Reviewerly/exHarmony huggingface dataset:
Improved Segmentation of Pulmonary Nodules Using Soft Computing Techniques with SegNet and Adversarial Networks

Lung cancer is seen as one of the most common lung diseases. For the patients having symptoms, the presence of lung nodules is checked by using various imaging techniques. Pulmonary nodules are detected in most of the cases having symptoms. But identifying the type of the nodule and the categorization still remains as a challenge. After confirming the presence of a nodule (benign or malignant) it takes several other steps to identify its characteristics. Improved imaging methods produce results within a short span of time. Research works are being conducted to increase the overall efficiency of the system. The proposed system considers authentic data sources for the study. The benign and malignant samples are considered for the generation of realistic large image sets. The generation of a large data set with the help of a generative adversarial network (GAN) is the first part of the work. The generated images using GAN cannot be differentiated from the original images even by a trained radiologist. This proves the importance of images generated using GAN. A GAN is able to generate 1024 × 1024 resolutions for natural images. Real data images are used to finetune the SegNet output. Through transfer learning, these weights are transferred to the system for segmentation of the images. The training process use real and generated images, which improve theefficiency of the network. The original data from LUNA 16 was used to further generate benign and malignant samples using GAN. A total of 440 images and their augmented images were used for training the GAN, and it generated 1,001,000 images. Hence the overall efficiency of the system was improved. To verify the results, the same various combinations and methods were considered and tabulated with various parameters. Methods with SegNet, GAN, and other combinations were evaluated to verify the efficiency of the system. Receiver operating characteristics were also plotted and compared with the area under the curve for verification of the results.
MTR-SAM: Visual Multimodal Text Recognition and Sentiment Analysis in Public Opinion Analysis on the Internet

Existing methods for monitoring internet public opinion rely primarily on regular crawling of textual information on web pages but cannot quickly and accurately acquire and identify textual information in images and videos and discriminate sentiment. The problems make this a challenging research point for multimodal information detection in an internet public opinion scenario. In this paper, we look at how to dynamically monitor the internet opinion information (mostly images and videos) that different websites post. Based on the most recent advancements in text recognition, this paper proposes a new method of visual multimodal text recognition and sentiment analysis (MTR-SAM) for internet public opinion analysis scenarios. In the detection module, a LK-PAN network with large sensory fields is proposed to enhance the CML distillation strategy, and an RSE-FPN with a residual attention mechanism is used to improve feature map representation. Second, it proposes that the original CTC decoder be replaced with a GTC method to solve earlier problems with text detection at arbitrary rotation angles. Additionally, the performance of scene text detection for arbitrary rotation angles is improved using a sinusoidal loss function for rotation recognition. Finally, the improved sentiment analysis model is used to predict the sentiment polarity of the text recognition results. The experimental results show that the new method proposed in this paper improves recognition speed by 31.77%, recognition accuracy by 10.78% on the video dataset, and the F1 score of the multimodal sentiment analysis model by 4.42% on the self-built internet public opinion dataset (lab dataset). The method proposed provides significant technical support for internet public opinion analysis in multimodal domains.
An Effective Framework for Intellectual Property Protection of NLG Models

Natural language generation (NLG) models combined with increasingly mature and powerful deep learning techniques have been widely used in recent years. Deployed NLG models in practical applications may be stolen or used illegally, and watermarking has become an important tool to protect the Intellectual Property (IP) of these deep models. Watermarking technique designs algorithms to embed watermark information and extracts watermark information for IP identification of NLG models can be seen as a symmetric signal processing problem. In terms of IP protection of NLG models, however, the existing watermarking approaches cannot provide reliable and timely model protection and prevent illegal users from utilizing the original performance of the stolen models. In addition, the quality of watermarked text sequences generated by some watermarking approaches is not high. In view of these, this paper proposes two embedding schemes to the hidden memory state of the RNN to protect the IP of NLG models for different tasks. Besides, we add a language model loss to the model decoder to improve the grammatical correctness of the output text sequences. During the experiments, it is proved that our approach does not compromise the performance of the original NLG models on the corresponding datasets and outputs high-quality text sequences, while forged secret keys will generate unusable NLG models, thus defeating the purpose of model infringement. Besides, we also conduct sufficient experiments to prove that the proposed model has strong robustness under different attacks.
Adaptive GUI Layout by Satisfying Fuzzy Constraints

