Reviewerly/exHarmony huggingface dataset:
AlphaMWE: Construction of Multilingual Parallel Corpora with MWE Annotations.

In this work, we present the construction of multilingual parallel corpora with annotation of multiword expressions (MWEs). MWEs include verbal MWEs (vMWEs) defined in the PARSEME shared task that have a verb as the head of the studied terms. The annotated vMWEs are also bilingually and multilingually aligned manually. The languages covered include English, Chinese, Polish, and German. Our original English corpus is taken from the PARSEME shared task in 2018. We performed machine translation of this source corpus followed by human post editing and annotation of target MWEs. Strict quality control was applied for error limitation, i.e., each MT output sentence received first manual post editing and annotation plus second manual quality rechecking. One of our findings during corpora preparation is that accurate translation of MWEs presents challenges to MT systems. To facilitate further MT research, we present a categorisation of the error types encountered by MT systems in performing MWE related translation. To acquire a broader view of MT issues, we selected four popular state-of-the-art MT models for comparisons namely: Microsoft Bing Translator, GoogleMT, Baidu Fanyi and DeepL MT. Because of the noise removal, translation post editing and MWE annotation by human professionals, we believe our AlphaMWE dataset will be an asset for cross-lingual and multilingual research, such as MT and information extraction. Our multilingual corpora are available as open access at github.com/poethan/AlphaMWE.
Investigating Rich Feature Sources for Conceptual Representation Encoding

Functional Magnetic Resonance Imaging (fMRI) provides a means to investigate human conceptual representation in cognitive and neuroscience studies, where researchers predict the fMRI activations with elicited stimuli inputs. Previous work mainly uses a single source of features, particularly linguistic features, to predict fMRI activations. However, relatively little work has been done on investigating rich-source features for conceptual representation. In this paper, we systematically compare the linguistic, visual as well as auditory input features in conceptual representation, and further introduce associative conceptual features, which are obtained from Small World of Words game, to predict fMRI activations. Our experimental results show that those rich-source features can enhance performance in predicting the fMRI activations. Our analysis indicates that information from rich sources is present in the conceptual representation of human brains. In particular, the visual feature weights the most on conceptual representation, which is consistent with the recent cognitive science study.
Debiasing Concept-based Explanations with Causal Analysis

Studying the concept-based explanation techniques, we provided evidences for potential existence of spurious association between the features and concepts due to unobserved latent variables or noise. We proposed a new causal prior graph that models the impact of the noise and latent confounding fron the estimated concepts. We showed that using the labels as instruments, we can remove the impact of the context from the explanations. Our experiments showed that our debiasing technique not only improves the quality of the explanations, but also improve the accuracy of predicting labels through the concepts. As future work, we will investigate other two-stage-regression techniques to find the most accurate debiasing method.
These Are the Cyberthreats Lurking in Your Supply Chain

Why It Matters Effective cybersecurity involves more than just erecting a firewall. You have to clean up your supply chain, too.
Multi-object Tracking with a Hierarchical Single-Branch Network

Recent Multiple Object Tracking (MOT) methods have gradually attempted to integrate object detection and instance re-identification (Re-ID) into a united network to form a one-stage solution. Typically, these methods use two separated branches within a single network to accomplish detection and Re-ID respectively without studying the inter-relationship between them, which inevitably impedes the tracking performance. In this paper, we propose an online multi-object tracking framework based on a hierarchical single-branch network to solve this problem. Specifically, the proposed single-branch network utilizes an improved Hierarchical Online Instance Matching (iHOIM) loss to explicitly model the inter-relationship between object detection and Re-ID. Our novel iHOIM loss function unifies the objectives of the two subtasks and encourages better detection performance and feature learning even in extremely crowded scenes. Moreover, we propose to introduce the object positions, predicted by a motion model, as region proposals for subsequent object detection, where the intuition is that detection results and motion predictions can complement each other in different scenarios. Experimental results on MOT16 and MOT20 datasets show that we can achieve state-of-the-art tracking performance, and the ablation study verifies the effectiveness of each proposed component.
Video Frame Prediction via Deep Learning

This paper provides new results over our previous work presented in ICIP 2019 on the performance of learned frame prediction architectures and associated training methods. More specifically, we show that using an end-to-end residual connection in the fully convolutional neural network (FCNN) provides improved performance. In order to provide comparative results, we trained a residual FCNN, a convolutional RNN (CRNN), and a convolutional long-short term memory (CLSTM) network for next frame prediction using the mean square loss. We performed both stateless and stateful training for recurrent networks. Experimental results show that the residual FCNN architecture performs the best in terms of peak signal to noise ratio (PSNR) at the expense of higher training and test (inference) computational complexity. The CRNN can be stably and efficiently trained using the stateful truncated backpropagation through time procedure, and requires an order of magnitude less inference runtime to achieve an acceptable performance in near real-time.
“Let’s Play a Game!” Serious Games for Arabic Children with Dictation Difficulties

