Reviewerly/exHarmony huggingface dataset:
Detection of Fraudulent Credit Card Transactions in Real Time Using SparkML and Kafka

As a result of the rapid expansion and development of E-Commerce, the use of credit card for online shopping and purchases has increased dramatically, and this rapid increase has led to a surge in fraudulent credit cards. Now being the most popular mode of payment in both offline and online purchases, credit card fraud has become a major menace. With the recent advancement in artificial intelligence, machine learning, data mining, sequence alignment, genetic programming, and fuzzy logic, innovative ways for detecting various credit card fraudulent activities have evolved. In our work, we have utilized SparkML and Scikit-Learn to develop and train several machine learning models for distinguishing fraudulent and legitimate transactions. We have also employed several data preprocessing techniques, such as class imbalance removal, which were implemented using various Spark packages. These machine learning models are then utilized to make predictions on Kafka-generated real-time data streams. Finally, we have used Streamlit in order to build an interface for displaying these predictions in real time.
On Using Conversational Frameworks to Support Natural Language Interaction in Intelligent Tutoring Systems

In this article, we analyze the potential of conversational frameworks to support the adaptation of existing tutoring systems to a natural language form of interaction. We have based our research on a pilot study, in which the open-source machine learning framework Rasa has been used to build a conversational agent that interacts with an existing intelligent tutoring system (ITS) called hypergraph-based problem solver (HBPS). This agent has been seamlessly integrated into the ITS to replace the previously available button-based user interface and allow the user to interact with HBPS in natural language. Once appropriately trained, the conversational agent was capable of identifying the intention of a given user utterance and extracting the relevant entities related to the message content, with average weighted F1-scores of 0.965 and 0.989 for intents and entities, respectively. Methodological guidelines are provided to generate a realistic training set that enables the creation of the required natural language understanding model and also evaluate the resulting system. These guidelines can be easily exported to other ITS and contexts to provide an enhanced interaction based on natural language processing methods.
Prediction of Osteosarcoma Using Machine Learning Techniques

Osteosarcoma is the name given to the most common bone malignancy which occurs in individuals of five to twenty-five years of age. This study focuses on finding out a prediction algorithm by comparing widely used algorithms on both feature, and image dataset. The aim is to find an algorithm with commendable accuracy which can be used in clinical settings. The work is done in two halves, i.e. on feature dataset and image dataset. The algorithms used for the feature dataset are Extra Trees Classifier, Convolutional Neural Network, XGBoost Classifier and DenseNet classifier. The concatenation of Viable and Necrotic Tumour gave the highest training accuracy of 96.22% followed by the group of Non-tumour versus Viable which gave the training accuracy of 94.56% compared to others. For the image dataset, Convolutional Neural Networks is used to predict Osteosarcoma. We managed to implement the model by utilizing trainable 2.5 million parameters of Convolutional Neural Network (CNN) which indicates that the model works on less complexity.
An Approach to Learn Structural Similarity Between Decision Trees Using Hungarian Algorithm

The prevailing data mining techniques emphasize on discovering patterns and extract useful information from facts already recorded in various databases. Prior research substantiates the belief that the decision tree methodology is one of the most prevalent technique for classification systems, predictive modeling, and interpretation as well as data manipulations. In this paper, we intend to delve deeper to find out structural similarity measure among decision trees induced from dataset using a naive approach. The purpose of this approach is to quantify the similarity which is an important factor, especially for data clustering. Decision tree similarity can be broadly classified under two categories (1) Structural Similarity (2) Semantic Similarity. Thus, the main focus of this paper is to determine the homogeneity between decision trees as the similarity measures are the key contributors in solving pattern recognition problems.
MobileNet + SSD: Lightweight Network for Real-Time Detection of Basketball Player

In sports applications, vision-based player detection is essential. For real-time activities like broadcasts and player identification, accuracy, efficiency, and minimal memory use are needed. The major difficulties in deploying object detection networks to embedded devices are the high computation and memory requirements. This paper proposes a mechanism of deep learning lightweight player detection pre-trained network (MobileNet) for Single-Shot Multibox Detector (SSD), which reduces the architecture weight file by reducing the number of convolutional layers and improves the computation speed. Therefore, MobileNetv1 is concatenated with the SSD framework of an efficient and accurate deep learning model for player detection in sports. The fundamental objective of this paper is to examine the accuracy and computation speed of the player detection approach (SSD), as well as the significance of a pre-trained deep learning model (MobileNet). The proposed model achieves 92.1% of precision, 81.3% of f1-score, and 12.4 MB of network weight file with an average frame rate of 57.2 frames per second (FPS) on the basketball dataset. The experimental results show that the MobileNetv1 + SSD designed for the purpose is more suitable for deployment in embedded devices for real-time player detection in sports.
Ensemble Learning Based Feature Selection for Detection of Spam in the Twitter Network

