Reviewerly/exHarmony huggingface dataset:
運用網路資料包絡分析法探究半導體公司之競爭力

&lt;p&gt;公司績效評估是複雜且重要的課題，除了評估企業經營績效之良寙外，主要是在了解公司決策能否落實執行。而公司內部各專責單位，其在資源分配與運用是否有效，直接影響到公司的經營績效。本研究運用一個新的價值創造程序，並藉由網絡資料包絡分析法來測量公司內部經濟活動的「生產績效」、「銷售績效」與「獲利績效」，以消除傳統一階段評估的黑箱（Black Box）。研究發現，整體上以「生產績效」較佳，次為「銷售績效」，而「獲利績效」則相對較差；另經KW檢定，各績效值在不同類型公司之間有顯著差異，顯示因經營方式的不同而導致績效評估結果的差異。其次，本研究並結合集群分析與多元尺度分析方法，以協助管理者了解組織本身之競爭優勢。&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;&lt;p&gt;Company performance evaluation is a complex and essential subject. In addition to evaluating the excellent performance of a company’s business performance, it is mainly to understand whether the company’s decisions can be implemented. The effective allocation and utilization of resources by each dedicated unit directly affect the company’s operating performance. This study uses a new value creation procedure. It uses network data envelopment analysis to measure the &amp;quot;production performance&amp;quot;, &amp;quot;sales performance&amp;quot; and &amp;quot;profit performance&amp;quot; of the company’s internal economic activities, to eliminate the black box of traditional one-stage evaluation (black box). The study found that, on the whole, &amp;quot;production performance&amp;quot; was the best, followed by &amp;quot;sales performance&amp;quot;, and &amp;quot;profit performance&amp;quot; was relatively poor; in addition, through the K-W test, there were significant differences in performance values among different types of companies, indicating that due to Differences in performance evaluation results due to differences in operating methods. Second, this study combines cluster analysis and multiscale analysis methods to help managers understand the organization’s competitive advantages.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
The charm of emojis: the heterogeneous effects of emoji use on users’ intention to share in online tourism rumor correction

Improving users’ intention to share rumor-correcting information is crucial for the tourism rumor correction. Based on the stimulus-organism-response (S-O-R) theoretical framework, this study explored the heterogeneous effects of emoji use on users’ sharing intention during tourism rumor correction using the experimental method. The results indicate that emoji use while correcting rumors can increase the persuasive effect, but the emoji meaning’s multipleness will reduce users’ intention to share. Furthermore, perceived information authenticity and processing fluency are mediating factors. Moreover, information sources is an effective moderator. For individual sources, the multiple meanings of emojis increase the negative effect on users’ sharing intention, whereas organizational sources can decrease this negative effect.
An Intelligent Detection Approach for Smoking Behavior

Smoking in public places not only causes potential harm to the health of oneself and others, but also causes hidden dangers such as fires. Therefore, for health and safety considerations, a detection model is designed based on deep learning for places where smoking is prohibited, such as airports, gas stations, and chemical warehouses, that can quickly detect and warn smoking behavior. In the model, a convolutional neural network is used to process the input frames of the video stream which are captured by the camera. After image feature extraction, feature fusion, target classification and target positioning, the position of the cigarette butt is located, and smoking behavior is determined. Common target detection algorithms are not ideal for small target objects, and the detection speed needs to be improved. A series of designed convolutional neural network modules not only reduce the amount of model calculations, speed up the deduction, and meet real-time requirements, but also improve the detection accuracy of small target objects (cigarette butts).
Solving the 0/1 Knapsack Problem Using Metaheuristic and Neural Networks for the Virtual Machine Placement Process in Cloud Computing Environment

Virtual machine placement (VMP) is carried out during virtual machine migration to choose the best physical computer to host the virtual machines. It is a crucial task in cloud computing. It directly affects data center performance, resource utilization, and power consumption, and it can help cloud providers save money on data center maintenance. To optimize various characteristics that affect data centers, VMs, and their runs, numerous VMP strategies have been developed in the cloud computing environment. This paper aims to compare the accuracy and efficiency of nine distinct strategies for treating the VMP as a knapsack problem. In the numerical analysis, we test out various conditions to determine how well the system works. We first illustrate the rate of convergence for algorithms, then the rate of execution time growth for a given number of virtual machines, and lastly the rate of development of CPU usage rate supplied by the nine methods throughout the three analyzed conditions. The obtained results reveal that the neural network algorithm performs better than the other eight approaches. The model performed well, as shown by its ability to provide near-optimal solutions to test cases.
Blind Image Deconvolution Using Variational Deep Image Prior

