Reviewerly/exHarmony huggingface dataset:
Importance-based Neuron Allocation for Multilingual Neural Machine Translation

Multilingual neural machine translation with a single model has drawn much attention due to its capability to deal with multiple languages.However, the current multilingual translation paradigm often makes the model tend to preserve the general knowledge, but ignore the language-specific knowledge.Some previous works try to solve this problem by adding various kinds of language-specific modules to the model, but they suffer from the parameter explosion problem and require specialized manual design.To solve these problems, we propose to divide the model neurons into general and language-specific parts based on their importance across languages.The general part is responsible for preserving the general knowledge and participating in the translation of all the languages, while the language-specific part is responsible for preserving the languagespecific knowledge and participating in the translation of some specific languages.Experimental results on several language pairs, covering IWSLT and Europarl corpus datasets, demonstrate the effectiveness and universality of the proposed method.
Future-Guided Incremental Transformer for Simultaneous Translation

Simultaneous translation (ST) starts translations synchronously while reading source sentences, and is used in many online scenarios. The previous wait-k policy is concise and achieved good results in ST. However, wait-k policy faces two weaknesses: low training speed caused by the recalculation of hidden states and lack of future source information to guide training. For the low training speed, we propose an incremental Transformer with an average embedding layer (AEL) to accelerate the speed of calculation of the hidden states during training. For future-guided training, we propose a conventional Transformer as the teacher of the incremental Transformer, and try to invisibly embed some future information in the model through knowledge distillation. We conducted experiments on Chinese-English and German-English simultaneous translation tasks and compared with the wait-k policy to evaluate the proposed method. Our method can effectively increase the training speed by about 28 times on average at different k and implicitly embed some predictive abilities in the model, achieving better translation quality than wait-k baseline.
Guiding Teacher Forcing with Seer Forcing for Neural Machine Translation

Although teacher forcing has become the main training paradigm for neural machine translation, it usually makes predictions only conditioned on past information, and hence lacks global planning for the future.To address this problem, we introduce another decoder, called seer decoder, into the encoder-decoder framework during training, which involves future information in target predictions.Meanwhile, we force the conventional decoder to simulate the behaviors of the seer decoder via knowledge distillation.In this way, at test the conventional decoder can perform like the seer decoder without the attendance of it.Experiment results on the Chinese-English, English-German and English-Romanian translation tasks show our method can outperform competitive baselines significantly and achieves greater improvements on the bigger data sets.Besides, the experiments also prove knowledge distillation the best way to transfer knowledge from the seer decoder to the conventional decoder compared to adversarial learning and L2 regularization.
From Discourse to Narrative: Knowledge Projection for Event Relation Extraction

Current event-centric knowledge graphs highly rely on explicit connectives to mine relations between events.Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs.The lack of high-quality labelled corpora further exacerbates that problem.In this paper, we propose a knowledge projection paradigm for event relation extraction: projecting discourse knowledge to narratives by exploiting the commonalities between them.Specifically, we propose Multi-tier Knowledge Projection Network (MKPNet), which can leverage multitier discourse knowledge effectively for event relation extraction.In this way, the labelled data requirement is significantly reduced, and implicit event relations can be effectively extracted.Intrinsic experimental results show that MKPNet achieves the new state-of-the-art performance, and extrinsic experimental results verify the value of the extracted event relations.
Taming Pre-trained Language Models with N-gram Representations for Low-Resource Domain Adaptation

Large pre-trained models such as BERT are known to improve different downstream NLP tasks, even when such a model is trained on a generic domain.Moreover, recent studies have shown that when large domain-specific corpora are available, continued pre-training on domain-specific data can further improve the performance of in-domain tasks.However, this practice requires significant domainspecific data and computational resources which may not always be available.In this paper, we aim to adapt a generic pretrained model with a relatively small amount of domain-specific data.We demonstrate that by explicitly incorporating the multi-granularity information of unseen and domain-specific words via the adaptation of (word based) ngrams, the performance of a generic pretrained model can be greatly improved.Specifically, we introduce a Transformer-based Domainaware N-gram Adaptor, T-DNA, to effectively learn and incorporate the semantic representation of different combinations of words in the new domain.Experimental results illustrate the effectiveness of T-DNA on eight lowresource downstream tasks from four domains.We show that T-DNA is able to achieve significant improvements compared to existing methods on most tasks using limited data with lower computational costs.Moreover, further analyses demonstrate the importance and effectiveness of both unseen words and the information of different granularities. 1
Semidefinite programming hierarchies for constrained bilinear optimization