The purpose of this paper is to present a gaming application (Mega Challenger) dedicated to students with dictation difficulties between seven and nine years old. The user interface of the game will be presented in this paper, as well as, design principles of the game. Also, experiments have been performed to test the effect of the Mega Challenger game on the performance achievement of students with dictation difficulties; a type of dysgraphia. A dictation achievement pre-assessment was held for the students based on learning difficulties standards, and all results were recorded for further analysis. After applying 48 training sessions using the “Mega Challenger” application for 16 weeks, we had a post-assessment, with the assistance of learning difficulties tutors and experts. According to the experiment, pre and post results of the students show a significant difference between the student’s performance in the pre-assessment and post-assessment. Usability evaluation of Mega Challenger based on ISO 25062:2006, for post-user study interviews with students’ teachers concluded that Mega Challenger decreases the assessment loads of teachers and is a promising tool for students who have dictation difficulty. Children with dictation difficulties achieve better academic results when they play the game and that they are more motivated in serious games.
Pembelajaran Bahasa Indonesia Menggunakan Microsoft Office Team 365 untuk SMA di Masa Pandemi

Currently, the world is faced with a pandemic of Corona Virus Disease or COVID-19. All activities such as teaching and learning activities (KBM) at schools and universities must be carried out at home or carried out boldly with distance learning to prevent the spread of COVID-19. Distance learning is an option, even a necessity. One of the applications that can be used to support distance learning is Microsoft Office Team 365 as used in SMA Negeri 5 Surakarta. The purpose of this study was to see the use of Microsoft Office Team 365 learning media in bold learning in SMA Negeri 5 Surakarta during the Pandemic Period. The method in this research is case research. The subjects of this study were students of class XII SMA Negeri 5 Surakarta. In one class an average of 30 students. Microsoft Office Team 365 learning media can be used as online learning during a pandemic. The features available in Microsoft Office Team 365 can support bold learning during a pandemic. Distance learning using IT-based media can optimize the use of learning media for Microfost Office Team 365 so that class XII students of SMA Negeri 5 Surakarta still benefit from the distance learning process in the midst of the COVID-19 pandemic.
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding

Data augmentation has been demonstrated as an effective strategy for improving model generalization and data efficiency. However, due to the discrete nature of natural language, designing label-preserving transformations for text data tends to be more challenging. In this paper, we propose a novel data augmentation frame-work dubbed CoDA, which synthesizes diverse and informative augmented examples by integrating multiple transformations organically. Moreover, a contrastive regularization is introduced to capture the global relationship among all the data samples. A momentum encoder along with a memory bank is further leveraged to better estimate the contrastive loss. To verify the effectiveness of the proposed framework, we apply CoDA to Transformer-based models on a wide range of natural language understanding tasks. On the GLUE benchmark, CoDA gives rise to an average improvement of 2.2%while applied to the Roberta-large model. More importantly, it consistently exhibits stronger results relative to several competitive data augmentation and adversarial training baselines (including the low-resource settings). Extensive experiments show that the proposed contrastive objective can be flexibly combined with various data augmentation approaches to further boost their performance, highlighting the wide applicability of the CoDA framework.
Pretrained Language Models and Backtranslation for English-Basque Biomedical Neural Machine Translation.

This paper describes the machine translation systems proposed by the University of Technology Sydney Natural Language Processing (UTS_NLP) team for the WMT20 English-Basque biomedical translation tasks. Due to the limited parallel corpora available, we have proposed to train a BERT-fused NMT model that leverages the use of pretrained language models. Furthermore, we have augmented the training corpus by backtranslating monolingual data. Our experiments show that NMT models in low-resource scenarios can benefit from combining these two training techniques, with improvements of up to 6.16 BLEU percentual points in the case of biomedical abstract translations.
Entanglement evolution of a three-qubit system after interaction with even three-mode nonlinear coherent state