Conventional deconvolution methods utilize hand-crafted image priors to constrain the optimization. While deep-learning-based methods have simplified the optimization by end-to-end training, they fail to generalize well to blurs unseen in the training dataset. Thus, training image-specific models is important for higher generalization. Deep image prior (DIP) provides an approach to optimize the weights of a randomly initialized network with a single degraded image by maximum a posteriori (MAP), which shows that the architecture of a network can serve as the hand-crafted image prior. Unlike conventional hand-crafted image priors, which are obtained through statistical methods, finding a suitable network architecture is challenging due to the unclear relationship between images and their corresponding architectures. As a result, the network architecture cannot provide enough constraint for the latent sharp image. This paper proposes a new variational deep image prior (VDIP) for blind image deconvolution, which exploits additive hand-crafted image priors on latent sharp images and approximates a distribution for each pixel to avoid suboptimal solutions. Our mathematical analysis shows that the proposed method can better constrain the optimization. The experimental results further demonstrate that the generated images have better quality than that of the original DIP on benchmark datasets.
GP-UNIT: Generative Prior for Versatile Unsupervised Image-to-Image Translation

Recent advances in deep learning have witnessed many successful unsupervised image-to-image translation models that learn correspondences between two visual domains without paired data. However, it is still a great challenge to build robust mappings between various domains especially for those with drastic visual discrepancies. In this paper, we introduce a novel versatile framework, Generative Prior-guided UNsupervised Image-to-image Translation (GP-UNIT), that improves the quality, applicability and controllability of the existing translation models. The key idea of GP-UNIT is to distill the generative prior from pre-trained class-conditional GANs to build coarse-level cross-domain correspondences, and to apply the learned prior to adversarial translations to excavate fine-level correspondences. With the learned multi-level content correspondences, GP-UNIT is able to perform valid translations between both close domains and distant domains. For close domains, GP-UNIT can be conditioned on a parameter to determine the intensity of the content correspondences during translation, allowing users to balance between content and style consistency. For distant domains, semi-supervised learning is explored to guide GP-UNIT to discover accurate semantic correspondences that are hard to learn solely from the appearance. We validate the superiority of GP-UNIT over state-of-the-art translation models in robust, high-quality and diversified translations between various domains through extensive experiments.
Sentimental Analysis of Movie Reviews in Tamil Text

Sentiment analysis is the act of recognizing and categorizing the emotions represented in text as positive, negative, or neutral. In numerous languages, including Tamil, machine learning techniques have been widely employed for sentiment analysis. Within that overview, let look at how the SVM (Support Vector Machine), k-nearest neighbor (k-NN), and LSTM (Long Time Short Memory) algorithms may be used for sentiment analysis in Tamil literature. To train and test the algorithms, the research used a dataset of Tamil text comments from YouTube reviews. The goal is to find the best sentiment analysis model in Tamil text that can be used to categorise the emotions reflected in fresh Tamil text data as positive, negative, or neutral. For this experiment, a dataset of Tamil text comments is acquired from YouTube reviews. The best model is then used to new Tamil text data to categorise sentiment.
Revolutionizing Education: Exploring the Potential of Blockchain based Incentive Systems

The current education system emphasizes memorization and high test scores, leading to disinterest and a lack of motivation among students. A more comprehensive educational approach that integrates hands-on learning, real-world applications and the development of critical thinking skills is proposed. Decentralization and transparency are two critical issues that impact the education system, and addressing them through initiatives such as increased openness in decision-making processes and accountability can improve the system. This research work proposes using Blockchain technology in education to provide recognition for students’ skills, secure and transparent verification of their academic history, and a more engaging and rewarding learning experience. A proof of concept is proposed, an incentive-based skill development platform that uses Ethereum smart contracts and cryptographic tokens to reward students’ efforts.
An Efficient Real-Time Indian Sign Language (ISL) Detection using Deep Learning

Indian Sign Language (ISL) detection has become crucially important due to the growing need to bridge the communication gap between the hearing and the non-hearing people. For the deaf and hard-of-hearing community in India, ISL is the main form of communication. However, since the majority of the population does not understand ISL, communication can become a challenge. Indian sign language detection system that uses deep convolutional neural networks (CNNs), Media Pipe, and OpenCV in real-time. The system is designed to detect and classify Indian sign language gestures in real-time using hand detection and tracking, gesture segmentation, feature extraction, and a trained CNN model. The system also includes a user-friendly interface and accessibility features such as text-to-speech, image to speech, text to image and webcam input to text display. The proposed system has the potential to enhance communication between deaf and hearing users and can be deployed in real-world settings to facilitate communication in different ways.
A Novel Model for Prediction of Next Word using Machine Learning

