Reviewerly/exHarmony huggingface dataset:
Ontology alignment evaluation for online assessment of e-learners: a new e-learning management system

Purpose This paper aims to propose a new assessment system module for handling the comprehensive answers written through the answer interface. Design/methodology/approach The working principle is under three major phases: Preliminary semantic processing: In the pre-processing work, the keywords are extracted for each answer given by the course instructor. In fact, this answer is actually considered as the key to evaluating the answers written by the e-learners. Keyword and semantic processing of e-learners for hierarchical clustering-based ontology construction: For each answer given by each student, the keywords and the semantic information are extracted and clustered (hierarchical clustering) using a new improved rider optimization algorithm known as Rider with Randomized Overtaker Update (RR-OU). Ontology matching evaluation: Once the ontology structures are completed, a new alignment procedure is used to find out the similarity between two different documents. Moreover, the objects defined in this work focuses on “how exactly the matching process is done for evaluating the document.” Finally, the e-learners are classified based on their grades. Findings On observing the outcomes, the proposed model shows less relative mean squared error measure when weights were (0.5, 0, 0.5), and it was 71.78% and 16.92% better than the error values attained for (0, 0.5, 0.5) and (0.5, 0.5, 0). On examining the outcomes, the values of error attained for (1, 0, 0) were found to be lower than the values when weights were (0, 0, 1) and (0, 1, 0). Here, the mean absolute error (MAE) measure for weight (1, 0, 0) was 33.99% and 51.52% better than the MAE value for weights (0, 0, 1) and (0, 1, 0). On analyzing the overall error analysis, the mean absolute percentage error of the implemented RR-OU model was 3.74% and 56.53% better than k-means and collaborative filtering + Onto + sequential pattern mining models, respectively. Originality/value This paper adopts the latest optimization algorithm called RR-OU for proposing a new assessment system module for handling the comprehensive answers written through the answer interface. To the best of the authors’ knowledge, this is the first work that uses RR-OU-based optimization for developing a new ontology alignment-based online assessment of e-learners.
EmoDialoGPT: Enhancing DialoGPT with Emotion

Perceiving user emotions and generating responses with specific emotions are of great significance to a social dialogue system. Much previous work still utilizes seq2seq models as the backbone to generate emotional responses. With the popularity of the generative pre-training models, we propose an emotional response generation model EmoDialoGPT based on DialoGPT by introducing emotion embedding and emotion prediction loss. In order to obtain a large-scale dialogue dataset with emotion labels, we train an emotion classifier and automatically annotate the emotion labels for the dialogue data. We evaluate our models from three aspects, including emotion expression, response quality and human evaluation. The experimental results show that, the proposed models outperform seq2seq models and baseline DialoGPT models on most metrics.
Jointly Prediction of Activities, Locations, and Starting Times for Isolated Elderly People

Restrictive public health measures such as isolation and quarantine have been used to reduce the pandemic virus’s transmission. With no proper treatment, older adults have been specifically advised to stay home, given their vulnerability to COVID-19. This pandemic has created an increasing need for new and innovative assistive technologies capable of easing the lives of people with special needs. Smart home systems have become widely popular in providing such assistive services to isolated older adults. These systems can provide better services to assist older people if it anticipates what activities inhabitants will perform ahead of time. For example, a smart home can prompt inhabitants to initiate essential activities like taking medicine using activity prediction. This paper proposes a multi-task activity prediction system that jointly predicts labels, locations, and starting times of future activities. The observed sequence of previous activities characterizes future activities. We use body activity information from wearable sensors and motion information from passive environmental sensors to sense activities of daily living of older adults. The activity prediction system consists of recurrent neural networks to capture temporal dependencies. This work also carries out several experiments on collected and existing real datasets to evaluate the system’s performance.
Multi-Class Sentiment Analysis from Turkish Tweets with RNN

Twitter is a social media platform where users can post their messages called ‘tweets’. Comment on a product, person, or event on Twitter; It takes reading and interpreting thousands of tweets to find out what emotion it represents. With sentiment analysis, it is possible to perform this process automatically in a short time. In this study; A data set consisting of Turkish tweets divided into 5 different emotion categories was used. Sentiment analysis was carried out using RNN architecture, which is a deep learning method. In the dataset, there are equal numbers of tweets for each of the emotions “angry”, “fear”, “happy”, “surprise”, “sad”. The success of the models established by performing multi-class sentiment analysis with LSTM, BiLSTM and GRU based on RNN architecture was compared. Highest accuracy; It has been in the model established with bidirectional LSTM, that is, BiLSTM, which is very successful in past and future word contexts.
A Knowledge-Based Fast Motion Planning Method Through Online Environmental Feature Learning

