Reviewerly/exHarmony huggingface dataset:
Findings of the WMT 2020 Shared Task on Chat Translation

We report the results of the first edition of the WMT shared task on chat translation. The task consisted of translating bilingual conversational text, in particular customer support chats for the English-German language pair (English agent, German customer). This task varies from the other translation shared tasks, i.e. news and biomedical, mainly due to the fact that the conversations are bilingual, less planned, more informal, and often ungrammatical. Furthermore, such conversations are usually characterized by shorter and simpler sentences and contain more pronouns. We received 14 submissions from 6 participating teams, all of them covering both directions, i.e. En->De for agent utterances and De->En for customer messages. We used automatic metrics (BLEU and TER) for evaluating the translations of both agent and customer messages and human document-level direct assessments (DDA) to evaluate the agent translations.
Data Security for School Service Top-Up Transactions Based on AES Combination Blockchain Technology

The application of Blockchain technology has begun to be widely accommodated in industrial and business practitioner environments as a safeguard of transaction security so that now including the education sector, non-business institutions enjoy the use of this technology to support the learning process. Information on the protected Blockchain can be in the form of transactions, assets, identities, and other information packaged in digital form. Information is collected in the form of blocks that are interrelated by using the hash function as cryptographic encryption. This research uses Blockchain for online pocket money top-up transactions for students. The use of a centralized Blockchain is centralized to reduce server procurement costs, but to increase the security of transaction information, modification of each block series is carried out using the AES cryptographic approach. The results showed that the attack by inserting a Cross-Site Scripting (XSS) script if you want to know the value of the top-up transaction amount, you must be able to hack the cryptographic process. This is supported by chain validation testing to determine how many block changes have been changed.
Explaining the Efficacy of Counterfactually Augmented Data

In attempts to produce machine learning models less reliant on spurious patterns in training data, researchers have recently proposed generating counterfactually augmented data through a human-in-the-loop process. As applied in NLP, given some documents and their (initial) labels, humans are tasked with revising the text to make a (given) counterfactual label applicable. Importantly, the instructions prohibit edits that are not necessary to flip the applicable label. Models trained on the augmented (original \emph{and} revised) data have been shown to rely less on semantically irrelevant words and to generalize better out of domain. While this work draws on causal thinking, casting edits as interventions and relying on human understanding to assess outcomes, the underlying causal model is not clear nor are the principles underlying the observed improvements in out-of-domain evaluation. In this paper, we explore a toy analog, using linear Gaussian models. Our analysis reveals interesting relationships between causal models, measurement noise, out-of-domain generalization, and reliance on spurious signals. Interestingly our analysis suggests that data corrupted by adding noise to causal features will degrade out-of-domain performance, while noise added to non-causal features may make models more robust out-of-domain. This analysis yields interesting insights that help to explain the efficacy of counterfactually augmented data. Finally, we present a large-scale empirical study that supports this hypothesis.
Single-Shot Depth Sensing With Pseudo Two-Dimensional Sequence Coded Discrete Binary Pattern

Depth sensing with single-shot structured light is widely employed for depth measurement of dynamic scenes. For conventional single-shot structured light techniques, the projected pattern is coded with regards to color, shape, and intensity, aiming at assigning each pixel a unique label. However, the features of the projected pattern are extremely susceptible to the surface texture of the measured object, resulting in errors during performing decoding. In this study, a robust and simple binary pattern is proposed for high-accuracy depth sensing. Only two gray values are utilized in the designed pattern, which is insensitive to environmental noise. The pseudo two-dimensional De Bruijn sequence coding method is employed to reduce the size of the decoding window for robust matching. Short slashes are adopted as features of the pattern that greatly simplifies the feature extraction and pattern decoding. Compared with the Kinect and time-of-flight (TOF) depth cameras, our proposed method can acquire high-precision depth in quantitative and qualitative experiments.
Plain‐language summaries: An essential component to promote knowledge translation

In this era of evidence-based practice, scholarly work such as peer-reviewed scientific publications plays a vital role in policy and decision-making at an individual, organisation and country-level. Alongside being considered an essential means of communicating scholarly work, scientific publications also investigate the specific domains that lack well-established literature and thereby inform scientists and researchers to thrive for the betterment of the publics' well-being. Thus, the main purpose of articulating the scientific, scholarly work should be to make it understandable and accessible to everyone, including the lay audience. However, oftentimes, researchers overlook the lay summaries while publishing the research findings.
Distant learning: challenges and risks of 2020

