Reviewerly/exHarmony huggingface dataset:
Research on NER Based on Register Migration and Multi-task Learning

The insufficient number of tags is currently the biggest constraint on named entity recognition (NER) technology, with only a small number of Registers (means the domain of language, which will be explained in Part I) currently having a corpus with sufficient tags. The linguistic features of different Registers vary greatly, and thus a corpus with sufficient labels cannot be applied to NER in other Registers. In addition, most of the current NER models are more designed for large samples with sufficient labels, and these models do not work well in small samples with a small number of labels. To address the above problems, this paper proposes a model T_NER based on the idea of migration learning and multi-task learning, which learns the common features of language by using the idea of multi-tasking, and passes the model parameters of neurons with common features of language learned from multiple well-labelled source domains to the neurons in the target domain to achieve migration learning based on parameter sharing. In baseline experiments, T_NER’s neurons outperformed the original models such as BiLSTM and BiGRU on a small-sample NER task; in formal experiments, the more the Registers in source domains, the better T_NER’s recognition of the target domain. The experiments demonstrate that T_NER can achieve NER for small samples and across Registers.
The representation of migrants in Spanish judicial decisions: using corpus data to refute hate speech

The phenomenon of immigration and its depiction in media texts have been examined profusely within the field of corpus-based discourse analysis ( Gabrielatos and Baker, 2008 ; Baker et al., 2013 ; and Blinder and Allen, 2016 ). This research seeks to present it as reflected in a corpus of 600 judicial decisions issued by Spanish courts in the years 2016 and 2017. This analysis was motivated by the rise of extreme right-wing parties in Europe in recent years. Such parties dehumanise immigrants and portray them as a threat to the welfare state. On first examination, the results appear to dissociate immigration and crime since a considerable percentage of the keywords obtained (about 20 percent) revolves around three major topoi (namely, ‘family’, ‘territory/access’ and ‘legal punishment’) and there is no evidence of any major offences or crimes amongst the top-ranking lexicon. The study of the collocate networks of the keywords within the category ‘legal punishment’ confirms our initial perception; in fact, out of twenty-one collocates, only the word delito (‘crime’) itself collocates with terms referring to typified crimes such as violencia (‘violence’). In parallel, the data were triangulated using the text-classification software UMUTextStats ( García-Díaz et al., 2018 ). The results of this second analysis also confirm our initial observations.
Optimal SVM Using an Improved FOA of Evolutionary Computing

To solve the problem that the traditional model has the problem of low recognition accuracy, this paper proposes a dynamic step size factor to realize the dynamic change of step size, which makes the fruit fly optimization algorithm (FOA) have strong local optimization ability and improve the convergence accuracy. The smell concentration determination formula is improved to realize the search of the algorithm in the negative value range. This paper constructs an improved FOA-SVM model and uses the heart dataset to evaluate the classification performance of the improved FOA-SVM algorithm. Finally, the gray texture joint feature parameters of coal and gangue are extracted as the input vector of the improved FOA-SVM and gangue are carried out. The experimental results show that the classification results of the improved FOA-SVM model proposed in this paper are good, and the average accuracy are 96.30%, which improves the classification performance by 2.1% compared with the original algorithm.
The Digital Transition in Criminal Trials: New Promises, New Risks, New Challenges

There is probably no field of social life which has not undergone radical changes in light of the ongoing digital transition, and the consequent transformation of almost every human activity. Law enforcement and criminal procedure could not lag behind these phenomena. It is therefore no surprise that a shift towards to new digital techniques has more or less slowly characterised the evolution of every criminal justice system. This contribution critically analyses the main results of the researches collected in this volume, with the aim of highlighting the tensions between conflicting interests and values, but also the difficult trade-offs that ICT instruments today require, if not always to improve, at least to maintain sustainable levels of fairness in the administration of criminal justice. Doubtless, investigative and procedural fairness remains the ultimate goal of modern criminal justice systems, and mostly of European case-laws, and this goal can surely not be abandoned in our digitalised world. Focusing on the new promises, the new risks, and finally the new challenges concerned with the digital transition in the fields of criminal proceedings and crime prevention provides a useful fil rouge to grasp the complexity of the ongoing evolution of criminal justice.
TFHM:A Traffic Feature Hiding Scheme Based on Generative Adversarial Networks

