Reviewerly/exHarmony huggingface dataset:
Geometric Pose Affordance: Monocular 3D Human Pose Estimation with Scene Constraints

Accurate estimation of 3D human pose from a single image remains a challenging task despite many recent advances. In this paper, we explore the hypothesis that strong prior information about scene geometry can be used to improve pose estimation accuracy. To tackle this question empirically, we have assembled a novel Geometric Pose Affordance dataset, consisting of multi-view imagery of people interacting with a variety of rich 3D environments. We utilized a commercial motion capture system to collect gold-standard estimates of pose and construct accurate geometric 3D models of the scene geometry. To inject prior knowledge of scene constraints into existing frameworks for pose estimation from images, we introduce a view-based representation of scene geometry, a multi-layer depth map, which employs multi-hit ray tracing to concisely encode multiple surface entry and exit points along each camera view ray direction. We propose two different mechanisms for integrating multi-layer depth information into pose estimation: input as encoded ray features used in lifting 2D pose to full 3D, and secondly as a differentiable loss that encourages learned models to favor geometrically consistent pose estimates. We show experimentally that these techniques can improve the accuracy of 3D pose estimates, particularly in the presence of occlusion and complex scene geometry.
A novel extended multimodal AI framework towards vulnerability detection in smart contracts

Current automatic data-driven vulnerability detection in smart contracts selects and processes features of interest under black box settings without empirical justification. In this paper, we propose a smart contract testing methodology that bestows developers with flexible, practical and customizable strategies to detect vulnerabilities. Our work enforces strong whitebox knowledge to a series of supervised multimodal tasks under static analysis. Each task encapsulates a vulnerability detection branch test and pipelines feature selection, dimension unification, feature fusion, model training and decision-making. We exploit multiple features made up of code and graph embeddings at the single modality level (intramodal settings) and across individual modalities (intermodal settings). We assign each task to either intramodal or intermodal settings, and show how to train state-of-the-art self-attentive bi-LSTM, textCNN, and random forest (RF) models to extract a joint multimodal feature representation per task. We evaluate our framework over 101,082 functions extracted from the SmartEmbed dataset, and rank each multimodal vulnerability mining strategy in terms of detection performance. Extensive experiments show that our work outperforms existing schemes, and the highest performance reaches 99.71%.
High-performance long-distance discrete-modulation continuous-variable quantum key distribution

We experimentally demonstrate a high-rate discretely modulated continuous-variable quantum key distribution over 80-km standard single-mode fiber with a 2.5 Gbaud, 16-symbol, two-ring constellation. With the help of well-designed digital signal processing algorithms, the excess noise of the system can be effectively suppressed. The achieved secret key rates are 49.02 Mbits/s, 11.86 Mbits/s, and 2.11 Mbits/s over 25-km, 50-km, and 80-km optical fiber, respectively, and achieve 67.4%, 70.0%, and 66.5% of the secret key rate performance of a Gaussian-modulated protocol. Our work shows that it is feasible to build a high-performance, long-distance continuous-variable quantum key distribution system with only a small constellation size.
Optimizing Machine Learning Algorithms for Landslide Susceptibility Mapping along the Karakoram Highway, Gilgit Baltistan, Pakistan: A Comparative Study of Baseline, Bayesian, and Metaheuristic Hyperparameter Optimization Techniques

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods such as metaheuristic algorithms, deep learning-based optimization, Bayesian optimization, and quantum optimization, and our paper focused mainly on metaheuristic and Bayesian optimization techniques and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment’s results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For instance, the metaheuristic algorithm boosted the random forest model’s overall accuracy by 5% and 3%, respectively, from baseline optimization methods GS and RS, and by 4% and 2% from baseline optimization methods GA and PSO. Additionally, for models like KNN and SVM, Bayesian methods with Gaussian processes had good results. When compared to the baseline algorithms RS and GS, the accuracy of the KNN model was enhanced by BO-TPE by 1% and 11%, respectively, and by BO-GP by 2% and 12%, respectively. For SVM, BO-TPE outperformed GS and RS by 6% in terms of performance, while BO-GP improved results by 5%. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.
Decoding the data economy: a literature review of its impact on business, society and digital transformation

