Reviewerly/exHarmony huggingface dataset:
AIP: A Named Entity Recognition Method Combining Glyphs and Sounds

In recent years, a large number of Chinese electronic texts have been produced in the process of information construction in various fields. Identifying specific entities in these electronic texts has become a major research focus. Most existing research methods use radicals to extract the glyph features of Chinese characters but have seen its limitation. This paper extracts the features of Chinese characters from three aspects: glyph features, phonetic features, and character features, and improves conventional feature extraction methods for each kind of feature. A new named entity recognition method (AIP) is proposed by transforming Chinese characters into corresponding images for glyph feature extraction, dividing pinyin into initials, vowels, and tones for phonetic feature extraction, and fine-tuning the A Lite Bert model for character feature extraction to improve the performance of the model. This paper compares the performance of the AIP model and mainstream neural network models on Chinese named entity recognition tasks on commonly used data sets and the data sets in specific domains. The results showed that AIP achieved better results than the related work. The F1 values on the two data sets are 94.4% and 80.5%, respectively, which validates the model's versatility.
ValAsp: A Tool for Data Validation in Answer Set Programming

Abstract The development of complex software requires tools promoting fail-fast approaches, so that bugs and unexpected behavior can be quickly identified and fixed. Tools for data validation may save the day of computer programmers. In fact, processing invalid data is a waste of resources at best, and a drama at worst if the problem remains unnoticed and wrong results are used for business. Answer Set Programming (ASP) is not an exception, but the quest for better and better performance resulted in systems that essentially do not validate data. Even under the simplistic assumption that input/output data are eventually validated by external tools, invalid data may appear in other portions of the program, and go undetected until some other module of the designed software suddenly breaks. This paper formalizes the problem of data validation for ASP programs, introduces a language to specify data validation, and presents valasp , a tool to inject data validation in ordinary programs. The proposed approach promotes fail-fast techniques at coding time without imposing any lag on the deployed system if data are pretended to be valid. Validation can be specified in terms of statements using YAML, ASP and Python. Additionally, the proposed approach opens the possibility to use ASP for validating data of imperative programming languages.
Scaling knowledge graph embedding models for link prediction

Developing scalable solutions for training Graph Neural Networks (GNNs) for link prediction tasks is challenging due to the inherent data dependencies which entail high computational costs and a huge memory footprint. We propose a new method for scaling training of knowledge graph embedding models for link prediction to address these challenges. Towards this end, we propose the following algorithmic strategies: self-sufficient partitions, constraint-based negative sampling, and edge mini-batch training. The experimental evaluation shows that our scaling solution for GNN-based knowledge graph embedding models achieves a 16x speed up on benchmark datasets while maintaining a comparable model performance to non-distributed methods on standard metrics.
Intelligent identification of radar intra-pulse signals

For the problem that traditional radar intra-pulse signals identification needs expert knowledge, a method of radar intra-pulse signals identification based on Choi-Williams Distribution (CWD) and Convolutional Neural Network (CNN) is proposed in this work. Firstly, the characteristics of the collected radar intra-pulse signals are acquired. Then, the feature images of these characteristics are preprocessed. Finally, the intelligent identification of radar intra-pulse signals based on CNN is realized. The experimental results show that when the Signal to Noise Ratio (SNR) is 5dB, the identification accuracy of algorithm model proposed in this work based on CWD and CNN can reach 87.95%, while that based on Continuous Wavelet Transform (CWT) and CNN can only reach 72.23%. The significance of this work further optimizes the feature extraction of radar intra-pulse signals and provides an empirical reference for radar intelligent identification in EW.
Multi-modal guided attention for live video comments generation

With the blooming of online video applications, live commenting is an emerging feature of online video sites. The live video comments generation (LVCG) task aims to generate live comments for videos while considering both the video and the surrounding comments made by other viewers. In this work, we aim to improve the relevance between live comments and videos by modeling the cross-modal interactions among different modalities. To overcome the problem of insufficient multimodal interactions for live video comments generation, we built two basic attention blocks: the self attention (SA) block that can model the dense intramodal interactions; and the x-guided attention (XGA) block to model the dense intermodal interactions. After that, by modular compositions of the SA and XGA blocks, we propose different multimodal transformer architectures to handle the multimodal features. Finally, experiments show that our proposed multimodal guided attention models significantly outperform previous methods in most of the metrics.
Increasing Context for Estimating Confidence Scores in Automatic Speech Recognition

