Miracle-dz/UniFAR-FTUs huggingface dataset:
Reactive Navigation in Crowds for Non-Holonomic Robots With Convex Bounding Shape

This letter describes a novel method for non-holonomic robots of convex shape to avoid imminent collisions with moving obstacles. The method's purpose is to assist navigation in crowds by correcting steering from the robot's path planner or driver. We evaluate its performance using a custom simulator which replicates real crowd movements and corresponding metrics which quantify agents’ efficiency and the robot's impact on the crowd and count collisions. We implement and evaluate the method on the standing wheelchair Qolo. In our experiments, it drives in autonomous mode using on-board sensing (LiDAR, RGB-D camera and a system to track pedestrians) and avoids collisions with up to five pedestrians and passes through a door.
Rice Disease Detection using Intensity Moments and Random Forest

Improvement of an automated method for recognizing and categorizing various plant diseases is an evolving research area. Usually, it is very time-consuming to recognize plant diseases in remote areas, because of the communication gap between the farmer and the specialist. A programmed layout can help a farmer to discern rice plant diseases. The automatic system that is referred to here can detect the main three types of rice leaf diseases (Bacterial leaf blight, Leaf blast, and Brown spot) by the Random Forest decision tree classifier. I n tensity moments are needed here for extracting features properly. This proposed system obtains 91.47% accuracy and can classify rice diseases nicely in their primary stage. By adding some more collaborative features, the obtained result can assist the developer to rapidly identify plant diseases. This will also help the agriculturalists in active decision-taking for defending the plant professionally from ample harm.
A Deep Neural Network Guided Testing Approach for Finite State Machines

Finite-State Machines (FSMs) lie at the heart of every digital system and verifying whether a given circuit implementation (B) of an FSM (A) conforms to its specification is an important task in the design cycle. In this work, a deep neural network (DNN) based technique for testing FSMs is developed. Given a set of transition functions that specify an FSM, a DNN is trained with the input-output sequences using the back-propagation algorithm. First, the input sequences and the corresponding output sequences (I/O-pairs) for A are constructed, and some of them are utilized to train the DNN. After training, the proposed DNN is validated with the rest of the I/O-pairs. Once the training and validation of the DNN is completed, it can be used for checking the correctness of its implementation (B) very quickly. Some inputs are applied to B and the observed output sequences are compared with those predicted by the proposed DNN. Based on the similarities between them, B is either declared as correct implementation of A, else it is declared as faulty implementation. The experiments are performed on the MCNC FSM benchmarks and certain faults are injected to form mutant FSMs. Experimental results reveal the efficacy of the proposed technique. Only a few tests are required to detect the presence of anomaly, if any. Hence, the test time is very less resulting in an average test time reduction of 85.66% compared to existing method. It is observed from the earlier works that this type of DNN based FSM testing method is presented first time.
Convolutional Neural Network for Diabetic Retinopathy Detection

The world’s projected blind population will reach 40 million by 2025. A modern fundus-based algorithm that approves the classification of retinal tissue needs to be improved in the early stages of healthy and diabetic retinopathy. In this experiment, we have introduced a convolution neural network approach to detecting diabetic retinopathy. We used the publicly accessible Apatos Blindness Detection database to train a convolution neural network, where the image is processed at an early stage, primarily involving image resizing, pixel rescaling, and label encoder. After that, an image is given to the convolution neural network model, to decide whether the patient has diabetic retinopathy or not. About 3789 color retinal images are used in experiments to train the proposed model and about 948 images are collected to test its efficiency in classification. Accuracy of 96.15$\backslash$%, Sensitivity 79$\backslash$%, Precision 89$\backslash$%, and F1-Score 84.1$\backslash$% and Area Under Score 0.82 is achieved using the Convolution Neural Network-based method.
Impact of Time Domain Features & Inertial Sensors on Activity Recognition using Randomized Selection

Activity performed by the user is one of the major components of context sensing. Now a day’s users are carrying Smartphones or wearable devices with them always. The device is fully equipped with the latest sensors, thus smart devices are prominently used in activity detection. The detection of activity is mainly dependent on three things-1- the sensors used for data collection, 2- the various features extracted from the raw data and 3-Machine Learning model used for training and testing. Researchers are using different sensors and extracting more numbers of features for getting better accuracy. However, feature dimensions are dependent on time of execution. Thus, an optimization is required between number of features used and its execution time. It is also required to find out the impact of different sensors on its accuracy and execution time. In this paper we have tried to discover the trade-off between number of features & sensor used with its accuracy and execution time. The evaluation of proposed work has been done by using publicly available dataset on UCI machine learning repository. Random selection methodology is used for selecting features and 5 popular machine learning algorithms is used to compare the results. The evaluation result shows that gyroscope helps in increasing accuracy if it is used along with accelerometer. We also conclude that features have significant effect on accuracy and execution time, and from various ML models Random forest & K nearest neighbor classifiers are providing better accuracy in most of the cases of Activity Recognition.
Effectuating Supervised Machine Learning Techniques for Multiclass Classification of Problematic Internet and Mobile Usage

