Reviewerly/exHarmony huggingface dataset:
Applications of IoT and Blockchain Technologies in Healthcare: Detection of Cervical Cancer Using Machine Learning Approaches

The Internet of Things is an emerging subject of specialized, social, and efficient inference. Such as User products, long-lasting goods, cars and trucks, business enterprise and utility elements, sensors, and many other devices which are connected with internet. Big Data, the term refers about the large amount of data will be stored for the purpose of data sharing, data mining, data storing in various fields and applications like Health care, Finance and Transportation. Blockchain technology is like a public ledger of patients medical records where maintained by the hospital with secret genetic technology. The blocks should not modify in the middle of the chain due to inbuilt mechanism. Machine learning is the one of the fast-growing methods in all kind of fields, especially in medical fields, it is used to detect the disease state of the patient at early stage. Thus, the paper refers about how the advanced technologies are IoT, Big Data, and Blockchain methodologies are helpful to diagnosis the Cervical cancer at earlier in medical fields. The dataset of Cervical cancer has been taken from MDE Laboratory with the amount of 150 records.
Web-Questionnaire-Based Corpus Creation Under Assumption of Human as Speech Targets

This paper presents a method that uses a web questionnaire to create a corpus containing spontaneous utterances of natural ideas, which may contain grammatical mistakes. In an experimental implementation of the method, the subjects were informed that they were receiving nursing care from a person, and they were required to answer a web-based questionnaire in which their responses were recorded as speech utterances. Compared to the Wizard of Oz approach and interview-based corpus-creation methods, the presented method simplifies the collection of utterances. Furthermore, we conducted a two-fold assessment to verify the effectiveness of the presented method. First, the approach exhibited a significant reduction in workload compared to interview-style utterance collection. Second, we compared the variety of expressions collected when subjects were informed that they were talking to a person with those collected when they were informed that they were communicating with a nursing robot. The results indicate that, although the number of utterances was larger for a robot than for a person, in terms of other metrics such as time efficiency index, the total number of morphemes, the average number of morphemes per utterance, the number of unique morphemes, and coefficient of variation, the utterances were larger for a human speech target than for a robot.
Restricting the Number of Times That Data Can Be Accessed in Cloud Storage Using TrustZone

Data assured deletion can achieve the deletion of outsourced data without possessing it physically in the cloud environment. However, most of existing schemes are designed and implemented on the traditional platforms, which might be not applicable to the scenarios that data is accessed via resource-constrained mobile terminals due to the bandwidth and computation consumptions. In this paper, we present a counter-based data assured deletion scheme using TrustZone, which can restrict the number of times that data can be accessed by restricting the number of times the encryption key can be used on resource-constrained mobile terminals. For this purpose, we bind the number of reads of a secure file to a virtual monotonic counter to count the number of times the key has been used. Then the currently used number of times is compared with the pre-set number of times to check the use condition is satisfied or not. If the comparison result is false, the key is destroyed securely and a public deletion proof is generated. Additionally, we check the hash-based integrity of the key information to detect and prevent the replay attack. In our design, the key retrieve is one-time, which can save the traffic cost. We only use some basic and common cryptographic operations provided by the operating system, which can save the computation cost. The physical security functionalities provided by TrustZone are taken advantage of, which provides a higher security guarantee for our scheme. The simulation implementation and results show our scheme is feasible and practical for use.
Empirical Analysis of Deep learning Techniques for Enhancing Patient Treatment Facilities in Healthcare Sector

The study investigates with Machine learning (ML), which is a type of neural network (AI) that empowers software programmers to start increasing prediction without being done with full to do so. Because data is so valuable, improving strategies for intelligently having to manage the now-ubiquitous content infrastructures is a necessary part of the process toward completely autonomous agents. In a nutshell, deep learning is a subset of machine learning that solves problems that machine learning alone cannot. Deep learning use neural networks to boost computing labour while delivering accurate results. NLP, speech recognition, and facial recognition are just a few of the fantastic uses of deep learning. For example, when you submit a photo of yourself and a buddy to Facebook, Facebook dynamically tags your colleague and proposes a name for you to use. To recognise a face, Facebook employs deep learning algorithms. Deep learning techniques comprehend spoken human languages and transform them to text. Deep learning, in tandem with IoT, might lead to a slew of game-changing advancements in the future. Monitoring cardiac rhythms, as well as glucose levels, may be challenging, and even those who are represented at medical institutions. Intermittent heart rate assessments cannot protect against sudden changes in vital signs, and standard techniques of heart rhythm surveillance used in hospitals require patients to be permanently attached to wired apparatus, limiting their mobility.
Machine Vision based Intelligent Breast Cancer Detection

