Abstract

The explosive growth of internet users and connected devices increased the threat vector surface. However, there is no single website or a search engine that provides information on vulnerabilities, threats, attacks, controls, etc. Ambiguity, bias and lack of credibility are some of the alarming issues while dealing with generic search engines on sensitive topics such as ‘Health’ and ‘Information Security’. A dedicated information security specific search engine benefits various stakeholders including security professionals, researchers, government, regulators and others. We implemented a fine grained approach that identifies sub-domains of information security, extracts related URLs and content and assesses search results credibility to enhance adoption of information security specific search engine. To identify sub-domains and extract seed and child URLs, a fine grained approach that extends an efficient Artificial Bee Colony algorithm was implemented. About 34,007 seed URLs and 400,726 child URLs of various sub-domains of the information security were extracted. The results of the proposed approach identified more URLs (seed and child) of sub-domains as compared to existing approaches while consuming less computing resources.

Abstract

Explainable AI aims to build systems that not only give high performance but also are able to provide insights that drive the decision making. However, deriving this explanation is often dependent on fully annotated (class label and local annotation) data, which are not readily available in the medical domain. Approach: This paper addresses the above-mentioned aspects and presents an innovative approach to classifying a lung nodule in a CT volume as malignant or benign, and generating a morphologically meaningful explanation for the decision in the form of attributes such as nodule margin, sphericity, and spiculation. A deep learning architecture that is trained using a multi-phase training regime is proposed. The nodule class label (benign/malignant) is learned with full supervision and is guided by semantic attributes that are learned in a weakly supervised manner. Results: Results of an extensive evaluation of the proposed system on the LIDC-IDRI dataset show good performance compared with state-of-the-art, fully supervised methods. The proposed model is able to label nodules (after full supervision) with an accuracy of 89.1% and an area under curve of 0.91 and to provide eight attributes scores as an explanation, which is learned from a much smaller training set. The proposed system's potential to be integrated with a sub-optimal nodule detection system was also tested, and our system handled 95% of false positive or random regions in the input well by labeling them as benign, which underscores its robustness. Conclusions: The proposed approach offers a way to address computer-aided diagnosis system design under the constraint of sparse availability of fully annotated images.

Abstract

Skin is one of the very important and largest organs of the human body that helps in regulating the body temperature and is responsible for sensations such as feel, touch, hot and cold. These days, skin problems are very common in day-to-day life. There may be many reasons for skin diseases: unbalanced and impure diet, several types of pollutions, or maybe family heredity. However, if skin disease after a long treatment does not show any sign of improvement or the skin cells grow abnormally, this may lead to skin cancer. There are many forms of skin cancer. For early and timely diagnosis of skin cancer, an efficient technique is required at utmost importance. Many people across the globe lost their lives due to the late diagnosis. Therefore, a technique that is cost-effective, quicker, and easily accessible needs a higher demand. These days for the classification of images, machine learning, and deep learning techniques proved to be the most efficient approach. In this paper, the dataset of several images of a benign and malignant tumor was taken and pre-processed. Once all the images were pre-processed, they are ready to fed in several CNN models. These models extract the features and pass the images to several machine learning classifiers for the classification of moles as benign or malignant. The results verify by using the classification approach it becomes very much easy for the dermatologist to easily detect the lesions and provide the appropriate treatment to the patient to save the life.

Abstract

The newly detected virus also called coronavirus spreads the disease Covid19. World Health Organization (WHO) confirmed this virus as a worldwide pandemic as it has infected millions of people and has taken away many lives across the globe. An infection caused by Covid19 disease majorly destroys the respiratory tract of human beings that ends with multiple organ failures or death in the worst case. In the present work, chest radiographs were provided as input to various deep learning CNN architectures for the purpose of feature extraction. After extracting the features, the images were provided as the input to various machine learning classifiers that classify the chest radiographs as Covid-19 positive, pneumonia infection, or healthy scans.

Abstract

Object tracking in a real-life scenario is very challenging due to occlusion. State-space models like Kalman and particle filters are well known to handle such a particular problem. The particle filter’s performance for solving such a problem depends on two issues - motion model and observation (i.e., likelihood) model. The question remains to exist due to the lack of useful observation and efficient motion models. This article presents an impressive observation model based on confidence (classification) score provided by introducing object-background prototypes based discriminative model. The proposed discriminative model is constructed with the prior knowledge of two classes (i.e., object and background) and tries to discriminate between three categories: an object, background, and occluded part of that object. The existing composite motion model handles the object motion and its scale. We also propose a model update technique that adapts the appearance changes of the object during tracking. We evaluate the proposed method on several challenging benchmark sequences. Analysis of the results concludes that the proposed technique can track fully (or partially) occluded object and the object in various complex environments.

