Reviewerly/exHarmony huggingface dataset:
An Overview of Causal Inference and its Applications in Health-care and Finance using methods such as Bayesian Networks and Granger’s Causality

Causal Inference is a discipline that involves discovering the causal connections between various statistically related quantities. Unlike Statistical Analysis, which focuses on answering the question of “what?” Causal inference is concerned with answering the question of “why?”. Causal inference has significant applications in the fields of Health-care, Finance, etc. In this paper, we will be exploring some fundamental concepts in Causal Inference and how it is used in the field of medicine and finance. In Section I we will be providing an introduction to the concept of causality, moving to the Section II we will be looking at various aspects of causal inference. Following that we will see basic concepts of causal model. In section IV, V we will be discussing the implementation of causal in the field of health-care and finance.
Searching by learning

In deep reinforcement learning, searching and learning techniques are two important components. They can be used independently and in combination to deal with different problems in AI. These results have inspired research into artificial general intelligence (AGI).We study table based classic Q-learning on the General Game Playing (GGP) system, showing that classic Q-learning works on GGP, although convergence is slow, and it is computationally expensive to learn complex games.This dissertation uses an AlphaZero-like self-play framework to explore AGI on small games. By tuning different hyper-parameters, the role, effects and contributions of searching and learning are studied. A further experiment shows that search techniques can contribute as experts to generate better training examples to speed up the start phase of training.In order to extend the AlphaZero-likeself-play approach to single player complex games, the Morpion Solitaire game is implemented by combining Ranked Reward method. Our first AlphaZero-based approach is able to achieve a near human best record.Overall, in this thesis, both searching and learning techniques are studied (by themselves and in combination) in GGP and AlphaZero-like self-play systems. We do so for the purpose of making steps towards artificial general intelligence, towards systems that exhibit intelligent behavior in more than one domain.
HyperSPNs: Compact and Expressive Probabilistic Circuits

Probabilistic circuits (PCs) are a family of generative models which allows for the computation of exact likelihoods and marginals of its probability distributions. PCs are both expressive and tractable, and serve as popular choices for discrete density estimation tasks. However, large PCs are susceptible to overfitting, and only a few regularization strategies (e.g., dropout, weight-decay) have been explored. We propose HyperSPNs: a new paradigm of generating the mixture weights of large PCs using a small-scale neural network. Our framework can be viewed as a soft weight-sharing strategy, which combines the greater expressiveness of large models with the better generalization and memory-footprint properties of small models. We show the merits of our regularization strategy on two state-of-the-art PC families introduced in recent literature -- RAT-SPNs and EiNETs -- and demonstrate generalization improvements in both models on a suite of density estimation benchmarks in both discrete and continuous domains.
Scrybe: A Secure Audit Trail for Clinical Trial Data Fusion

Clinical trials are a multi-billion-dollar industry. One of the biggest challenges facing the clinical trial research community is satisfying Part 11 of Title 21 of the Code of Federal Regulations [ 7 ] and ISO 27789 [ 40 ]. These controls provide audit requirements that guarantee the reliability of the data contained in the electronic records. Context-aware smart devices and wearable IoT devices have become increasingly common in clinical trials. Electronic Data Capture (EDC) and Clinical Data Management Systems (CDMS) do not currently address the new challenges introduced using these devices. The healthcare digital threat landscape is continually evolving, and the prevalence of sensor fusion and wearable devices compounds the growing attack surface. We propose Scrybe, a permissioned blockchain, to store proof of clinical trial data provenance. We illustrate how Scrybe addresses each control and the limitations of the Ethereum-based blockchains. Finally, we provide a proof-of-concept integration with REDCap to show tamper resistance.
Emotional Intelligence as a Psychological Factor of Students' Professional Competence

This article highlights human emotional intelligence and its general characteristics. The most important aspects of the future professional competence of future specialists will be analyzed. The levels of professional development through the development of emotional intelligence are grounded.
PEREKRUTAN ONLINE KARYAWAN PADA PT. TELKOM AKSES – JAMBI MENGGUNAKAN METODE PROFILE MATCHING:

Online recruitment recruitment or E-recruitment is a process of employee recruitment using technology and internet as main supporting facilities with the aim of providing convenience for the company or applicants. E-recruitment can be used in a process. The existence of a web-based employee recruitment system or e-recruitment, companies can choose their own employees right for the company, in addition the company can process data applicants in data base data applicants data does not accumulate in filing cabinets. In this study, the author discusses how the employee recruitment system at PT. Telkom Access - Jambi Using Method Matching Method. The system is built using the PHP programming language, its database application uses the MySQL method and the Matching Profile process as the decision-making reference. The result of this research is web-based e-recruitment. The existence of e-recruitment web-based is that it can be go for it in this site, friendly HR Department in looking at people who are comfortable and efficient in recruitment process (recruitment) effectively and efficiently, and easiness to see report receipt of employee.
Covid and the virtual classroom: the new normal?

