Reviewerly/exHarmony huggingface dataset:
Internal-led cyber frauds in Indian banks: an effective machine learning–based defense system to fraud detection, prioritization and prevention

Purpose The study aims to overview the different types of internal-led cyber fraud that have gained mainstream attention in recent major-value fraud events involving prominent Indian banks. The authors attempted to identify and classify cyber frauds and its drivers and correlate them for optimal mitigation planning. Design/methodology/approach The methodology opted for the identification and classification is through a detailed literature review and focus group discussion with risk and vigilance officers and cyber cell experts. The authors assessed the future of cyber fraud in the Indian banking business through the machine learning–based k-nearest neighbor (K-NN) approach and prioritized and predicted the future of cyber fraud. The predicted future revealing dominance of a few specific cyber frauds will help to get an appropriate fraud prevention model, using an associated parties centric (victim and offender) root-cause approach. The study uses correlation analysis and maps frauds with their respective drivers to determine the resource specific effective mitigation plan. Findings Finally, the paper concludes with a conceptual framework for preventing internal-led cyber fraud within the scope of the study. A cyber fraud mitigation ecosystem will be helpful for policymakers and fraud investigation officers to create a more robust environment for banks through timely and quick detection of cyber frauds and prevention of them. Research limitations/implications Additionally, the study supports the Reserve Bank of India and the Government of India's launched cyber security initiates and schemes which ensure protection for the banking ecosystem i.e. RBI direct scheme, integrated ombudsman scheme, cyber swachhta kendra (botnet cleaning and malware analysis centre), National Cyber Coordination Centre (NCCC) and Security Monitoring Centre (SMC). Practical implications Structured and effective internal-led plans for cyber fraud mitigation proposed in this study will conserve banks, employees, regulatory authorities, customers and economic resources, save bank authorities’ and policymakers’ time and money, and conserve resources. Additionally, this will enhance the reputation of the Indian banking industry and extend its lifespan. Originality/value The innovative insider-led cyber fraud mitigation approach quickly identifies cyber fraud, prioritizes it, identifies its prominent root causes, map frauds with respective root causes and then suggests strategies to ensure a cost-effective and time-saving bank ecosystem.
Characteristic automated search of cryptographic algorithms for distinguishing attacks (CASCADA)

Abstract Automated search methods based on Satisfiability Modulo Theory (SMT) problems are being widely used to evaluate the security of block ciphers against distinguishing attacks. While these methods provide a systematic and generic methodology, most of their software implementations are limited to a small set of ciphers and attacks, and extending these implementations requires significant effort and expertise. In this work, the authors present cryptographic algorithms for distinguishing attacks (CASCADA), an open-source Python library to evaluate the security of cryptographic primitives, specially block ciphers, against distinguishing attacks with bit-vector SMT solvers. The tool CASCADA implements the bit-vector property framework herein proposed and several SMT-based automated search methods to evaluate the security of ciphers against differential, related-key differential, rotational-XOR, impossible-differential, impossible-rotational-XOR, related-key impossible-differential, linear and zero-correlation cryptanalysis. The library CASCADA is the result of a huge engineering effort, and it provides many functionalities, a modular design, an extensive documentation and a complete suite of tests.
Presenting a Human Pupil Orbit Model (HPOM) for Eye-Gaze Tracking

Eye tracking technology has been continuously researched for application in various fields. In the past, studies have been conducted to interpret eye movements in 3D space in order to solve the problem of not being able to find the centre of rotation of the eye. In this paper, we propose a novel pre-processing method for eye-gaze tracking by monitoring the front of the face with a camera. Our method works regardless of the distance between the eye and the camera. The proposed method includes an analysis technique that simplifies conventional three-dimensional space analysis to two dimensions. The contribution this work presents is a method to simplify gaze direction detection. The errors in our model’s estimations appear to be under 1 pixel. In addition, our approach has an execution time of less than 1 s, enabling an adaptive model that responds to user movements in real time. The proposed method was able to overcome various problems that methods in existing studies still suffer from, including accurately finding the rotational centre of the user’s eye-ball. Moreover, even when a user’s pupil can only be monitored from a distance, our approach still makes it possible to produce accurate estimations.
An Italian lexical resource for incivility detection in online discourses