Accurate confidence measures for predictions from machine learning techniques play a critical role in the deployment and training of many speech and language processing applications. For example, confidence scores are important when making use of automatically generated transcriptions in training automatic speech recognition (ASR) systems, as well as down-stream applications, such as information retrieval and conversational assistants. Previous work on improving confidence scores for these systems has focused on two main directions: designing features correlated with improved confidence prediction; and employing sequence models to account for the importance of contextual information. Few studies, however, have explored incorporating contextual information more broadly, such as from the future, in addition to the past, or making use of alternative multiple hypotheses in addition to the most likely one. This article introduces two general approaches for encapsulating contextual information from lattices. Experimental results illustrating the importance of increasing contextual information for estimating confidence scores are presented on a range of limited resource languages where word error rates range between 30% and 60%. The results show that the novel approaches provide significant gains in the accuracy of confidence estimation.
Enumerating dissimilar minimum cost perfect and error-correcting bipartite matchings for robust data matching

Matchings between objects from two datasets, domains, or ontologies have to be computed in various application scenarios. One often used meta-approach — which we call bipartite data matching — is to leverage domain knowledge for defining costs between the objects that should be matched, and to then use the classical Hungarian algorithm to compute a minimum cost bipartite matching. In this paper, we introduce and study the problem of enumerating K dissimilar minimum cost bipartite matchings. We formalize this problem, prove that it is NP-hard, and present heuristics based on greedy dynamic programming. The presented enumeration techniques are not only interesting in themselves, but also mitigate an often overlooked shortcoming of bipartite data matching, namely, that it is sensitive w. r. t. the storage order of the input data. Extensive experiments show that our enumeration heuristics clearly outperform existing algorithms in terms of dissimilarity of the obtained matchings, that they are effective at rendering bipartite data matching approaches more robust w. r. t. random storage order, and that they significantly improve the upper bounds of state-of-the art algorithms for graph edit distance computation that are based on bipartite data matching.
Unequal adaptive visual recognition by learning from multi-modal data

Conventional domain adaptation tries to leverage knowledge obtained from the single source domain to recognize the data in the target domain, where only one modality exists in the source domain. This neglects the scenario that source domain can be acquired from multi-modal data, such as RGB data and depth data. In addition, conventional domain adaptation approaches generally assume source and target domains have the identical number of categories, which is quite restrict for real-world applications. In practice, the number of categories in the target domain is often less than that in the source domain. In this work, we focus on a more practical and challenging task that recognizes RGB data by learning from RGB-D data under an unequal label scenario, which suffers from three challenges: i) the addition of depth information, ii) the domain mismatch problem and iii) the negative transfer caused by unequal label numbers. Our main contribution is a novel method, referred to as unequal Distribution Visual-Depth Adaption (uDVDA), which takes advantage of depth data and handles domain mismatch problem under label inequality, simultaneously. Experiments show that uDVDA outperforms state-of-the-art models on different datasets, especially under unequal label scenario.
Low-Computational-Cost Algorithm for Inclination Correction of Independent Handwritten Digits on Microcontrollers

In recent years, the digitization of documents has progressed, and opportunities for handwritten document creation have decreased. However, handwritten notes are still taken for memorizing data, and automated digitalization is needed in some cases, such as making Excel sheets. When digitizing handwritten notes, manual input is required. Therefore, the automatic recognition and input of characters using a character recognition system is useful. However, if the characters are inclined, the recognition rate will be low. Therefore, we focus on the inclination correction problem of characters. The conventional method corrects the inclination and estimates the character line inclination. However, these methods do not work when characters exist in independent positions. Therefore, in this study, we propose a new method for estimating and correcting the tilt of independent handwritten digits by analyzing a circumscribed rectangle and other digital features. The proposed method is not based on an AI-based learning model or a complicated mathematical model. It is developed following a comparatively simple mathematical calculation that can be implemented on a microcontroller. Based on the results of the experiments using digits written in independent positions, the proposed method can correct the inclination with high accuracy. Furthermore, the proposed algorithm is low-computational cost and can be implemented in real-time on a microcontroller.
Print to Scan Resistant Watermarking based on Green Noise Diffusion Method using Machine Learning

