Reviewerly/exHarmony huggingface dataset:
Exploring Adversarial Attacks and Defenses in Deep Learning

The paper aims to take a deep dive into one of the emerging fields in Deep Learning namely, Adversarial attacks and defenses. We will first see what we mean when we talk of Adversarial examples and learn why they are important? After this, we will explore different types of Adversarial attacks and defenses. Here, we specifically tackle the cases associated with Image Classification. This is done by delving into their respective concepts along with understanding the tools and frameworks required to execute them. The implementation of the FGSM (Fast Gradient Signed Method) attack and the effectiveness of the Adversarial training defense to combat it are discussed. This is done by first analyzing the drop in accuracy from performing the FGSM attack on a MNIST CNN (Convolutional Neural Network) classifier followed by an improvement in the same accuracy metric by defending against the attack using the Adversarial training defense.
A Novel Training Algorithm for Neuro-control of Discrete-time Nonlinear Systems

Guntur District, AP, India Guntur District, AP, India Physical systems are inherently nonlinear, and traditional methods of controlling such systems are based on a first-order linear approximation. Even though several nonlinear control design methods have been proposed in the past decades, most of these require analytical models. In contrast, artificial neural networks have been used as models for control since the paper published by Narendra and Parthasarathy in 1990. These recurrent networks are trained using the back-propagation algorithm, popular in other contexts. The principal issue was the notoriously slow convergence. More recently, an online sequential learning algorithm was proposed, which had better convergence properties. However, this algorithm applies to feedforward neural networks with a single hidden layer. We propose extending this algorithm’s applicability to networks with two hidden layers. The extension is achieved by incorporating error back-propagation. Further, we demonstrate that this novel algorithm has better convergence properties.
Robotic Assistant to Surgeons for Inventory Handling

In today's world, there have been many robotic applications in the field of medicine, due to the rise of Industry 4.0. The majority of these applications focus on playing a core role in surgery assisting the surgeons. This paper, however, proposes an application that focuses on an equally important, yet less explored, role of assisting the surgery i.e., on the role of inventory handling. The paper focuses on implementing a robotic arm with speech and image detection capabilities. This arm would provide economic relief for any under-staffed hospitals, especially when there is a shortage of trained personnel. The robotic arm also helps in reducing any hospital-acquired infections.
Kannada Text Summarization using Extractive Technique

Surplus information on any topic is available in various resources including the World Wide Web, news articles, books, e-books, and blogs. A knowledge seeker might have to spend days together on assimilating the required content from the web. Moreover, most of the content available in multiple resources is repetitive. However, there is also the time constraint which plays a major part during assimilation of the content. Kannada is a regional language spoken in the southern part of India. It has various dialects based on geographic location. The amount of time involved in reading and understanding the Kannada text is user based and involves the language experience of the users. For most of them it is highly challenging and also time consuming. Instead a tool to automatically read the Kannada text content from various sources and summarize it is the need of the day. The proposed model aims to assist the readers by summarizing a given Kannada document. Automatic Kannada text summarization enables users to assimilate to required information from e resources in the shortest possible time. The project aims to build a natural language processing tool to automatically read Kannada text from any e-resource and summarize the same.
Deep Learning based Multi Sensor Data Fusion for Radar and IFF

Multi sensor data fusion is the process of deriving more accurate, consistent and useful information by integrating data from multiple sensors than that can be achieved using individual sensor. Conventional methods for data fusion require complex models with computationally expensive methods. This paper proposes a deep neural network based method for multi sensor data fusion using detections from both Radar and IFF sensors. A deep learning based Long Short Term Memory (LSTM) model is used for data fusion. LSTM models overcome the problem of exploding and vanishing gradients present in Recurrent Neural Networks. Bi-directional LSTM is used for forming tracks from sensor observations using proposed association. The proposed fusion system architecture can be extended to sensors other than Radar and IFF also. The tracks generated by using detections from both sensors are closer to ground truth than tracks generated from individual sensors. Data fusion from multiple sensors improves the accuracy of a wide range of applications such as target tracking and battlefield surveillance.
Anomaly Detection in Time Series Data using Data-Centric AI