The traffic encrypted by HTTPS on the Internet is increasing year by year. In most countries and regions, the proportion of encrypted traffic has exceeded 90%. The findings suggest that even encrypted traffic is at risk of privacy leakage. The anonymous communication network hides the communication relationship and transmission path between users through technologies such as mixnet and onion routing, but lacks a traffic feature hiding mechanism. Attackers can use the encrypted traffic analysis algorithm based on machine learning to de-anonymize the anonymous network. This paper proposes a traffic feature hiding scheme, called TFHM, which uses a generative adversarial network (GAN) to generate adversarial samples that are indistinguishable from target traffic, providing traffic feature hiding capabilities for anonymous networks and preventing listeners from classifying and analyzing anonymous traffic. According to the experimental results, the TFHM method can reduce the accuracy of encrypted traffic analysis methods based on Naive Bayes, Decision Tree, and SVM by 30%.
The 3/4 Conjecture for Fix-Free Codes With at Most Three Distinct Codeword Lengths

The 3/4 conjecture was posed 25 years ago by Ahlswede, Balkenhol, and Khachatrian, and states that if a multiset of positive integers has Kraft sum at most 3/4, then there exists a code that is both a prefix code and a suffix code with these integers as codeword lengths. We prove that the 3/4 conjecture is true whenever the given multiset of positive integers contains at most three distinct values.
Path Planning Algorithm for UAV Based on Smooth Rapidly Exploring Random Tree

To overcome the difficulties of poor real-time performance in UAV path planning, a smooth Rapidly Exploring Random Tree(RRT) algorithm based on a priori sampling and cost function is proposed in this paper. Thus the convergence of the path planning algorithm can be accelerated. The a priori information such as UAV dynamics constraints is used in the method to optimize the search space of expansion points, so the problem that original Rapidly Exploring Random Tree can hardly make full use of dynamics constraints is solved. At last, the feasibility of the algorithm is verified by the simulation. Moreover, the operation efficiency, convergence performance and planning capability are all improved.
A New 3-D PCA Regression Method for Manifold Dimension Reduction with Image Analysis

In this paper, we propose a new 3-D pca regression method for manifold dimension reduction with applications to image analysis. The proposed method is a novel image analysis method consisting of a regression algorithm of a structure designed based on an improved manifold 3-DPCA and an autoencoder capable of nonlinear expansion of PCA for efficient dimension reduction in large-capacity image data input. With the configuration of an autoencoder, a regression manifold 3-DPCA, which derives the best hyperplane through three-dimensional rotation of image pixel values, and a Bayesian rule structure similar to a deep learning structure, are applied. Conduct experiments for performance verification. Image is improved using fine dust image, and accuracy performance evaluation is performed through classification model. As a result, it can be confirmed that it is effective in performing deep learning.
Vision-based gesture modeling for signed expressions recognition

A vision-based method for signed expressions recognition consisting of searching for previously prepared word models in the analyzed utterances was developed. The models were created as temporal sequences of grouped descriptors of the place of articulation and the shape of the hand. The descriptors were created using the skeletons determined by the deep neural network and histogram of oriented gradients features. The classification was carried out using the Wagner–Fischer algorithm for determining Levenshtein distance, which was modified to search for fragments in sequences of discrete symbols. Considering the practical application of the developed method, even in the case of partially inaccurate recognition results, it was proposed to visualize the answers in the form of a word cloud. The method was tested for a challenging set of sign language expressions used in the office when applying for an ID card.
Clustering analysis of PM2.5 concentrations in the South Sumatra Province, Indonesia, using the Merra-2 Satellite Application and Hierarchical Cluster Method

&lt;abstract&gt; &lt;p&gt;The air quality monitoring system is the most prominent tool for monitoring air pollution levels, especially in areas where forest fires often occur. The South Sumatra Province of Indonesia is one of the greatest contributors to haze events in Indonesia due to peatlands fires. It does not sufficiently possess a ground monitoring system to cover rural areas, and thus, delayed actions can result in severe air pollution within this region. Therefore, the aim of this current study is to analyze the distribution and classification of PM2.5 observed from 2019 to 2021 within the South Sumatra Province, Indonesia. The acquisition of PM2.5 data was from the Merra-2 Satellite with a spatial resolution of 0.5˚ × 0.625˚ and an hourly interval. The hierarchical cluster analysis (HCA) was applied in this study for the clustering method. The result of the study revealed that the daily mean of PM2.5 levels varied from 5.9±0.01 to 21.3±0.03 μg/m&lt;sup&gt;3&lt;/sup&gt;. The study area was classified into three classes: high pollution areas (HPA), moderate pollution areas (MPA) and low pollution areas (LPA), based on the HCA method. The average level of PM2.5 observed in HPA was notably higher, at 16.8±0.02 μg/m&lt;sup&gt;3&lt;/sup&gt;, followed by MPA and LPA. Furthermore, this study indicated that the highest level of PM2.5 was found during 2019, with a severe haze event in the study area due to the intensive burning of forests, bush and peatlands. As a whole, the output of this study can be used by authorities for air quality management due to forest fire events in a certain area.&lt;/p&gt; &lt;/abstract&gt;
A Calculus with Recursive Types, Record Concatenation and Subtyping

