Miracle-dz/UniFAR-FTUs huggingface dataset:
Image based Voice Assistance for Visually Impaired

1Professor, Department of Electronics and Telecommunications, Xavier Institute of Engineering, Mumbai, India 2,3,4,5Student, Department of Electronics and Telecommunications, Xavier Institute of Engineering, Mumbai, India ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Nowadays Most of the blind rely on the conventional white cane. It's capability to deliver navigational freedom is limited. In our project the system will tell the user about the scene in front of them by capturing an image. The captured image will be processed and the objects and text in the image will be given as the output to the user in real time through voice. In this project we use the Raspberry Pi to process the data and Raspberry Pi Camera to detect the environment using Machine Learning and Deep Learning. We also add one switch in our project for the end user to decide when they want to explore the environment by clicking images. Our project Is divided in to two parts one is object detection and other is text detection. For object detection we used YOLO algorithm. Using yolo detection is a simple regression problem which takes an input image and learns the class probabilities and bounding box coordinates and predicts the classification score for each box for every class in training. For text detection we used SHTR technique using TensorFlow to detect the text within images and using pyttsx3 we can convert the text messages in to speech format for the users using earphone.
Strengthening the Global Framework for Leveraging Sport for Development and Peace

This background paper provides a critical review of major issues associated with the design, delivery, monitoring, and evaluation of sport for development and peace (SDP) projects. By referring to the concept of event leverage, it highlights in particular how various issues have hindered the development of sustainable impacts by SDP programmes. It is therefore argued that the use of process tracing frameworks (together with theory of change and theory of action) to assist the design, delivery, monitoring, and evaluation processes of SDP may offer a practical and theoretical solution for addressing the identified issues and for contributing to the development of methodological rigorousness.
An Approach Towards 3D Scene Reconstruction from an Uncalibrated Monocular Video Preceeded by an Exhaustive Study of Preprocessing of Images and Feature Detection Algorithms

National Informatics Centre, MietY, Govt of India -------------------------------------------------------------------------***---------------------------------------------------------------------Abstract 3D reconstruction of a scene is not only an emerging but also a challenging area of research work. In this work the novel approach is to develop a 3D reconstruction of the scene in a video sequence taken from a single moving uncalibrated camera. The first step of the process is to divide the video into number of frames and to detect and extract feature points. Then the requirement is to find matching correspondences within the adjacent frames, after which the frames can be stitched. In this connection, feature detection, extraction and matching have proved to be a very useful area in the study of image processing in recent times. It occupies a major section of research in the computer vision domain. But feature detection faces the problem of occlusion, view point dependence and scale. Several algorithms have been proposed till date for the feature detection purpose. Not only feature detecting proficiency, but a matching procedure has also been incorporated in which percentage of the match is calculated. Along with the matching proficiency, the speed of each algorithm is also compared by computing the processing time of the algorithm. Finally, a suitable and efficient algorithm is thus chosen for the feature detection purpose in 3D reconstruction process.
West Point Autonomous Vehicle Research and Design: Convolutional Neural Networks and Image Classification

Technological development within the domain of autonomous driving systems remains an important area of focus for government, industry, and academia. Large corporations are continuing research and development in unmanned delivery platforms; this technology holds the potential to substantially decrease costs and increase efficiency in supply chain management. For the military, autonomous systems show the potential to increase capability and improve Soldier safety. The West Point Autonomous Vehicle Research and Design (AVRAD) team developed an autonomous system, capable of competing in the 2020 Intelligent Ground Vehicle Competition (IGVC)--Self Drive Competition. IGVC leadership decided to cancel the 202 IGVC due to the COVID-19 pandemic. However, for the past 27 years, this international competition challenged teams to think creatively about evolving technologies of vehicle sensors, robotics, and system integration. During the event, teams race their autonomous systems through a course which tests the entry’s lane following ability and ability to avoid obstacles. While driving, the system builds a map of the immediate area by using data gathered from three front facing and two rear facing Mako G-319C cameras. Additionally, a Velodyne HDL-64E LiDAR senses and identifies each type of obstacle or critical image such as a stop sign. The images gathered are relayed to a Convolutional Neural Networks (CNN). The CNN algorithm classifies each image as a specific item, whether it is a traffic sign, person, or an entirely different obstacle. Finally, the vehicle management system uses the CNN’s classification output to map the immediate surroundings. With an established map, the system then determines the most appropriate action or pathway. This paper will explain, in detail, how the AVRAD team selected, tested, and improved a CNN capable of quickly and accurately detecting and recognizing common street side objects along its path.
Semantics Preserving Adversarial Examples

