Reviewerly/exHarmony huggingface dataset:
Discrete space reinforcement learning algorithm based on twin support vector machine classification

Reinforcement learning (RL) has become one of the key component of various machine learning algorithms in recent years. However, traditional RL algorithms lack convergence speed and accuracy in small-scale discrete space environment. Recently An et al. proposed RL algorithm based on support vector machines (SVMs) (Pattern Recognit. Lett. 111 (2018) 30-35) which adopts the Advantage Actor-Critic (A2C) framework and improves the speed and accuracy of convergence in discrete space. Owing to the advantages of twin support vector machines (TWSVMs) over SVMs, in this paper, we propose a RL algorithm based on TWSVM classification. The proposed algorithm adopts a modified A2C framework, where there are multiple Actors and a single Critic. Finally, we compare the performance of the proposed algorithm with some existing algorithms in traditional RL environment. Interestingly, the proposed algorithm outperforms the existing algorithms in terms of convergence speed and accuracy.
XAINES: Explaining AI with Narratives

Abstract Artificial Intelligence (AI) systems are increasingly pervasive: Internet of Things, in-car intelligent devices, robots, and virtual assistants, and their large-scale adoption makes it necessary to explain their behaviour, for example to their users who are impacted by their decisions, or to their developers who need to ensure their functionality. This requires, on the one hand, to obtain an accurate representation of the chain of events that caused the system to behave in a certain way (e.g., to make a specific decision). On the other hand, this causal chain needs to be communicated to the users depending on their needs and expectations. In this phase of explanation delivery, allowing interaction between user and model has the potential to improve both model quality and user experience. The XAINES project investigates the explanation of AI systems through narratives targeted to the needs of a specific audience, focusing on two important aspects that are crucial for enabling successful explanation: generating and selecting appropriate explanation content, i.e. the information to be contained in the explanation, and delivering this information to the user in an appropriate way. In this article, we present the project’s roadmap towards enabling the explanation of AI with narratives.
OrderlessFL

Industries produce a large amount of data that can improve Machine Learning models. However, due to privacy issues, the data cannot be shared. Several Federated Learning (FL) systems have been introduced as private alternatives without considering Byzantine actors. Also, these systems are affected by the gradient staleness problem. Several blockchain-based FL systems are introduced to address Byzantine actors, which rely on Proof-of-Work-based (PoW) protocols and suffer from their limitations. We introduce OrderlessFL, a safe permissioned blockchain-based FL system using flCRDT, a CRDT for concurrent ML training and mitigating gradient staleness.
OrderlessChain

Blockchains often use coordination-based consensus protocols to offer trust in a Byzantine environment and to serialize transactions to preserve the application's invariants. However, coordination can be a performance bottleneck. There exist application-level invariants known as Invariant-Confluence (I-confluence), which can be preserved without coordination and benefit from improved performance. We introduce OrderlessChain, a coordination-free permissioned blockchain for the safe execution of I-confluent applications.
Identification of Explainable Structures in Data with a Human-in-the-Loop

Abstract Explainable AIs (XAIs) often do not provide relevant or understandable explanations for a domain-specific human-in-the-loop (HIL). In addition, internally used metrics have biases that might not match existing structures in the data. The habilitation thesis presents an alternative solution approach by deriving explanations from high dimensional structures in the data rather than from predetermined classifications. Typically, the detection of such density- or distance-based structures in data has so far entailed the challenges of choosing appropriate algorithms and their parameters, which adds a considerable amount of complex decision-making options for the HIL. Central steps of the solution approach are a parameter-free methodology for the estimation and visualization of probability density functions (PDFs); followed by a hypothesis for selecting an appropriate distance metric independent of the data context in combination with projection-based clustering (PBC). PBC allows for subsequent interactive identification of separable structures in the data. Hence, the HIL does not need deep knowledge of the underlying algorithms to identify structures in data. The complete data-driven XAI approach involving the HIL is based on a decision tree guided by distance-based structures in data (DSD). This data-driven XAI shows initial success in the application to multivariate time series and non-sequential high-dimensional data. It generates meaningful and relevant explanations that are evaluated by Grice’s maxims.
Knowledge explorer

