Reviewerly/exHarmony huggingface dataset:
Multi-Scale Multi-Stage Single Image Super-Resolution Reconstruction Algorithm Based on Transformer

In this paper, creatively combining Transformer with image super-resolution reconstruction, we proposes a multi-scale multi-stage single image super-resolution reconstruction algorithm based on Transformer (MSTN). The algorithm uses Transformer as a feature sharing module, thus it realizes network parameter sharing, dynamically focuses on the correlation between feature information of adjacent stages, and then extracts the high-frequency texture information embedded in the current stage features from the feature information learned in the previous stage, which achieves a coarse-to-fine enhancement of image reconstruction. Experiments show that our method can not only per-form better image super-resolution reconstruction compared with other advanced methods, but also reduce the network parameters to a great extent.
Group Targets Splitting Algorithm for Ballistic Missile Penetration Applications

For missile defense systems, the sooner the group target split tracking of ballistic missile is completed, the more preparation time there will be. On the other hand, the group target split tracking too early may cause problems such as mixed tracking, wrong tracking and resources saturation during radar tracking process, which affect the combat performance of weapon systems. A group splitting algorithm for ballistic missile target tracking is proposed in this paper. First, the formation of group targets and the tracking strategy is analyzed, and the IMM-UKF filter framework is described. Then the mathematical model of the group splitting algorithm is established. The factors that affect the performance of the group splitting are discussed. Finally the multi-object optimization problem is solved by using Pareto method. Simulation and experimental results prove that the proposed algorithm can reach a balance between the instant splitting of group targets and the target association stability in a group.
USING VIDEO BLOGGING IN TEACHING SPEAKING AT THE FIRST GRADE STUDENTS OF SMA-IT AL-FITYAN SCHOOL GOWA

The purpose of this study was to determine the development of students' speaking after using the vlogging as medium in learning speaking skills on descriptive text material. This study used a quantitative method with a quasi-experimental group design. The population in this study were students of class X SMA-IT Al-Fityan School Gowa 2021/2022. The total number of students is 60 people. The sample was selected purposively, 15 students from Class XA were selected as the experimental class and 15 students from XB as the control class. The experimental class is taught by Vlogging. Both classes took Pre-Test and post-test. The instrument of collecting data is an oral test that focuses on describing one of the historical place. The results showed that there was a significant difference between the experimental class learning achievements on the post-test. The mean score and standard deviation of the post-test in the experimental class were (M=11.73, SD=1.387). The results of data analysis indicate that the Vlogging technique has a contribution to improve students' speaking skills in descriptive text material. In implementing vlogging, the researcher found several benefits, the first being that students really enjoyed learning to use vlogging. second, students can used vlogging to demonstrate ideas and give students' creativity and thirdly in today's era students easily access the internet and can learn where and whenever they want. Based on the research findings, the researcher concluded that the use of Vlogging as a learning medium was significant to improve students' speaking skills, especially in descriptive text material.
Representing Social Networks as Dynamic Heterogeneous Graphs

Graph representations for real-world social networks in the past have missed two important elements: (i) the multiplexity of connections and (ii) representing time. This paper presents a dynamic heterogeneous graph representation for social networks which includes time in every component of the graph, i.e., nodes and edges, each of different types that captures heterogeneity. We illustrate the power of this representation by presenting four time-dependent queries and deep learning problems that cannot easily be handled in conventional homogeneous graph representations. As a proof of concept we present a detailed representation of a relatively new social media platform (Steemit), which we use to illustrate both the dynamic querying capability as well as prediction tasks using Graph Neural Networks (GNNs). We also illustrate opportunities for future work in query optimization as well as new dynamic prediction tasks on heterogeneous graph structures.
Effective learning-based attack detection methods for the Internet of Things

Anomaly detection techniques have attracted more attention in research and industrial areas. Anomaly detection methods have been implemented in many tenders, such as detecting malicious traffic in networks and systems, discovering vulnerabilities in security systems, detecting fraud transactions in credit cards, detecting anomalies in imaging processing, and analyzing and visualizing data in various domains. The IoT ecosystem involves applications like intelligent homes, smart cities, and smart transportation systems. With the increasing necessity for analyzing IoT network behavior, it becomes difficult to efficiently apply traditional anomaly detection techniques. The conventional techniques that use deep learning (DL) or machine learning (ML) do not detect or monitor the IoT ecosystem efficiently and effectively because they do not consider the nature of the IoT ecosystem. Another issue with traditional anomaly detection techniques is that they recalculate training whenever any change from the start points. Furthermore, they depend on a static threshold throughout the training period. This does not fit with the nature of the IoT ecosystem, which is characterized by a dynamic environment. This chapter will discuss the autonomous anomaly detection system for the Internet of Things (IoT) using ML. Specifically, we focus on the dynamic threshold that can be adapted during the training time, such as the local-global ratio technique (LGR) method, which activates the rehabilitating merely when it is essential and precludes any superfluous variations from immaterial differences in the local profiles.
Research on Construction of RDF with HBase

