Reviewerly/exHarmony huggingface dataset:
Mortality Prediction in ICU Using Artificial Neural Networks

Abstract: Intensive Care Unit (ICU) death rate prediction supported artificial neural network, patients WHO have important unwellness and injury can get admitted to the unit. The dying rate for sufferers admitted to the unit can vary from Associate in Nursing underlying illness with a dying rate as low as one in twenty sufferers admitted for non-compulsory surgical operation and as excessive as one in 4 sufferers with metabolic process diseases. Artificial Neural Networks facilitate health care management selections, improve care and cut back value at same time by exploiting the applications of ANN within the care department. It helps to alter, minimize errors and predict additional correct diagnoses for the diseases or injuries that may cut back patients' risk of hospitalization supported given knowledge. Associate in Nursing empiric study was conducted with 4000+ adult patients from 100+ ICUs. The patients with ≥1 organ failure are forty sixth. Patients without infection receiving antibiotics stand for an hour. There have been 500+ deaths and 183 discharges from the unit. In 1627 patients admitted among twenty four h of the study day, the standardized mortality quantitative relation was zero.67. We have a tendency to develop our model exploitation of a synthetic Neural Network to predict the death rate of sick patients supported by their diseases, injuries, and medical records. This can additionally facilitate managing the unit beds in hospitals throughout the Associate in Nursing emergency.
Classical and quantum parts of conditional mutual information for open quantum systems

We study the classical, classical-quantum, and quantum parts of conditional mutual information in the ``system-environment-ancilla'' setting of open quantum systems. We perform the separation of conditional mutual information by generalizing the classification of correlations of quantum states. The condition for identifying the classical part of conditional mutual information is given by adapting the no-local-broadcasting theorem to this setting, while the condition for the classical-quantum part of conditional mutual information is obtained by considering the multipartite quantum discord and the no-unilocal-broadcasting theorem. For the quantum part of conditional mutual information, we further generalize the characterization of entanglement by quantum discord of state extensions to the multipartite setting to derive a generalized Koashi-Winter-type monogamy equality for conditional mutual information. Our results have explicit dependence on the extensions of the environment, which are useful for studying different environmental contributions to the quantum non-Markovianity of open quantum systems.
Facial Expression Recognition Using Convolutional Network

Abstract: This paper focuses on Emotion Recognition using facial expressions. It aims to detect facial expressions accurately and efficiently. Convolutional Neural Network has developed to help in recognizing emotions through facial expressions and classify them into seven basic categories which are: happy, sad, neutral, surprise, fear, disgust, and angry. So, Convolutional Neural Network is implemented to extract relevant features of the input images and classify them into seven labels. For evaluating the proposed model, Facial Emotion Recognition 2013 dataset is usedso that the model achieves the best accuracy rate. Facial expression recognition has been an activearea of research over the past few decades, and it is still challenging due to the high intra-class variation.
An Overview of Classification of Electrooculography (EOG) Signals by Machine Learning Methods

The distribution of the studies conducted between 2011-2021 in the fields of (Electrooculography) EOG and eye movements, EOG and wheelchair, EOG and eye angle, EOG and sleep state, EOG and mood estimation and EOG and game application was determined according to years, and the most cited studies were examined and presented. The study areas are listed as Eye Movement Classification, Wheelchair, Sleep state, Eye Angle, Mood State and Game Applications from the most to the least number of articles. When we examine in terms of the number of citations, they are listed as Sleeping state, Eye Movement Classification, Wheelchair, Eye Angle, Mood State and Game Applications, from the most to the least. In these studies, it has been tried to make the lives of people who have become disabled in various ways better by using the brain-computer interface with machine learning.
Online Transaction Security Using Face Recognition: A Review

Abstract: The growing development of the e-commerce market is of great significance in the world. In this online shopping process, the security of personal information and debit card or credit card information increases the popularity of e-commerce and is an important part. This paper provides limited information and is necessary for transferring money by online transactions by securing data and trust of customers. Facial recognition technology identifies a person's information through a digital image. It is automatically determined. It is mainly used in security systems. It matches facial recognition from different angles. It is mainly used in airports. It will recognize the face and we can avoid some unwanted fraud by using the facial recognition system. The fundamental gain of face popularity is used for fraud restrict and crime controlling motive due to the fact face pictures which have been archived and recorded, on the way to assist us to perceive someone later. Facial recognition identifies each distinct skin tone on the surface of a human face, such as curves on cheeks, eyes and nostrils, and more. The technology can also be used in very dark conditions and prevent identity theft.
THE INTERPRETATION OF STUDENTS’ EDUCATIONAL ACTIVITIES