A graphical user interface is equipped with an adaptive layout when it holds the ability to dynamically adjust its layout and structure of widgets based on conditions that evolve over time coming from the end user, the platform used, and the environment. More specifically, such a layout automatically adapts itself depending on the window dimensions of the application, the screen resolution, and the screen size, thus posing a series of constraints that, sometimes, could not be entirely satisfied together at once. To cope with this variation, we formulate the adaptive GUI layout as a fuzzy constraint satisfaction problem in which the solver attempts to satisfy the most important constraints first, such as an appropriate widget selection, then the least important constraints after.
ORIENTATE: automated machine learning classifiers for oral health prediction and research

The application of data-driven methods is expected to play an increasingly important role in healthcare. However, a lack of personnel with the necessary skills to develop these models and interpret its output is preventing a wider adoption of these methods. To address this gap, we introduce and describe ORIENTATE, a software for automated application of machine learning classification algorithms by clinical practitioners lacking specific technical skills. ORIENTATE allows the selection of features and the target variable, then automatically generates a number of classification models and cross-validates them, finding the best model and evaluating it. It also implements a custom feature selection algorithm for systematic searches of the best combination of predictors for a given target variable. Finally, it outputs a comprehensive report with graphs that facilitates the explanation of the classification model results, using global interpretation methods, and an interface for the prediction of new input samples. Feature relevance and interaction plots provided by ORIENTATE allow to use it for statistical inference, which can replace and/or complement classical statistical studies.Its application to a dataset with healthy and special health care needs (SHCN) children, treated under deep sedation, was discussed as case study. On the example dataset, despite its small size, the feature selection algorithm found a set of features able to predict the need for a second sedation with a f1 score of 0.83 and a ROC (AUC) of 0.92. Eight predictive factors for both populations were found and ordered by the relevance assigned to them by the model. A discussion of how to derive inferences from the relevance and interaction plots and a comparison with a classical study is also provided.ORIENTATE automatically finds suitable features and generates accurate classifiers which can be used in preventive tasks. In addition, researchers without specific skills on data methods can use it for the application of machine learning classification and as a complement to classical studies for inferential analysis of features. In the case study, a high prediction accuracy for a second sedation in SHCN children was achieved. The analysis of the relevance of the features showed that the number of teeth with pulpar treatments at the first sedation is a predictive factor for a second sedation.
Modeling and Simulation of Real-Time Virtual Machine Allocation in a Cloud Data Center

For dynamic resource scheduling in cloud data centers, a novel lightweight simulation system is proposed; two existing simulation systems at the application level for cloud computing are reviewed; and results gained using the suggested simulation system are examined and discussed. The usage of resources and energy efficiency in cloud data centers can be improved by load balancing and the consolidation of virtual machines. An aspect of dynamic virtual machine consolidation that directly affects resource usage and the quality of service the system is delivering is the timing of when it is ideal to reallocate Virtual Machines from an overloaded host [1]. Because server overloads result in a lack of resources and a decline in application performance, they have an impact on quality of service. In order to determine the best answer, existing approaches to the problem of host overload detection typically rely on statistical analysis inspired by nature. These strategies' drawbacks include the fact that they provide less-than-ideal outcomes and prevent the explicit articulation of a Quality-of-Service target. By optimizing the mean inter-migration time under the defined Quality of Service target ideally, we present a novel method for detecting host overload for any stationary workload that is known and a particular state configuration [2]. We demonstrate that our technique exceeds the best benchmark algorithm and offers over 88%of the performance of the ideal offline algorithm through simulations with real-world workload traces from more than a thousand Virtual Machines.
Leveraging Symbolic Knowledge Bases for Commonsense Natural Language Inference using Pattern Theory

