Reviewerly/exHarmony huggingface dataset:
Extracting Temporal Event Relation with Syntax-guided Graph Transformer

Extracting temporal relations (e.g., before, after, and simultaneous) among events is crucial to natural language understanding.One of the key challenges of this problem is that when the events of interest are far away in text, the context in-between often becomes complicated, making it challenging to resolve the temporal relationship between them.This paper thus proposes a new Syntax-guided Graph Transformer network (SGT) to mitigate this issue, by (1) explicitly exploiting the connection between two events based on their dependency parsing trees, and (2) automatically locating temporal cues between two events via a novel syntax-guided attention mechanism.Experiments on two benchmark datasets, MATRES and TB-DENSE, show that our approach significantly outperforms previous state-of-the-art methods on both end-to-end temporal relation extraction and temporal relation classification; This improvement also proves to be robust on the contrast set of MATRES.The code is publicly available at https://github.com/VT-NLP/Syntax-Guided-Graph-Transformer.
Towards a Human-like Open-Domain Chatbot

We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural network is simply trained to minimize perplexity of the next token. We also propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of a human-like multi-turn conversation. Our experiments show strong correlation between perplexity and SSA. The fact that the best perplexity end-to-end trained Meena scores high on SSA (72% on multi-turn evaluation) suggests that a human-level SSA of 86% is potentially within reach if we can better optimize perplexity. Additionally, the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher in absolute SSA than the existing chatbots we evaluated.
Parallel wave-based analog computing using metagratings

Wave-based signal processing has witnessed a significant expansion of interest in a variety of science and engineering disciplines, as it provides new opportunities for achieving high-speed and low-power operations. Although flat optics desires integrable components to perform multiple missions, yet, the current wave-based computational metasurfaces can engineer only the spatial content of the input signal where the processed signal obeys the traditional version of Snell's law. In this paper, we propose a multi-functional metagrating to modulate both spatial and angular properties of the input signal whereby both symmetric and asymmetric optical transfer functions are realized using high-order space harmonics. The performance of the designed compound metallic grating is validated through several investigations where closed-form expressions are suggested to extract the phase and amplitude information of the diffractive modes. Several illustrative examples are demonstrated to show that the proposed metagrating allows for simultaneous parallel analog computing tasks such as first- and second-order spatial differentiation through a single multichannel structured surface. It is anticipated that the designed platform brings a new twist to the field of optical signal processing and opens up large perspectives for simple integrated image processing systems.
VirusBoxing

Physical activity or exercise can improve people's health and reduce their risk of developing several diseases; most importantly, regular activity can improve the quality of life. However, lack of time is one of the major barriers for people doing exercise. High-intensity interval training (HIIT) can reduce the time required for a healthy exercise regime but also bring similar benefits of regular exercise. We present a boxing-based VR exergame called VirusBoxing to promote physical activity for players. VirusBoxing provides players with a platform for HIIT and empowers them with additional abilities to jab a distant object without the need to aim at it precisely. In this paper, we discuss how we adapted the HIIT protocol and gameplay features to empower players in a VR exergame to give players an efficient, effective, and enjoyable exercise experience.
SLA-Aware and Energy-Efficient Virtual Machine Placement and Consolidation in Heterogeneous DVFS Enabled Cloud Datacenter

Increasing demand for computational resource as services over the internet has led to the expansion of datacenter infrastructures. Thus, datacenter authorities are striving to adopt optimal power usage schemes to minimize costs, emissions and Service Level Agreement (SLA) violations in their task scheduling for heterogeneous computation centers. One of the most effective strategies to reduce datacenter energy consumption is to maximize the utilization of physical machines and shut down the idle ones. This can be realized through two main algorithms, namely virtual machine placement and virtual machine consolidation. The VM placement method is a dynamic process to put these virtual devices on physical machines. The consolidation technique, however, tries to improve physical machine efficiency through grouping and live migration of dispersed virtual machines on lower number of active physical machine. In this paper, a novel approach is proposed for improving the physical machine efficiency. The approach employs heuristics and meta-heuristic algorithms with eight performance criteria and is implemented on small to medium scale data centers using simulated cloud module. The results indicates that the proposed method showed up to 10.3%, 5.3%, and 12.5% the more significant efficiency rather best previous algorithms, respectively, in terms of the energy consumption, number of SLA violation and number of VMs migration.
Use of virtual reality in oncology: From the state of the art to an integrative model

