Reviewerly/exHarmony huggingface dataset:
Multi-channel Reverse Dictionary Model

A reverse dictionary takes the description of a target word as input and outputs the target word together with other words that match the description. Existing reverse dictionary methods cannot deal with highly variable input queries and low-frequency target words successfully. Inspired by the description-to-word inference process of humans, we propose the multi-channel reverse dictionary model, which can mitigate the two problems simultaneously. Our model comprises a sentence encoder and multiple predictors. The predictors are expected to identify different characteristics of the target word from the input query. We evaluate our model on English and Chinese datasets including both dictionary definitions and human-written descriptions. Experimental results show that our model achieves the state-of-the-art performance, and even outperforms the most popular commercial reverse dictionary system on the human-written description dataset. We also conduct quantitative analyses and a case study to demonstrate the effectiveness and robustness of our model. All the code and data of this work can be obtained on https://github.com/thunlp/MultiRD.
Investigating Word Meta-Embeddings by Disentangling Common and Individual Information

In the field of natural language processing, combining multiple pre-trained word embeddings has become a viable approach to improve word representations.However, there is still a lack of understanding of why such improvements can be achieved.In this paper, we investigate this issue by firstly proposing a novel word meta-embedding method.The proposed method tends to disentangle common and individual information from different word embeddings and learns representations for both.Based on the proposed method, we then carry out a systematic evaluation to provide a perspective on how common and individual information contributes to different tasks.Our intrinsic evaluation results suggest that common information is critical for word-level representations in terms of word similarity and relatedness.While, based on natural language inference, our extrinsic evaluation results show that common and individual information plays different roles and can complement each other.Further, both intrinsic and extrinsic evaluations reveal that explicitly separating common and individual information is beneficial for learning word meta-embeddings.
Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognition

Deep learning based object recognition methods have achieved unprecedented success in the recent years. However, this level of success is yet to be achieved on multimodal RGB-D images. The latter can play an important role in several computer vision and robotics applications. In this paper, we present spatial hierarchical analysis deep neural network, called ShaNet, for RGB-D object recognition. Our network consists of convolutional neural network (CNN) and recurrent neural network (RNNs) to analyse and learn distinctive and translationally invariant features in a hierarchical fashion. Unlike existing methods, which employ pre-trained models or rely on transfer learning, our proposed network is trained from scratch on RGB-D data. The proposed model has been tested on two different publicly available RGB-D datasets including Washington RGB-D and 2D3D object dataset. Our experimental results show that the proposed deep neural network achieves superior performance compared to existing RGB-D object recognition methods.
Towards Empirically Validated Remedies for Scrum Retrospective Headaches

Agile methods, especially Scrum, have become staples of the modern software development industry.Retrospective meetings are Scrum's instrument for process improvement and adaptation.They are considered one of the most important aspects of the Scrum method and its implementation in organizations.However, Retrospectives face their own challenges.Agile practitioners have highlighted common problems, i.e. headaches, that repeatedly appear in meetings and negatively impact the quality of process improvement efforts.To remedy these headaches, Retrospective activities, which can help teams think together and break the usual routine, have been proposed.In this research, we present case studies of educational and industry teams, investigating the effects of eleven Retrospective activities on five identified headaches.While we find evidence for the claimed benefits of activities in the majority of studied cases, application of remedies also led to new headaches arising.
Interpretation time for screening mammography as a function of the number of computer-aided detection marks

<bold>Purpose:</bold> Computer-aided detection (CAD) alerts radiologists to findings potentially associated with breast cancer but is notorious for creating false-positive marks. Although a previous paper found that radiologists took more time to interpret mammograms with more CAD marks, our impression was that this was not true in actual interpretation. We hypothesized that radiologists would selectively disregard these marks when present in larger numbers. <bold>Approach:</bold> We performed a retrospective review of bilateral digital screening mammograms. We use a mixed linear regression model to assess the relationship between number of CAD marks and ln (interpretation time) after adjustment for covariates. Both readers and mammograms were treated as random sampling units. <bold>Results:</bold> Ten radiologists, with median experience after residency of 12.5 years (range 6 to 24) interpreted 1832 mammograms. After accounting for number of images, Breast Imaging Reporting and Data System category, and breast density, the number of CAD marks was positively associated with longer interpretation time, with each additional CAD mark proportionally increasing median interpretation time by 4.35% for a typical reader. <bold>Conclusions:</bold> We found no support for our hypothesis that radiologists will selectively disregard CAD marks when they are present in larger numbers.
An improved teaching-learning-based optimization algorithm for Function Optimization