The proliferation of spam in Twitter poses serious social problems which affects society by causing people to commit crimes. For a better spam detection model, the challenge is to select optimal lightweight features that should be feasible to process a large number of tweets in very less time. This chapter focuses on discovering the optimal features from the dataset, developing various machine learning models for detection of spam and finally checking the stability of machine learning algorithms for varying sizes of training data. The dataset consists of user account and tweet based features from which the prominent features are selected using ensemble feature selection by employing different feature selection algorithms. Further, the effect of prominent features for spam detection is investigated using various machine learning algorithms. Comparative analysis of spam detection models is performed with the objective of finding the ones with the best performance and stability on large data sets of Twitter. The experimental results show that random forest classifiers perform better with an accuracy of 86.95%
Fast Adaptive Image Dehazing and Details Enhancement of Hazy Images

The majority of the existing methods for image dehazing are of more complexity, which exhibits more time for execution. Therefore, these algorithms may not be suitable for real-time image processing systems. Also, the existing methods do not consider the local variations of the hazy image that may result in over-saturation and under-saturation. Hence, there is a requirement to design a fast dehazing algorithm that adaptively dehazes according to the local region characteristics. In the proposed technique, a hazy image is first classified into ‘less-affected by haze’ and ‘more-affected by haze’ regions, on the basis of pixel intensity values. The image decomposition, image dehazing, and details enhancement are implemented separately in two blocks, namely ‘less-affected by haze’ and ‘more-affected by haze’ blocks, with different scale factors for adaptive dehazing. The results of these two blocks are fused based upon the regional categorization. The proposed algorithm produces good dehazed results at the rate of 25 frames per second.
Time Window Based Recommender System for Movies

Recommendation system is very popular nowadays with all e-commerce sites as well as in movie recommendation. A very common method to recommend movies to a target user is by finding users with similar tastes and recommending movies to that target user, which have been appreciated by the like-minded users. This simple but highly used approach is known as collaborative filtering. However, there might be a possible drawback in recommending movies just on the basis of plain collaborative filtering especially if the system is time-unaware. With time, preference of a user might change, and so in a time unaware system, the similarity measure calculated between the users may lack contextual basis. In this work, we propose an approach to make the recommendation algorithm time-aware by clustering the users of a system on the basis of time stamps. In collaborative filtering without the time context, users with huge gap in active years are also considered for calculating correlation; yet for any type of product, with increasing years, the products tend to change, as does the choice of users. Thus a time line is very important. Instead of clustering users around time, we have designed clusters around a target user to study the effect of time on the recommendations made to that particular user. The experiments conducted on the MovieLens-10M dataset produced encouraging results, which validates the efficacy of the proposed approach.
DOC: Text Recognition via Dual Adaptation and Clustering

More recently, unsupervised domain adaptation has been introduced to text image recognition tasks for serious domain shift problem, which can transfer knowledge from source domains to target ones. Moreover, in unsupervised domain adaptation for text recognition, there is no label information in the target domain to supervise the domain adaptation, especially at the character. Several existing methods regard a text image as a whole and perform only on global feature adaptation, neglecting local-level feature adaptation, i.e., characters. Others methods only focus their attention on word-level feature alignment while ignoring the categories of local-level characters. To address these issues, we propose a text recognition model via Dual adaptatiOn and Clustering, DOC for short. Regarding word-level, we construct a Global Discriminator for global feature adaptation to reduce text layout bias between source and target domains. Regarding character-level, we propose an Adaptive Feature Clustering (AFC) module, which can extract invariant character features through a local-level discriminator for adaptation. Moreover, it enhances the local-feature adaptation by a clustering scheme, which evaluates the feature adaptation by leveraging the knowledge from the source domain as much as possible. In this way, it can pay more attention to the differences in fine-grained characters. Extensive experiments on benchmark datasets demonstrate that our framework can achieve state-of-the-art performance. We have released the code. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
An Architecture to Develop an Automated Expert Finding System for Academic Events

