Reviewerly/exHarmony huggingface dataset:
Dialogue Explanation With Reasoning for AI

Explainable Artificial Intelligence is increasingly gaining attention in domains, such as self-driving cars and medical treatment. One of the most prevalent issues with these explainable models is that they are difficult to comprehend and have not been tested in real-world scenarios. In this research, I propose a dialogue-based explanation with reasoning for a rule-based system with the intention of utilising it in the future with a Neuro Symbolic AI system, to give machines the capacity to explain their actions or decisions using logic. We hypothesize that when a system makes a deduction that was, in some way, unexpected by the user then locating the source of the disagreement or misunderstanding is best achieved through a collaborative dialogue process that allows the participants to gradually isolate the cause. I also conduct a user evaluation for this hypothesis.
Learning to predict diverse human motions from a single image via mixture density networks

Human motion prediction, which plays a key role in computer vision, generally requires a past motion sequence as input. However, in real applications, a complete and correct past motion sequence can be too expensive to achieve. In this paper, we propose a novel approach to predicting future human motions from a much weaker condition, i.e., a single image, with mixture density networks (MDN) modeling. Contrary to most existing deep human motion prediction approaches, the multimodal nature of MDN enables the generation of diverse future motion hypotheses, which well compensates for the strong stochastic ambiguity aggregated by the single input and human motion uncertainty. In designing the loss function, we further introduce the energy-based formulation to flexibly impose prior losses over the learnable parameters of MDN to maintain motion coherence as well as improve the prediction accuracy by customizing the energy functions. Our trained model directly takes an image as input and generates multiple plausible motions that satisfy the given condition. Extensive experiments on two standard benchmark datasets demonstrate the effectiveness of our method in terms of prediction diversity and accuracy. • We propose a novel perspective for predicting human motion from the weakly informed conditions (i.e., a single image) by giving diverse plausible predictions. • We model human motion from the image domain to 3D space under mixture density networks and formulate energy-based priors to improve the prediction performance. • We quantitatively and qualitatively report extensive experimental results on Human3.6M and MPII datasets, and show the robustness of our method in terms of prediction accuracy and diversity.
Balancing Usability and Security of Graphical Passwords

Abstract Although most widely used authentication involves characters as passwords, but secure text-based passwords are complex and difficult to remember. Users want to have easy to remember passwords, but these are vulnerable to various kinds of attacks and are predictable. To address these problems, graphical passwords that involve selection of images and drawing lines have been proposed. The encoded images support creation of secure passwords and facilitate their memorability. Thus, they are considered as an alternative to strengthen password security while preserving usability because secure textual passwords become more complicated to use. The research addresses the issue of improving the user experiences during graphical authentication. This paper examines cued recall-based, pure recall-based and recognition-based approaches. The proposed scheme is based on recognition-based scheme that is selected as the least vulnerable to various attacks. The solution is currently under development, two qualitative usability testing sessions are performed and the participants’ feedback is discussed.
SAFE-OCC: A novelty detection framework for Convolutional Neural Network sensors and its application in process control

We present a novelty detection framework for Convolutional Neural Network (CNN) sensors that we call Sensor-Activated Feature Extraction One-Class Classification (SAFE-OCC). We show that this framework enables the safe use of computer vision sensors in process control architectures. Emergent control applications use CNN models to map visual data to a state signal that can be interpreted by the controller. Incorporating such sensors introduces a significant system operation vulnerability because CNN sensors can exhibit high prediction errors when exposed to novel (abnormal) visual data. Unfortunately, identifying such novelties in real-time is nontrivial. To address this issue, the SAFE-OCC framework leverages the convolutional blocks of the CNN to create an effective feature space to conduct novelty detection using a desired one-class classification technique. This approach engenders a feature space that directly corresponds to that used by the CNN sensor and avoids the need to derive an independent latent space. We demonstrate the effectiveness of SAFE-OCC via simulated control environments.
Optimization techniques for task scheduling criteria in IaaS cloud computing atmosphere using nature inspired hybrid spotted hyena optimization algorithm

