Reviewerly/exHarmony huggingface dataset:
Machine translation and legal terminology

This chapter addresses machine translation (MT) with an eye to legal terminology. The translation of legal terms and phrasemes may be fraught with contextual complexities, and context has long been the Achilles’ heel of MT. Nevertheless, neural MT (NMT) and statistical MT (SMT) have made considerable progress in recent years, thanks to data-driven approaches making use of potentially related corpora to overcome contextual obstacles. Such approaches and the potential frozenness or repetitiveness of legal terms and phrases may allow MT to overcome some of these obstacles. This chapter reviews contextual complexities surrounding legal terminology, NMT and SMT architectures, and research on MT and legal translation to determine what might be expected from data-driven MT in this context.
Automatic term recognition and legal language

Natural Language Processing (NLP) tools offer language scholars a wide array of possibilities to examine, amongst other, the lexicon in any text collection. This research was designed as an attempt to try to measure the degree of precision of three of these methods (Chung 2003; Drouin 2003; Scott 2008a) through their implementation on two corpora of Spanish and British judicial decisions which revolve around the topic of immigration. In addition, the last section of this chapter explores the lexical inventories extracted by each method (the top 500 candidate terms (CTs) in each case) by grouping them into ad hoc thematic categories, the most numerous being, as was to be expected, legal terms , followed by territory , evaluative items , crime and family .
Refined statistical attacks against searchable symmetric encryption using non-indexed documents

Searchable symmetric encryption(SSE) schemes allow clients to search encrypted data stored on remote servers but ensure efficient retrieval by revealing specific information about the queries, such as access patterns. Honest but curious servers can then use these leakages to infer keywords queried by users. However, most attack schemes can only achieve considerable recovery accuracy(over 30%) under favorable conditions. When the auxiliary information is weak, the inference attack schemes using statistical information can achieve the same or even better recovery accuracy. In this paper, an attack based on statistical information is proposed. Our attack iteratively solves the quadratic recovery query problem using a linear optimization solver. With a tiny number of known queries(which can be deleted by the server), the query recovery accuracy can reach about 95% without knowing the exact background knowledge of the documents stored by the client. This process not only makes our scheme outperforms other attack schemes in accuracy but also makes our attack execution more efficient than other schemes, up to a maximum difference of 1000 seconds. The attack can still achieve considerable accuracy even when the defense is applied to the SSE scheme; defenses can help the SSE scheme obfuscate the pattern leakages.
DEVELOPMENT OF AN ENHANCED C4.5 DECISION TREE ALGORITHM USING A MEMOIZED MAPREDUCE MODEL

Classification technique in data mining concentrates on the prediction of categorical or discrete target variables which is designed to be handled by the classical C4.5 decision tree algorithm, an algorithm whose aim is to produce a tree which accurately predicts the target variable for a new unseen data. However its recursive nature poses a limitation when huge volume of dataset is involved; making computation more complex and resulting in an inefficient implementation of the algorithm in terms of computing time, memory utilization and data complexity. Meanwhile, several researches have been done to control these limitations. One of such improvements is the parallelizing of the algorithm using the MapReduce model. This involves dividing the large dataset into smaller units and sharing them on multiple computers for parallel processing, but the recursive nature of the algorithm makes the cost of computing large number of repeated calculations quite high, which is our concern in this work. . This research is aimed at reducing computation time further, by using a memoized MapReduce model, which involves the saving of the result of previous calculations in a cache; hence, when same calculations are encountered again, the cached result is returned, thus re-computation is avoided. The cached result is considered a reduced cost compared to the computational cost of re-computation.
Towards Human-Level Safe Reinforcement Learning in Atari Library

Reinforcement learning (RL) is a powerful tool for training agents to perform complex tasks. However, from time-to-time RL agents often learn to behave in unsafe or unintended ways. This is especially true during the exploration phase, when the agent is trying to learn about its environment. This research acquires safe exploration methods from the field of robotics and evaluates their effectiveness compared to other algorithms that are commonly used in complex videogame environments without safe exploration. We also propose a method for hand-crafting catastrophic states, which are states that are known to be unsafe for the agent to visit. Our results show that our method and our hand-crafted safety constraints outperform state-of-the-art algorithms on relatively certain iterations. This means that our method is able to learn to behave safely while still achieving good performance. These results have implications for the future development of human-level safe learning with combination of model-based RL using complex videogame environments. By developing safe exploration methods, we can help to ensure that RL agents can be used in a variety of real-world applications, such as self-driving cars and robotics.
Monocular‐based collision avoidance system for unmanned aerial vehicle

