Reviewerly/exHarmony huggingface dataset:
Student Sentiment Analysis Using Various Machine Learning Techniques

In terms of social, psychological, physical, technological, and other elements, the educational system is undergoing significant transformation. Today, education is becoming a joint venture between the state, the market, and the community. Alternative education and training providers that place a greater emphasis on employability provide a problem, and university professors represent a particular breed of career academics that remain cut off from developments in the outside world. The sentiment analysis of student comments is presented in this work using a combination of Methodologies based on lexicons and machine learning. The textual feedback, which is often gathered around the conclusion of a semester, offers helpful insights into the general quality of teaching and makes insightful recommendations for ways to enhance instructional design. The article describes a sentiment analysis model trained using TF-IDF and linguistic characteristics to look at the opinions expressed by participants in their textual feedback. Additionally, a comparison among the existing sentiment analysis techniques is done.
Breast Cancer Classification using CNN with Transfer Learning Models

Breast cancer is the deadliest and most common cancer in the world. Early treatment of this cancer can help to nip it in the bud. In present medical setting, this cancer is identified by manual clinical procedures, which can lead to human errors and further delay the treatment procedure. So, we propose a Convolutional Neural Network (CNN) model employed with transfer learning approach with RESNET50, VGG19 and InceptionV3 algorithms. The histopathological image dataset is used to detect cancer cells in the tissues of the breast. We examine the performance of different models based on their accuracy, by varying different optimizers (Adam, SGDM and RMSProp) for each transfer learning model. The results show that the Inception-V3 model with Adam optimizer outperforms VGG19 and RESNET-50 in terms of accuracy.
Predicting Gender from Human or Non-human Social Media Profile Photos by using Transfer Learning

Social media profile photos can demonstrate a variety of information about a person, including her personality, behavior, preference, individuality, and gender. Prediction of gender from social media photos has a number of real life applications such as gender marketing and identification of camouflaged profile photos. Numerous techniques can be applied for determining gender from a user’s profile photos. In this study, we predict a user’s gender from her social media profile photos (i.e., Facebook, Twitter, and Instagram) by using multiple transfer learning models. While conventional methods are straightforward and can only determine gender based on human faces, we propose a novel model that determines gender based on both human faces and non-human pictures (i.e., a flower, animal, cartoon, doll, etc.). The model predicts the gender of a user based on the pattern of sharing profile photos with an outstanding accuracy of 95.75%.
Interactive Deep Neural Network for Aspect-Level Sentiment Analysis

Opinion mining, also known as sentiment analysis, is the act of analysing text written in natural language on a topic and categorizing it as positive, negative, or neutral based on the sentiments, emotions, and views stated in it. On the internet nowadays, the number of people who express their ideas through reviews is growing day by day. Manually analysing and extracting thoughts from such a large number of evaluations are nearly very complex and much tough to handle. The work analyses the deep learning techniques for extracting aspects in opinion mining in this research. Aspect extraction is a subtask of emotion investigation that needs discovering opinion targets in opinionated text, and identifying the precise characteristics of a product or service. A model for fine-grained aspect-based opinion mining is proposed that addresses important aspects of effective aspect-based opinion mining. Therefore, this work analyses and states the various methodologies that focuses on aspect based sentimental analysis. The implementation of this approach, is outside the scope of this paper.
Dynamic Spatial Sparsification for Efficient Vision Transformers and Convolutional Neural Networks

In this paper, we present a new approach for model acceleration by exploiting spatial sparsity in visual data. We observe that the final prediction in vision Transformers is only based on a subset of the most informative regions, which is sufficient for accurate image recognition. Based on this observation, we propose a dynamic token sparsification framework to prune redundant tokens progressively and dynamically based on the input to accelerate vision Transformers. Specifically, we devise a lightweight prediction module to estimate the importance of each token given the current features. The module is added to different layers to prune redundant tokens hierarchically. While the framework is inspired by our observation of the sparse attention in vision Transformers, we find that the idea of adaptive and asymmetric computation can be a general solution for accelerating various architectures. We extend our method to hierarchical models including CNNs and hierarchical vision Transformers as well as more complex dense prediction tasks. To handle structured feature maps, we formulate a generic dynamic spatial sparsification framework with progressive sparsification and asymmetric computation for different spatial locations. By applying lightweight fast paths to less informative features and expressive slow paths to important locations, we can maintain the complete structure of feature maps while significantly reducing the overall computations. Extensive experiments on diverse modern architectures and different visual tasks demonstrate the effectiveness of our proposed framework. By hierarchically pruning 66% of the input tokens, our method greatly reduces 31% ∼ 35% FLOPs and improves the throughput by over 40% while the drop of accuracy is within 0.5% for various vision Transformers. By introducing asymmetric computation, a similar acceleration can be achieved on modern CNNs and Swin Transformers. Moreover, our method achieves promising results on more complex tasks including semantic segmentation and object detection. Our results clearly demonstrate that dynamic spatial sparsification offers a new and more effective dimension for model acceleration. Code is available at https://github.com/raoyongming/DynamicViT.
Cooperative Perception With V2V Communication for Autonomous Vehicles

