Reviewerly/exHarmony huggingface dataset:
Intelligent Algorithm of Semantic Analysis Based on BP Neural Network

With the growth of scientific and technological information technology and the rapid popularization of the Internet, network big data and information technology are also growing rapidly. Information technology provides people with more information, and it also significantly increases the operating and management costs of my country’s Internet companies. In order to solve this problem thoroughly, people propose a new type of it development, research and business model, namely BP neural network technology. At present, BP neural network technology has been widely used in various application fields such as network storage, search engines, distributed computers, e-commerce, social networks, and has achieved rapid growth. This article mainly adopts the method of organically combining theoretical exploration and empirical research, and systematically analyzes the data collected through research based on the views and research contents of some scholars in recent years. Combining with the analysis of the data of intelligent semantic analysis algorithm, some relevant characteristics of BP neural network are summarized. This article mainly focuses on the research of an intelligent algorithm for image semantic analysis for image processing. The semantic analysis intelligent algorithm can well change the situation of target detection difficulties. This article uses an intelligent algorithm based on BP neural network to automatically analyze and distinguish differences. The final results of the research show that this paper uses the attention model and proposes a semantic analysis algorithm combined with graphic target detection through a multi-scale segmentation network. The experiment shows that the three performances of attention are 71.6, 56.5 and 49.3, which can be learned this algorithm is better than the same comparison algorithm in terms of three performance evaluation indexes.
Mine and Lattice Web Data using NLP

This Paper will be an exercise in net extraction, natural language processing (NLP), and named entity recognition (NER). For the NLP, we will primarily be using the open-supply Python libraries NLTK and Spacy. This paper is meant to be a demonstration of a use-case for net extraction and NLP, now no longer a complete novice educational to the use of both techniques. We extricated joins from a web page, at that point utilized those joins to extricate indeed more substance from the internet location. We utilized that substance to at that point extricate and upgrade that data utilizing outside APIs, ML clustering calculations, and NLP.
LITMUS Predictor: An AI Assistant for Building Reliable, High-Performing and Fair Multilingual NLP Systems

Pre-trained multilingual language models are gaining popularity due to their cross-lingual zero-shot transfer ability, but these models do not perform equally well in all languages. Evaluating task-specific performance of a model in a large number of languages is often a challenge due to lack of labeled data, as is targeting improvements in low performing languages through few-shot learning. We present a tool - LITMUS Predictor - that can make reliable performance projections for a fine-tuned task-specific model in a set of languages without test and training data, and help strategize data labeling efforts to optimize performance and fairness objectives.
Thrifty Neural Architecture Search for Medical Image Segmentation (Student Abstract)

Convolutional neural network (CNN) based image segmentation has been widely used in analyzing medical images and benefited many real-world disease diagnosis applications. However, existing advanced CNN-based medical image segmentation models usually contain numerous parameters that require massive computation and memory, limiting the applicability of these models in the data-constrained or hardware-constrained environments. By leveraging the recently proposed neural architecture search (NAS), this paper presents a novel approach, dubbed Thrifty NAS, to design computation and memory-efficient models for medical image segmentation automatically. The searched models by Thrifty NAS are with much fewer parameters while retaining competitive performance. More specifically, we design a micro level space for cell structure search and a macro level cell path for better network structure modeling. Extensive experimental results in different medical image datasets verify the effectiveness of the proposed method with competitive segmentation performance, especially with minuscule neural architecture model size, i.e., 0.61M that is superior to U-Net (7.76 M) and UNet++ (9.04 M).
SAS: Self-Augmentation Strategy for Language Model Pre-training

The core of self-supervised learning for pre-training language models includes pre-training task design as well as appropriate data augmentation. Most data augmentations in language model pre-training are context-independent. A seminal contextualized augmentation was recently proposed in ELECTRA and achieved state-of-the-art performance by introducing an auxiliary generation network (generator) to produce contextualized data augmentation for the training of a main discrimination network (discriminator). This design, however, introduces extra computation cost of the generator and a need to adjust the relative capability between the generator and the discriminator. In this paper, we propose a self-augmentation strategy (SAS) where a single network is utilized for both regular pre-training and contextualized data augmentation for the training in later epochs. Essentially, this strategy eliminates a separate generator and uses the single network to jointly conduct two pre-training tasks with MLM (Masked Language Modeling) and RTD (Replaced Token Detection) heads. It avoids the challenge to search for an appropriate size of the generator, which is critical to the performance as evidenced in ELECTRA and its subsequent variant models. In addition, SAS is a general strategy that can be seamlessly combined with many new techniques emerging recently or in the future, such as the disentangled attention mechanism from DeBERTa. Our experiments show that SAS is able to outperform ELECTRA and other state-of-the-art models in the GLUE tasks with similar or less computation cost.
Improving Ecommerce Performance by Dynamically Predicting the Purchased Items Using FUP Incremental Algorithm

