Reviewerly/exHarmony huggingface dataset:
Engineering an Intelligent Essay Scoring and Feedback System: An Experience Report

Artificial Intelligence (AI)/Machine Learning (ML)-based systems are widely sought-after commercial solutions that can automate and augment core business services. Intelligent systems can improve the quality of services offered and support scalability through automation. In this paper we describe our experience in engineering an exploratory system for assessing the quality of essays supplied by customers of a specialized recruitment support service. The problem domain is challenging because the open-ended customer-supplied source text has considerable scope for ambiguity and error, making models for analysis hard to build. There is also a need to incorporate specialized business domain knowledge into the intelligent processing systems. To address these challenges, we experimented with and exploited a number of cloud-based machine learning models and composed them into an application-specific processing pipeline. This design allows for modification of the underlying algorithms as more data and improved techniques become available. We describe our design, and the main challenges we faced, namely keeping a check on the quality control of the models, testing the software and deploying the computationally expensive ML models on the cloud.
Decentralized Trajectory Optimization for Multi-Agent Ergodic Exploration

Autonomous exploration is an application of growing importance in robotics. A promising strategy is ergodic trajectory planning, whereby an agent spends in each area a fraction of time which is proportional to its probability information density function. In this letter, a decentralized ergodic multi-agent trajectory planning algorithm featuring limited communication constraints is proposed. The agents' trajectories are designed by optimizing a weighted cost encompassing ergodicity, control energy and close-distance operation objectives. To solve the underlying optimal control problem, a second-order descent iterative method coupled with a projection operator in the form of an optimal feedback controller is used. Exhaustive numerical analyses show that the multi-agent solution allows a much more efficient exploration in terms of completion task time and control energy distribution by leveraging collaboration among agents.
HOIsim: Synthesizing Realistic 3D Human-Object Interaction Data for Human Activity Recognition

Correct understanding of human activities is critical for meaningful assistance by robots in daily life. The development of perception algorithms and Deep Learning models of human activity requires large-scale sensor datasets. Good real-world activity data is, however, difficult and time- consuming to acquire. Several precisely calibrated and time- synchronized sensors are required, and the annotation and labeling of the collected sensor data is extremely labor intensive.To address these challenges, we present a 3D activity simulator, "HOIsim", focusing on Human-Object Interactions (HOIs). Using HOIsim, we provide a procedurally generated synthetic dataset of two sample daily life activities "lunch" and "breakfast". The dataset contains out-of-the-box ground truth annotations in the form of human and object poses, as well as ground truth activity labels. Furthermore, we introduce methods to meaningfully randomize activity flows and the environment topology. This allows us to generate a large number of random variants of these activities in very less time.Based on an abstraction of the low-level pose data in the form of spatiotemporal graphs of HOIs, we evaluate the generated Lunch dataset only with two Deep Learning models for activity recognition. The first model, based on recurrent neural networks achieves an accuracy of 87%, whereas the other, based on transformers, achieves an accuracy of 94.7%.
Triastore: A Web 3.0 Blockchain Datastore for Massive IoT Workloads

The Internet of Things (IoT) revolution has introduced sensor-rich devices to an ever growing landscape of smart environments. A key component in the IoT scenarios of the future is the requirement to utilize a shared database that allows all participants to operate collaboratively, transparently, immutably, correctly and with performance guarantees. Blockchain databases have been proposed by the community to alleviate these challenges, however existing blockchain architectures suffer from performance issues. In this short paper we propose Triastore, a novel permissioned blockchain database system that carries out machine learning on the edge, abstracts machine learning models into primitive data blocks that are subsequently stored and retrieved from the blockchain. Triastore comprises of two internal routines, namely: (i) Proof of Federated Learning (PoFL), which trains in a distributed manner a global model for the ingested data; and (ii) Blockchain Consensus, which commits this generated model data on permissioned blockchain database. We present a detailed explanation of our data ingestion algorithm with relevant examples and carry out an experimental evaluation with image data from MNIST. The evaluation shows that our proposed data ingestion framework retains high levels of accuracy with low loss in data quality.
Representing Synchrophasor Data Using JSON

