Reviewerly/exHarmony huggingface dataset:
IMPACTS OF COVID-19 ON CORPORATE UNIVERSITY AND TRENDS

The main purpose of this article is to identify the main actions adopted by Corporate Universities (CUs) during the COVID-19 pandemic period, as well as trends for the post-pandemic period. To this end, a systematic literature review was conducted on Scopus and Web of Science databases. A questionnaire was developed and validated by experts in the field of Corporate Education, and the survey link was made available on the Qualtrics platform to those involved or interested in the topic. Interviews were also conducted with executives from CUs of large Brazilian organizations during the pandemic period. The questionnaire data were analyzed using descriptive statistics and inferential analysis, as well as content analysis of the interviews. As a result, the main actions taken by CUs focused on the qualification of their instructors, leaders, and professionals. There was an increase in investments by CUs, with an emphasis on the adoption of digital technologies, accompanied by actions aimed at engagement, motivation, and emotional well-being. The main trends for the post-COVID-19 period point to the expansion of short-term hybrid courses and changes in business models to adapt to the new challenges generated by the pandemic. One of the main consequences of the pandemic is the considerable recognition, by employees of organizations, users, and leaders of CUs, of the importance of social and humanities approaches adopted by them. Furthermore, it is understood that the relevant role of CUs tends to be reinforced based on the increasing value of the notion of sustainability in their courses and managerial training practices aimed at the sustainable development of organizations and society.
UP V-Ikot: Augmented Reality Mobile Application to Assist Campus Visits and Tours Inside the Up Diliman Campus

The traditional method of campus tours and visits is time and labor-intensive, relies on knowledgeable personnel, and has limitations due to time constraints and restricted areas. As universities have multiple buildings and large campuses, newcomers can easily get lost or confused. To supplement these tours, universities provide resources like websites and physical maps, but these also have limitations. Advancements in mobile technology have introduced Augmented Reality (AR) technology, which can provide an interactive and educational experience during campus tours and visits. Many universities have already developed their own AR-based campus touring mobile applications, but there are not many implementations using a hybrid approach that combines marker-less and marker-based tracking. This paper aims to promote an AR mobile application that utilizes a hybrid approach to aid campus students and visitors during campus tours and visits, and provide an alternative to improve the overall experience of self-guided campus tours and visits. The proposed application can enhance the engagement, interactivity, and educational experience of campus tours and visits, and can be valuable for universities in supplementing existing traditional tools.
Adaptive Load Balancing Using RR and ALB: Resource Provisioning in Cloud

Cloud Computing context, load balancing is an issue. With a rise in the number of cloud-based technology users and their need for a broad range of services utilizing resources successfully or effectively in a cloud environment is referred to as load balancing, has become a significant obstacle. Load balancing is crucial in storage systems to increase network capacity and speed up response times. The main goal is to present a new load-balancing mechanism that can balance incoming requests from users all over globally who are in different regions requesting data from remote data sources. This method will combine effective scheduling and cloud-based techniques. A dynamic load balancing method was developed to ensure that cloud environments have the ability to respond rapidly, in addition to running cloud resources efficiently and speeding up job processing times. Applications' incoming traffic is automatically split up across a number of targets, including Amazon EC2 instances, network addresses, and other entities by elastic load balancing. Elastic load balancing offers three distinct classifications of load balancer, and each one provides high availability, intelligent scalability, and robust security to guarantee the error-free functioning of your applications. Application load balancing and round robin are the two load balancing mechanisms in database cloud that are focus of this research study.
Research on Motion Control and Trajectory Planning Algorithm of Mobile Manipulator Based on Deep Learning

With the further development of industrialization and the expansion of the application field of robot technology, the manipulator fixed in a certain position can no longer meet the new requirements. In many unstructured working environments, the robot arm with a fixed base can’t complete the task well. At this time, the robot arm needs to be mobile, and the mobile robot arm can meet this demand. The core of the mobile robot arm is mainly composed of two parts, one is the upper part of the robot arm, and the other is the mobile chassis at the bottom. The mobile manipulator not only retains the flexibility of the manipulator, but also has a chassis that can move freely, which greatly expands the working range of the manipulator in space. Therefore, mobile manipulator has a good application prospect in industrial manufacturing, national defense, transportation, warehousing and logistics, service industries and other fields. With the rapid development of e-commerce and online shopping in China, more requirements are put forward for the automation and high efficiency of warehousing and logistics, and mobile robot arm is an excellent choice for warehousing and logistics industry. Therefore, no matter in scientific research or practical application, in-depth development is of great significance to its research and development.
Designing Multi-Modal Conversational Agents for the Kitchen with Older Adults: A Participatory Design Study