Prediction of next word is also known as language modeling and is an application of Natural Language Processing which helps in next word prediction. In the past, several studies employed various models to predict the outcomes, including federated text models and recurrent neural networks. In the suggested model, machine learning and TensorFlow techniques will be used to predict the five words. The text is understood by the machine learning algorithms, which then anticipate the next word to be used to finish the phrase. The proposed model helps the user to predict the next word. The model will be implemented using natural processing language algorithms and comparing the algorithms by using Wikipedia text files to train the model.
Exploring Techniques for Web Phishing Detection: A Comprehensive Survey

Phishing is a common social engineering attack in today’s world. By stealing confidential data through various manipulation techniques, it has interfered with people’s lives and had an impact on several companies. Phishing can be done in a number of ways, including through email, whaling, spear-phishing, click-bait adverts, and more. Attackers replicate an official website to create a website that appears legitimate to the user but is actually run by the attacker. The users eventually supply the cloned website with their login credentials and other sensitive information, including financial, personal, and even company information, which the attacker then uses for malevolent purposes. The phishing URLs (Uniform Resource Locator) must be recognized and ban them in order to stop these unethical behaviors. Techniques like machine learning and deep learning can be utilized for this. This study intends to provide a structured and comprehensive view of the research on phishing URL detection.
Determinants of Cloud Services and Customer Satisfaction in Indian Start-up Companies

The current market focuses on two critical aspects-speed and timeliness of the service providers, which are quintessential for cloud computing and service providers. This paper investigates the factors that Cloud Computing Service providers must maintain to keep their customers delighted. The review of literature carries the five most important factors cost, performance, accountability, security & privacy, and usability for cloud service customer satisfaction. This paper develops a questionnaire-based customer satisfaction model that helps cloud service providers understand the detriments of availing cloud services. The developed questionnaire was used to collect data from Indian start-ups using Analytic Hierarchy Process (AHP). The determining factors of the cloud service provider for customer satisfaction are identified by the ranking method. It was found that security and privacy factors are essential for customer satisfaction for start-ups.
Reference-Limited Compositional Zero-Shot Learning

Compositional zero-shot learning (CZSL) refers to recognizing unseen compositions of known visual primitives, which is an essential ability for artificial intelligence systems to learn and understand the world. While considerable progress has been made on existing benchmarks, we suspect whether popular CZSL methods can address the challenges of few-shot and few referential compositions, which is common when learning in real-world unseen environments. To this end, we study the challenging reference-limited compositional zero-shot learning (RL-CZSL) problem in this paper, i.e., given limited seen compositions that contain only a few samples as reference, unseen compositions of observed primitives should be identified. We propose a novel Meta Compositional Graph Learner (MetaCGL) that can efficiently learn the compositionality from insufficient referential information and generalize to unseen compositions. Besides, we build a benchmark with two new large-scale datasets that consist of natural images with diverse compositional labels, providing more realistic environments for RL-CZSL. Extensive experiments in the benchmarks show that our method achieves state-of-the-art performance in recognizing unseen compositions when reference is limited for compositional learning.
Fraud Detection in Online Payments using Machine Learning Techniques

People rely on online transactions for nearly everything in today’s environment. Online transactions offer several benefits, such as ease of use, viability, speedier payments, etc., but they also have some drawbacks, such as fraud, phishing, data loss, etc. As online transactions grow, there is a continuing risk of frauds and deceptive transactions that could violate a person’s privacy. In order to prevent high risk transactions, numerous commercial banks and insurance firms invested millions of rupees in the development of transaction detection systems. This research study has introduced a feature-engineered machine learning-based model for detecting transaction fraud. By processing as much data as it can, the algorithm can gain experience, strengthen its stability, and increase its performance. The effort to detect online fraud transactions can use these algorithms. In this, a dataset of specific online transactions is obtained. Then, with the aid of machine learning algorithms, unusual or distinctive data patterns that will be helpful in identifying any transactions that are fraudulent are discovered. The XGBoost algorithm is a cluster of decision trees, which will be utilized in order to achieve the best outcomes. This algorithm has recently taken control of the ML industry. Comparing this approach to other ML algorithms reveals that it is faster and more accurate.
Comparative Analysis of Statistical Optimizers for Logistic Regression

Logistic regression is one of the method of statistics and closely related to machine learning. Optimization technique plays very important role while building any Machine learning model. This research compares different optimization techniques which can be used for logistic regression; further it talks about which optimization technique can be the best optimization technique among the selected techniques to perform logistic regression based on prediction accuracy and loss obtained during experimental analysis. The obtained results demonstrate that RMSProp works well in practice and compares favorably to other optimization methods.
Machine Learning Models Applied in Sign Language Recognition