The internet has slowly become an inevitable part of every facet of our lives. With the power of the world wide web available at the touch of our fingertips, anything seems possible. But mental health disorders due to prolonged usage of the ever-evolving internet and mobile are also on the rise. Studies show there is a strong correlation between excessive internet usage and depression, lower self-esteem, Attention-Deficit Disorder (ADHD), impulsivity, hyperactivity and so on. In this paper, a system is proposed that classifies a persons’ internet/mobile usage into four classes (multi class) which are- Normal, Borderline, Critical and Severe. In collaboration with our institutions’ Counsellor and considering previous studies, a non-invasive questionnaire was developed to collect the data. The collected data was used to train some efficient and state-of-the-art machine learning models such as Logistic Regression, Decision Trees, Support Vector Machines (SVM), Xtreme Gradient Boosting (XGBoost), Random Forests and Light Gradient Boosting (LightGBM). The model with the highest accuracy was taken forward to deliver the best possible classification of a user into one of four categories. With thorough training and testing linear SVM with radial basis kernel returned the best accuracy and thus it was chosen to move forward with.
Identification of Drug Discovery for Patients Using Machine Learning

Deep learning place an very important role in the identification. In this paper an Identification of drug for medicine preparing machines has been proposed. In general, medicine name contains large characters in the medicine box, the drug identification can be converted into the largest character identification in the image in this proposed system. Firstly, Support Vector Machine (SVM) is used with Connected Component to recognize the text region which intern finds largest region to identify the drug name. Later Fragment Link is used for text division purpose that is it helps to divide the drug name into two elements. Those elements are Fragment and Link which join the full word according to the rules. For identification Convolutional neural network software is used. After identification the final output is played in audio player.
Chinese Temple shaped slotted monopole UWB patch antenna for Microwave Imaging Applications

A new Chinese Temple shaped (CTS) slotted monopole UWB patch antenna is introduced in this research for the purposes of microwave imaging applications such as breast imaging. Geometrically the antenna was being comprised of a slotted pentagonal radiator with a diminished slotted ground-plane carved rectangularly. On the patch and ground plane, numerous types of slots were explored. Slotted pentagonal-shape patch and slotted rectangular ground offer significant Gain over 2.4 to 7.8GHz frequency ranges, wide bandwidth, along with comparatively balanced radiation properties. Whole dimension of the prototype is 40×40×1.6mm3 with 0. 320λ×0. 320λ×0. 0128λ electrical dimension at starting frequency 2.4GHz. The antenna has a fractional bandwidth of 105.88% and an operating bandwidth of 5.4 GHz (2.4-7.8GHz). Several parameters are taken into account and optimized to maximize Gain, and the maximum gain value is 6 dBi. Efficiency reaches a maximum of 96.23% with an average of 94.32% within 2.4-7.8 GHz frequency ranges. Being obtainable and affordable, the prototype mounted on 1.6 mm thick FR-4 substrate with such a 4.6 and 0.002 Permeability and loss tangent respectively. Simulation performed through 3D simulator software CST Microwave Studio 2019 and HFSS. May the antenna really be a respectable contender to use in MI applications for its lightweight structure and high Gain.
Mobile Attendance based on Face Detection and Recognition using OpenVINO

The OpenVINO toolkit enables versatile computer vision with an Intel® Movidius™ Neural Compute Stick 2 connected to a Raspberry Pi. This small portable platform provides new opportunities for innovative solutions in computer vision applications and beyond. This paper investigates its feasibility for mobile attendance systems for settings such as classrooms or other scenarios that require headcount or roll call. Related studies of face-based systems are explored, while the advantages of the proposed system are highlighted. Although there are some positioning constraints, the proof-of-concept system processes an approximate average of five faces per second. That means it can take attendance in a lecture room of 90 students in about 18 seconds. A recognition accuracy of 98.1% with an f-score of 96.9% was yielded on a private classroom dataset captured with a modest RPi camera. These promising results were achieved using a tiny ResNet-18 architecture, producing significantly better results than MobileNet. Furthermore, it outperformed the recognition accuracy of other ‘lightweight’ methods used in the literature that do not run off embedded devices on publicly available datasets.
The Impact of India’s Cyber Security Law and Cyber Forensic On Building Techno-Centric Smartcity IoT Environment

