Reviewerly/exHarmony huggingface dataset:
On Importance Sampling-Based Evaluation of Latent Language Models

Language models that use additional latent structures (e.g., syntax trees, coreference chains, and knowledge graph links) provide several advantages over traditional language models.However, likelihood-based evaluation of these models is often intractable as it requires marginalizing over the latent space.Existing methods avoid this issue by using importance sampling.Although this approach has asymptotic guarantees, analysis is rarely conducted on the effect of decisions such as sample size, granularity of sample aggregation, and the proposal distribution on the reported estimates.In this paper, we measure the effect these factors have on perplexity estimates for three different latent language models.In addition, we elucidate subtle differences in how importance sampling is applied, which can have substantial effects on the final estimates, as well as provide theoretical results that reinforce the validity of importance sampling for evaluating latent language models.
Multi-Technology Correction Based 3D Human Pose Estimation for Jump Analysis in Figure Skating

Jump analysis in figure skating is important. Recovering the 3D pose of a figure skater has become increasingly important. However, issues such as restrictions from an athlete&rsquo;s clothing, self-occlusion, abnormal pose and so on will result in poor results. This paper proposes a multi-technology correction framework to obtain a 3D human pose. The framework consists of three key components: temporal information-based mutational point correction, multi-perspective-based reconstructed point selection and trajectory smoothness-based inaccurate point correction. Firstly, temporal information is used to correct the mutational points at the 2D level. Secondly, a multi-perspective is used to select the correct spatial points at the 3D level. Thirdly, trajectory smoothness is used to correct inaccuracies at the 3D level. This work will serve the purpose of displaying the 3D animated pose of a figure skater. The quality grade of the result rate on the test sequences is 87.25%.
Market Manipulation: An Adversarial Learning Framework for Detection and Evasion

We propose an adversarial learning framework to capture the evolving game between a regulator who develops tools to detect market manipulation and a manipulator who obfuscates actions to evade detection. The model includes three main parts: (1) a generator that learns to adapt original manipulation order streams to resemble trading patterns of a normal trader while preserving the manipulation intent; (2) a discriminator that differentiates the adversarially adapted manipulation order streams from normal trading activities; and (3) an agent-based simulator that evaluates the manipulation effect of adapted outputs. We conduct experiments on simulated order streams associated with a manipulator and a market-making agent respectively. We show examples of adapted manipulation order streams that mimic a specified market maker's quoting patterns and appear qualitatively different from the original manipulation strategy we implemented in the simulator. These results demonstrate the possibility of automatically generating a diverse set of (unseen) manipulation strategies that can facilitate the training of more robust detection algorithms.
Cross-denoising Network against Corrupted Labels in Medical Image Segmentation with Domain Shift

Deep convolutional neural networks (DCNNs) have contributed many breakthroughs in segmentation tasks, especially in the field of medical imaging. However, domain shift and corrupted annotations, which are two common problems in medical imaging, dramatically degrade the performance of DCNNs in practice. In this paper, we propose a novel robust cross-denoising framework using two peer networks to address domain shift and corrupted label problems with a peer-review strategy. Specifically, each network performs as a mentor, mutually supervised to learn from reliable samples selected by the peer network to combat with corrupted labels. In addition, a noise-tolerant loss is proposed to encourage the network to capture the key location and filter the discrepancy under various noise-contaminant labels. To further reduce the accumulated error, we introduce a class-imbalanced cross learning using most confident predictions at class-level. Experimental results on REFUGE and Drishti-GS datasets for optic disc (OD) and optic cup (OC) segmentation demonstrate the superior performance of our proposed approach to the state-of-the-art methods.
PeTra: A Sparsely Supervised Memory Model for People Tracking

We propose PeTra, a memory-augmented neural network designed to track entities in its memory slots.PeTra is trained using sparse annotation from the GAP pronoun resolution dataset and outperforms a prior memory model on the task while using a simpler architecture.We empirically compare key modeling choices, finding that we can simplify several aspects of the design of the memory module while retaining strong performance.To measure the people tracking capability of memory models, we (a) propose a new diagnostic evaluation based on counting the number of unique entities in text, and (b) conduct a small scale human evaluation to compare evidence of people tracking in the memory logs of PeTra relative to a previous approach.PeTra is highly effective in both evaluations, demonstrating its ability to track people in its memory despite being trained with limited annotation.
Adaptively Multi-Objective Adversarial Training for Dialogue Generation

