Reviewerly/exHarmony huggingface dataset:
WebRank: Language-Independent Extraction of Keywords from Webpages

We present a supervised method for keyword extraction from webpages. The method divides the HTML page into meaningful segments using document object model (DOM) and calculates a language independent feature vector for each word. Based on these, we generate a classification model that gives a likelihood for a word to be a keyword. The most likely words are then selected. We analyze the usefulness of the features on different datasets (news articles and service web pages) and compare different classification methods for the task. Results show that random forest performs best and provides up to 27.8 %- unit improvement compared to the best existing method.
Detection of High Impedance Fault Using Advanced ELM-based Neuro-fuzzy Inference System

In power systems, protective systems like circuit breakers and relays are used to detect abnormal conditions. High impedance fault (HIF) is a category of fault in power systems where energized conductors in contact with high impedance material, which cannot be detected by conventional protective measures. In this paper, an advanced neuro-fuzzy system called online sequential fuzzy extreme learning machine (OS-fuzzy-ELM) is utilized for efficient detection of HIF. OS-fuzzy-ELM is an adaptive strategy that combines the learning capability of the extreme learning machine (ELM) and the cognitive capability of the Takgi-Sugeno-Kang (TSK) fuzzy inference system. Discrete wavelet transform (DWT) and its energy signals are used for feature extraction. The simulation has been carried out using IEEE-9 bus standard distribution system, and the high impedance detection performance of OS-fuzzy-ELM is compared with ELM and SVM.
Computing the Distribution of Differentials over the Non-linear Mapping $$\chi $$

When choosing the non-linear layer in a symmetric design, the number of differentials with given differential probability (DP) gives information about how such non-linear layer may perform in the wide trail strategy. Namely, less differentials with high $$\mathrm {DP}$$ means less opportunity to form trails with high $$\mathrm {DP}$$ over multiple rounds. Multiple cryptographic primitives use the $$\chi $$ mapping as basis of their non-linear layer. Among them, Keccak- $$f$$ , Ascon, Xoodoo, and Subterranean. In the first three, the $$\chi $$ mapping operates on groups of few bits (5 in Keccak- $$f$$ and Ascon, and 3 in Xoodoo), while in Subterranean it operates on the full state, that is on 257 bits. In the former case, determining the number of differentials with given differential probability is an easy task, while the latter case is more involved. In this paper, we present a method to determine the number of differentials with given DP over $$\chi $$ operating on any number of bits.
Trajectory-based Modeling for Fraud Detection and Analytics: Foundation and Design

The trajectory concept is immersed in various socio-technical systems. Certainly, moving object data intended for location-based services is predominant, due to availability of spatiotemporal location data provided by current devices such as mobile devices, GPS devices, RFID and various sensors. This work examines the use of our trajectories meta-model given in previous works to detect and monitor frauds, from the point of view of trajectories semantic aspects. Modeling of frauds by trajectories gives meaning to the sequence of events that made it possible to detect them and to recognize the situations of their occurrence. The semantic dimension qualifies a fraud trajectory as a story that has both meaning and intention, described by episodes of semantic events related to the creation of the fraud, to its dynamic and to its ending. Fraud analytics will benefit from trajectory analytics for both big data ecosystems and spatiotemporal data mining algorithms and practices. Finally, we propose the architecture and structure of the software components that make up the proposed software system. The software design is based on the reactive manifesto and federates graph-oriented NoSQL databases and graph-based data science processing.
Object Detection with Shallow Feature Learning Network

A shallow feature learning network (SFLN) is proposed to explore the impact of shallow layers in object detection. In order to effectively use the shallow features and make up for the lack of semantic and contextual information at the shallow layers, we introduce two components: shallow feature fusion block (SFFB) and shallow feature fusion module (SFFM). First, we devise a SFFB to capture semantic and contextual information. Based on SFFB, we design a SFFM. The relatively high-level features in SFFM have abundant category information, which can further guide low-level features to select precise resolution details. The experimental results on Mini-COCO and RSOD dataset show the effectiveness of our proposed method.
Critical Insights on Cancer Detection Using Deep Learning

Cancer is grouped into many diseases that can originate in any organ of the human body. It is a type of tumor (cancerous), defined as the uncontrolled growth of damaged or abnormal cells. After originating, they spread to other parts of the body, causing tumors to grow there, too, called metastasis. Cancerous tumors are also known as malignant tumors. To prepare an effective treatment for cancer patients, it must be detected in the early phase. Machine learning and deep learning algorithms may assist in automating this task. One of the most commonly used deep learning techniques is Convolutional Neural Network (CNN). The present study reviews cancer detection using deep learning approaches by elaborating the datasets, results, limitations, and approaches.
Deep Covariance Feature and CNN-based End-to-End Masked Face Recognition