The purpose of this article is to study the interpretation of the essence of educational activities of university students from the point of view of different understanding of this concept by different subjects of the educational process. The author provides the results of the questionnaire of students of a pedagogical university, conducted in 2013-2014 and in 2022. Based on their comparative analysis, conclusions are made about the changes in the way the students interpret their own educational activities, as well as about the need to develop new training and assessment technologies at the university.
Genetic Algorithm with Machine Learning to Estimate the Optimal Objective Function Values of Subproblems

This paper addresses an optimization problem with two decision variable vectors. This problem can be divided into multiple subproblems when an arbitrary value is given to the first decision variable vector. In conventional genetic algorithms (GAs) for the problem, an individual is often expressed by the value of the first decision variable vector. In evaluating the individual, the value of the remaining decision variable vector is determined by metaheuristics or greedy algorithms. However, such GAs are time-consuming or not general-purpose. We propose a GA with a neural network model to estimate the optimal objective function values of the subproblems. Experimental results compared to other GAs show that the proposed method is effective.
Machine Learning

Abstract: Machine Learning (M.L) is a scientific study of statical modals and algorithms that computer used to perform a certain task. Learning algorithms used in many applications that our used in daily life. Image recognition is a well known for identify an object as a digital image, one of the reason it work so well is because a learning algorithm that has based on the intensity of the pixels black &amp; white images and color images. These algorithms are used for various purposes like predictive analytics, virtual assistants etc. The main advantage of using machine learning is that, once an algorithm grasp what to perform with particular data, then it works automatically.
A Neural Network Model for Business Performance Management Based on Random Matrix Theory

This paper uses random matrix theory to construct a neural network model for business performance management. The random sample covariance matrix of the random monitoring matrix is constructed, and the maximum eigenvalue and the minimum eigenvalue of the sample covariance matrix are solved. The ratio of eigenvalues is used to construct the eigenvalue detection index and determine the eigenvalue index detection threshold algorithm to judge the abnormal state of enterprise operation. The data of 66 listed Internet finance companies are selected, normalized, and correlation tested, and the index weights of each level are obtained using hierarchical analysis to derive the expected output of the BP neural network. Finally, the constructed BP neural network performance evaluation model is used for network training and simulation analysis, in which 192 data of 48 companies in the last four years are selected as training samples and 8 companies in the last four years are used as test samples to analyze the simulation output results. Using the original data instead of the main factor to join the BP neural network model, after two systematic optimizations, the final model with high accuracy, low mean square error, and low average error was formed. When applying the newly added data for testing, an accuracy of 95.98% was achieved for the ranking prediction, and the average deviation of 0.0021 points for the score prediction, which fully reflected the feasibility and adaptability of the model.
Entropy-driven Sampling and Training Scheme for Conditional Diffusion Generation

Denoising Diffusion Probabilistic Model (DDPM) is able to make flexible conditional image generation from prior noise to real data, by introducing an independent noise-aware classifier to provide conditional gradient guidance at each time step of denoising process. However, due to the ability of classifier to easily discriminate an incompletely generated image only with high-level structure, the gradient, which is a kind of class information guidance, tends to vanish early, leading to the collapse from conditional generation process into the unconditional process. To address this problem, we propose two simple but effective approaches from two perspectives. For sampling procedure, we introduce the entropy of predicted distribution as the measure of guidance vanishing level and propose an entropy-aware scaling method to adaptively recover the conditional semantic guidance. For training stage, we propose the entropy-aware optimization objectives to alleviate the overconfident prediction for noisy data.On ImageNet1000 256x256, with our proposed sampling scheme and trained classifier, the pretrained conditional and unconditional DDPM model can achieve 10.89% (4.59 to 4.09) and 43.5% (12 to 6.78) FID improvement respectively. The code is available at https://github.com/ZGCTroy/ED-DPM.
Detecting inference attacks involving sensor data in a multi‐database context: Issues &amp; challenges