Occlusion is a critical problem in the Autonomous Driving System. Solving this problem requires robust collaboration among autonomous vehicles traveling on the same roads. However, transferring the entirety of raw sensors' data among autonomous vehicles is expensive and can cause a delay in communication. This paper proposes a method called Realtime Collaborative Vehicular Communication based on Bird's-Eye-View (BEV) map. The BEV map holds the accurate depth information from the point cloud image while its 2D representation enables the method to use a novel and well-trained image-based backbone network. Most importantly, we encode the object detection results into the BEV representation to reduce the volume of data transmission and make real-time collaboration between autonomous vehicles possible. The output of this process, the BEV map, can also be used as direct input to most route planning modules. Numerical results show that this novel method can increase the accuracy of object detection by cross-verifying the results from multiple points of view. Thus, in the process, this new method also reduces the object detection challenges that stem from occlusion and partial occlusion. Additionally, different from many existing methods, this new method significantly reduces the data needed for transfer between vehicles, achieving a speed of 21.92 Hz for both the object detection process and the data transmission process, which is sufficiently fast for a real-time system.
An Intuitionistic Fuzzy Approach to Analysis Financial Risk Tolerance with MATLAB in Business

To deal with the ambiguity and uncertainty, fuzzy set theory and generalization may be used. One of the most active and productive fields of research and development over the past several years has been intuitionistic fuzzy control system theory. An intuitive fuzzy logic system is very similar to how people think. Tolerance for risk is a crucial component of investing. Higher net worth and more discretionary cash often allow business owners to take bigger risks with their investments. In order to examine the risk tolerance capacity in business, we design an intuitionistic fuzzy inference system in this research and introduce a new function called a triangular non-membership function in MATLAB.
Hybrid firefly algorithm with a new mechanism of gender distinguishing for global optimization

Firefly algorithm (FA) is a novel stochastic optimization technique mimicking the inter-attraction and movement of fireflies. As we all know that male and female fireflies exist in nature, which can be distinguished based on some certain definite characteristics. However, all fireflies are unisex in original firefly algorithm (OFA), which is not true biologically. Motivated by the physical characteristics and movement patterns of fireflies, we proposed a novel hybrid firefly algorithm with a new mechanism of gender distinguishing (HFA-GD). In our proposed algorithm, the population is divided into two subgroups based on gender, and improved position update formulas of male and female fireflies are proposed to reduce the count of attractions and overcome untimely convergence. Besides, to lessen the chance of FA's untimely convergence, mutation operators of fireflies with gender distinguishing and perturbation jumps of the global best firefly are introduced. Different from some FAs based on gender, male fireflies fly to brighter males to increase their chances of getting attention from other females, and the female flies to the brighter female in our proposed HFA-GD. The performance validity of HFA-GD is tested on 24 2006 Congress on Evolutionary Computation (CEC 2006) constrained optimization problems (COPs) and 28 CEC 2017 COPs, two constrained engineering optimization problems (CEOPs), and two parameter estimation problems. The comparison results of experiment with several meta-heuristic algorithms have demonstrated the effectiveness of HFA-GD.
A Bibliometric Analysis of Trending Mobile Teaching and Learning Research from the Social Sciences

