Reviewerly/exHarmony huggingface dataset:
Automatic and Accurate Expansion of Abbreviations in Parameters

Abbreviations are widely used in identifiers. However, they have severe negative impact on program comprehension and IR-based software maintenance activities, e.g., concept location, software clustering, and recovery of traceability links. Consequently, a number of efficient approaches have been proposed successfully to expand abbreviations in identifiers. Most of such approaches rely heavily on dictionaries, and rarely exploit the specific and fine-grained context of identifiers. As a result, such approaches are less accurate in expanding abbreviations (especially short ones) that may match multiple dictionary words. To this end, in this paper we propose an automatic approach to improve the accuracy of abbreviation expansion by exploiting the specific and fine-grained context. It focuses on a special but common category of abbreviations (abbreviations in parameter names), and thus it can exploit the specific and fine-grained context, i.e., the type of the enclosing parameter as well the corresponding formal (or actual) parameter name. The recent empirical study on parameters suggest that actual parameters are often lexically similar to their corresponding formal parameters. Consequently, it is likely that an abbreviation in a formal parameter can find its full terms in the corresponding actual parameter, and vice versa. Based on this assumption, a series of heuristics are proposed to look for full terms from the corresponding actual (or formal) parameter names. To the best of our knowledge, we are the first to expand abbreviations by exploiting the lexical similarity between actual and formal parameters. We also search for full terms in the data type of the enclosing parameter. Only if all such heuristics fail, the approach turns to the traditional abbreviation dictionaries. We evaluate the proposed approach on seven well known open-source projects. Evaluation results suggest that when only parameter abbreviations are involved, the proposed approach can improve the precision from 26 to 95 percent and recall from 26 to 65 percent compared against the state-of-the-art general purpose approach. Consequently, the proposed approach could be employed as a useful supplement to existing approaches to expand parameter abbreviations.
The impact of the Bitcoin bubble of 2017 on Bitcoin's P2P market

Peer-to-peer (P2P) exchange has an important role to play in global Bitcoin trading, yet most research into Bitcoin price behaviour focuses on exchange trading. An analysis of Bitcoin's P2P market indicates that when prices are measured relative to the going market rate, P2P traders in Bitcoin face different premiums and discounts depending on the currency traded, which in turn impacts on the bid-ask spread. In periods of extreme price changes such as the extraordinary price rise in Bitcoin occurring in 2017, measuring bid-ask spreads in relative terms masks the true picture of widening monetary bid-ask spreads.
A stable long-term object tracking method with re-detection strategy

In this work, we proposed a long-term tracking strategy to deal with the occlusion, out-of-plane rotation, and the confusing non-target object. Our tracking system is composed of two parts, the CA-CF tracker, an efficient correlation method for short-term tracking, and the SVM-based re-detector, which prevents the CA tracker from degradation. When the tracker works with confidence, the CA-CF module ensures an accurate tracking result and the SVM updates accordingly. When the response maps fluctuate heavily, the SVM switches to work as a re-detector and the tracker will be initialized. We also introduced to adopt both the maximum response criterion and the APCE criterion to judge the performance of the tracker in time. By evaluating our algorithm on the OTB benchmark datasets, we proposed to analyze the result affected by the parameters of our CA-CF-SVM strategy. The experimental results show that our method has a significant improvement than the state-of-the-art methods for the long-term tracking both in accuracy and robustness.
Efficient algorithms for graph regularized PLSA for probabilistic topic modeling

Probabilistic latent semantic analysis (PLSA) is a popular data analysis method with the objective to discover the underlying semantic structure of input data. In this work, we describe a method for probabilistic topic analysis in image and text based on a new representation of graph-regularized PLSA (GPLSA). In GPLSA, data entities are mapped to an undirected graph, where similarities between topic compositions on the graph are measured by the divergence between discrete probabilities. Such divergence is essentially incorporated as a graph-regularizer that augments the original PLSA algorithm. Furthermore, we extend the GPLSA algorithms to multiple data modalities based on the connections between data entities of each modality. We propose efficient multiplicative iterative algorithms for GPLSA with three popular regularizers, namely ℓ1, ℓ2 and symmetric KL divergences. In each case, we derive simple efficient numerical solutions that require only matrix arithmetic operations during the optimization. Experimental results demonstrate the efficacy of GPLSA over state-of-the-art methods.
Weakly supervised semantic segmentation using distinct class specific saliency maps

