Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal explanations for AI-based multisensor fusion

The recent resurgence in the effectiveness of artificial intelligence (AI) and machine learning (ML) techniques for image, text and signal processing has come with a growing recognition that these techniques are “inscrutable”: they can be hard for users to trust because they lack effective means of generating explanations for their outputs. Consequently, there is currently a great deal of research and development addressing this problem, producing a sizeable number of proposed explanation techniques for AI/ML approaches operating on a variety of data modalities. However, a problem that has received less attention is: what modality of explanation to choose for a particular user and task? For example, many techniques attempt to produce visualizations of the workings of an ML model, e.g., so-called “saliency maps” for a deep neural network, but there may be multiple reasons why this mode of explanation might not be appropriate for a user, including: (i) they may be operating at the edge of the network with a device that is not suited to receiving or displaying such a visualization; (ii) it may not be appropriate for security reasons to send them a visualization derived from the source imagery (e.g., if the location of the camera system is sensitive); (iii) this kind of explanation may be “too low level” for that user’s needs – they may require something more “causal”, for example. One approach that may address all three of these example issues would be to map the explanation from a visualization to a textual rationalization. In this paper we explore this issue of generating explanations in a range of modalities in the context of AI/ML services that operate on multisensor data and show that a “grammar-based” approach that separates atomic explanation-generation and communication actions offers sufficient scope and flexibility to address a set of mission scenarios.
Augmented Reality Application of Sign Language for Deaf People in Android Based on Smartphone

The aim of this study was to create augmented reality application that can display three-dimensional animated hand movements on a media card and implemented on the Android operating system. Modelling was begun with doing observation, in which the object of the observation was people who have a deficiency in hearing and speaking or often called deaf. The system made aimed at helping normal people in studying the letter cues when trying to communicate with the deaf. The results obtained is an application created with augmented reality and can be used by anyone having an Android-based smartphone that wants to recognize the letter cues by using letter card as the card marker. So that, when the camera is directed to the card compiled into a single word, the letter marker on the card will be detected by the application and will appear animated hand movement of each letter scanned by the camera in real-time. This is the purpose of the augmented reality on this letter recognition application, namely giving rise animation in real-time that can attract someone to study the letter cues.
Early Action Prediction With Generative Adversarial Networks

Action Prediction is aimed to determine what action is occurring in a video as early as possible, which is crucial to many online applications, such as predicting a traffic accident before it happens and detecting malicious actions in the monitoring system. In this paper, we address this problem by developing an end-to-end architecture that improves the discriminability of features of partially observed videos by assimilating them to features from complete videos. For this purpose, the generative adversarial network is introduced for tackling action prediction problem, which improves the recognition accuracy of partially observed videos though narrowing the feature difference of partially observed videos from complete ones. Specifically, its generator comprises of two networks: a CNN for feature extraction and an LSTM for estimating residual error between features of the partially observed videos and complete ones, and then the features from CNN adds the residual error from LSTM, which is regarded as the enhanced feature to fool a competing discriminator. Meanwhile, the generator is trained with an additional perceptual objective, which forces the enhanced features of partially observed videos are discriminative enough for action prediction. The extensive experimental results on UCF101, BIT, and UT-Interaction datasets demonstrate that our approach outperforms the state-of-the-art methods, especially for videos that less than 50% portion of frames is observed.
A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification

Brain cancer classification is an important step that depends on the physician’s knowledge and experience. An automated tumor classification system is very essential to support radiologists and physicians to identify brain tumors. However, the accuracy of current systems needs to be improved for suitable treatments. In this paper, we propose a hybrid feature extraction method with a regularized extreme learning machine (RELM) for developing an accurate brain tumor classification approach. The approach starts by preprocessing the brain images by using a min–max normalization rule to enhance the contrast of brain edges and regions. Then, the brain tumor features are extracted based on a hybrid method of feature extraction. Finally, a RELM is used for classifying the type of brain tumor. To evaluate and compare the proposed approach, a set of experiments is conducted on a new public dataset of brain images. The experimental results proved that the approach is more effective compared with the existing state-of-the-art approaches, and the performance in terms of classification accuracy improved from 91.51% to 94.233% for the experiment of the random holdout technique.
A New Machine Learning Approach for Anomaly Detection Using Metadata for Model Training

