Reviewerly/exHarmony huggingface dataset:
Terminology as a source of difficulty in translating international legal discourses: an empirical cross-genre study

Abstract Despite the persistent focus on terminology in legal translation studies, to date, no large-scale research has empirically explored the difficulty of terminology in translating legal genres. Approaches to translation difficulty in translation studies more broadly remain limited in scope. To fill this gap, a study was conducted to measure the difficulty associated with the translation of legal terminology and phraseology, as well as with terminology of other domains, in the LETRINT 1+ corpus, including nine representative genres of three institutional settings (the European Union, the United Nations and the World Trade Organization). For comparative purposes, four levels of translation difficulty were assigned to multiple terminological features by a group of specialized translators through a consensus-building process of annotation based on the cognitive effort estimated for translation decision-making. The difficulty scores obtained confirm the correlation between legal singularity and higher translation difficulty, as well as the connection of more commonly used legal terms and phrasemes, and core economic terms, with lower difficulty levels. The findings also provide evidence of the prominence of non-legal specialized terminology in institutional legal discourses, and the aggregate terminological difficulty levels of each genre examined, which can be particularly useful for informing translation quality assurance, project management and translator training.
A Natural Language Processing and deep learning based model for automated vehicle diagnostics using free-text customer service reports

Initial fault detection and diagnostics are imperative measures to improve the efficiency, safety, and stability of vehicle operation. In recent years, numerous studies have investigated data-driven approaches to improve the vehicle diagnostics process using available vehicle data. Moreover, data-driven methods are employed to enhance customer-service agent interactions. In this study, we demonstrate a machine learning pipeline to improve automated vehicle diagnostics. First, Natural Language Processing (NLP) is used to automate the extraction of crucial information from free-text failure reports (generated during customers' calls to the service department). Then, deep learning algorithms are employed to validate service requests and filter vague or misleading claims. Ultimately, different classification algorithms are implemented to classify service requests so that valid service requests can be directed to the relevant service department. The proposed model – Bidirectional Long Short Term Memory (BiLSTM) along with Convolution Neural Network (CNN) – shows more than 18% accuracy improvement in validating service requests compared to technicians' capabilities. In addition, using domain-based NLP techniques at preprocessing and feature extraction stages along with CNN-BiLSTM based request validation enhanced the accuracy (>25%), sensitivity (>39%), specificity (>11%), and precision (>11%) of Gradient Tree Boosting (GTB) service classification model. The Receiver Operating Characteristic Area Under the Curve (ROC-AUC) reached 0.82.
Usability study of a user-friendly AR assembly assistance

The Augmented Reality (AR) technology is proven suitable to support manual assembly activities. However, existing applications are hardly adapted to the users. To improve AR assembly assistance, we developed a user-friendly and intuitive user interface that was designed for specific user groups and validated in a usability study. The validation took place in a facility for handicapped people and in a manufacturing company. It confirms the intuitive and easy use of the elaborated user interface and demonstrates the importance of its user group's specific design.
FlexiNet: Fast and Accurate Vehicle Detection for Autonomous Vehicles

Autonomous vehicle has come to reach on the road; however accurate road perception in real-time is one of the crucial factors towards its success. The greatest challenge in this direction includes occlusion, truncation, lighting conditions, and complex backgrounds. In order to improve the accuracy and detection speed of vehicle detection, a dynamic scaling network is proposed that assists in constructing a balanced shape neural network to achieve optimum accuracy with minimal hardware. The net architecture is influenced by YOLOv5 and is composed of Cross-Stage Partial Network (CSPNet) as its backbone. In order to go even further, we have proposed an auto-anchor generating method that makes the network suitable for any datasets. Our neural network is fine-tuned by using activation, loss, and optimization functions so as to get the optimum results. Our experimental results demonstrate that the proposed net provides comparable performance of YOLOv4 and Faster R-CNN based on KITTI dataset as the benchmark.
Target-Aware State Estimation for Visual Tracking

Trackers based on the IoU prediction network (IoU-Net) have shown superior performance, which refines a coarse bounding box to an accurate one by maximizing the IoU between the target and the coarse box. However, the traditional IoU-Net is less effective in exploiting the limited but crucial supervision information contained in the initial frame, including the discriminative information between the target and backgrounds and the structure information of the initial target. Missing such information makes the IoU-Net less robust to background distractors and diverse variations of the target appearance. To address this issue, we propose a target-aware state estimation network for visual tracking. A gradient-guided feature adjustment module is built on an online discriminative model to generate target-aware features for constructing the state estimation network; it conveys the online learned discriminative information into the offline trained state estimation network. In addition, we propose a structure-aware integration module and embed it into the state estimation network, enabling the tracker to explicitly model the structure information of the initial target. Extensive experimental results on the VOT2018, OTB2015, UAV123, NFS30, TC128, TrackingNet, LaSOT, and VOT2018-LT datasets demonstrate that the proposed approach performs favorably against state-of-the-art trackers.
Realizing GANs via a Tunable Loss Function

