Reviewerly/exHarmony huggingface dataset:
The Effort of Reasoning: Modelling the Inference Steps of Boundedly Rational Agents

Abstract In this paper we design a new logical system to explicitly model the different deductive reasoning steps of a boundedly rational agent. We present an adequate system in line with experimental findings about an agent’s reasoning limitations and the cognitive effort that is involved. Inspired by Dynamic Epistemic Logic, we work with dynamic operators denoting explicit applications of inference rules in our logical language. Our models are supplemented by (a) impossible worlds (not closed under logical consequence), suitably structured according to the effect of inference rules, and (b) quantitative components capturing the agent’s cognitive capacity and the cognitive costs of rules with respect to certain resources (e.g. memory, time). These ingredients allow us to avoid problematic logical closure principles, while at the same time deductive reasoning is reflected in our dynamic truth clauses. We further show that our models can be reduced to awareness-like plausibility structures that validate the same formulas and we give a sound and complete axiomatization with respect to them. This approach to the agent’s internal deductive reasoning is finally combined with actions of external information.
The Weak Supervision Landscape

Many ways of annotating a dataset for machine learning classification tasks that go beyond the usual class labels exist in practice. These are of interest as they can simplify or facilitate the collection of annotations, while not greatly affecting the resulting machine learning model. Many of these fall under the umbrella term of weak labels or annotations. However, it is not always clear how different alternatives are related. In this paper we propose a framework for categorising weak supervision settings with the aim of: (1) helping the dataset owner or annotator navigate through the available options within weak supervision when prescribing an annotation process, and (2) describing existing annotations for a dataset to machine learning practitioners so that we allow them to understand the implications for the learning process. To this end, we identify the key elements that characterise weak supervision and devise a series of dimensions that categorise most of the existing approaches. We show how common settings in the literature fit within the framework and discuss its possible uses in practice.
Extracting Behavior Identification Features for Monitoring and Managing Speech-Dependent Smart Mental Illness Healthcare Systems

Speech is one of the major communication tools to share information among people. This exchange method has a complicated construction consisting of not the best imparting of voice but additionally consisting of the transmission of many-speaker unique information. The most important aim of this research is to extract individual features through the speech-dependent health monitoring and management system; through this system, the speech data can be collected from a remote location and can be accessed. The experimental analysis shows that the proposed model has a good efficiency. Consequently, in the last 5 years, many researchers from this domain come in front to explore various aspects of speech which includes speech analysis using mechanical signs, human system interaction, speaker, and speech identification. Speech is a biometric that combines physiological and behavioural characteristics. Especially beneficial for remote attack transactions over telecommunication networks, the medical information of each person is quite a challenge, e.g., like COVID-19 where the medical team has to identify each person in a particular region that how many people got affected by some disease and took a quick measure to get protected from such diseases and what are the safety measure required. Presently, this task is the most challenging one for researchers. Therefore, speech-based mechanisms might be useful for tracking his/her voice quality or throat getting affected. By collecting the database of people matched and comparing with his/her original database, it can be identified in such scenarios. This provides the better management system without touching and maintains a safe distance data that can be gathered and processed for further medical treatment. Many research studies have been done but speech-dependent approach is quite less and it requires more work to provide such a smart system in society, and it may be possible to reduce the chances to come into contact with viral effected people in the future and protect society for the same.
Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution

Multi-scale design has been considered in recent image super-resolution (SR) works to explore the hierarchical feature information. Existing multi-scale networks aim at building elaborate blocks or progressive architecture for restoration. In general, larger scale features concentrate more on structural and high-level information, while smaller scale features contain plentiful details and textured information. In this point of view, information from larger scale features can be derived from smaller ones. Based on the observation, in this article, we build a sequential hierarchical learning super-resolution network (SHSR) for effective image SR. Specially, we consider the inter-scale correlations of features, and devise a sequential multi-scale block (SMB) to progressively explore the hierarchical information. SMB is designed in a recursive way based on the linearity of convolution with restricted parameters. Besides the sequential hierarchical learning, we also investigate the correlations among the feature maps and devise a distribution transformation block (DTB). Different from attention-based methods, DTB regards the transformation in a normalization manner, and jointly considers the spatial and channel-wise correlations with scaling and bias factors. Experiment results show SHSR achieves superior quantitative performance and visual quality to state-of-the-art methods with near 34% parameters and 50% MACs off when scaling factor is × 4. To boost the performance without further training, the extension model SHSR + with self-ensemble achieves competitive performance than larger networks with near 92% parameters and 42% MACs off with scaling factor ×4.
Predicting the top-level ontological concepts of domain entities using word embeddings, informal definitions, and deep learning