Data mining or knowledge discovery is the way toward examining data according to substitute perspectives and summarizing it into accommodating information. This information can be then used to fabricate a pay, decreases costs, or both. Programming made with web mining as its key subject ought to permit clients to isolate information from a wide extent of assessments or centers, demand it, and sum up the affiliations perceived. Taking everything into account, information mining is the way toward discovering affiliations or models among many fields in colossal social instructive assortments. This paper effectively tracks down the rehashed bought things by clients. This proposed algorithm is having a higher running time than the existing FUP incremental algorithm. This algorithm efficiently finds the frequent items, and dynamically the items can be added. The entire history of the frequent item database was added and put into separate clusters. At last, we compare and choose the best-purchased items of the customer and also predict the past purchased items in the history. Based on the output, we can easily find the current status of the customer purchase.
Identification of Linear Latent Variable Model with Arbitrary Distribution

An important problem across multiple disciplines is to infer and understand meaningful latent variables. One strategy commonly used is to model the measured variables in terms of the latent variables under suitable assumptions on the connectivity from the latents to the measured (known as measurement model). Furthermore, it might be even more interesting to discover the causal relations among the latent variables (known as structural model). Recently, some methods have been proposed to estimate the structural model by assuming that the noise terms in the measured and latent variables are non-Gaussian. However, they are not suitable when some of the noise terms become Gaussian. To bridge this gap, we investigate the problem of identification of the structural model with arbitrary noise distributions. We provide necessary and sufficient condition under which the structural model is identifiable: it is identifiable iff for each pair of adjacent latent variables Lx, Ly, (1) at least one of Lx and Ly has non-Gaussian noise, or (2) at least one of them has a non-Gaussian ancestor and is not d-separated from the non-Gaussian component of this ancestor by the common causes of Lx and Ly. This identifiability result relaxes the non-Gaussianity requirements to only a (hopefully small) subset of variables, and accordingly elegantly extends the application scope of the structural model. Based on the above identifiability result, we further propose a practical algorithm to learn the structural model. We verify the correctness of the identifiability result and the effectiveness of the proposed method through empirical studies.
SASV Based on Pre-trained ASV System and Integrated Scoring Module

Based on the assumption that there is a correlation between anti-spoofing and speaker verification, a Total-Divide-Total integrated Spoofing-Aware Speaker Verification (SASV) system based on pre-trained automatic speaker verification (ASV) system and integrated scoring module is proposed and submitted to the SASV 2022 Challenge. The training and scoring of ASV and anti-spoofing countermeasure (CM) in current SASV systems are relatively independent, ignoring the correlation. In this paper, by leveraging the correlation between the two tasks, an integrated SASV system can be obtained by simply training a few more layers on the basis of the baseline pre-trained ASV subsystem. The features in pre-trained ASV system are utilized for logical access spoofing speech detection. Further, speaker embeddings extracted by the pre-trained ASV system are used to improve the performance of the CM. The integrated scoring module takes the embeddings of the ASV and anti-spoofing branches as input and preserves the correlation between the two tasks through matrix operations to produce integrated SASV scores. Submitted primary system achieved equal error rate (EER) of 3.07\% on the development dataset of the SASV 2022 Challenge and 4.30\% on the evaluation part, which is a 25\% improvement over the baseline systems.
Application of New Sensor Technology and Few-Shot Learning in Education Based on IoT Era

In recent years, with the rapid development of emerging Internet of Things technology and short-range wireless communication technology, smart healthcare monitoring network technology has become a research hotspot. It provides convenience for people and enhances the development of people's own healthcare awareness. This paper aims to study how to make its application in the field of smart healthcare education more applicable through the use of related technologies in the Internet of Things era and few-shot learning. For this reason, this paper proposes to optimize and improve the new sensor technology and the algorithm of few-shot learning, and to adjust some parameters as a whole. At the same time, related experiments and analysis are designed for the improved algorithm to study and understand its performance. The experimental results in this paper show that the improved algorithm improves its application effect by 36.9% and is relatively more applicable than the unimproved algorithm.
Sistem Informasi Profil Desa Mulyosri Kecamatan Prembun Kabupaten Kebumen Berbasis Website Menggunakan Metode Waterfall

