Reviewerly/exHarmony huggingface dataset:
Investigation of Fault Tolerant Scheduling Algorithm for Task in Cloud Systems Using Ant Colony Optimization

Cloud computing is defined as the sharing of computing and communication resources across a network of distributed data centres and is used by a wide range of systems. Fault tolerance is defined as a device's ability to gracefully respond to a sudden software or hardware failure. The addition of fault tolerance in cloud computing has several advantages, including improved failure recovery, lower infrastructure costs, and increased normal overall performance measurements. The ability to continue operating in the event of a power outage is the lowest level of fault tolerance. Many fault tolerant computer systems replicate all operations as a way to increase reliability by performing the same work on or more duplicate systems, so that if one fails, the other can take over. So, theoretically, fault tolerance techniques are used to anticipate disasters and take appropriate action before they occur. So, in a way, fault tolerance ideas are used to anticipate these disasters and take appropriate action before they occur. In this paper, we planned a set of rules as well as an aco to dynamically schedule appear real-time tasks with useful resource and fault-tolerant requirements directly to multiprocessor systems. They quantify the effectiveness of each of those strategies in improving the assure ratio, which would be defined as the percentage of general tasks completed within the system within the time limits. Our proposed Fault tolerance scheduling primarily based totally on heuristic algorithms has ambitions at reaching every fault tolerance and immoderate beneficial aid usage in the cloud. The experimental results show off that in comparison with Existing Dynamic Fault Tolerance Scheduling Techniques (DFTST) and Proposed Fault Tolerance Scheduling with Ant Colony Optimization (FTSACO) Algorithms extensively improves the common scheduling overall performance, achieves a more diploma of fault tolerance with immoderate beneficial aid usage, minimizes the advise reaction time and task rejection ratio, and reduces energy consumption.
Artificial Intelligence and Robotics Driving Tourism 4.0: An Exploration

With massive technological revolutions in place, Tourism 4.0 is gearing up to a new world of possibilities driven by the colossal amount of data generated from the tourist mobility across the world. In this context, AI and robotics are emerging out to be the game changers in the era of Tourism 4.0. This chapter aims to bring forth the various facets of AI and robotics driving the tourism industry towards a sustainable future. Some of the challenges that lie ahead have also been discussed in this work. It is concluded that with many new integrations to the field of AI and robotics, the tourism industry is expected to reach new heights of customization, service delivery, and experience management amplified by the accurate forecasting techniques in the days to come.
Can CS-AWARE be Adapted to the Needs of Different User Groups?

This chapter discusses the exploration of possibilities for adapting the CS-AWARE platform to the needs of different user groups, following an agile rapid prototyping and validation approach. This analysis is carried out by combining the results of the workshops assessed during CS-AWARE with an empirical evaluation regarding visualization techniques in state-of-the-art research. The results are used as a baseline regarding requirements for an interdependent multi-stakeholder collaborative environment encompassing the concepts of collaboration & communication, multi-stakeholder involvement, multi-stakeholder visualization, and situational awareness. A prototype testbed is used to evaluate the findings from employees working in cybersecurity as a focus group, as well as the general IT and economic sector to enable a cross-domain evaluation. The overall results focus on insights pertaining to data exploration, communication, and distinctions between research and market implementations.
Opinion Mining on Restaurant Rating Based on Aspects

Nowadays, most of the review rating about a restaurant is shared on the Internet about dineout, watching movie or dressing or a bottle of fine wine are all “experience goods for products”, that is, they have to be consumed or experienced in order for them to be assessed and evaluated. The Internet allows customers to contribute/share to their opinions and views based on the feedback/rating where user-generated content such as digital Word-of-Mouth (WoM) solutions, which are commonly looked at even more trusted and also trusted. Online client testimonials play a vital role and became significant which are expressed through the rating. Huge numbers of reviews/ratings are available for different products on different aspects to extract, and these ratings and reviews manually are a very difficult task. Opinion mining can extract the polarity of positive and negative words. By examining these reviews and rating the positive, negative polarity can be found. This paper basically focuses on classification of machine learning techniques.
Harris Corner Detection for Eye Extraction

Various algorithms and methodologies are available today for the detection and extraction of facial features like eyes which is an important aspect of face recognition used in many applications like biometrics, surveillance, and other areas. Image registration of various eye movements detecting different poses and alignment of the eyes has become a vast topic of research. A robust technique for detecting eyes is corner detection which is an approach in image processing and computer vision for extraction of features and other details of the images. In this paper a study of Harris corner detection approach has been discussed. Harris corner detection is applicable on the pre-processed images and the extracted features are compared with the feature data stored in the database. Harris detection method is a robust approach for significant feature detection and if combinedalong with another technique like non-maximum suppression gives much better output where corners need to be detected.
Semi-Supervised Medical Image Segmentation Based on Generative Adversarial Network