Nowadays applications produce and manage data of individual among which some may be sensitive and must be protected. Moreover, with the advent of smart applications, sensor data are produced by IoT devices in a huge quantity and sent to servers in the vicinity to be stored and processed. Meanwhile, newly discovered inference channels involving sensor data gives insights on personal data and raises new threats on individuals privacy. They escape the vigilance of traditional inference detection systems devoted to protecting personal data stored locally in a database. In this paper, we motivate the need of a distributed inference detection system acting in a general multi‐database context and we highlight the issues that such a system would face.
Combining Latent Factor Model for Dynamic Recommendations in Community Question Answering Forums

Community Question Answering (CQA) web service provides a platform for people to share knowledge. Quora, Stack Overflow, and Yahoo! Answers are few sites where questioners post their queries and answerers respond to their respective queries. Due to the ease of use and quick responsiveness of the CQA platform, these sites are being widely adopted by the community. For better usability, there is a dire need to route the question toward the relevant answerers. To fulfil this gap, recommender systems play an important role in identifying the relevant answerers. To map the user interests more effectively, this research work proposed a dynamic feature representation of the latent user attributes for user profiling. The latent features are mapped by leveraging the Latent Dirichlet Allocation (LDA) for topic modelling of user data. The proposed recommendation model segments the user profile based on these latent user profiles incorporating the incremental learning of the users’ interests to produce the relevant recommendations in near real time. The experimental setup generated recommendation lists of variable sizes and evaluated using multiple evaluation metrics, such as mean average precision, recall, throughput, and different quality metrics, such as discounted cumulative gain and mean reciprocal rank. The results showed that the proposed model provided a better quality of recommendations in CQA forums, which is promising for future research in this domain.
Humanlikeness and Aesthetic Customization's Effect on Trust, Performance, and Affect

Human-machine interactions have become a staple of people's daily lives through the use of mobile devices, robotics, and a myriad of smart technologies. Previous research has established that anthropomorphism can significantly affect subjective perceptions of, and interactions with, machines. Furthermore, the ability to customize digital tools has been shown to affect user preferences, video game enjoyment, and the efficacy of digital mental health interventions. This study examined whether the customization of a machine teammate could influence the performance of the human-machine team and generate an affective response on the part of the human teammate. To evaluate this premise, we developed a bomb-defusing task simulation using the Unity game engine wherein participants were randomly assigned to one of two (humanlike or machinelike) robot avatars or were given the ability to customize one. The customizable robot avatar allows the participant to select either a humanlike or machinelike robot and customize the color of the wheels and casing. The customization is aesthetic in nature and has no effect on the functionality of the robot. The game design incorporates a high-risk environment and uncertainty with respect to the bomb-defusing distance and required button presses to encourage cautious guidance of the robot. We predicted that the ability to customize the robot will increase performance and subjective measures of trust, affect, attachment, identification, immersion, and control. We also predicted that the humanlikeness of the robot would increase performance and our subjective measures. Finally, we expected to see a significant effect of customization and humanlikeness such that the customization and humanlikeness have an additive effect on performance and our subjective measures. The results of all analyses were nonsignificant. These results may help inform the design of such systems and address fears that customization could lead to over-empathizing with a machine teammate in a way that would reduce use in high-risk environments.
BlazePose GHUM Holistic: Real-time 3D Human Landmarks and Pose Estimation

We present BlazePose GHUM Holistic, a lightweight neural network pipeline for 3D human body landmarks and pose estimation, specifically tailored to real-time on-device inference. BlazePose GHUM Holistic enables motion capture from a single RGB image including avatar control, fitness tracking and AR/VR effects. Our main contributions include i) a novel method for 3D ground truth data acquisition, ii) updated 3D body tracking with additional hand landmarks and iii) full body pose estimation from a monocular image.
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds

In order for artificial agents to successfully perform tasks in changing environments, they must be able to both detect and adapt to novelty. However, visual novelty detection research often only evaluates on repurposed datasets such as CIFAR-10 originally intended for object classification, where images focus on one distinct, well-centered object. New benchmarks are needed to represent the challenges of navigating the complex scenes of an open world. Our new NovelCraft dataset contains multimodal episodic data of the images and symbolic world-states seen by an agent completing a pogo stick assembly task within a modified Minecraft environment. In some episodes, we insert novel objects of varying size within the complex 3D scene that may impact gameplay. Our visual novelty detection benchmark finds that methods that rank best on popular area-under-the-curve metrics may be outperformed by simpler alternatives when controlling false positives matters most. Further multimodal novelty detection experiments suggest that methods that fuse both visual and symbolic information can improve time until detection as well as overall discrimination. Finally, our evaluation of recent generalized category discovery methods suggests that adapting to new imbalanced categories in complex scenes remains an exciting open problem.
Development of an Electronic Smart Safe Box Using Private Blockchain Technology

