Reviewerly/exHarmony huggingface dataset:
Provable guarantees for decision tree induction: the agnostic setting

We give strengthened provable guarantees on the performance of widely employed and empirically successful {\sl top-down decision tree learning heuristics}. While prior works have focused on the realizable setting, we consider the more realistic and challenging {\sl agnostic} setting. We show that for all monotone functions~$f$ and parameters $s\in \mathbb{N}$, these heuristics construct a decision tree of size $s^{\tilde{O}((\log s)/\varepsilon^2)}$ that achieves error $\le \mathsf{opt}_s + \varepsilon$, where $\mathsf{opt}_s$ denotes the error of the optimal size-$s$ decision tree for $f$. Previously, such a guarantee was not known to be achievable by any algorithm, even one that is not based on top-down heuristics. We complement our algorithmic guarantee with a near-matching $s^{\tilde{\Omega}(\log s)}$ lower bound.
Artificial Intelligence: An Energy Efficiency Tool for Enhanced High performance computing

Power-consuming entities such as high performance computing (HPC) sites and large data centers are growing with the advance in information technology. In business, HPC is used to enhance the product delivery time, reduce the production cost, and decrease the time it takes to develop a new product. Today’s high level of computing power from supercomputers comes at the expense of consuming large amounts of electric power. It is necessary to consider reducing the energy required by the computing systems and the resources needed to operate these computing systems to minimize the energy utilized by HPC entities. The database could improve system energy efficiency by sampling all the components’ power consumption at regular intervals and the information contained in a database. The information stored in the database will serve as input data for energy-efficiency optimization. More so, device workload information and different usage metrics are stored in the database. There has been strong momentum in the area of artificial intelligence (AI) as a tool for optimizing and processing automation by leveraging on already existing information. This paper discusses ideas for improving energy efficiency for HPC using AI.
Approximate Cross-validation: Guarantees for Model Assessment and Selection

Cross-validation (CV) is a popular approach for assessing and selecting predictive models. However, when the number of folds is large, CV suffers from a need to repeatedly refit a learning procedure on a large number of training datasets. Recent work in empirical risk minimization (ERM) approximates the expensive refitting with a single Newton step warm-started from the full training set optimizer. While this can greatly reduce runtime, several open questions remain including whether these approximations lead to faithful model selection and whether they are suitable for non-smooth objectives. We address these questions with three main contributions: (i) we provide uniform non-asymptotic, deterministic model assessment guarantees for approximate CV; (ii) we show that (roughly) the same conditions also guarantee model selection performance comparable to CV; (iii) we provide a proximal Newton extension of the approximate CV framework for non-smooth prediction problems and develop improved assessment guarantees for problems such as `1-regularized ERM.
Multiple Instance Learning for Content Feedback Localization without Annotation

Automated Essay Scoring (AES) can be used to automatically generate holistic scores with reliability comparable to human scoring.In addition, AES systems can provide formative feedback to learners, typically at the essay level.In contrast, we are interested in providing feedback specialized to the content of the essay, and specifically for the content areas required by the rubric.A key objective is that the feedback should be localized alongside the relevant essay text.An important step in this process is determining where in the essay the rubric designated points and topics are discussed.A natural approach to this task is to train a classifier using manually annotated data; however, collecting such data is extremely resource intensive.Instead, we propose a method to predict these annotation spans without requiring any labeled annotation data.Our approach is to consider AES as a Multiple Instance Learning (MIL) task.We show that such models can both predict content scores and localize content by leveraging their sentence-level score predictions.This capability arises despite never having access to annotation training data.Implications are discussed for improving formative feedback and explainable AES models.
Students' Perceptions on Chatbots' Potential and Design Characteristics in Healthcare Education.