Virtual learning can be defined as software systems created to support the teaching- learning process that allows teachers and students to communicate in an integrated manner through cyberspace. The rapid developments in technology over the past few years, have led to the innovation of systems through which information can be delivered independent of location. The current COVID-19 climate has resulted in the restructuring of the educational system and the adaptation of technology globally, so as to portray didactic ideologies. Investigation of efficacy of the use of design software platforms in a virtual learning environment is paramount as digitation has become a significant  factor in the synthesis and dissemination of learning materials within the educational system. The use of virtual classrooms is deemed advantageous as it enables the incorporation of learning styles, decreases the incidences of learning barriers, allows for adherence of  Covid-19 restrictions, and enhances mental encoding and information retention. It also comes with certain disadvantages, which are  attributable to the technological divide, economic and technological infrastructure, the user’s knowledge, perception and usage of these technological advancements, and its possible alienation and educational dissatisfaction. What and who benefits with these learning platforms; Is this the new normal going forward?
LibGuides: Education: Databases

This guide one-stop-shop for services and resources to support classes in the College of Education.
Attributed Non-negative Matrix Multi-factorization for Data Representation

Non-negative matrix factorization (NMF) is an important method of latent data representation learning. Most of the existing NMF methods focus only on one single factorization and obtain one clustering solution. However, real data are usually complex and can be described from multiple attributes or sub-features. For example, face image consists of genders attribute and expressions attribute. And, the various attributes provide complementary information of data. Failing to explore multi-attribute representation and exploit the complementary information, it may be difficult to learn discriminative representation. In order to solve the above issue and obtain richer low-dimensional representations, we propose the Attributed Non-negative Matrix Multi-Factorization for Data Representation (ANMMF) model which simultaneously learns multiple low-dimensional representations from original data. By utilizing Hilbert Schmidt Independence Criterion (HSIC) to constrain the pairwise attributes, ANMMF enforces that each low-dimensional attribute representation is independent, which effectively mines complementary multi-attribute information embed in the original data. Further, graph Laplacian regularization is constrained to maintain the local geometrical structure. The low dimensional multi-attribute representation information embedded in the original data is fused to improve the clustering results. Finally, we develop the iterative updating schemes for the ANMMF model optimization, and extensive experiments on real-world databases demonstrate that our method has the most advanced performance compared with other related algorithms.
Institutional Logics in Large-Scale Agile Software Development Transformations

Abstract Transforming into agile ways of working in large organizations can be performed in different ways. Many organizations choose a defined large-scale agile software development framework but how the transformation is carried out could be based on different sorts of logics. This paper investigates institutional logics at play in large-scale agile transformations. By studying two case organizations, the paper aims at improving our understanding of large-scale transformations by viewing software development as an institution. The findings displays diverse impacts due to two differing institutional logics when transforming into large-scale agile software development by implementing the Scaled Agile Framework. One contribution of this paper is to show the possibilities of using two institutional logics, Agile toolbox logic and Agile rulebook logic, for analyzing impacts of agile transformations.
Peningkatan Keterampilan Mengajar Guru SD/MI melalui Pelatihan Media Pembelajaran Edugames Berbasis Teknologi: Quizizz dan Baamboozle