Artificial intelligence, especially deep learning, has sparked a great deal of interest in bioinformatics, particularly complications in clinical imaging. It has achieved great success by helping the CAD system achieve high-precision results. Despite this, detecting breast cancer on mammography images is still considered a critical challenge. The work aims to decrease FPR and FNR and increase the value of MCC. To achieve this goal, two state-of-the-art object detection models are used, YOLOv5 and Mask RCNN.YOLOv5 detects and classifies the mass as benign or malignant. Due to the spatial limitations of YOLOV5, the original model is modified to achieve the desired results. Mask RCNN detects the edges of tumours invading the breast parenchyma and also detects the size of the tumours. The size of the tumours defines the stage of cancer. The model was trained on the INbreast dataset with YOLOv5+Mask RCNN. The performance of the proposed model was evaluated compared to the original version of YOLOv5. The proposed technique achieves higher performance with a lower False-positive rate of 0.05 and False-negative rate of 0.03 and a high MCC value of 92.02%. The experiments performed show that the accuracy of YOLOv5 in combination with Mask RCNN is 0.06 higher than that of YOLOv5 alone. Additionally, this work could help determine the patient's prognosis and allow physicians to be more accurate and predictable at early-stage breast cancer detection.
An Efficient Text Summarization Using Term and Inverse Frequency With Key Phrase Identification in Malayalam Language

Malayalam is a morphologically rich language. Indian languages have several language genres like Indo-Aryan, Sino-Tibetan, and Dravidian languages, where Malayalam comes under the Dravidian language genres. Text summarization in Indian languages is hard because of their rich content and lack of easy availability of annotated data. Here we propose a summarization system for Malayalam language documents based on the Term Frequency &#x2013; Inverse Document Frequency (TF-IDF) scheme. Our summarizer will accept a single Malayalam text document as input, then by using the TF-IDF measurement scheme with keyword identification a summary document is generated. The proposed method successfully summarized Malayalam literature documents with 90.6&#x0025; accuracy.
PortiK: A computer vision based solution for real-time automatic solid waste characterization – Application to an aluminium stream

In Material Recovery Facilities (MRFs), recyclable municipal solid waste is turned into a precious commodity. However, effective recycling relies on effective waste sorting, which is still a challenge to sustainable development of our society. To help the operations improve and optimise their process, this paper describes PortiK, a solution for automatic waste analysis. Based on image analysis and object recognition, it allows for continuous, real-time, non-intrusive measurements of mass composition of waste streams. The end-to-end solution is detailed with all the steps necessary for the system to operate, from hardware specifications and data collection to supervisory information obtained by deep learning and statistical analysis. The overall system was tested and validated in an operational environment in a material recovery facility. PortiK monitored an aluminium can stream to estimate its purity. Aluminium cans were detected with 91.2% precision and 90.3% recall, respectively, resulting in an underestimation of the number of cans by less than 1%. Regarding contaminants (i.e. other types of waste), precision and recall were 80.2% and 78.4%, respectively, giving an 2.2% underestimation. Based on five sample analyses where pieces of waste were counted and weighed per batch, the detection results were used to estimate purity and its confidence level. The estimation error was calculated to be within ±7% after 5 minutes of monitoring and ±5% after 8 hours. These results have demonstrated the feasibility and the relevance of the proposed solution for online quality control of aluminium can stream.
Emergent Schr\"{o}dinger cat states during superradiant phase  transitions

Superradiant phase transitions (SPTs) are important for understanding light-matter interactions at the quantum level [1, 2], and play a central role in criticality-enhanced quantum sensing [3]. So far, SPTs have been observed in driven-dissipative systems [4-9], but the emergent light fields did not show any nonclassical characteristic due to the presence of strong dissipation. Here we report an experimental demonstration of the SPT featuring the emergence of a highly nonclassical photonic field, realized with a resonator coupled to a superconducting qubit, implementing the quantum Rabi model [10, 11]. We fully characterize the light-matter state by Wigner matrix tomography. The measured matrix elements exhibit quantum interference intrinsic of a photonic Schr\"{o}dinger cat state [12], and reveal light-matter entanglement. Besides their fundamental importance, these hitherto unobserved emergent quantum phenomena are useful for quantum metrology and fault-tolerant quantum computation.
Sequential Covariance Intersection Based on the Upper Bound of Correlation Coefficient

A sequential fusion of multiple partially correlated random vectors is investigated in this paper. The biggest difficulty in dealing with partially correlated random vectors is that the cross covariances are unknown, and this is also challenging for traditional covariance intersection algorithm to solve it in terms of partial correlation. Therefore, the covariance interaction algorithm based on the upper bound of the correlation coefficients of partially correlated random vectors is generalized to the sequential fusion of multiple partially correlated random vectors. The sequential fusion algorithm can be applied to the fusion of measurement information so as to perform estimation of Kalman filter, which generally requires to know the complete cross correlations. Finally, the simulation results show that the proposed sequential fusion algorithm has a tighter upper bound than the traditional covariance interaction.
OBTAINING EXACT SOLUTIONS OF VISCOINCOMPRESSIBLE PARALLEL FLOWS USING PYTHON