Abstract

We develop a theory of linear witnesses for detecting non-Markovianity, based on the geometric structure of the set of Choi states for all Markovian evolutions having Lindblad-type generators. We show that the set of all such Markovian Choi states form a convex and compact set under the small time interval approximation. Invoking geometric Hahn–Banach theorem, we construct linear witnesses to separate a given non-Markovian Choi state from the set of Markovian Choi states. We present examples of such witnesses for dephasing channel and Pauli channel in case of qubits. Furthermore, we have devised another method of detection NM of various qubit channels, by using projective measurements in the Bell basis. This can be done without the knowledge of what specific kind of channel we are dealing with. This gives us a huge operational advantage for NM detection. We further investigate the geometric structure of the Markovian Choi states to find that they do not form a polytope. This presents a platform to consider nonlinear improvement of non-Markovianity witnesses.

Abstract

For understanding an environmental variable in a given geographical space, finding the optimal number of nodes is a tedious task. For this purpose, a framework is proposed in this paper based on hierarchical agglomerative clustering along with geographical distance based cluster representation. The proposed framework helps remove the redundant nodes in a practical IoT network by choosing the optimal nodes based on the target reconstruction error in the spatially interpolated map. The approach is employed on the data collected by an IoT network of ten particulate matter (PM) nodes on the campus of IIIT Hyderabad, India. The performance of the proposed approach is also compared with that of the brute force approach, which provides the lower bound on the reconstruction error. The results show that the proposed approach performs very closely to the brute force approach in terms of the reconstruction error with much fewer computations.

Abstract

Radars with mmWave frequency modulated continuous wave (FMCW) technology accu- rately estimate the range and velocity of targets in their field of view (FoV). The targeted angle of arrival (AoA) estimation can be improved by increasing receiving antennas or by using multiple- input multiple-output (MIMO). However, obtaining target features such as target type remains challenging. In this paper, we present a novel target classification method based on machine learning and features extracted from a range fast Fourier transform (FFT) profile by using mmWave FMCW Radars operating in the frequency range of 77–81 GHz. The measurements are carried out in a variety of realistic situations, including pedestrian, automotive, and unmanned aerial vehicle (UAV) (also known as drone). Peak, width, area, variance, and range are collected from range FFT profile peaks and fed into a machine learning model. In order to evaluate the performance, various light weight classification machine learning models such as logistic regression, Naive Bayes, support vector machine (SVM), and lightweight gradient boosting machine (GBM) are used. We demonstrate our findings by using outdoor measurements and achieve a classification accuracy of 95.6% by using LightGBM. The proposed method will be extremely useful in a wide range of applications, including cost-effective and dependable ground station traffic management and control systems for autonomous operations, and advanced driver-assistance systems (ADAS). The presented classifi- cation technique extends the potential of mmWave FMCW Radar beyond the detection of range, velocity, and AoA to classification. mmWave FMCW Radars will be more robust in computer vision, visual perception, and fully autonomous ground control and traffic management cyber-physical systems as a result of the added new feature.

Abstract

The ability to predict the movements of the target person allows a person following robot (PFR) to coexist with the person while still complying with the social norms. In human-robot collaboration, this is an essential requisite for long-term time-dependent navigation and not losing sight of the person during momentary occlusions that may arise from a crowd due to static or dynamic obstacles, other human beings, or intersections in the local surrounding. The PFR must not only traverse to the previously unknown goal position but also relocate the target person after the miss, and resume following. In this paper, we try to solve this as a coupled motion-planning and control problem by formulating a model predictive control (MPC) controller with non-linear constraints for a wheeled differential-drive robot. And, using a human motion prediction strategy based on the recorded pose and trajectory information of both the moving target person and the PFR, add additional constraints to the same MPC, to recompute the optimal controls to the wheels. We make comparisons with RNNs like LSTM and Early Relocation for learning the best-predicted reference path.MPC is best suited for complex constrained problems because it allows the PFR to periodically update the tracking information, as well as to adapt to the moving person’s stride. We show the results using a simulated indoor environment and lay the foundation for its implementation on a real robot. Our proposed method offers a robust person following behaviour without the explicit need for policy learning or offline computation, allowing us to design a generalized framework.

Abstract

The Information Technology (Intermediary Guidelines and Digital Media Ethics Code) Rules, 2021 are designed to further empower the state and allow the executive considerable powers to shape public discourses. On the one hand, the state now demands access to all information about the content and origins of every digital communication, a measure that will weaken the right to privacy. On the other hand, digital content is now subject to both self-regulation as well as extensive surveillance and regulation designed to allow substantial control by the executive over content.