With the development of computer and information technology, mobile teaching has enjoyed pride of place among teaching mediums in the past two decades. To visually explore the mobile learning hotspots and trends present in international journals, this study adopted two science mapping tools (CiteSpace and VOSviewer) to first detect and then visualise emerging trends (i.e., hotspots) in the mobile learning literature. A total of 528 mobile learning articles published between 2003 and 2021 that appeared in 21 international educational technology journals indexed in the SSCI database were retrieved for bibliometric analysis. The results show (1) there was a remarkable increase in academic output in this field starting in 2008 that topped out in 2021; (2) co-authorship with academics from diverse countries/regions and institutions was evident; (3) three trending foci in the literature include defining mobile learning, designing learning systems, and exploring mobile learning effectiveness; and (4) the high-frequency co-cited publications focus on the effectiveness of mobile devices via different research methods. This study provides scholars with an accessible summary of the current trends in mobile learning, identifies the active researchers in this field, and reports on which outlets are most relevant for research produced on this topic. In addition, the findings have direct implications for the education and private sectors. Mobile devices are not widely adopted in classroom settings and are often considered a learning tool more suited for out-of-class assignments or practice. Therefore, it is necessary for information technology educators to invest in actively initiating the integration of mobile technology into the classroom. Those in the technology industry should aim to develop mobile devices and relevant educational applications/software that can be utilised not only within the confines of the classroom but also to bridge in-class and out-of-class learning.
Multilevel Feature Fusion for End-to-End Blind Image Quality Assessment

In this paper, a framework based on two feature extraction networks and a multilevel feature fusion (MFF) network is proposed. Multilevel degradation features can be obtained through this method, and combined with the human visual perception system, the local and global feature information contained in these features can be captured, which is conducive to the prediction of distorted images. First, a restored image approximating a reference image is generated by a restorative generative adversarial network (GAN). Furthermore, the multilevel degradation features of distorted images and the restored image features are extracted by EfficientNet. Second, the features extracted by EfficientNet are input into the MFF network and are fully expressed by the top-down, bottom-up and third edge joining methods. Moreover, the features provide more low-level details and high-level semantic features for the prediction of image quality scores. In addition, after the MFF stage, the framework calculates the score of each branch feature and obtains the average quality score. Experimental results show that our method achieves greatly improved prediction accuracy and performance on five standard databases.
CARET: Chain-Aware ROS 2 Evaluation Tool

This paper presents a tool to evaluate the latency of Robot Operating System (ROS) 2 applications called Chain-Aware ROS 2 Evaluation Tool (CARET). ROS 2 is designed to enhance the modularity of real-time robotic applications, including self-driving software such as Autoware. To analyze the performance of ROS 2 applications, CARET supports measurement functionalities for the callback latency, node latency, communication time between nodes, and end-to-end latency. To calculate each latency, CARET provides tracepoints, an architecture file, information on tracepoint connections, and a message tracking functionality. Furthermore, CARET can visualize different types of latency, such as bottlenecks and lost message, to analyze ROS 2 applications. The experimental results demonstrate that CARET can successfully measure the end-to-end latency of Autoware.Universe, which is ROS 2-based self-driving software.
Robust periodic blind watermarking based on sub-block mapping and block encryption

Digital watermarking is of critical importance in multimedia copyright protection. However, with the development of Internet technologies, the combined attacks of complex environments challenge to the robustness of traditional digital watermarking schemes. In order to tackle this problem, a robust periodic blind watermarking scheme based on sub-block mapping and block encryption is proposed in this paper to enhance robustness under combined attacks. Concretely, a watermark is encoded by the periodic encoding method to raise the fault tolerance rate. Then a block encryption technique is designed to encrypt periodic sub-blocks by utilizing different encoding methods to maintain the security of copyright information. To achieve high image quality, this scheme employs a sub-block mapping mechanism to map the encrypted periodic watermark into a smaller watermark to minimize the visual difference between host image and watermarked image. Finally, an improved Singular Value Decomposition (SVD) is conducted to embed a quaternary watermark that can extend to a gray-scale watermark. Experimental results demonstrate that the proposed watermarking scheme has good visual quality of watermarked images and is robust to image processing attacks, especially in cropping, scaling and combined attacks, which is better than other watermarking schemes.
Understanding generation Z consumers’ revisit intentions to robotic service restaurants