Automatically expert finding in user required domain is an important task and has a significance in the information retrieval community. The automatic expert finding task is challenging as the voluminous expertise-related information and heterogeneous data resources are available today. Research shows remarkable progress in the past few years, and various new techniques have been proposed. While using search engines, search outcomes vary from tens to hundreds. To overcome this drawback, an approach is built to develop an automated expert finding system for academic events and research committees. The study of a number of techniques used in the past has helped to propose this advanced framework. It presents the natural language model-based information retrieval to recover the meaning insights from the enormous amount of data available on the Internet.
Optimized Watermarking Scheme for Copyright Protection of Medical Images

A medical image watermarking system must meet three basic requirements: transparency, robustness, and payload capacity. Watermarking medical images is mostly concerned with ensuring they are robust against manipulation. The embedding factor is used in many robust watermarking algorithms to hide the secret information behind the cover medical image. Many watermarking algorithms have difficulty selecting appropriate embedding factors. An effective copyright protection algorithm for medical images uses particle swarm optimization (PSO) to provide robust, hybrid, and blind watermarking. The embedding factor is selected using PSO based on the medical image and watermark information. Compared to the existing algorithms, the proposed algorithm performs better in transparency and payload capacity based on experimental results and comparative analysis.
Generalizing Voice Presentation Attack Detection to Unseen Synthetic Attacks and Channel Variation

Automatic Speaker Verification (ASV) systems aim to verify a speaker’s claimed identity through voice. However, voice can be easily forged with replay, text-to-speech (TTS), and voice conversion (VC) techniques, which may compromise ASV systems. Voice presentation attack detection (PAD) is developed to improve the reliability of speaker verification systems against such spoofing attacks. One main issue of voice PAD systems is its generalization ability to unseen synthetic attacks, i.e., synthesis methods that are not seen during training of the presentation attack detection models. We propose one-class learning, where the model compacts the distribution of learned representations of bona fide speech while pushing away spoofing attacks to improve the results. Another issue is the robustness to variations of acoustic and telecommunication channels. To alleviate this issue, we propose channel-robust training strategies, including data augmentation, multi-task learning, and adversarial learning. In this chapter, we analyze the two issues within the scope of synthetic attacks, i.e., TTS and VC, and demonstrate the effectiveness of our proposed methods.
No Silver Bullets, Only Disciplined Reflections to Guide Course Corrections [From the Editor]

In this article I reflect on the legacy of Frederick P. Brooks on the discipline of software engineering through the perspective of his infamous “no silver bullet” quote.
Decoupled neural network training with re-computation and weight prediction

To break the three lockings during backpropagation (BP) process for neural network training, multiple decoupled learning methods have been investigated recently. These methods either lead to significant drop in accuracy performance or suffer from dramatic increase in memory usage. In this paper, a new form of decoupled learning, named decoupled neural network training scheme with re-computation and weight prediction (DTRP) is proposed. In DTRP, a re-computation scheme is adopted to solve the memory explosion problem, and a weight prediction scheme is proposed to deal with the weight delay caused by re-computation. Additionally, a batch compensation scheme is developed, allowing the proposed DTRP to run faster. Theoretical analysis shows that DTRP is guaranteed to converge to crical points under certain conditions. Experiments are conducted by training various convolutional neural networks on several classification datasets, showing comparable or better results than the state-of-the-art methods and BP. These experiments also reveal that adopting the proposed method, the memory explosion problem is effectively solved, and a significant acceleration is achieved.
Color Image Retrieval Method Using Low Dimensional Salient Visual Feature Descriptors for IoT Applications

Digital data are rising fast as Internet technology advances through many sources, such as smart phones, social networking sites, IoT, and other communication channels. Therefore, successfully storing, searching, and retrieving desired images from such large-scale databases are critical. Low-dimensional feature descriptors play an essential role in speeding up the retrieval process in such a large-scale dataset. A feature extraction approach based on the integration of color and texture contents has been proposed in the proposed system for the construction of a low-dimensional feature descriptor. In which color contents are quantified from a preprocessed quantized HSV color image and texture contents are retrieved from a Sobel edge detection-based preprocessed V-plane of HSV color image using a block level DCT (discrete cosine transformation) and gray level co-occurrence matrix. On a benchmark image dataset, the suggested image retrieval scheme is validated. The experimental outcomes were compared to ten cutting-edge image retrieval algorithms, which outperformed in the vast majority of cases.
CCAH: A CLIP-Based Cycle Alignment Hashing Method for Unsupervised Vision-Text Retrieval

