Reviewerly/exHarmony huggingface dataset:
Standing on the shoulders of giants: Seeding search-based multi-objective optimization with prior knowledge for software service composition

Search-Based Software Engineering, in particular multi-objective evolutionary algorithm, is a promising approach to engineering software service composition while simultaneously optimizing multiple conflicting Quality-of-Service (QoS) objectives. Yet, existing applications of evolutionary algorithms have failed to consider domain knowledge about the problem into the optimization, which is a perhaps obvious but challenging task. This paper aims to investigate different strategies of exploring and injecting knowledge about the problem into the Multi-Objective Evolutionary Algorithm (MOEA) by seeding. Further, we investigate various factors that contribute to the effectiveness of seeding, including the number of seeds, the importance of crossover operation and the similarity of historical problems. We conduced empirical evaluations with NSGA-II, MOEA/D and IBEA based on a wide spectrum of problem instances, including 10 different workflow structures, from 5 to 100 abstract services and 510 to 5.529 × 10203 candidate concrete services with diverse QoS on latency, throughput and cost, which was chosen from the real-world WS-DREAM dataset that contains 4500 QoS values. We found that, (i) all seeding strategies generally outperform their non-seeded counterparts under the same search budget with large statistical significance. Yet, they may involve relatively smaller compromise on one or two of the quality aspects among convergence, uniformity and spread. (ii) The implication of the number of seeds on the service composition problems is minimal in general (except for IBEA). (iii) In contrast to the non-seeded counterparts, the seeding strategies suffer much less implications by the crossover operation. (iv) The differences of historical problems, which are important for two proposed seeding strategies, can indeed affect the results in a non-linear manner; however, the results are still greatly better than the non-seeded counterparts even with up to 90% difference of the problem settings. The paper concludes that (i) When applying the seeding strategies, the number of seeds to be placed in is less important in general, except for the pre-optimization based strategies under IBEA. (ii) Eliminating or having less crossover is harmful for multi-objective service composition optimization, but the seeding strategies are much less sensitive to this operator than their non-seeded counterparts. (iii) For the history based seeding strategies, the seeds do not have to come from the most similar historical composition problem to achieve the best HV value, but a largely different historical problem should usually be avoided, unless they are the only available seeds.
Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm

The accumulation of metals in soil harms human health through different channels. Therefore, it is very important to conduct fast and effective non-destructive prediction of metals in the soil. In this study, we investigate the characteristics of four metal contents, namely, Sb, Pb, Cr, and Co, in the soil of the Houzhai River Watershed in Guizhou Province, China, and establish the content prediction back propagation (BP) neural network and genetic-ant colony algorithm BP (GAACA-BP) neural network models based on hyperspectral data. Results reveal that the four metals in the soil have different degrees of accumulation in the study area, and the correlation between them is significant, indicating that their sources may be similar. The fitting effect and accuracy of the GAACA-BP model are greatly improved compared with those of the BP model. The R values are above 0.7, the MRE is reduced to between 6% and 15%, and the validation accuracy is increased by 12–64%. The prediction ability of the model of the four metals is Cr &gt; Co &gt; Sb &gt; Pb. These results indicate the possibility of using hyperspectral techniques to predict metal content.
Distributed Multi-Agent Gaussian Regression via Finite-Dimensional Approximations

We consider the problem of distributedly estimating Gaussian processes in multi-agent frameworks. Each agent collects few measurements and aims to collaboratively reconstruct a common estimate based on all data. Agents are assumed with limited computational and communication capabilities and to gather M noisy measurements in total on input locations independently drawn from a known common probability density. The optimal solution would require agents to exchange all the M input locations and measurements and then invert an M×M matrix, a non-scalable task. Differently, we propose two suboptimal approaches using the first E orthonormal eigenfunctions obtained from the Karhunen-Loève (KL) expansion of the chosen kernel, where typically E≪M. The benefits are that the computation and communication complexities scale with E and not with M, and computing the required statistics can be performed via standard average consensus algorithms. We obtain probabilistic non-asymptotic bounds that determine a priori the desired level of estimation accuracy, and new distributed strategies relying on Stein's unbiased risk estimate (SURE) paradigms for tuning the regularization parameters and applicable to generic basis functions (thus not necessarily kernel eigenfunctions) and that can again be implemented via average consensus. The proposed estimators and bounds are finally tested on both synthetic and real field data.
Discourse Representation Parsing for Sentences and Documents