Effective security has become a vital part of our daily routine, for example securing cash, jewelry, documents, and other valuables. One way is to protect these items by keeping them in a safe box. However, current safe boxes are vulnerable to a number of security attacks such as physical, repudiation, anonymously modifying, etc. In this regard, this paper presents a robust smart safe box using the blockchain solution to ensure each transaction and secure it from unauthorized access. Also, it traces back the local safe items by reading back to blockchain data transactions and protecting them from thieves by tracking the burglar with a real-time tracking location Global Positioning System (GPS) tracker and capturing a picture and video recording through the external camera. Similarly, with more security enhancement using the TensorFlow framework to analyze optical detection and identify things inside the box using an internal camera. At the same time, each transaction is stored by using Write Once and Read Many (WORM) patterns saved in local private blockchain data block logs. Finally, it uses three-way authentication mechanisms to unlock the safe box that authenticates the users, (1) face recognition, (2) fingerprint and (3) the keypad password. The performance of the proposed smart safe box is tested by a number of hardware and blockchain tests. The results reveal that the proposed smart safe box ensures safe and authenticated access to the box and records each transaction using blockchain technology. Further, the performance evaluation of the private blockchain significantly improved the integrity and safeness of the proposed smart safe box.
Digital Learning Literacy Preference and Accessibility of Universitas Terbuka (UT)’s SUAKA-UT: An Evaluation towards Its System of Open Educational Resource (OER)

This article aims to analyze the preference and accessibility that determine the students' behavioural appreciation in using learning resources towards the Open Educational Resources (OER) system owned by the Universitas Terbuka (UT), a so-called SUAKA-UT. It used qualitative research methods with descriptive percentage analysis. Furthermore, the instrument employed a Sakal Likert questionnaire and limited interviews. The research population was a non-basic educational program, with the random sampling technique by selecting 123 students. The following sample details: 35 students in Makassar City, 19 Students in Sidrap Regency, 23 students in Tana Toraja Regency, 23 Students in Selayar Regency, and 23 students in Luwu Timur Regency. The results showed that the preference for online sources and accessibility is in the excellent category and had an average score of 4.10 out of ten SUAKA-UT service questions. They showed that, generally, the respondents strongly agree with the available learning resources since they can meet the needs and accessibility of online learning resources. Preference for online learning resources is a supporting tool for achieving independent learning success. The utilization of SUAKA-UT in the working area of UT Makassar went very well in providing easy access to learning resources. The UT has met students' needs and expectations so they can study flexibly. SUAKA-UT has become a preferred and has excellent acceptance as an online learning resource among the students. To maintain the existence of open learning sources of UT (SUAKA-UT), as a distance learning university with the motto of "Making Higher education," it must consistently innovate and update its online learning resources.
The Impact of Intellectual Capital on Improving the Performance of Productive Establishment (Baba Sweet Company as a Model)

This study aims to attempt the impact of intellectual capital on the performance of productive institution. To achieve the objectives of the study, a questionnaire has been developed and distributed to the employees of the company, and statistical methods (descriptive and analytical) were adopted for the purpose of answering the study questions and test hypotheses And a random sample of 56 questionnaires was selected .The study found a relation between the Intellectual Capital and the performance of Baba Sweet institution, The study recommended that Al Baba Sweets Company should restructure its intellectual assets in order to achieve efficiency and effectiveness.
Does technology assist to continue learning during pandemic? A sentiment analysis and topic modeling on online learning in south asian region

Online mode of education has been identified as the subtle solution to continue learning during the pandemic. However, the accessibility to online platforms, suitable devices, and connections are not equal across the globe thus raising the question of whether the opinion of the public in the South Asian region where the technology is not comparatively higher as in the western world would be the same as that to the global perspective. This study involves the sentiment analysis of natural language processing on recently tweeted data and concludes that the sentiment of the South Asian public remains positive as online education is the most suitable approach to overcome the learning difficulties during a pandemic. The study performs a ternary classification based on the polarity scores obtained from two robust lexicon-based sentiment analyzer tools namely VADER and TextBlob and observes that 63.2% of the tweets were positive, 30.5% of the tweets were neutral and around 6.3% of them were negative. Finally, topic modeling was also performed using the Latent Dirichlet Allocation method to gain insight into each of the classes.