Cyber Security and Cyber forensic infrastructure has always been a dependent component to achieve the goal of smart city, techno-centric ecosystem development. The non-existence of a Technology-Driven collaborated ecosystem of Cyber defense, cyber forensic, and cyber law has led to a situation of fragmenting the objective of techno-centric smart city evolvement. Trends of connecting humans and things in smart cities have merged everything in a single digital network and cybercriminals are getting integrated platforms to conduct cybercrimes. This indicates that there is a need for collaboration of Cybersecurity and cyber forensic infrastructure with the PPP model at the national and international levels for securing cyberspace. Different countries have adopted different approaches to deal with the exponential growth of cyber attacks depending upon the country’s specific cyber law. Due to lack of Integration between India’s Smart City Development Policy and Cyber Security Policy leading to a major roadblock in smart city development. Achievement of major Smart city objective of India become critical due to non-existence of collaborative approach between different cyber defense ecosystem pillars and country-specific regional cyber law. India’s Cybersecurity policy "National Cyber Security Policy" 2013 is significant approach but not enough to create a robust techno-centric smart city. In this paper, we have proposed collaborative PPP model cyber law and cybersecurity technology ecosystem to enable secured cyber confident citizenship in the techno-centric smart city environment of India. This paper addressed the need for a dynamic technology-driven cybersecurity law ecosystem , "Call the police" by technology in smart city to build India’s techno-centric stable smart city ecosystem.
Evolutionary Computing Assisted K-Means Clustering based MapReduce Distributed Computing Environment for IoT-Driven Smart City

In the last few years, the exponential rise in urban population and allied demands have alarmed governing agencies as well as industries to achieve more quality-of-service (QoS) oriented solutions to meet up-surging demands, especially towards real-time decision making, information exchange and knowledge-driven decisions. To achieve it, smart city concept which employs Internet- of-Things (IoT), distributed software computing, and BigData analytics has gained widespread attention. Though, inclusion of QoS-sensitive routing has helped enabling better and efficient sensory or node’s data collection and dissemination; however, ensuring optimal query-driven knowledge mining and information exchange has remained a challenge. Considering it as motivation, in this paper an evolutionary computing assisted K-Means clustering algorithm is developed for MapReduce computation in Hadoop distributed framework. The proposed method employs genetic algorithm to enhance centroid estimation as well as clustering, which as a result helped in achieving better clustering to support MapReduce. The proposed GA based K-Means clustering has been applied over Hadoop-MapReduce, where to achieve aforesaid centroid estimation and clustering enhancement Silhouette coefficient was used as the objective function. Here, GA-K Means was applied in such manner that it estimates optimized centroid and clusters simultaneously over Mapper and Reducer, which makes overall computation faster and more accurate.
Discrimination of human-written and human and machine written sentences using text consistency

The development of deep learning has made it possible to automatically generate sentences that could be misinterpreted as being written by humans in the field of natural language processing. As a result, the importance of the identity of the author of the text is beginning to be emphasized. In this paper, we propose a method to evaluate the consistency of sentences, which can distinguish between "sentences composed entirely of human-written texts" and "sentences with a mixture of human-written and machine-generated texts". In addition, we tested the consistency of the proposed method in an experiment, and confirmed that it was possible to discriminate two kinds of sentences in a mixed dataset of human written text and mixed text with higher accuracy than existing works. Furthermore, Kendall’s rank correlation coefficient and Mann-Whitney’s U-test in the sentence discrimination experiment confirmed that the proposed method showed a significant difference between the two types of sentences with a stronger correlation.
Text Based Hate-Speech Analysis

The definition of the term “hate speech” as per Oxford is ‘a speech that might involve abusive or threatening words which can have or can express pre-bias against a special community / group. This pre-bias can be anything like religion, race or sexual orientation, caste. ’ Hatred is generally based on ethnicity, religion, disability, gender, caste, and sexual orientation the internet and social media has become a powerful tool for such propagandist to spread hate and reach new audience. The anonymity and flexibility that the internet offers allow such haters to easily and safely propagate hate without any fear. Lack of regulation and legal policy worsens the situation a bit more. The need of the hour is for automated state of the art and scalable methods for hate speech detection and classification.This paper introduces two ensemble-based models in which the first one is based on Linear SVC, Logistic Regression, Random Forest and other based on Random Forest, KNN, Logistic Regression. Also, few deep learning models using self-trained and pre-trained word embeddings have been introduced for Twitter hate speech classification systems. The proposed research work has attempted to classify the tweets & assign them in one of the 3 categories i.e., Racist, Sexist or None.
PoseX: A Webcam-based Detection System to Prevent Postural Syndromes for Computer Users