Calculi with subtyping, a form of record concatenation and recursive types are useful to model objects with multiple inheritance. Surprisingly, almost no existing calculi supports the three features together, partly because the combination of subtyping and record concatenation is already known to be troublesome. Recently, a line of work on disjoint intersection types with a merge operator has emerged as a new approach to deal with the interaction between subtyping and record concatenation. However, the addition of recursive types has not been studied. In this paper we present a calculus that combines iso-recursive types with disjoint intersection types and a merge operator. The merge operator generalizes symmetric record concatenation, and the calculus supports subtyping as well as recursive types. We build on recent developments on the theory of iso-recursive subtyping using the so-called nominal unfolding rules to add iso-recursive types to a calculus with disjoint intersection types and a merge operator. The main challenge lies in the disjointness definition with iso-recursive subtyping. We show the type soundness of the calculus, decidability of subtyping, as well as the soundness and completeness of our disjointness definition. All the proofs are mechanized in the Coq theorem prover.
Federating Unlabeled Samples: A Semi-supervised Collaborative Framework for Whole Slide Image Analysis

Over the last decades, deep learning-based algorithms have witnessed tremendous progress in the medical field to assist pathologists in clinical decisions and reduce their workload. For these models to reach their full potential, access to large and diverse datasets is essential, but collaborations between hospitals are highly limited by privacy-related regulations. At the same time, medical institutions do not always have specialized pathologists to diagnose biopsies and label local data. To address these limitations, federated learning gained traction to enable multi-institution model training without sharing sensitive patient data. However, this technique is still in its infancy when it comes to digital pathology applications, and does not consider institutions with unlabeled data in federations. In this paper, we introduce a novel semi-supervised federated learning approach that promotes multi-institutional training of deep learning models while integrating unlabeled collaborating data sources into the federated setup. The experimental results show a better performance for models trained under a federated setting with both labeled and unlabeled data. Optimally, this framework will also bring the promise of assisting clinical decisions in hospitals that do not have specialized pathologists.
Collision Avoidance Simulation Using Voronoi Diagrams in a Centralized System of Holonomic Multi-agents

This work solves the Collision Avoidance problem in a simulation of a centralized system of holonomic multi-agents in a two dimensional space free of static obstacles. For this, we propose an implementation of three modules in an architecture: Threat Assessment Strategy (TAS), Path Planning Strategy (PPS), and Path Tracking Strategy (PTS). The Buffered Voronoi Cells represent the TAS. The PPS modules use two algorithms: the Analytical Geometric Algorithm (AGA) and the Receding Horizons Control (RHC) based on Quadratic Programming (QP) Algorithm. Finally, PTS controls the tracking according to fixed distance magnitudes in each iteration. The analysis of the results considers the computational execution time, the number of steps until convergence, and the calculation of optimal values. Also, these results are compared with the Optimal Reciprocal Collision Avoidance (ORCA) algorithm. In this way, our proposal successfully addresses and solves the collision avoidance problem but takes more execution time and number of steps compared with the ORCA algorithm. Besides, the number of steps of AGA is closer to ORCA, producing promising results with an accuracy of 95%.
GiTS: Gist-driven Text Segmentation

Text segmentation is a classical task in NLP that aims to split a document into several coherent sections. Existing work typically regards it as a binary classification task and predicts whether a paragraph or sentence is the end of a section through bottom-up paragraph content coherence modeling. However, the high-level gist, i.e., the essential meaning of a certain section, is not well utilized. In this work, we propose a gist-driven text segmentation model (GiTS), which locates section boundaries utilizing both the top-down section gist and the bottom-up paragraph contents. Specifically, GiTS consists of a gist generator and a boundary predictor. The RNN-based gist generator generates section gist in order to consider high-level content coherence and distinguishability. The boundary predictor predicts boundary through a pointer network. We also propose an auxiliary gist processor module, which can seamlessly extend GiTS to diverse NLP tasks, such as heading generation, question answering and text classification. Experimental results on text segmentation show our model outperforms the RNN-based state-of-the-art model by 6.83 % on average in terms of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$P_{k}$</tex> . We also show that combining GiTS with the auxiliary gist processor outperforms the state-of-the-art heading generation models by 22.68% in accuracy and 44.20% in Rouge-1.
Learning Generalized Knowledge From a Single Domain on Urban-Scene Segmentation

