Reviewerly/exHarmony huggingface dataset:
Analysis and comparison of various deep learning models to implement suspicious activity recognition in CCTV surveillance

The paper aims to analyze and compare various deep learning (DL) algorithms in order to develop a Suspicious Activity Recognition (SAR) system for closed-circuit television (CCTV) surveillance. Automated systems for detecting and classifying suspicious activities are crucial as technology’s role in safety and security expands. This paper addresses these challenges by creating a robust SAR system using machine learning techniques. It analyzes and compares evaluation metrics such as Precision, Recall, F1 Score, and Accuracy using various deep learning methods (convolutional neural network (CNN), Long short-term memory (LSTM) – Visual Geometry Group 16 (VGG16), LSTM – ResNet50, LSTM – EfficientNetB0, LSTM – InceptionNetV3, LSTM – DenseNet121, and Long-term Recurrent Convolutional Network (LRCN)). The proposed system improves threat identification, vandalism deterrence, fight prevention, and video surveillance. It aids emergency response by accurately classifying suspicious activities from CCTV footage, reducing reliance on human security personnel and addressing limitations in manual monitoring. The objectives of the paper include analyzing existing works, extracting features from CCTV videos, training robust deep learning models, evaluating algorithms, and improving accuracy. The conclusion highlights the superior performance of the LSTM-DenseNet121 algorithm, achieving an overall accuracy of 91.17% in detecting suspicious activities. This enhances security monitoring capabilities and reduces response time. Limitations of the system include subjectivity, contextual understanding, occlusion, false alarms, and privacy concerns. Future improvements involve real-time object tracking, collaboration with law enforcement agencies, and performance optimization. Ongoing research is necessary to overcome limitations and enhance the effectiveness of CCTV surveillance.
Comparing Multiple Extended Object Tracking with Point Based Multi Object Tracking for LiDAR in a Maritime Context

Abstract In this paper, we compare a recently developed multiple extended object tracking method with a point-based tracker for maritime applications by evaluating the methods on both real and simulated LiDAR data. Being able to track other vessels is a key part of maritime situational awareness, and multi-object tracking is well suited to this task. Traditionally, multi-object tracking uses the point approximation to simplify the target tracking problem, meaning that an object is assumed to only generate a single measurement. With recent advances in sensor technology, this approximation is no longer valid. Multiple extended object tracking instead makes use of methods that estimates the extent of a target using all available measurements. However, the target models used in extended object tracking are by necessity more complex. We compare these two approaches on real and simulated LiDAR data, which was gathered by tracking smaller vessels. We find that the extended object method has greater performance on simulated data and experimental data with comparatively little wake clutter. However, on experimental data with more wake clutter, the point-based method outperforms the extended object method since it is less sensitive to the disturbance caused by wake clutter. We can conclude that multiple extended object tracking methods have the potential to be more accurate for LiDAR data, but since the models are more complex, measurement sources need to be modeled correctly.
Organization of the educational process of future specialists in graphic design using digital technologies

Today we live in the era of global digitalization of society. The rapid development of technologies and digitalization captures an ever-increasing spectrum of human activity, including educational and creative areas. Digitazation is rethinking approaches to the organization of the educational process, creative and project activities at the global level. Digital technologies should be understood as one of the tools in design activity in contemporary art, as well as an area for creaƟng immaterial virtual products. The purpose of the article is to highlight the impact of digital technologies on the organizaƟon of the educational process of graphic design specialists. Materials and methods. To achieve the research goal, a set of theoreƟcal methods was used: analysis and generalizaƟon of scienƟfic and methodical sources for theoretical substantiation of research problems, modeling, and synthesis of the obtained data. Research results. The main goal of design educaƟon is the formaƟon of a qualified specialist, a person capable of mastering cultural experience and modern technologies, including digital ones. Design is a special type of activity in which innovative methods of project soluƟons and modern digital technologies are widely used and developed. Thus, the level of technological development and digitalization today is more than ever stimulating the development and promotion of design education. Digitazation promotes the introducƟon of webinars and video conferences into the educational process, it has changed the form and type of organization, as well as the availability of educational materials. Interactive media resources and digital technologies are increasingly used in modern design education. As with any evolving system, digitazation has a number of disadvantages and advantages. On the one hand, the development of digitalization gives designers an unlimited field for creativity and work. On the other hand, a number of problems and diﬃculties appear. For example, changes in the form of organization of work with the audience, including features and diﬀerences of physical objects and their digital copies, copyright, etc.
Accurate and Robust Eye Tracking with Ultrasound: A Computational Study