We introduce a novel semantic parsing task based on Discourse Representation Theory (DRT; Kamp and Reyle 1993).Our model operates over Discourse Representation Tree Structures which we formally define for sentences and documents.We present a general framework for parsing discourse structures of arbitrary length and granularity.We achieve this with a neural model equipped with a supervised hierarchical attention mechanism and a linguistically-motivated copy strategy.Experimental results on sentence-and documentlevel benchmarks show that our model outperforms competitive baselines by a wide margin.
Estimation of Primary Quantization Steps in Double-Compressed JPEG Images Using a Statistical Model of Discrete Cosine Transform

Double compression of images occurs when one compresses twice, possibly with different quality factors, a digital image. Estimation of the first compression parameter of such a double compression is of a crucial interest for image forensics since it may help revealing, for instance, the software or the source camera. This paper proposes an accurate method for estimating the primary quantization steps in double-compressed JPEG images. This original methodology is based on an accurate statistical model of discrete cosine transform (DCT) coefficients that has been proposed in our previous works. We also present a thorough analysis of the double compression properties, taking into account carefully the effect of round-off noise. This analysis is used to derive an accurate range of possible value for quantization of primary DCT coefficients with respect to the secondary quantization step. Using both the statistical model of quantized DCT coefficients and the range of possible values of first quantization step, a model of the twice quantized DCT coefficients is established. Eventually, it is proposed to estimate the primary quantization value by finding, among a set of possible candidates, the one that best match the proposed statistical model in terms of minimal symmetrized Kullback-Leibler (KL) divergence. The numerical experiments on large databases of real images and comparisons with state-of-the-art approaches emphasize the relevance of the proposed method.
Gradient-Based Inference for Networks with Output Constraints

Practitioners apply neural networks to increasingly complex problems in natural language processing, such as syntactic parsing and semantic role labeling that have rich output structures. Many such structured-prediction problems require deterministic constraints on the output values; for example, in sequence-to-sequence syntactic parsing, we require that the sequential outputs encode valid trees. While hidden units might capture such properties, the network is not always able to learn such constraints from the training data alone, and practitioners must then resort to post-processing. In this paper, we present an inference method for neural networks that enforces deterministic constraints on outputs without performing rule-based post-processing or expensive discrete search. Instead, in the spirit of gradient-based training, we enforce constraints with gradient-based inference (GBI): for each input at test-time, we nudge continuous model weights until the network’s unconstrained inference procedure generates an output that satisfies the constraints. We study the efficacy of GBI on three tasks with hard constraints: semantic role labeling, syntactic parsing, and sequence transduction. In each case, the algorithm not only satisfies constraints, but improves accuracy, even when the underlying network is stateof-the-art.
Take Goods from Shelves

Object detection for automatic visual checkout in self-service vending machines is attracting significant attention in the retail industry. However, several critical challenges have not received enough attention. First, large-scale, high-quality retail image datasets are urgently demanded to train and evaluate the detection models. Second, the trained models should be able to cope with the frequently added new goods at low cost, while most cutting-edge models cannot. In this paper, we propose a new hierarchical large-scale object detection dataset, called Take Goods from Shelves (TGFS), containing 38K images of 24 fine-grained and 3 coarse classes. A preliminary method for solving the goods-adding problem, called Faster R-CNN Class-incremental Object Detector (FCIOD), is also described and evaluated. In addition, several popular methods are benchmarked on the TGFS dataset.
Hindsight

The Adaptive bitrate algorithm (ABR) is an essential part of any HTTP-based video streaming service. Given the endless array of network environments, device capabilities, and content properties in a commercial setting, perfecting ABR remains challenging. To identify shortcomings effectively at a large scale, a scalable methodology is needed to evaluate ABR algorithms under various scenarios. The state-of-the-art method is to evaluate a production ABR retrospectively with an optimal ABR algorithm. However, optimal ABR is an NP-hard problem and therefore is costly to be deployed at a commercial scale. As a result, shortcomings in the field are often identified through manual inspection. The process is labor-intensive and often relies on experience and intuitions built from reviewing the characteristics of a large number of sessions. Motivated by our operational experience, in this paper we propose an efficient approximation for the optimal ABR problem, thus enabling large-scale deployment and benchmarking of production ABR algorithms.
Boosting Dialog Response Generation