At present, segmentation for medical image is mainly based on fully supervised model training, which consumes a lot of time and labor for dataset labeling. To address this issue, we propose a semi-supervised medical image segmentation model based on a generative adversarial network framework for automated segmentation of arteries. The network is mainly composed of two parts: a segmentation network for medical image segmentation and a discriminant network for evaluating segmentation results. In the initial stage of network training, a fully supervised training method is adopted to make the segmentation network and the discrimination network have certain segmentation and discrimination capabilities. Then a semi-supervised method is adopted to train the model, in which the discriminant network will generate pseudo-labels on the results of the segmentation for semi-supervised training of the segmentation network. The proposed method can use a small part of annotated dataset to realize the segmentation of medical images and effectively solve the problem of insufficient medical image annotation data.
Parallel Sponge-Based Authenticated Encryption With Side-Channel Protection and Adversary-Invisible Nonces

Since its birth in 2000, Authenticated Encryption (AE) has been a hot research topic, and many new features have been proposed to boost its security or performance. The block cipher was the dominant primitive in constructing AE schemes, followed by stream ciphers and compression functions until the sponge construction emerged in 2011. Sponge-based AE schemes provide functional characteristics such as parallelizability, incrementality, and online. They also offer security features for protection against active or passive adversaries. Currently, there exist parallel sponge-based AE schemes, but they are not protected against simple power analysis (SPA) and differential power analysis (DPA). On the other hand, sponge-based AE schemes that protect against such attacks are serial and cannot be parallelized. Furthermore, sponge-based AE schemes handle the nonces in a way that could allow misuse. So, sponge-based AE schemes that hide the nonce from adversaries are also an open problem. This work aims to bridge these gaps by proposing a parallel sponge-based AE with side-channel protection and adversary-invisible nonces (PSASPIN), using parallel fresh rekeying and the duplex mode of the sponge construction. A leveled implementation is used to implement the key generation part using a pseudorandom function (PRF) based on the Galois field multiplication. The data processing (the rekeyed) part is implemented using the sponge-based duplex mode. Finally, the security proof of the proposed scheme is provided using game-based theory according to the PRP/PRF switching lemma, and its performance is analyzed.
Pattern-Based Syntactic Simplification of Compound and Complex Sentences

With the advent of new technologies, simplifying the text automatically has been very popular and of high importance among natural language researchers during the last decade. The predominant research done in the area of Automatic Sentence Simplification(ASS) is inclined to either lexical or syntactical simplification of sentences. From the literature survey, it is observed that existing research in lexical simplification makes use of word substitution technique. This causes word sense ambiguity in cases where the word synonyms are not appropriate for a sentence in the given context. In contrast syntactical simplification though is accurate and applicable to Natural Language Processing (NLP) tasks requires tremendous efforts to construct rules for a given domain. The research proposes a framework called, Pattern-based Automatic Syntactic Simplification(PASS) which identifies sentences and applies rules based on grammatical patterns to simplify the sentences thereby making it more generic for NLP tasks. PASS is evaluated by human experts to rate the usefulness of the framework based on fluency, adequacy and simplicity of the sentences. Furthermore, the framework is automatically evaluated with the available online corpus using automatic metrics of SARI, BLEU, and FKGL. The proposed approach generates promising results in the field of ASS and could be used as a preliminary module for NLP tasks as well as other natural language-related applications like summarization, anaphora resolution, question-answering, and many more.
Video synopsis algorithm based on two-stage target tubes grouping

Video synopsis generates a concentrated video that can be browsed quickly. With the increase of condensation ratio, more pseudo collisions between target tubes will occur. To solve this problem, this paper proposed a video synopsis algorithm based on two-stage target tubes grouping. In the first stage, using the hypergraph to analyze the collision relationship between target tubes, and the target tubes are grouped according to the hyper-edges. In the second stage, a clustering algorithm based on equal distance nearest neighbor sampling is proposed to group the target tubes. Then, selecting target tubes according to the selection principle of quantity priority between groups and length priority within groups (QPB-LPG) . Finally, these target tubes are rearranged to generate concentrated videos with smaller pseudo collisions. The experimental results show that this algorithm can significantly reduce the pseudo collision between target tubes without reducing the frame condensation ratio and frame compact rate compared with existing video concentration algorithms, and the feasibility of the method is fully verified.
Rethinking Embedded Unsupervised Feature Selection: A Simple Joint Approach