The Covid-19 pandemic has changed many things, included learning methods in schools. All learning activities switch to an online system. This change raised several teaching obstacles faced by SD/MI teachers in Indonesia. The obstacle faced was the lack of mastery of technology for teachers so that the learning media used was less attractive. Based on a survey conducted, as many as 79.2% of parents stated that teachers mostly provide explanations of material through videos in WhatsApp groups and 45.8% of teachers provide video links from Youtube. Boredom was experienced by students during learning. Therefore, the community service team collaborates with partner schools to organize educational media training activities. In this case, the edugames taught are Quizizz and Baamboozle. This activity was attended by nine teachers from MI Asshibyan Dampaan, Cerme District, Gresik Regency. Activities taken place in a hybrid (offline and online). Quizizz materials were conducted offline at school on the first day, while bamboozle materials were conducted online through Zoom Meetings on the second day. After the presentation of the material by the team, the teacher also practiced compiling his own learning media with team assistance and then applied it in their respective classes. After the training activities, participants were able to arrange practice questions using the Quizizz learning media. The bamboozle media has not been implemented because the class does not take place via Zoom Meeting. The team hopes that this learning media can be applied continuously in online and offline classes.
Exploiting Domain-Aware Aspect Similarity for Multi-Source Cross-Domain Sentiment Classification

We propose a novel framework exploiting domain-aware aspect similarity for solving the multisource cross-domain sentiment classification problem under the constraint of little labeled data.Existing works mainly focus on identifying the common sentiment features from all domains with weighting based on the coarse-grained domain similarity.We argue that it might not provide an accurate similarity measure due to the negative effect of domain-specific aspects.In addition, existing models usually involve training sub-models using a small portion of the labeled data which might not be appropriate under the constraint of little labeled data.To tickle the above limitations, we propose a domain-aware topic model to exploit the fine-grained domain-aware aspect similarity.We utilize the novel domain-aware linear layer to control the exposure of various domains to latent aspect topics.The model discovers latent aspect topics and also captures the proportion of latent aspect topics of the input.Next, we utilize the proposed topicattention network for training aspect models capturing the transferable sentiment knowledge regarding particular aspect topics.The framework finally makes predictions according to the aspect proportion of the testing data for adjusting the contribution of various aspect models.Experimental results show that our proposed framework achieves the state-of-the-art performance under the constraint of little labeled data.The framework has 71% classification accuracy when there are only 40 labeled data.The performance increases to around 82% with 200 labeled data.This proves the effectiveness of the fine-grained domain-aware aspect similarity measure.
PMA LibGuides: PMA Collections Research: Accession Numbers

Recommended steps to help you search for information about objects in the Museum’s permanent collection.
The HARDNESS POLYURETHANE AND NATURAL RUBBER EFFECT ON ADHESIVENESS OF AIRLESS TIRES

Abstract: Airless tires are the best innovation for military tactical vehicles in carrying out the Army's main tasks for military tactical even peace support operations and war military operations. However, in its development there are still imperfections in the results, its fabrications in the adhesion between polyurethane and natural rubber. Therefore, the researchers aimed to develop the adhesion between polyurethane and natural rubber. In this research, a quantitative experimental method was used, namely by varying the hardness of natural rubber and polyurethane to determine the right hardness for airless tires for maximum adhesion. The problem that occurs is that the adhesive glue with the tire tread is problematic, resulting in a tire tread condition without air being released. In carrying out the research, a function test was carried out with a planned weight of 4,500 Kg. Based on the results of the tests that carried out, the correct result was obtained, namely the elongation at break of the most appropriate type of polyurethane L100, which was 9.287 MPa. In order to achieve the goal, namely the appropriate level of elasticity between polyurethane and natural rubber so as to be able to glue the spokes and tire treads, in the end the wheel rotation will rotate according to the desired elasticity.
Research Guides: ELED 424: Methods & Materials for Teaching Literacy in the Intermediate Grades: Teach Award Winners

Children's Literature And Other Materials For Reading And Language Arts In Elementary School
Chinese Medical Speech Recognition with Punctuated Hypothesis

Automatic Speech Recognition (ASR) technology presents the possibility for medical professionals to document patient record, diagnosis, postoperative care, patrol records, and etc. that are now done manually. However, earlier research aimed on Chinese medical speech corpus (ChiMeS) has two shortcomings: first is the lack of punctuation, resulting in reduced readability of the output transcript, and second is the poor recognition error rate, affecting its application put to the fields. Accordingly, the contributions of this paper consist of: (1) A punctuated Chinese medical corpus psChiMeS-14 newly annotated from ChiMeS-14, which is the collection of 516 anonymized medical record readouts of 867 minutes long, recorded by 15 professional nursing staff from Taipei Hospital of the Ministry of Health and Welfare. psChiMeS-14 is manually punctuated with: colons, commas, and periods, ready for general end-to-end ASR models. (2) A self-attention based speech recognition solution by conformer networks. Trained by and tested on psChiMeS-14 corpus, the solutions deliver state-of-the-art recognition performance: CER (character error rate) 10.5%, and KER (Keyword error rate) of 13.10%, respectively, which is contrasted to the 15.70% CER and the 22.50% KER by an earlier reported Joint CTC/Attention architecture.
Improving Classification Performance under Imbalanced Data Conditions using Generative Adversarial Networks