Summary Cloud computing has garnered unprecedented growth in recent years in the field of Information Technology. It has emerged as a high‐performance computing option owing to its infrastructure that comprises of heterogeneous collection of autonomous computers and adaptable network architecture. The tasks that are scheduled in an optimized manner for their execution could be classified under NP‐hard problems. Though meta‐heuristic scheduling algorithms emerge as scheduling options, they need to be much more consistent while dealing with the dynamic set up of the cloud environment. In this paper, we had proposed a multi‐objective meta‐heuristic scheduling algorithm namely Quasi Oppositional Genetic Spotted Hyena Optimization (QOGSHO) algorithm that globally optimizes the makespan, resource consumption and SLA violation QoS parameters, thereby improving the performance. The algorithm proposed is an amalgamated product of meta‐heuristic algorithms like Quasi Oppositional Based Learning (QOBL), Spotted Hyena Optimization (SHO), and Genetic Algorithm (GA). The performance efficiency of the proposed QOGSHO algorithm had been compared with various scheduling algorithms using uniform datasets by varying the data instance sizes in a simulated cloud environment. The obtained results clearly justify the task scheduling efficiency of the proposed algorithm with respect to the QoS parameters namely makespan, resource utilization and SLA violation.
Rain Drop Service and Biometric Verification Based Blockchain Technology for Securing the Bank Transactions from Cyber Crimes Using Weighted Fair Blockchain (WFB) Algorithm

Cybercrime is an online crime committing fraud, stealing identities, violating privacy or hacking the personal information. A high level of information security in banking can be attained through striving to achieve an integrity, confidentiality, availability, assurance, and accountability. This Pandemic situation (COVID-19) paved the way for the customers to avoid traditional ways of banking and adapt to digital transactions. This banking digitalization increases in the utilization of cashless transactions like digital money (Cryptocurrency). Cyber security is imperative to preserve sensitive information, therefore, Blockchain technology has been adapted to provide security. Transactions done via Blockchain are tested through every block, which makes transactions secure and helps the banking system to work faster. The proposed algorithm WFB is used to estimate the average queue rate and avoid unwanted block generation. Then the trapezoidal fuzzy technique optimizes the allocation of blocks. An objective of this investigation is to enhance the security in banking systems from Cybercrimes by verifying Rain Drop Service (RDS) and Fingerprint Biometric without the need of any central authority. Once the service is completed, the service is a dropout and the following new service will be provided (Hence the name RDS). For the strong authentication scheme to fight against bank fraud, RSA encryption technique has been implemented successfully. Therefore, Blockchain technology increases the need for cyber security as a part of design architecture which intends to detect the stemming attacks in real time instead of repairing the damage.
Theme-Aware Semi-Supervised Image Aesthetic Quality Assessment

Image aesthetic quality assessment (IAQA) has aroused considerable interest in recent years and is widely used in various applications, such as image retrieval, album management, chat robot and social media. However, existing methods need an excessive amount of labeled data to train the model. Collecting the enormous quantity of human scored training data is not always feasible due to a number of factors, such as the expensiveness of the labeling process and the difficulty in correctly classifying data. Previous studies have evaluated the aesthetic of a photo based only on image features, but have ignored the criterion bias associated with the themes. In this work, we present a new theme-aware semi-supervised image quality assessment method to address these difficulties. Specifically, the proposed method consists of two steps: a representation learning step and a label propagation step. In the representation learning step, we propose a robust theme-aware attention network (TAAN) to cope with the theme criterion bias problem. In the label propagation step, we use preliminary trained TAAN by step one to extract features and utilize the label propagation with a cumulative confidence (LPCC) algorithm to assign pseudo-labels to the unlabeled data. This enables use of both labeled and unlabeled data to train the TAAN model. To the best of our knowledge, this is the first time that a semi-supervised learning method to address image aesthetic assessment problems has been studied. We evaluate our approach on three benchmark datasets and show that it can achieve almost the same performance as a fully supervised learning method for a small number of samples. Furthermore, we show that our semi-supervised approach is robust to using varying quantities of labeled data.
Cluster Representation of the Structural Description of Images for Effective Classification

The problem of image recognition in the computer vision systems is being studied.The results of the development of efficient classification methods, given the figure of processing speed, based on the analysis of the segment representation of the structural description in the form of a set of descriptors are provided.We propose three versions of the classifier according to the following principles: "object-etalon", "object descriptor-etalon" and "vector description of the object-etalon", which are not similar in level of integration of researched data analysis.The options for constructing clusters over the whole set of descriptions of the etalon database, separately for each of the etalons, as well as the optimal method to compare sets of segment centers for the etalons and object, are implemented.An experimental rating of the efficiency of the created classifiers in terms of productivity, processing time, and classification quality has been realized of the applied.The proposed methods classify the set of etalons without error.We have formed the inference about the efficiency of classification approaches based on segment centers.The time of image processing according to the developed methods is hundreds of times less than according to the traditional one, without reducing the accuracy.
An Image Edge Detection Algorithm Based on Multi-Feature Fusion

