Reviewerly/exHarmony huggingface dataset:
Learning Automated Essay Scoring Models Using Item-Response-Theory-Based Scores to Decrease Effects of Rater Biases

In automated essay scoring (AES), scores are automatically assigned to essays as an alternative to grading by humans.Traditional AES typically relies on handcrafted features, whereas recent studies have proposed AES models based on deep neural networks to obviate the need for feature engineering.Those AES models generally require training on a large dataset of graded essays.However, assigned grades in such a training dataset are known to be biased owing to effects of rater characteristics when grading is conducted by assigning a few raters in a rater set to each essay.Performance of AES models drops when such biased data are used for model training.Researchers in the fields of educational and psychological measurement have recently proposed item response theory (IRT) models that can estimate essay scores while considering effects of rater biases.This study, therefore, proposes a new method that trains AES models using IRT-based scores for dealing with rater bias within training data.
harAGE: A Novel Multimodal Smartwatch-based Dataset for Human Activity Recognition

This work introduces the harAGEdataset: a novel multimodal smartwatch-based dataset for Human Activity Recognition (HAR) with more than 17 hours of data collected from 19 participants using a Garmin Vivoactive 3 device. The dataset contains samples from resting, lying, sitting, standing, washing hands, walking, running, stairs climbing, strength workout, flexibility workout, and cycling activities. The resting activity, excluded from the set of activities to recognise, was explicitly conducted while avoiding stressors and external stimuli, so the data collected can be used to compute the personal, baseline heart rate at rest. We also present the HAR-based models trained using the accelerometer data to recognise different sets of activities. Specifically, we focus on different strategies to combine, fuse, and enrich the accelerometer measurements, so they can be used end-to-end. Model performances are assessed following a Leave-One-Subject-Out Cross-Validation (LOSO-CV) approach, and we use the Unweighted Average Recall (UAR) as the evaluation metric to compare the ground truth and the inferred information. The best UAR score of 98.1 % is obtained when recognising the static and the dynamic activities, excluding the samples corresponding to the washing hands, strength workout, and flexibility workout activities. When recognising the specific activities included in these two sets, the model with the best performance scores a UAR of 70.1 %. Finally, when recognising all the activities considered in the harAGEdataset, the highest UAR achieved is 64.3 %.
A Blockchain-based Secured and Privacy-Preserved Personal Healthcare Record Exchange System

The demand for personal healthcare record (PHR) exchange among electronic medical record (EMR) systems and patients is growing along with remarkable efforts to improve the quality, safety, privacy-preserving, and efficiency of healthcare information delivery. Currently, in EMR systems, there are problems of fragmented communication and lack of interoperability for health care professionals, hospitals, clinics and patients to appropriately access and securely share a patient’s EMR. Moreover, in the context of the recent soaring of security breaches and surveillance in incidents reported, a solution to ensure the safety and privacy of data is essential. Blockchain can ensure trust, security, and privacy of individual data in the digital world. By leveraging blockchain features, we propose HoloCare system for providing a single source from the patient’s PHR for authentication, permission granting and remote access to the health information data from other EMR systems. To demonstrate the practicability and effectiveness of HoloCare, we utilize HL7 FHIR, known as a standard for healthcare data exchange in a wide variety of contexts from PHR-based data sharing, server communication among and within healthcare providers.
UAV Detection Based on Improved YOLOv4 Object Detection Model

With the development of UAV technology, the improper use of UAVs has seriously endangered public safety. In order to meet the needs of UAV intrusion detection, this paper proposes a method for UAV image detection using deep learning technology. This method performs lightweight processing in the YOLOv4 object detection model to obtain a faster detection speed model, and uses the CA attention mechanism module to replace the original SE attention mechanism module to build a new model (called CA-YOLOv4-L) with stronger object detection capabilities. Use the improved model to test the UAV pictures and the results show that: The improved model CA-YOLOv4-L achieves an average accuracy of 94.63% on UAV images, a detection speed of 39.66FPS, and a 34.8% reduction in parameters compared to the original model. The proposed method can effectively identify the UAV in the picture, can identify multiple UAV targets, and estimate the position of the UAV in the image.
Legal Technology: Assessment of the Legal Tech Industry’s Potential