Abstract The exponential growth of social media has brought an increasing propagation of online hostile communication and vitriolic discourses, and social media have become a fertile ground for heated discussions that frequently result in the use of insulting and offensive language. Lexical resources containing specific negative words have been widely employed to detect uncivil communication. This paper describes the development and implementation of an innovative resource, namely the Revised HurtLex Lexicon, in which every headword is annotated with an offensiveness level score. The starting point is HurtLex, a multilingual lexicon of hate words. Concentrating on the Italian entries, we revised the terms in HurtLex and derived an offensive score for each lexical item by applying an Item Response Theory model to the ratings provided by a large number of annotators. This resource can be used as part of a lexicon-based approach to track offensive and hateful content. Our work comprises an evaluation of the Revised HurtLex lexicon.
When Birds of a Feather Instagram Together: Debating the Image of Islam in Echo Chambers and Through Trench Warfare on Social Media

Social media contributions surrounding the Charlie Hebdo attacks have been key in the creation and evolution of the image of Islam online. While the attacks were seen as an affront to French values and ways of life, online exchanges have traveled around the globe. Especially with social media platforms, such as Twitter and Instagram, offering immediate translations of captions has increased the reach of posts immensely. Specifically, hashtag use has the ability to transcend national boundaries and helps in the creation of echo chambers and or fuels trench warfare online. This article extends on this work by examining how the image of Islam is transmitted and changed on Instagram through the use of the hashtag #CharlieHebdo. Specifically, the following three primary research questions are examined: (1) How does hashtag co-occurrence, in the discussion surrounding Charlie Hebdo, indicate echo chamber behavior? (2) How does trench warfare impact the debate surrounding the image of Islam within the #CharlieHebdo conversation? (3) Which categories can posts be put into based on other hashtags that are used simultaneously? To answer these questions, Instagram posts, posted nearly 6 years after the Charlie Hebdo attack was perpetrated in Paris and during the time of the trials of the suspects took place, are used as a case study. It was found that Islamophobia within the Charlie Hebdo debate online indeed indicates echo chamber behavior. The significance of this study was found to be two-fold. First, it extends the research on detecting echo chamber behavior through social network analysis and sentiment analysis of co-used hashtags. Second, it highlights the fact that trench warfare is a well understood tactic by Instagram users, for the hijacking of hashtags, and the role it plays in the polarization of hashtag communities.
ICACIA: An Intelligent Context-Aware framework for COBOT in defense industry using ontological and deep learning models

Most of the world’s most advanced defense technologies are robots, and the defence industry is slowly moving toward including AI in the military robots they build. For these smart robots to make their own decisions about where to go and what to do, they need to be limited by several algorithms that run continuously and at the same time. Autonomy is the range of automated systems that can be adapted to a specific mission, residual risk, and level of team cohesion between humans and robots. Self-driving robotic systems should be collaborative, which means they should be able to interact actively with humans in a shared space or in proximity to humans and robots. Human–Robot Collaboration (HRC) works better when these COBOTs are aware of their surroundings. Mobile Robot (MR) teams whose perceptual and cognitive abilities are very well developed can help a lot with context awareness. To work well with humans, these robots should know what is going on with their human and other robot teammates so they can make decisions on their own. Also, robots should be able to share information about their surroundings so that humans can benefit from a better understanding of the situation. At the same time, humans should be able to see what the robots are doing. In this paper, we propose a knowledge-based framework for humans and robots to work together to understand the context of Defense missions. An ontological model of contexts for missions, agents, and situations; a knowledge base comprising all the tools necessary for a sort of situation; and an efficient and reliable method of collaborative learning are some of its main contributions. The framework works well in terms of how long it takes for people to talk to each other. As the team continues to expand, it can also easily manage communication challenges and a widely differing event frequency range.
Fast Mining and Forecasting of Co-evolving Epidemiological Data Streams

Given a large, semi-infinite collection of co-evolving epidemiological data containing the daily counts of cases/deaths/recovered in multiple locations, how can we incrementally monitor current dynamical patterns and forecast future behavior? The world faces the rapid spread of infectious diseases such as SARS-CoV-2 (COVID-19), where a crucial goal is to predict potential future outbreaks and pandemics, as quickly as possible, using available data collected throughout the world. In this paper, we propose a new streaming algorithm, EPICAST, which is able to model, understand and forecast dynamical patterns in large co-evolving epidemiological data streams. Our proposed method is designed as a dynamic and flexible system, and is based on a unified non-linear differential equation. Our method has the following properties: (a) Effective: it operates on large co-evolving epidemiological data streams, and captures important world-wide trends, as well as location-specific patterns. It also performs real-time and long-term forecasting; (b) Adaptive: it incrementally monitors current dynamical patterns, and also identifies any abrupt changes in streams; (c) Scalable: our algorithm does not depend on data size, and thus is applicable to very large data streams. In extensive experiments on real datasets, we demonstrate that EPICAST outperforms the best existing state-of-the-art methods as regards accuracy and execution speed.
Rax: Composable Learning-to-Rank Using JAX