Abstract Conversational agents (CA) are increasingly used to manage and coordinate household chores and everyday activities at home. However, these technologies should be adaptive to age-specific characteristics in order to be considered beneficial for the ageing population. This study presents a participatory design of a conversational agent to provide cognitive support in recipe following and nutrition advice for adults aged 65 and over. Through a qualitative thematic analysis, the study explores older adults’ expectations, interactions and experiences with the agent in order to identify age-specific challenges of interacting with CAs. Data consists of a participatory design workshop with eight older adults (aged 65 and over), followed by a Wizard of Oz study with ten older adults interacting with the agent in the kitchen environment in a laboratory setting. Results demonstrate that older adults consider conversational agents as beneficial for providing personalised recipe recommendations, advising the user to choose appropriate ingredients and reminding them of their dietary intake. When interacting with the agent older adults displayed challenges with confirmation and repetition, questioning and correcting, the lack of conversational responses, and difficulties in hearing and understanding the multi-modal interaction. Older adults experience agents as collaborators, but not as conversational partners. The study concludes that the accessibility and inclusiveness of conversational agents regarding voice interaction could be improved by further developing participatory methods with older adults.
Specificity-aware Federated Learning with Dynamic Feature Fusion Network for Imbalanced Medical Image Classification

Recently, federated learning has become a powerful technique for medical image classification due to its ability to utilize datasets from multiple clinical clients while satisfying privacy constraints. However, there are still some obstacles in federated learning. Firstly, most existing methods directly average the model parameters collected by medical clients on the server, ignoring the specificities of the local models. Secondly, class imbalance is a common issue in medical datasets. In this paper, to handle these two challenges, we propose a novel specificity-aware federated learning framework that benefits from an Adaptive Aggregation Mechanism (AdapAM) and a Dynamic Feature Fusion Strategy (DFFS). Considering the specificity of each local model, we set the AdapAM on the server. The AdapAM utilizes reinforcement learning to adaptively weight and aggregate the parameters of local models based on their data distribution and performance feedback for obtaining the global model parameters. For the class imbalance in local datasets, we propose the DFFS to dynamically fuse the features of majority classes based on the imbalance ratio in the min-batch and collaborate the rest of features. We conduct extensive experiments on a dermoscopic dataset and a fundus image dataset. Experimental results show that our method can achieve state-of-the-art results in these two real-world medical applications.
Positive-Negative Receptive Field Reasoning for Omni-Supervised 3D Segmentation

Hidden features in the neural networks usually fail to learn informative representation for 3D segmentation as supervisions are only given on output prediction, while this can be solved by omni-scale supervision on intermediate layers. In this paper, we bring the first omni-scale supervision method to 3D segmentation via the proposed gradual Receptive Field Component Reasoning (RFCR), where target Receptive Field Component Codes (RFCCs) is designed to record categories within receptive fields for hidden units in the encoder. Then, target RFCCs will supervise the decoder to gradually infer the RFCCs in a coarse-to-fine categories reasoning manner, and finally obtain the semantic labels. To purchase more supervisions, we also propose an RFCR-NL model with complementary negative codes (i.e., Negative RFCCs, NRFCCs) with negative learning. Because many hidden features are inactive with tiny magnitudes and make minor contributions to RFCC prediction, we propose Feature Densification with a centrifugal potential to obtain more unambiguous features, and it is in effect equivalent to entropy regularization over features. More active features can unleash the potential of omni-supervision method. We embed our method into three prevailing backbones, which are significantly improved in all three datasets on both fully and weakly supervised segmentation tasks and achieve competitive performances.
How to stop AI deepfakes from sinking society — and science

Deceptive videos and images created using generative AI could sway elections, crash stock markets and ruin reputations. Researchers are developing methods to limit their harm. Deceptive videos and images created using generative AI could sway elections, crash stock markets and ruin reputations. Researchers are developing methods to limit their harm.
MIED : An Improved Graph Neural Network for Node Embedding in Heterogeneous Graphs