Detecting the anomalous data points in the time-series data is a crucial task in most of the industrial applications where time is a key component. As time-series data is used for forecasting/predicting the values, building a most accurate model is essential. If the input data consists of anomalies, then the model fails to perform well and so does the future prediction. The conventional method of building a good predictive model suggests to improve the model performance by applying regularization techniques, performing feature engineering or by experimenting with different combinations of activation functions and/or loss functions along with number of neurons and hidden layers in a neural network. But, such a model-centric approach fails miserably in real-time applications. Data-centric approach where the input data itself must be updated and corrected is a novel technique in solving the issues faced by model-centric approach. This paper proposes a technique of using data-centric approach to detect anomalies in time series data. Several models using model-centric approach are demonstrated and proved to be underperforming with high False Negatives. Whereas, the data-centric approach proved to achieve 100% performance in correctly identifying the anomalous data points.
Automated Analogue Dial Reading in Cockpits

A number of functional aircrafts still feature traditional cockpits with analogue gauges and dials in the modern world. In order to simplify aircraft navigation and allow the pilot to focus only on the most important information, technological intervention is imperative. This would also help minimize human error, log data and keep up with advancements in the modernization of aircraft displays. This paper has been focused on automating the task of analogue dial reading in cockpits, when there is a fixed-position camera available for capturing cockpit panel images. The Single Shot Detector Mobile Net v2(SSD) is used for gauge detection and cropping as it brings a good balance between speed and reliability. The Automated Analogue Dial Reading (AADR) system then preprocesses the detected gauges and finds candidate pointer lines that constitute the needle entity, using the Hough transform method. An optimal line depicting the pointer of the needle is then found from the lot, and the reading of the dial is obtained by associating the inclination of the needle with the dial configurations. Experimental results reveal that the cockpit gauges are detected from cockpit images with an accuracy of 98.64%, and the readings obtained are 98% accurate.
Design and Development of a Smart Campus using Augmented Reality(SCAR)

Real-Time human-machine interaction in multidisciplinary fields has gained more attention in recent years and it has become a basic requirement for the day. Augmented Reality (AR) is one such technology that allows users to perceive the real world superimposed with some virtual contents. AR has gone mainstream in various industries like automobiles, healthcare, manufacturing, robotics, education and so on. We have proposed an AR-based mobile application (SCAR) with some key features to develop a smart campus. The key features are Interactive Resume Viewer, 3D Visualization of Electronic Components and AR Based Indoor Navigation. SCAR app was designed and developed on the Unity Real-Time Development Platform integrated with Vuforia SDK. Validation of the proposed model was tested with case studies under real-time scenarios.
Communication Efficient Federated Learning Framework with Local Momentum

With the recent progress of AI, large amount of data generated in distributed Internet of Things (IoT) devices can be used to build different kinds of models that are helpful to improve people’s daily life. For example, language models can improve the speech recognition performance. Federated learning enables the distributed clients to jointly learn a model with data preserve in local, which provide a promising solution to leverage the massive data. However, in federated learning, the model learned by the local devices need to be repeatedly transmit to the server, which poses communication overhead. To tackle the communication issue, this paper proposes a communication efficient federated learning framework that utilize local momentum term to accelerate the convergence speed. Convergence guarantee under non-convex case is provided. Experiment on EMNIST and CIFAR10 dataset demonstrate that proposed method can effectively increase the convergence speed.
Preferred Benchmarking Criteria for Systematic Taxonomy of Embedded Platforms (STEP) in Human System Interaction Systems

The rate of progress in the field of Artificial Intelligence (AI) and Machine Learning (ML) has significantly increased over the past ten years and continues to accelerate. Since then, AI has made the leap from research case studies to real production ready applications. The significance of this growth cannot be undermined as it catalyzed the very nature of computing. Conventional platforms struggle to achieve greater performance and efficiency, what causes a surging demand for innovative AI accelerators, specialized platforms and purpose-built computes. At the same time, it is required to provide solutions for assessment of ML platform performance in a reproducible and unbiased manner to be able to provide a fair comparison of different products. This is especially valid for Human System Interaction (HSI) systems that require specific data handling for low latency responses in emergency situations or to improve user experience, as well as for preserving data privacy and security by processing it locally. Taking it into account, this work presents a comprehensive guideline on preferred benchmarking criteria for evaluation of ML platforms that include both lower level analysis of ML models and system-level evaluation of the entire pipeline. In addition, we propose a Systematic Taxonomy of Embedded Platforms (STEP) that can be used by the community and customers for better selection of specific ML hardware consistent with their needs for better design of ML-based HSI solutions.
Fine-Grained Soccer Actions Classification Using Deep Neural Network

