Reviewerly/exHarmony huggingface dataset:
Research on the security sharing method of power grid dispatching control data based on Alliance Blockchain and Zero-Knowledge Proof technology

With the widespread application of power system digital technology and the advancement of dispatch control cloud construction, the development of power grid regulation business presents the basic characteristics of many business participants, frequent business interactions, and large data exchange. The alliance chain technology in blockchain technology has become an important basis for the research of this paper because of its native support for multi-party business scenarios. Based on the alliance blockchain technology, this paper designs a multi-channel data isolation scheme for the grid dispatching control business to achieve efficient data sharing within a controllable range. At the same time, in order to ensure the security of the private data in the grid dispatching control business in the business interaction process. Furthermore, this paper designs a business interaction process for private data based on the zero-knowledge verification algorithm. Combined with smart contract technology, it automatically completes the entire network verification process of data encryption and zero-knowledge credentials, and realizes data security interaction in secret scenarios.
Pixel-Level Classification of Five Histologic Patterns of Lung Adenocarcinoma

Lung adenocarcinoma is the most common histologic type of lung cancer. The pixel-level labeling of histologic patterns of lung adenocarcinoma can assist pathologists in determining tumor grading with more details than normal classification. We manually annotated a dataset containing a total of 1000 patches (200 patches for each pattern) of 512 × 512 pixels and 420 patches (contains test sets) of 1024 × 1024 pixels according to the morphological features of the five histologic patterns of lung adenocarcinoma (lepidic, acinar, papillary, micropapillary, and solid). To generate an even large amount of data patches, we developed a data stitching strategy as a data augmentation for classification in model training. Stitched patches improve the Dice similarity coefficient (DSC) scores by 24.06% on the whole-slide image (WSI) with the solid pattern. We propose a WSI analysis framework for lung adenocarcinoma pathology, intelligently labeling lung adenocarcinoma histologic patterns at the pixel level. Our framework contains five branches of deep neural networks for segmenting each histologic pattern. We test our framework with 200 unclassified patches. The DSC scores of our results outpace comparing networks (U-Net, LinkNet, and FPN) by up to 10.78%. We also perform results on four WSIs with an overall accuracy of 99.6%, demonstrating that our network framework exhibits better accuracy and robustness in most cases.
What Do We Mean by ‘That’s a Fallacious Narrative’?

Abstract This paper tries to offer a descriptive account of the normative workings of evaluative fallacy charges directed to narratives. In order to do that, I first defend the continuity and mutual dependence, as based on a dynamical conception of argument, between the ‘belief conception’ and the ‘argumentative conception’ of fallacy. Then, I construe a catalogue of ‘fallacy charges’ based on both such a continuity and the variety of counterarguments explored by the theoretical framework of Argument Dialectics. And finally, I apply these ideas and distinctions in the analysis of four examples of published texts in which the charge of ‘fallacious narrative’ is issued by a discursive agent against other discursive agents’ either full-fledged narratives or narrative assumptions. The analyses confirm some of the characteristics mentioned in the catalogue as well as the argumentative nature of fallacy charges, even when the censored discourse does not exactly or explicitly contain an argument. The analyses also help understand the distinction between a rather concrete ‘linguistic’ use of the term narrative and a more abstract and elusive ‘discursive’ one, in which the difficulties of both identifying the object of censorship and the exact meaning of the fallacy charge multiply.
Enhance the frame accumulation accuracy of breast multispectral transmission image by the improved Powell registration algorithm

Multispectral transmission imaging provides a new direction for the early screening of breast cancer. Frame accumulation technology can effectively solve the problem of low grayscale and poor multispectral transmission images quality, but the accuracy of the result will be reduced due to the shift between image frames caused by human respiration and jitter. Therefore, an improved Powell registration algorithm based on normalized mutual information is proposed to register sequence images to improve the accuracy of images after frame accumulation. Firstly, the video is acquired and preprocessed through the designed breast transmission image acquisition system. Then, the coarse registration parameters are obtained by the "Rotation Translation scanning" method, which are used as the initial point of Powell algorithm for fine registration to obtain the registered image sequence. Finally, frame accumulation is performed on the registered images. The registration accuracy and the image quality of frame accumulation after registration are evaluated respectively. The results show that the improved method can raise the registration accuracy of the set floating image to more than 98.9%. This method not only improves the registration accuracy of Powell algorithm, but also solves the influence of jitter on frame accumulation technology, and provides a reference for the recognition and segmentation of breast blood vessels or tumors in multispectral transmission images.
Hand-drawn sketch and vector map matching based on topological features