We report a new approach to train machine learning (ML) models for binary classification in order to detect anomalies in application log records. Contrary to the common use of actual values of different log fields, we used metadata of the log records (“log schema”) to train and test our ML models. Our objective was to use ML models to automatically detect anomalous log records that may carry sensitive or restricted information and thus prevent their inadvertent transfer (“leakage”) from the source to destination environments. In addition to all the controls and measures currently in place to prevent such data leakage, our ML model approach provides an additional layer of data security to further reduce the possibility of potential data leaks. Several ML models (decision tree (DT), random forest (RF) and Gradient Boosted Tree (GBT)) were trained using a combination of real (class: “normal”) and synthetic (class: “suspicious”) metadata for approximately five million log records. The metadata for “normal” records were extracted from the schema of real historical log records that do not contain “sensitive” or “restricted” information. The metadata for likely “suspicious” records were simulated via artificially injecting structural violations that are not observed in the known “normal” log records. The final prediction (“normal” or “suspicious”) for each new record was based upon the use of a voting classifier. The three ML models (DT, RF and GBT) in our solution all individually yield high average accuracy in predictions (1.0, 0.99 and 1.0, respectively) over multiple experimental runs. Accordingly, the voting classifier consistently yields highly accurate predictions (1). Combined, our results suggest that the use of a combination of real and synthetic metadata derived from log schema and a voting classifier can be successfully applied to build a robust ML model solution for anomaly detection in log records. Keywords—anomaly detection, machine learning, decision tree, random forest, gradient boosted tree.
Sensitivity analysis of queue-based AQM over network parameters

The comparative analysis of the active queue management (AQM) has been seen in the past work, while the comparative analyses for combined multiple output responses or performance parameters viz., throughput, delay, and loss rate are rarely seen in the literature. The comparative analysis using single variable comprising the output performance parameters has been attempted. The sensitivity of the network parameters over the throughput, delay, and loss rate for different AQM schemes has been measured. An attempt has also been made to stabilise the AQM algorithm that aims to achieve a low-loss rate and a high throughput. The models for the stabilisation of queue size of random early detection (RED) based on the network parameter ma x p have been studied. The comparative performance analysis of existing stabilised models has also been presented with author's modified RED. The key idea is to modify the existing RED algorithm to achieve stabilisation in queue length at routers with the reduced loss rate as compared to RED.
Multimodal Sentiment and Gender Classification for Video Logs

: Sentiment analysis has recently attracted an immense attention from the social media research community. Until recently, the focus has been mainly on textual features before new directions are proposed for integration of other modalities. Moreover, combining gender classiﬁcation with sentiment recognition is a more challenging problem and forms new business models for directed-decision making. This paper explores a sentiment and gender classiﬁcation system for Arabic speakers using audio, textual and visual modalities. A video corpus is constructed and processed. Different features are extracted for each modality and then evaluated individually and in different combinations using two machine learning classiﬁers: support vector machines and logistic regression. Promising results are obtained with more than 90% accuracy achieved when using support vector machines with audio-visual or audio-text-visual features.
Automated Headlight Intensity Control and Obstacle Alerting System

Headlight intensity of vehicles poses a great danger during night travel. The drivers of most vehicles use high bright beam while driving at night. This causes inconvenience for the person travelling from the opposite direction. To avoid such incidents. The proposed system can be demonstrated with the help of two vehicles where the high beam of vehicle can be controlled with help of other car coming in opposite direction and vice versa using LDR sensor and zigbee communication which avoid the accidents to greater extent. We are designing a prototype of automatic headlight intensity control system and expected to dim the headlight to avoid this glare. This beam causes a temporary blindness to a person resulting in road accidents during the night. This automatically switches the high beam into low beam thus reducing the glare effect by sensing the approaching vehicle. This model concept eliminates the requirement of manual switch by the driver which is not done at all time.
An IOT based Wearable Smart Glove for Remote Monitoring of Rheumatoid Arthritis Patients

Rheumatoid Arthritis is a disabling and painful disease of finger joints affecting mainly the elderly people and requiring continuous medications and physical therapy. Traditional arthritis measurements require labor intensive examination by clinical staff. These manual measurements are inaccurate and subject to observer variations. Nowadays the use of wearable technologies is spreading and rapidly becoming a trend promising to offer key benefits in the management of chronic diseases especially at home. Here, we propose an affordable Smart Glove instrument for assisting physiotherapists in remotely analysing patients finger flexions when performing diverse activities at home. An E-textile based glove uses flex and force sensors, and an Arduino platform to transmit motion data to the physiotherapists using a smart phone using a dedicated App. The flex sensor on the index finger detects and estimates the motion, a BLE (Bluetooth Low Energy) Nano is used for processing and wireless transmission. The wearable smart glove uses a lithium ion 3.3V rechargeable 400mAH battery for consuming power. This Smart Glove also helps in monitoring the patient’s response to either medication and/or diverse recommended movements. The data collected can be used to analyse the status of the patient with time and also in assisting care givers to change planned activities or exercises when needed.
Convolutional Neural Network for Detection and Classification with Event-based Data