The sampling-based partial motion planning algorithm has come into widespread application in dynamic mobile robot navigation due to its low calculation costs and excellent performance in avoiding obstacles. However, when confronted with complicated scenarios, the motion planning algorithms are easily caught in traps. In order to solve this problem, this paper proposes a knowledge-based fast motion planning algorithm based on Risk-RRT, which guides motion planning by constructing a topological feature tree and generating a heuristic path from the tree. Firstly, an online topological feature learning method is proposed to simultaneously extract the features during the motion of the robot by means of the dual-channel scale filter and the secondary distance fusion. The learning process is completed until the feature points can represent arbitrary obstacle-free grid points of the whole map. Secondly, the topological feature tree is constructed with environmental feature points and the heuristic motion planning can be carried out on the feature tree. For one map, once the construction of the feature tree finishes, it can be reused as a prior knowledge in the following heuristic motion planning process, which will further improve the efficiency of searching feasible paths. The experimental results demonstrate that our proposed method can remarkably reduce the time taken to find a heuristic path and enhance the success rate of navigation in trapped environments.
Multi‐mode standardization under extreme time‐pressure – the case of COVID‐19 contact‐tracing apps

The present study investigates the standardization process of contact tracing apps during the COVID‐19 pandemic. Due to the epidemiological urgency, and differing from classical examples in the literature, this process is characterized by a compressed timeframe. In this setting, we investigate the role of different standard‐setting modes and their interaction through the lens of multi‐mode standardization. We find that the processes of standard setting through market competition or inclusive multi‐stakeholder committees proved time‐consuming and inefficient in addressing the immediate needs during this major global health crisis. Multi‐mode standardization between committees, market players, and governments equally proved unable to coordinate a standard. Ultimately, a so far neglected actor, namely platform owners, proved to be pivotal in coordinating a widely‐adopted standard. Our research extends multi‐mode standardization with platform owners as a further standardization actor of proliferating importance given the increasing pervasiveness of platforms in numerous contexts. The present article provides implications for the interplay between different modes of standard setting in general, and the setting of technological standards in crises in particular.
A folksonomy-based collaborative filtering method for crowdsourcing knowledge-sharing communities

Purpose This paper proposes a recommendation method that mines the semantic relationship between resources and combine it with collaborative filtering (CF) algorithm for crowdsourcing knowledge-sharing communities. Design/methodology/approach First, structured tag trees are constructed based on tag co-occurrence to overcome the tags' lack of semantic structure. Then, the semantic similarity between tags is determined based on tag co-occurrence and the tag-tree structure, and the semantic similarity between resources is calculated based on the semantic similarity of the tags. Finally, the user-resource evaluation matrix is filled based on the resource semantic similarity, and the user-based CF is used to predict the user's evaluation of the resources. Findings Folksonomy is a knowledge classification method that is suitable for crowdsourcing knowledge-sharing communities. The semantic similarity between resources can be obtained according to the tags in the folksonomy system, which can be used to alleviate the data sparsity and cold-start problems of CF. Experimental results show that compared with other algorithms, the algorithm in this paper performs better in mean absolute error (MAE) and F 1, which indicates that the proposed algorithm yields better performance. Originality/value The proposed folksonomy-based CF method can help users in crowdsourcing knowledge-sharing communities to better find the resources they need.
An Alternating Rank-<i>k</i> Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization

An Alternating Rank-k Nonnegative Least Squares Framework (ARkNLS) for Nonnegative Matrix Factorization
A Python Script for Geometric Interval Classification in QGIS: A Useful Tool for Archaeologists

Graduated colour maps, created through the mathematical classification of quantitative variables, are frequently used in archaeology. A Python script for implementing a classification method based on geometric intervals in QGIS is presented here. This method is more suitable than the standard methods in case the quantitative attribute to be classified follows a right-skewed distribution, which is common among archaeological data. After an overview of the main classification methods, this paper focuses on the benefits of the geometric interval subdivision scheme, describes the technical features of the script and demonstrates how it works. A final thought on the advantages of using FLOSS is proposed.
Multi-Frequency Residual Convolutional Neural Network for Steganalysis of Color Images