Naive neural dialogue generation models tend to produce repetitive and dull utterances. The promising adversarial models train the generator against a well-designed discriminator to push it to improve towards the expected direction. However, assessing dialogues requires consideration of many aspects of linguistics, which are difficult to be fully covered by a single discriminator. To address it, we reframe the dialogue generation task as a multi-objective optimization problem and propose a novel adversarial dialogue generation framework with multiple discriminators that excel in different objectives for multiple linguistic aspects, called AMPGAN, whose feasibility is proved by theoretical derivations. Moreover, we design an adaptively adjusted sampling distribution to balance the discriminators and promote the overall improvement of the generator by continuing to focus on these objectives that the generator is not performing well relatively. Experimental results on two real-world datasets show a significant improvement over the baselines.
Discovering Latent Class Labels for Multi-Label Learning

Existing multi-label learning (MLL) approaches mainly assume all the labels are observed and construct classification models with a fixed set of target labels (known labels). However, in some real applications, multiple latent labels may exist outside this set and hide in the data, especially for large-scale data sets. Discovering and exploring the latent labels hidden in the data may not only find interesting knowledge but also help us to build a more robust learning model. In this paper, a novel approach named DLCL (i.e., Discovering Latent Class Labels for MLL) is proposed which can not only discover the latent labels in the training data but also predict new instances with the latent and known labels simultaneously. Extensive experiments show a competitive performance of DLCL against other state-of-the-art MLL approaches.
TADS: Learning Time-Aware Scheduling Policy with Dyna-Style Planning for Spaced Repetition

Spaced repetition technique aims at improving long-term memory retention for human students by exploiting repeated, spaced reviews of learning contents. The study of spaced repetition focuses on designing an optimal policy to schedule the learning contents. To the best of our knowledge, none of the existing methods based on reinforcement learning take into account the varying time intervals between two adjacent learning events of the student, which, however, are essential to determine real-world schedule. In this paper, we aim to learn a scheduling policy that fully exploits the varying time interval information with high sample efficiency. We propose the Time-Aware scheduler with Dyna-Style planning (TADS) approach: a sample-efficient reinforcement learning framework for realistic spaced repetition. TADS learns a Time-LSTM policy to select an optimal content according to the student's whole learning history and the time interval since the last learning event. Besides, Dyna-style planning is integrated into TADS to further improve the sample efficiency. We evaluate our approach on three environments built from synthetic data and real-world data based on well-recognized cognitive models. Empirical results demonstrate that TADS achieves superior performance against state-of-the-art algorithms.
Active 3D Motion Visualization Based on Spatiotemporal Light-Ray Integration

In this paper, we propose a method of visualizing 3D motion with zero latency. This method achieves motion visualization by projecting special high-frequency light patterns on moving objects without using any feedback mechanisms. For this objective, we focus on the time integration of light rays in the sensing system of observers. It is known that the visual system of human observers integrates light rays in a certain period. Similarly, the image sensor in a camera integrates light rays during the exposure time. Thus, our method embeds multiple images into a time-varying light field, such that the observer of the time-varying light field observes completely different images according to the dynamic motion of the scene. Based on this concept, we propose a method of generating special high-frequency patterns of projector lights. After projection onto target objects with projectors, the image observed on the target changes automatically depending on the motion of the objects and without any scene sensing and data analysis. In other words, we achieve motion visualization without the time delay incurred during sensing and computing.
SR-CL-DMC: P-frame coding with Super-Resolution, Color Learning, and Deep Motion Compensation

This paper proposes a deep learning based video coding framework to greatly increase the compression ratio and keep the video quality by efficiently leveraging the information from a reference. In the encoder, the input frame is compressed by down-sampling to a lower resolution, eliminating color information, and then encoding residual between the current frame and the reference frame using Versatile Video Coding (VVC). The decoder consists of two main parts: Super-Resolution with Color Learning (SR-CL), and Deep Motion Compensation (DMC). For the SR-CL part, we adopt Restoration-Reconstruction Deep Neural Network to firstly restore the missing information from compression at low resolution and compression without color. And then, the sampling degradation at high-resolution is compensated. For the DMC part, we adopt recursive-feedback architectures to propose an optical flow estimation and refinement using Dilated Inception Blocks. As a result, the work achieves 64:1 compression ratio with 41.81/41.34 dB PSNR and 0.9959/0.9962 MS-SSIM on the validation/test set provided by the CLIC P-frame track challenge.
Phonetic and Visual Priors for Decipherment of Informal Romanization

