Reviewerly/exHarmony huggingface dataset:
Continual Learning for Neural Machine Translation

Neural machine translation (NMT) models are data-driven and require large-scale training corpus.In practical applications, NMT models are usually trained on a general domain corpus and then fine-tuned by continuing training on the in-domain corpus.However, this bears the risk of catastrophic forgetting that the performance on the general domain is decreased drastically.In this work, we propose a new continual learning framework for NMT models.We consider a scenario where the training is comprised of multiple stages and propose a dynamic knowledge distillation technique to alleviate the problem of catastrophic forgetting systematically.We also find that the bias exists in the output linear projection when fine-tuning on the in-domain corpus, and propose a bias-correction module to eliminate the bias.We conduct experiments on three representative settings of NMT application.Experimental results show that the proposed method achieves superior performance compared to baseline models in all settings. 1
Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection

The existence of multiple datasets for sarcasm detection prompts us to apply transfer learning to exploit their commonality.The adversarial neural transfer (ANT) framework utilizes multiple loss terms that encourage the source-domain and the target-domain feature distributions to be similar while optimizing for domain-specific performance.However, these objectives may be in conflict, which can lead to optimization difficulties and sometimes diminished transfer.We propose a generalized latent optimization strategy that allows different losses to accommodate each other and improves training dynamics.The proposed method outperforms transfer learning and meta-learning baselines.In particular, we achieve 10.02% absolute performance gain over the previous state of the art on the iSarcasm dataset.
robROSE: A robust approach for dealing with imbalanced data in fraud detection

A major challenge when trying to detect fraud is that the fraudulent activities form a minority class which make up a very small proportion of the data set. In most data sets, fraud occurs in typically less than $$0.5\%$$ of the cases. Detecting fraud in such a highly imbalanced data set typically leads to predictions that favor the majority group, causing fraud to remain undetected. We discuss some popular oversampling techniques that solve the problem of imbalanced data by creating synthetic samples that mimic the minority class. A frequent problem when analyzing real data is the presence of anomalies or outliers. When such atypical observations are present in the data, most oversampling techniques are prone to create synthetic samples that distort the detection algorithm and spoil the resulting analysis. A useful tool for anomaly detection is robust statistics, which aims to find the outliers by first fitting the majority of the data and then flagging data observations that deviate from it. In this paper, we present a robust version of ROSE, called robROSE, which combines several promising approaches to cope simultaneously with the problem of imbalanced data and the presence of outliers. The proposed method achieves to enhance the presence of the fraud cases while ignoring anomalies. The good performance of our new sampling technique is illustrated on simulated and real data sets and it is shown that robROSE can provide better insight in the structure of the data. The source code of the robROSE algorithm is made freely available.
Analytical Study on Performance of Cloud Computing with Respect to Data Security

The new technology of computer-oriented services is cloud computing. Cloud computing is becoming increasingly common and continues to expand due to rapid growth in the “cloud computing” sector. It makes major safety issues in big organizations more cost-effective since they pool valuable resources. As a result of the growing demand for more clouds and data, many security problems such as confidentiality, integrity and authentication could emerge in an open setting. These innovations have transformed the shape of conventional IT. This technology provides numerous advantages to IT companies, despite being challenged to meet their maturity. This paper has been debated Data security and privacy concerns are critical for both cloud architecture hardware and applications. The algorithm preserves confidential data across the cloud through encryption and decryption. This algorithm may be used for improving data confidentiality in the results assessment.
METHOD FOR OPTIMIZING OF MOBILE ROBOT TRAJECTORY IN REPELLER SOURCES FIELD