Current steganalyzers based on deep learning mostly adopt wider or deeper designs to improve detection performance.However, an overly complex network increases the training cost and is not conducive to its expansion and optimization.Moreover, steganalysis pays more attention to high-frequency information corresponding to the image texture.However, the deeper the network, the more likely it is to learn lowfrequency information corresponding to the image content, which is inconsistent with the goal of steganalysis.In response to these problems, a multi-frequency residual deep convolutional neural network for steganalysis of color images called MFRNet is proposed in this paper.We apply the idea of multi-frequency analysis to steganography detection for the first time, effectively controlling the network scale.By designing columns of different depths, it can learn different frequency components of steganographic noise at the same time.The detection performance is better than the existing networks that only learn a single frequency component of steganographic noise at the same depth.Therefore, it can achieve a good detection performance with a lighter architecture.In addition, by designing residual basic blocks with different residual shortcuts, different scales of steganographic noise residuals can be calculated at the same time, which can effectively suppress the interference of image content, so as to better reduce the impact of steganography algorithm mismatch and payload mismatch than the existing methods.The experimental results on PPG-LIRMM-COLOR showed that the proposed MFRNet outperformed the state-of-the-art model WISERNet.
Study of the subtyping machine of nominal subtyping with variance

This is a study of the computing power of the subtyping machine behind Kennedy and Pierce's nominal subtyping with variance. We depict the lattice of fragments of Kennedy and Pierce's type system and characterize their computing power in terms of regular, context-free, deterministic, and non-deterministic tree languages. Based on the theory, we present Treetop---a generator of C# implementations of subtyping machines. The software artifact constitutes the first feasible (yet POC) fluent API generator to support context-free API protocols in a decidable type system fragment.
ROECS: A Robust Semi-direct Pipeline Towards Online Extrinsics Correction of the Surround-view System

Generally, a surround-view system (SVS), which is an indispensable component of advanced driving assistant systems (ADAS), consists of four to six wide-angle fisheye cameras. As long as both intrinsics and extrinsics of all cameras have been calibrated, a top-down surround-view with the real scale can be synthesized at runtime from fisheye images captured by these cameras. However, when the vehicle is driving on the road, relative poses between cameras in the SVS may change from the initial calibrated states due to bumps or collisions. In case that extrinsics' representations are not adjusted accordingly, on the surround-view, obvious geometric misalignment will appear. Currently, the researches on correcting the extrinsics of the SVS in an online manner are quite sporadic, and a mature and robust pipeline is still lacking. As an attempt to fill this research gap to some extent, in this work, we present a novel extrinsics correction pipeline designed specially for the SVS, namely ROECS (Robust Online Extrinsics Correction of the Surround-view system). Specifically, a "refined bi-camera error" model is firstly designed. Then, by minimizing the overall "bi-camera error" within a sparse and semi-direct framework, the SVS's extrinsics can be iteratively optimized and become accurate eventually. Besides, an innovative three-step pixel selection strategy is also proposed. The superior robustness and the generalization capability of ROECS are validated by both quantitative and qualitative experimental results. To make the results reproducible, the collected data and the source code have been released at https://cslinzhang.github.io/ROECS/.
1D Convolutional Neural Network with Long Short-Term Memory for Human Activity Recognition

Human activity recognition aims to determine the actions or behavior of a person based on the time series data. In recent year, more large human activity recognition datasets are available as it can be collected in easier and cheaper ways. In this work, a 1D Convolutional Neural Network with Long Short-Term Memory Network for human activity recognition is proposed. The 1D Convolutional Neural Network is employed to learn high-level representative features from the accelerometer and gyroscope signal data. The Long Short-Term Memory network is then used to encode the temporal dependencies of the features. The final classification is performed with a softmax classifier. The proposed 1D Convolutional Neural Network with Long Short-Term Memory Network is evaluated on MotionSense, UCI-HAR, and USC-HAD datasets. The class distributions of these datasets are imbalanced. In view of this, adjusted class weight is proposed to mitigate the imbalanced class issue. Furthermore, early stopping is utilized to reduce the overfitting in the training. The proposed method achieved promising performance on MotionSense, UCI-HAR, and USC-HAD datasets, with F1-score of 98.14%, 91.04%, and 76.42%, respectively.
Secure E-Voting System using Blockchain technology and authentication via Face recognition and Mobile OTP

