Reviewerly/exHarmony huggingface dataset:
Revisiting Iterative Back-Translation from the Perspective of Compositional Generalization

Human intelligence exhibits compositional generalization (i.e., the capacity to understand and produce unseen combinations of seen components), but current neural seq2seq models lack such ability. In this paper, we revisit iterative back-translation, a simple yet effective semi-supervised method, to investigate whether and how it can improve compositional generalization. In this work: (1) We first empirically show that iterative back-translation substantially improves the performance on compositional generalization benchmarks (CFQ and SCAN). (2) To understand why iterative back-translation is useful, we carefully examine the performance gains and find that iterative back-translation can increasingly correct errors in pseudo-parallel data. (3) To further encourage this mechanism, we propose curriculum iterative back-translation, which better improves the quality of pseudo-parallel data, thus further improving the performance.
Efficient and Scalable Structure Learning for Bayesian Networks: Algorithms and Applications

Structure Learning for Bayesian network (BN) is an important problem with extensive research. It plays central roles in a wide variety of applications in Alibaba Group. However, existing structure learning algorithms suffer from considerable limitations in real-world applications due to their low efficiency and poor scalability. To resolve this, we propose a new structure learning algorithm LEAST, which comprehensively fulfills our business requirements as it attains high accuracy, efficiency and scalability at the same time. The core idea of LEAST is to formulate the structure learning into a continuous constrained optimization problem, with a novel differentiable constraint function measuring the acyclicity of the resulting graph. Unlike with existing work, our constraint function is built on the spectral radius of the graph and could be evaluated in near linear time w.r.t. the graph node size. Based on it, LEAST can be efficiently implemented with low storage overhead. According to our benchmark evaluation, LEAST runs 1-2 orders of magnitude faster than state-of-the-art method with comparable accuracy, and it is able to scale on BNs with up to hundreds of thousands of variables. In our production environment, LEAST is deployed and serves for more than 20 applications with thousands of executions per day. We describe a concrete scenario in a ticket booking service in Alibaba, where LEAST is applied to build a near real-time automatic anomaly detection and root error cause analysis system. We also show that LEAST unlocks the possibility of applying BN structure learning in new areas, such as large-scale gene expression data analysis and explainable recommendation system.
The Modality Card Deck: Co-Creating Multi-Modal Behavioral Expressions for Social Robots with Older Adults

Robots have been proposed as intelligent technology that can support the independent living and health of older adults. While significant advances are being made regarding hardware and intelligent software to support autonomous actions of robots, less emphasis has been put on designing robot behavior that is comprehensible and pleasant for older adults. However, good usability and user experience are crucial factors for acceptance and long-term use. One way to actively engage older adults in behavioral design for social robots is participatory design. The Modality Card Deck is proposed, a tool that helps to engage older adults in human-robot interaction design process and participate in design decision for robot behavior. The cards guide the users towards creating ideas for design solutions which are detailed enough to be implemented by interaction designers and software developers. This paper provides a detailed description of the Modality Card Deck and presents an evaluation of the tool in the scope of a case study. In the case study, the card deck was used in participatory design workshops with older adults to develop multi-modal robot behaviors for the Pepper robot and a quiz game application. After describing the procedure of the case study, the workshop results and learnings about working with the Modality Card Deck and older adults are presented.
N-ary Constituent Tree Parsing with Recursive Semi-Markov Model

In this paper, we study the task of graph-based constituent parsing in the setting that binarization is not conducted as a pre-processing step, where a constituent tree may consist of nodes with more than two children.Previous graphbased methods on this setting typically generate hidden nodes with the dummy label inside the n-ary nodes, in order to transform the tree into a binary tree for prediction.The limitation is that the hidden nodes break the sibling relations of the n-ary node's children.Consequently, the dependencies of such sibling constituents might not be accurately modeled and is being ignored.To solve this limitation, we propose a novel graph-based framework, which is called "recursive semi-Markov model".The main idea is to utilize 1-order semi-Markov model to predict the immediate children sequence of a constituent candidate, which then recursively serves as a child candidate of its parent.In this manner, the dependencies of sibling constituents can be described by 1-order transition features, which solves the above limitation.Through experiments, the proposed framework obtains the F1 of 95.92% and 92.50% on the datasets of PTB and CTB 5.1 respectively.Specially, the recursive semi-Markov model shows advantages in modeling nodes with more than two children, whose average F1 can be improved by 0.3-1.1 points in PTB and 2.3-6.8 points in CTB 5.1.
Gleipnir: toward practical error analysis for Quantum programs