This paper applies the solution of ODE's encounter in fluid mechanics by augmenting it with symbolic Python. The implementation procedure has been illustrated for two types of parallel flows, i.e., Couette and Hagen Poiseuille flow. The suggestive results thus obtained are plotted and presented using Matplotlib. The manuscript will help beginners of fluid mechanics solve the fluid flow problems computationally and produce the results in a better way.
COLORIZATION OF GRAYSCALE IMAGES IN DEEP LEARNING

One of the most exciting Deep Learning applications is color grading black and white pictures. Coloring a grayscale image is a simple exercise for the human mind in general; we learn to fill in missing shades in colouring books from a young age. This task used to necessitate a large number of people involvement and complex tasks, however in the recent years, thanks to AI and Deep Learning, the entire process can be automated from start to finish. In this paper, I looked at a number of publications that presented various AI and Deep Learning methodologies. I'll go over each approach and tactic that has been utilised to investigate different Neural Networks in Deep Learning and how they may be used to achieve incredible outcomes. Throughout this paper, I'll start by incorporating whatever the researchers achieved, and I'll add a wholly separate generator model as well as some changes to the training technique, which will significantly reduce the overall size of the required dataset whilst producing remarkable outcomes. A GAN based model is proposed and trained on the COCO dataset. A new experimental model has been introduced which marks as the final model of our system. We notice a slight decrease in the loss function in the final model in comparison to the first model. The baseline model computed a loss of 7.6 % and the final model computed a loss of 6.7% which denotes that the final model produces results with higher accuracy
Harmony versus Voicing. Modeling Local-Level Salience and Stability in Jazz after 1960

This article considers the counterpoint in jazz between the implied, underlying form and what is actually played in a given performance. My observations are framed within a model for jazz listening called “Stable Norms and Salient Deviations” (SNSD). Detailed analysis of an improvisation by jazz/R&B musician Robert Glasper demonstrates how my ideas about voicings are applicable to melodic improvisation. In addition to investigating theoretical and analytical matters, the essay explores the creative potential of my approach. Dieser Artikel betrachtet den Kontrapunkt im Jazz zwischen der implizierten zugrunde liegenden Form und dem, was tatsächlich in einer bestimmten Aufführung gespielt wird. Meine Beobachtungen sind in ein Modell für das Jazz-Hören mit dem Namen „stabile Normen und markante Abweichungen“ eingebettet. Die detaillierte Analyse einer Improvisation des Jazz-/R&B-Musikers Robert Glasper zeigt, wie meine Ideen zu Voicings auf melodische Improvisation anwendbar sind. Neben der Untersuchung theoretischer und analytischer Fragen untersucht der Essay das kreative Potenzial meines Ansatzes.
Predicting solar radiation with Artificial Neural Network based on urban geometrical classification

This research introduces the adaptation and development of an open-source Artificial Neural Network (ANN) with the aim of predicting solar radiation for newly generated neighbourhoods in Aswan, Egypt as an example of a hot arid zone. The outcomes are the result of training the ANN on a database of classified urban geometries and their solar radiation simulation results for local weather conditions. The classification of this database was first introduced and discussed in (Lila and Lannon 2019). This paper discusses the different stages of developing the ANN code and its final version capabilities. The ANN code was developed to differentiate the training process from the prediction code to allow for the reuse of the trained ANN in multiple tests. The ANN code was tested for different database sizes to predict individual buildings’ solar radiation and was also used to predict solar radiation for urban configurations that were not part of the training process. The results of these ANN predictions were compared to conventional solar radiation simulation results to establish the accuracy and time saved.
Genetic-WFC: Extending Wave Function Collapse With Genetic Search

This article presents genetic wave function collapse (WFC), a procedural level generation algorithm that mixes genetic optimization with WFC, a local adjacency constraints propagation algorithm. We use a synthetic player to evaluate the novelty, safety, and complexity of the generated levels. Novelty is maximized when the synthetic player goes on tiles not visited for a long time, safety is related to how far it can see, and complexity evaluates the variability of the surrounding tiles. WFC extracts constraints from example levels, and allows us to perform the genetic search on levels with few local asset placement errors, while using as little level design rules as possible. We show that we are able to rely on WFC while optimizing the results, first by influencing WFC asset selection and then by reencoding the chosen modules back to our genotype, in order to optimize crossover. We compare the fitness curves and best maps of our method with other approaches. We then visually explore the kind of levels we are able to generate by sampling different values of safety and complexity, giving a glimpse of the variability that our approach is able to reach.
Ostracism and Knowledge Hiding: Sequential Mediation of Efficacy Needs and Psychological Distress