While progress has been made in crafting adversarial examples with visually imperceivable changes, constructing semantically meaningful ones remains a challenge. In this paper, we propose a framework to create semantics preserving adversarial examples. The motivating principle behind our proposal is that semantics are better captured in the low-level embedding space of the inputs than in the noisy high-level feature space. Our proposal uses a manifold learning method to first learn the low-dimensional geometric summaries of the inputs via statistical inference. Then, we perturb the elements of the learned manifold to create adversarial examples. To ensure that the semantics of the adversarial examples are preserved, we develop a manifoldinvariant adversarial perturbation technique that induces the perturbed elements to remain in the manifold, while satisfying adversarial constraints. Our evaluations on toy data, images and text show that our proposed attack can produce adversarial examples that retain a greater degree of semantic similarity compared to existing attacks. Furthermore, our attack also has a high attack success rate against various certified and non-certified defenses.
A Study on Automatic Pedestrian Detection Using Computer Vision

Pedestrian Detection in images or videos plays a vital role in ADAS (Advanced Driver Assistance System) and AV (Autonomous Vehicle). The problem of pedestrian detection has been considered as a vital one in the domain of Computer Vision and Pattern Recognition. It is formulated as the problem of detecting and tracking the pedestrians in images or videos. Many techniques have been proposed in the past to solve this problem accurately and efficiently. However, the challenges occur at three different levels of pedestrian detection, such as video acquisition, human detection and its tracking. The challenges in video are illumination variation, abrupt motion, clutter present in the background, etc. Pedestrian detection and tracking challenges such as body attire and pose, occlusion, different illumination parameters in different scenarios, etc. These results in reducing the rate of attaining high accuracy. This paper provides a study of some well-known existing techniques for pedestrian detection and tracking in images. Some benchmark datasets are discussed, performance evaluation metrics are also presented.
Can the novel inflammatory markers be used in the prediction of Gestational Diabetes Mellitus or to strengthen the screening test?

DOI: 10.4328/ACAM.20362 Received: 2020-10-04 Accepted: 2020-11-03 Published Online: 2020-11-15 Printed: 2021-06-01 Ann Clin Anal Med 2021;12(6):621-625 Corresponding Author: Deniz Simsek, Mimar Sinan Mah. Emniyet Cad. Polis Okulu Karşısı Yıldırım, Bursa, Turkey. E-mail: drdenizsimsek@gmail.com P: +90 224 294 40 00 – 4037 F: +90 224 366 69 25 Corresponding Author ORCID ID: https://orcid.org/0000-0002-0975-9457 Abstract Aim: The pathophysiology of Gestational diabetes mellitus (GDM) has not been exposed properly, however, some clues designate the systematic inflammation. New inflammatory markers such as mean platelet volume (MPV), neutrophiltolymphocyte ratio (NLR), plateletto-lymphocyte ratio (PLR), and platelet distribution width (PDW) might play a crucial role in predicting or diagnosing GDM. In this study, we aimed to evaluate the possible association between the CBC parameters (MPV, PDW, NLR, PLR values) which were sampled on the test day of the glucose challenge test and GDM. Material and Methods: Pregnant women who underwent 50 gr GCT were evaluated. Among them, patients with an abnormal 100 g oral glucose tolerance test (OGTT), and normal OGTT results were recruited in groups 1 and 2, respectively.Women with normal GCT were included in Group 3 as controls. Patients who had complete hemogram count on the day of GCT or OGTT were reviewed.The MPV, NLR, PLR and PDW parameters were analyzed statistically between groups. Results: A total of 9819 patients were screened with 50gr GCT over a 3-year period. The distribution of patient numbers within the groups in groups 1, 2, and 3 was 167, 400, and 610 patients, respectively. There was no statistically significant difference between Group 1 and Group 2 for NLR, PLR, Platelet, MPV, PDW, neutrophil, lymphocyte, and hemoglobin (p>0.05). Healthy patients had higher MPV and lower PDW values than patients with GDM, which differed significant. Discussion: Neither the novel inflammatory markers PLR, and NLR, nor the platelet parameters PDW, and MPV seemed to be beneficial to predict GDM or strengthen the 50g glucose challenge test during the routine screening period. These markers might be related with chronic circumstances, and the usefulness of novel inflammatory markers might be revealed in the near future.
De-Anonymization of Electronic Mail