Practical error analysis is essential for the design, optimization, and evaluation of Noisy Intermediate-Scale Quantum(NISQ) computing. However, bounding errors in quantum programs is a grand challenge, because the effects of quantum errors depend on exponentially large quantum states. In this work, we present Gleipnir, a novel methodology toward practically computing verified error bounds in quantum programs. Gleipnir introduces the (ρ,δ)-diamond norm, an error metric constrained by a quantum predicate consisting of the approximate state ρ and its distance δ to the ideal state ρ. This predicate (ρ,δ) can be computed adaptively using tensor networks based on the Matrix Product States. Gleipnir features a lightweight logic for reasoning about error bounds in noisy quantum programs, based on the (ρ,δ)-diamond norm metric. Our experimental results show that Gleipnir is able to efficiently generate tight error bounds for real-world quantum programs with 10 to 100 qubits, and can be used to evaluate the error mitigation performance of quantum compiler transformations.
Use and evaluation of information from social media: A longitudinal cohort study

This cohort study examined longitudinal changes in undergraduates' use of social media as information sources in academic and everyday-life contexts, covering types of social media used, purposes of use, and evaluation strategies. Web surveys were used, collecting data from 1,355 and 928 undergraduates in 2013 and 2019. The study found that students used most types of social media less in 2019 than 2013 in both contexts. Only media-sharing services bucked this downward pattern. Overall, a majority of social media types underwent changes in the top three purposes of use. The 2019 cohort used most evaluation strategies more frequently in both contexts. However, they favored simplistic strategies relying on superficial physical cues when evaluating information. Based on the findings, this study provided recommendations for information literacy education and interface design, such as covering multimedia information evaluation, leveraging various heuristics to prime careful use of social media and analytical evaluation of information.
Construction of orthonormal wavelets

This paper gives a self-contained presentation of Meyer and Mallat’s basic theory of multiresolution analysis and the construction of orthonormal wavelet bases in ℝ n . In fact, this is done in the more general setting of an arbitrary lattice in ℝ n with a strictly expanding matrix which preserves this lattice. The most familiar case is the integer lattice ℤ n with matrix equal to twice the identity. This general theory is applied to obtain a construction of Daubechies’ compactly supported wavelets. Also included is a discussion of subband coding, which gives an efficient algorithm for computing wavelet coefficients and for reconstructing a function from these coefficients.
Trimmer: An Automated System for Configuration-Based Software Debloating

Software bloat has negative implications for security, reliability, and performance. To counter bloat, we propose <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> , a static analysis-based system for pruning unused functionality. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> removes code that is unused with respect to user-provided command-line arguments and application-specific configuration files. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> uses concrete memory tracking and a custom inter-procedural constant propagation analysis that facilitates dead code elimination. Our system supports both context-sensitive and context-insensitive constant propagation. We show that context-sensitive constant propagation is important for effective software pruning in most applications. We introduce <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparse constant propagation</i> that performs constant propagation only for configuration-hosting variables and show that it performs better (higher code size reductions) compared to constant propagation for all program variables. Overall, our results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> reduces binary sizes for real-world programs with reasonable analysis times. Across 20 evaluated programs, we observe a mean binary size reduction of 22.7 percent and a maximum reduction of 62.7 percent. For 5 programs, we observe performance speedups ranging from 5 to 53 percent. Moreover, we show that winnowing software applications can reduce the program attack surface by removing code that contains exploitable vulnerabilities. We find that debloating using <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Trimmer</small> removes CVEs in 4 applications.
Medical Internet of Things to Realize Elderly Stroke Prevention and Nursing Management