Edge detection is one of the core steps of image processing and computer vision. Accurate and fine image edge will make further target detection and semantic segmentation more effective. Holistically-Nested edge detection (HED) edge detection network has been proved to be a deep-learning network with better performance for edge detection. However, it is found that when the HED network is used in overlapping complex multi-edge scenarios for automatic object identification. There will be detected edge incomplete, not smooth and other problems. To solve these problems, an image edge detection algorithm based on improved HED and feature fusion is proposed. On the one hand, features are extracted using the improved HED network: the HED convolution layer is improved. The residual variable convolution block is used to replace the normal convolution enhancement model to extract features from edges of different sizes and shapes. Meanwhile, the empty convolution is used to replace the original pooling layer to expand the receptive field and retain more global information to obtain comprehensive feature information. On the other hand, edges are extracted using Otsu algorithm: Otsu-Canny algorithm is used to adaptively adjust the threshold value in the global scene to achieve the edge detection under the optimal threshold value. Finally, the edge extracted by improved HED network and Otsu-Canny algorithm is fused to obtain the final edge. Experimental results show that on the Berkeley University Data Set (BSDS500) the optimal data set size (ODS) F-measure of the proposed algorithm is 0.793; the average precision (AP) of the algorithm is 0.849; detection speed can reach more than 25 frames per second (FPS), which confirms the effectiveness of the proposed method.
An improved multiple populations quantum genetic algorithm

Abstract With the in-depth study of quantum genetic algorithm (QGA), the defect of premature convergence limits its development more and more. In order to further improve QGA, we propose an improved QGA based on multi population (IQGA). In the process of population initialization in IQGA, we generate multiple populations to avoid a single population falling into a local optimal value. We also abandon the fixed angle used by the traditional quantum revolving gate, and design a strategy to adjust the angle adaptively according to the difference from the optimal solution. In addition, we introduce a population catastrophe strategy to deal with the premature convergence of each population. The populations are connected based on migration operation, and the optimal solution of each population obtained by each iteration is collected in the elite group. A series of simulation experiments show that our improvement strategies of IQGA are effective.
Resolution Identification of Encrypted Video Streaming Based on HTTP/2 Features

With the inevitable dominance of video traffic on the Internet, Internet service providers (ISP) are striving to deliver video streaming with high quality. Video resolution, as a direct reflection of video quality, is a key factor of the video quality of experience (QoE). Since the displayed information of video cannot be observed by ISPs, ISPs can only measure the video resolution from traffic. However, with HTTP/2 being gradually adopted in video services, the multiplexing feature of HTTP/2 allows audio and video chunks to be mixed during transmission, making existing monitoring approaches unusable. In this article, we propose a method called H2CI to monitor resolution for adaptive encrypted video traffic under HTTP/2. We consider the size of the mixed data for identification. Specifically, H2CI consists of a length restoration method to extract restored fingerprints and a fingerprint-matching method for fine-grained resolution identification. The experimental results show that H2CI can achieve more than 98% accuracy for fine-grained resolution identification. Our method can be effectively applied to infer the adaptation behavior of encrypted video streaming and monitor the QoE of video services under HTTP/2.
Deep learned BLSTM for online handwriting modeling simulating the Beta-Elliptic approach

The beta-elliptic model (BEM) has proven a great success in several applications such as handwriting recognition and analysis, handwriting identification, the effect of age on the kinematics of hand movements, etc. With the emergence of deep learning technologies during the last years and their application in several fields, the implementation of beta-elliptic model with a simple multi-stage deep learned recurrent neural network (RNN) is required. In this paper, we propose a new online handwriting trajectory modeling by simulating the beta-elliptical approach to limit the calculation time and to have an end-to-end description system meeting the needs of mobile device users. The developed model deploys a multi-stage architecture based deep learned recurrent neural network (RNN) with bidirectional Long Short-Term Memory (BLSTM) that simulates the process of extracting the dynamic and geometric parameters composing the beta-elliptical vector. This architecture encompasses the pre-processing, segmentation, and approximation steps of the trajectory in its two velocity and geometric profiles and is traced by only neural computation sequences. To evaluate our model, a similarity degree between the beta-elliptic and BLSTM estimation models is measured by means of MAE (mean absolute error) and RMSE (Root Mean Square Error) metrics. Experimental results on LMCA and ADAB datasets show the efficiency of the proposed RNN model for online handwriting trajectory modeling consisting of 3.75%, 5.26% for RMSE, and 1.69%, 2.75% for MAE respectively.
Real-Time Video Processing for Ship Detection Using Transfer Learning

