Reviewerly/exHarmony huggingface dataset:
Explainable Machine Learning Models for Credit Risk Analysis: A Survey

Data analytics in financial applications has evolved as a result of technological improvements, increased data availability, and the necessity for financial institutions to make better-informed decisions in an increasingly complicated and competitive industry. Machine learning models have recently advanced and been integrated into data analytics, allowing researchers and practitioners to make sense of large and complex data and extract relevant insights and patterns. Financial institutions can use machine learning models to identify potential borrowers who may fail to meet financial obligations and to take appropriate action to avoid such situations. Such predictive analysis is termed credit risk analysis and is extensively used by lending organizations. In this case, financial and other information about potential borrowers is gathered from various sources and supplied to machine learning models, which may estimate a lender's eligibility and categorize them accordingly. Although these models predict problematic or safe borrowers with high accuracy, they provide no rationale or explanation for their judgments. As a result, these models are commonly misinterpreted, and the process is typically implemented as a black-box model, in which the model accepts data as input and produces some output without explaining the decision-making process. Nonetheless, because regulated financial services must utilize a transparent decision-making process, machine learning models' explainability becomes crucial for acceptable adoption. Recently, researchers addressed this issue by offering explainability techniques that can explain the decision-making process of machine learning models, which will encourage financial institutions to employ machine learning models for data analytics. This chapter investigates recent advances in credit risk analysis using explainable machine learning models and offers insights for future research in this area.
Binary Mixed-Digit Data Compression Codes

One of the most important trade-offs in data compression is between the compression ratio and decoding speed. The latter can be increased due to a step structure of a codeword length distribution. E.g., in byte-aligned codes (ETDC, SCDC, or RPBC), codewords are composed of whole bytes and thus can be processed easily and quickly. However, this is achieved at the cost of compression ratio. We investigate a new family of data compression codes with codewords composed of digits of different bit lengths. Thus we called them mixed-digit codes. Their codeword length distribution is agile, allowing us to outperform the byte-aligned, Fibonacci, and some other recently invented variable-length codes both in compression ratio and decoding speed. We developed and tested the encoding, fast decoding, and optimal code search algorithms.
An Optimized Control on Delay and Transmission Rate Over Wireless Video Streaming Channels

The quality of video data transmitted in real-time over internet is often affected with an end-to-end delay. It further burdens the video data transmission in addition with data rate distortion. Hence, it is necessary to optimize the delay and rate behavior using a diagnostic optimistic mechanism. In this paper, we develop a machine learning mechanism that considers the delay and data as an important factor in delivering the video to the destined user without delay or buffering. The machine learning using artificial neural network (ANN)-based Markov Chain maintains the trade-off between the delay and data rate and considers both the factors as a severe constraint in affecting the performance of data delivery over wireless channels. The machine learning offers scheduling of data packets that tends to reduce the data minimization, increases the transmission rate, and reduces the delay. The scheduling sets the priority of each data and avoids retransmission of data. The experimental verification shows the efficacy of the model over existing state-of-the-art mechanism in terms of transmission errors and delay.
Comic Character Recognition (CCR): Extraction of Speech Balloon Context and Character of Interest in Comics

Document analysis is an active field of research which can attain a complete understanding of semantics of a given document. Further, digitalization of physical forms of various data has become vital in today’s data extraction. Comic digitalization is becoming widespread as it is one of the easily understandable graphic content and is attention seeking from early education readers to middle aged groups. Extracting the frames, panels, and speech balloons from digital comic is crucial for techniques that felicitate comic reading. However, Automatic Panel Extraction for Digital Comic is challenging, largely because of its layout design, visual symbols, speech balloons attached to almost all the panels, throughout the page. In this proposed work, it is proposed to automatically extract panels from digital pages using contour analysis and watershed canny operator. The first method identifies the difference between frames associated with the panels, whereas the second method identifies the difference in the color between the panel and the gutters. Speech balloons are segmented by methods of K-means clustering and contour analysis. K-means clustering is used to identify the closest related components, and contour analysis is used to differentiate the speech balloons from the other components in the comic panel. Text Area Recognition is a subtle approach that is implemented by Optical Character Recognition (OCR). And finally, comic character extraction has three steps of annotating the object, training the model, and detection of the comic character. The annotation is performed by bounding box algorithm, followed by training of the custom model using a pre-trained YOLOV3 algorithm. Once the custom model is trained, it is provided with comic strips as input to detect the dominant characters with their probability of confidence, which is the correctness of comic character. The implementation considerably serves better in performance when compared to the previous traditional methods of comic component analysis and extraction. Evaluation on the same has also been performed for the trained as well as input dataset.
Improving the Effectiveness of Cyberdefense Measures