With the emergence of the global epidemic of COVID-19, face recognition systems have achieved much attention as contactless identity verification methods. However, covering a considerable part of the face by the mask poses severe challenges for conventional face recognition systems. This paper proposes an automated Masked Face Recognition (MFR) system based on the combination of a mask occlusion discarding technique and a deep-learning model. Initially, a pre-processing step is carried out in which the images pass three filters. Then, a Convolutional Neural Network (CNN) model is proposed to extract the features from unoccluded regions of the faces (i.e., eyes and forehead). These feature maps are employed to obtain covariance-based features. Two extra layers, i.e., Bitmap and Eigenvalue, are designed to reduce the dimension and concatenate these covariance feature matrices. The deep covariance features are quantized to codebooks combined based on Bag-of-Features (BoF) paradigm. Finally, a global histogram is created based on these codebooks and utilized for training an SVM classifier. The proposed method is trained and evaluated on Real-World-Masked-Face-Recognition-Dataset (RMFRD) and Simulated-Masked-Face-Recognition-Dataset (SMFRD) achieves an accuracy of 95.07% and 92.32 %, respectively, showing its competitive performance compared to the state-of-the-art. Experimental results prove that our system has high robustness against noisy data and illumination variations.
Privacy-Preserving Fog-Based Multi-Location Task Allocation in Mobile Crowdsourcing

As a wave of the rapidly approaching future, vehicles are becoming increasingly &#x201C;smarter&#x201D; via equipping with abundant resources for data sensing, processing, and transmitting. To fully make use of these resources, mobile crowdsourcing applications have attracted particular interests from academia and industry, and they are extensively integrated with fog computing for obtaining low latency and location sensitivity. In this paper, we consider a fog-based task allocation service for mobile crowdsourcing tasks with multiple locations, where a task is allocated to the worker whose future trajectory has the smallest Hausdorff semi-distance to the task locations. However, as fog nodes are not fully trusted, there may exist privacy concerns related to the workers and the task owners. To the best of our knowledge, although Hausdorff semi-distance has been applied in various applications, none of the existing works can support privacy-preserving Hausdorff semi-distance evaluation or <tex>$k$</tex> nearest neighbor queries. Aiming at this issue, we design a privacy-preserving fog-based multi-location task allocation scheme. Specifically, based on a symmetric homomorphic encryption technique, we build two privacy-preserving protocols for i) computing min/max value from an array and ii) retrieving the key linked to the minimum value from an array of key-value pairs. Then, the proposed scheme is built upon these two protocols. After that, we conduct rigid security analysis and extensive experiments to demonstrate the security and efficiency of our proposed scheme, respectively. The results indicate that our proposed scheme is not only privacy-preserving, but also efficient in terms of both computation and communication costs.
Discrete fractional Hankel transform based on a nonsymmetric kernel matrix

A discrete fractional Hankel transform (DFRHT) is developed based on the eigen decomposition of a nonsymmetric involutory kernel matrix Y of a discrete Hankel transform (DHT). The two eigen spaces pertaining to the two distinct eigenvalues of Y are not orthogonal because Y is not a normal matrix. Explicit expressions are derived for the oblique projection matrices of Y on its eigen spaces by applying a contributed generalized spectral theorem of linear algebra. Expressions are derived for the dimensions of the two eigen spaces in terms of the trace of matrix Y. An analytic form is derived for the inverse of a modal matrix of Y in order to circumvent the need for numerically evaluating it when computing the sophisticated fractional transform. The fact that the Hankel transform (HT) is self-reciprocating implies that taking the HT once can be viewed as a rotation by an angle π radians in the space – Hankel frequency plane. Being a generalization of the HT, the fractional Hankel transform (FRHT) of fractional order a corresponds to a rotation by an arbitrary angle α where α=πa. Initial orthonormal bases are individually generated for the two eigen spaces by the singular value decomposition of the oblique projection matrices on those spaces. In order for the DFRHT to approximate its continuous counterpart, namely the FRHT, it is desirable to define it in terms of eigenvectors which are as close as possible to samples of the eigen functions of the FRHT. After deriving a three term recursion formula for the eigen functions, a sampling scheme is proposed such that a finite number of samples will be representative of the behavior of those functions. Final optimal orthonormal eigenvectors are individually generated for the two eigen spaces.
Modulation Signal Recognition Based on Feature Fusion and Residual CNN in Fractal Noise Background