We give asymptotically converging semidefinite programming hierarchies of outer bounds on bilinear programs of the form $\mathrm{Tr}\big[M(X\otimes Y)\big]$, maximized with respect to semidefinite constraints on $X$ and $Y$. Applied to the problem of quantum error correction this gives hierarchies of efficiently computable outer bounds on the optimal fidelity for any message dimension and error model. The first level of our hierarchies corresponds to the non-signalling assisted fidelity previously studied by [Leung & Matthews, IEEE Trans.~Inf.~Theory 2015], and positive partial transpose constraints can be added and used to give a sufficient criterion for the exact convergence at a given level of the hierarchy. To quantify the worst case convergence speed of our hierarchies, we derive novel quantum de Finetti theorems that allow imposing linear constraints on the approximating state. In particular, we give finite de Finetti theorems for quantum channels, quantifying closeness to the convex hull of product channels as well as closeness to local operations and classical forward communication assisted channels. As a special case this constitutes a finite version of Fuchs-Schack-Scudo's asymptotic de Finetti theorem for quantum channels. Finally, our proof methods also allow us to answer an open question from [Brand\~ao & Harrow, STOC 2013] by improving the approximation factor of de Finetti theorems with no symmetry from $O(d^{k/2})$ to $\mathrm{poly}(d,k)$, where $d$ denotes local dimension and $k$ the number of copies.
A Novel Texture Descriptor: Circular Parts Local Binary Pattern

Local Binary Pattern (LBP) are considered as a classical descriptor for texture analysis, it has mostly been used in pattern recognition and computer vision applications. However, the LBP gets information from a restricted number of local neighbors which is not enough to describe texture information, and the other descriptors that get a large number of local neighbors suffer from a large dimensionality and consume much time. In this regard, we propose a novel descriptor for texture classification known as Circular Parts Local Binary Pattern (CPLBP) which is designed to enhance LBP by extending the area of neighborhood from one to a region of neighbors using polar coordinates that permit to capture more discriminating relationships that exists amongst the pixels in the local neighborhood which increase efficiency in extracting features. Firstly, the circle is divided into regions with a specific radius and angle. After that, we calculate the average gray-level value of each part. Finally, the value of the center pixel is compared with these average values. The relevance of the proposed idea is validate in databases Outex 10 and 12. A complete evaluation on benchmark data sets reveals CPLBP's high performance. CPLBP generates the score of 99.95 with SVM classification.
General condition of quantum teleportation by one-dimensional quantum walks

We extend the scheme of quantum teleportation by quantum walks introduced by Wang et al. (2017). First, we introduce the mathematical definition of the accomplishment of quantum teleportation by this extended scheme. Secondly, we show a useful necessary and sufficient condition that the quantum teleportation is accomplished rigorously. Our result classifies the parameters of the setting for the accomplishment of the quantum teleportation.
VR-UML: The Unified Modeling Language in Virtual Reality – An Immersive Modeling Experience

Software models in the Unified Modeling Language (UML) can been created or automatically reverse-engineered and used for quickly gaining structural insights into larger, legacy, or unfamiliar software. But as the size, structural complexity, and interdependencies between software components in larger systems grows, two-dimensional viewing and modeling has limitations, and new ways of visualizing larger models and numerous associated diagrams of different types are needed to intuitively convey structural and relational insights. To investigate the feasibility of using Virtual Reality (VR) to create an immersive UML-based software modeling experience, this paper contributes a VR solution concept for visualizing, navigating, modeling, and interacting with software models using UML notation. An implementation shows its feasibility while an empirical evaluation highlights its potential.
A Study on Movement Interface in Mobile Virtual Reality

This study proposes an interface for providing mobile interaction suitable for mobile virtual reality (VR) and analyzes it through comparative experiments.The proposed interface is premised on not using additional equipment except for the mobile headmounted display(HMD) in consideration of accessibility and usability.And the interface that controls the movement interaction using the user's gaze is designed in two phases.The key is to minimize the occurrence of negative factors such as VR sickness that can be caused by straight line movement in virtual reality.To this end, two phases are designed: an interface composed of forward/backward buttons to move the gaze toward the ground, and an interface composed of left and right buttons on the front in consideration of the gaze change in real walking motion.An application that can compare and analyze movement interactions through the proposed interface is produced, and a survey experiment is conducted to analyze the user's satisfaction with the interface experience and the negative impact on the movement process.It was confirmed that the proposed movement interaction reduced negative effects such as VR sickness along with a satisfactory interface experience for users.
Multimodal Visibility Deep Learning Model Based on Visible-Infrared Image Pair