We present and illustrate a recursive model which automatically organizes relationships between indicators of compromise (IoC) into richer information sets. It combines insights from natural language processing, supervised clustering, and network analysis to identify relations between IoC and thus to reduce information fragmentation. The quality of this combined information improves with every IoC that is added to the network, so that defenders can generate a more comprehensive and complete picture about a threat almost in real time and at very low transaction cost.
Implementing GANs for Image Generation and Spectral Normalization

The main aim of generative adversarial networks (GAN) and other machines when doing spectral image normalization is to have the images match perceived colors that humans and some animals can see when they look at it. Spectral normalization aims to present the original perception that would have been produced by human visual system. It is mainly executed with neural networks, commonly with generative adversarial networks (GAN). In this paper, the authors assess the impact of spectral normalization on deep convolutional generative adversarial network configurations. They study how normalizing the spectral channels of a deep convolutional GAN affects the networks. Experiments have shown that using spectral normalization in the process of training GANs improves stability and results in cleaner images.
Individual Career Versus Corporate Security: A Simulation of CSO Investment Choices

For corporate security officers (CSOs), investment decisions about IT security are more challenging than microeconomic models would suggest. Large budgets are not necessarily associated with effective corporate protection, whereas cybersecurity breaches negatively affect individual career prospects irrespective of prior investments. In this chapter we build on the Gordon-Loeb model to develop a recursive model which simulates investment dynamics, CSO reputation and inter-firm migration as well as cyberdefense effectiveness. We argue that a positive (negative) dynamic should exist between high (low) CSO reputation and effective corporate protection, and we simulate this hypothesized relationship by a Monte Carlo process which uses data from real cybersecurity breaches.
Approximate Cartesian Tree Matching: An Approach Using Swaps

Cartesian tree pattern matching consists of finding all the factors of a text that have the same Cartesian tree than a given pattern. There already exist theoretical and practical solutions for the exact case. In this paper, we propose the first algorithm for solving approximate Cartesian tree pattern matching. We consider Cartesian tree pattern matching with one swap: given a pattern of length m and a text of length n we present two algorithms that find all the factors of the text that have the same Cartesian tree of the pattern after one transposition of two adjacent symbols. The first algorithm uses a characterization of a linear representation of the Cartesian trees called parent-distance after one swap and runs in time $$\varTheta (mn)$$ using $$\varTheta (m)$$ space. The second algorithm generates all the parent-distance tables of sequences that have the same Cartesian tree than the pattern after one swap. It runs in time $$\mathcal {O}((m^2 + n)\log {m})$$ and has $$\mathcal {O}(m^2)$$ space complexity.
Largest Repetition Factorization of Fibonacci Words

A factorization of a string w is said to be a repetition factorization of w if every factor in the factorization is a repetition (i.e., the factor has a period shorter than or equal to the half of its length). Inoue et al. [TOCS 2022] showed how to compute the largest/smallest repetition factorization of a given string w of length n in $$O(n \log n)$$ time and O(n) space, by reducing the problems to the longest/shortest path problems on the repetition graph built on w. Inoue et al. also considered repetition factorizations on Fibonacci words, and posed a conjecture on the size $$S_{F_k}$$ of the largest repetition factorization of the k-th Fibonacci word $$F_k$$ . In this work, we provide a complete proof for this problem, by showing that $$S_{F_k}$$ is given by the recurrence $$S_{F_k} = S_{F_{k-1}} + S_{F_{k-2}} + 1$$ for every $$k \ge 15$$ .
Efficient Parameterized Pattern Matching in Sublinear Space

The parameterized matching problem is a variant of string matching, which is to search for all parameterized occurrences of a pattern P in a text T. In considering matching algorithms, the combinatorial natures of strings, especially periodicity, play an important role. In this paper, we analyze the properties of periods of parameterized strings and propose a generalization of Galil and Seiferas’s exact matching algorithm (1980) into parameterized matching, which runs in $$O(\pi |T|+|P|)$$ time and $$O(\log {|P|}+|\mathrm{\Pi }|)$$ space in addition to the input space, where $$\mathrm{\Pi }$$ is the parameter alphabet and $$\pi $$ is the number of parameter characters appearing in P plus one.
Space-Time Trade-Offs for the LCP Array of Wheeler DFAs