Deep neural networks have made significant progress in various tasks under the assumption of the same distribution between training and testing data. However, the obtained domain-specific knowledge often suffers from performance degradation when facing out-of-distribution data. Towards addressing the degradation, a critical requirement of such networks is the generalization capability to unseen domains, which is the goal of domain generalization (DG). This paper attempts to learn generalized knowledge from a single synthetic domain and then apply it to real and unknown scenarios. Specifically, we propose a contour-aware instance normalization module to effectively learn domain-invariant features via a novel weight-updating strategy, which can largely exploit the generalized information from the observed data. In addition, a category-level contrastive learning mechanism is proposed through understanding the semantic discrepancy and relevance among samples to mitigate the interference of domain-specific features on classification. Extensive experiments together with ablation studies on widely-adopted datasets are conducted to demonstrate the effectiveness of our design and show the superiority of our method over other state-of-the-art schemes on the task of urban-scene segmentation.
CMPD: Context-Based Malicious Parameter Detection for APIs

Abstract The Application Program Interface (API) plays an important role as the channel for data interaction between programs, while the widespread use of APIs has brought security risks that cannot be ignored. The adversary can perform various Web attacks, including SQL Injection and Cross-Site Scripting (XSS), by tampering with the parameters of API. Efficient detection of parameter tampering attacks for API is critical to ensure the system is running in the expected condition, further avoiding data leakage and property loss. Previous works always utilize the rule-based method or simple learning-based method to detect parameter tampering attacks. However, they ignore the contextual information of the API tokens and thus have a poor performance. In this paper, we propose the C ontext-based M alicious P arameter D etection (CMPD) framework to detect the parameter tampering attacks for APIs. We use a neural network language model to learn the distribution of the parameters, parameter names, and URLs and then use a tree model to detect the malicious query based on the high dimensional API embedding. Experiments show that CMPD outperforms all baseline, including rule-based method, Support Vector Machine (SVM), and Autoencoder, on CSIC 2010 dataset with $$F_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> value reaching 0.971. CMPD can also achieve a 0.895 $$F_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> value when training data is reduced to 20% and can achieve a 0.910 $$F_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>F</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> value when negative examples are reduced to 1%.
Distributionally Robust Federated Learning for Differentially Private Data

Local differential privacy (LDP) is a prominent approach and widely adopted in federated learning (FL) to preserve the privacy of local training data. It also nicely provides a rigorous privacy guarantee with computational efficiency in theory. However, a strong privacy guarantee with local differential privacy can degrade the adversarial robustness of the learned global model. To date, very few studies focus on the interplay between LDP and the adversarial robustness of federated learning. In this paper, we observe that LDP adds random noise to the data to achieve privacy guarantee of local data, and thus introduces uncertainty to the training dataset of federated learning. This leads to decreased robustness. To solve this robustness problem caused by uncertainty, we propose to leverage the promising distributionally robust optimization (DRO) modeling approach. Specifically, we first formulate a distributionally robust and private federated learning problem (DRPri). While our formulation successfully captures the uncertainty generated by the LDP, we show that it is not easily tractable. We thus transform our DRPri problem to another equivalent problem, under the Wasserstein distance-based uncertainty set, which is named the DRPri-W problem. We then design a robust and private federated learning algorithm, RPFL, to solve the DRPri-W problem. We analyze RPFL and theoretically show it satisfies differential privacy with a robustness guarantee. We evaluate algorithm RPFL by training classifiers on real-world datasets under a set of well-known attacks. Our experimental results show our algorithm RPFL can significantly improve the robustness of the trained global model under differentially private data by up to 4.33 times.
Age Progression and Regression using cycleGAN

Face detection and face related processing are emerging areas of research in computer vision and are rapidly growing topics of large interest. Facial age progression and regression is one of such interesting new generation applications. Face processing techniques have been developed during the last decade to address the difficulty of producing realistic aged faces for various smart system applications. In this paper, age progression and regression of frontal faces is achieved using Cycle Generative Adversarial Neural Network (CycleGAN). CycleGAN is a variant of the GAN architecture which is used to generate images with effects of face aging and rejuvenation. UTKFace dataset, which is an unpaired face dataset, is used for training of the proposed network. The trained network is evaluated subjectively as well as objectively for the accuracy of the age progression and regression of various face Images.
Fall Detection Algorithm for the Elderly based on YOLOv5s

With the aggravation of the aging society, how to monitor the fall of the elderly living alone indoors is widely concerned by the society. In recent years, the monitoring methods based on environmental sensors and wearable portable devices have achieved good results in both quality and efficiency. However, the practical promotion of them is faced with the problems of complex equipment and high cost. This paper presents a fall behavior monitoring method for the elderly based on computer vision. Firstly, in the home environment, the elderly in the home scene are monitored by a network camera, and the video data is uploaded to the cloud to extract behavioral characteristics and judge whether they fall. Secondly, the YOLOv5s algorithm is used to train the target detection model and detect falls, which has the characteristics of fast, accurate and good robustness. We conducted experiments on a dataset of 1500 images to systematically analyze the strengths and weaknesses of our approach and training model.