Abstract The use of information and communication technologies (ICT) in the sector of legal services has resulted in the emergence of a new category of services known as legal technology (legal tech). This article aims at defining the current state of research concerning the matter, confirming its interdisciplinary nature and examining the level of its popularity. The strategy assumed for the article has influenced the order and sequence of the topics covered starting from an introduction to legal technology together with analysis of the context of the definition of the term (legal tech) (“Introduction” section), through a detailed discussion of the methodology of systematic literature review, its results and an appraisal of the popularity of the notions (“Materials and Methods” and “Bibliometric Analysis” sections), the application of the thematic analysis method (“Thematic Analysis of the Reference Repository” section), Google Trends analysis (“Analysis of the Popularity of the Terms ‘Legal Technology’ or ‘Legal Tech’ (Google Trends)” section), and finally the conclusions (“Conclusions” section). The research methodology covers a systematic literature review, quantitative bibliometric analysis, the thematic analysis method, and — complementarily — popularity analysis performed using the Google Trends analytical tool. The article confirms the multidisciplinary nature of legal technology as a subject matter, indicating the thematic categories corresponding with the notion under investigation. It contains a description of the geographical segmentation and difference in that regard at a global level. The author has verified the presence of publications on legal technology and shown that the future of the legal services sector lies in an interdisciplinary juxtaposition of the classic legal sciences with entirely new areas, i.e. IT, artificial intelligence, and data analysis.
Impact of Cognitive Biases on Progressive Visualization

Progressive visualization is fast becoming a technique in the visualization community to help users interact with large amounts of data. With progressive visualization, users can examine intermediate results of complex or long running computations, without waiting for the computation to complete. While this has shown to be beneficial to users, recent research has identified potential risks. For example, users may misjudge the uncertainty in the intermediate results and draw incorrect conclusions or see patterns that are not present in the final results. In this article, we conduct a comprehensive set of studies to quantify the advantages and limitations of progressive visualization. Based on a recent report by Micallef et al., we examine four types of cognitive biases that can occur with progressive visualization: uncertainty bias, illusion bias, control bias, and anchoring bias. The results of the studies suggest a cautious but promising use of progressive visualization - while there can be significant savings in task completion time, accuracy can be negatively affected in certain conditions. These findings confirm earlier reports of the benefits and drawbacks of progressive visualization and that continued research into mitigating the effects of cognitive biases is necessary.
Enabling Trustworthiness in Sustainable Energy Infrastructure Through Blockchain and AI-Assisted Solutions

Network trustworthiness is a critical component of network security, as it builds on positive inter-actions, guarantees, transparency, and accountability. And with the growth of smart city services and applications, trustworthiness is becoming more important. Most current network trustworthiness solutions are insufficient, particularly for critical infrastructures where end devices are vulnerable and easily hacked. In terms of the energy sector, blockchain technology transforms all currencies into digital modes, thereby allowing one person to manage and exchange energy with others. This has drawn the attention of experts in many fields as a safe, low-cost platform to track billions of transactions in a distributed energy economy. Security and trust issues are still relatively new in the current centralized energy management scheme. With blockchain technology, a decentralized energy infrastructure enables parties to establish micro- grid trading energy transactions and apply artificial intelligence (AI). Using AI in energy systems enables machines to learn various parameters, such as predicted required amounts, excess amounts, and trusted partners. In this article, we envision a cooperative and distributed framework based on cutting-edge computing, communication, and intelligence capabilities such as AI and blockchain in the energy sector to enable secure energy trading, remote monitoring, and trustworthiness. The proposed framework can also enable secure energy trading at the edge devices and among multiple devices. There are also discussions on difficulties, issues, and design principles, as well as spotlights on some of the more popular solutions.
Reconstructing Dual Learning for Neural Voice Conversion Using Relatively Few Samples

This paper introduces a dual learning system for neural voice conversion (DualVC) using relatively few samples based on the symmetry of the speech conversion task. The system contains a pair of sequence-to-sequence neural networks that have the same structure but are trained in opposite directions. The objective function of the dual model training is the sum of paired conversion loss and reconstruction loss during the dual training circle. The models in the two directions are trained alternately to guide each other by the corresponding reconstruction loss. Furthermore, curriculum learning techniques are used to load models in existing fields into the current task to accelerate the rapid iteration and convergence of the model. The experiment on the voice conversion task with the proposed DualVC and curriculum learning strategy obtained a comparable naturalness and similarity with only a 30% dataset than the BaseVC model trained on the full dataset.
Detection of DoH Tunneling using Semi-supervised Learning method