We introduce a tunable GAN, called $\alpha$-GAN, parameterized by $\alpha\in$(0, $\infty$], which interpolates between various f-GANs and Integral Probability Metric based GANs (under constrained discriminator set). We construct $\alpha-$ GAN using a supervised loss function, namely, $\alpha-$ loss, which is a tunable loss function capturing several canonical losses. We show that $\alpha-$ GAN is intimately related to the Arimoto divergence, which was first proposed by Österriecher (1996), and later studied by Liese and Vajda (2006). We posit that the holistic understanding that $\alpha-$ GAN introduces will have practical benefits of addressing both the issues of vanishing gradients and mode collapses.
Selective ensemble of classifiers trained on selective samples

Classifier ensembles are characterized by the high quality of classification, thanks to their generalizing ability. Most existing ensemble algorithms use all learning samples to learn the base classifiers that may negatively impact the ensemble’s diversity. Also, the existing ensemble pruning algorithms often return suboptimal solutions that are biased by the selection criteria. In this work, we present a proposal to alleviate these drawbacks. We employ an instance selection method to query a reduced training set that reduces both the space complexity of the formed ensemble members and the time complexity to classify an instance. Additionally, we propose a guided search-based pruning schema that perfectly explores large-size ensembles and brings on a near-optimal subensemble with less computational requirements in reduced memory space and improved prediction time. We show experimentally how the proposed method could be an alternative to large-size ensembles. We demonstrate how to form less-complex, small-size, and high-accurate ensembles through our proposal. Experiments on 25 datasets show that the proposed method can produce effective ensembles better than Random Forest and baseline classifier pruning methods. Moreover, our proposition is comparable with the Extreme Gradient Boosting Algorithm in terms of accuracy.
Blockchain for Cybersecurity: Systematic Literature Review and Classification

Blockchain has transitioned beyond the hype to reality, as evidenced by the amount of research it has attracted and by its commercial applications. One popular application of blockchain is in cybersecurity, which is the focus of this paper. Specifically, we performed a systematic literature review of blockchain use cases for cybersecurity, while focusing on articles published over the past decade. Based on our analysis of 111 articles, we developed a classification framework using the thematic analysis approach. This classification framework is designed to offer readers a comprehensive perspective of the potential of blockchain to enhance cybersecurity in different contexts. The findings have implications for research and practice.
Blockchain consensus mechanism based on quantum zero-knowledge proof

Referring to the consensus mechanisms of traditional blockchain, a new consensus mechanism of quantum blockchain is designed based on the randomness and the irreversibility of quantum measurement and quantum zero-knowledge proof. This new consensus mechanism does not involve a great deal of computing resources and hence has fewer energy consumption, shorter time delay and higher throughput. Furthermore, the new consensus mechanism could withstand 51% attack.
PEERAssist: Leveraging on Paper-Review Interactions to Predict Peer Review Decisions

Peer review is the widely accepted method of research validation. However, with the deluge of research paper submissions accompanied with the rising number venues, the paper vetting system has come under a lot of stress. Problems like dearth of adequate reviewers, finding appropriate expert reviewers, maintaining the quality of the reviews are steadily and strongly surfacing up. To ease the peer review workload to some extent, here we investigate how an Artificial Intelligence (AI)-powered review system would look like. We leverage on the paper-review interaction to predict the decision in the reviewing process. We do not envisage an AI reviewing papers in the near-future, but seek to explore a human-AI collaboration in the decision-making process where the AI would leverage on the human-written reviews and paper full-text to predict the fate of the paper. The idea is to have an assistive decision-making tool for the chairs/editors to help them with an additional layer of confidence, especially with borderline and contrastive reviews. We use cross-attention between the review text and paper full-text to learn the interactions and henceforth generate the decision. We also make use of sentiment information encoded within peer-review texts to guide the outcome. Our initial results show encouraging performance on a dataset of paper+peer reviews curated from the ICLR openreviews. We make our codes and dataset (https://github.com/PrabhatkrBharti/PEERAssist) public for further explorations. We re-iterate that we are in an early stage of investigation and showcase our initial exciting results to justify our proposition.
Cross-view Self-supervised Learning via Momentum Statistics in Batch Normalization