Due to the advantages of low storage cost and fast retrieval efficiency, deep hashing methods are widely used in cross-modal retrieval. Images are usually accompanied by corresponding text descriptions rather than labels. Therefore, unsupervised methods have been widely concerned. However, due to the modal divide and semantic differences, existing unsupervised methods cannot adequately bridge the modal differences, leading to suboptimal retrieval results. In this paper, we propose CLIP-based cycle alignment hashing for unsupervised vision-text retrieval (CCAH), which aims to exploit the semantic link between the original features of modalities and the reconstructed features. Firstly, we design a modal cyclic interaction method that aligns semantically within intramodality, where one modal feature reconstructs another modal feature, thus taking full account of the semantic similarity between intramodal and intermodal relationships. Secondly, introducing GAT into cross-modal retrieval tasks. We consider the influence of text neighbour nodes and add attention mechanisms to capture the global features of text modalities. Thirdly, Fine-grained extraction of image features using the CLIP visual coder. Finally, hash encoding is learned through hash functions. The experiments demonstrate on three widely used datasets that our proposed CCAH achieves satisfactory results in total retrieval accuracy. Our code can be found at: https://github.com/CQYIO/CCAH.git.
A conformal predictive system for distribution regression with random features

Distribution regression is the regression case where the input objects are distributions. Many machine learning problems can be analyzed in this framework, such as multi-instance learning and learning from noisy data. This paper attempts to build a conformal predictive system (CPS) for distribution regression, where the prediction of the system for a test input is a cumulative distribution function (CDF) of the corresponding test label. The CDF output by a CPS provides useful information about the test label, as it can estimate the probability of any event related to the label and be transformed to prediction interval and prediction point with the help of the corresponding quantiles. Furthermore, a CPS has the property of validity as the prediction CDFs and the prediction intervals are statistically compatible with the realizations. This property is desired for many risk-sensitive applications, such as weather forecast. To the best of our knowledge, this is the first work to extend the learning framework of CPS to distribution regression problems. We first embed the input distributions to a reproducing kernel Hilbert space using kernel mean embedding approximated by random Fourier features, and then build a fast CPS on the top of the embeddings. While inheriting the property of validity from the learning framework of CPS, our algorithm is simple, easy to implement and fast. The proposed approach is tested on synthetic data sets and can be used to tackle the problem of statistical postprocessing of ensemble forecasts, which demonstrates the effectiveness of our algorithm for distribution regression problems.
LimitAccess: on-device TinyML based robust speech recognition and age classification

Abstract Automakers from Honda to Lamborghini are incorporating voice interaction technology into their vehicles to improve the user experience and offer value-added services. Speech recognition systems are a key component of smart cars, enhancing convenience and safety for drivers and passengers. In the future, safety-critical features may rely on speech recognition, but this raises concerns about children accessing such services. To address this issue, the LimitAccess system is proposed, which uses TinyML for age classification and helps parents limit children’s access to critical speech recognition services. This study employs a lite convolutional neural network (CNN) model for two different reasons: First, CNN showed superior accuracy compared to other audio classification models for age classification problems. Second, the lite model will be integrated into a microcontroller to meet its limited resource requirements. To train and evaluate our model, we created a dataset that included child and adult voices of the keyword “open”. The system approach categorizes voices into age groups (child, adult) and then utilizes that categorization to grant access to a car. The robustness of the model was enhanced by adding a new class (recordings) to the dataset, which enabled our system to detect replay and synthetic voice attacks. If an adult voice is detected, access to start the car will be granted. However, if a child’s voice or a recording is detected, the system will display a warning message that educates the child about the dangers and consequences of the improper use of a car. Arduino Nano 33 BLE sensing was our embedded device of choice for integrating our trained, optimized model. Our system achieved an overall F1 score of 87.7% and 85.89% accuracy. LimitAccess detected replay and synthetic voice attacks with an 88% F1 score.