ESS -- Edinburgh Stroke Study data collected by NHS Lothian as well as radiology reports created in NHS Tayside). Our comparison shows that a hand-crafted system is the most accurate way to automatically label EHR, but machine learning approaches can provide a feasible alternative where resources for a manual system are not readily available.
Talents Recommendation with Multi-Aspect Preference Learning

Discovering talents has always been a crucial mission in recruitment and applicant selection program. Traditionally, hunting and identifying the best candidate for a particular job is executed by specialists in human resources department, which requires complex manual data collection and analysis. In this paper, we propose to seek talents for companies by leveraging a variety of data from not only online professional networks (e.g., LinkedIn), but also other popular social networks (e.g., Foursquare and Last.fm). Specifically, we extract three distinct features, namely global, user and job preference to understand the patterns of talent recruitment, and then a Multi-Aspect Preference Learning (MAPL) model for applicant recommendation is proposed. Experimental results based on a real-world dataset validate the effectiveness and usability of our proposed method, which can achieve nearly 75% accuracy at best in recommending candidates for job positions.
The role of knowledge management in organizational performance: case study of University of Malang, Indonesia

The present study puts forward a model that examines variables of human resources management practices, organizational culture, knowledge management and organizational performance. By this model, it intends to explore the effects of human resources management practices and organizational culture on organizational performance. Particularly, it intends to find out their relationship with knowledge management as a mediator. The research applies SmartPLS for data analysis, with 163 respondents by means of saturation sampling technique. The results display several facts. First, both HRD practices and organizational culture have significant effects on knowledge management. Second, while knowledge management and organizational culture have a significant effect on organizational performance, HRD practices do not significantly affect organizational performance. It makes knowledge management only serve a partial mediation between human resources management practices and organizational performance, and serve a full mediation between organizational culture and organizational performance.
Examining Alaska’s Earthquakes on Land and Sea

The Alaska Amphibious Community Seismic Experiment is taking a close look at seismic activity along the Alaska Peninsula to understand earthquakes in this little-studied region.
DYNECOM: Augmenting Empathy in VR with Dyadic Synchrony Neurofeedback

In a novel experimental setting, we augmented a variation of traditional compassion meditation with our custom built VR environment for multiple concurrent users.The system incorporates respiration and brainwave based biofeedback that enables responsiveness to the shared physiological states of the users.The presence of another user's avatar in the shared virtual space supported low level social interactions and provided active targets for evoked compassion.We enhanced interoception and the deep empathetic processes involved in compassion meditation with real time visualizations of breathing rates and the level of approach motivation assessed from EEG frontal asymmetry, and the dyadic synchrony of those signals between the two users.We found how the different biofeedback types increased both the amount of physiological synchrony between the users and their self-reported empathy, illustrating how dyadic synchrony biofeedback can expand the possibilities of biofeedback in affective computing and VR solutions for health and wellness.
Intelligent transaction: definition, modes, and research directions

Purpose In the crowd intelligence networking era, the smart connections of human, machines and things enable point-to-point trustable transactions and distributed efficient collaboration; the smart connections among government, enterprises, organizations and the public would enable active participation of the public in society management and decision-making and improve the efficiency of government management and services. All interactions among various agents can be viewed as the transaction activity. The social division of labor system drives the evolution of transaction. The transaction mode also differentiated into different patterns with the development of human society. What will be the intelligent transaction in the crowd intelligence networking era? What will be the transactions modes and rules in the crowd intelligence networking era? The answers to these questions are of great importance to the future development of transactions. Design/methodology/approach The authors review the evolution of traditional transaction and transaction modes and analyze the driving forces of it. They attempt to give the definitions of intelligent transaction and intelligent transaction mode. They also review the traditional transaction modes and rules, analyze the characteristics of the intelligent transaction and classify the intelligent transaction modes. Findings The authors find the intelligent transaction is mainly reflected in the intellectualization of transaction subject, transaction object and transaction process. They summarize the characteristics of intelligent transaction and develop four modes for the intelligent transactions based on the modularization level of the transaction objects and the quantity of transaction subjects, including the demand side and the supply side. The authors also show representative examples to further illustrate rules and features of these transaction modes and point out the potential research directions. Originality/value This study is among the first to analyze the characteristics of the intelligent transaction, and the proposed division framework of the intelligent transaction modes could not only add value to the future research of intelligent transaction modes and rules but also help to guide the transactions in the crowd intelligence network.
Interpreting Black Box Models with Statistical Guarantees.