Abstract In this paper, using an entangled three-mode field state, we study the production of entanglement in a three- qubit system. The entangled three-mode field state is defined as superposed three-mode nonlinear coherent state, which is shown to be entangled as an increasing function of the coherency parameter. The interaction between the field state and a system of three separable qubits is considered under Jaynes–Cummings model. Our study reveals that the three-qubit system becomes entangled after the interaction and the entanglement can be tuned by changing the coherency parameter of the initial field state. In addition, we show how population inversion is affected by the interaction.
Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling

Metric learning is a technique in manifold learning to find a projection subspace for increasing and decreasing the inter- and intra-class variances, respectively. Some metric learning methods are based on triplet learning with anchor-positive-negative triplets. Large margin metric learning for nearest neighbor classification is one of the fundamental methods to do this. Recently, Siamese networks have been introduced with the triplet loss. Many triplet mining methods have been developed for Siamese nets; however, these techniques have not been applied on the triplets of large margin metric learning. In this work, inspired by the mining methods for Siamese nets, we propose several triplet mining techniques for large margin metric learning. Moreover, a hierarchical approach is proposed, for acceleration and scalability of optimization, where triplets are selected by stratified sampling in hierarchical hyper-spheres. We analyze the proposed methods on three publicly available datasets.
Informatization Strategies of Education and Teaching Management in the Era of Cloud Computing and Big Data

Abstract In the era of cloud computing and big data, the integrated processing of information and data has become the trend of various industries. College education and teaching management should be actively innovated and optimized, change traditional models and methods, build education and teaching management system, and improve management level. Based on this, this article will analyze the importance of the informatization of education and teaching management in the era of cloud computing and big data, and propose ways to construct the informatization of education and teaching management to help the relevant.
Gamification as instrument of professional orientation for schoolchildren

Researched is the problem of new methods of vocational guidance of schoolchildren, because traditional forms lose their effectiveness and relevance. Such new method, as gamification, might ensure maximum involvement of participants in vocational guidance process, allow to “test” a particular profession in game created reality, and that will help to choose future career soundly. The purpose of this study is to justify relevance of incorporating gamification technology into career counseling activities and to develop practical recommendations on the use of live-action games to form psychological readiness of schoolchildren for professional self-determination. The use of gaming technologies and mechanic will allow the student to be immersed in created alternative reality, in what he gets the opportunity to feel like a specialist performing real labor function that gives a clear understanding of a certain professional activity. The authors substantiate the main advantages of gamification in career-oriented work, such as involvement, motivation, replayability and development of meta-subject skills (soft skills). The article offers three basic scenarios of live action games, as a career guidance activities for high school students, developed on the basis of the authorities’ specifics. The authors substantiate effectiveness of application of innovative methods in vocational guidance of schoolchildren. The scenarios proposed by the authors might be used for any professional activity, lend themselves to adaptation and modification, and not only increase effectiveness of career guidance measures due to the high degree of involvement of participants, but also develop meta-subject skills of senior classes’ schoolchildren.
RusIdiolect: A New Resource for Authorship Studies

Problems of determining the author of texts (authorship attribution) or her/his characteristics (authorship profiling) using data mining techniques, although often solved disjointly, in fact are related to idiolect identification and should be studied in unified framework with obligatory account for interaction of as much factors of idiolectal variation (both author-based factors – gender, age, cognitive ability, personality traits, etc. and text-based factors – genre, topic, mode, etc.) as possible. Despite an enormous number of papers proposing rigorous methods of authorship analysis and a high social impact of these tasks, practical applicability of the techniques is questioned. This is because of the underestimation of interaction of the above-mentioned factors of idiolectal variation, which may result, for example, in topic, not author identification, in case of lack of topic control in authorship experiments, and other misleading conclusions. A small number of appropriate corpora also hampers progress in idiolect studies. This paper introduces a new freely available resource RusIdiolect which allows users to search for factors of idiolectal variation related to both author (person, gender, age, etc.) and text (register, mode, genre) as well as to perform full-text search. Database structure is outlined, as well as its possible applications in idiolect studies. The necessity to further develop corpora supplied with information on factors of idiolectal variation to facilitate this research area is highlighted.
Gender Variation in LIS Research Productivity: A Case Study of SRELS Journal of Information Management

The present study examines the publication pattern of female and male researchers for SRELS journal of Information Management , a leading journal in the field of library and information science. The study emphasized about the position of women in research and the gender variation in research productivity. The paper present bibliometric analysis on research output of 464 research publication contributed by 877 researchers, for the period of 2011-2018. The study focuses on authorship pattern, pattern of co- authorship, pattern of collaboration by gender, and average collaboration coefficient. The annual growth rate of female researchers in SRELS journal during the period is fluctuating every year and it is highest in year 2015 i.e 45.8. It is also noticed that the average author per article is 1.89 and average female per paper is 0.54 which is very unsatisfactory. It is clear from the study that multi authorship pattern is leading in this journal. Women researchers work has few international collaborative papers. The study also shows that single male author is higher than female authors, and female productivity increases when they collaborate with a male authors in their research work.
A minimalistic verified bootstrapped compiler (proof pearl)