The article discusses the procedure for correcting the trajectory of a robotic platform (RTP) on a plane in order to reduce the probability of its defeat/detection in the field of a finite number of repeller sources. Each of these sources is described by a mathematical model of some factor of counteraction to the RTP. This procedure is based, on the one hand, on the concept of a characteristic probability function of a system of repeller sources, which allows us to assess the degree of influence of these sources on the moving RTP. From this concept follows the probability of its successful completion used here as a criterion for optimizing the target trajectory. On the other hand, this procedure is based on solving local optimization problems that allow you to correct individual sections of the initial trajectory, taking into account the location of specific repeller sources with specified parameters in their vicinity. Each of these sources is characterized by the potential, frequency of impact, radius of action, and parameters of the field decay. The trajectory is adjusted iteratively and takes into account the target value of the probability of passing. The main restriction on the variation of the original trajectory is the maximum allowable deviation of the changed trajectory from the original one. If there is no such restriction, then the task may lose its meaning, because then you can select an area that covers all obstacles and sources, and bypass it around the perimeter. Therefore, we search for a local extremum that corresponds to an acceptable curve in the sense of the specified restriction. The iterative procedure proposed in this paper allows us to search for the corresponding local maxima of the probability of RTP passage in the field of several randomly located and oriented sources, in some neighborhood of the initial trajectory. First, the problem of trajectory optimization is set and solved under the condition of movement in the field of single source with the scope in the form of a circular sector, then the result is extended to the case of several similar sources. The main problem of the study is the choice of the General form of the functional at each point of the initial curve, as well as its adjustment coefficients. It is shown that the selection of these coefficients is an adaptive procedure, the input variables of which are characteristic geometric values describing the current trajectory in the source field. Standard median smoothing procedures are used to eliminate oscillations that occur as a result of the locality of the proposed procedure. The simulation results show the high efficiency of the proposed procedure for correcting the previously planned trajectory.
Highly Efficient Knowledge Graph Embedding Learning with Orthogonal Procrustes Analysis

Knowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications.However, existing studies focus on improving the final model performance without acknowledging the computational cost of the proposed approaches, in terms of execution time and environmental impact.This paper proposes a simple yet effective KGE framework which can reduce the training time and carbon footprint by orders of magnitudes compared with state-of-the-art approaches, while producing competitive performance.We highlight three technical innovations: full batch learning via relational matrices, closed-form Orthogonal Procrustes Analysis for KGEs, and non-negative-sampling training.In addition, as the first KGE method whose entity embeddings also store full relation information, our trained models encode rich semantics and are highly interpretable.Comprehensive experiments and ablation studies involving 13 strong baselines and two standard datasets verify the effectiveness and efficiency of our algorithm.
What the foundations of quantum computer science teach us about chemistry

With the rapid development of quantum technology, one of the leading applications that has been identified is the simulation of chemistry. Interestingly, even before full scale quantum computers are available, quantum computer science has exhibited a remarkable string of results that directly impact what is possible in a chemical simulation with any computer. Some of these results even impact our understanding of chemistry in the real world. In this Perspective, we take the position that direct chemical simulation is best understood as a digital experiment. While on the one hand, this clarifies the power of quantum computers to extend our reach, it also shows us the limitations of taking such an approach too directly. Leveraging results that quantum computers cannot outpace the physical world, we build to the controversial stance that some chemical problems are best viewed as problems for which no algorithm can deliver their solution, in general, known in computer science as undecidable problems. This has implications for the predictive power of thermodynamic models and topics such as the ergodic hypothesis. However, we argue that this Perspective is not defeatist but rather helps shed light on the success of existing chemical models such as transition state theory, molecular orbital theory, and thermodynamics as models that benefit from data. We contextualize recent results, showing that data-augmented models are a more powerful rote simulation. These results help us appreciate the success of traditional chemical theory and anticipate new models learned from experimental data. Not only can quantum computers provide data for such models, but they can also extend the class and power of models that utilize data in fundamental ways. These discussions culminate in speculation on new ways for quantum computing and chemistry to interact and our perspective on the eventual roles of quantum computers in the future of chemistry.
Heterogeneous Relational Graph Neural Networks with Adaptive Objective for End-to-End Task-Oriented Dialogue

End-to-end task-oriented dialogue systems, which provide a natural and informative way for human–computer interaction, are gaining more and more attention. The main challenge of such dialogue systems is how to effectively incorporate external knowledge bases into the learning framework. However, existing approaches usually overlook the natural graph structure information in the knowledge base and the relevant information between the knowledge base and the dialogue history, which makes them deficient in handling the above challenge. Besides, existing methods ignore the entity imbalance problem and treat different entities in system responses indiscriminately, which limits the learning of hard target entities. To address the two challenges, we propose Heterogeneous Relational Graph Neural Networks with Adaptive Objective (HRGNN-AO) for end-to-end task-oriented dialogue systems. In the method, we explore effective heterogeneous relational graphs to jointly capture multi-perspective graph structure information from the knowledge base and the dialogue history, which ultimately facilitates the generation of informative responses. Moreover, we design two components, shared-private parameterization and hierarchical attention mechanism, to solve the overfitting and confusion problems in the heterogeneous relational graph, respectively. To handle the entity imbalance problem, we propose an adaptive objective, which dynamically adjusts the weights of different target entities during the training process. The experimental results show that HRGNN-AO is effective in generating informative responses and outperforms state-of-the-art dialogue systems on the SMD and extended Multi-WOZ 2.1 datasets.
Design, Validation and Implementation of a Questionnaire to Assess Teenagers’ Digital Competence in the Area of Communication in Digital Environments