Abstract This article presents relevance of distance learning technologies in the current stage of public relations evolution owing to the development level of electronic devices for data transfer, processes of economic integration, the need to “socialize” for people with limited mobility, the need for lifelong learning, and changing conditions of interaction during 2020 th pandemic. Based on the analysis of legal as well as organizational and methodological documentation, the available educational experience of general and higher education teachers and our observations, an attempt is made to summarize the challenges of distance learning caused by the imperfection of distance learning regulation, insufficient development of theoretical backgrounds and methodology of distance learning. The mechanism for using ICT technologies in distance learning is also insufficiently developed. This article reveals the decline risks in the quality of education and threats to the health of students in the realization of distance learning. Our own experience in the implementation of distance learning using the “flipped classroom” technology is given.
SGBBA: An Efficient Method for Prediction System in Machine Learning using Imbalance Dataset

A real world big dataset with disproportionate classification is called imbalance dataset which badly impacts the predictive result of machine learning classification algorithms.Most of the datasets faces the class imbalance problem in machine learning.Most of the algorithms in machine learning work perfectly with about equal samples counts for every class.A variety of solutions have been suggested in the past time by the different researchers and applied to deal with the imbalance dataset.The performance of these methods is lower than the satisfactory level.It is very difficult to design an efficient method using machine learning algorithms without making the imbalance dataset to balance dataset.In this paper we have designed an method named SGBBA: an efficient method for prediction system in machine learning using Imbalance dataset.The method that is addressed in this paper increases the performance to the maximum in terms of accuracy and confusion matrix.The proposed method is consisted of two modules such as designing the method and method based prediction.The experiments with two benchmark datasets and one highly imbalanced credit card datasets are performed and the performances are compared with the performance of SMOTE resampling method.F-score, specificity, precision and recall are used as the evaluation matrices to test the performance of the proposed method in terms of any kind of imbalance dataset.According to the comparison of the result of the proposed method computationally attains the effective and robust performance than the existing methods.
Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning From Label Fuzzy Proportions

The presence of necrosis is associated with tumor progression and patient outcomes in many cancers, but existing analyses rarely adopt quantitative methods because the manual quantification of histopathological features is too expensive. We aim to accurately identify necrotic regions on hematoxylin and eosin (HE)-stained slides and to calculate the ratio of necrosis with minimal annotations on the images. An adaptive method named Learning from Label Fuzzy Proportions (LLFP) was introduced to histopathological image analysis. Two datasets of liver cancer HE slides were collected to verify the feasibility of the method by training on the internal set using cross validation and performing validation on the external set, along with ensemble learning to improve performance. The models from cross validation performed relatively stably in identifying necrosis, with a Concordance Index of the Slide Necrosis Score (CISNS) of 0.9165±0.0089 in the internal test set. The integration model improved the CISNS to 0.9341 and achieved a CISNS of 0.8278 on the external set. There were significant differences in survival (p = 0.0060) between the three groups divided according to the calculated necrosis ratio. The proposed method can build an integration model good at distinguishing necrosis and capable of clinical assistance as an automatic tool to stratify patients with different risks or as a cluster tool for the quantification of histopathological features. We presented a method effective for identifying histopathological features and suggested that the extent of necrosis, especially micronecrosis, in liver cancer is related to patient outcomes.
Artificial Intelligence and Intellectual Property Law - Position Statement of the Max Planck Institute for Innovation and Competition of 9 April 2021 on the Current Debate

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Machine Learning Classifiers Based Classification For IRIS Recognition

Classification is the most widely applied machine learning problem today, with implementations in face recognition, flower classification, clustering, and other fields. The goal of this paper is to organize and identify a set of data objects. The study employs K-nearest neighbors, decision tree (j48), and random forest algorithms, and then compares their performance using the IRIS dataset. The results of the comparison analysis showed that the K-nearest neighbors outperformed the other classifiers. Also, the random forest classifier worked better than the decision tree (j48). Finally, the best result obtained by this study is 100% and there is no error rate for the classifier that was obtained.
Convergence and Accuracy Trade-Offs in Federated Learning and Meta-Learning