Abstract Obstacle avoidance based on a monocular camera is a challenging task due to the lack of 3D information for Unmanned Aerial Vehicle. Recent methods based on Convolutional Neural Networks for monocular depth estimation and obstacle detection become widely used. However, collision avoidance with depth estimation usually suffers from long computational time and low avoidance success rate. A new collision avoidance system is proposed which uses monocular camera and intelligent algorithm to avoid obstacles on real time processing. Several experiments have been conducted on crowded environments with several object types. The results show outstanding performance in terms of obstacles avoidance and system response time compared to contemporary approaches. This makes the proposed approach of high potential to be integrated in crowded environments.
Kernel extreme learning with harmonized bat algorithm for prediction of pyrene toxicity in rats

Abstract Polycyclic aromatic hydrocarbons (PAHs) are organic pollutants and manufactured substances conferring toxicity to human health. The present study investigated whether pyrene, a type of PAH, harms rats. Our research provides an effective feature selection strategy for the animal dataset from Wenzhou Medical University's Experimental Animal Center to thoroughly examine the impacts of PAH toxicity on rat features. Initially, we devised a high‐performance optimization method (SCBA) and added the Sobol sequence, vertical crossover and horizontal crossover mechanisms to the bat algorithm (BA). The SCBA‐KELM model, which combines SCBA with the kernel extreme learning machine model (KELM), has excellent accuracy and high stability for selecting features. Benchmark function tests are then used in this research to verify the overall optimization performance of SCBA. In this paper, the feature selection performance of SCBA‐KELM is verified using various comparative experiments. According to the results, the features of the genes PXR, CAR, CYP2B1/2 and CYP1A1/2 have the most impact on rats. The SCBA‐KELM model's classification performance for the gene dataset was 100%, and the model's precision value for the public dataset was around 96%, as determined by the classification index. In conclusion, the model utilized in this research is anticipated to be a reliable and valuable approach for toxicological classification and assessment.
Research on the Constructivism-Based Blended Learning Model in the Professional English Writing

Abstract: The application of constructivist ideas in professional English writing teaching can help students construct meaningful understanding of course content while learning English, thereby improving their English writing and comprehension abilities. It enables students to explore course knowledge more deeply and ensures comprehensive improvement of students' professional English literacy under the influence of blended learning. Therefore, in the process of reforming and innovating professional English writing teaching activities, teachers should reexamine the guidance of constructivist ideas and choose constructivist theory, zone of proximal development theory, and input-output theory as guiding principles. They should explore group cooperative blended learning models, blended learning models in segmented classrooms, and blended learning models supported by communicative context creation. This approach effectively highlights the scientific and rational nature of educational reform and promotes high-quality advancement of professional English writing teaching.
Image Restoration Using Deep Learning Based Image Completion

Digital images can experience various disturbances in acquisition and storage, one of which is a disturbance indicated by damage to certain areas of the image field and causes the loss of some of the information represented by the image. One of the ways to restore an image experiencing disturbances like this is with image completion technology. Image completion is an image restoration technology capable of filling in or completing missing or corrupted parts of an image. Various methods have been developed for this image completion, starting from those based on basic image processing to the latest relying on artificial intelligence algorithms. This study aims to develop and implement an image completion model based on deep learning with the transfer learning method from the completion.net architecture. Using the Facesrub training dataset consisting of a collection of unique facial photos allows the model to understand facial attributes better. Compared to conventional image completion based on image patches, the method developed in this study can perform image filling in image gaps with more realistic results. Based on visual tests conducted on respondents, the results obtained enable respondents to understand all the information represented by the restored image, similar to the original image.
Automation of interaction—interaction design at the crossroads of user experience (UX) and artificial intelligence (AI)

Abstract Interaction design/HCI seems to be at a crossroads. On the one hand, it is still about designing for engaging user experiences (UX). Still, on the other hand, it seems to be increasingly about reducing interaction and automating human–machine interaction through the use of AI and other new technologies. In this paper, we explore this seemingly unavoidable gap. First, we discuss the fundamental design rationality underpinning interaction and automation of interaction from the viewpoints of classic theoretical standpoints. We then illustrate how these two come together in interaction design practice. Here we examine four examples from already published research on automation of interaction, including how different levels of automation of interaction affect or enable new practices, including coffee making, self-tracking, automated driving, and conversations with AI-based chatbots. Through an interaction analysis of these four examples, we show (1) how interaction and automation are combined in the design, (2) how interaction is dependent on a certain level of automation, and vice versa, and (3) how each example illustrates a different balance between, and integration of interaction and automation. Based on this analysis, we propose a two-dimensional design space as a conceptual construct that takes these aspects into account to understand and analyze ways of combining interaction and automation in interaction design. We illustrate the use of the proposed two-dimensional design space, discuss its theoretical implications, and suggest it as a useful tool—when designing for engaging user experiences (UX), with interaction and automation as two design materials.
Adv-Triplet Loss for Sparse Attack on Facial Expression Recognition