Ontology development is a challenging task that encompasses many time-consuming activities. One of these activities is the classification of the domain entities (concepts and instances) according to top-level concepts. This activity is usually performed manually by an ontology engineer. However, when the set of entities increases in size, associating each entity to the proper top-level ontological concept becomes challenging and requires a high level of expertise in both the target domain and ontology engineering. This paper proposes a deep learning approach that automatically classifies domain entities into top-level concepts using their informal definitions and the word embedding of the terms that represent them. From these inputs, we feed a deep neural network consisting of two modules: a feed-forward neural network and a bi-directional recurrent neural network with long short-term units. Our architecture combines both outputs of these modules into a dense layer and provides the probabilities of each candidate class. For validating our proposal, we have developed a dataset based on the OntoWordNet ontology, which provides a classification of WordNet synsets into concepts specified by DOLCE-lite-plus top-level ontology. Our experiments show that our proposal outperforms the baseline approaches by 6% regarding the F-score. In addition, our proposal is less affected by the polysemy in the terms that represent the domain entities than the compared approaches. Consequently, our proposal can consider more instances during its training than the baseline methods.
Privacy Protection Technology Based on Machine Learning and Intelligent Data Recognition

The machine learning algorithm is gradually being applied to various fields and has become the core technology to achieve artificial intelligence. The success of machine learning cannot be achieved without the support of large amounts of data and computing power, which are usually collected through crowdsourcing and learned online. The data collected for machine learning training often contains some personal and sensitive information, including personal mobile phone numbers, ID numbers, and medical information. How to protect these private data at low cost and efficiently is an important problem. Aiming at this kind of problem, this article starts with the privacy problem in machine learning and the way of being attacked and summarizes the privacy protection methods and characteristics in the machine learning algorithm. Then, for the classification accuracy of the different algorithm that uses noise to protect privacy, a deep difference privacy protection method combined with a convolutional neural network is proposed. This method perfectly integrates the features of difference and Gaussian distribution and can obtain the privacy budget of each layer of the neural network. Finally, the stochastic gradient descent algorithm's gradient value is employed to set the Gaussian noise scale and preserve the data's sensitive information. The experimental results demonstrated that by adjusting the parameters of the depth differential privacy model based on differences in private information in the data, a balance between the availability and privacy protection of the training data set could be reached.
Experiences on the Improvement of Logic-Based Anaphora Resolution in English Texts

Anaphora resolution is a crucial task for information extraction. Syntax-based approaches are based on the syntactic structure of sentences. Knowledge-poor approaches aim at avoiding the need for further external resources or knowledge to carry out their task. This paper proposes a knowledge-poor, syntax-based approach to anaphora resolution in English texts. Our approach improves the traditional algorithm that is considered the standard baseline for comparison in the literature. Its most relevant contributions are in its ability to handle differently different kinds of anaphoras, and to disambiguate alternate associations using gender recognition of proper nouns. The former is obtained by refining the rules in the baseline algorithm, while the latter is obtained using a machine learning approach. Experimental results on a standard benchmark dataset used in the literature show that our approach can significantly improve the performance over the standard baseline algorithm used in the literature, and compares well also to the state-of-the-art algorithm that thoroughly exploits external knowledge. It is also efficient. Thus, we propose to use our algorithm as the new baseline in the literature.
Credit Card Fraud Prediction Using XGBoost

With the development of technology, the internet and eCommerce online payment has become an essential mode of payment. Nowadays, credit card payment is a convenient mode of payment online as well as offline transactions. As online credit card payment increases, fraud transactions are likewise increasing day by day. Increasing fraud transactions in the online payment system became a more significant challenge for banks, companies, and researchers. Therefore, it is essential to have an efficient methodology to detect fraud transactions while payment has completed via credit card. Although many traditional approaches are already available for fraud transaction prediction, however, existing methods lack accuracy, and it can be increased by ensemble techniques such as XGBoost. In this paper, we use an ensemble approach that is XGBoost (eXtreme Gradient Boosting) for credit card fraud prediction. The results are compared with existing machine learning approaches.
A comparison of online hate on reddit and 4chan