Over the past 20 years, virtual reality (VR) has been the subject of growing interest in oncology. More and more researchers are studying the effects of virtual environments to contribute to current thinking on technologies likely to support patients undergoing oncological treatment. Recent research highlights how VR can divert attention while reducing anxiety in stressful healthcare situations through its multisensory and participative nature. VR appears to be a promising tool capable of reducing cancer-related anxiety symptoms, improving treatment adherence, and increasing satisfaction with oncology care. While the literature reports these positive effects in the therapeutic management of cancer, few studies have focused on theoretical models capable of explaining the psychological benefits of virtual immersion. This literature review provides a theoretical framework combining results from all relevant empirical work in oncology. The review can help researchers identify the optimal conditions for using VR in oncology and bridge the gap between divergent devices, modalities, and practices (e.g., headmounted displays, environments, interactivity, immersion time).
High-Resolution Drone Detection Based on Background Difference and SAG-YOLOv5s

To solve the problem of low accuracy and slow speed of drone detection in high-resolution images with fixed cameras, we propose a detection method combining background difference and lightweight network SAG-YOLOv5s. First, background difference is used to extract potential drone targets in high-resolution images, eliminating most of the background to reduce computational overhead. Secondly, the Ghost module and SimAM attention mechanism are introduced on the basis of YOLOv5s to reduce the total number of model parameters and improve feature extraction, and α-DIoU loss is used to replace the original DIoU loss to improve the accuracy of bounding box regression. Finally, to verify the effectiveness of our method, a high-resolution drone dataset is made based on the public data set. Experimental results show that the detection accuracy of the proposed method reaches 97.6%, 24.3 percentage points higher than that of YOLOv5s, and the detection speed in 4K video reaches 13.2 FPS, which meets the actual demand and is significantly better than similar algorithms. It achieves a good balance between detection accuracy and detection speed and provides a method benchmark for high-resolution drone detection under a fixed camera.
The Significant and Challenges of Extended Reality Technologies in Learning and Training during Covid-19 Pandemic

Extended Reality (XR) technologies can play a significant role in proving huge value to education following the changed circumstances universities faced during pandemic. This study presents Virtual Reality (VR) as a means of enhancing learning in education and training field during outbreak. This paper represents the use of XR technologies in a wide variety of settings, including the context of the education, learning, and training. Considering the most significant papers with a total of 2,270 articles from conferences and journals were obtained through online search was conducted from databases such as Google Scholar, Scopus, Web of Science (WoS), IEEE Xplore, ACM Digital Library, Springer Link, Research Gate, and Academia. The number of papers released, and the number of references obtained in both databases have a substantial-high influence. Researchers performing literature searches using bibliographic databases as their initial and dominant resource to customized and filtered sort out the most relevant publications examined based on abstract and key words such as Extended reality, Virtual reality, VR training, COVID-19, Distance education, Virtual environments, Education, Virtual Laboratories. According to the findings, XR equips students to gain professional skills to their subject as well as to increase the performance of learning quality and improve training.
YOLO-Submarine Cable: An Improved YOLO-V3 Network for Object Detection on Submarine Cable Images

