Reviewerly/exHarmony huggingface dataset:
Proposal of Motion Judgment Algorithm Based on Joint Angle of Variable Elastic Assist Suit with High Back Drivability

In recent years, the burden per worker has increased due to a decrease in the working population. Wearable assist suits have been developed as one of the methods for solving the problem. To extend the assist suit to practical situations, it is necessary to provide a motion judgment interface for judging the motion of a wearer. Therefore, in our study, a motion judgment algorithm is proposed for assist suits, based on variable viscoelasticity. The proposed algorithm judges sitting, standing-up, stance, sitting-down, and gait using only the joint angle information of the suit and verification is performed using human joint angles obtained by motion capture. Thus, the motion judgment rate is 90% or more for sitting, standing-up, stance, and sitting-down, and 80% or more for gait, confirming the usefulness of motion judgment. Additionally, based on these results, further verification is performed on an actual machine. As a result, in a series of motions starting from the sitting to the standing-up, the stance, and the gait, the motion judgement is successful five times from the sitting to the standing-up, the stance, and once in gait. In a series of motions from sitting to standing-up, the stance, and sitting-down, the motion judgment is successful five times during sitting; five times during sitting, stance, and sitting-down; and three times during standing-up. In this way, it is confirmed that the proposed method can judge the motion only by angle information, although there is a problem in a success rate depending on the motion.
Optimal Elliptic Curve Cryptography-Based Effective Approach for Secure Data Storage in Clouds

With the rapid development of cloud advancement, a data security challenge has emerged. In this paper, a technique based on elliptical cryptography and cuckoo search algorithm is proposed. With this technique, data owners securely store their data files in the cloud server. Initially the user sends a file storage request to store a file in a cloud server provider (CSP). The input file is checked whether it is sensitive or non-sensitive by the user. If the file is sensitive, then it would be split and stored in different virtual machines (VMs), and if the file is non-sensitive, then it would be assigned in a single VM. This approach was used for the first time as per the survey. To add further security, the sensitive data retrieval needs an encryption process that is supported by the proposed algorithm. If the data owner stores the sensitive data to cloud server, the data owner's document is encrypted by the double encryption technique. Here RSA and optimal elliptic curve cryptography (OECC) algorithm is used to encrypt the document with high security. The authors have used cuckoo search algorithm to identify the optimal key in ECC. This paper has proposed a novel cryptography approach for delivering mass distributed storage by which user's original data cannot be directly reached by cloud operators. Hence, this research has proved that the proposed work will give better securable data storage solving the security issues.
CTC-Synchronous Training for Monotonic Attention Model

Monotonic chunkwise attention (MoChA) has been studied for the online streaming automatic speech recognition (ASR) based on a sequence-to-sequence framework. In contrast to connectionist temporal classification (CTC), backward probabilities cannot be leveraged in the alignment marginalization process during training due to left-to-right dependency in the decoder. This results in the error propagation of alignments to subsequent token generation. To address this problem, we propose CTC-synchronous training (CTC-ST), in which MoChA uses CTC alignments to learn optimal monotonic alignments. Reference CTC alignments are extracted from a CTC branch sharing the same encoder with the decoder. The entire model is jointly optimized so that the expected boundaries from MoChA are synchronized with the alignments. Experimental evaluations of the TEDLIUM release-2 and Librispeech corpora show that the proposed method significantly improves recognition, especially for long utterances. We also show that CTC-ST can bring out the full potential of SpecAugment for MoChA.
Leveraging Linguistic Linked Data for Cross-Lingual Model Transfer in the Pharmaceutical Domain

We describe the use of linguistic linked data to support a cross-lingual transfer framework for sentiment analysis in the pharmaceutical domain. The proposed system dynamically gathers translations from the Linked Open Data (LOD) cloud, particularly from Apertium RDF, in order to project a deep learning-based sentiment classifier from one language to another, thus enabling scalability and avoiding the need of model re-training when transferred across languages. We describe the whole pipeline traversed by the multilingual data, from their conversion into RDF based on a new dynamic and flexible transformation framework, through their linking and publication as linked data, and finally their exploitation in the particular use case. Based on experiments on projecting a sentiment classifier from English to Spanish, we demonstrate how linked data techniques are able to enhance the multilingual capabilities of a deep learning-based approach in a dynamic and scalable way, in a real application scenario from the pharmaceutical domain.
End-to-End Speech-to-Dialog-Act Recognition

