Reviewerly/exHarmony huggingface dataset:
Exploring Multi-Stage Information Interactions for Multi-Source Neural Machine Translation

Existing studies for multi-source neural machine translation (NMT) either separately model different source sentences or resort to the conventional single-source NMT by simply concatenating all source sentences. However, there exist two drawbacks in these approaches. First, they ignore the explicit word-level semantic interactions between source sentences, which have been shown effective in the embeddings of multilingual texts. Second, multiple source sentences are simultaneously encoded by an NMT model, which is unable to fully exploit the semantic information of each source sentence. In this paper, we explore multi-stage information interactions for multi-source NMT. Specifically, we first propose a multi-source NMT model that performs information interactions at the encoding stage. Its encoder contains multiple semantic interaction layers, each of which sequentially consists of (1) monolingual semantic interaction sub-layer, which is based on the self-attention mechanism and used to learn word-level monolingual contextual representations of source sentences, and (2) cross-lingual semantic interaction sub-layer, which leverages word alignments to perform fine-grained semantic transitions among hidden states of different source sentences. Furthermore, at the training stage, we introduce a mutual distillation based training framework, where single-source models and ours perform information interactions. Such framework can fully exploit the semantic information of each source sentence to enhance our model. Extensive experimental results on the WMT14 English-German-French dataset show our method exhibits significant improvements upon competitive baselines.
Reversible Data Hiding Technique Using Multi-layer Perceptron Based Prediction and Adaptive Histogram Bin Shifting

In a prediction error-based reversible data hiding technique, a pixel value is predicted using the values at its neighboring pixels in order to obtain the prediction error. Considering the varied neighborhood of the pixels in an image, it is difficult to come up with a mathematical relation or rule between a pixel value and the values in its neighborhood. Hence, in this paper, a novel multi-layer perceptron-based pixel prediction technique is proposed. The multi-layer perceptron is trained to predict a pixel value by considering the values only at the 12 not yet been traversed pixels (according to a raster scan traversal) in a \(5\times 5\) neighborhood of the pixel. Finally, according to an adaptive embedding strategy, one or two bits of data are hidden in the prediction error of the pixel. An adaptive prediction error histogram bin shifting technique is used for embedding the data bits. A better performance of the proposed method over other existing methods is experimentally observed.
Strain Acquisition Framework and Novel Bending Evaluation Formulation for Compression-Loaded Composites Using Digital Image Correlation

Consistent and reproducible data are key for material characterization. This work presents digital image correlation (DIC) strain acquisition guidelines for compression-loaded carbon fiber composites. Additionally, a novel bending criterion is formulated which builds up on the DIC strain data so that it is able to completely replace state-of-the-art tactile strain devices. These guidelines are derived from a custom test setup that simultaneously investigates the front and side view of the specimen. They reflect both an observation and post-processing standpoint. It is found that the DIC-based strain progress matches closely with state-of-the-art strain gauges up to failure initiation. The new bending evaluation criterion allows the bending state-and therefore, the validity of the compression test-to be monitored analogously to the methodology defined in the standards. Furthermore, the new bending criterion eliminates a specific bending mode, caused by an offset of clamps, which cannot be detected by the traditional strain gauge-based monitoring approach.
Heterogeneous Face Recognition with Attention-guided Feature Disentangling

This paper proposes an attention-guided feature disentangling framework (AgFD) to eliminate the large cross-modality discrepancy for Heterogeneous Face Recognition (HFR). Existing HFR methods either focus only on extracting identity features or impose linear/no independence constraints on the decomposed components. Instead, our AgFD disentangles the facial representation and forces intrinsic independence between identity features and identity-irrelevant variations. To this end, an Attention-based Residual Decomposition Module (AbRDM) and an Adversarial Decorrelation Module (ADM) are presented. AbRDM provides hierarchical complementary feature disentanglement, while ADM is introduced for decorrelation learning. Extensive experiments on the challenging CASIA NIR-VIS 2.0 Database, Oulu-CASIA NIR&VIS Database, BUAA-VisNir Database, and IIIT-D Viewed Sketch Database demonstrate the generalization ability and competitive performance of the proposed method.
Cloud-Edge Computing Resource Collaborative Optimization Method for Power Distribution Fault Analysis Service

The cloud-edge computing architecture of distribution network can meet the computing and communication requirements of most novel and traditional distribution services. However, the demand for computing resources of fault service is often greater than the resource capacity of edge computing terminal. Therefore, based on the cloud-edge collaborative architecture, this paper proposes a collaborative optimization method of cloud and edge computing resources for fault service in distribution network. Firstly, this paper elaborates the characteristics of fault service in distribution network, and describes the possibility of cloud-edge collaborative information interaction and microservice offloading based on container technology. Then, a cloud-edge collaborative service computing model and the microservice model of fault service are established. According to the offloading mechanism, a microservice offloading decision optimization model is established, which take the system operation cost and calculation delay as the comprehensive measuring index. Finally, the method proposed in this paper is simulated by MATLAB, and the simulation results show that this method can effectively reduce the microservice response time of distribution network and meet the computing resource requirements of fault service.
Shape Estimation and Tracking using Spherical Double Fourier Series for Three-Dimensional Range Sensors