This paper presents an ultrasound simulation platform to synthesize realistic ultrasound eye tracking data as a function of transducer/ system design, sensor noise, eye/ face occlusion, and headset slippage. Simulation data were synthesized using a single face with adjustable gaze and eyelid opening in the presence of headset slippage. The data generated using this face/eye model was input into a machine learning algorithm to jointly estimate gaze and headset slippage. We achieved gaze root-mean-square-error (RMSE) of 0.085° and 0.756° without and with headset slippage, respectively. We anticipate that the proposed end-to-end simulation pipeline will enable tractable design optimization of wearable ultrasound devices and facilitate further investigation of ultrasound sensing solutions as a complementary technology to camera-based eye-tracking for AR/VR applications.
AI DBMS in modern-day applications

The future of computing is anticipated to be shaped by the fusion of DBMS (Database Management Systems) and AI (Artificial Intelligence). This union is of utmost importance to advance DBMS technology and enable next-generation computing Although DBMS and AI systems are well-established technology, research, and development into the combination of AI and databases is still in its early phases. The availability of vast volumes of shared data for knowledge processing, effective data, knowledge management, and intelligent data processing is the driving force behind this convergence. The Intelligent Database Interface (IDI) architecture was developed to protect the significant investments made in current databases. Numerous well-liked techniques and developments for fusing AI with databases have been examined in-depth through extensive study and published articles.
Deep Learning Techniques for Computer Aided Diagnosis of Various Cancers

Deep learning has emerged as a key machine learning technology in object detection, attracting attention for its results in medical imaging analysis. Convolutional neural networks (CNNs) are the most commonly used deep learning algorithms for this purpose, and they play an important role in the identification and potential early diagnosis of cancer. This chapter presents a survey on the advancements in deep learning techniques for cancer detection and diagnosis and hopes to provide an overview of the progress in this field. The chapter covers the progress in the diagnosis of the deadliest of cancers through computer aided diagnosis (CAD) and discusses the challenges of deep learning techniques used for cancer detection and diagnosis such as segmentation, classification, analysis of region of interest (ROI), cancer, denoising, and filtering using popular state-of-the-art deep learning architectures such as convolutional neural networks, fully convolutional networks, and autoencoders. The chapter presents details of deep learning techniques based on the type of cancer and provides insight into the field’s progress.
Intermediate Task Ensembling for Sarcasm Detection

Navigating nuanced linguistic features through machine learning plays an important role in academic, private, and public sectors as researchers and developers use computational methods to parse, process, and understand an ever-increasing volume of natural language text. While recent advances in natural language processing and large language models have improved access to state-of-the-art performance, partly through the evolution of pre-training and fine-tuning processes, some particularly difficult linguistic tasks, such as sarcasm detection, remain to be solved. Such tasks can be highly disruptive to computational systems that rely on natural language processing. In this paper, we approach sarcasm detection by leveraging the RoBERTa model, a two-step fine-tuning process called Intermediate Fine-Tuning, and ensembling theory. We establish baselines, create ensembles, explore ensemble predictions, and analyze both baseline and ensemble performance. This research shows that intermediate fine-tuning can create sufficiently performant and diverse inducers for ensembles, and that those ensembles may also outperform single-model baselines on sarcasm detection tasks.
Exploring Multilingual Word Embedding Alignments in BERT Models: A Case Study of English and Norwegian

Contextual language models, such as transformers, can solve a wide range of language tasks ranging from text classification to question answering and machine translation. Like many deep learning models, the performance heavily depends on the quality and amount of data available for training. This poses a problem for low-resource languages, such as Norwegian, that can not provide the necessary amount of training data. In this article, we investigate the use of multilingual models as a step toward overcoming the data sparsity problem for minority languages. In detail, we study how words are represented by multilingual BERT models across two languages of our interest: English and Norwegian. Our analysis shows that multilingual models similarly encode English-Norwegian word pairs. The multilingual model automatically aligns semantics across languages without supervision. Additionally, our analysis also shows that embedding a word encodes information about the language to which it belongs. We, therefore, believe that in pre-trained multilingual models’ knowledge from one language can be transferred to another without direct supervision and help solve the data sparsity problem for minor languages.
Heuristic Search of Heuristics

How can we infer the strategies that human participants adopt to carry out a task? One possibility, which we present and discuss here, is to develop a large number of strategies that participants could have adopted, given a cognitive architecture and a set of possible operations. Subsequently, the (often many) strategies that best explain a dataset of interest are highlighted. To generate and select candidate strategies, we use genetic programming, a heuristic search method inspired by evolutionary principles. Specifically, combinations of cognitive operators are evolved and their performance compared against human participants’ performance on a specific task. We apply this methodology to a typical decision-making task, in which human participants were asked to select the brighter of two stimuli. We discover several understandable, psychologically-plausible strategies that offer explanations of participants’ performance. The strengths, applications and challenges of this methodology are discussed.
Hybrid Dual-Resampling and Cost-Sensitive Classification for Credit Risk Prediction

