Reviewerly/exHarmony huggingface dataset:
Estimating Software Development Efforts Using a Random Forest-Based Stacked Ensemble Approach

Software Project Estimation is a challenging and important activity in developing software projects. Software Project Estimation includes Software Time Estimation, Software Resource Estimation, Software Cost Estimation, and Software Effort Estimation. Software Effort Estimation focuses on predicting the number of hours of work (effort in terms of person-hours or person-months) required to develop or maintain a software application. It is difficult to forecast effort during the initial stages of software development. Various machine learning and deep learning models have been developed to predict the effort estimation. In this paper, single model approaches and ensemble approaches were considered for estimation. Ensemble techniques are the combination of several single models. Ensemble techniques considered for estimation were averaging, weighted averaging, bagging, boosting, and stacking. Various stacking models considered and evaluated were stacking using a generalized linear model, stacking using decision tree, stacking using a support vector machine, and stacking using random forest. Datasets considered for estimation were Albrecht, China, Desharnais, Kemerer, Kitchenham, Maxwell, and Cocomo81. Evaluation measures used were mean absolute error, root mean squared error, and R-squared. The results proved that the proposed stacking using random forest provides the best results compared with single model approaches using the machine or deep learning algorithms and other ensemble techniques.
Image‐denoising algorithm based on improved K‐singular value decomposition and atom optimization

The traditional K-singular value decomposition (K-SVD) algorithm has poor image-denoising performance under strong noise. An image-denoising algorithm is proposed based on improved K-SVD and dictionary atom optimization. First, a correlation coefficient-matching criterion is used to obtain a sparser representation of the image dictionary. The dictionary noise atom is detected according to structural complexity and noise intensity and removed to optimize the dictionary. Then, non-local regularity is incorporated into the denoising model to further improve image-denoising performance. Results of the simulated dictionary recovery problem and application on a transmission line dataset show that the proposed algorithm improves the smoothness of homogeneous regions while retaining details such as texture and edge.
How to embrace variation and accept uncertainty in linguistic and psycholinguistic data analysis

Abstract The use of statistical inference in linguistics and related areas like psychology typically involves a binary decision: either reject or accept some null hypothesis using statistical significance testing. When statistical power is low, this frequentist data-analytic approach breaks down: null results are uninformative, and effect size estimates associated with significant results are overestimated. Using an example from psycholinguistics, several alternative approaches are demonstrated for reporting inconsistencies between the data and a theoretical prediction. The key here is to focus on committing to a falsifiable prediction, on quantifying uncertainty statistically, and learning to accept the fact that – in almost all practical data analysis situations – we can only draw uncertain conclusions from data, regardless of whether we manage to obtain statistical significance or not. A focus on uncertainty quantification is likely to lead to fewer excessively bold claims that, on closer investigation, may turn out to be not supported by the data.
Optimizing Deeper Transformers on Small Datasets

It is a common belief that training deep transformers from scratch requires large datasets.Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained models during finetuning.This work shows that this does not always need to be the case: with proper initialization and optimization, the benefits of very deep transformers can carry over to challenging tasks with small datasets, including Textto-SQL semantic parsing and logical reading comprehension.In particular, we successfully train 48 layers of transformers, comprising 24 fine-tuned layers from pre-trained RoBERTa and 24 relation-aware layers trained from scratch.With fewer training steps and no task-specific pre-training, we obtain the state-of-the-art performance on the challenging cross-domain Text-to-SQL parsing benchmark Spider 1 .We achieve this by deriving a novel Data-dependent Transformer Fixedupdate initialization scheme (DT-Fixup), inspired by the prior T-Fixup work (Huang et al., 2020).Further error analysis shows that increasing depth can help improve generalization on small datasets for hard cases that require reasoning and structural understanding.
Evaluation Examples are not Equally Informative: How should that change NLP Leaderboards?

Leaderboards are widely used in NLP and push the field forward.While leaderboards are a straightforward ranking of NLP models, this simplicity can mask nuances in evaluation items (examples) and subjects (NLP models).Rather than replace leaderboards, we advocate a re-imagining so that they better highlight if and where progress is made.Building on educational testing, we create a Bayesian leaderboard model where latent subject skill and latent item difficulty predict correct responses.Using this model, we analyze the ranking reliability of leaderboards.Afterwards, we show the model can guide what to annotate, identify annotation errors, detect overfitting, and identify informative examples.We conclude with recommendations for future benchmark tasks.
Real-time motion-induced error compensation for 4-step phase-shifting profilometry