We study a family of algorithms, which we refer to as local update methods, generalizing many federated and meta-learning algorithms. We prove that for quadratic models, local update methods are equivalent to first-order optimization on a surrogate loss we exactly characterize. Moreover, fundamental algorithmic choices (such as learning rates) explicitly govern a trade-off between the condition number of the surrogate loss and its alignment with the true loss. We derive novel convergence rates showcasing these trade-offs and highlight their importance in communication-limited settings. Using these insights, we are able to compare local update methods based on their convergence/accuracy trade-off, not just their convergence to critical points of the empirical loss. Our results shed new light on a broad range of phenomena, including the efficacy of server momentum in federated learning and the impact of proximal client updates.
Transformational Educational Leadership and the Innovative Strategies Engaging Online Faculty for the Excellent Teaching Performance in the United States

This research paper summarizes the arguments and counterarguments within the scientific discussion on the transformational educational leadership and the innovative strategies engaging online faculty for the excellent teaching performance in the United States. There are various leadership traits of academic leadership to assess engagement amongst online adjunct faculty in the online platform of higher education. This research study focuses on the lived experiences of academic leaders to aid online adjunct faculty in trying to increase the sense of inclusion, engagement, and support. Applying the lived experiences of academic leader could retrieve elements within online adjunct faculty so they can have a positive contribution towards the institution. Support is needed to improve engagement, to assist adequate academic leadership. Systematization of the literary sources and approaches for solving the problem the academic leadership indicates that incorporate individualized strategies when the leader recognizes the value online adjunct faculty bring to their institutions. The relevance of this scientific problem decision is that the themes within this article breakdown the vital impact leaders have with their online adjunct faculty. The wide-ranging factors discussed in this article were categorized to demonstrate the critical components that leadership strategies if implemented effectively, could aid in an engagement component and inclusion for online adjunct faculty. Factors that are vital for academic leadership could be the use of leadership strategies to promote engagement and inclusion within the online environment outlined in this scientific article. The research paper presents the results of an empirical analysis all strategies incorporated in finding the material, history of online education, remote learning environment, leadership traits, leadership support for remote, adjunct faculty, leadership characteristic inactive online adjunct faculty engagement, leadership challenges, authentic relationship, benefits of engagement, communication obstacles on the virtual platforms, and theories within leadership presented a more in-depth understanding on the topic of online adjunct faculty engagement and inclusion. The results of the research can be useful by all academic leaders from all undergraduate, graduate, post-graduate level around the world.
Geometrically Enriched Latent Spaces

A common assumption in generative models is that the generator immerses the latent space into a Euclidean ambient space. Instead, we consider the ambient space to be a Riemannian manifold, which allows for encoding domain knowledge through the associated Riemannian metric. Shortest paths can then be defined accordingly in the latent space to both follow the learned manifold and respect the ambient geometry. Through careful design of the ambient metric we can ensure that shortest paths are well-behaved even for deterministic generators that otherwise would exhibit a misleading bias. Experimentally we show that our approach improves interpretability of learned representations both using stochastic and deterministic generators.
Adaptive ensemble perception tracking

Recently, tracking models based on bounding box regression (such as region proposal networks), built on the Siamese network, have attracted much attention. Despite their promising performance, these trackers are less effective in perceiving the target information in the following two aspects. First, existing regression models cannot take a global view of a large-scale target since the effective receptive field of a neuron is too small to cover the target with a large scale. Second, the neurons with a fixed receptive field (RF) size in these models cannot adapt to the scale and aspect ratio changes of the target. In this paper, we propose an adaptive ensemble perception tracking framework to address these issues. Specifically, we first construct a per-pixel prediction model, which predicts the target state at each pixel of the correlated feature. On top of the per-pixel prediction model, we then develop a confidence-guided ensemble prediction mechanism. The ensemble mechanism adaptively fuses the predictions of multiple pixels with the guidance of confidence maps, which enlarges the perception range and enhances the adaptive perception ability at the object-level. In addition, we introduce a receptive field adaption model to enhance the adaptive perception ability at the neuron-level, which adjusts the RF by adaptively integrating the features with different RFs. Extensive experimental results on the VOT2018, VOT2016, UAV123, LaSOT, and TC128 datasets demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods in terms of accuracy and speed.
Exact classical noise master equations: Applications and connections

