Reviewerly/exHarmony huggingface dataset:
Sediment Classification of Acoustic Backscatter Image Based on Stacked Denoising Autoencoder and Modified Extreme Learning Machine

Acoustic backscatter data are widely applied to study the distribution characteristics of seabed sediments. However, the ghosting and mosaic errors in backscatter images lead to interference information being introduced into the feature extraction process, which is conducted with a convolutional neural network or auto encoder. In addition, the performance of the existing classifiers is limited by such incorrect information, meaning it is difficult to achieve fine classification in survey areas. Therefore, we propose a sediment classification method based on the acoustic backscatter image by combining a stacked denoising auto encoder (SDAE) and a modified extreme learning machine (MELM). The SDAE is used to extract the deep-seated sediment features, so that the training network can automatically learn to remove the residual errors from the original image. The MELM model, which integrates weighted estimation, a Parzen window and particle swarm optimization, is applied to weaken the interference of mislabeled samples on the training network and to optimize the random expression of input layer parameters. The experimental results show that the SDAE-MELM method greatly reduces mutual interference between sediment types, while the sediment boundaries are clear and continuous. The reliability and robustness of the proposed method are better than with other approaches, as assessed by the overall classification effect and comprehensive indexes.
Weight Decay Scheduling and Knowledge Distillation for Active Learning

Although convolutional neural networks perform extremely well for numerous computer vision tasks, a considerably large amount of labeled data is required to ensure a good outcome. Data labeling is labor-intensive, and in some cases, the labeling budget may be limited. Active learning is a technique that can reduce the labeling required. With this technique, the neural network selects on its own the unlabeled data most helpful for learning, and then requests the human annotator for the labels. Most existing active learning methods have focused on acquisition functions for an effective selection of the informative samples. However, in this paper, we focus on the data-incremental nature of active learning, and propose a method for properly tuning the weight decay as the amount of data increases. We also demonstrate that the performance can be improved by knowledge distillation using a low-performance teacher model trained from the previous acquisition step. In addition, we present a novel perspective of the weight decay, which provides a regularization effect by limiting the number of effective parameters and channels in the convolutional filter. We validate our methods on the MNIST, CIFAR-10, and CIFAR-100 datasets using convolutional neural networks of various sizes.
Semi-Supervised Neural Architecture Search

Neural architecture search (NAS) relies on a good controller to generate better architectures or predict the accuracy of given architectures. However, training the controller requires both abundant and high-quality pairs of architectures and their accuracy, while it is costly to evaluate an architecture and obtain its accuracy. In this paper, we propose SemiNAS, a semi-supervised NAS approach that leverages numerous unlabeled architectures (without evaluation and thus nearly no cost). Specifically, SemiNAS 1) trains an initial accuracy predictor with a small set of architecture-accuracy data pairs; 2) uses the trained accuracy predictor to predict the accuracy of large amount of architectures (without evaluation); and 3) adds the generated data pairs to the original data to further improve the predictor. The trained accuracy predictor can be applied to various NAS algorithms by predicting the accuracy of candidate architectures for them. SemiNAS has two advantages: 1) It reduces the computational cost under the same accuracy guarantee. On NASBench-101 benchmark dataset, it achieves comparable accuracy with gradient-based method while using only 1/7 architecture-accuracy pairs. 2) It achieves higher accuracy under the same computational cost. It achieves 94.02% test accuracy on NASBench-101, outperforming all the baselines when using the same number of architectures. On ImageNet, it achieves 23.5% top-1 error rate (under 600M FLOPS constraint) using 4 GPU-days for search. We further apply it to LJSpeech text to speech task and it achieves 97% intelligibility rate in the low-resource setting and 15% test error rate in the robustness setting, with 9%, 7% improvements over the baseline respectively.
Improving Limited Labeled Dialogue State Tracking with Self-Supervision

Existing dialogue state tracking (DST) models require plenty of labeled data.However, collecting high-quality labels is costly, especially when the number of domains increases.In this paper, we address a practical DST problem that is rarely discussed, i.e., learning efficiently with limited labeled data.We present and investigate two self-supervised objectives: preserving latent consistency and modeling conversational behavior.We encourage a DST model to have consistent latent distributions given a perturbed input, making it more robust to an unseen scenario.We also add an auxiliary utterance generation task, modeling a potential correlation between conversational behavior and dialogue states.The experimental results show that our proposed self-supervised signals can improve joint goal accuracy by 8.95% when only 1% labeled data is used on the MultiWOZ dataset.We can achieve an additional 1.76% improvement if some unlabeled data is jointly trained as semi-supervised learning.We analyze and visualize how our proposed self-supervised signals help the DST task and hope to stimulate future data-efficient DST research.
Study on the Moving Target Tracking Based on Vision DSP