In this paper, a novel measurement model based on spherical double Fourier series (DFS) for estimating the 3D shape of a target concurrently with its kinematic state is introduced. Here, the shape is represented as a star-convex radial function, decomposed as spherical DFS. In comparison to ordinary DFS, spherical DFS do not suffer from ambiguities at the poles. Details will be given in the paper. The shape representation is integrated into a Bayesian state estimator framework via a measurement equation. As range sensors only generate measurements from the target side facing the sensor, the shape representation is modified to enable application of shape symmetries during the estimation process. The model is analyzed in simulations and compared to a shape estimation procedure using spherical harmonics. Finally, shape estimation using spherical and ordinary DFS is compared to analyze the effect of the pole problem in extended object tracking (EOT) scenarios.
Estimation of Vehicle Count, Class, and Speed on Highways Using a Computer Vision-Based Technique

Real-time traffic flow analysis is beneficial for the extraction of vehicle-based information which can be used for further data predictions. Apply computer vision-based techniques on a video feed obtained from a mounted camera over a highway; the vehicle-related events such as count, class, and speed can be cognized. Background subtraction is employed to locate the motion of the vehicles. The moving vehicles are seen as white blobs after thresholding the video frame. Further processing of the blobs and their behavior can give way to the tracking of the vehicles along with their mentioned events.
Tackling the Score Shift in Cross-Lingual Speaker Verification by Exploiting Language Information

This paper contains a post-challenge performance analysis on cross-lingual speaker verification of the IDLab submission to the VoxCeleb Speaker Recognition Challenge 2021 (VoxSRC-21). We show that current speaker embedding extractors consistently underestimate speaker similarity in within-speaker cross-lingual trials. Consequently, the typical training and scoring protocols do not put enough emphasis on the compensation of intra-speaker language variability. We propose two techniques to increase cross-lingual speaker verification robustness. First, we enhance our previously proposed Large-Margin Fine-Tuning (LM-FT) training stage with a mini-batch sampling strategy which increases the amount of intra-speaker cross-lingual samples within the mini-batch. Second, we incorporate language information in the logistic regression calibration stage. We integrate quality metrics based on soft and hard decisions of a VoxLingua107 language identification model. The proposed techniques result in a 11.7% relative improvement over the baseline model on the VoxSRC-21 test set and contributed to our third place finish in the corresponding challenge.
MonoSOD: Monocular Salient Object Detection based on Predicted Depth

Salient object detection (SOD) can directly improve the performance of tasks like obstacle detection, semantic segmentation and object recognition. Such tasks are important for robotic and other autonomous navigation systems. State-of-the-art SOD methodologies, provide improved performance by incorporating depth information, usually acquired using additional specialized sensors, e.g., RGB-D cameras. This introduces an overhead to the overall cost and flexibility of such systems. Nevertheless, the recent advances of machine learning, have provided models, capable of generating depth map approximations, given a single RGB image. In this work, we propose a novel monocular SOD (MonoSOD) methodology, based on a two-branch CNN autoencoder architecture capable of predicting depth maps and estimating saliency through a trainable refinement scheme. Its application on benchmark datasets, indicates that its performance is comparable to that of state-of-the-art SOD methods relying on RGB-D data. Therefore, it could be considered as a lower-cost alternative of such methods for future applications.
AutoAct: An Auto Labeling Approach Based on Activities of Daily Living in the Wild Domain

Annotation problem in Human Activity Recognition (HAR) has always been a major concern. The main problem of human-based annotation is that it often leads to misclassification due to uneagerness, hectic working and living conditions, misleading emotional fluctuation, etc. A solution for that can be the use of a fully automated annotation system. Here, we proposed a unique method for developing a pre-trained model which will be used as the auto annotation tool, using two completely different benchmark Activities of Daily Living (ADL) in the wild data sets. Our proposed auto labeling system has been trained in varied conditions which reduce the bias from location, people, and device, and makes our system very robust. Here, we used a unique mixture of augmentation, scaling, and boosting to increase accuracy, reduce overfitting, and handle imbalanced class problems. The accuracy achieved for auto annotation in case of data set 1, data set 2, and combined data set are 92%, 87%, and 73% respectively. Also, performance evaluation has been done using precision, recall, f1 score, and ROC curve, and the results achieved are promising.
Conditioning Style on Substance: Plans for Narrative Observation

We consider a robot tasked with observing its environment and later selectively summarizing what it saw as a vivid, structured narrative. The robot interacts with an uncertain environment, modelled as a stochastic process, and must decide what events to pay attention to (substance), and how to best make its recording (style) for later compilation of its summary. If carrying a video camera, for example, it must decide where to be, what to aim the camera at, and which stylistic selections, like the focus and level of zoom, are most suitable. This paper examines planning algorithms that help the robot predict events that (1)will likely occur; (2)would be useful in telling a tale; and (3)may be hewed to cohere stylistically. The third factor, a time-extended requirement, is entirely neglected in earlier, simpler work. With formulations based on underlying Markov Decision Processes, we compare two algorithms: a monolithic planner that jointly plans over events and style pairs and a decoupled approach that prescribes style conditioned on events. The decoupled approach is seen to be effective and much faster to compute, suggesting that computational expediency justifies the separation of substance from style. Finally, we also report on our hardware implementation.
Video Analytics Architecture with Metadata Event-Engine for Urban Safe Cities