The green-noise diffused watermarking method is resistant to printing and scanning, and the embedded pattern is not easily visible. We extended this method to enable the extraction of watermark information from cropped images by embedding marker patterns. We used machine learning and embedded five green-noise patterns, including four value patterns and one marker pattern. Correct watermark extraction rate of over 95% are obtained from the printed images. We also embedded sub-information in the marker pattern to obtain block synchronization from the cropped images. Furthermore, the introduction of RS codes allowed us to obtain an extraction correctness rate close to 100% from both electronic and printed images.
AutoPoD-Mobile—Semi-Automated Data Population Using Case-like Scenarios for Training and Validation in Mobile Forensics

The complexity and constant changes in mobile forensics require special training of investigators with datasets that are as realistic as possible. Even today, the generation of training data is almost exclusively done manually. This paper presents a novel open-source framework called AutoPoD-Mobile. The framework supports the creation of case-based scenarios. Even more, the semi-automated provision of datasets for mobile forensics is enabled. Thus, the behavior of suspects interacting with each other can be simulated. The result combines mobile device data from normal device usage and case-related information. This way helps validate mobile forensic tools, test new techniques, and create realistic training datasets in the mobile forensics domain. The results of a proof-of-concept trial in a realistic deployment environment will also be presented. The paper concludes with a discussion of the results and identifies options for future improvements.
The cloud ecosystem

The chapter starts with an overview of the cloud ecosystem and an in-depth discussion of cloud delivery models and services in Section 2.1; this is followed by the analysis of cloud computing at Amazon, Google, Microsoft, and IBM in Sections 2.2, 2.3, 2.4, and 2.5, respectively. Sections 2.6 and 2.7 cover the pervasive issue of vendor lock-in and the prospects of cloud interoperability. The presentation of Service Level Agreements (SLAs) in Section 2.8 is followed by a discussion of responsibility sharing between cloud users and cloud service providers in Section 2.9. User experience is analyzed in Section 2.10, while Section 2.11 covers software licensing. Major challenges faced by cloud computing are discussed in Section 2.12. The cloud is changing the enterprise ecosystem at a stunning speed as documented in Section 2.13. The last section of the chapter contains exercises and problems.
An ISO/IEC 81346-inspired domain specific language to extract time series data for analytics

OPC UA information models support standardized models of industrial assets, but have problematic support for queries. SPARQL is the query language of Semantic Web Technologies (SWT). Translation of OPC UA information models to SWT and using SPARQL has been proposed as a solution to querying OPC UA information models and to extracting time series data from them. We argue that users of SPARQL based OPC UA queries may be overloaded by the amount of background knowledge required. In addition to knowing SPARQL, users must understand the industrial standards that are used for representing their industrial asset, how they are encoded using OPC UA information models and represented in SWT after translation. For the case of ISO/IEC 81346 (RDS) providing contextualization and IEC 61850 describing sensor data from equipment we create a domain specific query language for extracting time series data for analytics purposes which produces much shorter queries with lower requirements for technical knowledge than their SPARQL equivalents. We describe the expressiveness of the language, and argue that it is well suited to the needs of data scientists and data engineers. A grammar together with a translator to SPARQL is open sourced under the Apache 2.0 license.
An Algorithm for Automatic Recognition of Chinese Spatial Prepositional Phrases for Academic Texts

Automatic recognition of prepositional phrases has always been pursued in English translation, and its accurate recognition is crucial for various activities and applications in the field of natural language processing. This paper proposes an automatic recognition algorithm for Chinese spatial prepositional phrases in academic texts, which can not only identify parallel prepositional phrases but also improve the recognition accuracy of nested prepositional phrases. First, the method employs a simple noun phrase recognition model to identify and fuse phrase information contained in the corpus, thereby simplifying the corpus and minimizing the internal complexity of prepositional phrases. Second, the method uses the CRF model to identify nested inner prepositional phrases; if there is nesting, it identifies the inner layer of nesting; if there is no nesting, it identifies prepositional phrases; finally, it fuses the inner prepositions identified in the initial corpus phrase, modifies its feature information, and retrains the foreign preposition phrase recognition model for recognition. After the internal and external prepositional phrases are automatically identified, the double error correction method is used to correct the identified prepositional phrases. The experimental analysis shows that the accuracy rate, recall rate, and <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <mi>F</mi> </math> -value of the method for identifying prepositional phrases are 95.33%, 94.32%, and 94.73%, respectively, which are higher than those of the other methods for identifying prepositional phrases, which effectively improves the recognition rate of prepositional phrases.
A semantic element representation model for malicious domain name detection