The commonsense natural language inference (CNLI) tasks aim to select the most likely follow-up statement to a contextual description of ordinary, everyday events and facts. Current approaches to transfer learning of CNLI models across tasks require many labeled data from the new task. This paper presents a way to reduce this need for additional annotated training data from the new task by leveraging symbolic knowledge bases, such as ConceptNet. We formulate a teacher-student framework for mixed symbolic-neural reasoning, with the large-scale symbolic knowledge base serving as the teacher and a trained CNLI model as the student. This hybrid distillation process involves two steps. The first step is a symbolic reasoning process. Given a collection of unlabeled data, we use an abductive reasoning framework based on Grenander's pattern theory to create weakly labeled data. Pattern theory is an energy-based graphical probabilistic framework for reasoning among random variables with varying dependency structures. In the second step, the weakly labeled data, along with a fraction of the labeled data, is used to transfer-learn the CNLI model into the new task. The goal is to reduce the fraction of labeled data required. We demonstrate the efficacy of our approach by using three publicly available datasets (OpenBookQA, SWAG, and HellaSWAG) and evaluating three CNLI models (BERT, LSTM, and ESIM) that represent different tasks. We show that, on average, we achieve 63% of the top performance of a fully supervised BERT model with no labeled data. With only 1,000 labeled samples, we can improve this performance to 72%. Interestingly, without training, the teacher mechanism itself has significant inference power. The pattern theory framework achieves 32.7% accuracy on OpenBookQA, outperforming transformer-based models such as GPT (26.6%), GPT-2 (30.2%), and BERT (27.1%) by a significant margin. We demonstrate that the framework can be generalized to successfully train neural CNLI models using knowledge distillation under unsupervised and semi-supervised learning settings. Our results show that it outperforms all unsupervised and weakly supervised baselines and some early supervised approaches, while offering competitive performance with fully supervised baselines. Additionally, we show that the abductive learning framework can be adapted for other downstream tasks, such as unsupervised semantic textual similarity, unsupervised sentiment classification, and zero-shot text classification, without significant modification to the framework. Finally, user studies show that the generated interpretations enhance its explainability by providing key insights into its reasoning mechanism.
Tell me how you write and I'll tell you what you read: a study on the writing style of book reviews

Purpose The authors’ goal is to investigate variations in the writing style of book reviews published on different social reading platforms and referring to books of different genres, which enables acquiring insights into communication strategies adopted by readers to share their reading experiences. Design/methodology/approach The authors propose a corpus-based study focused on the analysis of A Good Review, a novel corpus of online book reviews written in Italian, posted on Amazon and Goodreads, and covering six literary fiction genres. The authors rely on stylometric analysis to explore the linguistic properties and lexicon of reviews and the authors conducted automatic classification experiments using multiple approaches and feature configurations to predict either the review's platform or the literary genre. Findings The analysis of user-generated reviews demonstrates that language is a quite variable dimension across reading platforms, but not as much across book genres. The classification experiments revealed that features modelling the syntactic structure of the sentence are reliable proxies for discerning Amazon and Goodreads reviews, whereas lexical information showed a higher predictive role for automatically discriminating the genre. Originality/value The high availability of cultural products makes information services necessary to help users navigate these resources and acquire information from unstructured data. This study contributes to a better understanding of the linguistic characteristics of user-generated book reviews, which can support the development of linguistically-informed recommendation services. Additionally, the authors release a novel corpus of online book reviews meant to support the reproducibility and advancements of the research.
Security policies and trust issues in cloud computing

The focus on cloud security principles, as well as the support provided by cloud specialists within an enterprise, is essential for cloud service clients. Adopting key security approaches and guidelines offers various benefits such as promoting interoperability, preventing client lock-in, and simplifying transitions. These policy norms ensure that basic accepted procedures are being followed by both the enterprise and cloud specialist organisations, and certifications are available for certain principles. These guidelines also provide a way for cloud service clients to evaluate cloud service providers. This paper focuses on the trust and security policies in a distributed computing environment, as well as the various open discussion issues such as trusted execution environment, remote access, collusion threat, data privacy, and endpoint identification.
Ethical Design for Wellbeing and Affective Health

Emotional wellbeing and mental health are topics of much social significance, which are also reflected in the growing HCI work aimed to support them. Research in this area covers a broad space from affective computing to affective interaction approach, and the ethical design of wellbeing and mental health technologies has become much needed. This talk will provide design exemplars of technologies for wellbeing and mental health, with an emphasis on the importance of supporting emotional awareness and regulation. The talk will also highlight the value of existing research for articulating novel design implications for ethical wellbeing and mental health technologies.
Segmented sensor blockchain based on state and block compression

Abstract In the sensor blockchain network, sensors need to collect data continuously and upload it to the blockchain network for verification of data integrity. The deployment of smart contracts on the chain can be used to authenticate data visitors. Historical data is valuable for data visitors, but it is useless for sensor nodes that can continue to link data only with current information. With the passage of time, the data of the sensor blockchain network will continue to grow. A node data storage mode that can adapt to the continuous expansion of the blockchain scale is needed, which can encourage nodes to join the network to participate in consensus and jointly maintain the security of the blockchain data. This paper provides a segmented storage solution based on state and block compression for sensor device nodes with limited storage capacity. After experimental verification, this scheme can significantly reduce the storage cost. It allows nodes to use blockchain for a long time in sensor blockchain networks with large transaction volumes and a large number of smart contracts and allows new nodes to be deployed faster.
An Overview of Speech Acts in English

