Reviewerly/exHarmony huggingface dataset:
The Past, Problems, and Potential of Readability Analysis

Readability analysis combines statistical modeling, theoretical linguistics, and psychological theory to determine the accessibility level of writing samples. This study has a long history and broad impact, yet typically uses extremely simple statistical tools (in particular linear regressions). This article briefly reviews key stages in the history of readability, and discusses present issues and potential future benefits these tools offer.
A Hybrid Blind Watermarking With Redundant Discrete Wavelet and Hadamard Transform

The rapid growth in usage of digital technologies brings tremendous interest to the field of digital authentication. Digital watermarking is one of the most powerful authentication techniques used to restrict unauthorized usage of copyrighted images. Unique logo can be inserted into different copies of the same digital image for different clients. This will curtail the illegal usage. Most of the earlier works used Redundant Discrete Wavelet (RDWT) transform in combination with SVD. These watermarking schemes suffered with false positive problem. Hence, in this work, a hybrid blind watermarking is developed by using a combination of Redundant Discrete Wavelet (RDWT) and Hadamard transform to get rid of false positive problem and to produce imperceptible, robust and secure watermarking scheme.
A comparative study on optical flow for facial expression analysis

Optical flow techniques are becoming increasingly performant and robust when estimating motion in a scene, but their performance has yet to be proven in the area of facial expression recognition. In this work, a variety of optical flow approaches are evaluated across multiple facial expression datasets, so as to provide a consistent performance evaluation. The aim of this work is not to propose a new expression recognition technique, but to understand better the adequacy of existing state-of-the art optical flow for encoding facial motion in the context of facial expression recognition. Our evaluations highlight the fact that motion approximation methods used to overcome motion discontinuities have a significant impact when optical flows are used to characterize facial expressions.
Single-Domain Generalization in Medical Image Segmentation via Test-Time Adaptation from Shape Dictionary

Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medical field where the data sharing is highly concerned and sometimes prohibitive due to privacy issue. This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains. We present a novel approach to address this problem in medical image segmentation, which extracts and integrates the semantic shape prior information of segmentation that are invariant across domains and can be well-captured even from single domain data to facilitate segmentation under distribution shifts. Besides, a test-time adaptation strategy with dual-consistency regularization is further devised to promote dynamic incorporation of these shape priors under each unseen domain to improve model generalizability. Extensive experiments on two medical image segmentation tasks demonstrate the consistent improvements of our method across various unseen domains, as well as its superiority over state-of-the-art approaches in addressing domain generalization under the worst-case scenario.
Channelized Axial Attention – considering Channel Relation within Spatial Attention for Semantic Segmentation

Spatial and channel attentions, modelling the semantic interdependencies in spatial and channel dimensions respectively, have recently been widely used for semantic segmentation. However, computing spatial and channel attentions separately sometimes causes errors, especially for those difficult cases. In this paper, we propose Channelized Axial Attention (CAA) to seamlessly integrate channel attention and spatial attention into a single operation with negligible computation overhead. Specifically, we break down the dot-product operation of the spatial attention into two parts and insert channel relation in between, allowing for independently optimized channel attention on each spatial location. We further develop grouped vectorization, which allows our model to run with very little memory consumption without slowing down the running speed. Comparative experiments conducted on multiple benchmark datasets, including Cityscapes, PASCAL Context, and COCO-Stuff, demonstrate that our CAA outperforms many state-of-the-art segmentation models (including dual attention) on all tested datasets.
Blockchain Technology as an Emerging Technology in the Insurance Market

Introduction: Blockchain is gaining attention in various industries and sectors. It is described as an emergent technology with immense possibilities similar to how the internet has revolutionised how businesses are currently carried out. Still, various sectors have either not adopted or are in a very nascent stage to adopt blockchain technology in their operations. The current research examines how blockchain can be used in the insurance sector. This industry was chosen as it is extremely relevant in today’s world and directly bears its economy.Purpose: To determine the current and future path in which the insurance industry is moving about blockchain technology adoption and find synergy between blockchain technology and the insurance business.Need for study: The insurance industry is highly relevant in today’s world and directly bears the country’s economy. Additionally, blockchain is an emergent technology with immense possibilities similar to how the internet has revolutionised how businesses are done. The current research looks at how blockchain can be used in the insurance business.Methodology: A systematic literature review was conducted in this study by reviewing literature related to blockchain technology and the insurance sector. Science direct was used as a source of information. For this study, the literature review approach was chosen since it allows us to trace the growth of the subject matter and identify the patterns that have formed through time.Findings: The study found that the insurance sector has recognised the latent benefits of blockchain technology and has begun to develop its usage in selected cases such as fraud prevention and risk assessment.Practical implications: The current study can be referred to by academicians, marketers, industry people, and policymakers. The study encourages companies and academicians to further investigate the usage of blockchain in insurance.
Scaling Up Influence Functions