The rapid integration of the Internet into our daily lives has led to many benefits but also to a number of new, wide-spread threats such as online hate, trolling, bullying, and generally aggressive behaviours. While research has traditionally explored online hate, in particular, on one platform, the reality is that such hate is a phenomenon that often makes use of multiple online networks. In this article, we seek to advance the discussion into online hate by harnessing a comparative approach, where we make use of various Natural Language Processing (NLP) techniques to computationally analyse hateful content from Reddit and 4chan relating to the 2020 US Presidential Elections. Our findings show how content and posting activity can differ depending on the platform being used. Through this, we provide initial comparison into the platform-specific behaviours of online hate, and how different platforms can serve specific purposes. We further provide several avenues for future research utilising a cross-platform approach so as to gain a more comprehensive understanding of the global hate ecosystem.
Chapter 11. Constructing a typological questionnaire with distributional semantic models

The paper presents a methodology for automatic construction of lexical typological questionnaires for qualitative semantic domains (e.g. sharp, straight, thick, or smooth). Our algorithm is based on data from a monolingual corpus; it constructs a list of collocations for the corresponding lexemes, computes a vector representation for every collocation, clusters the vector space into semantically homogeneous groups and extracts the three central elements from every cluster. We compare the resulting questionnaires against test data from the semantic domains that are already well studied manually. The algorithm demonstrates high quality results and can be used in the practice of lexical typological research.
Robust landmark graph-based clustering for high-dimensional data

High-dimensional data has attracted much attention because it contains more comprehensive information about samples. How to cluster these high-dimensional data has become a crucial topic in unsupervised learning. Existing clustering methods often show limited applicability due to their high computational complexity and low anti-noise ability. To address this issue, we propose a novel robust landmark graph-based clustering algorithm for high-dimensional data (RLGCH), which inherits the advantages of both k-means++ and graph-based clustering by using the results of k-means++ as pseudo labels for landmark graph-based clustering. In particular, RLGCH can achieve more reasonable clustering effectiveness than methods that just operate in the low-dimensional space or the original space since it performs k-means++ in the low-dimensional space and landmark graph-based spectral clustering in the original feature space. To avoid post-processing after optimization, the embedded factor matrix is constrained as an indicator matrix rather than a simple nonnegative matrix. To enhance the clustering robustness, the L2,1-norm is adopted to minimize the error of results between k-means++ and landmark graph-based clustering. To solve the model of RLGCH, we established a novel efficient optimization strategy to obtain all sample categories directly. Combining our clustering model and optimization strategy, the computational complexity is reduced to linear and insensitive to data dimensions. Extensive experiments on seven real-world datasets and sixteen noisy datasets show that compared with other state-of-the-art methods, RLGCH can improve the clustering efficiency and robustness greatly while guaranteeing comparable or even better clustering effectiveness.
A text as unique as a fingerprint: Text analysis and authorship recognition in a Virtual Learning Environment of the Unified Health System in Brazil

Authorship Attribution, the act of deducing the author of a given text based on its writing characteristics, is an issue with an extensive history. It refers to the task of properly recognizing the text’s author within a specific group of candidates, based on relevant features extracted from the text (Stylometry). Hence, stylometry identifies relevant attributes that define a space in which authors can be distinguished. Because writers use language in different ways to express their ideas, linguistic variations make it possible to recognize authorship. The definition of the author’s text is discussed, in this article, as an auxiliary tool in the distance education platform of the Ministry of Health, AVASUS. Therefore, the stylometric features were extracted from the collected data set, and different classification algorithms were trained. The objective was to predict the authorship of texts with more than 30 characters. As a result, an acknowledgment text for the Virtual Learning Environment of the Unified Health System in Brazil was obtained. The precision achieved in the classification process was 92% in some classifiers. This aspect suggests that techniques for extracting stylometric features may be used to recognize the author of a given text.
Magic state injection on the rotated surface code