Abstract All quantum systems are subject to noise and imperfections due to stray fields, inhomogeneities or drifting experimental controls. An understanding of the effects of noise and decoherence is critical to the progress towards fully functional quantum devices. In this perspective, we focus on noise in quantum systems which are modelled by a dynamic stochastic parameter in the Hamiltonian. We will outline exact evolution equations describing the ensemble average dynamics for a variety of common noise types and their connections. We will also highlight an approximate evolution equation valid in the weak noise limit for an arbitrary classical stochastic field. This framework should serve as a starting point for identifying signatures of differing noise types and optimisation of robust control protocols.
Multi-Scale Squeeze U-SegNet with Multi Global Attention for Brain MRI Segmentation

In this paper, we propose a multi-scale feature extraction with novel attention-based convolutional learning using the U-SegNet architecture to achieve segmentation of brain tissue from a magnetic resonance image (MRI). Although convolutional neural networks (CNNs) show enormous growth in medical image segmentation, there are some drawbacks with the conventional CNN models. In particular, the conventional use of encoder-decoder approaches leads to the extraction of similar low-level features multiple times, causing redundant use of information. Moreover, due to inefficient modeling of long-range dependencies, each semantic class is likely to be associated with non-accurate discriminative feature representations, resulting in low accuracy of segmentation. The proposed global attention module refines the feature extraction and improves the representational power of the convolutional neural network. Moreover, the attention-based multi-scale fusion strategy can integrate local features with their corresponding global dependencies. The integration of fire modules in both the encoder and decoder paths can significantly reduce the computational complexity owing to fewer model parameters. The proposed method was evaluated on publicly accessible datasets for brain tissue segmentation. The experimental results show that our proposed model achieves segmentation accuracies of 94.81% for cerebrospinal fluid (CSF), 95.54% for gray matter (GM), and 96.33% for white matter (WM) with a noticeably reduced number of learnable parameters. Our study shows better segmentation performance, improving the prediction accuracy by 2.5% in terms of dice similarity index while achieving a 4.5 times reduction in the number of learnable parameters compared to previously developed U-SegNet based segmentation approaches. This demonstrates that the proposed approach can achieve reliable and precise automatic segmentation of brain MRI images.
Motion Direction Inconsistency-Based Fight Detection for Multiview Surveillance Videos

Nowadays, with the increasing number of surveillance cameras, human behavior detection is of importance for public security. Detection of fight behavior using video surveillance is an essential and challenging research field. We propose a multiview fight detection method based on statistical characteristics of the optical flow and random forest. Cyberphysical systems for monitoring can obtain timely and accurate information from this method. Two novel descriptors named Motion Direction Inconsistency (MoDI) and Weighted Motion Direction Inconsistency (WMoDI) are defined to improve the performance of existing methods for videos with different shooting views and solve the misjudgment on nonfight, such as running and talking. First, YOLO V3 algorithm is applied to mark the motion areas, and then, the optical flow is computed to extract descriptors. Finally, Random Forest is used for classification based on statistical characteristics of descriptors. The evaluation results on CASIA dataset demonstrate that the proposed method can improve the accuracy and reduce the rate of missing alarm and false alarm for the detection, and it is very robust against videos with different shooting views.
M[eye]cro

We present M[eye]cro an interaction technique to select on-screen objects and navigate menus through the synergistic use of eye-gaze and thumb-to-finger microgestures. Thumb-to-finger microgestures are gestures performed with the thumb of a hand onto the fingers of the same hand. The active body of research on microgestures highlights expected properties including speed, availability and eye-free interaction. Such properties make microgestures a good candidate for multitasking. However, while praised, the state-of-the-art hypothesis stating that microgestures could be beneficial for multitasking has never been quantitatively verified. We study and compare M[eye]cro to a baseline, i.e., a technique based on physical controllers, in a cockpit-based context. This context allows us to design a controlled experiment involving multitasking with low- and high-priority tasks in parallel. Our results show that performances of the two techniques are similar when participants only perform the selection task. However, M[eye]cro tends to yield better time performance when participants additionally need to treat high-priority tasks in parallel. Results also show that M[eye]cro induces less fatigue and is mostly preferred.