Chatbots may have the potential to support healthcare education by enabling personalized learning. Trust is a pre-requisite for the users to accept the chatbots. In this study we analyzed students' assignments of the MSc course "User Needs, Requirements Engineering and Evaluation" at Karolinska Institutet, aiming to explore the chatbots' potential in healthcare education and the design characteristics of chatbots that may enhance the trust. The students identified two courses: pharmacology and medical law, that have the potential to leverage chatbots' characteristics. Our analyses on the design characteristics they suggested resulted in: recognition; visibility of system status; anthropomorphism in communication; knowledge expertise, linguistic consistency; realistic interaction. Our results are in line with previous research. Future studies could investigate the educational impact on the learning outcomes and students' satisfaction when interacting with chatbots.
Textual content prediction via fuzzy attention neural network model without predefined knowledge

Text analysis is a popular technique for finding the most significant information from texts including semantic, emotional, and other hidden features, which became a research hotspot in the last few years. Specially, there are some text analysis tasks with judgment reports, such as analyzing the criminal process and predicting prison terms. Traditional researches on text analysis are generally based on special feature selection and ontology model generation or require legal experts to provide external knowledge. All these methods require a lot of time and labor costs. Therefore, in this paper, we use textual data such as judgment reports creatively to perform prison term prediction without external legal knowledge. We propose a framework that combines value-based rules and a fuzzy text to predict the target prison term. The procedure in our framework includes information extraction, term fuzzification, and document vector regression. We carry out experiments with real-world judgment reports and compare our model's performance with those of ten traditional classification and regression models and two deep learning models. The results show that our model achieves competitive results compared with other models as evaluated by the RMSE and R-squared metrics. Finally, we implement a prototype system with a user-friendly GUI that can be used to predict prison terms according to the legal text inputted by the user.
Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer

Purpose Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosinâstained frozen tissue sections of SLNs in breast cancer patients. Materials and Methods A total of 297 digital slides were obtained from frozen SLN sections, which include postâneoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for 6 weeks with two P40 GPUs. The algorithms were assessed in terms of the area under receiver operating characteristic curve (AUC). Results The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting. Key words: Breast Neoplasms, Deep Learning, Frozen Sections, Neoplasm Metastasis, Sentinel Lymph Node
Approach to Reasoning about Uncertain Temporal Data in OWL 2

In this paper, we propose an ontology-based approach for representing and reasoning about certain and uncertain temporal data. It handles temporal data in terms of quantitative time intervals and points and the qualitative relations between them (e.g., “before”). It includes three parts. (1) We extend the 4D-fluents approach with certain ontological components to represent the handled temporal data in OWL 2. (2) We extend the Allen’s interval algebra to reason about certain and uncertain time intervals. We adapt these relations to allow relating a time interval and a time point, and two time points. All relations can be used for temporal reasoning by means of transitivity tables. (3) The extended Allen’s algebra instantiates the 4D-fluents-based representation. Inferences are based on SWRL rules. Based on this ontology, a prototype is implemented and integrated into an ontology-based memory prosthesis for Alzheimer’s patients to handle certain and uncertain temporal data inputs.
OntoMath$${}^{Edu}$$: A Linguistically Grounded Educational Mathematical Ontology

We present the first release of OntoMath $${}^{Edu}$$ , a new educational mathematical ontology. The ontology is intended to be used as a Linked Open Data hub for mathematical education, a linguistic resource for intelligent mathematical language processing and an end-user reference educational database. The ontology is organized in three layers: a foundational ontology layer, a domain ontology layer and a linguistic layer. The domain ontology layer contains language-independent concepts, covering secondary school mathematics curriculum. The linguistic layer provides linguistic grounding for these concepts, and the foundation ontology layer provides them with meta-ontological an-notations. The concepts are organized in two main hierarchies: the hierarchy of objects and the hierarchy of reified relationships. For our knowledge, OntoMath $${}^{Edu}$$ is the first Linked Open Data mathematical ontology, that respects ontological distinctions provided by a foundational ontology; represents mathematical relationships as first-oder entities; and provides strong linguistic grounding for the represented mathematical concepts.
Creative AI: From Expressive Mimicry to Critical Inquiry

The nascent field of what has come to be known as “creative AI” consists of a range of activities at the intersections of new media arts, human-computer interaction, and artificial intelligence. This article provides an overview of recent projects that emphasise the use of machine learning algorithms as a means to identify, replicate, and modify features in existing media, to facilitate new multimodal mappings between user inputs and media outputs, to push the boundaries of generative art experiences, and to critically investigate the role of feature detection and pattern identification technologies in contemporary life. Despite the proliferation of such projects, recent advances in applied machine learning have not yet been incorporated into or interrogated by creative AI projects, and this article also highlights opportunities for computational artists working in this area. The article concludes by envisioning how creative AI practice could include delineating the boundaries of what can and cannot be learned by extracting features from artefacts and experiences, exploring how new forms of interpretation can be encoded into neural networks, and articulating how the interaction of multiple machine learning algorithms can be used to generate new insight into the intertwining sociotechnical systems that encompass our lives.
An ANN-based gesture recognition algorithm for smart-home applications