Synchrophasor technology offers myriad possibilities in terms of novel operation, control and analysis of electrical power system infrastructure. Unfortunately, accessing synchrophasor data is complicated by data markup standards and transport protocols. This paper presents an alternative approach which is built on trusted methodologies and techniques used in data science, and proposes that the widely used JSON format is used for synchrophasor data encapsulation. The structure described has been deployed in a University lab for several years, it facilitates various live analysis and presentation methods, as-well-as data archival. An advantage of the text based JSON format is that it can be inspected by researchers directly and the data can be directly loaded into virtually all analysis software. An agreed data structure streamlines collaboration, exchange of research and replication of results in a development environment. The schema can also be used for compression, security and rejecting bad data in a production environment. Tools for handling JSON data are readily available and are commercially tested as JSON is so widely used.
Cross-Domain Fault Diagnosis of Rotating Machinery Using Discriminative Feature Attention Network

In recent industrial applications, machine learning technology is proving useful in preventing equipment failures in advance through early failure diagnosis. In particular, we show that different domains can be linked through adversarial learning with data available in different working conditions to facilitate the training of the model, as it is impractical to acquire data for all conditions in real-world applications. Nevertheless, the initial failure is a difficult problem to diagnose because it does not show a significant difference from the normal data between different conditions. Moreover, if only the domain discriminator is judged when adapting the domain, it tends to easily cause misclassification, so the reliability of the detection result needs to be improved. In this study, we propose a new learning method that improves classification performance by sharing the classification characteristics of the classifier for each task with the target domain characteristic generator. The proposed mechanism uses spatial attention to extract the focused partial information of the feature generator and discriminator, and further enhances task-specific features using the attention mechanism between the two extracted information types. Addresses the challenge of implementing both domain adaptation and classification. Extensive experimentation demonstrates efficiency and improved classification performance on benchmark and real-world application datasets. In real machine cases, the classification accuracy is improved by almost 4%. In addition, the negative impact on false alarms was lowered by increasing the classification accuracy of minimum failures. Convincingly demonstrate model effectiveness by performing an empirical analysis of the method through ablation analysis and visualization.
Hierarchical Spatial Pyramid Network For Cervical Precancerous Segmentation By Reconstructing Deep Segmentation Networks

Cervical cancer is one of the leading causes of cancer death in women aged 20 to 39 years, which emphasizes the importance of cervical precancerous diagnosis and treatment. Although there are many attempts on medical image processing, the research on the automatic diagnosis of cervical precancerous pathology is still scarce. In this paper, a challenging end-to-end automatic segmentation task for cervical precancerous diagnosis is focused. Specifically, considering that the diagnosis of cervical lesions relies heavily on spatial information, a hierarchical spatial pyramid network (HSP-Net) is proposed to enhance the representation ability of cervical structural features. First, a vertical hierarchical spatial pyramid (V-HSP) network is devised to aggregate the multiscale information during the feature extraction of the encoder. Second, a horizontal hierarchical spatial pyramid (H-HSP) network is designed to fuse information of multiscale receptive fields before and after cascading features from different branches. Experiments on the public dataset MTCHI demonstrate that HSP-Net achieves the state-of-the-art performance, reflecting the potential to assist doctors and patients clinically.
Automated Summary Scoring with ReaderBench

Text summarization is an effective reading comprehension strategy. However, summary evaluation is complex and must account for various factors including the summary and the reference text. This study examines a corpus of approximately 3,000 summaries based on 87 reference texts, with each summary being manually scored on a 4-point Likert scale. Machine learning models leveraging Natural Language Processing (NLP) techniques were trained to predict the extent to which summaries capture the main idea of the target text. The NLP models combined both domain and language independent textual complexity indices from the ReaderBench framework, as well as state-of-the-art language models and deep learning architectures to provide semantic contextualization. The models achieve low errors – normalized MAE ranging from 0.13–0.17 with corresponding R2 values of up to 0.46. Our approach consistently outperforms baselines that use TF-IDF vectors and linear models, as well as Transfomer-based regression using BERT. These results indicate that NLP algorithms that combine linguistic and semantic indices are accurate and robust, while ensuring generalizability to a wide array of topics.
Analysis of charisma, comfort and realism in CG characters from a gender perspective