In science and medicine, model interpretations may be reported as discoveries of natural phenomena or used to guide patient treatments. In such high-stakes tasks, false discoveries may lead investigators astray. These applications would therefore benefit from control over the finite-sample error rate of interpretations. We reframe black box model interpretability as a multiple hypothesis testing problem. The task is to discover important features by testing whether the model prediction is significantly different from what would be expected if the features were replaced with uninformative counterfactuals. We propose two testing methods: one that provably controls the false discovery rate but which is not yet feasible for large-scale applications, and an approximate testing method which can be applied to real-world data sets. In simulation, both tests have high power relative to existing interpretability methods. When applied to state-of-the-art vision and language models, the framework selects features that intuitively explain model predictions. The resulting explanations have the additional advantage that they are themselves easy to interpret.
INFORMATION TECHNOLOGIES IN BULGARIAN SCHOOL EDUCATION

Appears in: INTED2019 Proceedings Publication year: 2019Pages: 5226-5235ISBN: 978-84-09-08619-1ISSN: 2340-1079doi: 10.21125/inted.2019.1302Conference name: 13th International Technology, Education and Development ConferenceDates: 11-13 March, 2019Location: Valencia, Spain
TO THE QUESTION OF PHYSICAL IMPLEMENTATION OF OPTICAL NEURAL NETWORKS

The quality and depth of content Web of Science offers to researchers, authors, publishers, and institutions sets it apart from other research databases. The inclusion of News of NAS RK. Series of geology and technical sciences in the Emerging Sources Citation Index demonstrates our dedication to providing the most relevant and influential content of geology and engineering sciences to our community.Қазақстан Республикасы Ұлттық ғылым академиясы "ҚР ҰҒА Хабарлары.Геология жəне техникалық ғылымдар сериясы" ғылыми журналының
The Beginning of Base Editing: An Interview with Alexis C. Komor and Nicole M. Gaudelli

The CRISPR JournalVol. 2, No. 2 InterviewThe Beginning of Base Editing: An Interview with Alexis C. Komor and Nicole M. GaudelliKevin Davies, Alexis C. Komor, and Nicole M. GaudelliKevin DaviesKevin Davies, Executive Editor, The CRISPR Journal, New Rochelle, New York, E-mail: kdavies@liebertpub.com; Alexis C. Komor, Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, E-mail: akomor@ucsd.edu or Nicole M. Gaudelli, Beam Therapeutics, Cambridge, Massachusetts, E-mail Address: ngaudelli@beamtx.comSearch for more papers by this author, Alexis C. KomorKevin Davies, Executive Editor, The CRISPR Journal, New Rochelle, New York, E-mail: kdavies@liebertpub.com; Alexis C. Komor, Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, E-mail: akomor@ucsd.edu or Nicole M. Gaudelli, Beam Therapeutics, Cambridge, Massachusetts, E-mail Address: ngaudelli@beamtx.comSearch for more papers by this author, and Nicole M. GaudelliKevin Davies, Executive Editor, The CRISPR Journal, New Rochelle, New York, E-mail: kdavies@liebertpub.com; Alexis C. Komor, Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, California, E-mail: akomor@ucsd.edu or Nicole M. Gaudelli, Beam Therapeutics, Cambridge, Massachusetts, E-mail Address: ngaudelli@beamtx.comSearch for more papers by this authorPublished Online:18 Apr 2019https://doi.org/10.1089/crispr.2019.29050.kdaAboutSectionsView articleView Full TextPDF/EPUB Permissions & CitationsPermissionsDownload CitationsTrack CitationsAdd to favorites Back To Publication ShareShare onFacebookTwitterLinked InRedditEmail View articleFiguresReferencesRelatedDetailsCited by2022: A Wild Year for Short Reads in Genome Sequencing? Keith Robison16 February 2022 | GEN Biotechnology, Vol. 1, No. 1CRISPR Genome Editing: Into the Second Decade Geulah Livshits16 February 2022 | GEN Biotechnology, Vol. 1, No. 1Base Editors Flex Sights on Sickle-Cell Disease Anna Cereseto, Thomas J. Cradick, and Kevin Davies20 April 2021 | The CRISPR Journal, Vol. 4, No. 2 Volume 2Issue 2Apr 2019 InformationCopyright 2019, Mary Ann Liebert, Inc., publishersTo cite this article:Kevin Davies, Alexis C. Komor, and Nicole M. Gaudelli.The Beginning of Base Editing: An Interview with Alexis C. Komor and Nicole M. Gaudelli.The CRISPR Journal.Apr 2019.81-90.http://doi.org/10.1089/crispr.2019.29050.kdaPublished in Volume: 2 Issue 2: April 18, 2019PDF download
Using fuzzy-based association rule mining to improve production systems for chemical product development