Weakly supervised segmentation has drawn considerable attention, because of the high costs associated with the creation of pixel-wise annotated image datasets that are used for training fully supervised segmentation models. We propose a weakly supervised semantic segmentation method using CNN-based class-specific saliency maps and fully connected CRF. To obtain distinct class-specific saliency maps (DCSM) that can be used as unary potentials of CRF, we propose a novel method of estimating class saliency maps, which significantly improves the method proposed by Simonyan et al. (2014) through the following improvements: (1) using CNN derivatives with respect to feature maps of the intermediate convolutional layers with up-sampling instead of an input image; (2) subtracting the saliency maps of other classes from the saliency maps of the target class to differentiate target objects among other objects; (3) aggregating multiple-scale class saliency maps to compensate for the low resolution in feature maps. In addition, we propose the use of a novel algorithm for estimating segmentation “Easiness” combined with the proposed saliency-based method. Wei et al. (2016) recently demonstrated that a fully supervised segmentation model enhanced the performance of weakly supervised segmentation by training the model using the estimated initial masks in a weakly supervised setting. However, the initial estimated masks tend to include some noise, which sometimes produces erroneous results. Therefore, we focus on improving the quality of the initial estimated masks for training a fully supervised segmentation model. We propose a method for retrieving “good seeds” by predicting the segmentation “Easiness” of images based on the consistency of the outputs under different conditions. We illustrate that our proposed method can retrieve “good seeds”. Despite of the trade-off between training data quality and the number of training images, retrieved images can improve the accuracy of weakly supervised segmentation by combining data augmentation.
User behavior prediction via heterogeneous information preserving network embedding

User behavior prediction with low-dimensional vectors generated by user network embedding models has been verified to be efficient and reliable in real applications. However, most user network embedding models utilize homogeneous properties to represent users, such as attributes or user network structure. Though some works try to combine two kinds of properties, the existing works are still not enough to leverage the rich semantics of users. In this paper, we propose a novel heterogeneous information preserving user network embedding model, which is named HINE, for user behavior classification in user network. HINE applies attributes, user network connection, user network structure, and user behavior label information for user representation in user network embedding. The embedded vectors considering these multi-type properties of users contribute to better user behavior classification performances. Experiments verified the superior performances of the proposed approach on real-world complex user network dataset.
Supersingular Isogeny Diffie–Hellman Authenticated Key Exchange

We propose two authenticated key exchange protocols from supersingular isogenies. Our protocols are the first post-quantum one-round Diffie–Hellman type authenticated key exchange ones in the following points: one is secure under the quantum random oracle model and the other resists against maximum exposure where a non-trivial combination of secret keys is revealed. The security of the former and the latter is proven under isogeny versions of the decisional and gap Diffie–Hellman assumptions, respectively. We also propose a new approach for invalidating the Galbraith–Vercauteren-type attack for the gap problem.
Evolution-Strategy-Based Automation of System Development for High-Performance Speech Recognition

The state-of-the-art large vocabulary speech recognition systems consist of several components including hidden Markov model and deep neural network. To realize the highest recognition performance, numerous meta-parameters specifying the designs and training setups of these components must be optimized. A prominent obstacle in system development is the laborious effort required by human experts in tuning these meta-parameters. To automate the process, we propose to tune the meta-parameters of a whole large vocabulary speech recognition system using the evolution strategy with a multi-objective Pareto optimization. As the result of the evolution, the system is optimized for both low word error rate and compact model size. Since the approach requires repeated training and evaluation of the recognition systems that require large computation, we make use of parallel computation on cloud computers. Experimental results show the effectiveness of the proposed approach by discovering appropriate configuration for large vocabulary speech recognition systems automatically.
Gear fault diagnosis based on a new wavelet adaptive threshold de-noising method

Purpose This paper aims to explore a new wavelet adaptive threshold de-noising method to resolve the shortcomings of wavelet hard-threshold method and wavelet soft-threshold method, which are usually used in gear fault diagnosis. Design/methodology/approach A new threshold function and a new determined method of threshold for each layer are proposed. The principle and the implementation of the algorithm are given. The simulated signal and the measured gear fault signal are analyzed, and the obtained results are compared with those from wavelet soft-threshold method, wavelet hard-threshold method and wavelet modulus maximum method. Findings The presented wavelet adaptive threshold method overcomes the defects of the traditional wavelet threshold method, and it can effectively eliminate the noise hidden in the gear fault signal at different decomposition scales. It provides more accurate information for the further fault diagnosis. Originality/value A new threshold function is adopted and the multi-resolution unbiased risk estimation is used to determine the adaptive threshold, which overcomes the defect of the traditional wavelet method.
Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning

Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled independently. Here we propose a convolutional neural network based multi-task learning approach to perform both tasks simultaneously, such that, the network learns better representation of the data with improved generalization performance. Probabilistic formulation of the problem enables learning the task uncertainties during the training, which are used to automatically compute the weights for the tasks. We performed a five fold cross-validation of the myocardium segmentation obtained from the proposed multi-task network on 97 patient 4-dimensional cardiac cine-MRI datasets available through the STA-COM LV segmentation challenge against the provided gold-standard myocardium segmentation, obtaining a Dice overlap of 0.849 ± 0.036 and mean surface distance of 0.274 ± 0.083 mm, while simultaneously estimating the myocardial area with mean absolute difference error of 205 ± 198 mm2.
Alignment-based genetic programming for real life applications

A recent discovery has attracted the attention of many researchers in the field of genetic programming: given individuals with particular characteristics of alignment in the error space, called optimally aligned, it is possible to reconstruct a globally optimal solution. Furthermore, recent preliminary experiments have shown that an indirect search consisting of looking for optimally aligned individuals can have benefits in terms of generalization ability compared to a direct search for optimal solutions. For this reason, defining genetic programming systems that look for optimally aligned individuals is becoming an ambitious and important objective. Nevertheless, the systems that have been introduced so far present important limitations that make them unusable in practice, particularly for complex real-life applications. In this paper, we overcome those limitations, and we present the first usable alignment-based genetic programming system, called nested alignment genetic programming (NAGP). The presented experimental results show that NAGP is able to outperform two of the most recognized state-of-the-art genetic programming systems on four complex real-life applications. The predictive models generated by NAGP are not only more effective than the ones produced by the other studied methods but also significantly smaller and thus more manageable and interpretable.
Learning Coupled Convolutional Networks Fusion for Video Saliency Prediction

Visual saliency provides important information for understanding scenes in many computer vision tasks. The existing video saliency algorithms mainly focus on predicting spatial and temporal saliency maps. However, these maps are simply fused without considering the complex dynamic scenes in videos. To overcome this drawback, we propose a deep convolutional fusion framework for video saliency prediction. The proposed model, which is based on coupled fully convolutional networks (FCNs), effectively encodes the spatiotemporal information by integrating spatial and temporal features. We demonstrate that this information is helpful for accurately fusing the spatial and temporal saliency maps according to changes in video scenes. In particular, we gradually design three different deep fusion architectures to investigate how to better utilize the spatiotemporal information. Moreover, we propose a reasonable sampling strategy for selecting suitable training sets for the coupled FCNs. Through extensive experiments, we demonstrate that our model outperforms the state-of-the-art algorithms on four public video saliency data sets.
Principled deep neural network training through linear programming

Deep learning has received much attention lately due to the impressive empirical performance achieved by training algorithms. Consequently, a need for a better theoretical understanding of these problems has become more evident and multiple works in recent years have focused on this task. In this work, using a unified framework, we show that there exists a polyhedron that simultaneously encodes, in its facial structure, all possible deep neural network training problems that can arise from a given architecture, activation functions, loss function, and sample size. Notably, the size of the polyhedral representation depends only linearly on the sample size, and a better dependency on several other network parameters is unlikely. Using this general result, we compute the size of the polyhedral encoding for commonly used neural network architectures. Our results provide a new perspective on training problems through the lens of polyhedral theory and reveal strong structure arising from these problems.
Fast phase-shifting technique for 3-D surface micro-topography measurement

This paper presents a two-step phase-shifting interferometry technique for fast inspection of uniformity of optoelectronic samples. The in-line configuration was applied to make full use of the space-bandwidth product (SBP) of the camera and hence can resolve finer spatial details of the sample. Two-step phase-shifting (TSPS) algorithm was used to reconstruct the object with only two in-line interferograms generated when a quarter wave plate (QWP) is placed in the light path of one arm of the Mach-Zehnder interferometer. Experimental results show that the extracted phases calculated by the TSPS technique match with the phases extracted by the three-step phase-shifting (THSPS) technique.
Nonclassical correlations for quantum metrology in thermal equilibrium