Knowledge graphs are a rapidly growing paradigm and technology stack for integrating large-scale, heterogeneous data in an AI-ready form, i.e., combining data with the formal semantics required to understand it. However, toolchains that support data synthesis and knowledge discovery through information organization, search, filtering, and visualization have been developed at a pace lagging knowledge graph technology. In this paper, we present Knowledge Explorer, an open-source faceted search interface that provides environmentally intelligent services for interactively browsing and navigating KnowWhereGraph. Currently one of the largest open knowledge graphs, KnowWhereGraph contains over 12 billion statements with rich spatial and temporal information from more than 30 data layers. With an extensive collection of facets, Knowledge Explorer enables spatial, temporal, full-text, and expert search with dereferencing functionality to support "follow-your-nose" exploration, and it allows users to narrow their search by selecting facets. Given the size of the underlying graph and dependency on GeoSPARQL, we have improved query performance by implementing Elasticsearch indexing, spatial query generation, and caching. Knowledge Explorer is capable of retrieving information within seconds, answering a wide variety of competency questions posed by researchers, humanitarian relief organizations, and the broader public, thus helping better perform tasks such as cross-gazetteer place retrieval and disaster assessment from global to local geographic scales.
Error-output recurrent multi-layer Kernel Reservoir Network for electricity load time series forecasting

Electricity is one of the most consumed commodities in the modern world. Electricity load prediction models are used to plan distribution operations to balance the equilibrium of demand and supply. This necessity has increased the number of recent research works. They employed several learning algorithms, such as support vector regression, to predict demands. However, these algorithms have high computational cost and too many user-defined parameters that directly impact their performance. Recently, randomization-based learning algorithms have been widely tested because they performed well at a lower cost. However, still, there was a main drawback: uncertainty in approximation and learning. This work employed a kernel trick to solve the uncertainty problem. A kernel with reservoir-state layers was used to solve the problem. The kernel reservoir-state layers from the echo state network not only transformed features into high-dimensional space, but also enhanced the forecasting ability by learning temporal information. Additionally, the proposed model also had a multi-step prediction ability that used previous forecasting errors to update the output weights in the current step to prevent an accumulated error problem. We compared our proposed model with single-layer and multi-layer variants of Extreme Learning Machine, Echo State Network, and Random Vector Functional Link on ten electrical load data sets. The proposed model showed the best performance on 9/10 data sets in terms of Mean Square Error or Symmetric Mean Absolute Percentage Error. These findings implied that the proposed algorithm was superior in forecasting long-term electricity load.
Tracking changes using Kullback-Leibler divergence for the continual learning

Recently, continual learning has received a lot of attention. One of the significant problems is the occurrence of concept drift, which consists of changing probabilistic characteristics of the incoming data. In the case of the classification task, this phenomenon destabilizes the model’s performance and negatively affects the achieved prediction quality. Most current methods apply statistical learning and similarity analysis over the raw data. However, similarity analysis in streaming data remains a complex problem due to time limitation, non-precise values, fast decision speed, scalability, etc. This article introduces a novel method for monitoring changes in the probabilistic distribution of multi-dimensional data streams. As a measure of the rapidity of changes, we analyze the popular Kullback-Leibler divergence. During the experimental study, we show how to use this metric to predict the concept drift occurrence and understand its nature. The obtained results encourage further work on the proposed methods and its application in the real tasks where the prediction of the future appearance of concept drift plays a crucial role, such as predictive maintenance.
ALQAC 2022: A Summary of the Competition

This paper summarizes the second Automated Legal Question Answering Competition (ALQAC 2022). ALQAC aims to tackle the tasks of legal text processing for low-resource languages. This year, we organize two tasks on Vietnamese statute law documents: Legal Document Retrieval (Task 1) and Legal Question Answering (Task 2). Task 1 requires a system to retrieve the related legal articles to a legal question, and Task 2 requires a system to accomplish the answer to a legal question. There are 4 teams that participated in Task 1 with 10 runs, and 3 teams that participated in Task 2 with 9 runs. In this paper, we describe the tasks and the methods of each team, then we provide the official results and a discussion.
Graph Network based Approaches for Multi-modal Movie Recommendation System

The three times increase of SonyLiv viewers during the Tokyo Olympic, the 10% hike of YouTube users during the isolation era of covid-pandemic, and the 19% growth in Netflix user count due to the fastest growth of OTT, etc. have made the digital platform’s mode all-time active and specific. The hourly increase of users’ interactions and the e-commerce platform’s desire of letting users engage on their sites are pushing researchers to shape the virtual digital web as user specific and revenue-oriented. This paper develops a deep learning-based approach for building a movie recommendation system with three main aspects: (a) using a knowledge graph to embed text and meta information of movies, (b) using multi-modal information of movies like audio, visual frames, text summary, meta data information to generate movie/user representations without directly using rating information; this multi-modal representation can help in coping up with cold-start problem of recommendation system (c) a graph attention network based approach for developing regression system. For meta encoding, we have built knowledge graph from the meta information of the movies directly. For movie-summary embedding, we extracted nouns, verbs, and object to build a knowledge graph with head-relation-tail relationships. A deep neural network, as well as Graph attention networks, are utilized for measuring performance in terms of RMSE score. The proposed system is tested on an extended MovieLens-100K data-set having multi-modal information. Experimental results establish that only rating-based embeddings in the current setup outperform the state-of-the-art techniques but usage of multi-modal information in embedding generation performs better than its single-modal counterparts. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Sentiment Classification for Beauty-fashion Reviews