Recently, Conte et al. generalized the longest-common prefix (LCP) array from strings to Wheeler DFAs, and they showed that it can be used to efficiently determine matching statistics on a Wheeler DFA [DCC 2023]. However, storing the LCP array requires $$ O(n \log n) $$ bits, n being the number of states, while the compact representation of Wheeler DFAs often requires much less space. In particular, the BOSS representation of a de Bruijn graph only requires a linear number of bits, if the size of alphabet is constant. In this paper, we propose a sampling technique that allows to access an entry of the LCP array in logarithmic time by only storing a linear number of bits. We use our technique to provide a space-time trade-off to compute matching statistics on a Wheeler DFA. In addition, we show that by augmenting the BOSS representation of a k-th order de Bruijn graph with a linear number of bits we can navigate the underlying variable-order de Bruijn graph in time logarithmic in k, thus improving a previous bound by Boucher et al. which was linear in k [DCC 2015].
Fixpoints

The basic idea of the universal evaluators presented in the preceding chapter is that a function is computable when it is programmable.
A Simple Grammar-Based Index for Finding Approximately Longest Common Substrings

We show how, given positive constants $$\epsilon $$ and $$\delta $$ , and an $$\alpha $$ -balanced straight-line program with g rules for a text T[1..n], we can build an O(g)-space index that, given a pattern P[1..m], in $$O(m\log ^\delta g)$$ time finds w.h.p. a substring of P that occurs in T and whose length is at least a $$(1 - \epsilon )$$ fraction of the longest common substring of P and T. The correctness can be ensured within the same expected query time.
On the Number of Factors in the LZ-End Factorization

Kreft and Navarro [DCC 2010] introduced a restricted variant of the well-known Lempel-Ziv factorization, called the LZ-End factorization. Only recently Kempa and Saha [SODA2022] were able to obtain a good upper bound on the size of the LZ-End factorization in terms of the size of the LZ factorization. We extend their approach to improve the upper bound by a doubly-logarithmic factor.
On Suffix Tree Detection

A suffix tree is a fundamental data structure for string processing and information retrieval, however, its structure is still not well understood. The suffix trees reverse engineering problem, which its research aims at reducing this gap, is the following. Given an ordered rooted tree T with unlabeled edges, determine whether there exists a string w such that the unlabeled-edges suffix tree of w is isomorphic to T. Previous studies on this problem consider the relaxation of having the suffix links as well as assume a binary alphabet. This paper is the first to consider the suffix tree detection problem, in which the relaxation of having suffix links as input is removed. We study suffix tree detection on two scenarios that are interesting per se. We provide a suffix tree detection algorithm for general alphabet periodic strings. Given an ordered tree T with n leaves, our detection algorithm takes $$O(n+|\varSigma |^p)$$ -time, where p is the unknown in advance length of a period that repeats at least 3 times in a string S having a suffix tree structure identical to T, if such S exists. Therefore, it is a polynomial time algorithm if p is a constant and a linear time algorithm if, in addition, the alphabet has a sub-linear size. We also show some necessary (but insufficient) conditions for binary alphabet general strings suffix tree detection. By this we take another step towards understanding suffix trees structure.
Data Analytics Incorporated with Machine Learning Approaches in Finance

From the last few decades, a huge volume of financial data has been generated from the various heterogeneous sources of financial institutions. This data contains valuable or interesting information not easy or nearly impossible to get manually or using traditional approaches. This key information drives the business, marketing, and financial services very effectively, more economically, and with high growth in less time. This can be achieved through the fusion of data analytics (DA) techniques with finance. The rise of the DA approach from a few decades has had a great impact on the way of research or new findings. This study analyzes the literature available on various applications of DA in finance and develops an understanding for the researchers. This knowledge can utilize to resolve typical issues in the financial and decentralized financial domains and explored new ideas with their models.
Cooperative data-driven modeling

Data-driven modeling in mechanics is evolving rapidly based on recent machine learning advances, especially on artificial neural networks. As the field matures, new data and models created by different groups become available, opening possibilities for cooperative modeling. However, artificial neural networks suffer from catastrophic forgetting, i.e. they forget how to perform an old task when trained on a new one. This hinders cooperation because adapting an existing model for a new task affects the performance on a previous task trained by someone else. The authors developed a continual learning method 1 that addresses this issue, applying it here for the first time to solid mechanics. In particular, the method is applied to recurrent neural networks to predict history-dependent plasticity behavior, although it can be used on any other architecture (feedforward, convolutional, etc.) and to predict other phenomena. This work intends to spawn future developments on continual learning that will foster cooperative strategies among the mechanics community to solve increasingly challenging problems. We show that the chosen continual learning strategy can sequentially learn several constitutive laws without forgetting them, using less data to achieve the same error as standard (non-cooperative) training of one law per model.
The use of multimedia in English language teaching to support English teaching practice

