Reviewerly/exHarmony huggingface dataset:
GoldenWind at SemEval-2021 Task 5: Orthrus - An Ensemble Approach to Identify Toxicity

Many new developments to detect and mitigate toxicity are currently being evaluated.We are particularly interested in the correlation between toxicity and the emotions expressed in online posts.While toxicity may be disguised by amending the wording of posts, emotions will not.Therefore, we describe here an ensemble method to identify toxicity and classify the emotions expressed on a corpus of annotated posts published by Task 5 of Se-mEval 2021-our analysis shows that the majority of such posts express anger, sadness and fear.Our method to identify toxicity combines a lexicon-based approach, which on its own achieves an F1 score of 61.07%, with a supervised learning approach, which on its own achieves an F1 score of 60%.When both methods are combined, the ensemble achieves an F1 score of 66.37%.
Stochastic approach for assessing the predictability of chaotic time series using reservoir computing

The applicability of machine learning for predicting chaotic dynamics relies heavily upon the data used in the training stage. Chaotic time series obtained by numerically solving ordinary differential equations embed a complicated noise of the applied numerical scheme. Such a dependence of the solution on the numeric scheme leads to an inadequate representation of the real chaotic system. A stochastic approach for generating training time series and characterizing their predictability is suggested to address this problem. The approach is applied for analyzing two chaotic systems with known properties, the Lorenz system and the Anishchenko–Astakhov generator. Additionally, the approach is extended to critically assess a reservoir computing model used for chaotic time series prediction. Limitations of reservoir computing for surrogate modeling of chaotic systems are highlighted.
Historical stages in the formation of the scientific knowledge system in a theoretical and methodological context

Current tendencies question the role of science in modern society, force returning to the processes of formation of the scientific paradigm. The latter was complex and nonlinear, and the formation of scientific principles of cognition was their natural result. Throughout human history, the knowledge about the objective world has been acquired and used in various, historically necessary forms – both in the methodology of cognition and in the method of systematisation, which was determined by the level of their accumulation. The accumulation of knowledge took place in different ways: in the process of direct practical activity, on the basis of supposedly “foreign” contemplation and as a result of conscious influence on an object of study (experiment) with their different “specific weight” at different historical stages. As for the systematisation, the need for which was determined by systemic nature of an object of knowledge and the social nature of knowledge, throughout the history of mankind its forms differed considerably, but, in the end, were reduced to three main ones.
On the Limitations of Visual-Semantic Embedding Networks for Image-to-Text Information Retrieval

Visual-semantic embedding (VSE) networks create joint image-text representations to map images and texts in a shared embedding space to enable various information retrieval-related tasks, such as image-text retrieval, image captioning, and visual question answering. The most recent state-of-the-art VSE-based networks are: VSE++, SCAN, VSRN, and UNITER. This study evaluates the performance of those VSE networks for the task of image-to-text retrieval and identifies and analyses their strengths and limitations to guide future research on the topic. The experimental results on Flickr30K revealed that the pre-trained network, UNITER, achieved 61.5% on average Recall@5 for the task of retrieving all relevant descriptions. The traditional networks, VSRN, SCAN, and VSE++, achieved 50.3%, 47.1%, and 29.4% on average Recall@5, respectively, for the same task. An additional analysis was performed on image-text pairs from the top 25 worst-performing classes using a subset of the Flickr30K-based dataset to identify the limitations of the performance of the best-performing models, VSRN and UNITER. These limitations are discussed from the perspective of image scenes, image objects, image semantics, and basic functions of neural networks. This paper discusses the strengths and limitations of VSE networks to guide further research into the topic of using VSE networks for cross-modal information retrieval tasks.
Racist or Sexist Meme? Classifying Memes beyond Hateful

Memes are the combinations of text and images that are often humorous in nature.But, that may not always be the case, and certain combinations of texts and images may depict hate, referred to as hateful memes.This work presents a multimodal pipeline that takes both visual and textual features from memes into account to (1) identify the protected category (e.g.race, sex etc.) that has been attacked; and (2) detect the type of attack (e.g.contempt, slurs etc.).Our pipeline uses state-ofthe-art pre-trained visual and textual representations, followed by a simple logistic regression classifier.We employ our pipeline on the Hateful Memes Challenge dataset with additional newly created fine-grained labels for protected category and type of attack.Our best model achieves an AUROC of 0.96 for identifying the protected category, and 0.97 for detecting the type of attack.We release our code at https://github.com/harisbinzia/HatefulMemes
Streaming Multi-Talker Speech Recognition with Joint Speaker Identification