Fault-tolerant quantum computing based on the surface code requires magic states to achieve universality. The initially prepared magic states have low fidelity and need to be purified by a costly procedure called magic state distillation. The high spatial-temporal cost for distillation can be reduced by improving the fidelity of initial magic states. Prior works have investigated state injection approaches on the regular surface code. In this work, we propose a magic state injection method for the rotated surface code that requires fewer physical qubits to encode one logical qubit. Analytical results show that our method could achieve lower logical error rates than the state-of-the-art approach for the regular surface code in [1]. When both the two-qubit gate and single-qubit operation error rates are p, the new method results in a logical error rate to 34p/15 compared to 46p/15 in [1]. This fidelity improvement and fewer qubit encoding requirement would help reduce the overhead of fault-tolerant quantum computation, which is crucial for practical implementation in the near term.
SRAFE: Siamese Regression Aesthetic Fusion Evaluation for Chinese Calligraphic Copy

Abstract Evaluation of calligraphic copy is the core of Chinese calligraphy appreciation and inheritance. However, previous aesthetic evaluation studies often focussed on photos and paintings, with few attempts on Chinese calligraphy. To solve this problem, a Siamese regression aesthetic fusion method is proposed, named SRAFE, for Chinese calligraphy based on the combination of calligraphy aesthetics and deep learning. First, a dataset termed Evaluated Chinese Calligraphy Copies (E3C) is constructed for aesthetic evaluation. Second, 12 hand‐crafted aesthetic features based on the shape, structure, and stroke of calligraphy are designed. Then, the Siamese regression network (SRN) is designed to extract the deep aesthetic representation of calligraphy. Finally, the SRAFE method is built by fusing the deep aesthetic features with the hand‐crafted aesthetic features. Experimental results show that scores given by SRAFE are similar to the aesthetic evaluation label of E3C, proving the effectiveness of the authors’ method.
Construction and Application of Virtual Reality-Based Sports Rehabilitation Training Program

This paper adopts virtual reality technology to conduct in-depth research and analysis on sports rehabilitation training, designs a corresponding sports rehabilitation training program, and applies it to practice. An AR algorithm based on dynamic target tracking under VSLAM is proposed. The algorithm can effectively reject dynamic targets in static scenes while ensuring that the virtual objects registered based on dynamic template target tracking are still in the world coordinate system of VSLAM. To facilitate patients’ hand function rehabilitation training, this paper uses OpenPose for 2D gesture pose recognition, combines camera pose information and depth information provided by VSLAM to map key points of the hand into the world coordinate system, and then completes the interaction by collision detection algorithm. The virtual interaction module is implemented in this paper to meet the demand for multiuser off-site interaction in virtual training. This paper uses the Unity3D software and Photon Server server to create a VR virtual scene and design a user interaction mechanism to realize a system that supports multiple users to train together online, which effectively extends the application scope of the VR training system. The module utilizes Unity3D’s VR development capabilities to develop VR virtual basketball gym scenes and single-player offline interaction mechanisms such as virtual user single shooter and shooter; then, Photon Server is used to design and implement a multiuser remote login system and a multiuser interpass mechanism, thus achieving the effect of multiplayer remote online interaction in the same VR space. Finally, the proposed module was validated, and the results proved the effectiveness of the sports rehabilitation training program.
Model checking of vulnerabilities in smart contracts

Despite the benefits that the Blockchain technology brings to many application fields, its adoption does not come without challenges. Smart contracts, which are at the core of 2nd generation blockchains, can often be riddled with vulnerabilities that can be exploited to attack the platform and threaten its security. It is therefore crucial for the protection of the designed systems to prove the correctness of the smart contracts to be deployed. Approaches have been proposed to detect generic vulnerabilities like reentrancy, but the results would often include false positives where the detected bug is either non existent or not exploitable. Besides, such approaches do not offer to check contract-specific properties. The work presented in this paper is situated as part of a formal approach that we have proposed in an attempt to bridge this gap. This previously outlined approach is based on the transformation of Solidity smart contracts into Coloured Petri nets, which provides the possibility to verify smart contracts with reference to properties expressed as Linear Temporal Logic (LTL) formulae. Herein we extend our previous work on mainly two levels: first, by taking into account the concept of function calls in the transformation and second, by focusing on the LTL properties that can define the correctness of a smart contract. Such properties can be specific to the control- or data-flow of the contracts being checked. They can also be used to express vulnerabilities as we showcase by proposing LTL formalizations for six vulnerabilities from the literature. We then leverage the capability of the Helena model checker to detect these vulnerabilities while discerning their exploitability, as well as check temporal-based contract-specific properties.