Purpose The data economy, emerging from the current hyper-technological landscape, is a global digital ecosystem where data is gathered, organized and exchanged to create economic value. This paper aims to shed light on the interplay of the different topics involved in the data economy, as found in the literature. The study research provides a comprehensive understanding of the opportunities, challenges and implications of the data economy for businesses, governments, individuals and society at large, while investigating its impact on business value creation, knowledge and digital business transformation. Design/methodology/approach The authors conducted a literature review that generated a conceptual map of the data economy by analyzing a corpus of research papers through a combination of machine learning algorithms, text mining techniques and a qualitative research approach. Findings The study findings revealed eight topics that collectively represent the essential features of data economy in the current literature, namely (1) Data Security, (2) Technology Enablers, (3) Business Implications, (4) Social Implications, (5) Political Framework, (6) Legal Enablers, (7) Privacy Concerns and (8) Data Marketplace. The study resulting model may help researchers and practitioners to develop the concept of data economy in a structured way and provide a subset of specific areas that require further research exploration. Practical implications Practically, this paper offers managers and marketers valuable insights to comprehend how to manage the opportunities deriving from a constantly changing competitive arena whose value is today also generated by the data economy. Social implications Socially, the authors also reveal insights explaining how the data economy features may be exploited to build a better society. Originality/value This is the first paper exploring the data economy opportunity for business value creation from a critical perspective.
HuatuoGPT, Towards Taming Language Model to Be a Doctor

Hongbo Zhang, Junying Chen, Feng Jiang, Fei Yu, Zhihong Chen, Guiming Chen, Jianquan Li, Xiangbo Wu, Zhang Zhiyi, Qingying Xiao, Xiang Wan, Benyou Wang, Haizhou Li. Findings of the Association for Computational Linguistics: EMNLP 2023. 2023.
Using information technology to support empirical SLA research

This article argues the case for more widespread adoption of information technology in second language acquisition research. The extent to which electronic datasets and analysis tools such as concordancers and parsers are being adopted across applied linguistics research more widely is briefly reviewed. Reasons for relative lack of adoption of such tools within SLA to date are discussed, and a case is made for use of the CHILDES suite of tools to support SLA research. The background to the CHILDES project in L1 acquisition research is explained, and a detailed account is provided of how CHILDES has been adapted to support a corpus-based programme of research into French L2. The article concludes with a brief assessment of the strengths and weaknesses of CHILDES, and an outline of issues to be addressed if the full benefits of information technology are to be realised in SLA.
Hypothesis testing for finite mixture models

Hypothesis testing for finite mixture model has long been a challenging problem. The standard likelihood ratio test (LRT) does not have the usual asymptotic χ2 distribution partly because the mixture model is not identifiable under null hypothesis. A simple class of hypothesis test procedures for finite mixture models based on goodness of fit (GOF) test statistics is investigated. The suggested hypothesis test procedure is easy to understand and use and can be applied to many mixture models with continuous data. Five commonly used goodness of fit test statistics are considered and compared. The limit distribution of test statistics is simulated based on the bootstrap method. It is demonstrated that a simple application of GOF test statistics to finite mixture models can provide comparable or even superior hypothesis test performance compared to the existing cutting edge EM test method through extensive simulation studies. The effectiveness of GOF test to choose the number components is also demonstrated based on limited empirical studies and a real data application.
Context-Aware Asset Search for Graphic Design

Graphic design tools provide powerful controls for expert-level design creation, but the options can often be overwhelming for novices. This paper proposes Context-Aware Asset Search tools that take the current state of the user's design into account, thereby providing search and selections that are compatible with the current design and better fit the user's needs. In particular, we focus on image search and color selection, two tasks that are central to design. We learn a model for compatibility of images and colors within a design, using crowdsourced data. We then use the learned model to rank image search results or color suggestions during design. We found counterintuitive behavior using conventional training with pairwise comparisons for image search, where models with and without compatibility performed similarly. We describe a data collection procedure that alleviates this problem. We show that our method outperforms baseline approaches in quantitative evaluation, and we also evaluate a prototype interactive design tool.
Feature Selection for Unsupervised Domain Adaptation Using Optimal Transport