In multi-talker scenarios such as meetings and conversations, speech processing systems are usually required to transcribe the audio as well as identify the speakers for downstream applications. Since overlapped speech is common in this case, conventional approaches usually address this problem in a cascaded fashion that involves speech separation, speech recognition and speaker identification that are trained independently. In this paper, we propose Streaming Unmixing, Recognition and Identification Transducer (SURIT) -- a new framework that deals with this problem in an end-to-end streaming fashion. SURIT employs the recurrent neural network transducer (RNN-T) as the backbone for both speech recognition and speaker identification. We validate our idea on the LibrispeechMix dataset -- a multi-talker dataset derived from Librispeech, and present encouraging results.
LiSP-XK: Extended Light-Weight Signcryption for IoT in Resource-Constrained Environments

There is an increasing drive to provide improved levels of trust within an Internet-of-Things (IoTs) environments, but the devices and sensors used tend to be limited in their capabilities for dealing with traditional cryptography methods.Resource constraints and security are often the two major concerns of IIoT (Industrial IoT applications and big data generation at the present time.The strict security measures are often not significantly resource-managed and therefore, negotiation normally takes place between these.Following this, various light-weight versions of generic security primitives have been developed for IIoT and other resource-constrained sustainability.In this paper, we address the authentication concerns for resource-constrained environments by designing an efficient authentication protocol.Our authentication scheme is based on LiSP (light-weight Signcryption Protocol); however, some further customization has been performed on it to make it more suitable for IIoT-like resource-constrained environments.We use Keccack as the hash function in the process and Elli for light-weight public-key cryptography.We name our authentication scheme: Extended light-weight Signcryption Protocol with Keccack (LiSP-XK).The paper outlines a comparative analysis on our new design of authentication against a range of state-of-the-art schemes.We find the suitability of LiSP-XK for IIoT like environments due to its lesser complexity and less energy consumption.Moreover, the signcryption process is also beneficial in enhancing security.Overall the paper shows that LiSP-XK is overall 35% better in efficiency as compared to the other signcryption approaches.
Face recognition using enhancement discrete wavelet transform based on MATLAB

&lt;p&gt;In this work, it was proposed to compress the color image after de-noise by proposing a coding for the discrete transport of new wavelets called discrete chebysheve wavelet transduction (DCHWT) and linking it to a neural network that relies on the convolutional neural network to compress the color image. The aim of this work is to find an effective method for face recognition, which is to raise the noise and compress the image in convolutional neural networks to remove the noise that caused the image while it was being transmitted in the communication network. The work results of the algorithm were calculated by calculating the peak signal to noise ratio (PSNR), mean square error (MSE), compression ratio (CR) and bit-per-pixel (BPP) of the compressed image after a color image (256×256) was entered to demonstrate the quality and efficiency of the proposed algorithm in this work. The result obtained by using a convolutional neural network with new wavelets is to provide a better CR with the ratio of PSNR to be a high value that increases the high-quality ratio of the compressed image to be ready for face recognition.&lt;/p&gt;
A Survey on Blockchain-Based Search Engines

With the increasing growth of different types of data, search engines have become an essential tool on the Internet. Every day, billions of queries are run through few search engines with several privacy violations and monopoly problems. The blockchain, as a trending technology applied in various fields, including banking, IoT, education, etc., can be a beneficial alternative. Blockchain-based search engines, unlike monopolistic ones, do not have centralized controls. With a blockchain-based search system, no company can lay claims to user’s data or access search history and other related information. All these data will be encrypted and stored on a blockchain. Valuing users’ searches and paying them in return is another advantage of a blockchain-based search engine. Additionally, in smart environments, as a trending research field, blockchain-based search engines can provide context-aware and privacy-preserved search results. According to our research, few efforts have been made to develop blockchain use, which include studies generally in the early stages and few white papers. To the best of our knowledge, no research article has been published in this regard thus far. In this paper, a survey on blockchain-based search engines is provided. Additionally, we state that the blockchain is an essential paradigm for the search ecosystem by describing the advantages.
A Survey on Immersive Visualization

<p indent=0mm>Immersive visualization is an emerging research field. It aims to make full use of human perception and operation capabilities through the immersion and engagement offered by the immersive interface to support data analysis, reasoning, and decision-making. The latest research on immersive visualization is reviewed. Firstly, the design and evaluation of the visual and non-visual perception in information visualization are introduced. Secondly, the newest interaction methods from the aspects of touch, gesture, gaze, and navigation are summarized. Afterward, the visualization authoring toolkit in terms of desktop, mobile, and mix terminals is overviewed. Finally, the industry trends, applications, and challenges of immersive visualization are discussed.
GAEN: Graph Attention Evolving Networks