DNS over HTTPS (DoH) is an upgrade of traditional DNS, enhancing privacy and security. DoH prevents man-in-the-middle attacks such as eavesdropping and manipulating DNS data by using the HTTPS protocol to encrypt the data between the DoH client and the DoH-based DNS resolver. However, DoH may cause unintended security vulnerability, information leakage via malicious DoH tunneling. Since the DoH traffic is not recognizable from other HTTPS traffic, secret encrypted DoH messages can be easily leaked out of an enterprise network. Although some countermeasures have been proposed, they still have limitations. To solve this problem, we propose a Semi-supervised machine learning classifier to detect malicious DoH tunneling. The experiment results show a significant improvement compared to the other previous works.
Finding nash equilibrium for imperfect information games via fictitious play based on local regret minimization

Finding Nash equilibrium in the domain of imperfect information games as a challenging problem has received much attention. Neural Fictitious Self-Play (NFSP) is a popular model-free machine learning algorithm and has computed approximate Nash equilibrium on such games. However, the deep reinforcement learning method used to approximate the best response in NFSP requires reaching a fully observable Markov state, while the states in imperfect information games are partially observable and non-Markovian, which results in a poor approximation of the best response. Thus, NFSP needs more iterations to converge. In this study, we present a new reinforcement learning method that is inspired by counterfactual regret minimization to relax the Markov requirement by iteratively updating policy according to the regret matching process. Combining this new reinforcement learning algorithm with fictitious play, we further present a novel algorithm to find approximate Nash equilibrium in zero-sum imperfect information games. Experimental results in three benchmark games show that this new algorithm can find approximate Nash equilibrium effectively and converge much faster compared with baseline.
An approach towards the response surface linearization via ANN-based cascade scheme for regression modeling in Healthcare

The digitalization of medicine, which has become widespread in recent years, opens up new opportunities for diagnosing and monitoring the patient’s condition. A large amount of digital data allows artificial intelligence tools to solve various problems in this area. In the case of solving significantly nonlinear tasks, the accuracy of its processing has priority. Existing methods, single models, and sometimes ensemble methods do not provide sufficient prediction accuracy. Response surface linearization procedures provide the ability to improve prediction accuracy when a nonlinear machine learning algorithm or an artificial neural network does not give satisfactory results. The disadvantage of this approach is a significant increase in the duration of procedures for both training and application of the method for large data sets. However, this shortcoming can be compensated by applying the high-speed machine learning method. That is why in this paper, we propose a new cascade scheme based on the use of hybrid, non-iterative ANN, which provides both high accuracy and high speed of the training procedure. The proposed scheme is based on the algorithm developed by the authors, which provides the possibility of efficient processing of data sets of different volumes. The experimental modeling was based on a real data set from the Healthcare domain. The optimal parameters of the proposed scheme are experimentally established. High accuracy of operation of the cascade scheme based on various performance indicators is received. By comparison with other methods of this class, the high efficiency of the application of the cascade scheme is established.
Active Learning of Markov Decision Processes using Baum-Welch algorithm

Cyber-physical systems (CPSs) are naturally modelled as reactive systems with nondeterministic and probabilistic dynamics. Model-based verification techniques have proved effective in the deployment of safety-critical CPSs. Central for a successful application of such techniques is the construction of an accurate formal model for the system. Manual construction can be a resource-demanding and error-prone process, thus motivating the design of automata learning algorithms to synthesise a system model from observed system behaviours.This paper revisits and adapts the classic Baum-Welch algorithm for learning Markov decision processes and Markov chains. For the case of MDPs, which typically demand more observations, we present a model-based active learning sampling strategy that choses examples which are most informative w.r.t. the current model hypothesis. We empirically compare our approach with state-of-the-art tools and demonstrate that the proposed active learning procedure can significantly reduce the number of observations required to obtain accurate models.
PANDA: Lightweight non-interactive privacy-preserving data aggregation for constrained devices