In this paper, we propose a new feature selection method for unsupervised domain adaptation based on the emerging optimal transportation theory. We build upon a recent theoretical analysis of optimal transport in domain adaptation and show that it can directly suggest a feature selection procedure leveraging the shift between the domains. Based on this, we propose a novel algorithm that aims to sort features by their similarity across the source and target domains, where the order is obtained by analyzing the coupling matrix representing the solution of the proposed optimal transportation problem. We evaluate our method on a well-known benchmark data set and illustrate its capability of selecting correlated features leading to better classification performances. Furthermore, we show that the proposed algorithm can be used as a pre-processing step for existing domain adaptation techniques ensuring an important speed-up in terms of the computational time while maintaining comparable results. Finally, we validate our algorithm on clinical imaging databases for computer-aided diagnosis task with promising results. Code related to this paper is available at: https://leogautheron.github.io/ and Data related to this paper is available at: https://github.com/LeoGautheron/ECML2018-FeatureSelectionOptimalTransport
Training Compact Deep Learning Models for Video Classification Using Circulant Matrices

In real world scenarios, model accuracy is hardly the only factor to consider. Large models consume more memory and are computationally more intensive, which make them difficult to train and to deploy, especially on mobile devices. In this paper, we build on recent results at the crossroads of Linear Algebra and Deep Learning which demonstrate how imposing a structure on large weight matrices can be used to reduce the size of the model. Building on these results, we propose very compact models for video classification based on state-of-the-art network architectures such as Deep Bag-of-Frames, NetVLAD and NetFisherVectors. We then conduct thorough experiments using the large YouTube-8M video classification dataset. As we will show, the circulant DBoF embedding achieves an excellent trade-off between size and accuracy.
Business environmental analysis for textual data using data mining and sentence-level classification

Purpose The purpose of this paper is to propose a methodology to analyze a large amount of unstructured textual data into categories of business environmental analysis frameworks. Design/methodology/approach This paper uses machine learning to classify a vast amount of unstructured textual data by category of business environmental analysis framework. Generally, it is difficult to produce high quality and massive training data for machine-learning-based system in terms of cost. Semi-supervised learning techniques are used to improve the classification performance. Additionally, the lack of feature problem that traditional classification systems have suffered is resolved by applying semantic features by utilizing word embedding, a new technique in text mining. Findings The proposed methodology can be used for various business environmental analyses and the system is fully automated in both the training and classifying phases. Semi-supervised learning can solve the problems with insufficient training data. The proposed semantic features can be helpful for improving traditional classification systems. Research limitations/implications This paper focuses on classifying sentences that contain the information of business environmental analysis in large amount of documents. However, the proposed methodology has a limitation on the advanced analyses which can directly help managers establish strategies, since it does not summarize the environmental variables that are implied in the classified sentences. Using the advanced summarization and recommendation techniques could extract the environmental variables among the sentences, and they can assist managers to establish effective strategies. Originality/value The feature selection technique developed in this paper has not been used in traditional systems for business and industry, so that the whole process can be fully automated. It also demonstrates practicality so that it can be applied to various business environmental analysis frameworks. In addition, the system is more economical than traditional systems because of semi-supervised learning, and can resolve the lack of feature problem that traditional systems suffer. This work is valuable for analyzing environmental factors and establishing strategies for companies.
Function Space Particle Optimization for Bayesian Neural Networks

While Bayesian neural networks (BNNs) have drawn increasing attention, their posterior inference remains challenging, due to the high-dimensional and over-parameterized nature. To address this issue, several highly flexible and scalable variational inference procedures based on the idea of particle optimization have been proposed. These methods directly optimize a set of particles to approximate the target posterior. However, their application to BNNs often yields sub-optimal performance, as such methods have a particular failure mode on over-parameterized models. In this paper, we propose to solve this issue by performing particle optimization directly in the space of regression functions. We demonstrate through extensive experiments that our method successfully overcomes this issue, and outperforms strong baselines in a variety of tasks including prediction, defense against adversarial examples, and reinforcement learning.
Targets of Online Hate Speech in Context

