Reviewerly/exHarmony huggingface dataset:
A robust reputation iterative algorithm based on Z-statistics in a rating system with thorny objects

With the explosive growth of online alternative objects, the availability of a credible rating system could play a valuable role for users who are evaluating and/or choosing items. Due to the existence of spammers, many methods have been proposed that employ a reputation-based mechanism in the last few years. However, these methods only consider spammers’ behaviours but neglect the effect of thorny objects, which are difficult to evaluate because of their intrinsic uncertainty and complexity with respect to the problem context, knowledge gaps, and various user criteria. To solve this problem, we propose a new reputation iterative algorithm based on Z-statistics (ZS), which eliminates the effect of thorny objects and strengthens the ability to deal with spammers. In this article, the proposed method makes effectiveness comparisons with other typical methods using a rating example and a simulated rating system, and presents additional effectiveness and complexity analyses of these results. Finally, the experimental results also demonstrate that the ZS algorithm outperforms other methods in simultaneously dealing with the effects of spammers and thorny objects.
Development of English Grammatical Competence Through the Use of the Project Method

The paper considers the features of applying the project method as the latest pedagogical technology in the process of developing English grammar skills in students of philological specialities. The relevance of the study is conditioned by the need to find an effective methodology for implementing video projects (a type of project method) in teachers’ universities. The purpose of the study is to find out the effectiveness of using video project technology to improve the grammatical competence of philology students. In the course of the study, the method of critical analysis of literature was applied to investigate the goals, methods, and means of project technology and to formulate the conclusions; the method of scientific observation of the educational process contributed to the direct observation of students’ activities; the method of studying and generalising the experience of teachers revealed methodological techniques and methods in the development of proper grammar skills of students; the questionnaire method helped to establish the level of knowledge of students about the ways of learning and teaching a foreign language; the experimental method was used to prove the research hypothesis; the method of systematisation and generalisation was used in the analysis of findings. It is found that the essence of the concept of grammatical competence of a philology student consists in a set of knowledge about the grammatical organisation of the language system, and in the ability to operate with this knowledge in a certain speech activity. It is proved that by improving grammar, students develop their speech competencies — in reading, writing, listening, and speaking. The expediency of using information and communication technologies in studying English grammar, especially in distance learning, is substantiated. The use of the project method is proposed to improve the grammatical and informational skills of future foreign language teachers. It is confirmed that this method stimulates the development of cognitive and creative skills of students, is based on the systematic organisation of searches of a problem-based educational nature, encourages the independent acquisition of Information, its comprehension, and application in practice. In addition, project technology helps students acquire teamwork skills, careful planning, and increased motivation to improve their own communication competence
ProjecString: Turning an Everyday String Curtain into an Interactive Projection Display

We present ProjecString, a touch-sensitive string curtain projection display that encourages novel interactions via touching, grasping, and seeing and walking through the display. We embed capacitive-sensing conductive chains into an everyday string curtain, turning it into both a space divider and an interactive display. This novel take on transforming an everyday object into an interactive projection surface with a unique translucent property creates novel interactions that are both immersive and isolating.
CADA: Communication-Adaptive Distributed Adam

Stochastic gradient descent (SGD) has taken the stage as the primary workhorse for large-scale machine learning. It is often used with its adaptive variants such as AdaGrad, Adam, and AMSGrad. This paper proposes an adaptive stochastic gradient descent method for distributed machine learning, which can be viewed as the communication-adaptive counterpart of the celebrated Adam method - justifying its name CADA. The key components of CADA are a set of new rules tailored for adaptive stochastic gradients that can be implemented to save communication upload. The new algorithms adaptively reuse the stale Adam gradients, thus saving communication, and still have convergence rates comparable to original Adam. In numerical experiments, CADA achieves impressive empirical performance in terms of total communication round reduction.
A Guide to Global Quantum Key Distribution Networks

We describe systems and methods for the deployment of global quantum key distribution (QKD) networks covering transoceanic, long-haul, metro, and access segments of the network. A comparative study of the state-of-the-art QKD technologies is carried out, including both terrestrial QKD via optical fibers and free-space optics, as well as spaceborne solutions via satellites. We compare the pros and cons of various existing QKD technologies, including channel loss, potential interference, distance, connection topology, deployment cost and requirements, as well as application scenarios. Technical selection criteria and deployment requirements are developed for various different QKD solutions in each segment of networks. For example, optical fiber-based QKD is suitable for access networks due to its limited distance and compatibility with point-to-multipoint (P2MP) topology; with the help of trusted relays, it can be extended to long-haul and metro networks. Spaceborne QKD on the other hand, has much smaller channel loss and extended transmission distance, which can be used for transoceanic and long-haul networks exploiting satellite-based trusted relays.
A Framework for Conditional Statement Technical Debt Identification and  Description