In this paper, a modulated signal recognition method based on feature fusion and ResCNN (FF-ResCNN) under generalized fractal noise background is proposed. Firstly, in the feature extraction and fusion part, considering the robustness, anti-noise and interpretability of the features, the fusion feature vectors of generalized fractal spectrum, instantaneous feature and high-order cumulant feature are constructed. Secondly, in order to suppress the possible gradient and degradation problems, the ResCNN model is used to train and classify the fusion features when the CNN model is used to further collect the deep-level abstract features. Finally, feature extraction, feature fusion and classification experiments are carried out for nine modulated signals under two noise types (GWN and fractal noise). Experimental results show that in the case of generalized fractal noise, when SNR=0dB, the recognition accuracy of signal modulation type reaches 95.42%, which verifies the superiority of the proposed method. At the same time, the simulation analysis is carried out for different feature fusion schemes, which provides a valuable reference for feature combination optimization under different noise environment.
Face Recognition Based on Local Statistical Features and Artificial Neural Network

In this paper a face recognition method based on image segmentation, statistical features, and neural network is proposed which is composed of three main steps of (1) preprocessing, (2) extraction of statistical features including mean, standard deviation, skewness, and kurtosis, and (3) classification using a perceptron neural network with one hidden layer. The proposed method benefits the advantage of simplicity in implementation. In addition, the simulation results show that the proposed method could achieve the recognition accuracy of 99.8% which outperforms the competitive methods of principal component analysis (PCA) (8.25-13.34% improvement), k-nearest neighbors (11.95-17.54% improvement), local binary pattern (4.45-10.04% improvement), support vector machine (SVM) combined with the PCA (0.19-2.18% improvement), and convolutional neural network (up to 0.64% improvement).
FedGR: A Lossless-Obfuscation Approach for Secure Federated Learning

Federated learning is a promising new technology in the field of artificial intelligence. However, the unprotected model gradient parameters in federated learning may reveal sensitive participants information. To address this problem, we present a secure federated learning framework called FedGR. We use Paillier homomorphic encryption to design a new gradient security replacement algorithm, which eliminates the connections between gradient parameters and user sensitive data. In addition, we revisit the previous work by Aono and Hayashi(IEEE TIFS 2017) and show that, with their method, the user&#x0027;s local computing burden is too heavy. We then proved FedGR has the following characteristics to solve this problem: 1) The system does not leak any information to the server. 2) Compared with that of ordinary deep learning systems, the accuracy of federated training results yielded by our system remains unchanged. 3)The proposed approach greatly reduces the user&#x0027;s local computing overhead.
Small Object Detection with Improved CenterNet

Detection of small objects is a notorious challenge due to their properties of low resolution and less information. Most existing works aim for general object detection while ignoring the impact of the object size, resulting in high missed detection rate of small objects, making it difficult to apply object detection in real scenarios such as autonomous driving. The implementation of an efficient detection algorithm for small objects is thus worth discussing. In this paper, we propose a small object detection framework with improved CenterNet. Specifically, the proposed framework consists of four parts. The super-resolution (SR) subnetwork enhances the information contained in small objects by generating an SR version of the original image. The future pyramid subnetwork fuses different levels of features thereby retaining semantic information of small objects. The center localization header and the size regression header are combined to detect objects via prediction of object center and object size. In this way, the performance of object detection is improved especially on small objects. We have conducted comprehensive experiments on MS COCO benchmark, and the experimental results have proved the effectiveness of our framework, especially in the detection of small objects better than existing methods.
A Modified Memory-Based Collaborative Filtering Algorithm based on a New User Similarity Measure

Data sparsity remains to be a critical concern for recommendation systems since it results in low accuracy and poor recommendation quality. To address this problem, collaborative filtering techniques based on user similarity have been applied but existing implementations have not been shown to sufficiently address the problem of data sparsity. Thus, this paper presents an enhanced memory-based collaborative filtering algorithm utilizing a new similarity measure that identifies co-rated items and computes user similarity to overcome the data sparsity problem and improve the recommendation quality which can be adopted for various applications. Results of the study show that the use of the new similarity measure has improved the determination of user similarity than when using the traditional Cosine, Euclidean Distance, and Pearson Correlation similarity metrics.
Deep Learning based Campus Assistive Chatbot

With the rapidly increasing dependence and expectations on online platforms to provide faster responses during all hours of the day, a web-based chatbot could be considered as the ideal solution to do so in the educational domain. A chatbot is a conversational agent capable of emulating human interactions. The purpose of this project is to implement a chatbot for a college website in order to make it more accessible, convenient and to enhance user experience. Campus navigation through Google Maps is an additional feature which is integrated into the chatbot to better assist the user. A menu containing frequently used links is provided which redirects the webpage according to the user’s choice. Using Artificial Intelligence and Deep Learning technologies, a chatbot EVA was created. Natural Language Processing is incorporated to allow the user to communicate with the chatbot using natural language input. A Sequential Model is developed using Neural Networks and the concept of Bag of Words is utilized. Tkinter is the tool used to set up the Graphical User Interface. A basic level performance test was conducted which revealed an efficiency rate of 89%. Finally, the chatbot was integrated with a college website which resulted in the generation of a functional college chatbot. EVA not only provides concise answers to the user’s queries, but also necessary links to information regarding course content, extracurricular activities as well as directions to various locations on campus as per the user’s requirement.
KAFE: Knowledge and Frequency Adapted Embeddings