Phase-shifting profilometry has been widely used in high-accuracy three-dimensional (3D) shape measurement. However, for dynamic scenarios, the object motion will lead to extra phase shift and then motion-induced error. Convenient and efficient motion-induced error compensation is still challenging. Therefore, we proposed a real-time motion-induced error compensation method for 4-step phase-shifting profilometry. The four phase-shifting images are divided into two groups to calculate two corresponding wrapped phases, one from the first three fringes and the other from the last three fringes. As the motion-induced error doubles the frequency of the projected fringes, the average phase can effectively compensate the motion-induced error because there is a π/2 phase shift between the adjacent frames. Furthermore, we designed a time sequence by recycling the projection fringes in a proper order, and the efficiency of 3D reconstruction could be effectively improved. This method performs pixel-wise error compensation, based on which we realized 50 fps real-time 3D measurement by GPU acceleration. Experimental results demonstrate that the proposed method can effectively reduce the motion-induced error.
NEU-chatbot: Chatbot for admission of National Economics University

In the last few years, intelligent chatbot systems have been prevalent in various application fields, especially in education. Therefore, the demand for such online consulting services like chatbots is getting higher respectively. However, most communications between potential students and universities are performed manually, which is very time-consuming procedure, becoming a burden on the head of admissions. In this paper, we introduce an AI-based chatbot where students can instantly get daily updates of curriculum, admission for new students, tuition fees, IELTS writing task II score, etc. Our chatbot was developed by Deep Learning models, which are already integrated into the Rasa framework. We also proposed a rational pipeline for Vietnamese chatbots with our data preprocessing to obtain optimal accuracy and to avoid the overfitting of the model. Our model can detect more than fifty types of questions from users' input with an accuracy of 97.1% on test set. The chatbot was applied for National Economics University's official admission Fanpage on the Facebook platform, which is the most famous social network in Vietnam. This research shows detailed guidelines on how to build an AI chatbot from scratch, and the techniques we used, which can be applied to any language globally.
Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors

Fully automated and volumetric segmentation of critical tumors may play a crucial role in diagnosis and surgical planning. One of the most challenging tumor segmentation tasks is localization of pancreatic ductal adenocarcinoma (PDAC). Exclusive application of conventional methods does not appear promising. Deep learning approaches has achieved great success in the computer aided diagnosis, especially in biomedical image segmentation. This paper introduces a framework based on convolutional neural network (CNN) for segmentation of PDAC mass and surrounding vessels in CT images by incorporating powerful classic features, as well. First, a 3D-CNN architecture is used to localize the pancreas region from the whole CT volume using 3D Local Binary Pattern (LBP) map of the original image. Segmentation of PDAC mass is subsequently performed using 2D attention U-Net and Texture Attention U-Net (TAU-Net). TAU-Net is introduced by fusion of dense Scale-Invariant Feature Transform (SIFT) and LBP descriptors into the attention U-Net. An ensemble model is then used to cumulate the advantages of both networks using a 3D-CNN. In addition, to reduce the effects of imbalanced data, a multi-objective loss function is proposed as a weighted combination of three classic losses including Generalized Dice Loss (GDL), Weighted Pixel-Wise Cross Entropy loss (WPCE) and boundary loss. Due to insufficient sample size for vessel segmentation, we used the above-mentioned pre-trained networks and fine-tuned them. Experimental results show that the proposed method improves the Dice score for PDAC mass segmentation in portal-venous phase by 7.52% compared to state-of-the-art methods in term of DSC. Besides, three dimensional visualization of the tumor and surrounding vessels can facilitate the evaluation of PDAC treatment response.
Customers’ perceptions of hotel AI-enabled voice assistants: does brand matter?

Purpose Extending the technology acceptance model (TAM) to a new context, the purpose of this paper is to propose an integrative model of the brand of artificial intelligence-enabled voice assistants (AI-EVA) and customers’ perceptions and behavioral intentions of using AI-EVA in hotels. Moderating effects of construal levels and hotel scales were examined. Design/methodology/approach This paper adopted a mixed method approach. A qualitative and phenomenological methodology was adopted in Study 1 to explore hotel customers’ experience with AI-EVA. Study 2 applied experimental design to investigate the effects of the brand of AI-EVA and construal level on customers’ perceptions and behavioral intentions of using AI-EVA. Based on Studies 1 and 2 results, Study 3 examined how the brand of AI-EVA and hotel scale affect customers’ perceptions and behavioral intentions of using AI-EVA during hotel stays. Findings This research found that customers perceive a higher level of perceived usefulness, perceived ease of use and anthropomorphism when AI-EVA is branded (vs off-brand). Perceived usefulness positively affects customers’ intention to use and to spread positive word-of-mouth. Anxiety of using AI-enabled devices and privacy concerns inhibit customers’ intention to use AI-EVA. Anthropomorphism increases customers’ willingness to spread positive word-of-mouth. Construal level moderates the effect of the brand of AI-EVAs on perceived ease of use and anthropomorphism. Hotel scale moderates the effect of brand on perceived usefulness. Originality/value This paper is one of the first attempts to uncover and integrate different factors underlying customers’ perceptions of using AI-EVA in an extended TAM in hotel settings. This paper provides an integrative model extending the TAM to a new context by deploying a mixed-method approach across three studies.
A color image encryption algorithm based on hyperchaotic map and DNA mutation

