Reviewerly/exHarmony huggingface dataset:
Width-based search for multi agent privacy-preserving planning

In multi-agent planning, preserving the agents' privacy has become an increasingly popular research topic. For preserving the agents' privacy, agents jointly compute a plan that achieves mutual goals by keeping certain information private to the individual agents. Unfortunately, this can severely restrict the accuracy of the heuristic functions used while searching for solutions. It has been recently shown that, for centralized planning, blind search algorithms such as width-based search can solve instances of many existing domains in low polynomial time when they feature atomic goals. Moreover, the performance of goal-oriented search can be improved by combining it with width-based search. In this paper, we investigate the usage of width-based search in the context of (decentralised) collaborative multi-agent privacy-preserving planning, addressing the challenges related to the agents' privacy and performance. In particular, we show that width-based search is a very effective approach over several benchmark domains, even when the search is driven by heuristics that roughly estimate the distance from goal states, computed without using the private information of other involved agents. Moreover, we show that the use of width-based techniques can significantly reduce the number of messages transmitted among the agents, better preserving their privacy and improving their performance. An experimental study presented in the paper analyses the effectiveness of our techniques, and compares them with the state-of-the-art of collaborative multi-agent planning.
Experimental lossless data compressor

x3 is a lossless optimizing dictionary-based data compressor. The algorithm uses a combination of a dictionary, context modeling, and arithmetic coding. Optimization adds the ability to find the most appropriate parameters for each file. Even without optimization, x3 can compress data with a compression ratio comparable to the best dictionary compression methods like LZMA, zstd, or Brotli. Our work differs from others by explicitly inserting phrases into the dictionary. This is a gap not yet covered by any other compression method.
Depth-2 neural networks under a data-poisoning attack

In this work, we study the possibility of defending against data-poisoning attacks while training a shallow neural network in a regression setup. We focus on doing supervised learning with realizable labels for a class of depth-2 finite-width neural networks, which includes single-filter convolutional networks. In this class of networks, we attempt to learn the true network weights generating the labels in the presence of a malicious oracle doing stochastic, bounded and additive adversarial distortions on the true labels, during training. For the gradient-free stochastic algorithm that we construct, we prove worst-case near-optimal trade-offs among the magnitude of the adversarial attack, the weight approximation accuracy, and the confidence achieved by the proposed algorithm. As our algorithm uses mini-batching, we analyze how the mini-batch size affects convergence. We also show how to utilize the scaling of the outer layer weights to counter data-poisoning attacks on true labels depending on the probability of attack. Lastly, we give experimental evidence demonstrating how our algorithm outperforms stochastic gradient descent under different input data distributions, including instances of heavy-tailed distributions.
Application of Genetic Algorithm for Complexity Metrics-Based Classification of Ontologies with ELECTRE Tri

The selection of suitable ontologies for reuse remains a challenging task. Although there have been attempts to cluster and classify ontologies using machine learning techniques, little or no studies have applied Multi-Criteria Decision Making (MCDM) techniques to classify ontologies. This study applies a MCDM model known as ELECTRE Tri to classify 200 ontologies using a Genetic Algorithm. The classes were defined a priori according to four ontology quality dimensions, namely, Accuracy, Understandability, Cohesion, and Conciseness. These dimensions were measured with 13 complexity metrics of ontology. The parameters for the ELECTRE Tri model were inferred from a set of assignments by taking a metaheuristic approach with the Genetic Algorithm. The experimental results show that the ELECTRE Tri model has successfully classified the ontologies in the dataset into the 3 classes with ratios of 21.5%, 24% and 54.5%, respectively. These results constitute interesting insights for the selection and reuse of the ontologies.
Learning Approximate Invariance Requires Far Fewer Data

Efficient learning, that is, learning with small datasets is difficult for current deep learning models. Invariance has been conjectured to be the key for its generalization potential. One of the most used procedure to learn invariant models is data augmentation (DA). Data augmentation can be performed offline by augmenting the data before any training, or it can be performed online during training. However, applying those technique won’t yield better generalization gains every time. We frame this problem as the stability of generalization gains made by invariance inducing techniques. In this study we introduced a new algorithm to train an approximate invariant priors before posterior training of Bayesian Neural Network (BNN). Furthermore, we compared the generalization stability of our invariance inducing algorithm with online DA and offline DA on MNIST and Fashion MNIST with three perturbation processes: rotation, noise, and rotation+noise. Results showed that learning approximate invariant priors requires less exposure to the perturbation process, but it leads BNN to more stable generalization gains during posterior training. Finally, we also show that invariance inducing techniques enhance uncertainty in Bayesian Neural Networks.
Facial Expression Recognition with Manifold Learning and Graph Convolutional Network