This article describes the process of design, validation, and implementation (N = 609) of a questionnaire drawn up ad hoc to assess the digital competence of compulsory education students (ages 11 to 13) in the area of communication. The test measures students’ knowledge, skills, and attitudes in the six competences that make up the area of communication, as established in the Framework for the Development and Knowledge of Digital Competence in Europe (DigComp): interacting through new technologies, sharing of information and content, online citizen participation, collaboration through digital technologies, netiquette, and digital identity management. The purposes of the study are to design and validate an instrument to assess compulsory education students’ digital competences in the area of communication based on their knowledge, skills, and attitudes and to analyse such instrument’s psychometric characteristics with special emphasis on its reliability and validity. The method used consisted of the implementation of various psychometric validation techniques and the analysis of the results based on statistical descriptions. Items show adequate discrimination and difficulty indices. Validity was guaranteed through expert judgement and factorial analysis of the test. The conclusion stresses the pressing need for education centres to provide students with adequate educational-communicative training.
An Intelligent Resource Reservation for Crowdsourced Live Video Streaming Applications in Geo-Distributed Cloud Environment

Crowdsourced live video streaming (livecast) services such as Facebook Live, YouNow, Douyu and Twitch are gaining more momentum recently. Allocating the limited resources in a cost-effective manner while maximizing the Quality of Service (QoS) through real-time delivery and the provision of the appropriate representations for all viewers is a challenging problem. In our paper, we introduce a machine-learning based predictive resource allocation framework for geo-distributed cloud sites, considering the delay and quality constraints to guarantee the maximum QoS for viewers and the minimum cost for content providers. First, we present an offline optimization that decides the required transcoding resources in distributed regions near the viewers with a trade-off between the QoS and the overall cost. Second, we use machine learning to build forecasting models that proactively predict the approximate transcoding resources to be reserved at each cloud site ahead of time. Finally, we develop a Greedy Nearest and Cheapest algorithm (GNCA) to perform the resource allocation of real-time broadcasted videos on the rented resources. Extensive simulations have shown that GNCA outperforms the state-of-the art resource allocation approaches for crowdsourced live streaming by achieving more than 20% gain in terms of system cost while serving the viewers with relatively lower latency.
MIMOQA: Multimodal Input Multimodal Output Question Answering

Multimodal research has picked up significantly in the space of question answering with the task being extended to visual question answering, charts question answering as well as multimodal input question answering.However, all these explorations produce a unimodal textual output as the answer.In this paper, we propose a novel task -MIMOQA -Multimodal Input Multimodal Output Question Answering in which the output is also multimodal.Through human experiments, we empirically show that such multimodal outputs provide better cognitive understanding of the answers.We also propose a novel multimodal question-answering framework, MExBERT, that incorporates a joint textual and visual attention towards producing such a multimodal output.Our method relies on a novel multimodal dataset curated for this problem from publicly available unimodal datasets.We show the superior performance of MExBERT against strong baselines on both the automatic as well as human metrics.
Making data meaningful: guidelines for good quality open data

In the most recent editorial for the The Journal of Social Psychology (JSP), J. Grahe (2021) set out and justified a new journal policy: publishing papers now requires authors to make available all...
Assertion Detection in Clinical Notes: Medical Language Models to the Rescue?

In order to provide high-quality care, health professionals must efficiently identify the presence, possibility, or absence of symptoms, treatments and other relevant entities in freetext clinical notes.Such is the task of assertion detection -to identify the assertion class (present, possible, absent) of an entity based on textual cues in unstructured text.We evaluate state-of-the-art medical language models on the task and show that they outperform the baselines in all three classes.As transferability is especially important in the medical domain we further study how the best performing model behaves on unseen data from two other medical datasets.For this purpose we introduce a newly annotated set of 5,000 assertions for the publicly available MIMIC-III dataset.We conclude with an error analysis that reveals situations in which the models still go wrong and points towards future research directions.
How to introduce expert feedback in one-class support vector machines for anomaly detection?