In the digital era where hacking and bypassing a system is easy, tampering of data is always possible leading to bad situations. Blockchain is used to store data which is near impossible to change or tamper with as it is very secure in nature. Voting as a process in any nation is an essential event and if votes get miscalculated by any external source it will be harmful. To avoid such kinds of situations and making it more comfortable blockchain technology comes in acknowledgment. This paper proposes a decentralized national e-voting system based on blockchain technology. It includes an admin panel to schedule the voting, manage candidates and declare the results. The web application will provide the users with an interface to enter their Aadhar card ID (text input) and a photo of themselves at the time of voting. The eligibility of the voter will be checked at the time they enter their Aadhar card ID. Eligible voter's phone numbers will be verified via One Time Password (OTP). After voter verification, individual voters will be considered eligible for voting. During voting, voters will be monitored through a webcam/front camera. The votes will be stored in a blockchain and any tampering would be detected easily. The address and the corresponding constituency will be checked in the backend. Voting results will be declared on a specified date and will be handled by the admin. The results will be displayed graphically with various options to choose from and will also include past results and statistics.
Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference

Zero-shot recognition (ZSR) aims to perform visual classification by category in the absence of training samples. The focus in most traditional ZSR models is using semantic knowledge about familiar categories to represent unfamiliar categories with only the visual appearance of an unseen object. In this research, we consider not only visual information but context to enhance the classifier’s cognitive ability in a multi-object scene. We propose a novel method, contextual inference, that uses external resources such as knowledge graphs and semantic embedding spaces to obtain similarity measures between an unseen object and its surrounding objects. Using the intuition that close contexts involve more related associations than distant ones, distance weighting is applied to each piece of surrounding information with a newly defined distance calculation formula. We integrated contextual inference into traditional ZSR models to calibrate their visual predictions, and performed extensive experiments on two different datasets for comparative evaluations. The experimental results demonstrate the effectiveness of our method through significant enhancements in performance.
Intellectual Property Rights Management Using Blockchain

This paper describes the potential of blockchain technology to offer novel solutions in the Intellectual Property space. Traditionally, IPRs have been managed and regulated by centralized institutions like governments or administrative bodies of the region where IP holders want to secure and enforce rights. However, as markets get globalized and digitized there is a pressing need to look for more reliable and feasible solutions. In this paper, we describe various contemporary techniques for IPR management and the challenges associated with them. We have also proposed an unprecedented framework for a model based on consortium blockchain which can aid in resolving the issues related to IPR management.
Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous

Collaboration requires agents to align their goals on the fly. Underlying the human ability to align goals with other agents is their ability to predict the intentions of others and actively update their own plans. We propose hierarchical predictive planning (HPP), a model-based reinforcement learning method for decentralized multiagent rendezvous. Starting with pretrained, single-agent point to point navigation policies and using noisy, high-dimensional sensor inputs like lidar, we first learn via self-supervision motion predictions of all agents on the team. Next, HPP uses the prediction models to propose and evaluate navigation subgoals for completing the rendezvous task without explicit communication among agents. We evaluate HPP in a suite of unseen environments, with increasing complexity and numbers of obstacles. We show that HPP outperforms alternative reinforcement learning, path planning, and heuristic-based baselines on challenging, unseen environments. Experiments in the real world demonstrate successful transfer of the prediction models from sim to real world without any additional fine-tuning. Altogether, HPP removes the need for a centralized operator in multiagent systems by combining model-based RL and inference methods, enabling agents to dynamically align plans.
The Emergence of Adversarial Communication in Multi-Agent Reinforcement Learning

Many real-world problems require the coordination of multiple autonomous agents. Recent work has shown the promise of Graph Neural Networks (GNNs) to learn explicit communication strategies that enable complex multi-agent coordination. These works use models of cooperative multi-agent systems whereby agents strive to achieve a shared global goal. When considering agents with self-interested local objectives, the standard design choice is to model these as separate learning systems (albeit sharing the same environment). Such a design choice, however, precludes the existence of a single, differentiable communication channel, and consequently prohibits the learning of inter-agent communication strategies. In this work, we address this gap by presenting a learning model that accommodates individual non-shared rewards and a differentiable communication channel that is common among all agents. We focus on the case where agents have self-interested objectives, and develop a learning algorithm that elicits the emergence of adversarial communications. We perform experiments on multi-agent coverage and path planning problems, and employ a post-hoc interpretability technique to visualize the messages that agents communicate to each other. We show how a single self-interested agent is capable of learning highly manipulative communication strategies that allows it to significantly outperform a cooperative team of agents.