Due to the strain on land resources, marine energy development is expanding, in which the submarine cable occupies an important position. Therefore, periodic inspections of submarine cables are required. Submarine cable inspection is typically performed using underwater vehicles equipped with cameras. However, the motion of the underwater vehicle body, the dim light underwater, and the property of light propagation in water lead to problems such as the blurring of submarine cable images, the lack of information on the position and characteristics of the submarine cable, and the blue–green color of the images. Furthermore, the submarine cable occupies a significant portion of the image as a linear entity. In this paper, we propose an improved YOLO-SC (YOLO-Submarine Cable) detection method based on the YOLO-V3 algorithm, build a testing environment for submarine cables, and create a submarine cable image dataset. The YOLO-SC network adds skip connections to feature extraction to make the position information of submarine cables more accurate, a top-down downsampling structure in multi-scale special fusion to reduce the network computation and broaden the network perceptual field, and lightweight processing in the prediction network to accelerate the network detection. Under laboratory conditions, we illustrate the effectiveness of these modifications through ablation studies. Compared to other algorithms, the average detection accuracy of the YOLO-SC model is increased by up to 4.2%, and the average detection speed is decreased by up to 1.616 s. The experiments demonstrate that the YOLO-SC model proposed in this paper has a positive impact on the detection of submarine cables.
Enhancing Cybersecurity Using Blockchain Technology Based on IoT Data Fusion

In this study, blockchain technology is used to strengthen the security of wireless sensing data in the network architecture of the Internet of Things. For many organic farms that contain crops with high unit prices, the environmental parameters of the crops grown on the farm are often regarded as confidential data. First, a wireless sensing network is used to transmit sensing data to the farm’s front-end integrated microcontroller for fusion of the sensing data. After fusion of the sensing data is completed, the processed data is transmitted to the data processing center of the entire farm for encapsulation of the blockchain algorithm. Data are sent to the cloud database for storage after they are packaged by the system. At the same time, the data in the cloud database can be managed and analyzed by remote operators. Data encapsulation based on blockchain technology is used to effectively prevent any data from being stolen or destroyed by hackers. A complete blockchain encapsulation database system is implemented in the experimental stage, and the operator can use this system to encrypt the data that have been fused at the remote end for blockchain technology. In the encryption process of this blockchain, the security of data processing can be specifically enhanced by the system. Finally, each encapsulated block of data is securely stored in a private cloud database. There is a function to check whether the data in the cloud have been tampered with. A very innovative approach to using blockchain technology is proposed in this article to enhance the security of data processing.
The effect of sensory marketing factors on customer loyalty during Covid 19: Exploring the mediating role of customer satisfaction

The loyalty of the customers is very important for the survival of the organizations. Therefore, the prime objective of the present study was to examine the effect of sensory factors namely visual factors, auditory factors, and touch factors on customer satisfaction and loyalty. For this purpose, data were collected from the respondents of restaurants in the UAE in the form of survey questionnaires. A convenience sampling technique was adopted for this purpose. The usable response rate from the data collected was 75.78%. For the analysis of the data gathered PLS-SEM was used for which smart PLS was used as the tool to examine the data. The findings of the study revealed that visual factors play a very important role in developing customer satisfaction and loyalty. The same was the findings regarding the positive role of the other two sensory factors. Moreover, the mediating role of customer satisfaction was also confirmed in the present study. The gaps filled by the study are mentioned and discussed. These results can be used by academicians and policymakers as well.
Real-Time Implementation of Face Recognition and Emotion Recognition in a Humanoid Robot Using a Convolutional Neural Network

Robots can mimic humans, including recognizing faces and emotions. However, relevant studies have not been implemented in real-time humanoid robot systems. In addition, face and emotion recognition have been considered separate problems. This study proposes a combination of face and emotion recognition for real-time application in a humanoid robot. Specifically, face and emotion recognition systems were developed simultaneously using convolutional neural network architectures. The model was compared to well-known architectures, such as AlexNet and VGG16, to determine which is better for implementation in humanoid robots. Data used for face recognition were primary data taken from 30 electrical engineering students after preprocessing, resulting in 18,900 data points. Emotion data of surprise, anger, neutral, smile, and sad were taken from the same respondents and combined with secondary data for a total of 5,000 data points for training and testing. The test was carried out in real time on a humanoid robot using the two architectures. The face and emotion recognition accuracy was 85% and 64%, respectively, using the AlexNet model. VGG16 yielded recognition accuracies of 100% and 73%, respectively. The proposed model architecture showed 87% and 67% accuracies for face recognition and emotion recognition, respectively. Thus, VGG16 performs better in recognizing faces as well as emotions, and it can be implemented in humanoid robots. This study also provides a method for measuring the distance between the recognized object and robot with an average error rate of 2.52%.
Cost function for low-dimensional manifold topology assessment