Realistic characters from movies, games and simulations can make viewers feel strange (discomfort), an effect known as the Uncanny Valley (UV) theory. However, can the genders of CG characters and the genders of viewers change perceived comfort? In addition, can the genders (both characters and viewers) also influence the perceived charisma? Can the realism of a character also influence these aspects? This work aims to evaluate the perception of women and men about female and male characters, created using Computer Graphics (CG), presented in various media (movies, games, computer simulations, among others). Our goal is to answer the following questions: (i) How does the comfort perceived by people of both tested genders (female and male) relate to the genders of the characters? and (ii) Is the charisma influenced by the realism of the characters, considering the subjects and genders of the characters? We conducted perceptual studies on characters created using CG in images and videos through questionnaires. Our results indicated that the gender of the subjects and characters affected comfort, charisma and perceived realism. In addition, we also revisited the aspect of the UV theory (perception of comfort and human likeness) and found coherent curves compared to many works in the literature.
Approximation theorems on graphs

Analysis of functions on combinatorial graphs is an emerging field attracting more and more attention. In this paper, we study the approximation of functions defined on combinatorial graphs by functions in Paley–Wiener spaces. First, we use a family of graph translation operators to define the modulus of smoothness, which has several properties similar to their counterparts in the classical approximation theory. Next, we establish Jackson’s and Bernstein’s inequalities for functions defined on graphs. Finally, we provide an estimation on the decay of graph Fourier coefficients in terms of the modulus of smoothness. These results lead to a theory of approximation of functions on combinatorial graphs and have potential applications to filtering, denoising, data dimension reduction, image processing and learning theory.
Real and Virtual Token Economy Applied to Games: A Comparative Study Between Cryptocurrencies

Monetization is a way to earn money from products, softwares or services. Many video game companies use monetization to sell items, bonus or even money to customize the user experience or achieve quests easily in their games. Even though the cryptocurrencies are becoming more and more popular, few games use cryptocurrencies as a real form of monetization, which means that there is a potential for its application. The purpose of this paper is to compare the most used cryptocurrencies for games, based on some important characteristics, discuss which is the most suitable cryptocurrency for gaming platforms and propose an architecture for gaming companies use inside the games using cryptocurrencies.
Sequential Pattern Mining of Large Combinable Items with Values for a Set-of-items Recommendation

Next-item recommendation solutions based on sequential pattern mining have been widely used in empirical studies. However, current solutions do not consider recommendations involving large combinations of items with varied values. For example, inspecting many specimens is key to understanding a patient's current health status and to checking a medical prescription's effectiveness. Typically, a specimen inspection may involve dozens of inspection items selected from more than a thousand possible items, with each item being associated with a measured value. The values themselves will differ for different items. Based on the pattern of previous item values, recommending the next specimen inspection from a huge number of candidate ones, requires that a combination of many inspection items must be processed efficiently. This paper presents a method for vectorizing a combination of items and item values, and identifying clusters of item-set types. From the item-set types that best suit the target input, a set of items is recommended using both frequency and uniqueness. The method was tested experimentally, using real data from a university hospital's electronic medical record system. The results showed that the proposed method can successfully recommend specific item types with the highest precision and recall ratios. The validity of the results was confirmed by the hospital's medical staff.
Human interaction recognition method based on parallel multi-feature fusion network

Human activity recognition is a key technology in intelligent video surveillance and an important research direction in the field of computer vision. However, the complexity of human interaction features and the differences in motion characteristics at different time periods have always existed. In this paper, a human interaction recognition algorithm based on parallel multi-feature fusion network is proposed. First of all, in view of the different amount of information provided by the different time periods of action, an improved time-phased video down sampling method based on Gaussian model is proposed. Second, the Inception module uses different scale convolution kernels for feature extraction. It can improve network performance and reduce the amount of network parameters at the same time. The ResNet module mitigates degradation problem due to increased depth of neural networks and achieves higher classification accuracy. The amount of information provided in the motion video in different stages of motion time is also different. Therefore, we combine the advantages of the Inception network and ResNet to extract feature information, and then we integrate the extracted features. After the extracted features are merged, the training is continued to realize parallel connection of the multi-feature neural network. In this paper, experiments are carried out on the UT dataset. Compared with the traditional activity recognition algorithm, this method can accomplish the recognition tasks of six kinds of interactive actions in a better way, and its accuracy rate reaches 88.9%.
Towards Synchronous Model of Non-emotional Conversational Gesture Generation in Humanoids