A novel cross-view self-supervised learning (CVSSL) method via momentum statistics in batch normalization is presented in this paper. The problem of accuracy degradation in small-batch cases is currently common in self-supervised learning. Our method introduces the cross-view loss and the momentum statistics in batch normalization to solve the accuracy degradation problem in small-batch cases. Experimental results show that our method can drastically outperform the state-of-the-art self-supervised learning method in small-batch cases on the STL-10 dataset.
Close latency-security trade-off for the Nakamoto consensus

Bitcoin is a peer-to-peer electronic cash system invented by Nakamoto in 2008. While it has attracted much research interest, its exact latency and security properties remain open. Existing analyses provide security and latency (or confirmation time) guarantees that are too loose for practical use. In fact the best known upper bounds are several orders of magnitude larger than a lower bound due to a well-known private-mining attack. This paper describes a continuous-time model for blockchains and develops a rigorous analysis that yields close upper and lower bounds for the latency-security trade-off. For example, when the adversary controls 10% of the total mining power and the block propagation delays are within 10 seconds, a Bitcoin block is secured with less than 10-3 error probability if it is confirmed after four hours, or with less than 10-9 error probability if confirmed after ten hours. These confirmation times are about two hours away from their corresponding lower bounds. To establish such close bounds, the blockchain security question is reduced to a race between the Poisson adversarial mining process and a renewal process formed by a certain species of honest blocks. The moment generation functions of relevant renewal times are derived in closed form. The general formulas from the analysis are then applied to study the latency-security trade-off of several well-known proof-of-work longest-chain cryptocurrencies. Guidance is also provided on how to set parameters for different purposes.
A Survey of Instructional Approaches in the Requirements Engineering Education Literature

Requirements engineering (RE) has established itself as a core software engineering discipline. It is well acknowledged that good RE leads to higher quality software and considerably reduces the risk of failure or exceeding budgets of software development projects. Therefore, it is of vital importance to train future software engineers in RE and educate future requirements engineers to adequately manage requirements in various projects. However, to date there exists no central concept of what the most useful educational approaches are in RE education in order to best interweave theory with practice. To lay the foundation for this important mission, we conducted a systematic literature review. In this paper, we report on the results and provide a synthesis of instructional approaches in RE education. Findings show that experiential learning through projects, collaboration, and realistic stakeholder involvement are among the most promising trends to teach both RE theory and develop student soft skills.
Diagnosing post-traumatic stress disorder using electronic medical record data

This study proposes a predictive model that uses structured data and unstructured narrative notes from Electronic Medical Records to accurately identify patients diagnosed with Post-Traumatic Stress Disorder (PTSD). We utilize data from primary care clinicians participating in the Manitoba Primary Care Research Network (MaPCReN) representing 154,118 patients. A reference sample of 195 patients that had their PTSD diagnosis confirmed using a manual chart review of structured data and narrative notes, and PTSD negative patients is used as the gold standard data for model training, validation and testing. We assess structured and unstructured data from eight tables in the MaPCReN namely, patient demographics, disease case, examinations, medication, billing records, health condition, risk factors, and encounter notes. Feature engineering is applied to convert data into proper representation for predictive modeling. We explore serial and parallel mixed data models that are trained on both structured and unstructured data to identify PTSD. Model performances were calculated based on a highly skewed hold-out test dataset. The serial model that uses both structured and text data as input, yielded the highest values in sensitivity (0.77), F-measure (0.76), and AUC (0.88) and the parallel model that uses both structured and text data as the input obtained the highest positive predicted value (PPV) (0.75). Diseases such as PTSD are difficult to diagnose. Information recorded in the chart note over multiple visits of the patients with the primary care physicians has higher predictive power than structured data and combining these two data types can increase the predictive capabilities of machine learning models in diagnosing PTSD. While the deep-learning model outperformed the traditional ensemble model in processing text data, the ensemble classifier obtained better results in ingesting a combination of features obtained from both data types in the serial mixed model. The study demonstrated that unstructured encounter notes enhance a model's ability to identify patients diagnosed with PTSD. These findings can enhance quality improvement, research, and disease surveillance related to PTSD in primary care populations.
Convergence-Guaranteed Trajectory Planning for a Class of Nonlinear Systems With Nonconvex State Constraints