Teaching-learning-based optimization (TLBO) algorithm, one of the representative intelligent optimization algorithms. TLBO imitates the teaching-learning process in the classroom. This paper briefly introduces the original TLBO and then proposes an improved teaching-learning-based optimization algorithm (CTLBO). Firstly, a competing-based initializing strategy is proposed to get the class with better grades. Secondly, a preview process is added to enhance the rate of convergence. Thirdly, students whose grades have not improved will be given remedial training. The teacher will teach these students again and their learning ability will be reduced accordingly. Finally, CTLBO performs function optimization on the benchmark functions, and obtains better experimental results than the compared optimization algorithm.
Modelling Latent Skills for Multitask Language Generation

We present a generative model for multitask conditional language generation. Our guiding hypothesis is that a shared set of latent skills underlies many disparate language generation tasks, and that explicitly modelling these skills in a task embedding space can help with both positive transfer across tasks and with efficient adaptation to new tasks. We instantiate this task embedding space as a latent variable in a latent variable sequence-to-sequence model. We evaluate this hypothesis by curating a series of monolingual text-to-text language generation datasets - covering a broad range of tasks and domains - and comparing the performance of models both in the multitask and few-shot regimes. We show that our latent task variable model outperforms other sequence-to-sequence baselines on average across tasks in the multitask setting. In the few-shot learning setting on an unseen test dataset (i.e., a new task), we demonstrate that model adaptation based on inference in the latent task space is more robust than standard fine-tuning based parameter adaptation and performs comparably in terms of overall performance. Finally, we examine the latent task representations learnt by our model and show that they cluster tasks in a natural way.
Robust Template-Based Non-Rigid Motion Tracking Using Local Coordinate Regularization

In this paper, we propose our template-based non-rigid registration algorithm to address the misalignments in the frame-to-frame motion tracking with single or multiple commodity depth cameras. We analyze the deformation in the local coordinates of neighboring nodes and use this differential representation to formulate the regularization term for the deformation field in our non-rigid registration. The local coordinate regularizations vary for each pair of neighboring nodes based on the tracking status of the surface regions. We propose our tracking strategies for different surface regions to minimize misalignments and reduce error accumulation. This method can thus preserve local geometric features and prevent undesirable distortions. Moreover, we introduce a geodesic-based correspondence estimation algorithm to align surfaces with large displacements. Finally, we demonstrate the effectiveness of our proposed method with detailed experiments.
Ultimate neutron and x-ray radiography images compression using artificial bee colony and firefly optimization algorithms

Optimum compression ratio (OCR) of neutron and x-ray radiography images with minimum decomposition distortion is the main target of this paper. Artificial bee colony (ABC) and firefly optimal algorithms are applied for achieving this goal. These algorithms are applied in conjunction with proposed image encoding/decoding and decomposition/decompression algorithms. The encoding/decoding algorithms depend on binary image, Huffman, and hierarchical trees. In addition, the decompression algorithms use real dual-tree transform, Hilbert transform, real a trou transform, and Gaussian pyramidal transform. A fitness function (FF) that correlates to compression ratio and image quality is conducted. The OCR with the best radiography image quality is the purpose of the suggested FF. A comparison between the optimized compressed ratio of x-ray and neutron radiography images for both optimization techniques is demonstrated. The proposed algorithms are validated. For the ABC algorithm, optimum values of 80.3610% and 80.3610% are fulfilled for x-ray and neutron images via hierarchical trees with Hilbert transforms. An optimum value of 85.333333% is attained by the Huffman algorithm with discrete cosine transform for both images. For the firefly optimization algorithm (FOA), the hierarchical trees with Hilbert transform achieve an optimum value of 97.0499% for both images. The binary image encoding with pyramidal transform gives optimum values of 68.8055% and 77.4566% for x-ray and neutron images, respectively. The FOA is noted to accomplish more advanced results over the ABC algorithm for compressed radiography images. Additionally, the binary image algorithm with a trou decompression provides an optimum value of 76.77% for neutron radiography images. The achieved optimum values of neutron images are better than of x-ray images due to the absorption of more energy by neutron radiation. Robustness and the significant task of the supposed FF are clarified. This confirms the applicability of image compression optimization algorithms with neutron and x-ray radiography images.
Integrated electronics in 130 nm CMOS process for quantum key distribution sender device

Quantum Key Distribution (QKD) is the most mature method for implementing commercial quantum communications in practice. As part of the miniaturization of practical QKD devices, an integrated electronic system in the 130 nm complementary metal oxide semiconductor process is presented for the QKD sender device. The electronics provide driving signals for the optics at the sender terminal of the quantum channel in QKD and consist mainly of three key modules, namely, a laser diode driver with a high slew rate, a high-speed physical random number generator, and a pre-driver for the electro-optic modulator. The electronic system is designed to operate at frequencies as high as 625-MHz to accommodate the frequency of the QKD system. The high degree of integration is advantageous for miniaturizing QKD sender devices.
A Modified Symbiotic Organism Search Algorithm with Lévy Flight for Software Module Clustering Problem