We address efficient calculation of influence functions for tracking predictions back to the training data. We propose and analyze a new approach to speeding up the inverse Hessian calculation based on Arnoldi iteration. With this improvement, we achieve, to the best of our knowledge, the first successful implementation of influence functions that scales to full-size (language and vision) Transformer models with several hundreds of millions of parameters. We evaluate our approach in image classification and sequence-to-sequence tasks with tens to a hundred of millions of training examples. Our code is available at https://github.com/google-research/jax-influence.
PTAU

Current question answering systems are insufficient when confronting real-life scenarios, as they can hardly be aware of whether a question is answerable given its context. Hence, there is a recent pursuit of unanswerability of a question and its attribution. Attribution of unanswerability requires the system to choose an appropriate cause for an unanswerable question. As the task is sophisticated for even human beings, it is expensive to acquire labeled data, which makes it a low-data regime problem. Moreover, the causes themselves are semantically abstract and complex, and the process of attribution is heavily question- and context-dependent. Thus, a capable model has to carefully appreciate the causes, and then, judiciously contrast the question with its context, in order to cast it into the right cause. In response to the challenges, we present PTAU, which refers to and implements a high-level human reading strategy such that one reads with anticipation. In specific, PTAU leverages the recent prompt-tuning paradigm, and is further enhanced with two innovatively conceived modules: 1) a cause-oriented template module that constructs continuous templates towards certain attributing class in high dimensional vector space; and 2) a semantics-aware label module that exploits label semantics through contrastive learning to render the classes distinguishable. Extensive experiments demonstrate that the proposed design better enlightens not only the attribution model, but also current question answering models, leading to superior performance.
Factors influencing the implementation of cloud accounting: evidence from small and medium enterprises in Oman

Purpose This study aims to investigate the factors influencing the adoption of cloud accounting (CA) in Oman’s small and medium enterprises (SMEs). The research model is developed based on relationships between technology, organisational and environmental contexts. Design/methodology/approach This study used a questionnaire to collect data from a sample of SMEs in Oman’s information and communication sector. In total, 300 enterprises were selected, and the questionnaire was distributed to the executives. The questionnaires valid for analysis were 159. The collected data were analysed using structural equation modelling through analysis of a moment structures software. Findings This study tested seven factors, namely, support from top management, firm size, infrastructure (technology readiness), security and privacy, compatibility, competitive pressure and relative advantage. The results revealed that compatibility has a significant impact on the adoption of CA. Practical implications This study suggests the mangers in SMEs should play a more decisive role in identification of technological, organisational and environmental factors that affect the success of implementing CA in a comprehensive model. Originality/value This study constitutes a management strategy that helps the enterprises in light of limited economic resources and concerns about the use of cloud services to make the appropriate decision in adopting CA.
Real Estate Tokenisation via Non Fungible Tokens

This paper presents a crypto Non Fungible Token (NFT) for real estate information. Current issues with real estate are the lack of asset information availability, and once data is retrieved, its veracity. Moreover, data can be out of data or artificially manipulated for an economic benefit. An example could be the fire alarm inspection logs. On the other hand, the main benefits of tokenisation are 1) decentralisation from a single source of authority, 2) encryption of data that avoids tampering, and 3) digital proof of ownership. The NFT registers real estate static and dynamic information to be traded in the data marketplace between the different users and stakeholders such as investors, occupiers, insurers and property managers. The information registered in the NFT contains real estate deeds, transactions and relevant parameters that change over time. The infrastructure of the proposed real estate NFT consists of a private token in a private blockchain entirely developed for this application rather than ERC-1155 or ERC-721; therefore, a new crypto token is created. Furthermore, the consensus protocol is a mixture between the proof of work and proof of stake mechanisms by choosing random validators with staked Cryptocurrency already, such as Ethereum. Experimental results show that the increment of the NFT at every transaction does not produce an equal increment of mining time or mining nonce, making the NFT predictable for higher complex models.
On debugging the performance of configurable software systems