The goal of this paper is to analyze and build an algorithm to recognize hand gestures applying to smart home applications.The proposed algorithm uses image processing techniques combing with artificial neural network (ANN) approaches to help users interact with computers by common gestures.We use five types of gestures, namely those for Stop, Forward, Backward, Turn Left, and Turn Right.Users will control devices through a camera connected to computers.The algorithm will analyze gestures and take actions to perform appropriate action according to users requests via their gestures.The results show that the average accuracy of proposal algorithm is 92.6 percent for images and more than 91 percent for video, which both satisfy performance requirements for real-world application, specifically for smart home services.The processing time is approximately 0.098 second with 10 frames/sec datasets.However, accuracy rate still depends on the number of training images (video) and their resolution.
Blockchain and Social Media

Blockchain is a distributed ledger where transactions are stored in blocks and spread across a peer-to-peer network where every node holds a copy of the ledger. Encryption is a base concept for blockchain. Blockchain is a peer-to-peer technology where the integrity of digital information is protected. The blockchain ledger records every sequence of transactions from beginning to end whether it is many or one. There are certain factors which influence shifting of social media with underlying blockchain technology. Verification of online identities is in increased demand. Fake IDs and vulnerability to fraud can be mitigated where the identities of customers can be verified using blockchain technology and smart contracts. Blockchain-based social media use digital currencies to reward creators and viewers. They are a great alternative to current traditional social media platforms. Some of the platforms of blockchain-based social media are: Steemit, Obsidian, Earn, Indorse, Social X, Enlte, Voice, Sapien, Sola, Ong.Social, Minds, SoMee, Smoke, Alfa, and so on.
Delta Schema Network in Model-Based Reinforcement Learning

This work is devoted to unresolved problems of Artificial General Intelligence - the inefficiency of transfer learning. One of the mechanisms that are used to solve this problem in the area of reinforcement learning is a model-based approach. In the paper we are expanding the schema networks method which allows to extract the logical relationships between objects and actions from the environment data. We present algorithms for training a Delta Schema Network (DSN), predicting future states of the environment and planning actions that will lead to positive reward. DSN shows strong performance of transfer learning on the classic Atari game environment.
Soft Classification Techniques for Breast Cancer Detection and Classification

Breast cancer is known to be one of the most common cancers among women, often fatal. The reasons for death are mainly due to imprecision or delay in diagnosis. Early treatment helps to cure malignant growth and prevent its recurrence. The objective of this paper is to build a model to detect and correctly classify the tumor with high accuracy. In order to accomplish this, we compare Support Vector Machine (SVM) with five other Machine Learning (ML) Algorithms, namely, Decision Tree Classifier (CART), Naive Bayes Classifier (NB), Logistic Regression (LR), Linear Discriminant Analysis (LDA) and K Nearest Neighbor (KNN). ML Algorithms are known for their efficiency in data classification and are therefore widely used for diagnostic purposes in the medical field. We have evaluated the efficiency of SVM using precision, recall, ROC area and accuracy estimates. The best performance was achieved by the SVM method resulting in the highest accuracy.
Application of Swarm Robotic System in a Dynamic Environment using Cohort Intelligence

The nature inspired Swarm Intelligence has laid the foundation for many eclectic applications. This work considers a solution to one such application of Search and Rescue operation, based on Cohort Intelligence (CI) methodology which aims at modelling the behaviour of candidates based on the interaction amongst each other to achieve a common goal. Every candidate improves its own behaviour by observing all other candidates in the cohort. This method results in the refinement of the performance of the entire system. The research done so far in this application using CI is associated with robots deployed in a static alien establishment. However, this assumption is not suitable in real-life scenarios. In this paper, the obstacle avoidance and path planning of a swarm of robots was implemented while considering a dynamic alien establishment. The problem of robots occasionally getting stuck in the non-convex obstacles has also been solved using a perturbation technique. The following test cases were implemented - No Obstacle Case (NOC), Stationary Obstacles Case (SOC), Single Dynamic Obstacle Case (SDOC), Multiple Dynamic Obstacles Case with Same Velocity (MDOC-SV) and Multiple Dynamic Obstacles Case with Different Velocities (MDOC-DV).
Convergence and determinants of anti-corruption disclosure among extractive firms in Africa