<p indent=0mm>In order to enhance the robustness of the visibility deep learning model under a small training dataset, this paper proposes a multi-modal visibility deep learning model based on visible-infrared image pairs. Apart from conventional visibility deep learning models, the visible-infrared image pairs are used as observation data. First, raw data set is preprocessed to generate visible-infrared image pairs with identical resolution and view range using image registration. Then we construct a new convolutional neural network structure including three CNN streams, which are connected in parallel. The feature maps of each stream are extracted and fused from low layer to deep layer by propagation. Finally, the visibility range level is classified by softmax layer based on the output feature descriptor of full connected layer. The experimental results demonstrate that, compared with conventional visibility deep learning models, both accuracy and robustness are strongly enhanced using the proposed method, especially for small training datasets.
SiGMoiD: A super-statistical generative model for binary data

In modern computational biology, there is great interest in building probabilistic models to describe collections of a large number of co-varying binary variables. However, current approaches to build generative models rely on modelers’ identification of constraints and are computationally expensive to infer when the number of variables is large ( N ~100). Here, we address both these issues with S uper-stat i stical G enerative Mo del for b i nary D ata (SiGMoiD). SiGMoiD is a maximum entropy-based framework where we imagine the data as arising from super-statistical system; individual binary variables in a given sample are coupled to the same ‘bath’ whose intensive variables vary from sample to sample. Importantly, unlike standard maximum entropy approaches where modeler specifies the constraints, the SiGMoiD algorithm infers them directly from the data. Due to this optimal choice of constraints, SiGMoiD allows us to model collections of a very large number ( N &gt;1000) of binary variables. Finally, SiGMoiD offers a reduced dimensional description of the data, allowing us to identify clusters of similar data points as well as binary variables. We illustrate the versatility of SiGMoiD using multiple datasets spanning several time- and length-scales.
A Security-Enhanced Image Communication Scheme Using Cellular Neural Network

In the current network and big data environment, the secure transmission of digital images is facing huge challenges. The use of some methodologies in artificial intelligence to enhance its security is extremely cutting-edge and also a development trend. To this end, this paper proposes a security-enhanced image communication scheme based on cellular neural network (CNN) under cryptanalysis. First, the complex characteristics of CNN are used to create pseudorandom sequences for image encryption. Then, a plain image is sequentially confused, permuted and diffused to get the cipher image by these CNN-based sequences. Based on cryptanalysis theory, a security-enhanced algorithm structure and relevant steps are detailed. Theoretical analysis and experimental results both demonstrate its safety performance. Moreover, the structure of image cipher can effectively resist various common attacks in cryptography. Therefore, the image communication scheme based on CNN proposed in this paper is a competitive security technology method.
On crossing fitness valleys with majority-vote crossover and estimation-of-distribution algorithms

The benefits of using crossover in crossing fitness gaps have been studied extensively in evolutionary computation. Recent runtime results show that majority-vote crossover is particularly efficient at optimizing the well-known Jump benchmark function that includes a fitness gap next to the global optimum. Also estimation-of-distribution algorithms (EDAs), which use an implicit crossover, are much more efficient on Jump than typical mutation-based algorithms. However, the allowed gap size for polynomial runtimes with EDAs is at most logarithmic in the problem dimension n.
Resource utilization prediction technique in cloud using knowledge based ensemble random forest with LSTM model

Future computation of cloud datacenter resource usage is a provoking task due to dynamic and Business Critic workloads. Accurate prediction of cloud resource utilization through historical observation facilitates, effectively aligning the task with resources, estimating the capacity of a cloud server, applying intensive auto-scaling and controlling resource usage. As imprecise prediction of resources leads to either low or high provisioning of resources in the cloud. This paper focuses on solving this problem in a more proactive way. Most of the existing prediction models are based on a mono pattern of workload which is not suitable for handling peculiar workloads. The researchers address this problem by making use of a contemporary model to dynamically analyze the CPU utilization, so as to precisely estimate data center CPU utilization. The proposed design makes use of an Ensemble Random Forest-Long Short Term Memory based deep architectural models for resource estimation. This design preprocesses and trains data based on historical observation. The approach is analyzed by using a real cloud data set. The empirical interpretation depicts that the proposed design outperforms the previous approaches as it bears 30%–60% enhanced accuracy in resource utilization.
Classification and reconstruction of spatially overlapping phase images using diffractive optical networks