Nonclassical correlations beyond entanglement might provide a resource in quantum information tasks, such as quantum computation or quantum metrology. Quantum discord is a measure of nonclassical correlations to which entanglement belongs as a subset. Exploring the operational meaning of quantum discord as a resource in quantum information processing tasks, such as quantum metrology, is of essential importance to our understanding of nonclassical correlations. In our recent work [Phys. Rev. A 98, 012115 (2018)], we considered a protocol---which we call the greedy local thermometry protocol---for estimating the temperature of thermal equilibrium states from local measurements, elucidating the role of diagonal discord in enhancing the protocol sensitivity in the high-temperature limit. In this paper, we extend our results to a general greedy local parameter estimation scenario. In particular, we introduce a quantum discord---which we call discord for local metrology---to quantify the nonclassical correlations induced by the local optimal measurement on the subsystem. We demonstrate explicitly that discord for local metrology plays a role in sensitivity enhancement in the high-temperature limit by showing its relation to loss in quantum Fisher information. In particular, it coincides with diagonal discord for estimating a linear coupling parameter.
HMGOWM: A Hybrid Decision Mechanism for Automating Migration of Virtual Machines

Large-scale data centers have been widely used for cloud services, and the stability of various cloud services has received additional attention from users. Although service disruptions are not as catastrophic as they once were, their impact might be more extensive than before. These outages may trigger the migration of virtual machines (VMs) located in the failure node. However, the access time of each VM is random, unlike the accident time, which can be predicted. This means that traditional migration caused by service interruptions may result in a large number of unwanted migrations, regardless of the user’s downtime experience. Migration is an expensive process in terms of the resources needed as well as the degradation of application performance during migration. A balance between the recovery time of the service (to minimize the migration resulting from a given placement) and the downtime experience of the users (to minimize the impact of access interruptions) is needed. In this paper, we propose HMGOWM, a hybrid decision-making mechanism for automating the migration of VMs. Our proposed mechanism extends the original VM migration performance cost model, greatly reducing the downtime experience of the users. To achieve high performance and a good load balance, a multi-objective monitoring system for both VMs and physical machine nodes and an adaptive VM migration-scheduling scheme for the OpenStack cloud platform are proposed. Extensive experiment results indicate that the downtime experienced by users can be efficiently reduced and that the implementation of HMGOWM outperforms the original scheduling of the OpenStack cloud platform.
3-D Printed Object Authentication Based on Printing Noise and Digital Signature

With the development of 3-D printing and reverse engineering, the protection of intellectual property of 3-D printed objects is becoming a prominent problem. In order to authenticate 3-D printed objects, an authentication scheme based on printing noise and digital signature is proposed. First, the noises introduced in the 3-D printing and observation are investigated. Thereafter, a special authentication mark is designed for extracting the printing noise. Based on this, a 3-D printed object authentication framework is built and it is composed of two processes-registration and verification. In the registration, the printing noise of the authentication mark is extracted and signed by digital signature. While in the verification, the signature is verified and then the printing noise of the authentication mark is extracted. After that, the extracted printing noise is matched with the one acquired in the registration. Experimental results and analysis show that the proposed scheme can reliably accomplish the authentication of the 3-D printed object with high precision and that it can achieve high security and good robustness.
Gating mechanism based Natural Language Generation for spoken dialogue systems

Recurrent Neural Network (RNN) based approaches have recently shown promising in tackling Natural Language Generation (NLG) problems. This paper presents an approach to leverage gating mechanisms, in which we incrementally propose three additional semantic cells into a traditional RNN model: a Refinement cell to filter the sequential inputs before RNN computations, an Adjustment cell, and an Output cell to select semantic elements and gate a feature vector during generation. The proposed gating-based generators can learn from unaligned data by jointly training both sentence planning and surface realization to generate natural language utterances. We conducted extensive experiments on four different NLG domains in which the results empirically show that the proposed methods not only achieved better performance on all the NLG domains in comparison with previous gating-based, attention-based methods, but also obtained highly competitive results compared to a hybrid generator.
Generating Style-Specific Chinese Tang Poetry With a Simple Actor-Critic Model

Recent studies in sequence-to-sequence learning demonstrate that recurrent neural network (RNN) encoder-decoder structure can do well in Chinese classical poetry generation. With topic words or first line as the encoder input, an entire poem is then incrementally generated by the decoder from left to right with the highest probability. However, this locally incremental nature of decoding model can lead to the incongruity of style between the front and back of the poem generated. Inspired by the behavior of people tending to plan and associate the following parts in advance when they are writing poems, this paper employs a simple actor-critic method to generate style-specific Chinese poems. We design a style matching reward function and employ a value network with Monte Carlo search as the critic to estimate the future rewards of the desired style for poem generation. This approach makes the generation process more flexible and controllable. The experimental results demonstrate that our approach can generate three specific styles of high-quality poetry, and enhance the consistency of style of generated poems.