In reduced-order modeling, complex systems that exhibit high state-space dimensionality are described and evolved using a small number of parameters. These parameters can be obtained in a data-driven way, where a high-dimensional dataset is projected onto a lower-dimensional basis. A complex system is then restricted to states on a low-dimensional manifold where it can be efficiently modeled. While this approach brings computational benefits, obtaining a good quality of the manifold topology becomes a crucial aspect when models, such as nonlinear regression, are built on top of the manifold. Here, we present a quantitative metric for characterizing manifold topologies. Our metric pays attention to non-uniqueness and spatial gradients in physical quantities of interest, and can be applied to manifolds of arbitrary dimensionality. Using the metric as a cost function in optimization algorithms, we show that optimized low-dimensional projections can be found. We delineate a few applications of the cost function to datasets representing argon plasma, reacting flows and atmospheric pollutant dispersion. We demonstrate how the cost function can assess various dimensionality reduction and manifold learning techniques as well as data preprocessing strategies in their capacity to yield quality low-dimensional projections. We show that improved manifold topologies can facilitate building nonlinear regression models.
Task Failure Prediction Using Machine Learning Techniques in the Google Cluster Trace Cloud Computing Environment

Cloud computing has grown into a critical technology by enabling ground-breaking capabilities for Internet-dependent computer platforms and software applications. As cloud computing systems continue to expand and develop, the need for a more guaranteed, reliant service, and an early task execution status from Cloud Service Providers (CSP) is vital. Additionally, efficient prediction of task failure significantly improves the running time as well as resource utilization in cloud computing. Task failure forecasting in the cloud is regarded as a challenging task based on the literature review conducted in this study. To address these issues, the goal of this study aimed to create fast machine learning approaches for reliably predicting task failure in cloud computing and analyzing their performance using multiple assessment criteria. The Google cluster dataset was used in this study, coupled with Artificial Neural Network (ANN), Support Vector Machine (SVM), and a stacking ensemble method, to forecast job failure in a cloud computing context. The results show that the proposed models can predict the failed tasks both effectively and efficiently. The stacking ensemble outperformed the experimented models, reaching a 99.8%. The suggested paradigm could greatly benefit cloud service providers by decreasing wasted resources and costs associated with task failures.
Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem

The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.
Detecting Anomalies in Financial Data Using Machine Learning Algorithms

Bookkeeping data free of fraud and errors are a cornerstone of legitimate business operations. The highly complex and laborious work of financial auditors calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques nowadays are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalous in general ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of deep learning and two unsupervised ML techniques such as isolation forest and autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting and auditing contexts.
Effects of Dynamical Decoupling and Pulse-Level Optimizations on IBM Quantum Computers

Currently available quantum computers are prone to errors. Circuit optimization and error mitigation methods are needed to design quantum circuits to achieve better fidelity when executed on NISQ hardware. Dynamical decoupling (DD) is generally used to suppress the decoherence error and different DD strategies have been proposed. Moreover, the circuit fidelity can be improved by pulse-level optimization, such as creating hardware-native pulse-efficient gates. This paper implements all the popular DD sequences and evaluates their performances on IBM quantum chips with different characteristics for various well-known quantum applications. Also, we investigate combining DD with pulse-level optimization method and apply them to QAOA to solve Max-Cut problem. Based on the experimental results, we found that DD can be a benefit for only certain types of quantum algorithms, while the combination of DD and pulse-level optimization methods always has a positive impact. Finally, we provide several guidelines for users to learn how to use these noise mitigation methods to build circuits for quantum applications with high fidelity on IBM quantum computers.