Real-world networked systems often show dynamic properties with continuously evolving network nodes and topology over time. When learning from dynamic networks, it is beneficial to correlate all temporal networks to fully capture the similarity/relevance between nodes. Recent work for dynamic network representation learning typically trains each single network independently and imposes relevance regularization on the network learning at different time steps. Such a snapshot scheme fails to leverage topology similarity between temporal networks for progressive training. In addition to the static node relationships within each network, nodes could show similar variation patterns (e.g., change of local structures) within the temporal network sequence. Both static node structures and temporal variation patterns can be combined to better characterize node affinities for unified embedding learning. In this paper, we propose Graph Attention Evolving Networks (GAEN) for dynamic network embedding with preserved similarities between nodes derived from their temporal variation patterns. Instead of training graph attention weights for each network independently, we allow model weights to share and evolve across all temporal networks based on their respective topology discrepancies. Experiments and validations, on four real-world dynamic graphs, demonstrate that GAEN outperforms the state-of-the-art in both link prediction and node classification tasks.
Preserving Geo-Indistinguishability of the Emergency Scene to Predict Ambulance Response Time

Emergency medical services (EMS) provide crucial emergency assistance and ambulatory services. One key measurement of EMS’s quality of service is their ambulances’ response time (ART), which generally refers to the period between EMS notification and the moment an ambulance arrives on the scene. Due to many victims requiring care within adequate time (e.g., cardiac arrest), improving ARTs is vital. This paper proposes to predict ARTs using machine-learning (ML) techniques, which could be used as a decision-support system by EMS to allow a dynamic selection of ambulance dispatch centers. However, one well-known predictor of ART is the location of the emergency (e.g., if it is urban or rural areas), which is sensitive data because it can reveal who received care and for which reason. Thus, we considered the ‘input perturbation’ setting in the privacy-preserving ML literature, which allows EMS to sanitize each location data independently and, hence, ML models are trained only with sanitized data. In this paper, geo-indistinguishability was applied to sanitize each emergency location data, which is a state-of-the-art formal notion based on differential privacy. To validate our proposals, we used retrospective data of an EMS in France, namely Departmental Fire and Rescue Service of Doubs, and publicly available data (e.g., weather and traffic data). As shown in the results, the sanitization of location data and the perturbation of its associated features (e.g., city, distance) had no considerable impact on predicting ARTs. With these findings, EMSs may prefer using and/or sharing sanitized datasets to avoid possible data leakages, membership inference attacks, or data reconstructions, for example.
Design and Experimental Validation of an LPV Pure Pursuit Automatic Steering Controller

This paper addresses the problem of designing control systems for steering an autonomous vehicle, for varying speed, ensuring stability and sustaining performance, taking into account comfort and safety. A novel automatic lateral control architecture is proposed combining Linear Parameter Varying (LPV) control theory and the Pure-Pursuit algorithm. The response of the robust controller is investigated for tracking a reference trajectory. The implementation is based on the reduction of the polytopic LPV model. In addition, to illustrate the results, the synthesized controller has been tested first in simulations, and then to an automated electric Renault Zoe in a test track providing encouraging results.
InfOCF-Web: An Online Tool for Nonmonotonic Reasoning with Conditionals and Ranking Functions

InfOCF-Web provides implementations of system P and system Z inference, and of inference relations based on c-representation with respect to various inference modes and different classes of minimal models. It has an easy-to-use online interface for computing ranking models of a conditional knowledge R, and for answering queries and comparing inference results of nonmonotonic inference relations induced by R.
Multi-Objective Reinforcement Learning for Designing Ethical Environments

AI research is being challenged with ensuring that autonomous agents learn to behave ethically, namely in alignment with moral values. A common approach, founded on the exploitation of Reinforcement Learning techniques, is to design environments that incentivise agents to behave ethically. However, to the best of our knowledge, current approaches do not theoretically guarantee that an agent will learn to behave ethically. Here, we make headway along this direction by proposing a novel way of designing environments wherein it is formally guaranteed that an agent learns to behave ethically while pursuing its individual objectives. Our theoretical results develop within the formal framework of Multi-Objective Reinforcement Learning to ease the handling of an agent's individual and ethical objectives. As a further contribution, we leverage on our theoretical results to introduce an algorithm that automates the design of ethical environments.
Regional Network Education Information Collection Platform for Smart Classrooms based on Big Data Technology