Rax is a library for composable Learning-to-Rank (LTR) written entirely in JAX. The goal of Rax is to facilitate easy prototyping of LTR systems by leveraging the flexibility and simplicity of JAX. Rax provides a diverse set of popular ranking metrics and losses that integrate well with the rest of the JAX ecosystem. Furthermore, Rax implements a system of ranking-specific function transformations which allows fine-grained customization of ranking losses and metrics. Most notably Rax provides approx_t12n: a function transformation (t12n) that can transform any of our ranking metrics into an approximate and differentiable form that can be optimized. This provides a systematic way to directly optimize neural ranking models for ranking metrics that are not easily optimizable in other libraries. We empirically demonstrate the effectiveness of Rax by benchmarking neural models implemented using Flax and trained using Rax on two popular LTR benchmarks: WEB30K and Istella. Furthermore, we show that integrating ranking losses with T5, a large language model, can improve overall ranking performance on the MS MARCO passage ranking task. We are sharing the Rax library with the open source community as part of the larger JAX ecosystem at https://github.com/google/rax.
Automated Machine Learning &amp; Tuning with FLAML

In this tutorial, we will provide an in-depth and hands-on tutorial on Automated Machine Learning & Tuning with a fast python library FLAML. We will start with an overview of the AutoML problem and the FLAML library. In the first half of the tutorial, we will then give a hands-on tutorial on how to use FLAML to automate typical machine learning tasks in an end-to-end manner with different customization options and how to perform general tuning tasks on user-defined functions. In the second half of the tutorial, we will introduce several advanced functionalities of the library. For example, zero-shot AutoML, fair AutoML, and online AutoML. We will close the tutorial with several open problems, and challenges learned from AutoML practice.
Online News Sentiment Classification Using DistilBERT

The ability of pre-trained BERT model to achieve outstanding performances on many Natural Language Processing (NLP) tasks has attracted the attention of researchers in recent times.However, the huge computational and memory requirements have hampered its widespread deployment on devices with limited resources.The concept of knowledge distillation has shown to produce smaller and faster distilled models with less trainable parameters and intended for resource-constrained environments.The distilled models can be fine-tuned with great performance on a wider range of tasks, such as sentiment classification.This paper evaluates the performance of DistilBERT model and other pre-canned text classifiers on a Covid-19 online news binary classification dataset.The analysis shows that despite having fewer trainable parameters than the BERT-based model, the DistilBERT model achieved an accuracy of 0.94 on the validation set after only two training epochs.The paper also highlights the usefulness of the ktrain library in facilitating the building, training, and application of state-of-the-art Machine Learning and Deep Learning models.
A Novel Multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies

Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. Multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.
Maneuvering extended object tracking based on constrained expectation maximization

This paper addresses the problem of maneuvering extended object tracking, in which the object extension (OE) and the turn rate are identified simultaneously. Due to its non-linearity with respect to the turn rate, the state transition is converted into a linear form of a newly defined hyper-parametric vector by parameter substitution. For the hyper-parametric equality constraints (HECs) introduced in the transformation, a constrained expectation conditional maximization algorithm is designed. The HECs are projected onto the conditional expectation function for regularization, so as to realize the iterative identification of multi-parameter (i.e., OE and turn rate). The transformation and CECM optimization bring the advantage of avoiding nonlinear model approximation, which is important for the convergence and accuracy of state estimation. Finally, simulation results demonstrate the superiority of the proposed method in terms of both estimation accuracy and identification effectiveness.
Extracting Case Indices from Convolutional Neural Networks: A Comparative Study