The existing detection methods of algorithmically generated malicious domain names lack theoretical modelling methods for domain name element composition. To address this problem, a semantic element representation model for domain names is constructed based on the set of semantic elements of domain names and the probabilistic context free grammar model. The model first analyses and categorises the constituent elements of the domain name, and then proposes a syntax tree analysis method for the semantical relationships between the elements, which enables efficient representation of multiple elements in domain names. Based on the proposed model, the malicious domain names are categorised into four categories: random character-based, word-based, predicted character-based, and multi-element hybrid. Experiments are conducted to analyse the anomalies and concealment of domain names, the results denote that there are significant differences between malicious and legitimate domain names, as well as between malicious domain names, and the comparative experimental results denote the proposed model can effectively improve the detection accuracy of malicious domain names.
Investigating the Impact of Information Sharing in Human Activity Recognition

The accuracy of Human Activity Recognition is noticeably affected by the orientation of smartphones during data collection. This study utilized a public domain dataset that was specifically collected to include variations in smartphone positioning. Although the dataset contained records from various sensors, only accelerometer data were used in this study; thus, the developed methodology would preserve smartphone battery and incur low computation costs. A total of 175 different features were extracted from the pre-processed data. Data stratification was conducted in three ways to investigate the effect of information sharing between the training and testing datasets. After data balancing using only the training dataset, ten-fold and LOSO cross-validation were performed using several algorithms, including Support Vector Machine, XGBoost, Random Forest, Naïve Bayes, KNN, and Neural Network. A very simple post-processing algorithm was developed to improve the accuracy. The results reveal that XGBoost takes the least computation time while providing high prediction accuracy. Although Neural Network outperforms XGBoost, XGBoost demonstrates better accuracy with post-processing. The final detection accuracy ranges from 99.8% to 77.6% depending on the level of information sharing. This strongly suggests that when reporting accuracy values, the associated information sharing levels should be provided as well in order to allow the results to be interpreted in the correct context.
Optimization of matched filters for VHR image segmentation

The availability of very high resolution remotely sensed images has made the use of spectral information alone insufficient for class recognition, so the integration of spatial information into the segmentation task becomes necessary. Spatial information takes into account the surrounding of a pixel, rather than dealing with a pixel as an isolated item. In this paper, inspired by matched filtering theory, we propose a new technique for spatial feature extraction. The technique consists of using 2D kernels convolved with the spectral bands. The convolution operation bears valuable spatial information about the pixel under consideration. The concatenation of the extracted spatial features and spectral features of pixels feeds a support vector machine (SVM) to segment the image of interest. To cope with the complexity of images having objects of varying sizes (i.e. urban areas), we adopted a hierarchical strategy. That is, kernels with increasing size are applied to extract different spatial features corresponding to different objects. Thus, we have extended the use of matched filters from single object enhancement to multi-object enhancement. A challenging step in designing the matched filters is the two-dimensional kernels coefficients selection, which we formulated as an optimization problem within a particle swarm optimization framework. The optimization process was driven by two different fitness functions, the cross-validation SVM accuracy and the Bhattacharyya distance, which were both evaluated on training samples. We assessed the proposed procedure on two very high-resolution images having different spatial resolutions. The obtained results showed significant improvement in terms of the overall classification accuracy (over 10% for both images). Moreover, visual inspection of the segmented images, in addition to pepper and salt elimination, revealed significant improvement in many objects not detected by the spectral method. Our method of extracting spatial features seems to be very efficient.
Para-functional engineering: cognitive challenges

Self-adaptive behavior can be defined as the behavior that allows an agent to adapt to a context using her/his/its resources. The property of being ‘self-adaptive’ implies considering some preliminary sources or elicitors for such skill. In the case of machine learning, all the learning or self-adaptive behavior mechanisms are related to algorithmic models of mathematical nature, while in the case of humans more subtle neurochemical and symbolic processes (logical and linguistic) are present. The purpose of this paper is to offer a theoretical analysis of the basic mechanisms related to learning processes, always oriented towards the creation of artificial cognitive systems which can implement such bioinspired mechanisms. Parafunctionality is the key innovative concept we introduce for applying bioinspired cognition to machine learning exploring a real mechanism still unexplored.