Anomaly detection consists of detecting elements of a database that are different from the majority of normal data. The majority of anomaly detection algorithms considers unlabeled datasets. However, in some applications, labels associated with a subset of the database (coming for instance from expert feedback) are available providing useful information to design the anomaly detector. This paper studies a semi-supervised anomaly detector based on support vector machines, which takes the best of existing supervised and unsupervised support vector machines algorithms. The proposed algorithm allows the maximum proportion of vectors detected as anomalies and the maximum proportion of errors in the supervised data to be controlled, through two hyperparameters defining these proportions. Simulations conducted on various benchmark datasets show the interest of the proposed semi-supervised anomaly detection method.
Scale-Invariant Scale-Channel Networks: Deep Networks That Generalise to Previously Unseen Scales

The ability to handle large scale variations is crucial for many real world visual tasks. A straightforward approach for handling scale in a deep network is to process an image at several scales simultaneously in a set of scale channels. Scale invariance can then, in principle, be achieved by using weight sharing between the scale channels together with max or average pooling over the outputs from the scale channels. The ability of such scale channel networks to generalise to scales not present in the training set over significant scale ranges has, however, not previously been explored. In this paper, we present a systematic study of this methodology by implementing different types of scale channel networks and evaluating their ability to generalise to previously unseen scales. We develop a formalism for analysing the covariance and invariance properties of scale channel networks, and explore how different design choices, unique to scaling transformations, affect the overall performance of scale channel networks. We first show that two previously proposed scale channel network designs do not generalise well to scales not present in the training set. We explain theoretically and demonstrate experimentally why generalisation fails in these cases. We then propose a new type of foveated scale channel architecture}, where the scale channels process increasingly larger parts of the image with decreasing resolution. This new type of scale channel network is shown to generalise extremely well, provided sufficient image resolution and the absence of boundary effects. Our proposed FovMax and FovAvg networks perform almost identically over a scale range of 8, also when training on single scale training data, and do also give improved performance when learning from datasets with large scale variations in the small sample regime.
Online Learning Solution: Ice Breaking Application to Increase Student Motivation

The purpose of this study was to find out the effectiveness of ice breaking as an effort to increase the studentsâ€™ learning motivation. This research was conducted in the Science Education Study Program of STKIP PGRI Nganjuk, East Java, Indonesia. The sample in this study was 25 students in the third semester who took the mechanics course.The sample used in this study was taken using purposive sampling technique. This type of comparative research is used in this study. Data collection techniques used, questionnaires, observation sheets, and documentation. This type of research is comparative which is used to examine differences in student learning motivation before and after being treated. The results of the study were based on the Paired T Test shows value probability/ p Paired T test value of 0.000. This means that there are differences in student learning motivation after being given the ice breaking learning model. This ice breaking was implemented as an effort to reduce saturation and boredom of the students during the learning process. The new thing that is obtained is that the application of games can increase learning motivation
A new construction of weightwise perfectly balanced Boolean functions

In this paper, we first introduce a class of quartic Boolean functions. And then, the construction of weightwise perfectly balanced Boolean functions on $ 2^m $ variables are given by modifying the support of the quartic functions, where $ m $ is a positive integer. The algebraic degree, the weightwise nonlinearity, and the algebraic immunity of the newly constructed weightwise perfectly balanced functions are discussed at the end of this paper.
On Cosets Weight Distribution of Doubly-Extended Reed-Solomon Codes of Codimension 4

We consider the [ q+1, q-3,5] <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">q</sub> 3 generalized doubly-extended Reed-Solomon code of codimension 4 as the code associated with the twisted cubic in the projective space PG(3,q). Basing on the point-plane incidence matrix of PG(3,q), we obtain the number of weight 3 vectors in all the cosets of the considered code. This allows us to classify the cosets by their weight distributions and to obtain these distributions. The weight of a coset is the smallest Hamming weight of any vector in the coset. For the cosets of equal weight having distinct weight distributions, we prove that the difference between the w-th components, 3 <; w ≤ q+1, of the distributions is uniquely determined by the difference between the 3-rd components. This implies an interesting (and in some sense unexpected) symmetry of the obtained distributions.