The development of information technology and the internet is growing. This can be seen by the many places that provide internet facilities to obtain information. One of the uses of the internet in terms of information processing can be applied to the design of a website-based information system. Website-based information systems make it easy to disseminate information quickly and accurately. Based on the interviews conducted, the dissemination of information in Mulyosri Village still uses the manual method, thus making the agency's performance in providing information to the public less than optimal. To overcome this problem, a website-based information system was built to make it easier for the public to access information in Mulyosri Village. The development method used in this research is the waterfall method which consists of five steps of development, namely: (1) Requirement, (2) Design, (3) Implementation, (4) Verification, and (5) Maintenance. The system is made using the PHP programming language and uses a MySQL database. After the information system is complete, blackbox testing is carried out to see the feasibility of the system. This research produces a profile information system in Mulyosri Village that can be used and accessed by Mulyosri Village residents. In addition, based on the results of blackbox testing, it shows that the system has been running well and as expected. Thus it can be said that this system is feasible to use. Keywords: information system, village profile, website, waterfall method
An Adversarial Framework for Generating Unseen Images by Activation Maximization

Activation maximization (AM) refers to the task of generating input examples that maximize the activation of a target class of a classifier, which can be used for class-conditional image generation and model interpretation. A popular class of AM method, GAN-based AM, introduces a GAN pre-trained on a large image set, and performs AM over its input random seed or style embeddings, so that the generated images are natural and adversarial attacks are prevented. Most of these methods would require the image set to contain some images of the target class to be visualized. Otherwise they tend to generate other seen class images that most maximizes the target class activation. In this paper, we aim to tackle the case where information about the target class is completely removed from the image set. This would ensure that the generated images truly reflect the target class information residing in the classifier, not the target class information in the image set, which contributes to a more faithful interpretation technique. To this end, we propose PROBEGAN, a GAN-based AM algorithm capable of generating image classes unseen in the image set. Rather than using a pre-trained GAN, PROBEGAN trains a new GAN with AM explicitly included in its training objective. PROBEGAN consists of a class-conditional generator, a seen-class discriminator, and an all-class unconditional discriminator. It can be shown that such a framework can generate images with the features of the unseen target class, while retaining the naturalness as depicted in the image set. Experiments have shown that PROBEGAN can generate unseen-class images with much higher quality than the baselines. We also explore using PROBEGAN as a model interpretation tool. Our code is at https://github.com/csmiler/ProbeGAN/.
QoE-Centric Multi-User mmWave Scheduling: A Beam Alignment and Buffer Predictive Approach

In this paper, we consider the multi-user scheduling problem in millimeter wave (mmWave) video streaming networks, which comprise a streaming server and several users, each requesting a video stream with a different resolution. The main objective is to optimize the long-term average quality of experience (QoE) for all users. We tackle this problem by considering the physical layer characteristics of the mmWave network, including the beam alignment overhead due to pencil-beams. To develop an efficient scheduling policy, we leverage the contextual multi-armed bandit (MAB) models to propose a beam alignment overhead and buffer predictive streaming solution, dubbed B2P-Stream. The proposed B2P-Stream algorithm optimally balances the trade-off between the overhead and users' buffer levels and improves the QoE by reducing the beam alignment overhead for users of higher resolutions. We also provide a theoretical guarantee for our proposed method and prove that it guarantees a sub-linear regret bound. Finally, we examine our proposed framework through extensive simulations. We provide a detailed comparison of the B2P-Stream against uniformly random and Round-robin (RR) policies and show that it outperforms both of them in providing a better QoE and fairness. We also analyze the scalability and robustness of the B2P-Stream algorithm with different network configurations.
Industrial Chain Data Sharing and Circulation of Blockchain and Big Data Technology

In the era of big data, the value of data is infinite. With the development of the Internet of Things, everything is interconnected, and the sharing and circulation of data are particularly important. This paper was aimed at studying the data sharing system based on blockchain and big data technology. Based on blockchain and big data technology, this paper proposes a data-sharing system based on an HDFS file system. And it strengthens the security of data sharing based on blockchain technology, proposes a security key for industrial data, and greatly upgrades the security of data sharing. Experiments in this paper have proved that the data-sharing system in this paper has strong robustness, and choosing the appropriate <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>k</mi> </math> and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M2"> <mi>n</mi> </math> can take into account both the computational overhead and security, such as (3, 5), (5, 10), and (6, 10).
Enhancing Automated Software Traceability by Transfer Learning from Open-World Data