Last century philosophers made a great step forward in understanding the way in which we use words in order to achieve something in the real world. They pointed to the fact that we perform speech acts when we speak. The production of speech acts is governed by some rules which help us understand the utterance meaning and decode the speaker meaning. Moreover, in a discourse, the speech acts are organized linearly in sequences of micro-speech acts, and hierarchically in macro-speech acts, which are associated with an entire piece of discourse. As the right choice of speech acts in advertisements contribute to the achievement of the advertiser’s goals, the present article selects its examples from various advertisements placed in some English magazines.
Promoting accuracy in low-magnification histopathology grading: With augmentation and multi-dilation model

Advances in artificial intelligence have facilitated the automated grading of histopathology slides. Yet, the magnification of whole slide scanners (WSS) has restrained the accuracy of patch-based grading. In this work, we found that augmentation can significantly promote grading performance under this issue, even when the data volume is large (>140 K). With augmentation and a multi-dilation model, the CovXNet, we yielded a Balanced Accuracy of 92.13%, which is the current highest for the Breast Histopathology Dataset (40X magnification) also the first time both sensitivity and specificity >90%. However, in this focused grading task, augmentation only improves models with high invariance (the CovXNet and BCA-CNN). Pre-trained ResNet has lower invariance in this task, but fine-tuning can significantly improve both accuracy and invariance. For the CropNet attention model, adapting with max pooling but not augmentation offers promotions. Additionally, this work also found two types of common errors in high-starred codes, when using random.shuffle for data-label composited array, or the integrated shuffle function of ImageDataGenerator, which fake a higher accuracy by masking class 0 as class 1. Using Sklearn.shuffle instead is safer. All codes are available on our GitHub.
Biometric watermarking: an application-based review

The advancement of technology will require us to improve the security of data being transmitted. Nowadays, biometric authentication systems are used in many sectors, including banking, industrial, and security. There are several security risks associated with biometric systems in the system database where the biometric templates are stored and in the network between the system database and the biometric sensor. Biometric watermarking helps to protect the images by embedding biometric information on the images so that copyright protection can be ensured. It has been widely used in various applications such as online image theft identification, e-healthcare applications, and e-voting applications. This review paper gives an overview of biometric watermarking, its architecture, an application-based review, criteria used for performance analysis of such systems. The objective is to provide enough background information for researchers to quickly gain a high-level understanding and help the readers to apply biometric watermarking in their applications.
An Automatic Generation and Verification Method of Software Requirements Specification

The generation of standardized requirements specification documents plays a crucial role in software processes. However, the manual composition of software requirements specifications is a laborious and time-consuming task, often leading to errors that deviate from the actual requirements. To address this issue, this paper proposes an automated method for generating requirements specifications utilizing a knowledge graph and graphviz. Furthermore, in order to overcome the limitations of the existing automated requirement generation process, such as inadequate emphasis on data information and evaluation, we enhance the traditional U/C matrix by introducing an S/U/C matrix. This novel matrix represents the outcomes of data/function systematic analysis, and verification is facilitated through the design of inspection rules. Experimental results demonstrate that the requirements specifications generated using this method achieve standardization and adherence to regulations, while the devised S/U/C inspection rules facilitate the updating and iteration of the requirements specifications.
Listen Veronica! Can You Give Me a Hand With This Bug?

Developing software implies looking for documentation, following tutorials, making implementation decisions, encountering errors, and overcoming them. Behind each aspect is the developer’s reasoning that, if not collected, is lost after the implementation. Conversely, if captured and linked to the code, the developers’ reasoning and motivations for each step they accomplish can become a valuable asset, meaningful for them and other developers. Looking for a mechanism to capture such knowledge seamlessly, we present Veronica. It is a conversational agent integrated directly into Visual Studio Code that, based on the developers’ self-explanatory reasoning, records memos and links them with the code they are writing. Furthermore, Veronica can interact with the web browser to automatically gather the sources consulted by the developer and attach them to the code. We validated our approach by conducting a usability study with eight participants that positively assessed the tool’s usefulness and suggested improvements in the graphical interface.