In the process of addressing, when people use words to express indistinctly, they often draw simple sketches to assist expression, which helps people to form a simple spatial scene in the brain and correspond to the actual scene one by one, and finally locate and find the target address. How to establish an one-to-one mapping relationship between the spatial objects in the hand-drawn sketch and in the vector map is the key to the realization of map addressing and location, and this process is also the process of map matching. This paper aims to address difficult problems associated with the features of hand-drawn sketches and vector map matching in order to improve the use of all potential matching points designed for application in hand-drawn sketches and spatial relation matrix structures of vector maps. To accomplish this, we use the N-queen problem solving process and improve the tabu search algorithm. In the matching process under the constraint of a single spatial relationship, and the hierarchical matching process under the constraint of multiple spatial relations, this study verifies the quality of the spatial relationship and the feasibility and effectiveness of the matching method of hand-drawn sketches and vector maps using the improved tabu search algorithm.
Unsupervised generalization of correlated quantum dynamics on disordered lattices

The application of unsupervised machine learning to the study of physical problems is an emerging field aimed at leveraging their outstanding capabilities to distill essential information from complex data sets. Here we use Generative Adversarial Networks (GANs), deep learning algorithms mainly used to synthesize realistic images, for the analysis of a physical problem: the propagation of interacting quantum particles on disordered chains, characterized by the emergence of complex quantum correlations. We find that GANs can learn these correlations and identify the physical control parameters in a completely unsupervised manner. After training on a data set of unlabeled examples, the algorithm can generate, without further calculations, a much larger number of unseen yet physically correct new instances. Most notably, the knowledge distilled in the algorithm's latent space identifies the relevant control parameter, the level of disorder in our case, even though this was not highlighted in the training set. This allows tuning the disorder level in the generated samples to values the algorithm was not explicitly trained on, thereby uncovering a fundamental physical phenomenon---the crossover from nearly free propagation to localization. These results demonstrate the ability of deep neural networks to learn and generalize physical rules by scanning examples and suggest their use in physical analysis and discovery.
Automatic SQL to HQL-NoSQL Querying using PostgreSQL and Integrated Hive-Hbase

The amount of digital data is constantly growing in almost all fields. This data is divided into two categories, structured and unstructured data. Non-structural databases known as NoSQL became one of the main fields of big data. Many companies are still using relational databases like PostgreSQL and MySQL. But with the rapid evolution and diversity of stored data, companies find themselves obliged to use big data tools like HBase or Hive. Big data is characterized by its capacity, speed, and ability to store diverse types of data. Data analysis and high storage capacity are the main reasons for companies to search for new database systems. Data migration to new systems is associated with the modification of the existing data and applications. This process costs a lot to adopt new specialists to handle this transition. Furthermore, due to different sources of data in old systems, e.g., real-time applications that are continuously collecting new data, companies will not be able to leave relational databases. For this reason, we present a system, termed Automatic Query Language, or AQL in short form, for migrating data from PostgreSQL to integrated HBase/Hive databases. In addition, we provide a platform that allows any user to query automatically PostgreSQL, Hive, and HBase databases using SQL query only. Querying the system is related to where each big data tool’s performance is better. After the platform was completed, we were able to insert and select data from both relational databases and big data components. Join operation was not a problem because complex queries for analysis were executed using Hive which was integrated with HBase. The tested AQL system proved that HBase can insert data with more efficiency than PostgreSQL and Hive, and that select query in Hive has a better performance than PostgreSQL for big data size, whereas, for small data size, the performance of PostgreSQL is better.
Enhancing Clinical Data Analysis by Explaining Interaction Effects between Covariates in Deep Neural Network Models

Deep neural network (DNN) is a powerful technology that is being utilized by a growing number and range of research projects, including disease risk prediction models. One of the key strengths of DNN is its ability to model non-linear relationships, which include covariate interactions. We developed a novel method called interaction scores for measuring the covariate interactions captured by DNN models. As the method is model-agnostic, it can also be applied to other types of machine learning models. It is designed to be a generalization of the coefficient of the interaction term in a logistic regression; hence, its values are easily interpretable. The interaction score can be calculated at both an individual level and population level. The individual-level score provides an individualized explanation for covariate interactions. We applied this method to two simulated datasets and a real-world clinical dataset on Alzheimer's disease and related dementia (ADRD). We also applied two existing interaction measurement methods to those datasets for comparison. The results on the simulated datasets showed that the interaction score method can explain the underlying interaction effects, there are strong correlations between the population-level interaction scores and the ground truth values, and the individual-level interaction scores vary when the interaction was designed to be non-uniform. Another validation of our new method is that the interactions discovered from the ADRD data included both known and novel relationships.
A GIS-Based Multidimensional Evaluation Method for Solar Energy Potential in Shanxi Province, China