Facial Expression Recognition (FER) has the ability to detect human affect state. Most of the methods employed for FER task do not really consider the correlation among FER data labels to resolve data annotation and ambiguity problems. Label Distribution Learning (LDL) application to FER considerably address this, but only in the presence of data with distribution labels. Therefore, methods that would recover label distribution from logical labels are required. This work is presenting a graph-based label enhancement approach with manifold learning and Graph Convolutional Network (GCN) for facial expression recognition. The manifold learning approach transforms FER data as a graphical problem, where the data points are considered as nearest neighbours represent graph nodes, with the motive of representing the distances along the edges of the neighbouring graph with the approximate distances along the manifold. This process uses the nearest neighbour graph to learn the geometric structure in FER data, which also learn the possible correlation among the data labels. The graphical convolutional network is employed to incorporate the information provided in the manifold learning and the logical description of the data to classify the nodes of the graph using the information of the nearest neighbours. The experiment conducted on the Binghampton University-3D Facial Expression (BU-3DFE) and the Cohn Kanade extension (CK+) data shows that the model gives promising results.
A Novel Approach for Social Media Content Filtering Using Machine Learning Technique

A completely interconnected world is possible because of social networks, which allow individuals to interact, share ideas, and organize themselves into digital environments. Both online behavior and online content processing are critical for security applications to be successful. Extremists now have an easy way to spread their ideas and beliefs via social media. Cyberbullying and the spread of false news and phoney reviews on social media are two of the numerous security risks that arise as a result of this. This necessitates the need for the creation of a method to identify and minimize dangerous information on social networks. Risks and the extent of their repercussions are subjectively evaluated in the context of security occurrences. Societal consequences of negative impressions can be dire. Citizens’ polls are routinely used to measure these sentiments, but they take a long time and don’t adjust well to shifting security dynamics. In light of this, we developed a machine learning-based social media content filtering strategy. In order to train our model to identify fraudulent tweets on the Twitter network, we used the four different machine learning approach to find the malicious comments.
Analysis of N-Way K-Shot Malware Detection Using Few-Shot Learning

Solving machine learning problems with small-scale training datasets becomes an emergent research area to fill the opposite end of big data applications. Attention is drawn to malware detection using few-shot learning, which is typically formulated as N-way K-shot problems. The aims are to reduce the effort in data collection, learn the rare cases, reduce the model complexity, and increase the accuracy of the detection model. The performance of the malware detection model is analyzed with the variation in the number of ways and the number of shots. This facilitates the understanding on the design and formulation of three algorithms namely relation network, prototypical network, and relation network for N-way K-shot problems. Two benchmark datasets are selected for the performance evaluation. Results reveal the general characteristics of the performance of malware detection model with fixed ways and varying shots, and varying ways and fixed shots based on the trends of results of 30 scenarios.
Deep Matrix Factorization for Multi-view Clustering Using Density-Based Preprocessing

Using algebraic tools to advance autonomous data mining is difficult because the best clustering techniques do not scale with the dataset’s modulo/cardinality, especially in this era of data surge. This paper suggested a pre-processing strategy based on density to compact primary data through a collection of representative points while maintaining the original data’s density and distribution. This study used data compression to preserve the dataset’s density distribution and features. Most multiview clustering approaches use a single-layer formulation. Since the mapping between the new presentation and the primary data contains intricate hierarchical details, it’s more efficient to investigate hidden structures hierarchically. The emphasis is on learning the complementary information to solve the clustering problem. This study presented a deep matrix factorization framework that made use of multi-view clustering (MVC). Semi-nonnegative matrix factorization (NMF) learns multi-view data hierarchies layer-by-layer. To effectively mine shared information from each view, the output layer must be identical. Graph regularizers combine deep structure layer representations to maintain geometry across views. Benchmark results show the model’s effectiveness.
A Systematic Review of Recommendation System Based on Deep Learning Methods

Recommender Systems (RSs) play an essential role in assisting online users in making decisions and finding relevant items of their potential preferences or tastes via recommendation algorithms or models. This study aims to provide a systematic literature review of deep learning-based RSs that can guide researchers and practitioners to better understand the new trends and challenges in the area. Several publications were gathered from the Web of Science digital library from 2012 to 2022. We systematically review the most commonly used models, datasets, and metrics in RSs. At last, we discuss the potential direction of the future work.
Security on Social Media Platform Using Private Blockchain