This study was implemented in private Islamic secondary school in Yogyakarta. This study aims to support student motivation in studying English with multimedia as the assistance. The use of multimedia such as pictures, videos, and games are expected to create an active atmosphere of learning and teaching. This practice was conducted for four times and the result show that students were very engage while studying English and the class atmosphere become more fun and less monotone. How is the use of multimedia mediates activity to teach formal invitation in a private Islamic Secondary School? I learned that students demonstrated better learning engagement during the lesson. In addition, it also encourage the students to be more creative and active. &#x0D; Keywords: Multimedia: Learning Media: Teaching English
Reinforcement Learning of Action and Query Policies With LTL Instructions Under Uncertain Event Detector

Reinforcement learning (RL) with linear temporal logic (LTL) objectives can allow robots to carry out symbolic event plans in unknown environments. Most existing methods assume that the event detector can accurately map environmental states to symbolic events; however, uncertainty is inevitable for real-world event detectors. Such uncertainty in an event detector generates multiple branching possibilities on LTL instructions, confusing action decisions. Moreover, the queries to the uncertain event detector, necessary for the task's progress, may increase the uncertainty further. To cope with those issues, we propose an RL framework, Learning Action and Query over Belief LTL (LAQBL), to learn an agent that can consider the diversity of LTL instructions due to uncertain event detection while avoiding task failure due to the unnecessary event-detection query. Our framework simultaneously learns 1) an embedding of belief LTL, which is multiple branching possibilities on LTL instructions using a graph neural network, 2) an action policy, and 3) a query policy that decides whether or not to query for the event detector. Simulations in a 2D grid world and image-input robotic inspection environments show that our method successfully learns actions to follow LTL instructions even with uncertain event detectors.
VD-HEN: Capturing Semantic Dependencies for Source Code Vulnerability Detection With a Hierarchical Embedding Network

This article proposes a semantic dependency capture method for source code vulnerability detection of an open source framework based on a hierarchical embedded network. It extracts syntactic information and structural information through a statement embedding network and program embedding network, respectively. Experimental results show that the proposed method is superior to other methods.
Implicit Point Function for LiDAR Super-Resolution in Autonomous Driving

LiDAR super-resolution is a relatively new problem in which we seek to fill in the blanks between measured points when a low-resolution LiDAR is given, making a high-resolution LiDAR or even a resolution-free LiDAR. Recently, several research works have been reported regarding LiDAR super-resolution. However, most of the works on LiDAR super-resolution have the drawback that they first transform 3D LiDAR point cloud into 2D depth map and upsample the LiDAR output by applying the image super-resolution method, ignoring the 3D geometric information of the point cloud obtained from a LiDAR. To solve the above problem, we propose a new deep learning network named as implicit point function (IPF). The basic idea of IPF is that when we are given low-resolution point cloud and a query ray, we generate the 3D target point embeddings on the query ray using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">on-the-ray positional embedding</i> and local features, preserving the 3D geometric information of the given point cloud. Then, we aggregate them into one target point via the attention mechanism. IPF enables us to learn continuous representation of 3D space from low-resolution LiDAR and upsample a small number of layers to any number that we want. Finally, our IPF is applied to large-scale synthetic dataset and real dataset, and its validity is demonstrated by comparing with the previous methods.
Distributed Ledger Technologies for Electronic Health Care: IOTA-Based Remote Patient Monitoring and Telemedicine System

We propose using IOTA in electronic health-care systems to ensure better adaptability, anonymity, and compatibility. Quantitative results suggest that IOTA outperforms other blockchain solutions in terms of speed as well as energy consumption. Our middleware offers cross-platform compatibility and easy integration with any system.
CB-SAGE: A novel centrality based graph neural network for floor plan classification

Graph Neural Networks (GNNs) have emerged as one of the most prominent research areas in accomplishing machine learning tasks over graphical networks. GNNs are prominently used in performing tasks like semi-supervised node classification, link prediction, and community detection. GraphSAGE is one of the most recent GNN models which is being used to accomplish these tasks. A floor plan is an architectural design of a building that represents various floor compartments. In this paper, we represent floor plan(s) as graph(s). We characterize floor room (compartment) classification as a node classification problem. We propose a variation of the traditional GraphSAGE (sample and aggregation) algorithm: Centrality based GraphSAGE (CB-SAGE), which captures the structural properties of the network. We use the average clustering coefficient and average betweenness centrality to capture structure properties. We compute CB scores for all the floor plan graphs. Top 70% nodes (based on CB scores) are selected for the training. During the training, we append the betweenness centrality score of each node as an additional feature in the feature matrix for the embedding process. We conduct experiments on the House-GAN dataset, which contains 1,43,184 vectorized floor plan images. The proposed method outperforms the current state-of-art models in accomplishing the task of floor plan classification. We compare our results with the traditional machine learning approach (MLP) and other GNN-based methods. Our approach achieves an accuracy of 96.70%, which is significantly (approximately 16%) higher than other state-of-the-art methods.