Neural models have become one of the most important approaches to dialog response generation.However, they still tend to generate the most common and generic responses in the corpus all the time.To address this problem, we designed an iterative training process and ensemble method based on boosting.We combined our method with different training and decoding paradigms as the base model, including mutual-information-based decoding and reward-augmented maximum likelihood learning.Empirical results show that our approach can significantly improve the diversity and relevance of the responses generated by all base models, backed by objective measurements and human evaluation.
Operational characterization of incompatibility of quantum channels with quantum state discrimination

A collection of quantum channels is called incompatible if they cannot be obtained as marginals from a single channel. No-cloning theorem is the most prominent instance of incompatibility of quantum channels. We show that every collection of incompatible channels can be more useful than compatible ones as preprocessings in a state discrimination task with multiple ensembles of states. This is done by showing that the robustness of channel incompatibility which is a measure for incompatibility of channels exactly quantifies the maximum advantage in the state discrimination. We also show that incompatibility of quantum measurement and channel has a similar operational interpretation. Finally, we demonstrate that our result with respect to channel incompatibility includes all other kinds of incompatibility as special cases.
Robust Face Image Super-Resolution via Joint Learning of Subdivided Contextual Model

In this paper, we focus on restoring high-resolution facial images under noisy low-resolution scenarios. This problem is a challenging problem as the most important structures and details of captured facial images are missing. To address this problem, we propose a novel local patch-based face super-resolution (FSR) method via the joint learning of the contextual model. The contextual model is based on the topology consisting of contextual sub-patches, which provide more useful structural information than the commonly used local contextual structures due to the finer patch size. In this way, the contextual models are able to recover the missing local structures in target patches. In order to further strengthen the structural compensation function of contextual topology, we introduce the recognition feature as additional regularity. Based on the contextual model, we formulate the super-resolved procedure as a contextual joint representation with respect to the target patch and its adjacent patches. The high-resolution image is obtained by weighting contextual estimations. Both quantitative and qualitative validations show that the proposed method performs favorably against state-of-the-art algorithms.
Exploiting Future Word Contexts in Neural Network Language Models for Speech Recognition

Language modeling is a crucial component in a wide range of applications including speech recognition. Language models (LMs) are usually constructed by splitting a sentence into words and computing the probability of a word based on its word history. This sentence probability calculation, making use of conditional probability distributions, assumes that there is little impact from approximations used in the LMs, including the word history representations and finite training data. This motivates examining models that make use of additional information from the sentence. In this paper, future word information, in addition to the history, is used to predict the probability of the current word. For recurrent neural network LMs (RNNLMs), this information can be encapsulated in a bi-directional model. However, if used directly, this form of model is computationally expensive when trained on large quantities of data, and can be problematic when used with word lattices. This paper proposes a novel neural network language model structure, the succeeding-word RNNLM, su-RNNLM, to address these issues. Instead of using a recurrent unit to capture the complete future word contexts, a feedforward unit is used to model a fixed finite number of succeeding words. This is more efficient in training than bi-directional models and can be applied to lattice rescoring. The generated lattices can be used for downstream applications, such as confusion network decoding and keyword search. Experimental results on speech recognition and keyword spotting tasks illustrate the empirical usefulness of future word information, and the flexibility of the proposed model to represent this information.
Ensemble of cuckoo search variants

Cuckoo search is a simple yet effective evolutionary algorithm for solving numerical optimization problems. Recently, many variants of cuckoo search have been developed to further enhance the performance. These improved versions have different capabilities in tackling the optimization problems with different properties, so it is difficult to determine which algorithm is best for all problems. To address this issue, we present a new cuckoo search algorithm named the ensemble cuckoo search variant. In this developed version, a candidate pool consisting of three different cuckoo search algorithms is first constructed. According to the previous experiences in producing promising solutions, an adaptive scheme is then used to determine the probability that each algorithm can be assigned to distinct individuals in the current population. Also, an external archive is embedded to further discourage premature convergence. To assess the performance of this ensemble algorithm, 42 test problems derived from CEC 2005 and CEC 2013 are employed. Experimental results indicate that the proposed algorithm is a competitive method compared with seven well-established cuckoo search variants and several other well-known evolutionary algorithms.
Enriching Neural Models with Targeted Features for Dementia Detection