The class imbalance in financial data sets is prevalent and problematic when evaluating credit risks. This paper proposes a Hybrid dual Resampling and cost-sensitive classification approach by creating heuristically balanced data sets. Given an imbalanced credit data set, a synthetic minority class is generated using a resampling learning technique based on Gaussian mixture modelling from the minority class data. Simultaneously, k-means clustering is applied to the majority class. Then, feature selection is performed using an Extra Tree Ensemble technique. Finally, a cost-sensitive logistic model is estimated and applied to predict the probability of default using the heuristically balanced datasets. The results show that the proposed technique achieves superior performance in comparison with other imbalanced preprocessing approaches.
A Knowledge Rich Task Planning Framework for Human-Robot Collaboration

In this paper, we position ourselves within the context of collaborative planning. Drawing upon our recent research, we introduce a novel, knowledge rich task planning framework that represents and reasons and effectively addresses agents’ first-order false beliefs to solving a task planning problem with a shared goal. Our contributions to this work are as follows: First, to enhance the reasoning abilities of an existing framework, an intuitive observability model for situation assessment is addressed, and for that, an improved knowledge modeling is considered. This effectively captures agents’ predictable false beliefs and motivates us to exploit the power of off-the-shelf knowledge reasoners. Second, a new planning approach that incorporates this improved encoding. And, to show the effectiveness of our planner and present our initial findings and proof of concept, we conduct a thorough use case analysis.
Detecting Bias in University News Articles: A Comparative Study Using BERT, GPT-3.5 and Google Bard Annotations

This study focuses on the detection of bias in news articles from a British research-intensive university, given the substantial significance of higher education institutions as information sources and their considerable influence in shaping public opinion. While prior research has underscored the existence of bias in news content, there has been limited exploration of bias detection in the specific realm of higher education news articles. To address this gap, we adopt a similar approach to Raza et al. [9] by utilising DistilBert to classify news articles published on a university website. Our primary objective is to evaluate the performance of the model in detecting bias within this domain. Furthermore, we utilise the capabilities of GPT-3.5-turbo for annotation tasks, supported by recent studies showcasing its effectiveness. In addition, we conduct performance comparisons by testing Google’s Bard for annotation tasks alongside GPT-3.5-turbo. To assess the quality of DistilBert, GPT-3.5-turbo, and Google’s Bard annotations, we acquired ground truth labels through Amazon Mturk to annotate a subset of our data. The experimental findings demonstrate that the DistilBert model, trained on the MBIC dataset, shows moderate performance in detecting biased language in university news articles. Moreover, the assessment of GPT-3.5-turbo and Google Bard annotations against human-annotated data reveals a low level of accuracy, highlighting their unreliable annotations in this domain.
Estimations of Professional Experience with Panel Data to Improve Salary Predictions

Predicting salaries is crucial in business. While prediction models can be trained on large and real salary datasets, they typically lack information regarding professional experience, an essential factor for salary. We investigate various regression techniques for the estimation of professional experience based on data from the Socio-Economic Panel (SOEP) to augment data sets. We further show how to integrate such models into applications and evaluate the usefulness for salary prediction on a large real payroll dataset.
Automating Question Generation From Educational Text

The use of question-based activities (QBAs) is wide-spread in education, traditionally forming an integral part of the learning and assessment process. In this paper, we design and evaluate an automated question generation tool for formative and summative assessment in schools. We present an expert survey of one hundred and four teachers, demonstrating the need for automated generation of QBAs, as a tool that can significantly reduce the workload of teachers and facilitate personalized learning experiences. Leveraging the recent advancements in generative AI, we then present a modular framework employing transformer based language models for automatic generation of multiple-choice questions (MCQs) from textual content. The presented solution, with distinct modules for question generation, correct answer prediction, and distractor formulation, enables us to evaluate different language models and generation techniques. Finally, we perform an extensive quantitative and qualitative evaluation, demonstrating trade-offs in the use of different techniques and models.
Towards Goal-Oriented Agents for Evolving Problems Observed via Conversation