Machine learning for extracting case features can provide great benefit over feature engineering for retrieval in poorly understood or hard to characterize domains. The effectiveness of machine learning with deep neural networks has prompted much interest in neural network approaches to feature learning in case-based reasoning, with several works showing the value of feature extraction from input data using convolutional neural networks. Those approaches are based on plausible assumptions about where in the networks to extract features for maximal usefulness. This paper presents an empirical evaluation of those underlying assumptions. We compare three extraction approaches, for an image classification task: the most common feature extraction method, extracting after the convolution layer; a recently proposed alternative, extracting after the densely-connected layers; and a new approach, extracting after the densely-connected layers using multiple networks. Our results show that the latter two approaches substantially increase case retrieval accuracy in example-sparse domains, to which case-based reasoning systems are commonly applied.
High-Level Simulation of Embedded Software Vulnerabilities to EM Side-Channel Attacks

Attacks on embedded devices using the electromagnetic (EM) side channel have proliferated. Predicting software vulnerability to such attacks requires an ability to simulate EM fields during software development rather than relying on expensive lab-based measurements. We propose a modeling approach capable of synthesizing instruction-level EM traces for arbitrary software, using a one-time pre-characterization of a processor. Reducing the cost of dictionary construction is a major contribution of this paper. Results on a set of benchmarks show that synthesized traces are accurate in estimating EM emanations with less than 5% mean absolute percentage error (MAPE) compared to measurements. Furthermore, synthesized traces predict control flow leakage with an accuracy of 87% or more based on the side-channel vulnerability factor (SVF) metric.
Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study

Explainability in AI is becoming increasingly important as we delegate more safety-critical tasks to intelligent decision support systems. Case-Based Reasoning (CBR) systems are one way to build such systems. Understanding how results are created by a CBR system has become an important task in their development process. In this work, we present how visualizations can help developers and domain experts to evaluate the CBR systems behavior and provide insights to further develop CBR systems in their application scenarios. This paper presents an overview of SupportPrim, a CBR system for the management of musculoskeletal pain complaints, and presents methods that explain its retrieval and similarity measures through visualizations that help to evaluate the system’s performance. In the case study, we conduct experiments within the SupportPrim CBR system using differently weighted global similarity measures to compare their effect on the retrieval. This work shows that providing suitable explanations for the CBR system’s stakeholders increases the likelihood of its adoption, and visualizations allow the creation of different explanations for the different users throughout the development phase, thus allowing for better modeling and usage of the system.
A multi‐preference integrated algorithm for deep learning based recommender framework

Summary Nowadays, the online recommender systems based collaborative filtering methods are widely employed to model long term user preferences (LTUP). The deep learning methods, like recurrent neural networks (RNN) have the potential to model short‐term user preferences (STUP). There is no dynamic integration of these two models in the existing recommender systems. Therefore, in this article, a multi‐preference integrated algorithm (MPIA) for deep learning based recommender framework (DLRF) is proposed to perform the dynamic integration of these two models. Moreover, the MPIA addresses improper data and to improve the performance for creating recommendations. This algorithm is depending on an enhanced long short term memory (LSTM) with additional controllers to consider relative information. Here, experiments are carried out by Amazon benchmark datasets, then obtained outcomes are compared with other existing recommender systems. From the comparison, the experimental outcomes show that the proposed MPIA outperforms existing systems under performance metrics, like area under curve, F1‐score. Consequently, the MPIA can be integrated with real time recommender systems.
A data skew-based unknown traffic classification approach for TLS applications

With the continuous development of network technology, the volume of encrypted traffic from unknown applications rises sharply, posing a significant challenge to conventional traffic classification methods. While these methods achieve a certain level of success in recognizing specific application traffic, they fail to classify unknown traffic, especially encrypted traffic. Existing traffic classification methods are usually constrained by the assumption that classes encountered in testing are also present in training, which is not consistent with the open environment of the real world. In this paper, we propose a novel data skew-based classification method for Transport Layer Security (TLS) application unknown traffic (DSCU) to achieve consistent classification of TLS applications. First, DSCU constructs skew data, and then the one-class classifiers generated based on the skew data limit the input space scope of the known class and reserve space for the unknown class. This enables DSCU to separate known flows (i.e., flows from applications contained in the training set) from unknown flows (i.e., flows without any application information regarding them during training). After separation, the fine-grained classification of known flows can improve the accuracy of known flow classification. Three groups of experiments conducted on a real-world dataset covering 25 applications show that DSCU reliably achieves outstanding performance on TLS flow classification.