Recently, various embedded methods for unsupervised feature selection have been put forward. However, most of them adopt a two-step strategy, i.e., selecting <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> top-ranked dimensions according to a learned order of all features, then conducting K-means clustering for evaluation. This commonly used strategy usually results in a group of sub-optimal features, because the selected <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> top-ranked features are seldom the desired top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> dimensions. To address this problem, we rethink the two steps in a joint manner and propose a simple yet effective approach called <b>U</b> nsupervised <b>F</b> eature <b>S</b> election with <b>S</b> eparability ( <b>UFS<inline-formula><tex-math notation="LaTeX">$^{\mathbf{2}}$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msup><mml:mrow/><mml:mn mathvariant="bold">2</mml:mn></mml:msup></mml:math><inline-graphic xlink:href="chang-ieq4-3178715.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula></b> ) to simultaneously select features and cluster data. More specifically, a binary vector is seamlessly integrated into K-means to select an exact number of features for clustering. Different from previous embedded methods involving <inline-formula><tex-math notation="LaTeX">$l_{2,1}$</tex-math></inline-formula> -norm, our joint model explicitly uses the parameter <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> (i.e., the number of selected features). Afterwards, a customized term for the binary vector is designed to maximize the separability among selected feature dimensions. In order to solve the formulated 0-1 integer programming problem, an iterative algorithm is developed. Finally, we evaluate the proposed approach extensively on different datasets. Despite the relative simplicity, UFS <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> remarkably and generally outperforms state-of-the-art baselines.
A Machine Learning Pipeline for Mortality Prediction in the ICU

Mortality risk prediction for patients admitted into the intensive care unit (ICU) is a crucial and challenging task, so that clinicians are able to respond with timely and appropriate clinical intervention. This becomes more urgent under the background of COVID-19 as a global pandemic. In recent years, electronic health records (EHR) have been widely adopted, and have the potential to greatly improve clinical services and diagnostics. However, the large proportion of missing data in EHR poses challenges that may reduce the accuracy of prediction methods. We propose a cohort study that builds a pipeline that extracts ICD-9 codes and laboratory tests from public available electronic ICU databases, and improve the in-hospital mortality prediction accuracy using a combination of neural network missing data imputation approach and decision tree based outcome prediction algorithm. We show the proposed approach achieves a higher area under the ROC curve, ranging from 0.88-0.98, compared with other well-known machine learning methods applied to similar target population. It also offers clinical interpretations through variable selection. Our analysis also shows that mortality prediction for neonates was more challenging than for adults, and that prediction accuracy decreases as patients stayed longer in the ICU.
Deep Learning-Based Differential Distinguisher for Lightweight Cipher GIFT-COFB

Cryptography is concerned with the methods of secure communication between two or more parties. In recent years, the use of deep learning architecture in cryptanalysis of lightweight ciphers has increased manifold. In this paper, our work includes developing a deep neural network-based differential distinguisher for round-reduced GIFT-COFB lightweight block cipher. This work is inspired by the work of Gohr and Baksi et al. who found out an efficient way of mounting differential attack on block ciphers using machine learning. In our work, we first generate two types of datasets using two different sets of differential classes and then build a multi-layer perceptron network which can distinguish between 2 and 6 rounds of GIFT-COFB cipher data from random data. We also use an improvised model with slight changes in the original approach of the differential distinguisher presented by Baksi et al. which gives a higher accuracy.
Federated Adam-Type Algorithm for Distributed Optimization With Lazy Strategy

For large-scale machine learning tasks, distributing data in multiple clients, and using distributed optimization algorithms with a parameter server can accelerate the training process. The federated average algorithm has been widely used for distributed optimization via training local models in parallel and aggregating local models in a server to obtain the global model. To further improve the performance of the federated average algorithm, a novel federated learning algorithm have been proposed in this article by embedding a lazy strategy in the distributed Adam-type algorithm. In the proposed algorithm, the learning rate is adjusted adaptively in local update and lazy update strategy is applied on the second-order momentum of clients to make the learning rate identical. The convergence of the proposed algorithm is provided for both convex and nonconvex loss functions. Experiments have been conducted on MNIST digit recognition data set and CIFAR-10 data set. Experimental results show that the proposed algorithm can significantly reduce the communication overhead, thereby reduce the training time by 60% for CIFAR-10 data set, and the proposed algorithm achieve better performance than the federated average algorithm and its momentum version.
Monitoring Employees Entering and Leaving the Office with Deep Learning Algorithms