Purpose The purpose of this paper is to investigate the influencing factors of generation Z (Gen Z) consumers’ revisit intentions to robotic restaurants in the post-pandemic times. Design/methodology/approach Using a sample of Gen Z consumers with dining experiences in an Indian restaurant using service robots, the study empirically tests a research framework based on stimulus–organism–response theory. Findings The study explains how Gen Z consumer’s perceptions of functional attributes (i.e. perceived usefulness and perceived ease of use), socio-emotional attribute (i.e. perceived safety) and relational attribute (i.e. trust) shape their attitude, which in turn leads to their evaluations of performance outcomes and intention to revisit the robotic restaurants. The findings also indicate that perceived risk reduction of viral infection moderates the impact of performance outcomes on revisit intention. Originality/value Due to the outbreak of COVID-19 pandemic, the changing preferences of consumers have resulted in an increase in demand for restaurants offering robotic services. To support the long-term viability of service robots in restaurant services, the current study investigates what elements of service robots can determine consumers’ intentions to revisit the robotic restaurants during post-pandemic times.
Infomax-based deep autoencoder network for recognition of multi-element geochemical anomalies linked to mineralization

In recent years, deep autoencoder networks (DANs) have shown enormous potential to achieve state-of-the-art performance for recognizing multi-element geochemical anomalies related to mineralization. By training a DAN, multi-element signatures of geochemical background are learned by higher-level representations of input signals, providing key references to quantify reconstruction errors linked to complex patterns of metal-vectoring geochemical anomalies in non-linear Earth systems. However, the learning of geochemical background representations may be suppressed by redundant mutual information from inter-element correlations and by mixed information of elemental concentration data caused by multiplicative cascade geo-processes. To deal with these issues, we conceptualized an idea of a new deep learning architecture called Info‒DAN, chaining the information maximization (Infomax) processor to the training network of stacked autoencoders. Infomax is an adaptive learning algorithm from information theory paradigms which aims at maximizing the information flow (joint entropy) passed through a feed-forward neural network processor. It was adopted to encode original multi-element data into independent source signals associated with different geochemical sub-populations and to prevent the dilution of background representations caused by inter-element information redundancy. The recovered source signals were then fed into a DAN processor to assist in modeling the improved representations of geochemical background populations and in enhancing complex anomaly patterns. The Info‒DAN technique was applied to stream sediment geochemical data pertaining to the Moalleman district, NE Iran, for performance appraisal in recognition of metal-vectoring geochemical anomalies. Evaluation tools comprising success-rate curves and prediction-area plots indicated that anomaly patterns derived from Info‒DAN, compared to those from a stand-alone DAN, reveal a stronger spatial correlation between ore-controlling fractures/faults and locations of known metal occurrences. The findings suggest that, thanks to the proposed algorithm, complex patterns of geochemical anomalies can be quantified with improved generalization accuracy as well as practical insights for vectoring towards metal exploration targets.
Using Personas in the development of eHealth interventions for chronic pain: A scoping review and narrative synthesis

Behavioral eHealth interventions can enhance self-management and improve well-being in people with chronic pain. The development of these interventions calls for a user-centered approach to ensure that patient needs are appreciated. However, it may be challenging to involve patients; particularly during the early stages of the process. Fictional user profiles, known as Personas, can represent needs and guide designing eHealth interventions. This article provides a comprehensive overview of the use of Personas in the development of behavioral eHealth interventions for people with chronic pain with the aim to identify benefits and challenges.Bibliographic databases (Medline, Web of Science Core Collection, PsycInfo, CINAHL) and registries (PubMed Central, medaRxiv) were systematically searched. In a double-reviewing process, n = 6830 hits and n = 351 full-texts were screened and read. Ten peer-reviewed studies published between 2017 and 2022 were included in the narrative synthesis.Ten studies reported using "Pain Personas" in the development of eHealth interventions for such purposes as to gain a shared understanding of the user and to discuss solutions in team meetings, or for patients to identify with (if Personas are included in the intervention). Personas were based on qualitative and/or quantitative data. However, the procedure for creating Personas was only described in half of the included studies (n = 5). These five studies provided descriptive details of the Personas (i.e., picture, name, narrative of their pain behavior, technological skills, and motivation).Although Personas have been used by pain researchers in recent projects and were highlighted as an important ingredient in the development process, available design guidelines for the creation and use of Personas are not followed or communicated transparently. Benefits and challenges when using Personas in the development of eHealth interventions for people with chronic pain are discussed to support future eHealth efforts and to improve the quality of eHealth innovation in the field of pain.