Blockchain could be a decentralized, shared and open progressed record that’s utilized to record trades over various PCs so that any expound record can’t be balanced retroactively without the alter of each following square. It gives us a modern framework to record a open history of exchanges without depending on any centralized trusted party. The property of decentralization, openness, permission less and tamper-resistant has pulled in significant intrigued in both scholastic and mechanical communities. In this paper we work on social media security using block chain. It the growth of the social media platforms, raise the need of the securing the data communicated. Now, the data which is communicated can be secured in number of ways, but the end-points of social media applications are not secure. In the paper we propose some technology using Blockchain for security of social media platform framework, which can ensure both information security and privacy while simultaneously increasing the trust of the public sectors. We tried to work on securing the end app, by making the innovative concept of use authentication using the Zig-Zag arrangement for security of social media platform.
A Biomedical Named Entity Recognition Framework with Multi-granularity Prompt Tuning

Deep Learning based Biomedical named entity recognition (BioNER) requires a large number of annotated samples, but annotated medical data is very scarce. To address this challenge, this paper proposes Prompt-BioNER, a BioNER framework using prompt tuning. Specifically, the framework is based on multi-granularity prompt fusion and achieves different levels of feature extraction through masked language model and next sentence prediction pre-trained tasks, which effectively reduces the model’s dependence on annotated data. To evaluate the overall performance of Prompt-BioNER, we conduct extensive experiments on 3 datasets. Experimental results demonstrate that BioNER outperforms the the-state-of-the-arts methods, and it can achieve good performance under low resource conditions.
An Semantic Similarity Matching Method for Chinese Medical Question Text

For the purpose of capturing the semantic information accurately and clarifying the user’s questioning intention, this paper proposes a novel, ensemble deep architecture BERT-MSBiLSTM-Attentions (BMA) which uses the Bidirectional Encoder Representations from Transformers (BERT), Multi-layer Siamese Bi-directional Long Short Term Memory (MSBiLSTM) and dual attention mechanism (Attentions) in order to solve the current question semantic similarity matching problem in medical automatic question answering system. In the preprocessing part, we first obtain token-level and sentence-level embedding vectors that contain rich semantic representations of complete sentences. The fusion of more accurate and adequate semantic features obtained through Siamese recurrent network and dual attention network can effectively eliminate the effect of poor matching results due to the presence of certain non-canonical texts or the diversity of their expression ambiguities. To evaluate our model, we splice the dataset of Ping An Healthkonnect disease QA transfer learning competition and “public AI star” challenge - COVID-19 similar sentence judgment competition. Experimental results with CC19 dataset show that BMA network achieves significant performance improvements compared to existing methods.
The Framework and Fundamental Use of ROS

After the correct installation of ROS, we will continue to learn the ROS framework with the initial usage. In the ROS framework section, we will help readers to understand the composition and resource distribution of ROS fully. The ROS framework consists of three levels: file system level, computational graph level, and community level; after understanding the basic framework of ROS, we will introduce the basics of ROS usage, mainly including the introduction of catkin, workspaces and their creation, creation and compilation of project packages, creation and compilation of ROS nodes and running them, the use of roslaunch, the creation of ROS messages and services, how to write test message publishers and subscribers in C++ or Python, how to write test Servers and Clients in C++ or Python, and more. The content of this chapter is the basis for conducting subsequent chapters, and you should be proficient in it.
Robot Voice Interaction Functions of Basic Theory

Voice is the most friendly and natural way for human-robot interaction. Voice interaction systems for robots include Automatic Speech Recognition (ASR), Semantic Understanding
Multimodal Network Based Graphs of Primitives Storage Concept for Web Mining CBIR

Nowadays multimedia databases are becoming more and more popular. Because there are many images uploaded to the Internet there is a need of efficient querying and storing them. Additionally, a method of efficient web mining method also should be researched. The proper querying and storing data in a multimedia database is very crucial. In this paper a new concept of using Multimodal Network for storing and organizing graph of primitives for web mining Content Based Image Retrieval has been proposed. The graphs of primitives has been developed in our previous works in order to provide a new CBIR method. The usage of Multimodal Network would be beneficial for storing different types of nodes (e.g. nodes used only for organizing the hierarchy, nodes storing graph of primitives of objects or nodes storing image files). Additionally, the paper describes also modified K-Mean clustering method for gathering together in clusters similar graphs of primitives. Because of limited time, only preliminary experiments has been performed.
Overview of Robots