We devise a color image encryption scheme via combining hyperchaotic map, cross-plane operation and gene theory. First, the hyperchaotic map used in the encryption scheme is analyzed and studied. On the basis of the dynamics of hyperchaotic map, a color image encryption scheme is designed. At the end of the encryption process, a DNA mutation operation is used to increase the encoding images’ randomness and to improve the encryption algorithm’s security. Finally, simulation experiments, performance analysis, and attack tests are performed to prove the effectiveness and security of the designed algorithm. This work provides the possibility of applying chaos theory and gene theory in image encryption.
Prototypical Contrast Adaptation for Domain Adaptive Semantic Segmentation

Unsupervised Domain Adaptation (UDA) aims to adapt the model trained on the labeled source domain to an unlabeled target domain. In this paper, we present Prototypical Contrast Adaptation (ProCA), a simple and efficient contrastive learning method for unsupervised domain adaptive semantic segmentation. Previous domain adaptation methods merely consider the alignment of the intra-class representational distributions across various domains, while the inter-class structural relationship is insufficiently explored, resulting in the aligned representations on the target domain might not be as easily discriminated as done on the source domain anymore. Instead, ProCA incorporates inter-class information into class-wise prototypes, and adopts the class-centered distribution alignment for adaptation. By considering the same class prototypes as positives and other class prototypes as negatives to achieve class-centered distribution alignment, ProCA achieves state-of-the-art performance on classical domain adaptation tasks, i.e., GTA5 $$\rightarrow $$ Cityscapes and SYNTHIA $$\rightarrow $$ Cityscapes. Code is available at ProCA .
Color-Gray Multi-Image Hybrid Compression–Encryption Scheme Based on BP Neural Network and Knight Tour

In the research of multi-image encryption (MIE), the image type and size are important factors that limit the algorithm design. For this reason, the multi-image (MI) hybrid encryption algorithm that can flexibly encrypt color images and grayscale images of various sizes is proposed. Based on this, combining the back propagation (BP) neural network compression technology and the MI hybrid encryption algorithm, an MI hybrid compression-encryption (MIHCE) scheme can be obtained to reduce the pressure of simultaneous transmission and storage of multiple cipher images. Besides, two chaotic maps are used in the scheme design process. By plotting the phase diagrams under different parameter conditions, the rich variation of the behavior of the chaotic maps in the phase space is exhibited. The MIHCE scheme based on the chaotic maps consists of three parts: 1) compressing the MI cube by using the BP neural network; 2) scrambling the compressed MI cube based on the knight tour problem and chaotic sequences; and 3) diffusing the scrambled MI cube. After the MIHCE is completed, the obtained cipher images are stored and transmitted. Subsequently, the security analysis and compression performance analysis prove the feasibility and safety of the designed compression-encryption scheme.
Neural-Response-Based Extreme Learning Machine for Image Classification

This paper proposes a novel and simple multilayer feature learning method for image classification by employing the extreme learning machine (ELM). The proposed algorithm is composed of two stages: the multilayer ELM (ML-ELM) feature mapping stage and the ELM learning stage. The ML-ELM feature mapping stage is recursively built by alternating between feature map construction and maximum pooling operation. In particular, the input weights for constructing feature maps are randomly generated and hence need not be trained or tuned, which makes the algorithm highly efficient. Moreover, the maximum pooling operation enables the algorithm to be invariant to certain transformations. During the ELM learning stage, elastic-net regularization is proposed to learn the output weight. Elastic-net regularization helps to learn more compact and meaningful output weight. In addition, we preprocess the input data with the dense scale-invariant feature transform operation to improve both the robustness and invariance of the algorithm. To evaluate the effectiveness of the proposed method, several experiments are conducted on three challenging databases. Compared with the conventional deep learning methods and other related ones, the proposed method achieves the best classification results with high computational efficiency.
Deep neural network model for group activity recognition using contextual relationship

In this paper, we present contextual relationship-based learning model using deep neural network for recognizing the activities performed by a group of people in a video sequence. The proposed model comprises of the context learning using a bottom-up approach, learning from individual human actions to group level activity as well as learning from the scene information. We build deep convolutional neural network model to capture human action-pose feature for a given input video sequence. To capture group level temporal flow changes, aggregated action-pose feature of persons within the context area are fed to deep recurrent neural network, which provides spatio-temporal group descriptor. Together with this, we build a scene level convolutional neural network, to extract scene level feature which improves the performance of group activity recognition. The probabilistic inference model, as an additional layer in deep neural network, added to ensemble the models and provide a unified deep learning framework. Experimental results show the efficiency of the proposed model on standard benchmark collective activity dataset in group activity recognition. We also present the evaluated results by varying different learning parameters, optimizers, especially recurrent neural network models long short-term memory and gated recurrent unit on the benchmark collective activity dataset.
Measuring the Effects of Stress on Mobile Interaction