Technical Debt (TD) occurs when development teams favour short-term operability over long-term stability. Since this places software maintainability at risk, technical debt requires early attention to avoid paying for extensively accumulated interest. Most of the existing work focuses on detecting technical debt using code comments, known as Self-Admitted Technical Debt (SATD). However, there are many cases where technical debt instances are not explicitly acknowledged but deeply hidden in the code. In this paper, we propose a framework that caters for the absence of SATD comments in code. Our Self-Admitted Technical Debt Identification and Description (SATDID) framework determines if technical debt should be self-admitted for an input code fragment. If that is the case, SATDID will automatically generate the appropriate descriptive SATD comment that can be attached with the code. While our approach is applicable in principle to any type of code fragments, we focus in this study on technical debt hidden in conditional statements, one of the most TD-carrying parts of code. We explore and evaluate different implementations of SATDID. The evaluation results demonstrate the applicability and effectiveness of our framework over multiple benchmarks. Comparing with the results from the benchmarks, our approach provides at least 21.18%, 43.93%, 14.24%, and 583.33% improvements in terms of Precision, Recall, F-1, and cumulative Bleu-4 scores, respectively. In addition, we conduct a human evaluation to the SATD comments generated by SATDID. In 1-5 and 0-5 scales for Acceptability and Understandability, the total means achieved by our approach are 3.128 and 3.172, respectively.
YolactEdge: Real-time Instance Segmentation on the Edge

We propose YolactEdge, the first competitive instance segmentation approach that runs on small edge devices at real-time speeds. Specifically, YolactEdge runs at up to 30.8 FPS on a Jetson AGX Xavier (and 172.7 FPS on an RTX 2080 Ti) with a ResNet-101 backbone on 550x550 resolution images. To achieve this, we make two improvements to the state-of-the-art image-based real-time method YOLACT: (1) applying TensorRT optimization while carefully trading off speed and accuracy, and (2) a novel feature warping module to exploit temporal redundancy in videos. Experiments on the YouTube VIS and MS COCO datasets demonstrate that YolactEdge produces a 3-5x speed up over existing real-time methods while producing competitive mask and box detection accuracy. We also conduct ablation studies to dissect our design choices and modules. Code and models are available at https://github.com/haotian-liu/yolact_edge.
Model-free readout-error mitigation for quantum expectation values

Measurements on current quantum processors are subject to hardware imperfections that lead to readout errors. These errors manifest themselves as a bias in quantum expectation values. Here, we propose a very simple method that forces the bias in the expectation value to appear as a multiplicative factor that can be measured directly and removed at the cost of an increase in the sampling complexity for the observable. The method assumes no specific form of the noise, but only requires that the noise is `weak' to avoid excessive sampling overhead. We provide bounds relating the error in the expectation value to the sample complexity.
More Industry-friendly: Federated Learning with High Efficient Design

Although many achievements have been made since Google threw out the paradigm of federated learning (FL), there still exists much room for researchers to optimize its efficiency. In this paper, we propose a high efficient FL method equipped with the double head design aiming for personalization optimization over non-IID dataset, and the gradual model sharing design for communication saving. Experimental results show that, our method has more stable accuracy performance and better communication efficient across various data distributions than other state of art methods (SOTAs), makes it more industry-friendly.
Learning to Stop: Dynamic Simulation Monte-Carlo Tree Search

Monte Carlo tree search (MCTS) has achieved state-of-the-art results in many domains such as Go and Atari games when combining with deep neural networks (DNNs). When more simulations are executed, MCTS can achieve higher performance but also requires enormous amounts of CPU and GPU resources. However, not all states require a long searching time to identify the best action that the agent can find. For example, in 19x19 Go and NoGo, we found that for more than half of the states, the best action predicted by DNN remains unchanged even after searching 2 minutes. This implies that a significant amount of resources can be saved if we are able to stop the searching earlier when we are confident with the current searching result. In this paper, we propose to achieve this goal by predicting the uncertainty of the current searching status and use the result to decide whether we should stop searching. With our algorithm, called Dynamic Simulation MCTS (DS-MCTS), we can speed up a NoGo agent trained by AlphaZero 2.5 times faster while maintaining a similar winning rate. Also, under the same average simulation count, our method can achieve a 61% winning rate against the original program.
Improving Image Captioning by Leveraging Intra- and Inter-layer Global  Representation in Transformer Network