Word embeddings are widely used in several Natural Language Processing (NLP) applications. The training process typically involves iterative gradient updates of each word vector. This makes word frequency a major factor in the quality of embedding, and in general the embedding of words with few training occurrences end up being of poor quality. This is problematic since rare and frequent words, albeit semantically similar, might end up far from each other in the embedding space. In this study, we develop KAFE (Knowledge And Frequency adapted Embeddings) which combines adversarial principles and knowledge graph to efficiently represent both frequent and rare words. The goal of adversarial training in KAFE is to minimize the spatial distinguishability (separability) of frequent and rare words in the embedding space. The knowledge graph encourages the embedding to follow the structure of the domain-specific hierarchy, providing an informative prior that is particularly important for words with low amount of training data. We demonstrate the performance of KAFE in representing clinical diagnoses using real-world Electronic Health Records (EHR) data coupled with a knowledge graph. EHRs are notorious for including ever-increasing numbers of rare concepts that are important to consider when defining the state of the patient for various downstream applications. Our experiments demonstrate better intelligibility through visualisation, as well as higher prediction and stability scores of KAFE over state-of-the-art.
Evaluation of Strategic Decision taken by Autonomous Agent using Explainable AI

Autonomous intrusion detection systems assess the data intelligently and take strategic decision to detect and mitigate cyber-attacks. These decisions have to be explained and evaluated for the transparency and correctness. Explainable Artificial Intelligent (XAI) methods that explore how features contribute or influence a decision taken using an algorithm can be useful for the purpose. XAI method of Testing with Concept Activation Vectors (TCAV) has been used recently to show the importance of high level concepts for a prediction class in order to deliver explanations in the way humans communicate with each other. This work explores the possibility of using TCAV to evaluate the strategic decision made by autonomous agents. A case study in the context of DoS attack is analysed to show that TCAV scores for various DoS attack classes and normal class of KDD99 data set can be used to evaluate the strategic decisions. The proposed method of analysis provides a quantifiable method to justify the current strategy or change in the strategy if required.
Glitch, the Post-digital Aesthetic of Failure and Twenty-First-Century Media

This paper aims to understand how everyday life is affected by new technological conditions through an inquiry into glitch, a concept that signifies moments of faulty interference in the regular operation of a technology and that is often labeled a ghost in the machine. By drawing on two concepts from cultural studies – spectrality and post-digital culture – it demonstrates how the imperfection-oriented aesthetic of glitch is today complicated by the technological tendency to bypass human awareness. By developing this argument through a reading of German electronic music group Oval’s influential glitch-based record 94diskont (1995), the paper shows how glitch’s signification of mediation, fragility and technological complexity has been modulated in recent years. This analysis is augmented through a consideration of Mark B.N. Hansen’s concept of ‘twenty-first-century media’, which takes as highly significant the tendency of contemporary media to operate beyond the thresholds of human cognition and perception. The paper suggests that, as a result of these new medial forms, the subversive potential of glitch-based artworks is impacted severely, but also that glitch’s status of ghost in the machine offers valuable resources for thinking through the media experiences afforded by 21st-century media. This paper thereby points to new potential modes of critique, and expands existing cultural debates about aesthetics, technology, and the constitution of everyday life.
Risk scenario-based value estimation of Bitcoin

The wild swings in Bitcoin's valuation keep attracting authorities' and policy-makers interest. Thus at present, many researchers are focus on analyzing and forecasting. The existing studies on Bitcoin price prediction are mainly in two ways: (1) study how economic factors, market and investor sentiment indicators influence Bitcoin price; (2) apply machine learning and artificial neural networks to predict the value of Bitcoin. This paper aims to implement a scenario analysis method to generate various hypothetical events and then determine their effects on the value of Bitcoin price. Scenario analysis is normally used to measure financial risk. In this paper, we propose a method that combines scenario analysis with historical data. We further aim to find the correlations among scenarios and examine the relationship between the significant shocks and Bitcoin prices. Our findings suggest that what-if analysis is a good way to measure the risk exposure of Bitcoin. The method can also be used for worse-scenario analysis to check how Bitcoin performs during crisis periods.