Deep learning has achieved significant improvements in a variety of tasks in computer vision applications with an open image dataset which has a large amount of data. However, the acquisition of a large number of the dataset is a challenge in real-world applications, especially if they are new eras for deep learning. Furthermore, the distribution of class in the dataset is often imbalanced. The data imbalance problem is frequently bottlenecks of the neural network performance in classification. Recently, the potential of generative adversarial networks (GAN) as a data augmentation method on minority data has been studied. This dissertation investigates using GAN and transfer learning to improve the performance of the classification under imbalanced data conditions. We first propose a classification enhancement generative adversarial networks (CEGAN) to enhance the quality of generated synthetic minority data and more importantly, to improve the prediction accuracy in data imbalanced condition. Our experiments show that approximating the real data distribution using CEGAN improves the classification performance significantly in data imbalanced conditions compared with various standard data augmentation methods.To further improve the performance of the classification, we propose a novel supervised discriminative feature generation method (DFG) for minority class dataset. DFG is based on the modified structure of Generative Adversarial Network consisting of four independent networks: generator, discriminator, feature extractor, and classifier. To augment the selected discriminative features of minority class data by adopting attention mechanism, the generator for class-imbalanced target task is trained while feature extractor and classifier are regularized with the pre-trained ones from large source data. The experimental results show that the generator of DFG enhances the augmentation of label-preserved and diverse features, and classification results are significantly improved on the target task.In this thesis, these proposals are deployed to bearing fault detection and diagnosis of induction motor and shipping label recognition and validation for logistics. The experimental results for bearing fault detection and diagnosis conclude that the proposed GAN-based framework has good performance on the imbalanced fault diagnosis of rotating machinery. The experimental results for shipping label recognition and validation also show that the proposed method achieves better performance than many classical and state-of-the-art algorithms.
LIGHTWEIGHT SECRET SHARING CRYPTOGRAPHIC SCHEME FOR DECENTRALIZED DISTRIBUTED WANET USING TRI-VARIATE POLYNOMIALS OVER ECC

WANET is a auto-configuring and infrastructure-free network of dynamic nodes joined through wireless devices. Since WANET is a decentralized distribution of resource constraint devices in which secure communication is a challenging task. In such a type of network, secure communication is established through lightweight security settlement. The present paper proposes a lightweight cryptographic scheme for decentralized and distributed WANET using the tri-variate technique over elliptic curves. For authentication,Shamir secret sharing technique is used.The key exchange between individual nodes are accomplished through the ECDH technique,and for secure communication a lightweight encryption/decryption algorithm, namelytinny encryption algorithm(TEA),which is one the fastest and most efficient lightweight algorithm.
Research Guides: (ECC) Using the Ellsworth Community College Library: Koha Tutorials

Need a refresher on how to use the library? The Electronic Card Catalog? The Article Databases?
Perspectives, they might be a-changin': A proactive-control take on the cognitive cost of maintaining one’s own perspective.

The world abounds with different perspectives, which necessitates balancing between maintaining the currently relevant perspective and flexibly switching between perspectives, if needed. Employing the distinction between reactive and proactive control (Braver, 2012), we argue that previous research on perspective-taking has mainly looked at the cost of activating reactive control to deal with what is happening now. Here we examine the cost of activating proactive control in order to be prepared for what might happen in the future. In three experiments, we embed a perspective-taking task (Samson et al., 2010) into a task-switching design and calculate perspective-mixing costs to capture proactive control. We show that a context in which perspective shifts might occur unpredictably (compared to a context in which such shifts are not expected) results in a poorer ability to maintain any perspective, but especially one's own. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
Diverge - Investigating the Consequences of Bad Comments

In large software development projects the majority of code comments are written at the beginning of the project and tend to not be updated when the code is rewritten. This commonly results in code ...
Using Twitter to build communities: a primer for libraries, archives, and museums

"Using Twitter to build communities: a primer for libraries, archives, and museums." Archives and Records, 42(3), pp. 332–333