The susceptibility of current Deep Neural Networks (DNNs) to adversarial examples has been a significant concern in deep learning methods. In particular, sparse adversarial examples represent a specific category of adversarial examples that can deceive the target model by perturbing only a few pixels in images. While existing sparse adversarial attack methods have shown achievements, the current results of sparsity and efficiency are inadequate and require significant improvements. This paper introduces an adv-triplet loss and proposes a search attack method to attack the Face Expression Recognition (FER) system with minimal pixel perturbations. Specifically, we propose an adv-triplet loss function and utilize its gradient information to generate pixels for adversarial examples. Extensive experiments conducted on the CK+ and Oulu-CASIA datasets demonstrate the superiority of our proposed method over several state-of-the-art sparse attack methods.
A Semantic Similarity Distance-Aware Contrastive Learning for Abstractive Summarization

Recently, contrastive learning has been extended from visual representation to summarization tasks. Abstractive summarization aims to generate a short description for a document while retaining significant information. At present, the methods of contrastive learning summarization focus on modeling the global semantics of source documents, targets and candidate summaries to maximize their similarities. However, they ignore the influence of sentence semantics in the source document. In this paper, we propose a sentence-level semantic similarity distance-aware contrastive learning method (SSDCL), which integrates the semantic similarity distance between summaries and sentences of source documents into the contrastive loss in the form of soft weights. Therefore, our model maximize the similarity between summaries and salient information, while minimizing the similarity between summaries and noise. We conducted extensive experiments on CNN/Daily Mail and XSum datasets to verify our model. The experimental results show that the proposed method achieved remarkable performance over the baseline and many advanced methods.
CASSOR: Class-Aware Sample Selection for Ordinal Regression with Noisy Labels

Ordinal regression aims at solving the classification problem, where the categories are related in a natural order. Due to the difficulty in distinguishing between highly relevant categories, label noise is frequently present in ordinal data. Moreover, the varying degrees of relevance between categories can lead to an inconsistent distribution of misclassification loss across categories, posing a challenge to select clean data consistently from all categories for training. To overcome this limitation, we develop a sample selection method termed ‘Class-Aware Sample Selection for Ordinal Regression’ (CASSOR). To be concrete, we devise a class-specific sample selection strategy in order to adaptively acquire sufficient clean examples for robust model training. Moreover, a label-ranking regularizer is designed to help guide the sample selection process via exploring the ordinal relationship between different examples. As a result, our proposed CASSOR is endowed with strong discrimination abilities on ordinal data. Intensive experiments have been performed on multiple real-world ordinal regression datasets, which firmly demonstrates the effectiveness of our method.
CDAN: Cost Dependent Deep Abstention Network

This paper proposes deep architectures for learning instance-specific abstain (reject) option multiclass classifiers. The proposed approach uses novel bounded multiclass abstention loss for multiclass classification as a performance measure. This approach uses rejection cost as the rejection parameter in contrast to coverage-based approaches. To show the effectiveness of the proposed approach, we experiment with several real-world datasets and compare them with state-of-the-art coverage-based and cost-of-rejection-based techniques. The experimental results show that the proposed method improves performance over the state-of-the-art approaches.
Partial Multi-label Learning with a Few Accurately Labeled Data

Partial Multi-label Learning is a multi-label classification problem where only candidate labels are given for training data. These candidate labels consist of relevant labels and false-positive labels. In this paper, we consider the PML when a few accurately labeled data are available. In practice, it is difficult to remove false-positive labels fully due to a large cost, but it is possible to do that in a few instances with a smaller cost. Conventional PML methods do not assume those accurately labeled data so it is hard to utilize data effectively. We propose a new algorithm called PML-VD to utilize those accurately labeled data. PML-VD first disambiguates the noisy-labeled data with both accurately labeled data and noisy labeled data and then learns a classifier. This two-stage approach enables the effective utilization of accurately labeled data without overfitting. Experiments on nine PML datasets shows the effectiveness of explicit utilization of accurately labeled data. In best cases, PML-VD improves 7% classification accuracy in terms of ranking loss.
Chinese Macro Discourse Parsing on Generative Fusion and Distant Supervision