Solar energy is considered one of the most hopeful alternative sources to avoiding dependence on fossil fuels, and it does not cause any air pollution. GIS-based solar energy potential evaluation is mainly focused on regional scale; further, more solar energy potential evaluation with building scale is calculated through observation data and mathematical model. Therefore, in this paper, a GIS-based joint solar energy potential evaluation is developed to evaluate the distributed photovoltaic potential and centralized photovoltaic potential. Shanxi province in China, which has abundant coal resources, is used as the study area. The raster grid scale is used as the minimum research scale, which could not only deal with the distributed photovoltaic potential but could also calculate the centralized photovoltaic potential. The obtained results indicate that the developed method could effectively deal with problems associated with the distributed photovoltaic potential and centralized photovoltaic potential in the raster grid scale.
Elliptic Curve-Based Query Authentication Protocol for IoT Devices Aided by Blockchain

Digital transformation has increased its proportion in the last few years and the Internet-on-Things (IoT) domain is not an exception, with more and more devices or sensors being connected to the Internet and transmitting different types of data. Usually, being part of more complex IT systems, it must be ensured that the IoT devices transmitting the data are authenticated components of the system before sending the data to a storage server. However, usually, IoT devices have limited computing power, therefore all of the work that they are doing should not be too expensive in terms of computations. This is the case for the authentication mechanism, too. Having this context, in this paper, we propose an authentication mechanism for IoT devices based on elliptic curves, which are known as having a low computational cost compared to other techniques used in cryptography that provide the same level of security. The proposed system includes a blockchain network that will verify the identity of the device which tries to connect within the system to send the data to the storage server, a process that will be made together with the storage server. Once the identity is valid, the blockchain records the transaction and the storage server initiates the data transmission process. Besides including a lightweight authentication mechanism, the proposed method has several other important properties due to it using the blockchain network. Compared to the related work that we analyzed, we show that the proposed authentication mechanism is secure against common attacks designed for IoT devices. The performance analysis shows that the authentication query made by the IoT device takes place in less than a second on both a MSP430F1611 microcontroller and a MICAz sensor.
Implementation of Kyai Policies in Improving a Culture of Reading and Writing in Islamic Boarding Schools Lirboyo Kediri Indonesia

The background of this research is the process of implementing policies into actions that aim to strengthen literacy culture. Students at the Lirboyo Islamic boarding school, Kediri. This study aimed to find the implementation of Kyai’s policies in improving the culture of reading and writing at the Lirboyo Islamic boarding school, Kediri. This study uses a qualitative approach. The research design used was field research using a case study design where the subject studied was leadership and literacy culture at the Lirboyo Islamic boarding school, Kediri. Data collection is used through participant observation, in-depth interviews, and documentation. Data analysis was carried out inductively, which included condensing data, presenting data, and drawing conclusions. Checking the validity of the data provides credibility, dependability, and confirmability. The results of the study show that the implementation of the cleric’s policy in strengthening the culture of reading and writing at the Lirboyo Islamic Boarding School, Kediri is (1) the cleric gives a mandate to the sections that handle it, (2) instructs the teacher to oversee the implementation of the program, (3) carried out by students with supervised by administrators, advisors, and teachers.
Investigating the potential of changing the smartphone system language to L2 for facilitating vocabulary learning and motivation

Few mobile-assisted vocabulary learning approaches fully correspond to the authenticity principle of the situated learning theory (SLT). A novel practice of changing the smartphone system language to second language (L2), which intends to better implement SLT in a real smartphone context, is the focus of this study. To explore the potential of using this under-researched approach to facilitate L2 vocabulary learning, a mixed-method design was adopted to investigate its impact on vocabulary acquisition, global language learning motivation, and vocabulary learning motivation. Participants’ pre- and post-test scores on the vocabulary knowledge test and motivation questionnaires were compared between the experimental and control groups. Interviews were also conducted to understand learners’ perceptions of this novel approach. Both quantitative and qualitative results indicated that the approach of changing smartphone system language to L2 was effective in increasing learners’ vocabulary knowledge gains and improving their global and vocabulary learning motivation. These findings contribute to the literature on L2 vocabulary learning and provide significant pedagogical implications.
Conceptual data systems architecture principles for information systems