As computers are widely used in daily life, computer users spend a lot of time staring at screens. long-term sitting postures lead to upper body pain which brings negative physical and mental impacts. Any exaggerated forward curvature in the upper back is called kyphosis. The common unhealthy postures are cervical kyphosis, thoracic kyphosis, and raised-shoulders. PoseX can recognize cervical spine kyphosis, thoracic spine kyphosis, and raised-shoulders from a facial image with only one general webcam. Based on the body key-points provided by OpenPose, PoseX uses the proportional relation between face width, shoulders width, and ears-shoulders height as the features to extract depth information from the RGB image. In our experiment, 15 computer users were recruited. The sitting scenarios include four tasks (normal, cervical kyphosis, thoracic kyphosis, and raised-shoulders) and three distance conditions (60 cm, 85 cm, 110 cm), PoseX records personalized parameters in 3 seconds and performs an accuracy higher or equal to 84 percent in each task. The proposed technique shows a promising low-cost health monitoring system for computer users.
A Survey on Fatigue and Drowsiness Detection Techniques in Driving

Drowsiness and fatigue could be fatal during driving as it can lead to accidents. So, there is an acute demand and need of drowsiness cum fatigue detection of drivers during driving sessions. In this paper some important and possible techniques are presented to give reader a comprehensive idea of how technology could save millions from traffic accidents. This paper presents the state-of-the-art review of various potent techniques which are presently in use. Various techniques used till date are majorly based on eye/head tracking and wheel steering pattern tracking. These techniques are categorized into five different categories i.e., Subjective reporting, biological characteristics, physical characteristics, vehicular characteristics, and hybrid characteristics. This paper also discusses the problems faced by the current technology which need to be addressed in future. We believe that this study offers a clear understanding of drowsiness and fatigue detection techniques and contribute to the improvements of the current technology in near future.
Decoding Emotions in Text Using GloVe Embeddings

The process of identifying human emotions in text is a relatively nascent research domain. While multiple approaches such as observing facial expressions from video, spoken expressions from audio, written expressions from the text, and physiology measured by wearable devices have been taken in the past, multi-modal approaches have shown promising results too. In this paper, we utilize the content of text to determine the emotions expressed therein by the writer. Semantic embeddings are derived from the text through the means of GloVe - "an unsupervised learning algorithm for obtaining vector representations for words" [1], which was chosen because of its ability to incorporate global statistics and not relying on the local statistics or local contextual information of words. The embeddings this obtained were passed through LSTM - "an extension of recurrent neural networks (RNNs) that is capable of handling long term dependencies" [2]. Our model was able to attain an overall F1 score of 0.93. While the model recognized joy and sadness better than other labels, it found surprise harder to detect. On emotion recognition tasks, our approach of using GloVe Embedding has not been extensively studied in the past.
Analysis on Prediction of Plant Leaf diseases using Deep Learning

As the global population continues to burgeon, increasing overall crop production is becoming imperative to ensure food safety for everyone. However, a myriad of plant diseases can sever the supply of essential crops. To tackle these diseases, it is first important to identify these diseases and monitor them on a large scale. To solve this problem, Tensorflow with Keras and OpenCV are used to build a detection system. Since automated farms tend to be large and spread out, the classifier has to be executed and controlled from a cloud computing environment. This system can process images of plants and detect common diseases. Using three convolution layers, ten nodes and forty epochs this model was able to achieve validation of more accuracy. The training set consisted of 11942 images and the validation set was thirty-five per cent of the training set in size i.e a total of 6421 images. It is believed that the detection systems like ours, can strengthen the farming industry and help secure food safety for all. OpenCV allows our system to detect patters in images and translate those patterns into data on which ML models can be built using Keras. While previous attempts at solving similar problems have been made, few models that can specifically target Indian plants have been made, especially with a dataset of this size and a model of forty epochs.
Parking Occupancy Prediction using Computer Vision with Location Awareness

Parking occupancy in large outdoor parking as well as at street parking slots is a big problem, and solutions for it were proposed in the past with limited success. Recent advances in Computer Vision (CV) algorithms made this technology more powerful by making models smaller and more precise. On the other hand development of hardware components made Edge devices more powerful with AI accelerators as built-in options. Based on this new development, we propose an architecture where the latest Smart Edge devices are used to calculate parking occupancy on the edge. The proposed solution uses newly available data streams to create a 3D model of the parking environment that will identify available on-street parking spaces with high precision.