Informal romanization is an idiosyncratic process used by humans in informal digital communication to encode non-Latin script languages into Latin character sets found on common keyboards.Character substitution choices differ between users but have been shown to be governed by the same main principles observed across a variety of languagesnamely, character pairs are often associated through phonetic or visual similarity.We propose a noisy-channel WFST cascade model for deciphering the original non-Latin script from observed romanized text in an unsupervised fashion.We train our model directly on romanized data from two languages: Egyptian Arabic and Russian.We demonstrate that adding inductive bias through phonetic and visual priors on character mappings substantially improves the model's performance on both languages, yielding results much closer to the supervised skyline.Finally, we introduce a new dataset of romanized Russian, collected from a Russian social network website and partially annotated for our experiments. 1
Convolutional Neural Networks with Compression Complexity Pooling for Out-of-Distribution Image Detection

To reliably detect out-of-distribution images based on already deployed convolutional neural networks, several recent studies on the out-of-distribution detection have tried to define effective confidence scores without retraining the model. Although they have shown promising results, most of them need to find the optimal hyperparameter values by using a few out-of-distribution images, which eventually assumes a specific test distribution and makes it less practical for real-world applications. In this work, we propose a novel out-of-distribution detection method termed as MALCOM, which neither uses any out-of-distribution sample nor retrains the model. Inspired by an observation that the global average pooling cannot capture spatial information of feature maps in convolutional neural networks, our method aims to extract informative sequential patterns from the feature maps. To this end, we introduce a similarity metric that focuses on shared patterns between two sequences based on the normalized compression distance. In short, MALCOM uses both the global average and the spatial patterns of feature maps to identify out-of-distribution images accurately.
Independent Skill Transfer for Deep Reinforcement Learning

Recently, diverse primitive skills have been learned by adopting the entropy as intrinsic reward, which further shows that new practical skills can be produced by combining a variety of primitive skills. This is essentially skill transfer, very useful for learning high-level skills but quite challenging due to the low efficiency of transferring primitive skills. In this paper, we propose a novel efficient skill transfer method, where we learn independent skills and only independent components of skills are transferred instead of the whole set of skills. More concretely, independent components of skills are obtained through independent component analysis (ICA), which always have a smaller amount (or lower dimension) compared with their mixtures. With a lower dimension, independent skill transfer (IST) exhibits a higher efficiency on learning a given task. Extensive experiments including three robotic tasks demonstrate the effectiveness and high efficiency of our proposed IST method in comparison to direct primitive-skill transfer and conventional reinforcement learning.
A survey on the global optimization problem using Kruskal–Wallis test

The article deals with experimental comparison and verification of stochastic algorithms for global optimization while searching the global optimum in dimensions 3 and 4 of selected testing functions in Matlab computing environment.To draw a comparison, we took the algorithms Controlled Random Search, Differential Evolution that we created for this test and implemented in Matlab, and fminsearch function which is directly built in Matlab.The basic quantities to compare algorithms were time complexity while searching the considered area and reliability of finding the global optimum of the 1st De Jong function, Rosenbrock's saddle, Ackley's function and Griewangk's function.The time complexity of the algorithms was determined by the number of test function evaluations during the global optimum search and we analysed the results of the experiment using the "Kruskal-Wallis test" non-parametric method.
Phase stepping through polarizing modulation in electronic speckle pattern interferometry

We have demonstrated a speckle out-of-plane interferometer that employs phase-stepping procedures by means of polarization modulation. The system generates circular polarization states with opposite signs at each arm of the system, which overlap at the output of the interferometer, to generate phase shifts operating a conventional linear polarizer; the emerging polarization states have been analyzed to obtain the shifts needed to process the optical phase. The phase-stepping technique is demonstrated with a two-step algorithm to measure out-of-plane displacement on a flat metal plate.
Recurrent Dirichlet Belief Networks for interpretable Dynamic Relational Data Modelling

The Dirichlet Belief Network~(DirBN) has been recently proposed as a promising approach in learning interpretable deep latent representations for objects. In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data. The proposed Recurrent-DBN has the following merits: (1) it infers interpretable and organised hierarchical latent structures for objects within and across time steps; (2) it enables recurrent long-term temporal dependence modelling, which outperforms the one-order Markov descriptions in most of the dynamic probabilistic frameworks; (3) the computational cost scales to the number of positive links only. In addition, we develop a new inference strategy, which first upward-and-backward propagates latent counts and then downward-and-forward samples variables, to enable efficient Gibbs sampling for the Recurrent-DBN. We apply the Recurrent-DBN to dynamic relational data problems. The extensive experiment results on real-world data validate the advantages of the Recurrent-DBN over the state-of-the-art models in interpretable latent structure discovery and improved link prediction performance.
Seq2seq Deep Learning Method for Summary Generation by LSTM with Two-way Encoder and Beam Search Decoder