To date, there are much increasing trends on adopting parameter free meta-heuristic algorithms for solving general optimization problems. With parameter free algorithms, there are no parameter controls for tuning. As such, the adoption of parameter free meta-heuristic algorithms is often straightforward. On the negative note, exploration (i.e. roaming the search space thoroughly) and exploitation (i.e. manipulating the current known best neighbor) are pre-set. As the search spaces are problem dependent, any pre-set exploration and exploitation can lead to entrapment in local optima. In this paper, we investigate the use of Lévy flight to enhance the exploration of a parameter free meta-heuristic algorithm, called Modified Symbiotic Organism Search Algorithm (MSOS), via its population initialization. Our experimentations involving the software module clustering problems have been encouraging, as MSOS gives competitive results against existing selected parameter free meta-heuristic algorithms. For all the given module clustering problems, MSOS generates overall best mean results.
Absolute phase unwrapping with SVM for fringe‐projection profilometry

Phase unwrapping is a fundamental task in phase-based profilometry. Existing spatial and temporal approaches are facing challenges such as error propagation and low efficiency. In this study, the authors propose a learning-based method that uses a support vector machine (SVM) to perform phase unwrapping, where the problem is solved as a classification task. To be specific, seven elements, extracted from the captured patterns and the wrapped phase, form the input feature vector and the fringe order is the output class. Besides, a radial basis function kernel SVM is adopted as the model. The proposed method is conducted independently for every pixel, and does not suffer from error propagation in the spatial unwrapping. Moreover, it needs fewer patterns than temporal unwrapping since only one phase map is required. Simulation and experimental results demonstrate that the proposed scheme produces precise depth maps, which are comparable with the complex quality-guided methods but at a much faster speed.
Coupling Appearance and Motion: Unsupervised Clustering for Object Segmentation Through Space and Time

In this chapter, we approach the unsupervised learningUnsupervised learning problem in both space and time head-on, by considering both spatial and temporal dimensions from the very beginning. We couple, from the start, the appearance of objects. It also defines their spatial properties with their motion, which defines their existence in time and provide a unique graph clusteringGraph clustering formulation in both space and time for the problem of unsupervised object discoveryUnsupervised object discovery in video. Again we resort to a graph formulation: we have one-to-one correspondences between graph nodes and video pixels. Graph nodes that belong to the main object of interest should form a strong cluster: they are linked through trajectories defined by pixels that belong to the same physical object point and they should also have similar appearance features along those trajectories. The clustering problem aims to maximize both their agreements in motion and their similarities in appearance. Therefore, we regard this object discovery problem as a graph clusteringGraph clustering problem for which we provide an efficient spectral approach: the object segmentation in the space-time volume is captured by the principal eigenvectorPrincipal eigenvector of a Feature-Motion matrixFeature-Motion matrix, which we introduce for the first time. Even though the matrix is huge, we never built it explicitly. However, our segmentation algorithm is guaranteed to converge to its principal eigenvectorPrincipal eigenvector. The eigenvector is known to provide an optimal solution to an eigenvalue problem, which does not depend on initialization and, for this reason, is very robust in practice and able to find the foreground object in the video as the strongest cluster in the space-time graph. The approach in this chapter naturally relates to the principles of unsupervised learningPrinciples of unsupervised learning presented in the introduction and also provides a first, lower level approach to coupling space and time processing. That is further explored at a higher semantic level in the final chapter, where we present our recurrent space-time graph neural networkRecurrent space-time graph neural networks model (Nicolicioiu et al. Neural information processing systems, [1]). In practice, our method, termed GO-VOS, achieves state-of-the-art results on three difficult datasets in the literature: DAVIS, SegTrack, and YouTube-Objects.
Linear Predictive Codes for Speech Recognition System at 121bps

This paper described the recognition of the phonetics related to numerical in Indian regional languages such as Marathi & Hindi by Nearest Neighbour rule.The segmentation is based on the location of the start and endpoints of the speech.The exact speech boundaries can be located and evaluated for linear predictive codes.The Linear Predictive Codes of phonetics related to numerical in Indian regional languages such as Marathi & Hindi forms the codebook.The optimum distance between the test and the codebook linear predictive codes can be determined by the Dynamic Time Warping technique.Depending on the distance, the word is recognized by the Nearest Neighbour rule.The accuracy of 88% is achieved with a high reduction in the memory requirements & good SNR.
PENNI: Pruned Kernel Sharing for Efficient CNN Inference