Mainly inspired by biological perception systems, event-based sensors provide data with many advantages such as timing precision, data compression and low energy consumption. In this work, it is analyzed how these data can be used to detect and classify cars, in the case of front camera automotive applications. The basic idea is to merge state of the art deep learning algorithms with event-based data integrated into artificial frames. When this preprocessing method is used in viewing purposes, it suggests that the shape of the targets can be extracted, but only when the relative speed is high enough between the camera and the targets. Eventbased sensors seems to provide a more robust description of the target’s trajectory than using conventional frames, the object only being described by its moving edges, and independently of lighting conditions. It is also highlighted how features trained on conventional greylevel images can be transferred to event-based data to efficiently detect car into pseudo images.
An Efficient Authentication Scheme for Blockchain-Based Electronic Health Records

In traditional electronic health records (EHRs), medical-related information is generally separately controlled by different hospitals and thus it leads to the inconvenience of information sharing. Cloud-based EHRs solve the problem of information sharing in the traditional EHRs. However, cloud-based EHRs suffer the centralized problem, i.e., cloud service center and key-generation center. This paper works on creating a new EHRs paradigm which can help in dealing with the centralized problem of cloud-based EHRs. Our solution is to make use of the emerging technology of blockchain to EHRs (denoted as blockchain-based EHRs for convenience). First, we formally define the system model of blockchain-based EHRs in the setting of consortium blockchain. In addition, the authentication issue is very important for EHRs. However, existing authentication schemes for blockchain-based EHRs have their own weak points. Therefore, in this paper, we also propose an authentication scheme for blockchain-based EHRs. Our proposal is an identity-based signature scheme with multiple authorities which can resist collusion attack out of $N$ from $N-1$ authorities. Furthermore, our scheme is provably secure in the random oracle model and has more efficient signing and verification algorithms than existing authentication schemes of blockchain-based EHRs.
Crowdsourcing Software Task Assignment Method for Collaborative Development

Software crowdsourcing is an emerging and promising software development model. It is based on the characteristics of Internet community intelligence, which makes it have certain advantages in development cost and product quality. Companies are increasingly using crowdsourcing to accomplish specific software development tasks. However, this development model still faces many challenges. One of the key issues is the collaboration between crowdsourced workers. Developer collaboration is important to software development, but workers in crowdsourcing come from an undefined network community, so it’s hard to guarantee that they can work together. This paper focuses on task assignment and uses the active time of workers as the basis of grouping to provide a solution for multi-task to multi-worker allocation. Based on the on-demand distribution model, this paper considers three factors: worker’s ability, task module complexity, and worker’s active time. First, the workers are divided into multiple collaborative candidate groups based on active time. Then, the Hungarian algorithm is used to select the optimal workers for each module from the collaborative candidate groups of each task, and the coordination candidate group replacement strategy is used to solve the assignment failure problem. Finally completing the assignment of all tasks within an assignment cycle. The experiments have shown that the proposed method increases the total utility by 25% and the success rate of distribution by 30% than the sequential assignment method. The proposed method can give a reasonable solution for software crowdsourcing task allocation based on collaborative development.
Optimal Prediction and Update for Box Set-Membership Filter

This paper investigates a box set-membership filter for nonlinear dynamic systems and on-line usage. To the best of our knowledge, although ellipsoid set-membership filter has more freedom degree to optimize a bounding estimation, it is computationally intractable to obtain an optimal prediction and update, and the approximation loss is uncertain in different scenarios. In this paper, we equivalently transform the prediction and update of the box set-membership filter to linear programing problems without loss of performance, respectively. Moreover, for a typical nonlinear dynamic system in target tracking, the remainder bound of the nonlinear function can be obtained analytically on-line. However, the ellipsoid bounding problem of the remainder usually needs to be relaxed to solve a semi-definite programming problem. Thus, the computational complexity of the optimal box set-membership filter is much less than that of the ellipsoid set-membership filter based on the semi-definite programming. Finally, a numerical example in target tracking demonstrates the effectiveness of the box set-membership filter. The proposed box set-membership filter can obtain a better trade-off between the filter accuracy and the computational complexity.
Assessment of Marginal Bone Loss Around Morse Taper Connection and Platform Switching Dental Implant: Clinical Observational Study