The fast growth of e-commerce markets helps companies bring their products closer to customers and lets users have many choices for online shopping. However, it causes the need to have a proper strategy to keep customers in every company. As a rising solution, sentiment analysis on users’ feedback using artificial intelligence is a timely-fashioned way for business owners to understand their customers and clients, which could help them improve their business against competitors. Therefore, in the scope of our research, we introduce our results on the task of customers’ review sentiment analysis using the dataset provided in the Fashion and Beauty Review Rating (one competition organized in Kaggle), where our solution reached first place with a score of 0.51269 RMSE. Our proposed solution combines deep learning models (Bidirectional Long Short-term Memory, Bidirectional Gated Recurrent Unit, Convolutional Neural Network) and a rule-based method (a method that uses linguistic rules to predict the rating of reviews). We can describe the solution in this paper with the support of analysis techniques to give more insightful points.
Classification of Manual Versus Autonomous Driving based on Machine Learning of Eye Movement Patterns

Recent advances in autonomous driving systems raise new questions about how to enhance the communication and takeover control between the system and the driver. Eye tracking technologies have shown their feasibility to recognize whether the driver’s gaze is directed ‘on-road’ or ‘off-road’. However, this binary information alone is not sufficient to infer the driver’s engagement in the driving task. In the present work, we take the next step and investigate how driving modes (autopilot, navigation system, and printed map) associated with different levels of engagement can be categorized from drivers’ gaze patterns. Using gaze data recorded in these three driving tasks along with several state-of-the-art machine learning methods, we demonstrate that the driving modes are associated with different gaze patterns. We achieved an average accuracy of 90.1% for binary and 80.3% for multi-class driving mode classification. Our findings pave the way for enhancing driver monitoring systems in (semi-) autonomous cars.
Reconfigurable Low-Threshold All-Optical Nonlinear Activation Functions Based on an Add-Drop Silicon Microring Resonator

The realization of optical nonlinear activation functions (NAFs) is essential for integrated optical neural networks (ONNs).Here, we propose and experimentally demonstrate a photonic method to implement reconfigurable and low-threshold alloptical NAFs based on a compact and high-Q add-drop microring resonator (MRR) on silicon.In the experiment, four different NAFs including softplus, radial basis, clamped ReLU, and sigmoid functions are realized by exploiting the thermo-optical (TO) effect of the MRR.The threshold to implement NAFs is as low as 0.08 mW.As a demonstration, a handwritten digit classification benchmark task is simulated based on a convolutional neural network (CNN) using the obtained activation functions, where an accuracy of 98% is realized.Thanks to the unique advantages of ultra-compact footprint and ultralow threshold, the proposed nonlinear unit is promising to be widely used in large-scale integrated ONNs.
Simple and fast calibration method for phase-only spatial light modulators

Phase-only spatial light modulators (SLMs) are widely used to engineer the phase of light in various applications. However, liquid-crystal-on-silicon SLMs have undesirable spatial variations in phase response and optical flatness across the SLM panel, which must be compensated for accurate phase control. Here, we introduce a simple and fast way to simultaneously extract these two types of SLM nonuniformities at single-pixel resolution using Twyman-Green interferometry without a piezoelectric transducer. By modulating the interference intensity via the SLM gray level, our approach requires N times fewer interferograms than typical N-step phase shift interferometry (PSI), while providing flatness correction as accurate as PSI. In practice, our calibration method works well with as few as 18 interferograms, which can be quickly acquired without concern for phase drift. We detail the calibration procedure and discuss the performance of our calibration.
Data coverage assessment on neural network based digital twins for autonomous control system

In a recently developed Nearly Autonomous Management and Control (NAMAC) system, neural networks (NNs) are used to develop digital twins for diagnosis (DT-Ds). However, NNs are not usually considered extrapolation models and may result in large errors if they are applied to unseen data outside the training data (uncovered). In this study, we propose a data coverage assessment (DCA) to determine if the NN-based DT-Ds are extrapolated based on their epistemic uncertainty. The uncertainty quantification algorithms and uncertainty thresholds are selected based on the confusion matrix of classifying evaluation data into covered or uncovered data. To demonstrate the adaptability of the proposed framework, we applied it to a basic feedforward neural network and a more advanced recurrent neural network based on a more nonlinear database. Case studies show that the proposed framework can distinguish unseen data for both basic and advanced applications with proper uncertainty quantification algorithms and thresholds.
Design and Development of an Augmented Reality E-Commerce 3D models