Stroke is a major disease that seriously endangers the lives and health of middle-aged and elderly people in our country, but its implementation of secondary prevention needs to be improved urgently. The application of IoT technology in home health monitoring and telemedicine, as well as the popularization of cloud computing, contributes to the early identification of ischemic stroke and provides intelligent, humanized, and preventive medical and health services for patients at high risk of stroke. This article clarifies the networking structure and networking objects of the rehabilitation system Internet of Things, clarifies the functions of each part, and establishes an overall system architecture based on smart medical care; the design and optimization of the mechanical part of the stroke rehabilitation robot are carried out, as well as kinematics and dynamic analysis. According to the functions of different types of stroke rehabilitation robots, strategies are given for the use of lower limb rehabilitation robots; standardized codes are used to identify system objects, and RFID technology is used to automatically identify users and devices. Combined with the use of the Internet and GSM mobile communication network, construct a network database of system networking objects and, on this basis, establish information management software based on a smart medical rehabilitation system that takes care of both doctors and patients to realize the system's Internet of Things architecture. In addition, this article also gives the recovery strategy generation in the system with the design method of resource scheduling method and the theoretical algorithm of rehabilitation strategy generation is given and verified. This research summarizes the application background, advantages, and past practice of the Internet of Things in stroke medical care, develops and applies a medical collaborative cloud computing system for systematic intervention of stroke, and realizes the module functions such as information sharing, regional monitoring, and collaborative consultation within the base.
Code is code and law is law—the law of digitalization and the digitalization of law

Abstract The article argues for a sharp analytical distinction between the realms of technology and of law. The question to what extent the law ‘can’ be digitalized relates to technology, whereas the question to what extent it ‘may’ be digitalized falls within the realm of the law. Against this backdrop, it is argued that digital technology does not challenge the law fundamentally. Instead, the crucial questions on the relationship between law and digitalization lie in the details. The article raises those questions and provides an analytical framework for the law of digitalization and the digitalization of law.
Cryptography in Financial Markets: Potential Channels for Future Financial Stability

Digital finance is assuming a significant part in the arrangement of financial services all over the world. Fast growth with digitalization, data analysis, and computing capacities allows for a whole new scope of financial services and transactions. This financial development empowered by digital financial technology (Fintech) has pulled in a ton of attention, as it could offer some potential for economic growth and development. As a part of the Fintech environment, cryptography has started to grow quickly and digital assets are acquiring in favorability among financial bankers and investors. Human behavior as they engage with financial activities is personally associated with the noticed market elements. However, with many existing theories and studies on the fundamental motivations of the conduct of people in financial frameworks, there is still restricted experimental derivation of the behavioral conduct of the financial agents from a definite market analysis. Cryptocurrency technology has given a map to this analysis with its voluminous data and its transparency of financial transactions. It has empowered us to perform inference on the personal conduct standards of users in the market, which we analyze in the bitcoin and ethereum cryptocurrency markets. In our study, we initially decide different properties of the cryptography users by complex network analysis. Financial cryptography is a difficult subject that necessitates abilities from a variety of seemingly unrelated fields. There is a serious risk that attempts to establish Financial Cryptography frameworks would simplify or omit key disciplines because they are caught between central banking and cryptography. This paper discusses research that attempts to limit the scope of Financial Cryptography. This model should assist the project, administrative, and requirements personnel by classifying each discipline into a seven-layer model of basic nature, where the link between each adjoining layer is evident. While this model is shown as effective, all models have cutoff points. This one does not present a design system or a protocol agenda. Furthermore, given the model's initial adaptation and the field, it should be viewed as a suggestion of complexity rather than a definitive approach.
Dynamic Gesture Recognition Combining Two-stream 3D Convolution with Attention Mechanisms

Benefits from the progress of computer hardware and computing power, natural and simple dynamic gesture recognition gets a lot of attention in human-computer interaction. In view of the requirement of the accuracy of dynamic gesture recognition in human-computer interaction, a method of dynamic gesture recognition that combines Two-stream Inflated 3D (I3D) Convolution Neural Network (CNN) with the Convolutional Block Attention Module (CBAM-I3D) is proposed. In addition, relevant parameters and structures of the I3D network model are improved. In order to improve the convergence speed and stability of the model, the Batch Normalization (BN) technology is used to optimize the network, which shortens the training time of the optimized network. At the same time, experimental comparisons with various Two-stream 3D convolution methods on the open source Chinese Sign Language (CSL) recognition dataset are performed. The experimental results show that the proposed method can recognize dynamic gestures well, and the recognition rate reaches 90.76%, which is higher than other dynamic gesture recognition methods. The validity and feasibility of the proposed method are verified.
lfl: An R package for linguistic fuzzy logic