The objective of this work is to train a chatbot capable of solving evolving problems through conversing with a user about a problem the chatbot cannot directly observe. The system consists of a virtual problem (in this case a simple game), a simulated user capable of answering natural language questions that can observe and perform actions on the problem, and a Deep Q-Network (DQN)-based chatbot architecture. The chatbot is trained with the goal of solving the problem through dialogue with the simulated user using reinforcement learning. The contributions of this paper are as follows: a proposed architecture to apply a conversational DQN-based agent to evolving problems, an exploration of training methods such as curriculum learning on model performance and the effect of modified reward functions in the case of increasing environment complexity.
On Reproducible Implementations in Unsupervised Concept Drift Detection Algorithms Research

In order to create reproducible experimentation and algorithms in machine learning and data mining research, reproducible descriptions of the algorithms are needed. These can be in the form of source code, pseudo code and prose. Efforts in academia commonly focus on accessibility of source code. Based on an internal study reproducing unsupervised concept drift detectors, this work argues that a publication’s content is equally important and highlights common issues affecting attempts at implementing unsupervised concept drift detectors. These include major issues prohibiting implementation entirely, as well as minor issues, which demand increased effort from the developer. The paper proposes the use of a checklist as a consistent tool to ensure better quality and reproducible publications of algorithms. The issues highlighted in this work could mark a starting point, although future work is required to ensure representation of more diverse areas of research in artificial intelligence.
Exploring Optimal Configurations in Active Learning for Medical Imaging

Medical imaging is a critical component of clinical decision-making, patient diagnosis, treatment planning, intervention, and therapy. However, due to the shortage of qualified radiologists, there is an increasing burden on healthcare practitioners, which underscores the need to develop reliable automated methods. Despite the development of novel computational techniques, interpreting medical images remains challenging due to noise and varying acquisition conditions. One promising solution to improve the reliability and accuracy of automated medical image analysis is Interactive Machine Learning (IML), which integrates human expertise into the model training process. Active learning (AL) is an important IML technique that can iteratively query for informative samples to be labeled by humans, leading to more data-efficient learning. To fully leverage the potential of active learning, however, it is crucial to understand the optimal setup for different components of an AL system. This paper presents an evaluation of the effectiveness of different combinations of data representation, model capacity, and query strategy for active learning systems designed for medical image classification tasks. The results of this evaluation show that employing raw image representations as input, in conjunction with a ResNet50 model and margin-based queries, yields more reliable and accurate automated methods for medical image analysis.
Intraoperative Diagnosis of Neoplastic Lesions by Imprint Cytology and Frozen Section with Histopathological Correlation

Introduction: Intraoperative consultation examinations are required by surgeons from pathologists for immediate important decisions regarding the optimal extent of surgery. Surgeons particularly want to know whether a lesion is malignant or not. Both frozen section (FS) and touch impression cytology serve this purpose well. Both provide accurate results in minutes while the patient is under anesthesia. The surgeon then modifies his surgical plan based on the intraoperative consultation from pathologist. Objectives: The objective of this study is to access the role of imprint cytology (IC) and FS in diagnosis of tumors of different organs, to correlate IC and FS findings with histopathological diagnosis and to determine their diagnostic accuracy. Materials and Method: This present prospective cross-sectional study was done in the pathology department of BKL Walawalkar Rural Medical College, Dervan, Chiplun, for a period of one and a half year. A total of 161 surgical specimens from various organs were studied using IC and FS. The results of both the techniques were compared with the gold standard histopathological diagnosis. Results: Statistical correlation of IC and FS with histopathology shows that true positive cases on IC are 88.7% and on FS are 85.1%. Sensitivity, specificity, and diagnostic accuracy of IC and FS are 95.7%, 81.8%, 94.7% and 93.2%, 78.9%, 92%, respectively. Combined diagnostic accuracy is 93.5%. Conclusion: IC and FSs are interdependable intraoperative diagnostic modalities. IC is a simple, accurate, rapid, and cost-effective diagnostic tool, intraoperatively. FS preserves crisp cellular and nuclear detail and also closely approximates permanent histopathology sections. Thus, a combination of IC and FS helps in achieving high diagnostic accuracy rates and in distinguishing benign and malignant lesions.
Essential Aspects to Bayesian Data Imputation

Data imputation holds significant importance in a variety of fields including risk management. Incomplete or missing data can hinder a thorough analysis of risks, making accurate decision making challenging. By employing imputation techniques to fill in the gaps, risk managers can obtain a more comprehensive and reliable understanding of the underlying risk factors. This, in turn, enables them to make informed decisions and develop effective strategies for risk mitigation. This note introduces the concept Bayesian data imputation.We collect and provide backgrounds needed for Bayesian data imputation when missing data are missing at random. Numerical examples are provided for demonstration.