In chemical product development, challenges lie in the determination of appropriate ingredients and parameter settings that lead to the desired product attributes. This relies heavily on the past knowledge and experience of the domain experts to generate feasible product candidates for verification. In this paper, a fuzzy-based association rule mining model (FbARM) is developed to provide knowledge support during chemical product development. Fuzzy-based association rule mining is applied to discover hidden relationships between parameters and the resultant product quality, followed by the use of fuzzy logic to generate recommendations on parameter settings. The feasibility of the FbARM is verified by means of a case study in a personal-care products manufacturing company. The results demonstrate the practical viability of the FbARM, while the learning ability of the FbARM allows a continuous improvement of the fuzzy rules, which is of paramount importance in responding to the changing requirements of the chemical industry.
An Efficient Proxy Ring Signature Without Bilinear Pairing*

Proxy ring signature is a proxy signature designed to protect the privacy of the proxy signer. In this paper, we propose a new proxy ring signature based on the Schnorr's signature. Our scheme combines the idea of the Schnorr's signature with the method of existing structure of proxy ring signature. In order to improve the performance, we eliminate the use of bilinear pairing operation. We only use the hash function in the signature verification phase. Because of above features, the proxy ring signature scheme we proposed is more efficient in computation. We also prove that our scheme is unforgeable against all kinds of adversaries in the random oracle model.
TECHNOPHOBIA AS A CULTURAL AND PSYCHOLOGICAL PHENOMENON: Theoretical Analysis

Today we can talk about a sufficient formation of both negative and positive attitudes towards the introduction of new technologies. This is the technophobia that continued to be fueled by such objective factors as job slashing caused by events and new inventions. Technophobia includes cognitive, emotional and behavioral components. Along with individual factors (self-efficacy, open-mindedness, anxiety level, user’s experience, emotional state, etc.), the formation of technophobia is influenced by interpersonal factors (communication with colleagues, friends and relatives on the subject of technologies), group and intergroup factors (clash of interests of different relevant groups in connection with the appearance of new technologies, group stereotypes – representations of typical users of the given technology, its developers, etc.), as well as societal factors (mass culture and mass media, the level of technological and economic development of a country, cross-cultural features). In this way, this is an increasingly important phenomenon, since the speed of innovation is very high. Thus, the purpose of this study is to present a theoretical analysis on this subject and to highlight the importance of conducting other research on this subject.
Tangible tabletops and dual reality for crisis management: case study with mobile robots and dynamic tangible objects

In this paper, we present an experimental study between tangible interaction and tactile interaction on tabletops in a dual reality environment. We recruited 32 participants to take part in a user study, which consists of remotely displacing robots and exploring a simulated disaster area using a tabletop and robot toys on its surface. We present our results and we focus on the differences between the two interaction techniques for remote control of robots. Our findings indicate that the tangible interaction outperforms the tactile interaction in usability and in terms of committed errors, classified by different criteria. Meanwhile they indicate also that for the user workload there is no significant difference between tangible and tactile interactions.
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models.