Alzheimer's disease (AD) is an irreversible brain disease that can dramatically reduce quality of life, most commonly manifesting in older adults and eventually leading to the need for full-time care.Early detection is fundamental to slowing its progression; however, diagnosis can be expensive, time-consuming, and invasive.In this work we develop a neural model based on a CNN-LSTM architecture that learns to detect AD and related dementias using targeted and implicitly-learned features from conversational transcripts.Our approach establishes the new state of the art on the De-mentiaBank dataset, achieving an F1 score of 0.929 when classifying participants into AD and control groups.
Atalaya at SemEval 2019 Task 5: Robust Embeddings for Tweet Classification

In this article, we describe our participation in HatEval, a shared task aimed at the detection of hate speech against immigrants and women.We focused on Spanish subtasks, building from our previous experiences on sentiment analysis in this language.We trained linear classifiers and Recurrent Neural Networks, using classic features, such as bagof-words, bag-of-characters, and word embeddings, and also with recent techniques such as contextualized word representations.In particular, we trained robust task-oriented subwordaware embeddings and computed tweet representations using a weighted-averaging strategy.In the final evaluation, our systems showed competitive results for both Spanish subtasks ES-A and ES-B, achieving the first and fourth places respectively.
A Comparison of Sentiment Analysis Methods on Amazon Reviews of Mobile Phones

The consumer reviews serve as feedback for businesses in terms of performance, product quality, and consumer service.In this research, we predict consumer opinion based on mobile phone reviews, in addition to providing an analysis of the most important factors behind reviews being classified as either positive, negative, or neutral.This insight could help companies improve their products as well as helping potential buyers to make the right decision.The research presented in this paper was carried out as follows: the data was pre-processed, before being converted from text to vector representation using a range of feature extraction techniques such as bag-of-words, TF-IDF, Glove, and word2vec.We study the performance of different machine learning algorithms, such as logistic regression, stochastic gradient descent, naive Bayes and convolutional neural networks.In addition, we evaluate our models using accuracy, F1score, precision, recall and log loss function.Moreover, we apply Lime technique to provide analytical reasons for the reviews being classified as either positive, negative or neutral.Our experiments revealed that convolutional neural network with word2vec as a feature extraction technique provides the best results for both the unbalanced and balanced versions of the dataset.
A multiscale object detection approach for remote sensing images based on MSE-DenseNet and the dynamic anchor assignment

Object detection has played an important role in the remote sensing (RS) image understanding domain, but it still suffers from challenges, including the complexity of the image background, the variety of objects and differences of target scales. Although many studies applying deep learning methods to object detection have been presented, effective methods of multiscale object detection in RS images are still lacking. In this paper, we propose a novel object detection approach based on the Multiscale-SELU-DenseNet (MSE-DenseNet) and the dynamic anchor assignment (DAA) strategy. We fuse a highly modified DenseNet and a feature pyramid network into the MSE-DenseNet to extract multi-level feature descriptions of RS images, and then we use them to detect objects on various scales. To further improve the detection performance, we propose a DAA strategy for assigning prior anchor boxes. The experimental results show that our proposed method outperforms state-of-the-art detection methods on the VHR-10 dataset and maintains a real-time detection speed.
Measurement of Interlaminar Tensile Strength and Elastic Properties of Composites Using Open-Hole Compression Testing and Digital Image Correlation

Advanced polymeric composites are increasingly used in high-performance aircraft structures to reduce weight and improve efficiency. However, a major challenge delaying the implementation of the advanced composites is the lack of accurate methods for material characterization. Accurate measurement of three-dimensional mechanical properties of composites, stress–strain response, strength, fatigue, and toughness properties, is essential in the development of validated analysis techniques accelerating design and certification of composite structures. In particular, accurate measurement of the through-thickness constitutive properties and interlaminar tensile (ILT) strength is needed to capture delamination failure, which is one of the primary failure modes in composite aircraft structures. A major technical challenge to accurate measurement of ILT properties is their strong sensitivity to manufacturing defects that often leads to unacceptable scatter in standard test results. Unacceptable failure mode in standard test methods is another common obstacle to accurate ILT strength measurement. Characterization methods based on non-contact full-field measurement of deformation have emerged as attractive alternative techniques allowing more flexibility in test configuration to address some of the limitations inherent to strain gauge-based standard testing. In this work, a method based on full-field digital image correlation (DIC) measurement of surface deformation in unidirectional open-hole compression (OHC) specimens is proposed and investigated as a viable alternative to assessing ILT stress–strain, strength, and fatigue properties. Inverse identification using a finite element model updating (FEMU) method is used for simultaneous measurement of through-thickness elastic constants with recovery of the maximum ILT stress at failure for characterization of strength and fatigue S–N curves.