The embedded visual tracking system has higher requirements for real-time performance and system resources, and this is a challenge for visual tracking systems with available hardware resources. The major focus of this study is evaluating the results of hardware optimization methods. These optimization techniques provide efficient utilization based on limited hardware resources. This paper also uses a pragmatic approach to investigate the real-time performance effect by implementing and optimizing a kernel correlation filter (KCF) tracking algorithm based on a vision digital signal processor (vision DSP). We examine and analyze the impact factors of the tracking system, which include DP (data parallelism), IP (instruction parallelism), and the characteristics of parallel processing of the DSP core and iDMA (integrated direct memory access). Moreover, we utilize a time-sharing strategy to increase the system runtime speed. These research results are also applicable to other machine vision algorithms. In addition, we introduced a scale filter to overcome the disadvantages of KCF for scale transformation. The experimental results demonstrate that the use of system resources and real-time tracking speed also satisfies the expected requirements, and the tracking algorithm with a scale filter can realize almost the same accuracy as the DSST (discriminative scale space tracking) algorithm under a vision DSP environment.
Robust Meta-learning for Mixed Linear Regression with Small Batches

A common challenge faced in practical supervised learning, such as medical image processing and robotic interactions, is that there are plenty of tasks but each task cannot afford to collect enough labeled examples to be learned in isolation. However, by exploiting the similarities across those tasks, one can hope to overcome such data scarcity. Under a canonical scenario where each task is drawn from a mixture of k linear regressions, we study a fundamental question: can abundant small-data tasks compensate for the lack of big-data tasks? Existing second moment based approaches show that such a trade-off is efficiently achievable, with the help of medium-sized tasks with $\Omega(k^{1/2})$ examples each. However, this algorithm is brittle in two important scenarios. The predictions can be arbitrarily bad (i) even with only a few outliers in the dataset; or (ii) even if the medium-sized tasks are slightly smaller with $o(k^{1/2})$ examples each. We introduce a spectral approach that is simultaneously robust under both scenarios. To this end, we first design a novel outlier-robust principal component analysis algorithm that achieves an optimal accuracy. This is followed by a sum-of-squares algorithm to exploit the information from higher order moments. Together, this approach is robust against outliers and achieves a graceful statistical trade-off; the lack of $\Omega(k^{1/2})$-size tasks can be compensated for with smaller tasks, which can now be as small as $O(\log k)$.
Forward Secure Conjunctive-Keyword Searchable Symmetric Encryption Using Shamir Threshold Secret Sharing Scheme

Searchable symmetric encryption (SSE) has been widely applied in the encrypted database for queries in practice. Although SSE is powerful and feature-rich, it is always plagued by information leaks. Some recent attacks point out that forward security which disallows leakage from update operations, now becomes a basic requirement for any newly designed SSE schemes. Many forward secure searchable symmetric encryption (FSSE) schemes supporting single-keyword search have been proposed. Only a few SSE schemes can satisfy forward security and support conjunctive-keyword search at the same time, which are realized by adopting inefficient or complicated cryptographic tools. In this paper, we use an efficient cryptographic tool, Shamir threshold secret sharing scheme, to design an efficient and secure conjunctive-keyword FSSE scheme. Our scheme achieves sub-linear efficiency, and can easily be used in any single-keyword FSSE scheme to obtain a conjunctive-keyword FSSE scheme. Compared with the current conjunctive-keyword FSSE scheme, our scheme has a better update and search efficiency.
Adopting explicit and implicit social relations by SVD++ for recommendation system improvement

Recommender systems suffer a set of drawbacks such as sparsity. Social relations provide a useful source to overcome the sparsity problem. Previous studies have utilized social relations or rating feedback sources. However, they ignored integrating these sources. In this paper, the limitations of previous studies are overcome by exploiting four sources of information, namely: explicit social relationships, implicit social relationships, users’ ratings, and implicit feedback information. Firstly, implicit social relationships are extracted through the source allocation index algorithm to establish new relations among users. Secondly, the similarity method is applied to find the similarity between each pair of users who have explicit or implicit social relations. Then, users’ ratings and implicit rating feedback sources are extracted via a user-item matrix. Furthermore, all sources are integrated into the singular value decomposition plus (SVD++) method. Finally, missing predictions are computed. The proposed method is implemented on three real-world datasets: Last.Fm, FilmTrust, and Ciao. Experimental results reveal that the proposed model is superior to other studies such as SVD, SVD++, EU-SVD++, SocReg, and EISR in terms of accuracy, where the improvement of the proposed method is about 0.03% for MAE and 0.01% for RMSE when dimension value (d) = 10.
Second-order guarantees in centralized, federated and decentralized nonconvex optimization

Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the sense that even simply verifying that a given point is a local minimum can be NP-hard [1]. Still, some relatively simple algorithms have been shown to lead to surprisingly good empirical results in many contexts of interest. Perhaps the most prominent example is the success of the backpropagation algorithm for training neural networks. Several recent works have pursued rigorous analytical justification for this phenomenon by studying the structure of the nonconvex optimization problems and establishing that simple algorithms, such as gradient descent and its variations, perform well in converging towards local minima and avoiding saddle-points. A key insight in these analyses is that gradient perturbations play a critical role in allowing local descent algorithms to efficiently distinguish desirable from undesirable stationary points and escape from the latter. In this article, we cover recent results on second-order guarantees for stochastic first-order optimization algorithms in centralized, federated, and decentralized architectures.
Evaluating age-related variations of gaze behavior for a novel digitized-digit symbol substitution test

Analysis of cognitive functioning from gaze behavior might serve as an early indicator of age related decline of cognitive functions. Standard psychological tests like the digit-symbol substitution test or the symbol-digit modalities test is used exclusively in this regard. In this paper, we have designed and developed a digitized version of the digit symbol substitution test. Three different versions have been designed in order to derive deeper insights of the user behavior. The test-retest validation of the versions reveals good correlation across sessions. Fur-ther, the difference in gaze behavior which might be used as an indicator of cognitive functions is tested for two different age groups (13 participants&lt;30 years and 11 participants &gt;40 years). It is seen that the designed digitized version along with the usage of physiological markers like eye tracking bestows additional information and is sensitive to age related factors which might be used for the assessment as well as for the training purpose in rehabilitation systems. Results show that the performance can be analyzed using gaze and pupillometric features in addition to the conventional test performance metrics. We derived an index to measure the performance related to visuo-spatial functioning on one of the de-signed versions of the test. Results of this index on the number of fixations for two age groups are found to be separated in a statistically significant (p&lt;0.05) manner. The age related difference (p&lt;0.05) is also evident in the pupillometric responses obtained.
Bengali News Headline Generation on the Basis of Sequence to Sequence Learning Using Bi-Directional RNN

The newspaper provides significant information every day. Each news describes every event largely, but the headline contained a summary of the news. A meaningful headline is very good for a reader who can understand the gist of corresponding news. Text generation is a language modelling where the machine can generate text automatically. Predict the next correct sequence of a text in the main concept of text generation. Using the text generation approach, automatic headline generation is the possible solution for any language. Many experiments have already been completed for English text generation but few in the Bengali language. Here, it has a big scope to generating an automatic news headlines generator for Bengali text using deep learning concept with bi-directional recurrent neural network (RNN). Bengali text summarization is our recent research work in Bengali NLP. During this research, Bengali text generation for text summarizer is challenging work. Thus, to solve these types of problem, automatic text generation is a good solution.
TPFN: Applying Outer Product Along Time to Multimodal Sentiment Analysis Fusion on Incomplete Data

Multimodal sentiment analysis (MSA) has been widely investigated in both computer vision and natural language processing. However, studies on the imperfect data especially with missing values are still far from success and challenging, even though such an issue is ubiquitous in the real world. Although previous works show the promising performance by exploiting the low-rank structures of the fused features, only the first-order statistics of the temporal dynamics are concerned. To this end, we propose a novel network architecture termed Time Product Fusion Network (TPFN), which takes the high-order statistics over both modalities and temporal dynamics into account. We construct the fused features by the outer product along adjacent time-steps, such that richer modal and temporal interactions are utilized. In addition, we claim that the low-rank structures can be obtained by regularizing the Frobenius norm of latent factors instead of the fused features. Experiments on CMU-MOSI and CMU-MOSEI datasets show that TPFN can compete with state-of-the art approaches in multimodal sentiment analysis in cases of both random and structured missing values.
A fully stochastic second-order trust region method

A stochastic second-order trust region method is proposed, which can be viewed as an extension of the trust-region-ish (TRish) algorithm proposed by Curtis et al. [A stochastic trust region algorithm based on careful step normalization. INFORMS J. Optim. 1(3) 200–220, 2019]. In each iteration, a search direction is computed by (approximately) solving a subproblem defined by stochastic gradient and Hessian estimates. The algorithm has convergence guarantees in the fully stochastic regime, i.e. when each stochastic gradient is merely an unbiased estimate of the gradient with bounded variance and the stochastic Hessian estimates are bounded. This framework covers a variety of implementations, such as when the stochastic Hessians are defined by sampled second-order derivatives or diagonal matrices, such as in RMSprop, Adagrad, Adam and other popular algorithms. The proposed algorithm has a worst-case complexity guarantee in the nearly deterministic regime, i.e. when the stochastic gradients and Hessians are close in expectation to the true gradients and Hessians. The results of numerical experiments for training CNNs for image classification and an RNN for time series forecasting are presented. These results show that the algorithm can outperform a stochastic gradient and first-order TRish algorithm.
GEOCHEMICAL ASSESSMENT OF TRACE METALS IN SOIL, STREAM WATER AND SELECTED FOOD CROPS AT KIBI GOLDFIELDS ENVIRONMENT, GHANA