Information systems alignment with enterprise evolution affects the performance of enterprises. The systems conceptual and data quality, development time and sustainable life cycle management, are issues for enterprise competitiveness. The ability to directly generate enterprise information systems from models has been thought of as a solution to improve on these issues. Model-driven systems have been a research topic for decades. Fundamental principles for a proven model-driven information systems architecture are outlined in this article. Architectural foundation is a separation of user communities conceptual domain from the information technical domain. The users domain is modeled as an information system model in three layers, conceptual data logic model, interface model and user community model. The technical domain is a platform, allowing the modeling and execution of such a model. These principles have been applied in practice and proven viable. Two platforms and applications, which adhere to these principles, are briefly described.
LWMD: A Comprehensive Compression Platform for End-to-End Automatic Speech Recognition Models

Recently end-to-end (E2E) automatic speech recognition (ASR) models have achieved promising performance. However, existing models tend to adopt increasing model sizes and suffer from expensive resource consumption for real-world applications. To compress E2E ASR models and obtain smaller model sizes, we propose a comprehensive compression platform named LWMD (light-weight model designing), which consists of two essential parts: a light-weight architecture search (LWAS) framework and a differentiable structured pruning (DSP) algorithm. On the one hand, the LWAS framework adopts the neural architecture search (NAS) technique to automatically search light-weight architectures for E2E ASR models. By integrating different architecture topologies of existing models together, LWAS designs a topology-fused search space. Furthermore, combined with the E2E ASR training criterion, LWAS develops a resource-aware search algorithm to select light-weight architectures from the search space. On the other hand, given the searched architectures, the DSP algorithm performs structured pruning to reduce parameter numbers further. With a Gumbel re-parameter trick, DSP builds a stronger correlation between the pruning criterion and the model performance than conventional pruning methods. And an attention-similarity loss function is further developed for better performance. On two mandarin datasets, Aishell-1 and HKUST, the compression results are well evaluated and analyzed to demonstrate the effectiveness of the LWMD platform.
Crowd Control, Planning, and Prediction Using Sentiment Analysis: An Alert System for City Authorities

Modern means of communication, economic crises, and political decisions play imperative roles in reshaping political and administrative systems throughout the world. Twitter, a micro-blogging website, has gained paramount importance in terms of public opinion-sharing. Manual intelligence of law enforcement agencies (i.e., in changing situations) cannot cope in real time. Thus, to address this problem, we built an alert system for government authorities in the province of Punjab, Pakistan. The alert system gathers real-time data from Twitter in English and Roman Urdu about forthcoming gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.). To determine public sentiment regarding upcoming anti-government gatherings (protests, demonstrations, assemblies, rallies, sit-ins, marches, etc.), the alert system determines the polarity of tweets. Using keywords, the system provides information for future gatherings by extracting the entities like date, time, and location from Twitter data obtained in real time. Our system was trained and tested with different machine learning (ML) algorithms, such as random forest (RF), decision tree (DT), support vector machine (SVM), multinomial naïve Bayes (MNB), and Gaussian naïve Bayes (GNB), along with two vectorization techniques, i.e., term frequency–inverse document frequency (TFIDF) and count vectorization. Moreover, this paper compares the accuracy results of sentiment analysis (SA) of Twitter data by applying supervised machine learning (ML) algorithms. In our research experiment, we used two data sets, i.e., a small data set of 1000 tweets and a large data set of 4000 tweets. Results showed that RF along with count vectorization performed best for the small data set with an accuracy of 82%; with the large data set, MNB along with count vectorization outperformed all other classifiers with an accuracy of 75%. Additionally, language models, e.g., bigram and trigram, were used to generate the word clouds of positive and negative words to visualize the most frequently used words.
A Study on Cloud Service Selection Using Weighted Sum Model

Cloud service selection is essential in selecting on-demand services on a subscription basis. Since there are many cloud services with comparable functionality, it is important to find out which one best meets the needs and goals of the customer. A cloud service is perfect for the customer choosing a cloud service can be complicated Because of this, decision-making becomes a problem Services performance and cost are fundamentally different. In addition, many characteristics of cloud services there are these entire providers into account when selecting should be taken. When there are Many criteria, a compromise to be done, because in most real-world situations, which A service and other on all criteria Does not perform better than everyone else.However, a service of certain criteria may be fundamentally more advanced, other when judged by other criteria Solutions can be better than that. Action research is multifaceted Criteria that make decisions. A subfield called Contains; it is multi-criteria Focus on problem solving methods pays. Multiple criteria there are many techniques for decision-making. QoS based service in the cloud Several MCDM techniques to choose from although published, very useful the choice of technique is still remains debatable.