Determining whether a configurable software system has a performance bug or it was misconfigured is often challenging. While there are numerous debugging techniques that can support developers in this task, there is limited empirical evidence of how useful the techniques are to address the actual needs that developers have when debugging the performance of configurable software systems; most techniques are often evaluated in terms of technical accuracy instead of their usability. In this paper, we take a human-centered approach to identify, design, implement, and evaluate a solution to support developers in the process of debugging the performance of configurable software systems. We first conduct an exploratory study with 19 developers to identify the information needs that developers have during this process. Subsequently, we design and implement a tailored tool, adapting techniques from prior work, to support those needs. Two user studies, with a total of 20 developers, validate and confirm that the information that we provide helps developers debug the performance of configurable software systems.
Predicting the type and target of offensive social media posts in Marathi

The presence of offensive language on social media is very common motivating platforms to invest in strategies to make communities safer. This includes developing robust machine learning systems capable of recognizing offensive content online. Apart from a few notable exceptions, most research on automatic offensive language identification has dealt with English and a few other high-resource languages such as French, German, and Spanish. In this paper, we address this gap by tackling offensive language identification in Marathi, a low-resource Indo-Aryan language spoken in India. We introduce the Marathi Offensive Language Dataset v.2.0 or MOLD 2.0 and present multiple experiments on this dataset. MOLD 2.0 is a much larger version of MOLD with expanded annotation to the levels B (type) and C (target) of the popular OLID taxonomy. MOLD 2.0 is the first hierarchical offensive language dataset compiled for Marathi, thus opening new avenues for research in low-resource Indo-Aryan languages. Finally, we also introduce SeMOLD, a larger dataset annotated following the semi-supervised methods presented in SOLID (Rosenthal et al. in SOLID: a large-scale semi-supervised dataset for offensive language identification. In: Findings of ACL, 2021).
Towards a Social VR-based Exergame for Elderly Users: An Exploratory Study of Acceptance, Experiences and Design Principles

For many elderly individuals, the aging experience is associated with a lack of social interaction and physical exercise that may negatively affect their health. To address these issues, researchers have designed experiences based on immersive virtual reality (VR) and 2D screen-based exergames. However, very few have studied the use of social VR for elderly, in which users can interact remotely through avatars in a single, shared, immersive virtual environment, using a head-mounted display. Additionally, there is limited research on the experience of elderly in performing interactive activities, especially game-based activities, in social VR. We conducted an exploratory study with 10 elderly people who never experienced VR before, to evaluate an avatar-mediated interaction-based social VR game prototype. Based on a mixed-methods approach, our study presents new insights into the usability, acceptance, and gameplay experience of elderly in a social VR game. Moreover, our study reflects upon design principles that should be considered when developing social VR games for elderly to ensure an engaging and safe user experience. Our results suggest that such games have a potential among the user group. Direct hand manipulation, based on hand tracking for interaction with 3D objects, presented an engaging and intuitive interaction paradigm, and the social game activity in VR was found to be enjoyable.
Gaussian Belief Trees for Chance Constrained Asymptotically Optimal Motion Planning

In this paper, we address the problem of sampling-based motion planning under motion and measurement un-certainty with probabilistic guarantees. We generalize traditional sampling-based, tree-based motion planning algorithms for deterministic systems and propose belief-A, a framework that extends any kinodynamical tree-based planner to the belief space for linear (or linearizable) systems. We introduce appropriate sampling techniques and distance metrics for the belief space that preserve the probabilistic completeness and asymptotic optimality properties of the underlying planner. We demonstrate the efficacy of our approach for finding safe low-cost paths efficiently and asymptotically optimally in simulation, for both holonomic and non-holonomic systems.
A Novel Data-Driven Method for Behind-the-Meter Solar Generation Disaggregation With Cross-Iteration Refinement

Photovoltaic (PV) generation is increasing in distribution systems following policies and incentives to promote zero-carbon emission societies.Most residential PV systems are installed behind-the-meter (BTM).Due to single meter deployment that measures the net load only, this PV generation is invisible to distribution system operators causing a negative impact on the distribution system planning and local supply and demand balance.This paper proposes a novel data-driven BTM PV generation disaggregation method using only net load and weather data, without relying on other PV proxies and PV panels' physical models.Long Short-Term Memory (LSTM) is employed to build a generation difference fitted model (GDFM) and a consumption difference fitted model (CDFM) derived from weather data.Both difference fitted models are refined by a crossiteration with mutual output.Finally, considering the photoelectric conversion properties, the disaggregated generation results are acquired by the refined GDFM of changing input.The proposed method has been tested with actual smart meter data of Austin, Texas and proves to increase the disaggregated accuracy as compared to current state-of-the-art methods.The proposed method is also applicable to disaggregate BTM PV systems of different manufacturing processes and types.
Evaluating Extreme Hierarchical Multi-label Classification