We need social robotics to assist humans with elderly care, health care, child education, entertainment, space exploration and hospitality. This task requires sharing a common space with human beings along with humanlike humanoid-human interactions that involve combining facial-expression analysis, speech understanding, gesture analysis and generation. Facial expression analysis, speech understanding, speech generation and hand-gesture recognition using camera and depth-sensors are increasingly being explored. Limited gesture generation is being explored in avatars (virtual) and more recently in humanoids. However, gesture generation is limited to head-gestures, and random and meaningless hand-gestures, which lack detailed classification of gestures and their translation to humanoid gesture generation. In this paper, we extend the hand-gesture classification in the psychology-domain to include haptic gestures and extend the conceptual dependency model for computational hand-gesture generation. We also present a model to generate different subclasses of hand-gestures based upon the notion of scene, object attributes, shallow anaphora analysis and speech-action temporal alignment at the sentence level.
Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.
The optimal filtering set problem with application to surrogate evaluation in genetic programming

Surrogate evaluation is common in population-based evolutionary algorithms where exact fitness calculation may be extremely time consuming. We consider a Genetic Program (GP) that evolves scheduling rules, which have to be evaluated on a training set of instances of a scheduling problem, and propose exploiting a small set of low size instances, called filter, so that the evaluation of a rule in a filter estimates the actual evaluation of the rule on the training set. The calculation of filters is modelled as an optimal subset problem and solved by a genetic algorithm. As case study, we consider the problem of scheduling jobs in a machine with time-varying capacity and show that the combination of the surrogate model with the GP termed SM-GP, outperforms the original GP.
A Method for Supervisory Control of Manipulator of Underwater Vehicle

A novel method for supervisory control of multilink manipulators mounted on underwater vehicles is considered. This method is designed to significantly increase the level of automation of manipulative operations, by the building of motion trajectories for a manipulator working tool along the surfaces of work objects on the basis of target indications given by the operator. This is achieved as follows: The operator targets the camera (with changeable spatial orientation of optical axis) mounted on the vehicle at the work object, and uses it to set one or more working point on the selected object. The geometric shape of the object in the work area is determined using clouds of points obtained from the technical vision system. Depending on the manipulative task set, the spatial motion trajectories and the orientation of the manipulator working tool are automatically set using the spatial coordinates of these points lying on the work object surfaces. The designed method was implemented in the C++ programming language. A graphical interface has also been created that provides rapid testing of the accuracy of overlaying the planned trajectories on the mathematically described surface of a work object. Supervisory control of an underwater manipulator was successfully simulated in the V-REP environment.
Learning Theories and Teaching Methodologies for the Design of Training in Digital Competence for Language Teachers: A Narrative Review

This paper provides a narrative review of learning theories and teaching methodologies that can be used to design training activities for an online or blended training workshop addressed to language teachers. The review was performed in a traditional, narrative manner and revolved around the exploration of recent, influential learning theories, such as Connectivism, Social Constructionism, and Ecological Theory. It also involved an exploration of contemporary teaching methodologies, such as game-based learning, problem-based learning, and virtual exchange, which were deemed important for language teachers in order to get an up-to-date overview of theoretical perspectives and improve their teaching. This review is part of the Digital Competence for Language Teachers (DC4LT) Erasmus+ KA2 project and its ultimate objective was to delineate an array of learning theories and teaching methodologies in order to enable trainers to select the ones that are most suitable for training workshops in digital competence for language teachers. The specific review concludes with implications for the design of a training workshop with replicability and transferability potential for future trainers.
Ensemble Learning in Credit Card Fraud Detection Using Boosting Methods

With the continuous prosperity of the financial market, credit card volume has always been booming these years. The fraud businesses are also raising rapidly. Under this circumstance, fraud detection has become a more and more valuable problem. But the proportion of the fraud is absolutely much lower than the genius transaction, so the imbalance dataset makes this problem much more challenging. In this paper we mainly tell how to cope with the credit card fraud detection problem by using boosting methods and also gave a contribution of the brief comparison between these boosting methods.