Resource Description Framework (RDF) is designed as a standard metadata model for data interchange on the Internet. Because of machine comprehensibility, it has been successfully used in many areas, such as the intelligent processing of numerous data. While the generation of RDF with relational database (RDB) receives much attention, little effort has been put into the automatic construction of RDF with HBase due to its flexible data structure. Since more data is stored in HBase, it is necessary to extract useful information from HBase. In this paper, we are devoted to construction of RDF with HBase. We put forward formal definitions of RDF and HBase and propose our strategy for generating RDF with HBase. We develop a prototype system to create RDF, and test results demonstrate the feasibility of our method.
A New Evaluation Method of Similarity Between Two SAR Images

Evaluation method of similarity between two synthetic aperture radar (SAR) images plays a key role in the SAR raw data simulation and the analysis of SAR imaging quality. Although the similarity between two SAR images is intuitive, it still lacks objective and quantitative method to evaluate it. Thus, an evaluation method named Otsu-PHash is proposed in this paper from the perspective of image similarity to solve the problem. The key steps of the proposed Otsu-PHash method are threshold segmentation and similarity comparison. Finally, the numerical experiment verified that our proposed method can be applied to evaluate similarity of the two SAR images.
Innovative informatics methods and algorithms for cancer research and management (NCI)

Federal Grants & ContractsVolume 47, Issue 4 p. 7-7 Grants Alerts Innovative informatics methods and algorithms for cancer research and management (NCI) First published: 18 January 2023 https://doi.org/10.1002/fgc.32818Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume47, Issue419 January 2023Pages 7-7 RelatedInformation
Combination of Hedge Algebra and Type-2 Fuzzy System for Electrocardiogram Signal Recognition and Classification

The paper presents a new application of Hedge Algebra in Type 2 (HAT2) Fuzzy System (FS) for electrocardiographical signal recognition and classification. The HAT2 integrates the capability of fuzzy logic in modeling uncertainties of data and the linguistic variables to describe and manipulate the knowledge in a way closer to the way humans describe facts and rules. An adaptive learning algorithm will be also proposed to train the system’s parameters for better fitting to the data samples. The proposed approach will be tested with the heart beat classification problem with different types of arrhythmias in the ECG signals taken from the Arrhythmia Databases of Massachusetts Institute of Technology and Boston's Beth Israel Hospital. The numerical results will be compared with other methods to show the high quality of proposed solution. The proposed solution is also shown being simple with low complexity of computation, which makes it suitable for use in IoT or portable devices for intelligent solutions used in modern healthcare systems.
Variable-length deep convolutional neural networks by Internet Protocol Addresses Whale Optimization Algorithm for random and complex image classification

While Deep Convolutional Neural Network (DCNN) is one of the most reliable deep learning methods for image classification, choosing the optimal DCNN architecture for a particular application can be quite challenging. This study aims to examine the usage of the Whale Optimization Algorithm (WOA) to discover the optimal structure for DCNNs automatically. Three advancements based on standard WOA are proposed to accomplish the objective. First, a novel Internet Protocol Address (IPA)-based coding approach is suggested, which makes it easier to encode DCNN layers utilizing whale vectors. The construction of variable-length DCNNs is then suggested using an Enfeebled layer to include certain whale vector dimensions. The learning procedure concludes by subdividing large datasets into smaller portions which are then arbitrarily assessed. In order to evaluate the proposed model’s effectiveness, it is compared to the performance of twenty-three different classifiers, including the state-of-the-art algorithm, on nine standard image classification benchmark datasets. The experimental results indicate that the suggested technique outperforms other benchmarks in 81 of 95 assessments. This variable-length approach is the first of its kind, utilizing WOA to evolve the topologies of DCNNs automatically.
Candid webinar explores Foundation Directory database