Spoken language understanding, which extracts intents and/or semantic concepts in utterances, is conventionally formulated as a post-processing of automatic speech recognition. It is usually trained with oracle transcripts, but needs to deal with errors by ASR. Moreover, there are acoustic features which are related with intents but not represented with the transcripts. In this paper, we present an end-to-end model which directly converts speech into dialog acts without the deterministic transcription process. In the proposed model, the dialog act recognition network is conjunct with an acoustic-to-word ASR model at its latent layer before the softmax layer, which provides a distributed representation of word-level ASR decoding information. Then, the entire network is fine-tuned in an end-to-end manner. This allows for stable training as well as robustness against ASR errors. The model is further extended to conduct DA segmentation jointly. Evaluations with the Switchboard corpus demonstrate that the proposed method significantly improves dialog act recognition accuracy from the conventional pipeline framework.
Intermediate Training of BERT for Product Matching.

Transformer-based models like BERT have pushed the state-of the-art for a wide range of tasks in natural language processing.General-purpose pre-training on large corpora allows Transformers to yield good performance even with small amounts of training data for task-specific fine-tuning. In this work, we apply BERT to the task of product matching in e-commerce and show that BERT is much more training data efficient than other state-of-the-art methods. Moreover, we show that we can further boost its effectiveness through an intermediate training step, exploiting large collections of product offers. Our intermediate training leads to strong performance (>90% F1) on new, unseen products without any product-specific fine-tuning. Further fine-tuning yields additional gains, resulting in improvements of up to 12% F1 for small training sets. Adding the masked language modeling objective in the intermediate training step in order to further adapt the language model to the application domain leads to an additional increase of up to 3% F1.
Integration of the 3D Environment for UAV Onboard Visual Object Tracking

Single visual object tracking from an unmanned aerial vehicle (UAV) poses fundamental challenges such as object occlusion, small-scale objects, background clutter, and abrupt camera motion. To tackle these difficulties, we propose to integrate the 3D structure of the observed scene into a detection-by-tracking algorithm. We introduce a pipeline that combines a model-free visual object tracker, a sparse 3D reconstruction, and a state estimator. The 3D reconstruction of the scene is computed with an image-based Structure-from-Motion (SfM) component that enables us to leverage a state estimator in the corresponding 3D scene during tracking. By representing the position of the target in 3D space rather than in image space, we stabilize the tracking during ego-motion and improve the handling of occlusions, background clutter, and small-scale objects. We evaluated our approach on prototypical image sequences, captured from a UAV with low-altitude oblique views. For this purpose, we adapted an existing dataset for visual object tracking and reconstructed the observed scene in 3D. The experimental results demonstrate that the proposed approach outperforms methods using plain visual cues as well as approaches leveraging image-space-based state estimations. We believe that our approach can be beneficial for trafficmonitoring, video surveillance, and navigation.
Preventing inert knowledge: Category status promotes spontaneous structure-based retrieval of prior knowledge.

Analogical comparison of 2 provided cases promotes spontaneous analogical transfer by encouraging a more abstract representation of a target principle. This is widely understood as a process of schema abstraction that aids retrieval from memory in the absence of superficial similarity. The category status hypothesis states that if knowledge about a target principle is represented as a relational category, it is easier to activate as a result of categorizing (as opposed to cue-based reminding). To investigate these 2 pathways to spontaneous transfer (schema retrieval vs. categorization), participants were assigned to different study conditions: (a) standard comparison of 2 analogs; (b) standard comparison followed by a second comparison of 2 new analogs; or (c) a guided Category-building task based on a foundation of sequential summarization with additional categorization supports. Experiment 1 showed improved spontaneous transfer in the Category-building task relative to Single-comparison-the gold standard for encouraging spontaneous transfer. Experiment 2 demonstrated that the Category-building task led to more frequent spontaneous structure-based retrieval than either comparison-based task. This suggests that the transfer improvements observed in Experiment 1 are attributable to the Category-building task making superficially dissimilar prior knowledge more accessible. Experiments 3a and 3b tested alternative explanations of this finding and provided evidence that the effectiveness of the Category-building task arises from encouraging the construal of a target principle as a relational category. These findings support categorization as a critical factor in explaining successful spontaneous analogical retrieval and toward instructional approaches that promote portable rather than inert knowledge. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Learning Black-Box Attackers with Transferable Priors and Query Feedback

This paper addresses the challenging black-box adversarial attack problem, where only classification confidence of a victim model is available. Inspired by consistency of visual saliency between different vision models, a surrogate model is expected to improve the attack performance via transferability. By combining transferability-based and query-based black-box attack, we propose a surprisingly simple baseline approach (named SimBA++) using the surrogate model, which significantly outperforms several state-of-the-art methods. Moreover, to efficiently utilize the query feedback, we update the surrogate model in a novel learning scheme, named High-Order Gradient Approximation (HOGA). By constructing a high-order gradient computation graph, we update the surrogate model to approximate the victim model in both forward and backward pass. The SimBA++ and HOGA result in Learnable Black-Box Attack (LeBA), which surpasses previous state of the art by considerable margins: the proposed LeBA significantly reduces queries, while keeping higher attack success rates close to 100% in extensive ImageNet experiments, including attacking vision benchmarks and defensive models. Code is open source at this https URL.
Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images.