Drawing on the conservation of resources theory and need-threat model, this study analyses the mediating role of efficacy needs and psychological distress on the relationship between workplace ostracism and knowledge-hiding behaviour of employees. Time-lagged data were collected from 225 employees working in the mobile telecommunication sector through online survey instruments prepared in light of past literature. SPSS23 and SmartPLS3 were used to analyse the data. The results obtained confirm the initial hypothesis that there exists a positive relationship between workplace ostracism and knowledge hiding. Furthermore, the results also established the sequential mediating roles of efficacy needs and psychological distress between workplace ostracism and knowledge-hiding behaviour of employees. This study adds to the literature suggesting that workplace ostracism represents a real threat to companies that aim to foster knowledge-sharing behaviours. Moreover, it reveals that efficacy needs and psychological distress are two underlying mechanisms behind the baseline relationship.
Revocable Hierarchical Identity-Based Authenticated Key Exchange

Identity-based authenticated key exchange (IB-AKE) would be considered to have an advantage in the sense that it does not require certificate management. However, IB-AKE has not both a key delegation functionality and a key revocation functionality. This leaves the problem of the burden to the private key generator when there are a large number of parties in the system, and the problem of the lack of a clear way to eliminate dishonest parties from the system. In this paper, we propose a new authentication mechanism called revocable hierarchical IB-AKE (RHIB-AKE), which can decentralize key generation and revocation performed by a PKG. We also propose a generic construction of RHIB-AKE from a revocable hierarchical identity-based key encapsulation mechanism (RHIB-KEM). We obtain the first RHIB-AKE schemes from pairings or lattices by our generic construction since RHIB-KEM is known to be constructed from them. For security, we show that our scheme resists against leakage of all combinations of master, static, current, and ephemeral secret keys except ones trivially break the security.
Cognitive analysis of the everyday cognition functions

As a result of the present research, the systematisation of the functions of everyday cognition was carried out, which made it possible to explicate its functions that had not previously been the subject of special philosophical consideration: ontological, regulatory, reflexive, selective, prognostic, programming function of the activity, behaviour and communication function of reproduction of social life. The cognitive mechanisms and communicative schemes of everyday cognition, its existential and methodological functions, the features of the functions of cognitive and digital socialisation, as well as the adaptive function of everyday cognition at the present stage are considered. It was concluded that everyday cognition is a key cognitive factor in the reproduction of social life.
Dynamic Survival Analysis for EHR Data with Personalized Parametric Distributions.

The widespread availability of high-dimensional electronic healthcare record (EHR) datasets has led to significant interest in using such data to derive clinical insights and make risk predictions. More specifically, techniques from machine learning are being increasingly applied to the problem of dynamic survival analysis, where updated time-to-event risk predictions are learned as a function of the full covariate trajectory from EHR datasets. EHR data presents unique challenges in the context of dynamic survival analysis, involving a variety of decisions about data representation, modeling, interpretability, and clinically meaningful evaluation. In this paper we propose a new approach to dynamic survival analysis which addresses some of these challenges. Our modeling approach is based on learning a global parametric distribution to represent population characteristics and then dynamically locating individuals on the time-axis of this distribution conditioned on their histories. For evaluation we also propose a new version of the dynamic C-Index for clinically meaningful evaluation of dynamic survival models. To validate our approach we conduct dynamic risk prediction on three real-world datasets, involving COVID-19 severe outcomes, cardiovascular disease (CVD) onset, and primary biliary cirrhosis (PBC) time-to-transplant. We find that our proposed modeling approach is competitive with other well-known statistical and machine learning approaches for dynamic risk prediction, while offering potential advantages in terms of interepretability of predictions at the individual level.
Augmented Shortcuts for Vision Transformers

Transformer models have achieved great progress on computer vision tasks recently. The rapid development of vision transformers is mainly contributed by their high representation ability for extracting informative features from input images. However, the mainstream transformer models are designed with deep architectures, and the feature diversity will be continuously reduced as the depth increases, i.e., feature collapse. In this paper, we theoretically analyze the feature collapse phenomenon and study the relationship between shortcuts and feature diversity in these transformer models. Then, we present an augmented shortcut scheme, which inserts additional paths with learnable parameters in parallel on the original shortcuts. To save the computational costs, we further explore an efficient approach that uses the block-circulant projection to implement augmented shortcuts. Extensive experiments conducted on benchmark datasets demonstrate the effectiveness of the proposed method, which brings about 1% accuracy increase of the state-of-the-art visual transformers without obviously increasing their parameters and FLOPs.