This study attempts to create a system to monitor employees entering and leaving the office using face recognition. In addition, the system also signals by LED when recognizing a staff who has clearance to enter or notifies those who do not in the area. Events of entering and leaving from staff are written into a log file for management purposes. The face-detection and image processing utilize Multi-task Cascaded Convolutional Network. Feature data is then extracted from the processed images by FaceNet, which is classified by the Support Vector Machine algorithm into a model. Information of employees and logs are saved in MySQL database, which is also used in a web application using Python and Django web framework.
Learning from Categorical Data Subject to Non-random Misclassification and Non-response Under Prior Quasi-Near-Ignorance Using an Imprecise Dirichlet Model

The problem of learning from categorical data is commonly known in many fields. Besides the principal complexity arising from non-correspondence between latent and manifest categories, other common obstacles are non-random non-response and lack of prior knowledge about probabilities of the latent categories. In this paper, we study a situation involving the intersection of these obstacles under the mild condition that one confirmed observation exists in each category; a setting that is highly expected to generally hold in most practices. Under this setting, we derive a quasi-near-ignorance prior for the chances of the latent categories employing the imprecise Dirichlet model. Then, we use this prior to obtain the corresponding posterior predictive probability. We prove that a non-trivial probability interval for the chance of the next observation is obtainable despite the complex nature of the problem.
Drive a Vehicle by Head Movements: An Advanced Driver Assistance System Using Facial Landmarks and Pose

In this paper, we propose an Advanced Driver Assistance System (ADAS) for people with disabilities. Our first prototype is a facial landmark and pose estimation system coupled with a drive-by-wire hardware. This is a Ford Fusion Plugin Hybrid research vehicle, and drive-by-wire conversion is done by Dataspeed, Rochester Hills MI. Our early prototypes were based on open-loop control, but later we have developed a facial landmark detection based Artificial Intelligence (AI) system controlling the steering column using the drive-by-wire feature of the vehicle. With the help of this system, people with disabilities are expected to drive motor vehicles with increased comfort and safety. In future versions of the system, we are planning to add more automation and assistance features for other use cases.
Situation Awareness for Autonomous Vehicles Using Blockchain-Based Service Cooperation

Efficient Vehicle-to-Everything enabling cooperation and enhanced decision-making for autonomous vehicles is essential for optimized and safe traffic. Real-time decision-making based on vehicle sensor data, other traffic data, and environmental and contextual data becomes imperative. As a part of such Intelligent Traffic Systems, cooperation between different stakeholders needs to be facilitated rapidly, reliably, and securely. The Internet of Things provides the fabric to connect these stakeholders who share their data, refined information, and provided services with each other. However, these cloud-based systems struggle to meet the real-time requirements for smart traffic due to long distances across networks. Here, edge computing systems bring the data and services into the close proximity of fast-moving vehicles, reducing information delivery latencies and improving privacy as sensitive data is processed locally. To solve the issues of trust and latency in data sharing between these stakeholders, we propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. We set up proof-of-concept experiments with Hyperledger Fabric and virtual cars to analyze the system throughput with secure unicast and multicast data transmissions. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.
A smart virtual vision system for health monitoring

One of the greatest challenges in regard to the elderly is the autonomous and healthy availability of appropriate beneficial and social functionalities to achieve the primary goal of prolonging independent living at home. In 2050, the forecast population of elderly people in India is about 300 million. The traditional kind of joint family in Indian culture has come under pressure due to family planning awareness, migration to cities, a lack of sustainability, and inadequate guidance from the elders. A virtual vision system (VVS) is a home-based monitoring device that captures the scene continuously to aid the user. The system helps the individual to navigate in a closed environment. The user can provide the system with commands, queries, and/or demands using free form natural language input to receive help. A wearable sensor that can track physiological parameters such as oxygen saturation, temperature, and pulse rate also can be integrated into the system. The risk of falling and potential injury is one reason for the elderly being placed in care facilities. The system also supports users by tracking their mobility, and helps to identify falls. The system autonomously patrols the user's environment without any user activity and checks if the user is well and has not suffered a fall. The VVS emphasis is on an application platform that incorporates distinct technical solutions such as biometric tools, remote doctor visits, emergency call and tracking systems, drug dispensers, and online shopping. In elderly care, continuous monitoring helps not only to focus on improving the senior's safety when a caregiver is not present, but also provides peace of mind to adult children who may be concerned about the welfare of their loved one. Such systems would be helpful for those in self-quarantine/isolation during this COVID-19 pandemic situation.