Background: Breast cancer is one of the most causes of death in women. Early diagnosis and detection of Invasive Ductal Carcinoma (IDC) is an important key for the treatment of IDC. Computer-aided approaches have great potential to improve diagnosis accuracy. In this paper, we proposed a deep learning-based method for the automatic classification of IDC in whole slide images (WSI) of breast cancer. Furthermore, different types of deep neural networks training such as training from scratch and transfer learning to classify IDC were evaluated. Methods: In total, 277524 image patches with 50×50-pixel size form original images were used for model training. In the first method, we train a simple convolutional neural network (named it baseline model) on these images. In the second approach, we used the pre-trained VGG-16 CNN model via feature extraction and fine-tuning for the classification of breast pathology images. Results: Our baseline model achieved a better result for the automatic classification of IDC in terms of F-measure and accuracy (83%, 85%) in comparison with original paper on this data set and achieved a comparable result with a new study that introduced accepted-rejected pooling layer. Also, transfer learning via feature extraction yielded better results (81%, 81%) in comparison with handcrafted features. Furthermore, transfer learning via feature extraction yielded better classification results in comparison with the baseline model. Conclusion: The experimental results demonstrate that using deep learning approaches yielded better results in comparison with handcrafted features. Also, using transfer learning in histopathology image analysis yielded significant results in comparison with training from scratch in much less time.
An Empirical Study to Investigate Different SMOTE Data Sampling Techniques for Improving Software Refactoring Prediction

The exponential rise in software systems and allied applications has alarmed industries and professionals to ensure high quality with optimal reliability, maintainability etc. On contrary software companies focus on developing software solutions at the reduced cost corresponding to the customer demands. Thus, maintaining optimal software quality at reduced cost has always been the challenge for developers. On the other hand, inappropriate code design often leads aging, smells or bugs which can harm eventual intend of the software systems. However, identifying a smell signifier or structural attribute characterizing refactoring probability in software has been the challenge. To alleviate such problems, in this research code-metrics structural feature identification and Neural Network based refactoring prediction model is developed. Our proposed refactoring prediction system at first extracts a set of software code metrics from object-oriented software systems, which are then processed for feature selection method to choose an appropriate sample set of features using Wilcoxon rank test. Once obtaining the optimal set of code-metrics, a novel ANN classifier using 5 different hidden layers is implemented on 5 open source java projects with 3 data sampling techniques SMOTE, BLSMOTE, SVSMOTE to handle class imbalance problem. The performance of our proposed model achieves optimal classification accuracy, F-measure and then it has been shown through AUC graph as well as box-plot diagram.
Angular displacement estimation enhanced by squeezing and parametric amplification

We theoretically study angular displacement estimation based on a modified Mach-Zehnder interferometer (MZI), in which two optical parametric amplifiers (PAs) are introduced into two arms of the standard MZI, respectively. The employment of PAs can both squeeze the shot noise and amplify the photon number inside the interferometer. When the unknown angular displacements are introduced to both arms, we derive the multiparameter quantum Cramér-Rao bound (QCRB) using the quantum Fisher information matrix approach, and the bound of angular displacement difference between the two arms is compared with the sensitivity of angular displacement using the intensity detection. On the other hand, in the case where the unknown angular displacement is in only one arm, we give the sensitivity of angular displacement using the method of homodyne detection. It can surpass the standard quantum limit (SQL) and approach the single parameter QCRB. Finally, the effect of photon losses on sensitivity is discussed.
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks

Adversarial training is a popular defense strategy against attack threat models with bounded Lp norms. However, it often degrades the model performance on normal images and the defense does not generalize well to novel attacks. Given the success of deep generative models such as GANs and VAEs in characterizing the underlying manifold of images, we investigate whether or not the aforementioned problems can be remedied by exploiting the underlying manifold information. To this end, we construct an On-Manifold (OM-ImageNet) dataset by projecting the ImageNet samples onto the manifold learned by StyleGSN. For this dataset, the underlying manifold information is exact. Using OM-ImageNet, we first show that adversarial training in the latent space of images improves both standard accuracy and robustness to on-manifold attacks. However, since no out-of-manifold perturbations are realized, the defense can be broken by Lp adversarial attacks. We further propose Dual Manifold Adversarial Training (DMAT) where adversarial perturbations in both latent and image spaces are used in robustifying the model. Our DMAT improves performance on normal images, and achieves comparable robustness to the standard adversarial training against Lp attacks. In addition, we observe that models defended by DMAT achieve improved robustness against novel attacks which manipulate images by global color shifts or various types of image filtering. Interestingly, similar improvements are also achieved when the defended models are tested on out-of-manifold natural images. These results demonstrate the potential benefits of using manifold information in enhancing robustness of deep learning models against various types of novel adversarial attacks.
Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning

While humans can solve a visual puzzle that requires logical reasoning by observing only few samples, it would require training over large amount of data for state-of-the-art deep reasoning models to obtain similar performance on the same task. In this work, we propose to solve such a few-shot (or low-shot) visual reasoning problem, by resorting to analogical reasoning, which is a unique human ability to identify structural or relational similarity between two sets. Specifically, given training and test sets that contain the same type of visual reasoning problems, we extract the structural relationships between elements in both domains, and enforce them to be as similar as possible with analogical learning. We repeatedly apply this process with slightly modified queries of the same problem under the assumption that it does not affect the relationship between a training and a test sample. This allows to learn the relational similarity between the two samples in an effective manner even with a single pair of samples. We validate our method on RAVEN dataset, on which it outperforms state-of-the-art method, with larger gains when the training data is scarce. We further meta-learn our analogical contrastive learning model over the same tasks with diverse attributes, and show that it generalizes to the same visual reasoning problem with unseen attributes.
Introducing Routing Uncertainty in Capsule Networks

Rather than performing inefficient local iterative routing between adjacent capsule layers, we propose an alternative global view based on representing the inherent uncertainty in part-object assignment. In our formulation, the local routing iterations are replaced with variational inference of part-object connections in a probabilistic capsule network, leading to a significant speedup without sacrificing performance. In this way, global context is also considered when routing capsules by introducing global latent variables that have direct influence on the objective function, and are updated discriminatively in accordance with the minimum description length (MDL) principle. We focus on enhancing capsule network properties, and perform a thorough evaluation on pose-aware tasks, observing improvements in performance over previous approaches whilst being more computationally efficient.
Deep Sparse Autoencoder Network for Facial Emotion Recognition

Deep neural network (DNN)Deep Neural Network (DNN) has been used as a learning model for modeling the hierarchical architecture of human brain. However, DNN suffers from problems of learning efficiency and computational complexity.
The role of news title for linking during preservation process in digital archives

Purpose The World Wide Web has become an essential platform for a news publication, and it has become one of the primary sources of information dissemination in the past few years. Electronic media, i.e., television channels, magazines and newspapers, have started publishing news online. This online information is prompt to be disappeared because of short life-span and imperative to be archived for the long-term and future generations. This paper presents a content-based similarity measure based on the headings of the news articles for linking digital news stories published in various newspapers during the preservation process that helps to ensure future accessibility. Design/methodology/approach To evaluate the accuracy and assess the effectiveness and worth of the proposed measure for linking news articles in Digital News Story Archive (DNSA), we adopted both, system-centric and user-centric (human judgment) evaluation over different datasets of news articles. Findings The proposed similarity measure is evaluated using different sizes of datasets, and the results are compared by both user-centric technique, i.e., expert judgment and system-centric techniques, i.e., cosine similarity measure, extended Jaccard measure and common ratio measure for stories (CRMS). The comparison helps to get a broader impact and can be helpful for generalization of the measure for different categories of news articles. Multiple experiments have conducted the findings of which showed that the measure presented viable results for national and international news, while best results for linking sports news articles during preservation based on headings. Originality/value The DNSA preserves a huge number of news articles from multiple news sources and to link with a vast collection, which encourages to introduce an efficient linking mechanism with few terms to manipulate. The CRMS is modified to deal with the headings of news articles as a part of the digital news stories preservation framework and comprehensively analysed.
Thermal entanglement in Tavis–Cummings model with Kerr nonlinearity

In this paper, we investigate entanglement between two two-level atoms when they simultaneously interact with a single-mode thermal field of a lossless cavity with Kerr medium. We show that a slight Kerr nonlinearity might enhance the degree of entanglement between the atoms. More interestingly, both atoms would somehow get entangled even when both atoms are initially in the excited state.