Although state-of-the-art (SOTA) CNNs achieve outstanding performance on various tasks, their high computation demand and massive number of parameters make it difficult to deploy these SOTA CNNs onto resource-constrained devices. Previous works on CNN acceleration utilize low-rank approximation of the original convolution layers to reduce computation cost. However, these methods are very difficult to conduct upon sparse models, which limits execution speedup since redundancies within the CNN model are not fully exploited. We argue that kernel granularity decomposition can be conducted with low-rank assumption while exploiting the redundancy within the remaining compact coefficients. Based on this observation, we propose PENNI, a CNN model compression framework that is able to achieve model compactness and hardware efficiency simultaneously by (1) implementing kernel sharing in convolution layers via a small number of basis kernels and (2) alternately adjusting bases and coefficients with sparse constraints. Experiments show that we can prune 97% parameters and 92% FLOPs on ResNet18 CIFAR10 with no accuracy loss, and achieve 44% reduction in run-time memory consumption and a 53% reduction in inference latency.
An Ensemble Learning-Based Cooperative Defensive Architecture Against Adversarial Attacks

Since Deep Neural Networks (DNNs) have been more and more widely used in safety-critical Intelligent System (IS) applications, the robustness of DNNs becomes a great concern in IS design. Due to the vulnerability of DNN models, adversarial examples generated by malicious attacks may result in disasters. Although there are plenty of defense methods for these adversarial attacks, existing methods can only resist special adversarial attacks. Meanwhile, the accuracy of existing methods degrades dramatically when they process nature examples. To address this problem, we propose an effective Cooperative Defensive Architecture (CDA) that can enhance the robustness of IS devices by integrating heterogeneous base classifiers. Because of the parallel mechanism in ensemble learning, the compressed heterogeneous base classifiers do not increase the prediction time on device. Comprehensive experimental results show that the modified DNNs by our approach cannot only resist adversarial examples more effectively than original model, but also achieve a high accuracy when they process nature examples.
Research on Music Visualization Based on Graphic Images and Mathematical Statistics

Music visualization can present music information through the visual way of graphic images, which is helpful to improve the accuracy and effectiveness of music information communication. In view of the shortcomings in the current music visualization field, this paper combines K-means clustering, fusion decision tree and other mathematical statistical methods on the basis of music graphic images to construct a music visualization model based on graphic images and mathematical statistics. First, the application principles of Schlieren imaging and laser Doppler imaging in the visualization of music graphic images are described. Secondly, on the basis of music graphic images, K-means clustering method is used to perform cluster analysis on music visualization information. Finally, through the fusion decision tree method, the classification of music visual information is studied. The actual case analysis and performance test results show the superiority of the music visualization method based on graphic images and mathematical statistics. This method can provide a scientific reference model and basis for the modern music industry to establish new visualization systems using graphics, images and mathematical statistics.
Retrieving Potential Causes from a Query Event

Different to traditional IR, which retrieves a set of topically relevant documents given a user query, we investigate causal retrieval, which involves retrieving a set of documents that describe a set of potential causes leading to an effect specified in the query. We argue that the nature of causal relevance should be different to that of traditional topical relevance. This is because although the causally relevant documents would have partial term overlap with the ones that are topically relevant for a query, yet it is expected that a majority of these documents would use a different set of terms to describe a number of causes possibly leading to their effects. To address this, we propose a feedback model to estimate a distribution of terms which are relatively infrequent but associated with high weights in the topically relevant distribution, leading to potential causal relevance. Our experiments demonstrate that such a feedback model turns out to be substantially more effective than traditional IR models and a number of other causality heuristic baselines.
Deep Volumetric Feature Encoding for Biomedical Images

Deep learning research has demonstrated the effectiveness of using pre-trained networks as feature encoders. The large majority of these networks are trained on 2D datasets with millions of samples and diverse classes of information. We demonstrate and evaluate approaches to transferring deep 2D feature spaces to 3D in order to take advantage of these and related resources in the biomedical domain. First, we show how VGG-19 activations can be mapped to a 3D variant of the network (VGG-19-3D). Second, using varied medical decathlon data, we provide a technique for training 3D networks to predict the encodings induced by 3D VGG-19. Lastly, we compare five different 3D networks (one of which is trained only on 3D MRI and another of which is not trained at all) across layers and patch sizes in terms of their ability to identify hippocampal landmark points in 3D MRI data that was not included in their training. We make observations about the performance, recommend different networks and layers and make them publicly available for further evaluation.