Several natural language processing (NLP) tasks are defined as a classification problem in its most complex form: Multi-label Hierarchical Extreme classification, in which items may be associated with multiple classes from a set of thousands of possible classes organized in a hierarchy and with a highly unbalanced distribution both in terms of class frequency and the number of labels per item. We analyze the state of the art of evaluation metrics based on a set of formal properties and we define an information theoretic based metric inspired by the Information Contrast Model (ICM). Experiments on synthetic data and a case study on real data show the suitability of the ICM for such scenarios.
Towards Robotic Knee Arthroscopy: Spatial and Spectral Learning Model for Surgical Scene Segmentation

Minimally invasive surgeries are complex to perform, and surgical outcomes are varied due to a limited view of the surgical scene. There is a lack of reliable vision systems that can identify and segment different tissue types intra-operatively. Here we introduce a novel approach towards overcoming this limitation. Our approach extracts geometric and spectral information captured by a miniaturized camera using deep learning algorithms. We have successfully implemented a deep neural network and trained it for in-situ labelling of soft and hard tissues acquired from clinically relevant cadaveric studies. Although presented and validated for knee arthroscopy our approach can be implemented across different endoscopic platforms. The intraoperative nature of tissue segmentation could be easily implemented in medical imaging systems for achieving better outcomes in minimally invasive procedures. In addition to segmenting different tissue types like, ACL, femur, tibia, cartilage, and meniscus, our network can also segment surgical tools present inside the knee cavity. The achieved dice similarity score for the tissue types femur, tibia, ACL, and meniscus were 0.91, 0.71, 0.39, and 0.62.
A Machine Learning Approach for the Classification of Falls and Activities of Daily Living in Agricultural Workers

Population aging is a global trend, and the highest proportion of elderly people in the workforce per unit of population is found in agricultural areas. However, few systematic studies have been conducted on farmer falls in the field of agricultural machinery. This study focuses on the application of classification methods for monitoring devices to detect fall/nonfall movements of farmworkers, where agricultural biomechanical factors are considered in detecting activities of daily living. In this study, we recorded and analyzed original acquisition datasets of signals obtained from two accelerometers and one gyroscope for 40 healthy individuals who performed various falls and activities of daily living (ADLs). Spatial characteristics were used to train the machine-learning classifiers to distinguish between fall and non-fall events. Supervised machine learning experiments evaluated the effectiveness of the proposed approach: the k-nearest neighbors (kNN) and support vector machine (SVM) algorithms achieved roc auc-scores of 0.999 in differentiating between falls and ADLs (binary-class classification). Moreover, an artificial neural network (ANN) classifier showed the highest performance in terms of classification roc auc-scores of 1.0. The evaluation metric demonstrated the highest performance in the analysis and evaluation of the signal obtained from the S2 acceleration sensor with a measurement range of &#x00B1;16 g. The proposed SVM classifier evaluations showed a 0.988 roc auc-score for sensor tests in multi-class classification, along with the highest performance in terms of the F1-score and Matthews Correlation Coefficient (MCC) over 84% in the multi-class classification model for distinguishing each of ADLs and Fall using &#x00B1;16 g acceleration sensor.
An estimation framework of regional rooftop photovoltaic potential based on satellite remote sensing images

This paper proposes a comprehensive framework for estimating the regional rooftop photovoltaic (PV) potential. The required rooftop information is extracted from Gao Fen-7 satellite images. In particular, the rooftop area is obtained using a semantic segmentation network. The azimuth and inclination angles are calculated based on the digital surface model. In addition, to improve the accuracy of the economic evaluation, buildings are divided into commercial and industrial buildings and residential buildings. Based on the difference in the roof inclination, the rooftops can be divided into flat roofs, on which the PV panels are installed with the optimal inclination angle, and sloped rooftops, on which the PV panels are installed in a lay-flat manner. The solar irradiation on the plane-of-array is calculated using the isotropic sky translocation model. Then, the available installed capacity and generation potential of the rooftop PV is obtained. Finally, the net present value, dynamic payback period, and internal rate of return are used to evaluate the economic efficiency of the rooftop PV project. The proposed framework is applied in the Da Xing district of Beijing, China, with a total area of 546.84 km2. The results show that the rooftop area and available installed capacity of PV are 25.63 km2 and 1487.45 MWp, respectively. The annual rooftop PV generation potential is 2832.23 GWh, with significant economic returns.