This chapter provides an introduction to the basic concepts of robots, including the definition, classification, development history, components, key technologies, and development trends of robots. Through this chapter, readers can have a clear understanding and appreciation of robots (especially service robots).
Recurrent Neural Networks as Electrical Networks, a Formalization

Since the 1980s, and particularly with the Hopfield model, recurrent neural networks or RNN became a topic of great interest. The first works of neural networks consisted of simple systems of a few neurons that were commonly simulated through analogue electronic circuits. The passage from the equations to the circuits was carried out directly without justification and subsequent formalisation. The present work shows a way to formally obtain the equivalence between an analogue circuit and a neural network and formalizes the connection between both systems. We also show which are the properties that these electrical networks must satisfy. We can have confidence that the representation in terms of circuits is mathematically equivalent to the equations that represent the network.
Relational Semantics for Normal Topological Quasi-Boolean Logic

This work introduces modal logics for varieties of normal topological quasi-Boolean algebras. Relational semantics for these modal logics using involutive frames are established. A discrete duality is given for involutive frames and normal topological quasi-Boolean algebras. Some results on Kripke-completeness and finite model property are given.
Debias in Deep Learning Recommender System

In recent years, plenty of researchers work on recommender systems, predicting users’ ratings or preferences for items based on massive data. Traditional statistical learning recommender system includes collaborative filtering (CF), matrix factorization (MF), factorization machine (FM), field-aware factorization machine (FFM), and gradient boosting decision tree (GBDT). Some deep learning algorithms like AutoRec and NeuralCF are introduced in this article. However, it is purposed that existing recommender systems are based on association, and there is a deviation from the actual situation. Thus, inverse propensity score (IPS) and doubly robust model are proposed to debias.
Training Environment for Rare Events Learning a Feasibility Study

Monitoring a city in general and its infrastructure and public transportation nodes (like metro/subway) in particular, is a part of the Teleste company activity. With a number of real-time video cameras counted in thousands, the system that in an intelligent way will support the situational awareness is a logical way to exploit the potential of the machine learning and machine vision achievements. In the paper we report and discuss applicability of the 3D scene generating tools (“engines”) to create a realistic, physically plausible and interactive training environment for simulation and generation of rare random events, interactive training scenarios with the help of the digital models of architecture and infrastructure artifacts, and of the crowd behavior.
Effects of Different Normalization on the ESRGAN

Nowadays, batch normalization (BN) has become the core component of deep learning. Thanks to the advantage of stable training, an image super-resolution reconstruction model, enhanced super-resolution generative adversarial networks (ESRGAN), and an image super-resolution reconstruction algorithm use BN to help its stable training. However, BN normalizes different types of information. This causes artifacts in the generated super-resolution image. Based on this, the generator of ESRGAN removes BN and the discriminator retains BN. However, BN in the discriminator will also normalize the information of different images, thus affecting the discriminator judgment. Motivated by this, we replace BN in the discriminator with layer normalization (LN), instance normalization (IN), group normalization (GN), and representative batch normalization (RBN) without adding any normalization operation. After a large number of experiments, ESRGAN reaches the state of the art when GN (PSNR:27.72, SSIM:0.8316) is used in the discriminator on the Set5 dataset.
Towards Automated Analysis of Rhetorical Categories in Students Essay Writings using Bloom’s Taxonomy

Essay writing has become one of the most common learning tasks assigned to students enrolled in various courses at different educational levels, owing to the growing demand for future professionals to effectively communicate information to an audience and develop a written product (i.e. essay). Evaluating a written product requires scorers who manually examine the existence of rhetorical categories, which is a time-consuming task. Machine Learning (ML) approaches have the potential to alleviate this challenge. As a result, several attempts have been made in the literature to automate the identification of rhetorical categories using Rhetorical Structure Theory (RST). However, RST do not provide information regarding students' cognitive level, which motivates the use of Bloom's Taxonomy. Therefore, in this research we propose to: i) investigate the extent to which classification of rhetorical categories can be automated based on Bloom's taxonomy by comparing the traditional ML classifiers with the pre-trained language model BERT, ii) explore the associations between rhetorical categories and writing performance. Our results showed that BERT model outperformed the traditional ML-based classifiers with 18% better accuracy, indicating it can be used in future analytics tool. Moreover, we found a statistical difference between the associations of rhetorical categories in low-achiever, medium-achiever and high-achiever groups which implies that rhetorical categories can be predictive of writing performance.