In this paper a deep neural network architecture is proposed for abstractive summarization, which aims generating brief summaries from long text documents. Unlike default sequence-to-sequence model with one-way encoder, the proposed solution has a two-way encoder with state-of-the-art recurrent neural networks type, LSTM (Long Short-Term Memory). One of the LSTMs starts from the beginning of the series, and the other from the end, so the final state is created by combining the inner states of them. Furthermore, two types of decoder were used, one for training, and an extended one for inferencing. The extended decoder is based on the beam search idea, which retains more than one candidate when generating sequence elements. The final prediction of the decoder will be the sequence with the highest probability. In the system the attention mechanism and appropriate embedding layer also help with text generation. Experiments are presented on the Newsroom dataset with relational article and summary pairs.
Unsupervised Twitter Sentiment Analysis on The Revision of Indonesian Code Law and the Anti-Corruption Law using Combination Method of Opinion Word and Agglomerative Hierarchical Clustering

The rejection on ratification of the revision of Indonesian Code Law or known as RKUHP and Corruption Law raises several opinions from various perspectives in social media. Twitter as one of many platforms affected, has more than 19.5 million users in Indonesia. Twitter is one of many social media in Indonesia where people can share their views, arguments, information, and opinions from all points of view. Since Twitter has a great diversity of users, it needs a system which is designed to determine the opinion tendency towards the problems or objects. The purpose of this study is to analyze the sentiment of Twitter users' tweets to reject the revision of the Law whether they have positive or negative sentiments using the Agglomerative Hierarchical Clustering method. The data that being used in this study were obtained from the results of crawling tweets based on hashtag (#) (#ReformasiDikorupsi). The next stage is pre-processing which consists of case folding, tokenizing, cleansing, sanitizing, and stemming. The extraction features Opinion words and Term Frequency (TF) which performs the process automatically. In the clustering stage, two clusters use three approaches; single linkage, complete linkage and average linkage. In the accuracy calculation phase, the writer uses the error ratio, confusion matrix, and silhouette coefficient. Therefore, the results are quite good. From 2408 tweets, the highest accuracy results are 61.6%.
Autonomous search in a social and ubiquitous Web

Recent W3C recommendations for the Web of Things (WoT) and the Social Web are turning hypermedia into a homogeneous information fabric that interconnects heterogeneous resources: devices, people, information resources, abstract concepts, etc. The integration of multi-agent systems with such hypermedia environments now provides a means to distribute autonomous behavior in worldwide pervasive systems. A central problem then is to enable autonomous agents to discover heterogeneous resources in worldwide and dynamic hypermedia environments. This is a problem in particular in WoT environments that rely on open standards and evolve rapidly—thus requiring agents to adapt their behavior at run time in pursuit of their design objectives. To this end, we developed a hypermedia search engine for the WoT that allows autonomous agents to perform approximate search queries in order to retrieve relevant resources in their environment in (weak) real time. The search engine crawls dynamic WoT environments to discover and index device metadata described with the W3C WoT Thing Description, and exposes a SPARQL endpoint that agents can use for approximate search. To demonstrate the feasibility of our approach, we implemented a prototype application for the maintenance of industrial robots in worldwide manufacturing systems. The prototype demonstrates that our semantic hypermedia search engine enhances the flexibility and agility of autonomous agents in a social and ubiquitous Web.
PuzzLing Machines: A Challenge on Learning From Small Data

Deep neural models have repeatedly proved excellent at memorizing surface patterns from large datasets for various ML and NLP benchmarks.They struggle to achieve human-like thinking, however, because they lack the skill of iterative reasoning upon knowledge.To expose this problem in a new light, we introduce a challenge on learning from small data, PuzzLing Machines, which consists of Rosetta Stone puzzles from Linguistic Olympiads for high school students.These puzzles are carefully designed to contain only the minimal amount of parallel text necessary to deduce the form of unseen expressions.Solving them does not require external information (e.g., knowledge bases, visual signals) or linguistic expertise, but meta-linguistic awareness and deductive skills.Our challenge contains around 100 puzzles covering a wide range of linguistic phenomena from 81 languages.We show that both simple statistical algorithms and state-of-the-art deep neural models perform inadequately on this challenge, as expected.We hope that this benchmark, available at https://ukplab.github.io/PuzzLing-Machines/, inspires further efforts towards a new paradigm in NLP-one that is grounded in human-like reasoning and understanding.