Software requirements traceability is a critical component of the software engineering process, enabling activities such as requirements validation, compliance verification, and safety assurance. However, the cost and effort of manually creating a complete set of trace links across natural language artifacts such as requirements, design, and test-cases can be prohibitively expensive. Researchers have therefore proposed automated link-generation solutions primarily based on information-retrieval (IR) techniques; however, these solutions have failed to deliver the accuracy needed for full adoption in industrial projects. Improvements can be achieved using deep-learning traceability models; however, their efficacy is impeded by the limited size and availability of project-level artifacts and links to serve as training data. In this paper, we address this problem by proposing and evaluating several deep-learning approaches for text-to-text traceability. Our method, named NLTrace, explores three transfer learning strategies that use datasets mined from open world platforms. Through pretraining Language Models (LMs) and leveraging adjacent tracing tasks, we demonstrate that NLTrace can significantly improve the performance of LM based trace models when training links are available. In such scenarios NLTrace outperforms the best performing classical IR method with an 188% improvement in F2 score and 94.01% in Mean Average Precision (MAP). It also outperforms the general LM based trace model by 7% and 23% for F2 and MAP respectively. In addition, NLTrace can adapt to low-resource tracing scenarios where other LM models can not. The knowledge learned from adjacent tasks enables NLTrace to outperform VSM models by 28% F2 on generation challenges when presented with a small number of training examples.
Different Types of CBIR Applications : A Survey

Visual information in large digital databases has been grown abundantly in the recent years and content based image retrieval (CBIR) attracts the interest of image processing oriented research people. The CBIR systems are vital performance due to the usage of features such as color, texture and shape of the query image. Searching a collection of images that have similarities with input images and without knowing the name of the image, makes a search system that applies the concept of content-based image retrieval (CBIR), is very necessary. The successful of the CBIR system creates the intension to apply it on many real world fields such as search engine, web and human disease analysis etc., This paper surveys a lot of applications based on the CBIR systems and this paper concentrates on the four types of applications which are belongs with medical field, remote sense imaging, forensic, security applications. The recent papers which are written showed that the applications which are reviewed with the explanation about technologies used to enhance the study of CBIR based applications and the methods which are adopted for CBIR in recent period. This paper is more useful for the people who want to continue their research in content based image retrieval.
DeepAnalyze

Crash localization, an important step in debugging crashes, is challenging when dealing with an extremely large number of diverse applications and platforms and underlying root causes. Large-scale error reporting systems, e.g., Windows Error Reporting (WER), commonly rely on manually developed rules and heuristics to localize blamed frames causing the crashes. As new applications and features are routinely introduced and existing applications are run under new environments, developing new rules and maintaining existing ones become extremely challenging.
The use of flash cards in teaching as a means of developing students’ verbal creativity

Problem and goal. Development of linguistic diversity, creative ideas in foreign language communication are key areas of international activity supported at the level of the United Nations. An important condition for effectiveness of any dialogue is verbal creativity. Preparation of the individual for professional creative thinking, for overcoming verbal stereotypes takes place in professionally-oriented communication. The authors suggest using interactive resources of flash cards in teaching to develop students’ verbal creativity. Metodology. Teaching a foreign language using flash cards is based on the Leitner method. Flash cards as thematic cards depicting objects (concepts) are also used for development of speech (vocabulary, morphology, word formation, syntax, pronunciation), and psychological processes leading to new results (fluency, originality, flexibility). The testing developed by the authors is carried out, it includes the parts “Vocabulary,” “Morphology,” “Word formation,” “Syntax,” “Pronunciation,” “Torrance figure test.” An experimental study was conducted at Vyatka State University during the study of the disciplines “Electronic resources in the professional activity,” “Foreign language.” 60 first-year students studying psychology (bachelor degree level) are involved. The Lexilize resource is used to create flash cards. Statistical processing of the results was performed using Pearson’s chi-square test. Results . Students study services for creating flash cards, use them to process new material, memorize and present concepts, word formation in a variety of contexts. Statistically significant differences in the qualitative changes that occurred in the system according to the levels of development of verbal creativity were revealed. Conclusion. The features of the presented version of using flash cards when teaching are described: combination with other means (mobile applications, simulators, infographics) and traditional forms of information transfer; interdisciplinary connections. Rules are formulated, implementation of which ensures effectiveness of using flash cards for development of verbal creativity.