1Student, Dept. of ECE, Jaypee Institute of Information Technology, Noida, India 2Assistant Professor, Dept. of ECE, Jaypee Institute of Information Technology, Noida, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract Everyone has their own style of writing, the way of focusing on a particular part of the sentence, vocabulary level and most of the time this can be used to infer the author of a particular text. This is affected by one’s learning source, region, country and from style one often read etc. Often one’s writing style, it really is a term that is used to describe the author of a specific document. Electronic mail assumes a significant function in day by day close to home correspondence. Likewise, email is regularly used to send official warning. Email makes it simpler for individuals to impart, yet tragically, it is more defenseless to assault[11] simultaneously. It thus becomes necessary to develop a mechanism that can de-anonymize such emails and identify any sort of spam, if present. One of the ongoing and rising patterns in initiation examination is stylometric[8] data is extracted using a computer programmed, highlights from the content of any text and look for a robust classification technique that can identify author of a text based on its content.
The impact of leakage on dynamic RON in GaN-on-Si HEMTs

Substrate ramps and stepped stress transient measurements are applied to study vertical charge transport mechanisms in GaN-on-Si power HEMTs. By choosing appropriate bias points for substrate stress it is possible to single out the dominant charge transport mechanism: at low negative biases transport through carbon-doped GaN manifests itself in negative transients with apparent activation energy (EA) = 0.29 eV, while at larger negative voltages transport through unintentionally doped GaN is characterized by positive transients (EA = 0.38 eV). We present experimental evidence for 3D variable range hopping taking place in C-doped GaN and 1D hopping along the dislocations in unintentionally doped GaN. By investigating transients obtained from bidirectional voltage steps of 10 V potential difference in the range 0 to -140 V, we observe that hopping transport through dislocations shows non-Ohmic behavior at low substrate biases, which manifests itself in a time constant τ strongly dependent on bias. We propose that this can be explained by the existence of a diode junction between the dislocation core and the 2D electron gas (2DEG).
Black-Box Reductions for Zeroth-Order Gradient Algorithms to Achieve Lower Query Complexity

Zeroth-order (ZO) optimization has been the key technique for various machine learning applications especially for black-box adversarial attack, where models need to be learned in a gradient-free manner. Although many ZO algorithms have been proposed, the high function query complexities hinder their applications seriously. To address this challenging problem, we propose two stagewise black-box reduction frameworks for ZO algorithms under convex and non-convex settings respectively, which lower down the function query complexities of ZO algorithms. Moreover, our frameworks can directly derive the convergence results of ZO algorithms under convex and non-convex settings without extra analyses, as long as convergence results under strongly convex setting are given. To illustrate the advantages, we further study ZO-SVRG, ZO-SAGA and ZO-Varag under strongly-convex setting and use our frameworks to directly derive the convergence results under convex and non-convex settings. The function query complexities of these algorithms derived by our frameworks are lower than that of their vanilla counterparts without frameworks, or even lower than that of state-of-the-art algorithms. Finally we conduct numerical experiments to illustrate the superiority of our frameworks.
Newer Dental Teaching Methods : Paradigm Shift from Teacher-Centered to Student-Centered Learning . Review

Information and communication technologies are being rapidly incorporated into our culture and their effect on the lives of people is becoming more and more evident. Their presence has entered the education sector, where a transition to a modern way of understanding teaching and in the ways in which it is taught has been made by their inclusion. The use of assessment or adjunct instruments as part of teaching has become more common in medical and dental schools in recent years. In the dental curriculum, educators have tried distinct teaching approaches. The new curriculum has moved from teacher-centered approaches to more methods for student learners. Various teaching techniques used in educational institutions are workshops, discussions, computer-assisted learning, audiovisual sources, video-based learning, presentation and role-playing. But for all of these new teaching methods, there is still no straightforward summary and no reference to the evidence for the most effective teaching form. Modern learning methods that are increasingly spreading around the world are not well-known in dentistry. Therefore, the purpose of this review was to focus on these modern teaching methodologies in order to attract the attention of dental staff to be more informed of these methods, particularly in the field of education. This helps dental institutions shift their model from one that focuses on teacher-led training to one that focuses on student-centered learning.
Graph Signaling Denoising via Unrolling Networks /Author=Chen, Siheng; Eldar, Yonina /CreationDate=June 8, 2021 /Subject=Artificial Intelligence, Machine Learning, Signal Processing