This paper shows how a small verified bootstrapped compiler can be developed inside an interactive theorem prover (ITP). Throughout, emphasis is put on clarity and minimalism.
Shape or Texture: Understanding Discriminative Features in CNNs

Contrasting the previous evidence that neurons in the later layers of a Convolutional Neural Network (CNN) respond to complex object shapes, recent studies have shown that CNNs actually exhibit a 'texture bias': given an image with both texture and shape cues (e.g., a stylized image), a CNN is biased towards predicting the category corresponding to the texture. However, these previous studies conduct experiments on the final classification output of the network, and fail to robustly evaluate the bias contained (i) in the latent representations, and (ii) on a per-pixel level. In this paper, we design a series of experiments that overcome these issues. We do this with the goal of better understanding what type of shape information contained in the network is discriminative, where shape information is encoded, as well as when the network learns about object shape during training. We show that a network learns the majority of overall shape information at the first few epochs of training and that this information is largely encoded in the last few layers of a CNN. Finally, we show that the encoding of shape does not imply the encoding of localized per-pixel semantic information. The experimental results and findings provide a more accurate understanding of the behaviour of current CNNs, thus helping to inform future design choices.
VoIP Speech Encryption System Using Stream Cipher with Chaotic Key Generator

Recently, with the development multimedia technologies and wireless telecommunication, Voice over Internet Protocol, becomes widely used in communication between connecting people, VoIP allows people that are connected to the local network or the Internet to make voice calls using digital connection instead of based on the analog traditional telephone network. The technologies of Internet doesn’t give any security mechanism and there is no way to guarntee that the voice streams will be transmitted over Internet or network have not been intercepted in between. In this paper, VoIP is developed using stream cipher algorithm and the chaotic cryptography for key generator. It is based on the chaotic maps for generating a one-time random key used to encrypt each voice data in the RTP packet. Chaotic maps have been used successfully for encryption bulky data such as voice, image, and video, chaotic cryptography has good properties such as long periodicity, pseudo-randomness, and sensitivity to initial conditions and change in system parameters. A VoIP system was successfully implemented based on the on ITU-T G.729 for voice codec, as a multimedia encoding of Real-time Transport Protocol payload data, then, apply a proposed method to generate three-mixed logistic chaotic maps [1] and then analysis the encryption/ decryption quality measures for speech signal based this method. The experimental work demonstrates that the proposed scheme can provide confidentiality to voice data with voice over IP performance quality, minimum lost in transmitted packet, minimum average delay, and minimum jitter.
The more legs the merrier: A new composition for symmetric (multi-)lenses

Lenses are a category theoretic construct and are used in a wide variety of applications. Symmetric lenses compose to, of course, form new symmetric lenses. Symmetric lenses are usually represented as spans of asymmetric lenses. In many applications, the fact that a symmetric lens might also be represented as a cospan of asymmetric lenses is important, especially for implementation purposes. However, the composition of symmetric lenses does not preserve the property that the lenses can be represented by cospans -- two such symmetric lenses may (and frequently do) compose to form a symmetric lens which cannot be represented as a cospan of asymmetric lenses. Thus preserving the factorisation to show how cospans of asymmetric lenses might be used in implementations becomes important. In 2018, the first work on multilenses was begun. Multilenses can be represented as multi-spans of asymmetric lenses (often called 'wide spans', these are spans with an arbitrary finite number of legs). In this paper we analyse a small but realistic example of a supply chain in which the cospan representations would be 'composed away' by ordinary symmetric lens composition, and introduce a new kind of composition which we call 'fusion' in which two ordinary symmetric lenses (spans with two legs) fuse to form a multilens with three legs preserving the cospan representations, and more generally, two symmetric multilenses, spans with say m and n legs, fuse to form a symmetric multilens with m+n-1 legs, again preserving cospan representations.
The Open Science Ecosystem: A Systematic Framework Anchored in Values, Ethics, and FAIRER Data

This paper develops an open science ecosystem in relation to values and ethics, and FAIRER (Findable, Accessible, Interoperable, Ethical, and Reproducible) data. It provides a framework for the systematic development of open science across disciplines and organizations.