Transformer-based architectures have shown great success in image captioning, where object regions are encoded and then attended into the vectorial representations to guide the caption decoding. However, such vectorial representations only contain region-level information without considering the global information reflecting the entire image, which fails to expand the capability of complex multi-modal reasoning in image captioning. In this paper, we introduce a Global Enhanced Transformer (termed GET) to enable the extraction of a more comprehensive global representation, and then adaptively guide the decoder to generate high-quality captions. In GET, a Global Enhanced Encoder is designed for the embedding of the global feature, and a Global Adaptive Decoder are designed for the guidance of the caption generation. The former models intra- and inter-layer global representation by taking advantage of the proposed Global Enhanced Attention and a layer-wise fusion module. The latter contains a Global Adaptive Controller that can adaptively fuse the global information into the decoder to guide the caption generation. Extensive experiments on MS COCO dataset demonstrate the superiority of our GET over many state-of-the-arts.
Imitating Interactive Intelligence

A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through natural language. Here we study how to design artificial agents that can interact naturally with humans using the simplification of a virtual environment. This setting nevertheless integrates a number of the central challenges of artificial intelligence (AI) research: complex visual perception and goal-directed physical control, grounded language comprehension and production, and multi-agent social interaction. To build agents that can robustly interact with humans, we would ideally train them while they interact with humans. However, this is presently impractical. Therefore, we approximate the role of the human with another learned agent, and use ideas from inverse reinforcement learning to reduce the disparities between human-human and agent-agent interactive behaviour. Rigorously evaluating our agents poses a great challenge, so we develop a variety of behavioural tests, including evaluation by humans who watch videos of agents or interact directly with them. These evaluations convincingly demonstrate that interactive training and auxiliary losses improve agent behaviour beyond what is achieved by supervised learning of actions alone. Further, we demonstrate that agent capabilities generalise beyond literal experiences in the dataset. Finally, we train evaluation models whose ratings of agents agree well with human judgement, thus permitting the evaluation of new agent models without additional effort. Taken together, our results in this virtual environment provide evidence that large-scale human behavioural imitation is a promising tool to create intelligent, interactive agents, and the challenge of reliably evaluating such agents is possible to surmount.
6-qubit Optimal Clifford Circuits

Clifford group lies at the core of quantum computation -- it underlies quantum error correction, its elements can be used to perform magic state distillation and they form randomized benchmarking protocols, Clifford group is used to study quantum entanglement, and more. The ability to utilize Clifford group elements in practice relies heavily on the efficiency of their circuit-level implementation. Finding short circuits is a hard problem; despite Clifford group being finite, its size grows quickly with the number of qubits $n$, limiting known optimal implementations to $n{=}4$ qubits. For $n{=}6$, the number of Clifford group elements is about $2.1{\cdot}10^{23}$. In this paper, we report a set of algorithms, along with their C/C++ implementation, that implicitly synthesize optimal circuits for all 6-qubit Clifford group elements by storing a subset of the latter in a database of size 2.1TB (1KB=1024B). We demonstrate how to extract arbitrary optimal 6-qubit Clifford circuit in $0.0009358$ and $0.0006274$ seconds using consumer- and enterprise-grade computers (hardware) respectively, while relying on this database.
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation

In this work, we propose TediGAN, a novel framework for multi-modal image generation and manipulation with textual descriptions. The proposed method consists of three components: StyleGAN inversion module, visual-linguistic similarity learning, and instance-level optimization. The inversion module maps real images to the latent space of a well-trained StyleGAN. The visual-linguistic similarity learns the text-image matching by mapping the image and text into a common embedding space. The instance-level optimization is for identity preservation in manipulation. Our model can produce diverse and high-quality images with an unprecedented resolution at 1024. Using a control mechanism based on style-mixing, our TediGAN inherently supports image synthesis with multi-modal inputs, such as sketches or semantic labels, with or without instance guidance. To facilitate text-guided multi-modal synthesis, we propose the Multi-Modal CelebA-HQ, a large-scale dataset consisting of real face images and corresponding semantic segmentation map, sketch, and textual descriptions. Extensive experiments on the introduced dataset demonstrate the superior performance of our proposed method. Code and data are available at https://github.com/weihaox/TediGAN.
Adaptive Weighted Discriminator for Training Generative Adversarial  Networks

Generative adversarial network (GAN) has become one of the most important neural network models for classical unsupervised machine learning. A variety of discriminator loss functions have been developed to train GAN's discriminators and they all have a common structure: a sum of real and fake losses that only depends on the actual and generated data respectively. One challenge associated with an equally weighted sum of two losses is that the training may benefit one loss but harm the other, which we show causes instability and mode collapse. In this paper, we introduce a new family of discriminator loss functions that adopts a weighted sum of real and fake parts, which we call adaptive weighted loss functions or aw-loss functions. Using the gradients of the real and fake parts of the loss, we can adaptively choose weights to train a discriminator in the direction that benefits the GAN's stability. Our method can be potentially applied to any discriminator model with a loss that is a sum of the real and fake parts. Experiments validated the effectiveness of our loss functions on an unconditional image generation task, improving the baseline results by a significant margin on CIFAR-10, STL-10, and CIFAR-100 datasets in Inception Scores and FID.
Regularization via Adaptive Pairwise Label Smoothing