Nonprofit Business AdvisorVolume 2023, Issue 401 p. 9-9 Resources Candid webinar explores Foundation Directory database First published: 12 January 2023 https://doi.org/10.1002/nba.31399Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume2023, Issue401February 2023Pages 9-9 RelatedInformation
Maestro-U: Leveraging Joint Speech-Text Representation Learning for Zero Supervised Speech ASR

Training state-of-the-art Automated Speech Recognition (ASR) models typically requires a substantial amount of transcribed speech. In this work, we demonstrate that a modality-matched joint speech and text model introduced in [1] can be leveraged to train a massively multilingual ASR model without any supervised (manually transcribed) speech for some languages. This paper explores the use of jointly learnt speech and text representations in a massively multilingual, zero supervised speech, real-world setting to expand the set of languages covered by ASR with only unlabeled speech and text in the target languages. Using the FLEURS dataset, we define the task to cover 102 languages, where transcribed speech is available in 52 of these languages and can be used to improve end-to-end ASR quality on the remaining 50. First, we show that by combining speech representations with byte-level text representations and use of language embeddings, we can dramatically reduce the Character Error Rate (CER) on languages with no supervised speech from 64.8% to 30.8%, a relative reduction of 53%. Second, using a subset of South Asian languages we show that Maestro-U can promote knowledge transfer from languages with supervised speech even when there is limited to no graphemic overlap. Overall, Maestro-U closes the gap to oracle performance by 68.5% relative and reduces the CER of 19 languages below 15%.
The SAP S/4HANA system and digital transformation in organizations

In the midst of technological advances and recurrent cyber attacks that affect the data systems of different organizations, the search for automation and acceleration in a structured way are the triggers for the beginning of the modernization of processes in Information Technology (IT). The SAP system, of German origin, is considered a strong system and has been used by medium and large companies, national and multinational. It has specific characteristics, such as automation and agility for internal processes, use of robots and Artificial Intelligence (AI), all of which is stored in the cloud. A SAP system promotes improvements in customer service, inventory control and purchasing needs, avoids or minimizes product losses, streamlining sales processes, asset management and allowing greater ease in consolidating information, including for administrative, accounting and financial sectors. In this context, SAP S/4HANA, or “SAP Business Suite 4 SAP HANA”, is the newest generation of the set of business solutions; is the most advanced version, created to simplify all transactions between organizations, as well as in their internal processes. As a research methodology, bibliographical research was adopted to answer the following question: how to secure data, optimize internal processes and ensure greater agility and savings for organizations? The objective of the article is to present the concepts inherent to each of the mentioned aspects, and to demonstrate the effectiveness of using the SAP S/4HANA system.
Single-Batch CNN Training using Block Minifloats on FPGAs

Training convolutional neural networks remains a challenge on resource-limited edge devices due to its intensive computations, large storage requirements, and high bandwidth. Error back-propagation, gradient generation, and weight update usually require high precision to guarantee model accuracy, which places a further burden on computation and bandwidth. This paper presents the first parallel FPGA CNN training accelerator with block minifloat datatypes. We first propose a heuristic bit-width allocation technique to derive a unified 8-bit block minifloat format with a sign bit, 2 exponent bits, and 5 mantissa bits. In contrast to previous techniques, the same data format is used for weights, activations, errors, and gradients. Using this format, accuracy similar to 32-bit single precision floating point is achieved and thus simplifies the FPGA-based designs of computational units such as multiply-and-add. In addition, we propose a unified Conv block to deal with Conv and transposed Conv in the forward and backward paths respectively; and a dilated Conv block with a weight kernel partition scheme for gradient generation. Both Conv blocks support non-unit stride, this being crucial for the residual connections that appear in modern CNNs. For training of ResNet20 on the CIFAR-10 dataset with a batch size of 1, our accelerator on a Xilinx Ultrascale+ ZCU102 FPGA achieves state-of-the-art single-batch throughput of 144.64 and 192.68 GOPs with and without batch normalisation layers respectively.
Dual Graph Convolutional Networks for Document-Level Event Causality Identification

Event causalities organize events into a graph according to causal logics, which assists humans in decision making by causal reasoning among events. Despite many efforts to identify event causalities, most of them assume that only one causality exists in a sentence or causalities only occur in adjacent sentences, leading to the incapability of detecting multiple causalities or document-level causalities. In this paper, we propose a novel model for document-level event causality identification named DocECI. We define two heterogeneous document graphs, namely text structure graph and mention relation graph, and encode them with relational graph convolutional networks, which gradually aggregate the information of multi-granular nodes in a cascade manner and capture the causality patterns. Experiments on a benchmark dataset show that DocECI outperforms existing models by a significant margin. Moreover, a new experiment is conducted on causality direction identification, which is overlooked by existing models.
Knowledge Transfer via Word Alignment and Its Application to Vietnamese POS Tagging