Big data technology plays an important role in optimizing the education intelligence by enhancing the learning experience through novel assessment strategies and predictive teaching. This work aims at improving the learning efficiency of users and increase the effective learning quality of the smart classroom concept using the big data technology. A regional network education information collection platform based on big data technology has been developed, which can collect student learning data for subsequent analysis and processing. The software architecture of this platform is mainly divided into basic layer, platform layer and access layer. The physical structure mainly includes web servers and Hadoop clusters. The big data acquisition platform of this education has excellent acquisition performance, and the data acquisition of different data item field length is larger than the expected index. The outcomes obtained reveals that the data item field is less than 20 and the acquisition amount is 145% of the expected result. When the data item field is between 20 and 40, the acquisition amount is 137% of the expected result. The collection is 116% of the expected result, when the data item field is between 40 and 50. When the data item field is between 50 and 60, the collection is 103% of the expected result. The comparison of the regular teaching method with the smart classroom based concept revealed that the big data platform is 87% better than the normal regular teaching methods in terms of study material as well as meaningful teacher-student interaction. The research on education big data can provide better education services for educational activities, drive the reform of teaching mode and optimize the teaching methods of education enabling the smart classroom concept.
i-Dataquest: A heterogeneous information retrieval tool using data graph for the manufacturing industry

Manufacturing industry needs access to the data in order to realise its activities but also to generate new value-added knowledge. Nevertheless, it is confronted with a large and growing volume of heterogeneous data which limits its ability to exploit them optimally. Moreover, the data are distributed within different heterogeneous information systems, which limits the relationship exploration under the information retrieval process. Usually, the challenge is addressed by trying to manage and normalize the data structure in order to faster searching and exploiting them in a manufacturing context. For their part, the authors present i-Dataquest, an information retrieval system supported by (i) a graph-oriented model built from the structured and unstructured data of the company and (ii) a query system answering 'what' and 'about what' and (iii) generating three different results: a list of items, a list of property values and a list of sentences. The i-Dataquest prototype is built using Neo4J for the graph system generation, ConceptNet for lexical resource management and StandfordNLP for natural language processing. An evaluation of the prototype's performance is conducted through a data set representing a drone manufacturer. The results show that the transformation of specific content such as tables in the graph and the semantic expansion of queries significantly improves the recall and precision measures. The results also suggest improving filtering less relevant results by considering particularly queries looking for a specific value.
Learning Innovation of Area-Based Approach to Sustainable Development Goals of Highland Community Resources Management of Social Studies Teachers in the Northern Thailand

The research aimed to study the context of area-based approach learning management and guidelines of area-based approach learning management for sustainable highland community resource management of social studies teachers in the learning center for Thai hill tribe communities in northern Thailand. The method of qualitative research was used. Purposive sampling was conducted to obtain key informants which were social studies teachers of the highland learning center, accounting for 285 people. This area is a development area of the royal project. Learning center for Thai hill tribe communities in northern Thailand have accessed to organize social studies teaching at the basic education level. The tools used in the research consisted of in-depth interviews and focus group discussion recording. Then, content analysis was conducted. &#x0D; &#x0D; From the research result, it was found that the context of area-based approach learning management for sustainable highland community resources management of social studies teachers consisted of: executing the process of area-based management by surveying and collecting village data to be used as knowledge in learning management for pre-school students and students of basic education and the illiterate; developing occupations for the interested group in communities; developing life skills by using the method of coordination with community leaders; building local networks; participating in meeting with the community ;and exchanging information in order to inform people in the community of external exchanges. Regarding the use of area-based knowledge of social studies teachers, they have implemented the knowledge until this has brought about guidelines for promoting community resources management. For example, the community has participatory development plans. The community has networks of natural resources and environmental conservation and citizens have local conscience. Results of sustainable highland development are the fact that the community has strong power in conserving traditions, inheriting tribe cultures, establishing local herbal plants groups to take care of community health. Moreover, there are groups developing local products, building community declarations and regulations for living together in the community peacefully. The guidelines of area-based approach learning management for sustainable highland community resource management of social studies teachers consisted of 5 components which were FPCIL, namely (1) F: Facilitator, (2) P: Participatory assessment and planning, (3) C: Community capital value added, (4) I: Innovation of knowledge and (5) L: Lesson-learned.