We propose an interpretable graph neural network framework to denoise single or multiple noisy graph signals. The proposed graph unrolling networks expand algorithm unrolling to the graph domain and provide an interpretation of the architecture design from a signal processing perspective. We unroll an iterative denoising algorithm by mapping each iteration into a single network layer where the feed-forward process is equivalent to iteratively denoising graph signals. We train the graph unrolling networks through unsupervised learning, where the input noisy graph signals are used to supervise the networks. By leveraging the learning ability of neural networks, we adaptively capture appropriate priors from input noisy graph signals, instead of manually choosing signal priors. To validate the proposed methods, we conduct extensive experiments on both real-world datasets and simulated datasets, and demonstrate that our methods have smaller denoising errors than conventional denoising algorithms and state-of-the-art graph neural networks. For denoising a single smooth graph signal, the normalized mean square error of the proposed networks is around 40% and 60% lower than that of graph Laplacian denoising and graph wavelets, respectively IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) c © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Mitsubishi Electric Research Laboratories, Inc. 201 Broadway, Cambridge, Massachusetts 02139 GRAPH SIGNAL DENOISING VIA UNROLLING NETWORKS Siheng Chen1 , Yonina C. Eldar2 1 Shanghai Jiao Tong University; 2 Weizmann Institute of Science
Decoding the Brain’s Algorit

Acts of cognition can be described at different levels of analysis: what behavior should characterize the act, what algorithms and representations underlie the behavior, and how the algorithms are physically realized in neural activity [1]. Theories that bridge levels of analysis offer more complete explanations by leveraging the constraints present at each level [2–4]. Despite the great potential for theoretical advances, few studies of cognition bridge levels of analysis. For example, formal cognitive models of category decisions accurately predict human decision making [5, 6], but whether model algorithms and representations supporting category decisions are consistent with underlying neural implementation remains unknown. This uncertainty is largely due to the hurdle of forging links between theory and brain [7–9]. Here, we tackle this critical problem by using brain response to characterize the nature of mental computations that support category decisions to evaluate two dominant, and opposing, models of categorization.We found that brain states during category decisions were significantly more consistent with latent model representations from exemplar [5] rather than prototype theory [10, 11]. Representations of individual experiences, not the abstraction of experiences, are critical for category decision making. Holding models accountable for behavior and neural implementation provides a means for advancing more complete descriptions of the algorithms of cognition.
Recommendation System for Education Service Using Multi Agent

Nowadays, the world is in knowledge age. Variety of new technologies and supports are being evolved for education. One of the most well known systems is recommendation system. Recommender system uses the opinions of a community of users to help individuals in that community more effectively. In this paper, the system propose a multi agent recommendation system for education service mainly on information retrieving and recommending course (subject) lists. This recommendation system is based on item-based collaborative filtering method for recommending related subjects on user’s interested subjects and multidimensional association rule method for providing knowledge about the relationship between the users and degree courses. These techniques made as a form of agent and perform separately in its corresponding sections. The system provides agent based information extraction by user's preferences. This system provides information and recommendation to users to help them to decide which are interested or to be chosen.
Semantic bug seeding: a learning-based approach for creating realistic bugs

When working on techniques to address the wide-spread problem of software bugs, one often faces the need for a large number of realistic bugs in real-world programs. Such bugs can either help evaluate an approach, e.g., in form of a bug benchmark or a suite of program mutations, or even help build the technique, e.g., in learning-based bug detection. Because gathering a large number of real bugs is difficult, a common approach is to rely on automatically seeded bugs. Prior work seeds bugs based on syntactic transformation patterns, which often results in unrealistic bugs and typically cannot introduce new, application-specific code tokens. This paper presents SemSeed, a technique for automatically seeding bugs in a semantics-aware way. The key idea is to imitate how a given real-world bug would look like in other programs by semantically adapting the bug pattern to the local context. To reason about the semantics of pieces of code, our approach builds on learned token embeddings that encode the semantic similarities of identifiers and literals. Our evaluation with real-world JavaScript software shows that the approach effectively reproduces real bugs and clearly outperforms a semantics-unaware approach. The seeded bugs are useful as training data for learning-based bug detection, where they significantly improve the bug detection ability. Moreover, we show that SemSeed-created bugs complement existing mutation testing operators, and that our approach is efficient enough to seed hundreds of thousands of bugs within an hour.