Online hate speech, especially on social media platforms, is the subject of both policy and political debate in Europe and globally - from the fragmentation of network publics to echo chambers and bubble phenomena, from networked outrage to networked populism, from trolls and bullies to propaganda and non-linear cyberwarfare. Both researchers and Facebook Community standards see the identification of the potential targets of hateful or antagonistic speech as key to classifying and distinguishing the latter from arguments that represent political viewpoints protected by freedom of expression rights. This research is an exploratory analysis of mentions of targets of hate speech in comments in the context of 106 public Facebook pages in Romanian and Hungarian from January 2015 to December 2017. A total of 1.8 million comments were collected through API interrogation and analyzed using a text-mining niche-dictionaries approach and co-occurrence analysis to reveal connections to events on the media and political agenda and discursive patterns. Findings indicate that in both countries the most prominent targets mentioned are connected to current events on the political and media agenda, that targets are most frequently mentioned in contexts created by politicians and news media, and that discursive patterns in both countries involve the proliferation of similar stereotypes about certain target groups.
Solving the security challenges of IoT with public key cryptography

With the Internet of Things (IoT) predicted to connect 20 billion devices by 2020, there is a strong case to be made for the adoption of identity-based public key cryptography to provide end-to-end security of data across IoT-enabled industries.
On researching activity tracking to support learning: a retrospective

Purpose This paper aims to discuss research and design of learning activities involving activity tracking and wearable activity tracking technology. Design/methodology/approach Three studies are summarized as part of a program of research that sought to design new learning activities for classroom settings. The first used data from a qualitative interview study of adult athletes who self-track. The second used video excerpts from a designed learning activity with a group of fifth grade elementary students. The third study draws largely on quantitative assessment data from an activity tracking unit enactment in a rural sixth grade class. Findings Activity tracking appears to provide opportunities for establishing benchmarks and calibration opportunities related to intensity of physical activities. Those features of activity tracking can be leveraged to develop learning activities where elementary students discover features of data and how data are affected by different distributions. Students can show significant improvement related to statistical reasoning in classroom instructional units that centralize the use of self-tracked data. Originality/value As activity tracking is becoming a more ubiquitous practice with increased pervasiveness and familiarity with mobile and wearable technologies, this paper demonstrates a topical intersection between the information and learning sciences, illustrates how self-tracking can be recruited for instructional settings, and it discusses concerns that have emerged in the past several years as the technology related to activity tracking begins to be used for educational purposes.
Digital assessment of mathematics: Opportunities, issues and criteria

Digital assessment of mathematics is becoming widespread, but still comes with limitations and constraints. A central question is how to design digital tests that assess mathematical knowledge in a valid way. Based on literature on validity and on assessment with and through technology, we identify arguments for and opportunities of digital assessment of mathematics, as well as its main issues. Through three case descriptions, different ways to design digital tests are explored. As a conclusion, we make a plea for assessment environments which offer rich opportunities for students to “do” mathematics and for test designers to design rich items; automated scoring also needs further development, with respect to the scoring of intermediate steps in problem-solving strategies.
Revisit Batch Normalization: New Understanding and Refinement via Composition Optimization

Batch Normalization (BN) has been used extensively in deep learning to achieve faster training process and better resulting models. However, whether BN works strongly depends on how the batches are constructed during training, and it may not converge to a desired solution if the statistics on the batch are not close to the statistics over the whole dataset. In this paper, we try to understand BN from an optimization perspective by providing an explicit objective function associated with BN. This explicit objective function reveals that: 1) BN, rather than being a new optimization algorithm or trick, is creating a different objective function instead of the one in our common sense; and 2) why BN may not work well in some scenarios.We then propose a refinement of BN based on the compositional optimization technique called Full Normalization (FN) to alleviate the issues of BN when the batches are not constructed ideally. The convergence analysis and empirical study for FN are also included in this paper.
Temporal Quilting for Survival Analysis

The importance of survival analysis in many disciplines (especially in medicine) has led to the development of a variety of approaches to modeling the survival function. Models constructed via various approaches oer dierent strengths and weaknesses in terms of discriminative performance and calibration, but no one model is best across all datasets or even across all time horizons within a single dataset. Because we require both good calibration and good discriminative performance over dierent time horizons, conventional model selection and ensemble approaches are not applicable. This paper develops a novel approach that combines the collective intelligence of different underlying survival models to produce a valid survival function that is well-calibrated and oers superior discriminative performance at dierent time horizons. Empirical results show that our approach provides signicant gains over the benchmarks on a variety of real-world datasets.