Purpose The purpose of this study is to examine the convergence and determinants of anti-corruption disclosures of extractive firms in Africa. Design/methodology/approach The study uses an unbalanced panel data of 27 firms operating in 5 African countries covering the period 2006 to 2018. Corporate data is collected from the global reporting initiative (GRI) database. The study uses an index to measure overall disclosure and individual items are coded as binary. The study uses fixed effects, panel logistic and panel-corrected standard error regression, depending on the type of dependent variable used. Findings The results indicate that the determinants of anti-corruption disclosure are membership in the United Nations global compact (UNGC) and Extractive Industry Transparency Initiative, multi-national enterprise status, corruption perception index and human development index (HDI). Specifically, UNGC membership and multi-national status enhance the disclosure on corruption analysis. Countries with a high prevalence of corruption tend to disclose more on corruption analysis. Disclosure on corruption training is high among firms that are UNGC signatories, countries with a high HDI and countries with a high prevalence of corruption. There is a weak effect of firm-level, industry-level and country-level factors on disclosures on corruption response. Research limitations/implications The study provides insights on the use of GRI 205: Anti-Corruption, which has relevant implications for practitioners, policymakers and the academic community. Originality/value This study is premier in exploring anti-corruption disclosure with a special focus on extractive firms in Africa. It is also unique in providing a test of both beta and sigma convergence among the firms.
ArtSim: Improved Estimation of Current Impact for Recent Articles

As the number of published scientific papers continuously increases, the need to assess paper impact becomes more valuable than ever. In this work, we focus on citation-based measures that try to estimate the popularity (current impact) of an article. State-of-the-art methods in this category calculate estimates of popularity based on paper citation data. However, with respect to recent publications, only limited data of this type are available, rendering these measures prone to inaccuracies. In this work, we present ArtSim, an approach that exploits paper similarity, calculated using scholarly knowledge graphs, to better estimate paper popularity for recently published papers. Our approach is designed to be applied on top of existing popularity measures, to improve their accuracy. We apply ArtSim on top of four well-known popularity measures and demonstrate through experiments its potential in improving their popularity estimates.
Training Time Reduction in Transfer Learning for a Similar Dataset Using Deep Learning

Training deep neural networks take a lot of time and computation. In this paper, we have discussed how we can reduce the training time for deep learning models if we have already trained a model for a similar dataset. The basic logic here is that for similar dataset the features stored in the deep neural net are similar and the only difference comes for the classification layers of deep neural so instead of training the whole net we just train the last layers for classifying the data and use the trained model weights on the rest of the layers; this method saves a lot of time.
Communication-Efficient Collaborative Learning of Geo-Distributed JointCloud from Heterogeneous Datasets

With the popularity of cloud computing, the use of services provided by the cloud is increasing. Due to the high communication cost, and data privacy issues, a new crosscloud collaborative computing model is demanded instead of a single giant cloud. Coping with federated learning and Joint-Cloud, we propose a federated learning-based collaborative learning framework, in which the distributed cloud entities are able to learn the same model collaboratively. As compared to traditional cloud-centric approaches, the framework for JointCloud can reduce the network bandwidth overhead and guarantee privacy. However, there are two crucial challenges in the federated manner: heterogeneity and high communication overhead. To address the heterogeneity, we propose a Teacher-Student mechanism, the key of which is a regularization term incorporated with the objective function so as to adjust the gradients from the clients among JointCloud with different data distribution. Then, based on the Teacher-Student mechanism, we further present a communication-efficient federated optimation approach via joint Identification-Verification to reduce the communication rounds. We conduct extensive experiments on Non-IID datasets. The experimental results demonstrate that the proposed framework can significantly reduce communication costs and improve performance.