Dental implant is now become a successful and wide spread used to replace missing teeth. Although studies is continue to enhance the success rate of this treatment and to overcome the considerable failure and limitation in its used.Several manufacturer companies recently advocate a new design like taper connection designs systems which make a considerable enhancement in overall success rate of dental implant .
Question and Answer Classification in Czech Question Answering Benchmark Dataset

In this paper, we introduce a new updated version of the Czech Question Answering database SQAD v2.1 (Simple Question Answering Database) with the update being devoted to improved question and answer classification. The SQAD v2.1 database contains more than 8,500 question-answer pairs with all appropriate metadata for QA training and evaluation. We present the details and changes in the database structure as well as a new algorithm for detecting the question type and the actual answer type from the text of the question. The algorithm is evaluated with more than 4,000 question answer pairs reaching the F1-measure of 88% for question typed and 85% for answer type detection.
Polyp Classification and Clustering from Endoscopic Images using Competitive and Convolutional Neural Networks

Understanding the type of Polyp present in the body plays an important role in medical diagnosis. This paper proposes an approach to classify and cluster the polyp present in an Endoscopic scene into malignant or benign class. CNN and Self Organizing Maps are used to classify and cluster from white light and Narrow Band (NBI) Endoscopic Images . Using Competitive Neural Network different polyps available from previous data are plotted with the new polyp according to their structural similarity. Such kind of presentation not only help the doctor in it’s easy understanding but also helps him to know what kind of medical procedures were followed in similar cases.
HS2: Active learning over hypergraphs with pointwise and pairwise queries

We propose a hypergraph-based active learning scheme which we term HS2; HS2 generalizes the previously reported algorithm S2 originally proposed for graph-based active learning with pointwise queries. Our HS2 method can accommodate hypergraph structures and allows one to ask both pointwise queries and pairwise queries. Based on a novel parametric system particularly designed for hypergraphs, we derive theoretical results on the query complexity of HS2 for the above described generalized settings. Both the theoretical and empirical results show that HS2 requires a significantly fewer number of queries than S2 when one uses S2 over a graph obtained from the corresponding hypergraph via clique expansion.
Series Arc Detection and Complex Load Recognition Based on Principal Component Analysis and Support Vector Machine

The increasing household loads make series arc faults more complex, which are difficult to be detected by traditional circuit breakers and lead to the frequent occurrence of residential fire accidents. In this paper, a comprehensive approach of complex load recognition and series arc detection is proposed on the basis of principal component analysis and support vector machine (PCA-SVM) combination model. Several typical loads were selected and analyzed, especially nonlinear and complex loads like power electronics load and multi-state load. Three time-domain parameters, maximum slip difference (MSD), zero current period (ZCP) and maximum Euclidean distance (MED), and nine frequency-domain harmonics information are collected to complex waveforms. To decrease the computation cost and further to enhance the response velocity, all the time-domain and frequency-domain information were blended and dimensionally reduced to three parameters by principal component analysis (PCA). Prior to the series arc detection, load recognition is trained and completed with the artificial intelligence (AI) algorithm. At last, the comprehensive model of load recognition and series arc detection is achieved based on a support vector machine (SVM). The accuracy of load recognition and series arc detection reaches 99.1% and 99.3%, respectively, demonstrating the excellent performances of the intelligent approach to diagnose the series arcing activities in modern household applications.
Smart transport and logistics: A Node‐RED implementation

A clever and efficient management of transport and logistics are fundamental in manufacturer companies, starting to adopt new methodologies, inspired to the emerging industry 4.0 principles. Such a behavior is influenced by the spreading of the Internet of Things (IoT) paradigm, helping to automate a lot of features, if not all, of products' management, from raw materials' purchase order to the final delivery to customers. Small and medium industries must face design issues and noncustomized solutions may not fit with their habitual data flow. Hence, the need of a tool, able to support designers and developers in defining the network architecture and messages' exchange, emerges. To this end, the use of Node‐RED, a flow‐based programming tool for the IoT, is proposed, by providing a comprehensive case study targeted to smart transport and logistics.