Label Smoothing (LS) is an effective regularizer to improve the generalization of state-of-the-art deep models. For each training sample the LS strategy smooths the one-hot encoded training signal by distributing its distribution mass over the non ground-truth classes, aiming to penalize the networks from generating overconfident output distributions. This paper introduces a novel label smoothing technique called Pairwise Label Smoothing (PLS). The PLS takes a pair of samples as input. Smoothing with a pair of ground-truth labels enables the PLS to preserve the relative distance between the two truth labels while further soften that between the truth labels and the other targets, resulting in models producing much less confident predictions than the LS strategy. Also, unlike current LS methods, which typically require to find a global smoothing distribution mass through cross-validation search, PLS automatically learns the distribution mass for each input pair during training. We empirically show that PLS significantly outperforms LS and the baseline models, achieving up to 30% of relative classification error reduction. We also visually show that when achieving such accuracy gains the PLS tends to produce very low winning softmax scores.
NHSS: A Speech and Singing Parallel Database

We present a database of parallel recordings of speech and singing, collected and released by the Human Language Technology (HLT) laboratory at the National University of Singapore (NUS), that is called NUS-HLT Speak-Sing (NHSS) database. We release this database to the public to support research activities, that include, but not limited to comparative studies of acoustic attributes of speech and singing signals, cooperative synthesis of speech and singing voices, and speech-to-singing conversion. This database consists of recordings of sung vocals of English pop songs, the spoken counterpart of lyrics of the songs read by the singers in their natural reading manner, and manually prepared utterance-level and word-level annotations. The audio recordings in the NHSS database correspond to 100 songs sung and spoken by 10 singers, resulting in a total of 7 hours of audio data. There are 5 male and 5 female singers, singing and reading the lyrics of 10 songs each. In this paper, we discuss the design methodology of the database, analyse the similarities and dissimilarities in characteristics of speech and singing voices, and provide some strategies to address relationships between these characteristics for converting one to another. We develop benchmark systems, which can be used as reference for speech-to-singing alignment, spectral mapping, and conversion using the NHSS database.
BayesTime: Bayesian Functional Principal Components for Sparse  Longitudinal Data

Modeling non-linear temporal trajectories is of fundamental interest in many application areas, such as in longitudinal microbiome analysis. Many existing methods focus on estimating mean trajectories, but it is also often of value to assess temporal patterns of individual subjects. Sparse principal components analysis (SFPCA) serves as a useful tool for assessing individual variation in non-linear trajectories; however its application to real data often requires careful model selection criteria and diagnostic tools. Here, we propose a Bayesian approach to SFPCA, which allows users to use the efficient leave-one-out cross-validation (LOO) with Pareto-smoothed importance sampling (PSIS) for model selection, and to utilize the estimated shape parameter from PSIS-LOO and also the posterior predictive checks for graphical model diagnostics. This Bayesian implementation thus enables careful application of SFPCA to a wide range of longitudinal data applications.
A combinatorial bijection on di-sk trees

A di-sk tree is a rooted binary tree whose nodes are labeled by $\oplus$ or $\ominus$, and no node has the same label as its right child. The di-sk trees are in natural bijection with separable permutations. We construct a combinatorial bijection on di-sk trees proving the two quintuples $(\LMAX,\LMIN,\DESB,\iar,\comp)$ and $(\LMAX,\LMIN,\DESB,\comp,\iar)$ have the same distribution over separable permutations. Here for a permutation $\pi$, $\LMAX(\pi)/\LMIN(\pi)$ is the set of values of the left-to-right maxima/minima of $\pi$ and $\DESB(\pi)$ is the set of descent bottoms of $\pi$, while $\comp(\pi)$ and $\iar(\pi)$ are respectively the number of components of $\pi$ and the length of initial ascending run of $\pi$. Interestingly, our bijection specializes to a bijection on $312$-avoiding permutations, which provides (up to the classical {\em Knuth--Richards bijection}) an alternative approach to a result of Rubey (2016) that asserts the two triples $(\LMAX,\iar,\comp)$ and $(\LMAX,\comp,\iar)$ are equidistributed on $321$-avoiding permutations. Rubey's result is a symmetric extension of an equidistribution due to Adin--Bagno--Roichman, which implies the class of $321$-avoiding permutations with a prescribed number of components is Schur positive. Some equidistribution results for various statistics concerning tree traversal are presented in the end.