Most previous studies on discourse parsing have utilized discriminative models to construct tree structures. However, these models tend to overlook the global perspective of the tree structure as a whole during the step-by-step top-down or bottom-up parsing process. To address this issue, we propose DP-GF, a macro Discourse Parser based on Generative Fusion, which considers discourse parsing from both process-oriented and result-oriented perspectives. Additionally, due to the small size of existing corpora and the difficulty in annotating macro discourse structures, DP-GF addresses the small-sample problems by proposing a distant supervision training method that transforms a relatively large-scale topic structure corpus into a high-quality silver-standard discourse structure corpus. Our experimental results on MCDTB 2.0 demonstrate that our proposed model outperforms the state-of-the-art baselines on discourse tree construction.
Incomplete Multi-view Weak-Label Learning with Noisy Features and Imbalanced Labels

A variety of modern applications exhibit multi-view multi-label learning, where each sample has multi-view features, and multiple labels are correlated via common views. Current methods usually fail to directly deal with the setting where only a subset of features and labels are observed for each sample, and ignore the presence of noisy views and imbalanced labels in real-world problems. In this paper, we propose a novel method to overcome the limitations. It jointly embeds incomplete views and weak labels into a low-dimensional subspace with adaptive weights, and facilitates the difference between embedding weight matrices via auto-weighted Hilbert-Schmidt Independence Criterion (HSIC) to reduce the redundancy. Moreover, it adaptively learns view-wise importance for embedding to detect noisy views, and mitigates the label imbalance problem by focal loss. Experimental results on four real-world multi-view multi-label datasets demonstrate the effectiveness of the proposed method.
RPL-SVM: Making SVM Robust Against Missing Values and Partial Labels

With increased data availability, data quality is the biggest problem in using AI models. The data may suffer from missing values and noisy values. Another major challenge is to get accurate labels for feature vectors in the training data. In contrast, in many applications, we can only get weak labels (for example, partial labels, positive-unlabeled data, bandit feedback, etc.). In this paper, we consider uncertainty in both features and labels. More specifically, we assume that feature vectors have missing attributes and are only given partial labels. We present a novel second-order cone programming framework to learn robust classifiers that can tolerate uncertainty in the observations of partially labeled multiclass classification problems. The proposed approach, RPL-SVM, is based on a chance-constrained framework. Experimental results show that RPL-SVM efficiently learns multiclass classifiers with missing values in a partial label setting. This demonstrates the remarkable resilience of RPL-SVM to real-world observational uncertainties.
Ancient Chinese Machine Reading Comprehension Exception Question Dataset with a Non-trivial Model

Ancient Chinese Reading Comprehension (ACRC) is challenging for the absence of datasets and the difficulty of understanding ancient languages. Further, among ACRC, entire-span-regarded (Entire spaN regarDed, END) questions are especially exhausting because of the input-length limitation of seminal BERTs, which solve modern-language reading comprehension expeditiously. To alleviate the datasets absence issue, this paper builds a new dataset ACRE (Ancient Chinese Reading-comprehension End-question). To tackle long inputs, this paper proposes a non-trivial model which is based on the convolution of multiple encoders that are BERT decedents, named EVERGREEN (EVidence-first bERt encodinG with entiRE-tExt coNvolution). Besides proving the effectiveness of encoding compressing via convolution, our experimental results also show that, for ACRC, first, neither pre-trained AC language models nor long-text-oriented transformers realize its value; second, the top evidence sentence along with distributed sentences are better than top-n evidence sentences as inputs of EVERGREEN; third, comparing with its variants, including dynamic convolution and multi-scale convolution, classical convolution is the best.
Image Quality Assessment Method Based on Cross-Modal

Deep learning methods have achieved remarkable results in the direction of image quality assessment tasks. However, most of the related studies focus only on image unimodal, ignoring the potential advantages that come with the development of cross-modal techniques. Cross-modal models have implied a wealth of information, which provides new research directions and possibilities in the field of image quality assessment. In this paper, the feasibility of cross-modal models in image quality assessment is first explored for the image quality binary classification task. Subsequently, the optimization prompting method is combined with the tuning of the image encoder in the cross-modal model so that the cross-modal model can be used for image quality assessment scoring. To verify the feasibility of the cross-modal model on the image quality assessment task, an empirical analysis was conducted on the binary image quality dataset PQD, and it was found that the F1 score improved by 18% over the baseline model. Further, we propose an adaptive cross-modal image quality assessment method AC-IQA. On the image quality scoring dataset, compared with the previous optimal methods, AC-IQA improves the PLCC and SROCC metrics on the TID2013 dataset by 5.5% and 9.5%, respectively, and on the KADID dataset by 6.2% and 5.2%.