Action recognition from video data modal is contemplation about recognizing features in spatial position and characterizing the temporal changes across these extracted spatial features. Soccer video action recognition is a categorized field of video action recognition. Soccer sports have enormous viewers across the globe, and there is a significant commercial impact on the broadcasters. High inter-class similarities of various soccer actions symbolize the fine-grained nature of action recognition. Soccer matches are broadcasted live and captured using shifting cameras rather than stationary cameras. The fine-grained nature of soccer actions and shifting camera possessions make the recognition more intricate than the coarse-grained. There exist only limited researches that address this issue. To this extent, we propose a deep learning approach that successfully categorizes ten different soccer actions from our custom-developed SoccerAct10 dataset. Feature extraction is accomplished utilizing transfer learning from state-of-the-art convolutional neural networks (CNN) models such as DenseNet201, InceptionResNetV2, MobileNetV2, ResNet152V2, and Xception. All these models were trained on a massive ImageNet dataset. Long short-term memory (LSTM), with the input features from CNN, models the temporal changes of soccer actions. LSTM has already proven its success in tackling the vanishing gradient. At the final layer, the softmax activation function yields the distributions of probabilities of each soccer action. Empirical evaluation demystifies the effectiveness of our proposed approach, distinguishing ten distinct soccer actions with 90% accuracy.
"Totally Unnecessary" or "Simply Convenient" – About Users and Non-Users of Voice Assistants

Voice assistants, and in particular smart speakers, have become very popular in the recent past. Their growing ability to interact with users in natural language makes them a convenient interface for control of entertainment systems, appliances, smart homes, IoT applications, and many Internet applications. Nevertheless, they are controversial devices, not only because of the obvious challenges for security and privacy of their users. This paper investigates the concerns and motivations of users of voice assistants, but also those of non-users who are a group that is often disregarded in an engineering context. Following a theoretical model of non-usage, a representative nationwide survey was conducted in Austria to obtain quantitative insight into the groups of users and non-users of voice assistants. While the results confirm several former works regarding the sociodemographic characteristics of the groups, we find that only 75% of the non-users actively resist against the technology, whereas 20% rather feel excluded from using it. One of the surprising results also was that users and non-users share the same security and privacy concerns.
A BERT-based Idiom Detection Model

Idioms are figures of speech that contradict the principle of compositionality. This disposition of idioms can misdirect Natural Language Processing (NLP) techniques, which mostly focus on the literal meaning of terms. In this paper, we propose a novel idiom detection model that distinguishes between literal and idiomatic expressions. It utilizes a token classification approach to fine-tune BERT(Bidirectional Encoder Representations from Transformers). It is empirically evaluated on four idiom datasets, yielding an accuracy of more than 0.94. This model adds to the robustness and diversity of NLP techniques available to process and understand increasing magnitudes of free-form text and speech. Furthermore, the social value of this model is in enabling non-native speakers to comprehend the nuances of a foreign language.
Effect of Embodiment and Improving Japanese Students’ English Pronunciation and Prosody with Humanoid Robot

The overall goal of this research project is a study using RALL (Robot Assisted Language Learning) for Japanese students to improve pronunciation and prosody skills, and to provide more evidence of the effect of embodiment on RALL in English pronunciation and prosody. The subjects are divided into two groups: ten participants who use a robot (RALL) and ten participants who use a screen-based e-learning via computer (non-RALL). The robot had been used for the experiment was NAO from Aldebaran. The experiment took around 3 weeks for one participant to complete all the lessons including three pronunciation lessons and two prosody lessons. In order to assess the experiment, independent t-tests were performed regarding comparison of RALL and non-RALL scores difference. Dependent t-tests were performed to assess the performance of each group. The results shows that there was no significant effect of embodiment between RALL and non-RALL group. However, RALL system could improve Japanese students` English pronunciation, prosody, and overall pronunciation and prosody aspect better than non-RALL system.
Explainable Black Box Models

The idea that black box models are unexplainable has been elevated to an axiom. In contrast to explainable models, where the involved parameters have a certain meaning that is understandable, black box models rely on principles that have no other function than to produce sophisticated mappings. The lack of explainability is sometimes compensated by applying a so-called post-hoc explainability method. Such a method is supposed to extract some meaning from the separately constructed black box model, but this technique is criticized for several reasons. In this paper we argue that there is an alternative to explainable models and to post-hoc explainability, by representing explanations as variables at either the input side or the output side of a black box model. This results in explainable black box models, where explanations are unrelated to the working principles, but they are still part of that same black box model.
Application of the Solution for Analysis of IT Systems Users Experience on the Example of Internet Bank Usage