This paper presents an R package that supports the use of fuzzy relational calculus and linguistic fuzzy logic in data processing applications. The lfl package enables computing compositions of fuzzy relations enhanced with distinct extensions, such as excluding features, unavoidable features, or generalized quantifiers. Furthermore, it provides tools for transformation of data into fuzzy sets representing linguistic expressions, mining of linguistic fuzzy association rules, and performing an inference on fuzzy rule bases using the Perception-based logical deduction (PbLD). The package also enables to use the Fuzzy rule-based ensemble, a tool for time series forecasting based on an ensemble of forecasts from several individual methods implemented in R. To the best of the authors' knowledge, there is no other open source software that would provide free tools covering the above-described fragments of the fuzzy modeling area. Therefore, we find highly desirable to allow the community to get familiar with the tools as well as their implementation.
Global Aggregation Then Local Distribution for Scene Parsing

Modelling long-range contextual relationships is critical for pixel-wise prediction tasks such as semantic segmentation. However, convolutional neural networks (CNNs) are inherently limited to model such dependencies due to the naive structure in its building modules (\eg, local convolution kernel). While recent global aggregation methods are beneficial for long-range structure information modelling, they would oversmooth and bring noise to the regions containing fine details (\eg,~boundaries and small objects), which are very much cared for the semantic segmentation task. To alleviate this problem, we propose to explore the local context for making the aggregated long-range relationship being distributed more accurately in local regions. In particular, we design a novel local distribution module which models the affinity map between global and local relationship for each pixel adaptively. Integrating existing global aggregation modules, we show that our approach can be modularized as an end-to-end trainable block and easily plugged into existing semantic segmentation networks, giving rise to the \emph{GALD} networks. Despite its simplicity and versatility, our approach allows us to build new state of the art on major semantic segmentation benchmarks including Cityscapes, ADE20K, Pascal Context, Camvid and COCO-stuff. Code and trained models are released at \url{https://github.com/lxtGH/GALD-DGCNet} to foster further research.
TRANSFORMATION OF EDUCATIONAL PROCESS IN COVID-19 PANDEMIC: A CASE OF IGOR SIKORSKY KYIV POLYTECHNIC INSTITUTE

The rapid spread of the SARS-CoV-2 coronavirus has led to the global COVID-19 pandemic and a lockdown was introduced in Ukraine in March 2020. This forced universities to urgently transform the traditional system of organisation of the educational process and transfer to distance learning. This study aims to evaluate the distance learning system organised by a technical university in COVID-19 lockdowns from the perspective of the students as the main stakeholders of education. The method of the survey as the most widely used research method of sociological research was used to collect and analyse the data obtained. In this context, a number of surveys involving from 2,721 to 9,000 students of technical and humanitarian specialities were conducted to evaluate the level of their satisfaction with the distance learning system. The results of the study showed that the transformations introduced in the organisation and realisation of the learning process were on the whole positively estimated by students, which means that the elements of distance learning should remain after the end of the lockdown restrictions. However, more research is needed to evaluate the diagnostic tools for preventing academic dishonesty.
How Greek Students Experienced Online Education during Covid-19 Pandemic in Order to Adjust to a Post-Lockdown Period

Following an unprecedented situation of confinement due to the Covid-19 pandemic, academic institutions were called to focus on supporting telecommunications technologies. For the first time, Higher Education went completely online. The authors of this research conducted an online voluntary survey where Greek students could answer questions about the distance education, they had experienced during the Covid-19 lockdown, particularly of the synchronous type. The purpose of this research was to investigate the major issues that created impediments to the students, both the technical barriers that made it difficult to communicate, and the teaching/learning challenges raised because of emerging trends. Recognizing the main problems that arose in the educational process during the lockdown period leads to a better communication in the future in the field of distance education. The students were informed about the research by the Student Counseling Center of their universities but also via posts on well-known student content websites. The research results have shown that most students attended synchronous communication online classes (the theoretical and the practical ones) in replacement of their face-to-face lessons. The students pointed out some negative aspects of online education concerning synchronous communication educational practices, but also how their classes were organized and presented. They referred to the main technical difficulties that occurred - on the part of the teacher - preventing a satisfactory communication, as to the practices that stressed them or to the lack of communication between students and teachers they experienced during the lockdown. Nevertheless, despite these problems, most students are interested in continuing online learning in combination with traditional courses in a classroom. Overall, this study provided important, additional information in respect of the students’ perceptions towards online education during the first quarantine.