This study assesses environmental receptors impacted by alluvial gold mining at Kibi Goldfields Limited in the Eastern region of Ghana to establish trace metals contamination risk. Specific objectives are to determine concentrations of (1) As, Cr, Ni and Pb in soils, drainage and food crops, using X-ray fluorescence, (2) Cl- in drainage using standard methods. Mean concentrations of As, Cr and Ni in soils are higher at impacted sites than control sites; while Pb and As concentrations in drainage exceeds the maximum contamination guideline values in drinking water. Using Cl- as a conservative tracer, As and Pb are adsorbing from water, while Cr and Ni are desorbing into water. In the food crops, bioaccumulation factors of As and Ni are profound at the impacted site than control sites; Except Cr in cocoyam at impacted sites, translocation factors are negligible in the food crops. Cr bioaccumulates in in cocoyam and plantain at the control site, while Ni bioaccumulates in only plantain at the impacted sites. Overall, the metal contamination is of concern in the receptors, except Pd in plants. Results from this study can be used for trace metals risk assessment in the environment.
High Capacity and Secure Watermarking for Medical Images Using Tchebichef Moments

Using Tchebichef Moments, this study has introduced a new method for imperceptible watermarking of the medical images with a high embedding capacity and robustness against the various attacks.The suggested method applies conditional quantization based on dither modulation to minimize the number of the moment orders that need to be changed.Reduction of modified moments increases the watermark imperceptibility and hence decreases the attacks probability.The proposed method has been examined in the presence of such factors as quantization step and watermark size.Experimental results show that average PSNR is 53.91 dB for 16384 embedding watermark bits.Moreover, the introduced algorithm has been tested for known attacks like scaling, cropping, noise, etc.It has been shown that our proposed method has proper security and robustness against various attacks.In addition, zero false positive rate on non-watermarked images has been obtained.
Grounded PCFG Induction with Images.

Recent work in unsupervised parsing has tried to incorporate visual information into learning, but results suggest that these models need linguistic bias to compete against models that only rely on text. This work proposes grammar induction models which use visual information from images for labeled parsing, and achieve state-of-the-art results on grounded grammar induction on several languages. Results indicate that visual information is especially helpful in languages where high frequency words are more broadly distributed. Comparison between models with and without visual information shows that the grounded models are able to use visual information for proposing noun phrases, gathering useful information from images for unknown words, and achieving better performance at prepositional phrase attachment prediction.
Improved 2 + 1 phase-shifting algorithm for suppressing ambient light influence

By constructing two phase-shifted fringe images and a flat image, the 2 + 1 phase-shifting algorithm can reduce the error caused by motion and realize real-time and high-resolution three-dimensional (3-D) measurement. However, the nonlinear gamma of the digital light processing and the ambient light will affect the light-field distribution of the flat image, and extensive experiments have shown that the distribution of the captured flat image is inconsistent with the DC component of the deformed patterns. Therefore, based on the linear calibration model, a more realistic DC component of the deformed patterns is deduced from the captured flat image. The AC components of the deformed patterns can be extracted more genuinely by subtracting the deduced DC component from deformed patterns. So, the influence of ambient light can be suppressed efficiently to improve 3-D measuring accuracy. The experimental results show the feasibility and validity of the proposed method.
An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks.

Typically, tokenization is the very first step in most text processing works. As a token serves as an atomic unit that embeds the contextual information of text, how to define a token plays a decisive role in the performance of a model. Even though Byte Pair Encoding (BPE) has been considered the de facto standard tokenization method due to its simplicity and universality, it still remains unclear whether BPE works best across all languages and tasks. In this paper, we test several tokenization strategies in order to answer our primary research question, that is, “What is the best tokenization strategy for Korean NLP tasks?” Experimental results demonstrate that a hybrid approach of morphological segmentation followed by BPE works best in Korean to/from English machine translation and natural language understanding tasks such as KorNLI, KorSTS, NSMC, and PAWS-X. As an exception, for KorQuAD, the Korean extension of SQuAD, BPE segmentation turns out to be the most effective. Our code and pre-trained models are publicly available at https://github.com/kakaobrain/kortok.