One of the most relevant worldwide problems is the inclusion of people with disabilities. In this research we want to help focusing in the people with hearing disabilities, being able to translate sign language into words that we could read. It is a common worldwide problem to be able to accurately predict the gestures of non-hearing people in order to be able to communicate efficiently with them and not have a barrier when they want to perform their daily activities. In order to that we propose a three phase method combining Data preparation(The dataset used for this is the “Australian Sign Language sings”, which is public and free to use) and cleaning phase, modeling using Random Forest Vector Support Machine and Neural Networks, able to optimize and qualify these models using the measures of accuracy, precision, recall and f1-score. Therefore, in this work we try to offer the highest possible quality measures to the prediction of signs in the Australian language with the mentioned dataset. This also opens the way for future research where more advanced supervised modeling techniques can be applied to improve the values obtained.
The Root of Civil Conversation: Spenser with (and by) Himself

Previous articleNext article No AccessVII. AfterwordThe Root of Civil Conversation: Spenser with (and by) HimselfJeff DolvenJeff DolvenPrinceton University, USA Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmailPrint SectionsMoreDetailsFiguresReferencesCited by Spenser Studies Volume 372023Companionable Thinking: Spenser with… Article DOIhttps://doi.org/10.1086/723096 Views: 34Total views on this site © 2023 The University of Chicago. All rights reserved.PDF download Crossref reports no articles citing this article.
Revealing landslide exposure of informal settlements in Medellín using Deep Learning

Large areas of informal settlements on the slopes of Medellín are exposed to landslide risk, but there exists no accurate and up-to date data set on the location and size of informal areas. It is thus difficult to develop mitigation strategies to reduce the risk for the local population. Here, we tackle the issue of inaccurate geodata and apply a CNN for the extraction of individual building footprints from orthophotos. With it we achieve a more reliable data base for a more precise estimation of the amount of exposed population in informal areas towards landslides.
Real Impact of the Blockchain in Securing a ToIP Network

Telephony over IP (ToIP) is a cost-saving communication technology based on voice over IP (VoIP) that enables enterprises to reduce communication fees. However, ToIP faces many security threats due to its IP-based nature. This work aims to improve ToIP security using cryptography and blockchain technology. The authors propose a secure approach to user registration, authentication, communication session establishment, and communication data storage. The proposed solution leverages blockchain technology to ensure the integrity, confidentiality, and availability of communication data. By implementing this solution, the researchers aim to enhance the security of ToIP networks and protect them from cyber threats. This approach provides a secure and reliable way to support ToIP services while preserving confidentiality and privacy.
Novel Framework for Generating Criminals Images Based on Textual Data Using Identity GANs

Text-to-image generation is a vital task in different fields, such as combating crime and terrorism and quickly arresting lawbreakers. For several years, due to a lack of deep learning and machine learning resources, police officials required artists to draw the face of a criminal. Traditional methods of identifying criminals are inefficient and time-consuming. This paper presented a new proposed hybrid model for converting the text into the nearest images, then ranking the produced images according to the available data. The framework contains two main steps: generation of the image using an Identity Generative Adversarial Network (IGAN) and ranking of the images according to the available data using multi-criteria decision-making based on neutrosophic theory. The IGAN has the same architecture as the classical Generative Adversarial Networks (GANs), but with different modifications, such as adding a non-linear identity block, smoothing the standard GAN loss function by using a modified loss function and label smoothing, and using mini-batch training. The model achieves efficient results in Inception Distance (FID) and inception score (IS) when compared with other architectures of GANs for generating images from text. The IGAN achieves 42.16 as FID and 14.96 as IS. When it comes to ranking the generated images using Neutrosophic, the framework also performs well in the case of missing information and missing data.
Intelligent IoT Network Awareness

IoT devices are everywhere sensing, collecting, storing, and computing massive amounts of data. In the Internet of Things scenario, diversified services will generate traffic with different characteristics and put forward different business requirements. The application based on network intelligent awareness plays a key role in effectively managing network and deepening the control of network. In this chapter, we propose an end-to-end IoT traffic classification method relying on a deep learning aided capsule network for the sake of forming an efficient classification mechanism that integrates feature extraction, feature selection, and classification model. Then, we propose a hybrid IDS architecture and introduce a machine learning aided detection method. In addition, we model the time-series network traffic by the recurrent neural network (RNN). The attention mechanism is introduced for assisting network traffic classification in the form of the following two models: the attention aids long short term memory (LSTM) and the hierarchical attention network (HAN). Finally, we propose to design a machine learning-based in-network Distributed Denial of Service (DDoS) detection framework. Benefit from switch processing performance, the in-network mechanism could achieve high scalability and line speed performance.