Automatic ship classification and detection is an interesting research field concerning maritime security. Automatic ship detection systems are important for maritime security and surveillance. These systems can be used to monitor marine traffic, illegal fishing and illegal activities which deal with the prospects of maritime security. This research has gained interest because many ships that are sailing on the ocean or sea do not install transponders which are used for tracking the ships. It will be a serious threat to the nation, mankind, and sea-life if we do not keep an eye on these kinds of ships. Therefore, in this we presented a novel Deep Learning method that will be used to detect ships by using satellite Images. This approach uses TensorFlow object detection API to detect objects in the images as we are concerned with object detection. The dataset used for this purpose is Maritime Satellite Imagery (MASATI)-v2 dataset which consists of various satellite images that are captured under different weather and dynamic conditions. As the real-time satellite monitoring is a kind of video thing, we proposed an approach to perform video processing to detect ships by using the model that is trained using MASATI-v2 dataset. For training the model we are using a Transfer Learning technique by utilizing (Single Shot Detector) SSD MobileNetV2. This algorithm is different from normal convolution neural networks, and it uses depth wise and pointwise separable filters to perform the task.
Mask R-CNN based Object Detection in Overhead Transmission Line from UAV Images

The transmission of electricity through Overhead Transmission Line (OTL) is a difficult task. The defects in OTL are caused by the presence of a bird's nest in the transmission tower, which frequently affects the entire surrounding area. Therefore in this article proposed two methods as preliminary research. In the first method, birds’ nests and transmission towers are located using template matching, a conventional technique. As a second method, introduced a convolutional neural network (CNN) with instance segmentation, which recognizes birds’ nests and transmission towers from images captured by an unmanned aerial vehicle (UAV). The second method is carried out through preprocessing, manual annotation, and feature extraction using the backend architecture Resnet-50 and trained into Region of Interest (ROI)Align, which produces output with two classes, as well as the bounding box and mask for the respective classes. When compared to traditional method, the proposed Mask Region based Convolutional Neural Network (Mask R-CNN) method achieves 98.7% accuracy.
Smart Healthcare System

Now a days, monitoring of various health parameters of patient at hospitals has become a widespread phenomenon. People are losing their lives in unanticipated way with heart attack by not finding symptoms of it before happening. We can decrease such unanticipated death rates by implementing the project named Patient Health Monitoring System. In this project we will design a system which monitors the patient health parameters like body temperature, heartbeat, blood pressure and so on by using Internet of Things (IoT). IoT is where data collection is done through various devices like sensors and data will be stored in Cloud where we can extract and analyze the data faster with efficiency by making interaction among them. The proposed technique screens the essential health parameters and transmits the information through a wireless communication which is additionally moved to a network by means of a wifi module. The information can be obtained anytime advancing the reception of the present status of the patient. The doctors, just as the caretaker are intimated quickly through a message service or an audio signaling device at the time of abrupt conditions or any sick indications. Moreover, complexity level, power consumption of our design System is low and is highly portable for healthcare monitoring of patients.
Adopting Assistive Technologies in Healthcare Processes: A Chatbot for Patients with Amyotrophic Lateral Sclerosis

The paper presents e-Health solutions for monitoring patients affected by Amyotrophic lateral sclerosis (ALS). Assistive technologies have a direct and positive impact on both the quality of life of patients and the overall management of the service. In the context of a wide project, we introduce an Ambient Assisted Living framework to investigate the adoption of telehealth and telemedicine applications in the healthcare process. First, we model the process of the hospital service where technological innovations have been applied. Second, we investigate the impact of a chatbot to improve the communications between operators and families. The chatbot obtained high adherence, compliance, and engagement of users. We provide evidence of how robust technological and organizational modelling can be successfully applied to support the staff of nutritionists staffs in ALS healthcare process. Furthermore, the solution discussed in this work provided a great impact during the COVID-19 emergency.
Learning to Detect with Constant False Alarm Rate

We consider the use of machine learning for hypothesis testing with an emphasis on target detection. Classical model-based solutions rely on comparing likelihoods. These are sensitive to imperfect models and are often computationally expensive. In contrast, data-driven machine learning is often more robust and yields classifiers with fixed computational complexity. Learned detectors usually provide high accuracy with low complexity but do not have a constant false alarm rate (CFAR) as required in many applications. To close this gap, we propose to add a term to the loss function that promotes similar distributions of the detector under any null hypothesis scenario. Experiments show that our approach leads to near CFAR detectors with similar accuracy as their competitors.