This paper proposes a Metapath-Infused Exponential Decay graph neural network (MIED) approach for node embedding in heterogeneous graphs. It is designed to address limitations in existing methods, which usually lose the graph information during feature alignment and ignore the different importance of nodes during metapath aggregation. Firstly, graph convolutional network (GCN) is applied on the subgraphs, which is derived from the original graph with given metapaths to transform node features. Secondly, an exponential decay encoder (EDE) is designed, in which the influence of nodes on starting point decays exponentially with a fixed parameter as they move farther away from it. Thirdly, a set of experiments is conducted on two selected datasets of heterogeneous graphs, i.e., IMDb and DBLP, for comparison purposes. The results show that MIED outperforms selected approaches, e.g., GAT, HAN, MAGNN, etc. Thus, our approach is proven to be able to take full advantage of graph information considering node weights based on distance aspects. Finally, relevant parameters are analyzed and the recommended hyperparameter setting is given.
Design of Fusion Filtering Method for Rectangular Descriptor Systems with Randomly Occurring Measurement Delays

In this paper, the rectangular descriptor systems with randomly occurring measurement delays are discussed and the related fusion filtering problem is considered, where the randomly occurring measurement delays are modeled by Bernoulli random variables. Firstly, matrix transformation, singular value decomposition method and augmentation technique are applied in turn to transform the original rectangular descriptor systems into a set of non-descriptor subsystems. Secondly, the local filter gains that minimize the filtering error covariances are given and the fusion filtering scheme for the original descriptor systems is given by applying the model transformation and covariance intersection (CI) fusion technique. Finally, a numerical simulation is used to show the effectiveness of the proposed fusion filtering criterion.
Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data

Alzheimer's disease (AD) is the most common form of dementia. Early and accurate detection of AD is crucial to plan for disease modifying therapies that could prevent or delay the conversion to sever stages of the disease. As a chronic disease, patient's multivariate time series data including neuroimaging, genetics, cognitive scores, and neuropsychological battery provides a complete profile about patient's status. This data has been used to build machine learning and deep learning (DL) models for the early detection of the disease. However, these models still have limited performance and are not stable enough to be trusted in real medical settings. Literature shows that DL models outperform classical machine learning models, but ensemble learning has proven to achieve better results than standalone models. This study proposes a novel deep stacking framework which combines multiple DL models to accurately predict AD at an early stage. The study uses long short-term memory (LSTM) models as base models over patient's multivariate time series data to learn the deep longitudinal features. Each base LSTM classifier has been optimized using the Bayesian optimizer using different feature sets. As a result, the final optimized ensembled model employed heterogeneous base models that are trained on heterogeneous data. The performance of the resulting ensemble model has been explored using a cohort of 685 patients from the University of Washington's National Alzheimer's Coordinating Center dataset. Compared to the classical machine learning models and base LSTM classifiers, the proposed ensemble model achieves the highest testing results (i.e., 82.02, 82.25, 82.02, and 82.12 for accuracy, precision, recall, and F1-score, respectively). The resulting model enhances the performance of the state-of-the-art literature, and it could be used to build an accurate clinical decision support tool that can assist domain experts for AD progression detection.
SLA based Workflow Scheduling algorithm in Cloud Computing using Haris Hawks optimization

Task Scheduling is crucial facet in cloud paradigm as virtual resources need to be provisioned to the variable requests coming onto cloud console from various users and more over that tasks are depends on each other which creates a workflow which is a difficult task for cloud service provider to provision these tasks over appropriate VMs. Inefficient mapping of tasks to VMs increases makespan and lead to violation of SLA between users, cloud provider. In this paper, we modeled a SLA based workflow scheduling algorithm focuses on minimization of makespan and SLA violations. This algorithm developed using Harris hawks optimization. Experimentation carried out using workflowsim. Random workload fed as input to algorithm and it is evaluated against existing baseline approaches and simulation results revealed that our proposed approach minimizes makespan and SLA violations over existing approaches by 40% and 43% respectively.
Suppressing communication errors using quantum-enabled forward error correction

Because noise is inherent to all measurements, optical communication requires error identification and correction to protect and recover user data. Yet, error correction, routinely used in classical receivers, has not been applied to receivers that take advantage of quantum measurement. Here, we show how information uniquely available in a quantum measurement can be employed for efficient error correction. Our quantum-enabled forward error correction protocol operates on quadrature phase shift keying (QPSK) and achieves more than 80 dB error suppression compared to the raw symbol error rate and approximately 40 dB improvement of symbol error rates beyond the QPSK classical limit. With a symbol error rate below 10−9 for just 11 photons per bit, this approach enables reliable use of quantum receivers for ultra-low power optical communications. Limiting optical power improves the information capacity of optical links and enables scalable networks with coexisting quantum and classical channels in the same optical fiber.
Asymmetric Contour Uncertainty Estimation for Medical Image Segmentation