Research shows that environmental factors such as ambient noise and cold ambience can render users situationally impaired, adversely affecting interaction with mobile devices. However, an internal factor which is known to negatively impact cognitive abilities -- stress -- has not been systematically investigated in terms of its impact on mobile interaction. In this paper, we report a study where we use the Trier Social Stress Test to induce stress on participants, and investigate its effect on three aspects of mobile interaction: target acquisition, visual search, and text entry. We find that stress reduces completion time and accuracy during target acquisition tasks, as well as completion time during visual search tasks. Finally, we are able to directly contrast the magnitude of these effects to previously published effects of environmentally-caused impairments. Our work contributes to the growing body of literature on situational impairments.
Security and Privacy Frameworks for Access Control Big Data Systems

In the security and privacy fields, Access Control (AC) systems are viewed as the fundamental aspects of networking security mechanisms. Enforcing AC becomes even more challenging when researchers and data analysts have to an... | Find, read and cite all the research you need on Tech Science Press
Develop Leave Application using Blockchain Smart Contract

In today's time, we use Leave Application System managed by some central authority. This can lead to corruption, misuse by a single authority or unnecessary time for approval. So, in this paper, we have developed smart contract architecture for Leave Management System using Solidity and Ethereum, which is a decentralized app architecture leveraged from Blockchain Technology. We also include smartphones as IoT devices by which any client can use this app. Due to the resource-constrained nature of these devices, the smart contract app is coupled with an alternative centralized architecture which is a classic client/server architecture with an underlying Blockchain backend.
EasyLabels: weak labels for scene segmentation in laparoscopic videos

We present a different approach for annotating laparoscopic images for segmentation in a weak fashion and experimentally prove that its accuracy when trained with partial cross-entropy is close to that obtained with fully supervised approaches. We propose an approach that relies on weak annotations provided as stripes over the different objects in the image and partial cross-entropy as the loss function of a fully convolutional neural network to obtain a dense pixel-level prediction map. We validate our method on three different datasets, providing qualitative results for all of them and quantitative results for two of them. The experiments show that our approach is able to obtain at least $$90\%$$ of the accuracy obtained with fully supervised methods for all the tested datasets, while requiring $$\sim 13$$ $$\times $$ less time to create the annotations compared to full supervision. With this work, we demonstrate that laparoscopic data can be segmented using very few annotated data while maintaining levels of accuracy comparable to those obtained with full supervision.
On Privacy Notions in Anonymous Communication

Abstract Many anonymous communication networks (ACNs) with different privacy goals have been developed. Still, there are no accepted formal definitions of privacy goals, and ACNs often define their goals ad hoc. However, the formal definition of privacy goals benefits the understanding and comparison of different flavors of privacy and, as a result, the improvement of ACNs. In this paper, we work towards defining and comparing privacy goals by formalizing them as privacy notions and identifying their building blocks. For any pair of notions we prove whether one is strictly stronger, and, if so, which. Hence, we are able to present a complete hierarchy. Using this rigorous comparison between notions, we revise inconsistencies between the existing works and improve the understanding of privacy goals.
Traditional and Heavy Tailed Self Regularization in Neural Network Models

Random Matrix Theory (RMT) is applied to analyze the weight matrices of Deep Neural Networks (DNNs), including both production quality, pre-trained models such as AlexNet and Inception, and smaller models trained from scratch, such as LeNet5 and a miniature-AlexNet. Empirical and theoretical results clearly indicate that the empirical spectral density (ESD) of DNN layer matrices displays signatures of traditionally-regularized statistical models, even in the absence of exogenously specifying traditional forms of regularization, such as Dropout or Weight Norm constraints. Building on recent results in RMT, most notably its extension to Universality classes of Heavy-Tailed matrices, we develop a theory to identify \emph{5+1 Phases of Training}, corresponding to increasing amounts of \emph{Implicit Self-Regularization}. For smaller and/or older DNNs, this Implicit Self-Regularization is like traditional Tikhonov regularization, in that there is a `size scale' separating signal from noise. For state-of-the-art DNNs, however, we identify a novel form of \emph{Heavy-Tailed Self-Regularization}, similar to the self-organization seen in the statistical physics of disordered systems. This implicit Self-Regularization can depend strongly on the many knobs of the training process. By exploiting the generalization gap phenomena, we demonstrate that we can cause a small model to exhibit all 5+1 phases of training simply by changing the batch size.