Discovering User-Interpretable Capabilities of Black-Box Planning Agents

Several approaches have been developed for answering users' specific questions about AI behavior and for assessing their core functionality in terms of primitive executable actions. However, the problem of summarizing an AI agent's broad capabilities for a user is comparatively new. This paper presents an algorithm for discovering from scratch the suite of high-level "capabilities" that an AI system with arbitrary internal planning algorithms/policies can perform. It computes conditions describing the applicability and effects of these capabilities in user-interpretable terms. Starting from a set of user-interpretable state properties, an AI agent, and a simulator that the agent can interact with, our algorithm returns a set of high-level capabilities with their parameterized descriptions. Empirical evaluation on several game-based scenarios shows that this approach efficiently learns descriptions of various types of AI agents in deterministic, fully observable settings. User studies show that such descriptions are easier to understand and reason with than the agent's primitive actions.Comment: KR 202

Environmental Factors Impacting Spam: An Initial Study

Spam is a source of serious concern for both e-mail users and Internet Service Providers (ISP). While prior research has focused on spam content and spam filtering techniques, this study focuses on country-level, macro-environmental conditions that facilitate spamming activity. Adopting a criminological perspective, this study draws upon the deviance-based theories of rational choice and routine activities to analyze the effects of scale, economic, and judicial factors on spamming activity. Analysis of archival data obtained from international organizations suggests that spamming is influenced by scale and economic factors, but not by judicial ones. The study contributes not only to a better understanding of spamming activity but also provides a foundation for future work on other related issues such as privacy and network security

DeepSleep: A ballistocardiographic deep learning approach for classifying sleep stages

Current techniques for tracking sleep are either obtrusive (Polysomnography) or low in accuracy (wearables). In this early work, we model a sleep classification system using an unobtrusive Ballistocardiographic (BCG)-based heart sensor signal collected from a commercially available pressure-sensitive sensor sheet. We present DeepSleep, a hybrid deep neural network architecture comprising of CNN and LSTM layers. We further employed a 2-phase training strategy to build a pre-trained model and to tackle the limited dataset size. Our model results in a classification accuracy of 74%, 82%, 77% and 63% using Dozee BCG, MIT-BIH’s ECG, Dozee’s ECG and Fitbit’s PPG datasets, respectively. Furthermore, our model shows a positive correlation (r = 0.43) with the SATED perceived sleep quality scores. We show that BCG signals are effective for long-term sleep monitoring, but currently not suitable for medical diagnostic purposes

Classification of executive functioning performance post-longitudinal tDCS using functional connectivity and machine learning methods

Executive functioning is a cognitive process that enables humans to plan, organize, and regulate their behavior in a goal-directed manner. Understanding and classifying the changes in executive functioning after longitudinal interventions (like transcranial direct current stimulation (tDCS)) has not been explored in the literature. This study employs functional connectivity and machine learning algorithms to classify executive functioning performance post-tDCS. Fifty subjects were divided into experimental and placebo control groups. EEG data was collected while subjects performed an executive functioning task on Day 1. The experimental group received tDCS during task training from Day 2 to Day 8, while the control group received sham tDCS. On Day 10, subjects repeated the tasks specified on Day 1. Different functional connectivity metrics were extracted from EEG data and eventually used for classifying executive functioning performance using different machine learning algorithms. Results revealed that a novel combination of partial directed coherence and multi-layer perceptron (along with recursive feature elimination) resulted in a high classification accuracy of 95.44%. We discuss the implications of our results in developing real-time neurofeedback systems for assessing and enhancing executive functioning performance post-tDCS administration.Comment: 7 pages, presented at the IEEE 20th India Council International Conference (INDICON 2023), Hyderabad, India, December 202

Rapid Control Prototyping of Five-Level MMC based Induction Motor Drive with different Switching Frequencies

In this paper, Rapid Control Prototyping (RCP) of five-level Modular Multilevel Converter (MMC) based Induction Motor (IM) drive performance is observed with different switching frequencies. The Semikron based MMC Stacks with two half-bridge each are tested with the switching logic generated by phase and level shifted based Sinusoidal Pulse Width Modulation (SPWM) technique. The switching logic is generated by the Typhoon Hardware in Loop (HIL) 402. The disadvantages of Multilevel Converter like not so good output quality, less modularity, not scalable and high voltage and current rating demand for the power semiconductor switches can be overcome by using MMC. In this work, the IM drive is fed by MMC and the experimentally the performance is observed. The performance of the Induction Motor in terms of speed is observed with different switching frequencies of 2.5kHz, 5kHz, 7.5kHz, 10kHz, 12.5kHz and the results are tabulated in terms of Total Harmonic Distortion (THD) of input voltage and current to the Induction Motor Drive. The complete model is developed using Typhoon HIL 2021.2 Version Real-Time Simulation Software

A prospective study of cervical lesions diagnosed by liquid based cytology in Western Rajasthan, India population

Background: Carcinoma cervix is the second most common malignancy of women in India after breast cancer. The present study was conducted to determine the spectrum of cervical lesions by liquid-based cytology in Western Rajasthan population.Methods: It is a Prospective study on 1087 cervical samples carried over a period of 1 year. Cervical samples were taken and processed by SurePath™ LBC.Results: Of total 1087 cases 959 were negative for intraepithelial lesion or malignancy (88.22%). 88 cases (8.09%) were reported as unsatisfactory. Among the non- neoplastic cases- bacterial vaginosis was reported in 209 cases (21.8%), Candida in 77 cases (8.02%), both Candida and bacterial vaginosis in 12 cases (1.25%), reactive cellular changes in 193 cases (20.12%), and Trichomonas vaginalis in 01 case. Among pre-malignant and malignant lesions, 40 cases (4.17%) the distribution was as follows-atypical squamous cells of undetermined significance 16(1.67%), atypical squamous cell-cannot rule out high grade 08 cases (0.83%), Low grade squamous intraepithelial lesion 04 cases (0.42%), high grade squamous intraepithelial lesion 07 cases (0.73%), Atypical glandular cell favoring neoplastic 01 case (0.15%), and squamous cell carcinoma 04 cases (0.42%). Histopathological co-relation of premalignant and malignant lesions was further studied.Conclusions: Liquid based cytology is an effective screening and diagnostic procedure for cervical abnormalities. Among pre-malignant and malignant lesions, histo-pathological correlation increased with increased grade of severity of lesions. To the best of knowledge, this is the largest study of liquid based cytology in the Western Rajasthan