Aleatoric uncertainty estimation is a critical step in medical image segmentation. Most techniques for estimating aleatoric uncertainty for segmentation purposes assume a Gaussian distribution over the neural network’s logit value modeling the uncertainty in the predicted class. However, in many cases, such as image segmentation, there is no uncertainty about the presence of a specific structure, but rather about the precise outline of that structure. For this reason, we explicitly model the location uncertainty by redefining the conventional per-pixel segmentation task as a contour regression problem. This allows for modeling the uncertainty of contour points using a more appropriate multivariate distribution. Additionally, as contour uncertainty may be asymmetric, we use a multivariate skewed Gaussian distribution. In addition to being directly interpretable, our uncertainty estimation method outperforms previous methods on three datasets using two different image modalities. Code is available at: https://github.com/ThierryJudge/contouring-uncertainty .
Filter Bank for Perfect Reconstruction of Light Field from Its Focal Stack

Focal stacks (FS) have attracted attention as an alternative representation of light field (LF). However, the problem of reconstructing LF from its FS is considered ill-posed. Although many regularization methods have been discussed, no method has been proposed to solve this problem perfectly. This paper showed that the LF can be perfectly reconstructed from the FS through a filter bank in theory for Lambertian scenes without occlusion if the camera aperture for acquiring the FS is a Cauchy function. The numerical simulation demonstrated that the filter bank allows perfect reconstruction of the LF.
A Generative Model for Structured Sentiment Analysis

Structured Sentiment Analysis (SSA) aims to extract the complete sentiment structure from a given text. Existing approaches predominantly rely on the interactions of words to predict the relationships between sentiment elements. While these methods have shown effectiveness, they overlook the rich label semantics associated with SSA tasks and necessitate extensive task-specific designs. In order to address the above problems, we propose a generative framework for tackling the SSA task. We designed two templates to transform the SSA task into a text generation problem, which facilitate the training process by formulating the SSA task as a text generation problem. Through experiments conducted on three SSA datasets, we demonstrate that our proposed generative approach outperforms all existing methods, thereby highlighting the advantages of employing the generative model for SSA.
Novel Dynamic S-Box Based on Password Key and Circle Map

Many cryptosystems and security techniques use substitution boxes to ensure the data’s secure communication. A new technique is presented for generating a robust S-box to fulfill security requirements. The AES algorithm represents a block cipher cryptographic algorithm. It was selected by the National Institute of Science and Technology as the optimal cryptographic algorithm in 2011. Through the study of the properties of original S-BOX, this algorithm has been subjected to a number of attacks (linear, differential, statistical, and interpolation), and original S-BOX has been static, which makes the attack strong and shows a weakness in the algorithm. It is necessary to make this algorithm more efficient and powerful through the improvement of the dynamic generation of the steps for the protection of textual data security. This paper proposes a dynamic S-Box based on the user's password key (8 chars), shifting, and a 1D circle map. The results in this work indicated that the suggested approach presents a secure S-BOX, which is considered to have 255 differences identified when 1 bit of the key is changed; therefore, about 99% of the S-Box has been changed. Also, an inverse table of S-Box (16*16) is generated via the S-Box output created from the above-mentioned suggestions for returning the values regarding the union of the column and the row for all the S-Box generated values. We examine the quality of our S-Box through various well-known performance parameters. All of the analysis yields very encouraging results, certifying that the generated S-box meets all criteria that are required for reliable and secure encryption. Just a few milliseconds are needed to implement it.
Unsupervised Anomaly Detection via Nonlinear Manifold Learning

Abstract Anomalies are samples that significantly deviate from the rest of the data and their detection plays a major role in building machine learning models that can be reliably used in applications such as data-driven design and novelty detection. The majority of existing anomaly detection methods either are exclusively developed for (semi) supervised settings, or provide poor performance in unsupervised applications where there is no training data with labeled anomalous samples. To bridge this research gap, we introduce a robust, efficient, and interpretable methodology based on nonlinear manifold learning to detect anomalies in unsupervised settings. The essence of our approach is to learn a low-dimensional and interpretable latent representation (aka manifold) for all the data points such that normal samples are automatically clustered together and hence can be easily and robustly identified. We learn this low-dimensional manifold by designing a learning algorithm that leverages either a latent map Gaussian process (LMGP) or a deep autoencoder (AE). Our LMGP-based approach, in particular, provides a probabilistic perspective on the learning task and is ideal for high-dimensional applications with scarce data. We demonstrate the superior performance of our approach over existing technologies via multiple analytic examples and real-world datasets.