The world is becoming more modernized with augmented reality, and this paper proposes an idea for a new way to improve the user experience of all e-commerce platforms. This strategy makes use of Transient Chaotic Neural Networks (TCNN) to develop a new marker-less visual tracking system that makes use of pictorial information. It is made up of point correspondences between image sequences. The purpose of this is to provide the easy visualization of the dimensional importance of an object in the perspective of the user’s own near environment to facilitate the making of accurate purchase decisions. This is accomplished by using Vuforia to create a 3D representation of the target object, Unity3D and C# to serve as the application’s framework, and Wikitude for tracking the surrounding environment. The outcomes of the experiments provide evidence of both the effectiveness and efficiency of the proposed model.
Deep Learning based Bangla Voice to Braille Character Conversion System

Braille is a communication method for sight-disabled people, which has not been incorporated that much into the modern education system. Lots of research have been done on braille systems but most of them are about the conversion of a text character to a braille pattern. However, so far no work has been done with Bangla voice to braille character conversion. This paper introduces a system that will help visually impaired people to start their very basic education with a technology that is capable of recognizing Bangla characters. When a Bangla character is uttered by a person, our system will convert that character into a braille pattern. Afterward, a person using a finger touch can easily recognize the corresponding braille for that character. This voice recognition is done using the Visual Geometry Group (VGG-16) model of Convolutional Neural Network (CNN) and Arduino Uno. A 16x4 LCD display is used to generate the braille pattern for the recognized character. After training the model with 50 epochs, we achieved 98% of train accuracy and 92.42% of test accuracy from the test data.
Translation, Psychometric Evaluation, and Validation of the Croatian Version of the User Experience Questionnaire (UEQ)

The User Experience Questionnaire (UEQ) is a standard, widely used questionnaire for evaluating user experience with many (software) products and services. This article describes UEQ translation into Croatian, which involved English and German language experts, and its verification based on an online survey conducted among 231 university students about perceived experience with different video conferencing tools in online learning during the COVID-19 lockdown. The sample was large enough to confirm the construct validity of the questionnaire. Psychometric evaluation and validation relied on factor and reliability analyses of all six scales of the full, 26-item UEQ and of the pragmatic and hedonic scales in the short, 8-item UEQ version. All scales show a rather high internal reliability, which confirms the validity of the Croatian translation. Croatian-speaking user experience researchers and practitioners can use it with confidence.
Fuzzy Clustering Algorithm Based on Improved Global Best-Guided Artificial Bee Colony with New Search Probability Model for Image Segmentation

Clustering using fuzzy C-means (FCM) is a soft segmentation method that has been extensively investigated and successfully implemented in image segmentation. FCM is useful in various aspects, such as the segmentation of grayscale images. However, FCM has some limitations in terms of its selection of the initial cluster center. It can be easily trapped into local optima and is sensitive to noise, which is considered the most challenging issue in the FCM clustering algorithm. This paper proposes an approach to solve FCM problems in two phases. Firstly, to improve the balance between the exploration and exploitation of improved global best-guided artificial bee colony algorithm (IABC). This is achieved using a new search probability model called PIABC that improves the exploration process by choosing the best source of food which directly affects the exploitation process in IABC. Secondly, the fuzzy clustering algorithm based on PIABC, abbreviated as PIABC-FCM, uses the balancing of PIABC to avoid getting stuck into local optima while searching for the best solution having a set of cluster center locations of FCM. The proposed method was evaluated using grayscale images. The performance of the proposed approach shows promising outcomes when compared with other related works.
Classification of Task Types in Software Development Projects

Managing software development processes is still a serious challenge and offers the possibility of introducing improvements that will reduce the resources needed to successfully complete projects. The article presents the original concept of classification of types of project tasks, which will allow for more beneficial use of the collected data in management support systems in the IT industry. The currently used agile management methods—described in the article—and the fact that changes during the course of projects are inevitable, were the inspiration for creating sets of tasks that occur in software development. Thanks to statistics for generating tasks and aggregating results in an iterative and incremental way, the analysis is more accurate and allows planning the further course of work in the project, selecting the optimal number of employees in task teams, and identifying bottlenecks that may decide on faster completion of the project with success. The use of data from actual software projects in the IT industry made it possible to classify the types of tasks and the necessary values for further work planning, depending on the nature of the planned software development project.