We identify a new variational inference scheme for dynamical systems whose transition function is modelled by a Gaussian process. Inference in this setting has either employed computationally intensive MCMC methods, or relied on factorisations of the variational posterior. As we demonstrate in our experiments, the factorisation between latent system states and transition function can lead to a miscalibrated posterior and to learning unnecessarily large noise terms. We eliminate this factorisation by explicitly modelling the dependence between state trajectories and the Gaussian process posterior. Samples of the latent states can then be tractably generated by conditioning on this representation. The method we obtain (VCDT: variationally coupled dynamics and trajectories) gives better predictive performance and more calibrated estimates of the transition function, yet maintains the same time and space complexities as mean-field methods. Code is available at: this http URL.
Privacy in Health Disparity Research

Background: Most research on identifying and understanding health disparities focused on measurement strategies and analytics design over administrative and survey data. What has received less attention, however, is the complex interplay between privacy concerns and the needs of health disparity research. Objectives: The main objective of the paper is to illustrate the fundamental challenges facing the reconciliation of privacy needs and health disparity research. Research Design: We investigated whether privacy disparity exists over raw, unprocessed, public health data, and then examined the effect of a state-of-the-practice data anonymization process and a state-of-the-art privacy-preserving data sharing technique on both privacy disparity and the efficacy of health disparity research. Results: We demonstrate that the essential elements for health disparity research, for example, the integration of demographics attributes such as race in public health datasets, could indeed lead to privacy disparity if not carefully treated. Discussions and Policy Implications: The findings in this paper indicate that regulators must carefully study the privacy implications before mandating the inclusion of certain attributes like race and geolocation in a released dataset. For example, the Affordable Care Act requires all federally supported public health programs to collect data on race, ethnicity, sex, geographic location, etc., to the extent feasible. Although doing so could help identify health disparities, if the collected data are directly released without proper sanitization, the released data may introduce privacy disparities, causing further harm to underserved populations.
Preferences and Ethical Priorities: Thinking Fast and Slow in AI

In AI, the ability to model and reason with preferences allows for more personalized services. Ethical priorities are also essential, if we want AI systems to make decisions that are ethically acceptable. Both data-driven and symbolic methods can be used to model preferences and ethical priorities, and to combine them in the same system, as two agents that need to cooperate. We describe two approaches to design AI systems that can reason with both preferences and ethical priorities. We then generalize this setting to follow Kahneman's theory of thinking fast and slow in the human's mind. According to this theory, we make decision by employing and combining two very different systems: one accounts for intuition and immediate but imprecise actions, while the other one models correct and complex logical reasoning. We discuss how such two systems could possibly be exploited and adapted to design machines that allow for both data-driven and logical reasoning, and exhibit degrees of personalized and ethically acceptable behavior.
Predicting Annotation Difficulty to Improve Task Routing and Model Performance for Biomedical Information Extraction

Yinfei Yang, Oshin Agarwal, Chris Tar, Byron C. Wallace, Ani Nenkova. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Progress Monitoring in Primary Education using Levumi: A Case Study

We present, as a case study, the web-based platform Levumi that enables teachers to easily monitor learningprogressions of children with a focus on elementary skills in reading, writing, and mathematics. Curriculumbasedmeasurements are used that can be administered economically in short time and - in many parts - inparallel for multiple children. The system is built around exchanging data between schools and educationalresearch, such that data with high ecological validity can be collected anonymously in order to gain insightsinto learning processes and in turn offer improved tests for teachers. For this case study, the acceptance anduse of the platform over the last years is evaluated with a focus on tests for reading abilities. The results showhow that many users are integrating the system in their daily teaching, learning progressions can be assessedand that the data is even usable for validation purposes.