In this article, we study the problem of trajectory planning for a class of nonlinear systems with convex state/control constraints and nonconvex state constraints with concave constraint functions. This corresponds to a challenging nonconvex optimal control problem. We present how to convexify the nonlinear dynamics without any approximation via a combination of variable redefinition and relaxation. We then prove that the relaxation is exact by designing an appropriate objective function. This exact relaxation result enables us to further convexify the nonconvex state constraints simply by linearization. As a result, an algorithm is designed to iteratively solve the obtained convex optimization problems until convergence to get a solution of the original problem. A unique feature of the proposed approach is that the algorithm is proved to converge and it does not rely on any trust-region constraint. High performance of the algorithm is demonstrated by its application to trajectory planning of UAVs and autonomous cars with obstacle avoidance requirements.
TiWS-iForest: Isolation forest in weakly supervised and tiny ML scenarios

Unsupervised anomaly detection tackles the problem of finding anomalies inside datasets without the labels availability; since data tagging is typically hard or expensive to obtain, such approaches have seen huge applicability in recent years. In this context, Isolation Forest is a popular algorithm able to define an anomaly score by means of an ensemble of peculiar trees called isolation trees. These are built using a random partitioning procedure that is extremely fast and cheap to train. However, we find that the standard algorithm might be improved in terms of memory requirements, latency and performances; this is of particular importance in low resources scenarios and in TinyML implementations on ultra-constrained microprocessors. Moreover, Anomaly Detection approaches currently do not take advantage of weak supervisions: being typically consumed in Decision Support Systems, feedback from the users, even if rare, can be a valuable source of information that is currently unexplored. Beside showing iForest training limitations, we propose here TiWS-iForest, an approach that, by leveraging weak supervision is able to reduce Isolation Forest complexity and to enhance detection performances. We showed the effectiveness of TiWS-iForest on real word datasets and we share the code in a public repository to enhance reproducibility.
Model-based clustering via skewed matrix-variate cluster-weighted models

Cluster-weighted models (CWMs) extend finite mixtures of regressions (FMRs) in order to allow the distribution of covariates to contribute to the clustering process. In this article, we introduce 24 matrix-variate CWMs which are obtained by allowing both the responses and covariates in each cluster to be modelled by one of four existing skewed matrix-variate distributions or the matrix-variate normal distribution. Endowed with greater flexibility, our matrix-variate CWMs are able to handle this kind of data in a more suitable manner. As a by-product, the four skewed matrix-variate FMRs are also introduced. Maximum likelihood parameter estimates are derived using an expectation-conditional maximization algorithm. Parameter recovery, classification assessment, and the capability of the Bayesian information criterion to detect the underlying groups are investigated using simulated data. Lastly, our matrix-variate CWMs, along with the matrix-variate normal CWM and matrix-variate FMRs, are applied to two real datasets for illustrative purposes.
Rethinking formal models of partially observable multiagent decision making

Multiagent decision-making in partially observable environments is usually modelled as either an extensive-form game (EFG) in game theory or a partially observable stochastic game (POSG) in multiagent reinforcement learning (MARL). One issue with the current situation is that while most practical problems can be modelled in both formalisms, the relationship of the two models is unclear, which hinders the transfer of ideas between the two communities. A second issue is that while EFGs have recently seen significant algorithmic progress, their classical formalization is unsuitable for efficient presentation of the underlying ideas, such as those around decomposition. To solve the first issue, we introduce factored-observation stochastic games (FOSGs), a minor modification of the POSG formalism which distinguishes between private and public observation and thereby greatly simplifies decomposition. To remedy the second issue, we show that FOSGs and POSGs are naturally connected to EFGs: by “unrolling” a FOSG into its tree form, we obtain an EFG. Conversely, any perfect-recall timeable EFG corresponds to some underlying FOSG in this manner. Moreover, this relationship justifies several minor modifications to the classical EFG formalization that recently appeared as an implicit response to the model's issues with decomposition. Finally, we illustrate the transfer of ideas between EFGs and MARL by presenting three key EFG techniques – counterfactual regret minimization, sequence form, and decomposition – in the FOSG framework.
Time Series Model Attribution Visualizations as Explanations

Attributions are a common local explanation technique for deep learning models on single samples as they are easily extractable and demonstrate the relevance of input values. In many cases, heatmaps visualize such attributions for samples, for instance, on images. However, heatmaps are not always the ideal visualization to explain certain model decisions for other data types. In this review, we focus on attribution visualizations for time series. We collect attribution heatmap visualizations and some alternatives, discuss the advantages as well as disadvantages and give a short position towards future opportunities for attributions and explanations for time series.