Consequential and systematic analysis of IT systems usage with the aim to improve end-user experience is one of the most crucial in the era of total life digitalization. In fact, there are different methods for user’s behaviour analysis in the context of his experience assessment; however the corresponding tools mostly are focused on visual representation of user’s activities and system states transitions. User’s journey maps are a common user experience (UX) notation, which, depending on the context, can be used in a variety of ways. In UX tools, it is supported at the user interface (UI) development and testing stages as extended notation in multiple possible shapes, sizes, and formats. eStepControl is a tool, which supports identification and visual representation of IT system users behaviour, integrating UX analysis based on users journeys maps and gives an ability to analyse data about user sessions – to identify the most interesting cases for UX specialist and to make appropriate conclusions. The goal of this paper is to demonstrate functionality of eStepControl on the example of an internet bank, which is a suitable and logical representative of high UX importance in IT systems. This article covers brief description of the approach used for UX analysis, based on machine learning algorithms, statistical analysis methods and graph search and transformations techniques, as well as shows real UX analysis use cases of eStepControl tool for one day usage of the internet bank.
A Multirobot Cooperative Area Coverage Search Algorithm Based on Bioinspired Neural Network in Unknown Environments

This article proposes a novel approach based on a bioinspired neural network (BIN) for multirobot area coverage search in unknown environments. We obtain the dynamic environment information in the search process of the multirobot by combining the BIN model with the grid map. We, then, design a search objective function to guide robots to explore the task area by applying the distributed model prediction control policy. Moreover, we further improve the search efficiency through a collaborative decision-making mechanism among robots. Finally, we conduct simulation studies, which demonstrate the effectiveness and superiority of the proposed approach.
GiTMO: An HDR10 Compatible Generic Tone-Mapping for Real-Time Video Play

Though the market of HDR displays for consumers is gaining maturity, low dynamic range (LDR) displays still hold a majority share in the consumer market. Many tone-mapping operators (TMO) have been developed to convert the HDR contents into a form suitable for LDR displays. This work proposes a generic tone-mapping operator (GiTMO) that can represent all global TMOs in a generic form of a one-dimensional array of a few coefficients. We use these coefficients and the existing HDR10 delivery pipeline to produce a tone-mapped version of the HDR content at the receiver end in real time. In our framework, coefficients are sent to the receiver as supplemental enhancement information (SEI) message of High Efficiency Video Coding (HEVC). Thus, HDR10, which is designed for HDR display, becomes compatible with LDR displays too. This can relieve the content producers from the need to release separate copies of HDR and LDR content. GiTMO, being generic and efficient, can generate LDR faithfully for all global TMOs, making the consumer end oblivious of the TMO used at the transmitter side. We perform extensive evaluations to validate the accuracy and efficiency of the GiTMO and the proposed content delivery format, and their benefits over the existing approaches.
A General Quantitative Analysis Framework for Attacks in Blockchain

Decentralized cryptocurrency systems have become primary targets for attackers due to substantial profit gain and economic rewards. A number of attack models have been proposed during last few years. However, the evaluation and comparison of those attack models remain problematic due to the lack of systematic framework to analyze them. In this work, we propose a general quantitative analysis framework for attack models in the network and consensus layer of blockchain. We identify the problem statement and evolution process. And we show how to apply our general framework in previous attacks such as selfish mining and bribery attack. We also explained that the framework is suitable for other attacks in blockchain. For further exploration, we simulate the success rate and benefits of different attacks through experiments. We provide several defensive strategies, and study how these strategies against previous attack models.
Robust Online Tracking With Meta-Updater

In a sequence, the appearance of both the target and background often changes dramatically. Offline-trained models may not handle huge appearance variations well, causing tracking failures. Most discriminative trackers address this issue by introducing an online update scheme, making the model dynamically adapt the changes of the target and background. Although the online update scheme plays an important role in improving the tracker's accuracy, it inevitably pollutes the model with noisy observation samples. It is necessary to reduce the risk of the online update scheme for better tracking. In this work, we propose a novel offline-trained Meta-Updater to address an important but unsolved problem: Is the tracker ready for updating in the current frame? The proposed module can effectively integrate geometric, discriminative, and appearance cues in a sequential manner, and then mine the sequential information with a designed cascaded LSTM module. Moreover, we strengthen the effect of appearance information on the module, i.e., the additional local outlier factor is introduced to integrate into a newly designed network. We integrate our meta-updater into eight different types of online update trackers. Extensive experiments on four long-term and two short-term tracking benchmarks demonstrate that our meta-updater is effective and has strong generalization ability.