Diffractive optical networks unify wave optics and deep learning to all-optically compute a given machine learning or computational imaging task as the light propagates from the input to the output plane. Here, we report the design of diffractive optical networks for the classification and reconstruction of spatially overlapping, phase-encoded objects. When two different phase-only objects spatially overlap, the individual object functions are perturbed since their phase patterns are summed up. The retrieval of the underlying phase images from solely the overlapping phase distribution presents a challenging problem, the solution of which is generally not unique. We show that through a task-specific training process, passive diffractive optical networks composed of successive transmissive layers can all-optically and simultaneously classify two different randomly-selected, spatially overlapping phase images at the input. After trained with ~ 550 million unique combinations of phase-encoded handwritten digits from the MNIST dataset, our blind testing results reveal that the diffractive optical network achieves an accuracy of > 85.8% for all-optical classification of two overlapping phase images of new handwritten digits. In addition to all-optical classification of overlapping phase objects, we also demonstrate the reconstruction of these phase images based on a shallow electronic neural network that uses the highly compressed output of the diffractive optical network as its input (with e.g., ~ 20-65 times less number of pixels) to rapidly reconstruct both of the phase images, despite their spatial overlap and related phase ambiguity. The presented phase image classification and reconstruction framework might find applications in e.g., computational imaging, microscopy and quantitative phase imaging fields.
Measurement-induced quantum walks

We investigate a tight-binding quantum walk on a graph. Repeated stroboscopic measurements of the position of the particle yield a measured "trajectory," and a combination of classical and quantum mechanical properties for the walk are observed. We explore the effects of the measurements on the spreading of the packet on a one-dimensional line, showing that except for the Zeno limit, the system converges to Gaussian statistics similarly to a classical random walk. A large deviation analysis and an Edgeworth expansion yield quantum corrections to this normal behavior. We then explore the first passage time to a target state using a generating function method, yielding properties like the quantization of the mean first return time. In particular, we study the effects of certain sampling rates that cause remarkable changes in the behavior in the system, such as divergence of the mean detection time in finite systems and decomposition of the phase space into mutually exclusive regions, an effect that mimics ergodicity breaking, whose origin here is the destructive interference in quantum mechanics. For a quantum walk on a line, we show that in our system the first detection probability decays classically like (time)^{-3/2}. This is dramatically different compared to local measurements, which yield a decay rate of (time)^{-3}, indicating that the exponents of the first passage time depend on the type of measurements used.
Balancing between stability and change in Agile teams

Purpose This study aims to create a better understanding of how practitioners implement and work Agile while balancing the tensions arising between stability and change. Design/methodology/approach A grounded theory approach was used to explore what happens in practice when software development teams implement and work Agile. The empirical data consists of twenty semi-structured interviews with practitioners working in fourteen different organizations and in six different Agile roles. Findings As a result, a substantive theory was presented of continuously balancing between stability and change in Agile teams. In addition, the study also proposes three guidelines that can help organizations about to change their way of working to Agile. Research limitations/implications The inherent limitation of a grounded theory study is that a substantial theory can only explain the specific contexts explored in that study. Thus, this study's contribution is a substantial theory that needs to be further developed and improved. Practical implications The proposed guidelines can help organizations about to change their way of working to Agile. They can also assist organizations in switching from “doing Agile” to “being Agile”, thus becoming more successful. Originality/value The new perspective that this study contributes is the fact that our discovered categories show that several inherent processes are ongoing at the same time in order to balance the need to have both stability and change.
Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy

Simultaneous machine translation (SiMT) generates translation before reading the entire source sentence and hence it has to trade off between translation quality and latency.To fulfill the requirements of different translation quality and latency in practical applications, the previous methods usually need to train multiple SiMT models for different latency levels, resulting in large computational costs.In this paper, we propose a universal SiMT model with Mixture-of-Experts Wait-k Policy to achieve the best translation quality under arbitrary latency with only one trained model.Specifically, our method employs multi-head attention to accomplish the mixture of experts where each head is treated as a wait-k expert with its own waiting words number, and given a test latency and source inputs, the weights of the experts are accordingly adjusted to produce the best translation.Experiments on three datasets show that our method outperforms all the strong baselines under different latency, including the state-of-the-art adaptive policy.