Privacy-preserving data aggregation is becoming a demanding necessity for many promising scenarios, e.g., health care analysis. Sensitive data are collected and aggregated in a privacy-preserving approach using current Internet of Things (IoT) technology, leading to immense challenge and consequent interest in developing secure computing algorithms for individual and organizational data. However, most existing solutions focus on specific evaluations (e.g., SUM), and they use heavy cryptographic techniques, which are far from practice for constrained IoT devices. The Trusted Execution Environment (TEE, implemented with Intel SGX) can assist in computing arbitrary functions and avoiding resource-consuming operations. Nevertheless, TEE is subject to several challenges because TEE is vulnerable to limited resource and even function violations, e.g., the attacker may bypass the boundary of TEE. In this paper, we propose a lightweight non-interactive privacy-preserving data aggregation scheme for resource-constrained devices, named PANDA, where TEE is introduced to bypass the trusted entities requirement and heavy overhead. Additionally, PANDA explores the certificate-aided function authorization to prevent leakage from unauthorized functions, and designs the public verifiable certificate management to detect the abnormal behaviors of the host to mitigate the external host compromise. We formalize PANDA with rigorous security analysis to indicate privacy protection against the compromised aggregator and analyst. The evaluation results show that PANDA has constant online communication cost and lightweight computation overhead for constrained devices, which is suitable for IoT applications.
Low c-differential uniformity for functions modified on subfields

In this paper, we construct some piecewise defined functions, and study their c-differential uniformity. As a by-product, we improve upon several prior results. Further, we look at concatenations of functions with low differential uniformity and show several results. For example, we prove that given $$\beta _i$$ (a basis of $$\mathbb {F}_{q^n}$$ over $$\mathbb {F}_q$$ ), some functions $$f_i$$ of c-differential uniformities $$\delta _i$$ , and $$L_i$$ (specific linearized polynomials defined in terms of $$\beta _i$$ ), $$1\le i\le n$$ , then $$F(x)=\sum _{i=1}^n\beta _i f_i(L_i(x))$$ has c-differential uniformity equal to $$\prod _{i=1}^n \delta _i$$ .
Generalizable Crowd Counting via Diverse Context Style Learning

Existing crowd counting approaches predominantly perform well on the training-testing protocol. However, due to large style discrepancies not only among images but also within a single image, they suffer from obvious performance degradation when applied to unseen domains. In this paper, we aim to design a generalizable crowd counting framework which is trained on a source domain but can generalize well on the other domains. To reach this, we propose a gated ensemble learning framework. Specifically, we first propose a diverse fine-grained style attention model to help learn discriminative content feature representations, allowing for exploiting diverse features to improve generalization. We then introduce a channel-level binary gating ensemble model, where diverse feature prior, input-dependent guidance and density grade classification constraint are implemented, to optimally select diverse content features to participate in the ensemble, taking advantage of their complementary while avoiding redundancy. Extensive experiments show that our gating ensemble approach achieves superior generalization performance among four public datasets. Codes are publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/wdzhao123/DCSL</uri> .
An Extension of Reversible Image Enhancement Processing for Saturation and Brightness Contrast

This paper proposes a reversible image processing method for color images that can independently improve saturation and enhance brightness contrast. Image processing techniques have been popularly used to obtain desired images. The existing techniques generally do not consider reversibility. Recently, many reversible image processing methods have been widely researched. Most of the previous studies have investigated reversible contrast enhancement for grayscale images based on data hiding techniques. When these techniques are simply applied to color images, hue distortion occurs. Several efficient methods have been studied for color images, but they could not guarantee complete reversibility. We previously proposed a new method that reversibly controls not only the brightness contrast, but also saturation. However, this method cannot fully control them independently. To tackle this issue, we extend our previous work without losing its advantages. The proposed method uses the HSV cone model, while our previous method uses the HSV cylinder model. The experimental results demonstrate that our method flexibly controls saturation and brightness contrast reversibly and independently.
Recognition of Usual Similar Activities of Dementia Patients via Smartwatches Using Supervised Learning

Currently, about 46.8 million people worldwide have dementia. More than 7.7 million new cases occur every year. Causes and triggers of the disease are currently unknown and a cure is not available. This makes dementia, along with cancer, one of the most dangerous diseases in the world. In the field of dementia care, this work attempts to use machine learning to classify the activities of individuals with dementia in order to track and analyze disease progression and detect disease-related changes as early as possible. In collaboration with two care communities, exercise data is measured using the Apple Watch Series 6. Consultation with several care teams that work with dementia patients on a daily basis revealed that many dementia patients wear watches.In this project, data from the aforementioned sensors is sent to the database at 20 data packets per second via a socket. DecisionTreeClassifier, KNeighborsClassifier, Logistic Regression, Fast Forest, Support Vector Machine, and Multilayer Perceptron classification algorithms are used to gain knowledge about locating, providing, and documenting motor skills during the course of dementia. The performance of the aforementioned algorithms for three similar activities of the dementia patients – writing, drinking and eating – will be investigated. The aim is to show the performance with which the activities can be recognized and how this knowledge can be used to support dementia documentation by nursing staff.