It is not difficult to build a linguistic tagger with a large annotated corpus. Labeled data becomes a big problem with low-resource languages such as Vietnamese. Due to the development and investment in research, there is no large and high-accuracy annotated corpus. This paper proposes a transfer learning strategy to build a high-quality tagger for Vietnamese using a bilingual corpus Vietnamese-English. Particularly, We inherit the strength of a POS tagger in English, which is constructed from a large-scale corpus, then transfer the knowledge to the Vietnamese POS tagger via word alignment. Experimental results show that the proposed method achieves high accuracy with $$94.97\%$$ . The transfer strategy depends mostly on the source tagger’s accuracy and the word alignment process’s performance, so the proposed strategy can be extended and applied to another low-resource language as long as there is a large bilingual corpus with a rich resource language.
Joint modeling method of question intent detection and slot filling for domain-oriented question answering system

Purpose Intent detection and slot filling are two important tasks in question comprehension of a question answering system. This study aims to build a joint task model with some generalization ability and benchmark its performance over other neural network models mentioned in this paper. Design/methodology/approach This study used a deep-learning-based approach for the joint modeling of question intent detection and slot filling. Meanwhile, the internal cell structure of the long short-term memory (LSTM) network was improved. Furthermore, the dataset Computer Science Literature Question (CSLQ) was constructed based on the Science and Technology Knowledge Graph. The datasets Airline Travel Information Systems, Snips (a natural language processing dataset of the consumer intent engine collected by Snips) and CSLQ were used for the empirical analysis. The accuracy of intent detection and F 1 score of slot filling, as well as the semantic accuracy of sentences, were compared for several models. Findings The results showed that the proposed model outperformed all other benchmark methods, especially for the CSLQ dataset. This proves that the design of this study improved the comprehensive performance and generalization ability of the model to some extent. Originality/value This study contributes to the understanding of question sentences in a specific domain. LSTM was improved, and a computer literature domain dataset was constructed herein. This will lay the data and model foundation for the future construction of a computer literature question answering system.
Logic Shrinkage: Learned Connectivity Sparsification for LUT-Based Neural Networks

Field-programmable gate array (FPGA)–specific deep neural network (DNN) architectures using native lookup tables (LUTs) as independently trainable inference operators have been shown to achieve favorable area-accuracy and energy-accuracy trade-offs. The first work in this area, LUTNet, exhibited state-of-the-art performance for standard DNN benchmarks. In this article, we propose the learned optimization of such LUT-based topologies, resulting in higher-efficiency designs than via the direct use of off-the-shelf, hand-designed networks. Existing implementations of this class of architecture require the manual specification of the number of inputs per LUT, K . Choosing appropriate K a priori is challenging. Doing so at even high granularity, for example, per layer, is a time-consuming and error-prone process that leaves FPGAs’ spatial flexibility underexploited. Furthermore, prior works see LUT inputs connected randomly, which does not guarantee a good choice of network topology. To address these issues, we propose logic shrinkage , a fine-grained netlist pruning methodology enabling K to be automatically learned for every LUT in a neural network targeted for FPGA inference. By removing LUT inputs determined to be of low importance, our method increases the efficiency of the resultant accelerators. Our GPU-friendly solution to LUT input removal is capable of processing large topologies during their training with negligible slowdown. With logic shrinkage, we improve the area and energy efficiency of the best-performing LUTNet implementation of the CNV network classifying CIFAR-10 by 1.54× and 1.31×, respectively, while matching its accuracy. This implementation also reaches 2.71× the area efficiency of an equally accurate, heavily pruned binary neural network (BNN). On ImageNet, with the Bi-Real Net architecture, employment of logic shrinkage results in a post-synthesis area reduction of 2.67× vs. LUTNet, allowing for implementation that was previously impossible on today’s largest FPGAs. We validate the benefits of logic shrinkage in the context of real application deployment by implementing a face mask detection DNN using a BNN, LUTNet, and logic-shrunk layers. Our results show that logic shrinkage results in area gains versus LUTNet (up to 1.20×) and equally pruned BNNs (up to 1.08×), along with accuracy improvements.