Intelligent video analysis from sources such as urban surveillance cameras is a prolific research area today. Multiple types of computer architectures offer a wide range of possibilities when addressing the needs of computer vision technologies. When it comes to real time processing for high level and complex event detections, however, some limitations may arise, such as the computing power in the edge or the cost of sending real time video to the cloud for running advanced algorithms. In this paper, we present a functional architecture of a complete video surveillance solution and we focus on the metadata-processing event engine which takes care of the high-level video processing that is decoupled from a low-level video analysis. The low-level video analysis running in the edge generates and publishes a flow of JSON messages structure containing the details of bounding boxes detected in each frame into an asynchronous messaging service. The metadata event engine is running in a remote cloud, far from the camera locations. We present the performance evaluation of this event engine under different circumstances simulating data coming simultaneously from multiple cameras, in order to study the best strategies when deploying and partitioning distributed processing tasks.
Dynamic Guidance for Decluttering Photographic Compositions

Unwanted clutter in a photo can be incredibly distracting. However in the moment, photographers have so many things to simultaneously consider, it can be hard to catch every detail. Designers have long known the benefits of abstraction for seeing a more holistic view of their design. We wondered if, similarly, some form of image abstraction might be helpful for photographers as an alternative perspective or "lens" with which to see their image. Specifically, we wondered if such abstraction might draw the photographer's attention away from details in the subject to noticing objects in the background, such as unwanted clutter. We present our process for designing such a camera overlay, based on the idea of using abstraction to recognize clutter. Our final design uses object-based saliency and edge detection to highlight contrast along subject and image borders, outlining potential distractors in these regions. We describe the implementation and evaluation of a capture-time tool that interactively displays these overlays and find that the tool is helpful for making users more confident in their ability to take decluttered photos that clearly convey their intended story.
Reminding the incremental language model via data-free self-distillation

Incremental language learning, which involves retrieving pseudo-data from previous tasks, can alleviate catastrophic forgetting. However, previous methods require a large amount of pseudo-data to approach the performance of multitask learning, and the performance decreases dramatically when there is significantly less pseudo-data than new task data. This decrease occurs because the pseudo-data are learned inefficiently and deviate from the real data. To address these issues, we propose reminding the incremental language model via data-free self-distillation (DFSD), which includes 1) self-distillation based on the Earth mover’s distance (SD-EMD) and 2) hidden data augmentation (HDA). SD-EMD can increase the efficiency of the model by adaptively estimating the knowledge distribution in all GPT-2 layers and effectively transferring data from the teacher model to the student model via adaptive self-multilayer-to-multilayer mapping. HDA can reduce deviations by decomposing the generation process via data augmentation and bootstrapping. Our experiments on decaNLP and text classification tasks with low pseudo-data sampling ratios reveal that the DFSD model outperforms previous state-of-the-art incremental methods. The advantages of DFSD become more apparent when there is less pseudo-data and larger deviations.
Research on 3D crack segmentation of CT images of oil rock core

In this paper, we propose a framework for CT image segmentation of oil rock core. According to the characteristics of CT image of oil rock core, the existing level set segmentation algorithm is improved. Firstly, an algorithm of Chan-Vese (C-V) model is carried out to segment rock core from image background. Secondly the gray level of image background region is replaced by the average gray level of rock core, so that image background does not affect the binary segmentation. Next, median filtering processing is carried out. Finally, an algorithm of local binary fitting (LBF) model is executed to obtain the crack region. The proposed algorithm has been applied to oil rock core CT images with promising results.
Guwen-UNILM: Machine Translation Between Ancient and Modern Chinese Based on Pre-Trained Models

Ancient Chinese literatures are not only the unique cultural heritage of China but also the treasures of world civilization. Nevertheless, it has become quite difficult for modern people to comprehend or even create ancient works with the evolution of language in the long history. Translation is therefore playing a key role in bridging the two eras. This paper is to develop an automatic translation method between ancient and modern Chinese literature. To start with, an open sourced sentence level parallel corpus of ancient-modern Chinese is established since there is no available parallel corpus open for use. As the seq2seq-based machine translation models do not work well on this task, the pre-trained model UNILM is then applied in our method considering the monolingual characteristics of this task. Furthermore, the ancient Chinese pre-trained model - Guwen-BERT is utilized to further improve the performance of the method. The quality of translation is evaluated by both Human Evaluation and two automatic metrics: a) case-sensitive BLEU scores and b) Imagery Conservation (I.C), which is first developed in this paper. The experimental results under all metrics show that our method can generate higher quality of translation.
Building a New-Generation Corpus for Empirical Translation Studies: The Dutch Parallel Corpus 2.0

This chapter introduces a new, updated version of the Dutch Parallel Corpus, a bidirectional parallel corpus of expert translations for Dutch><English and Dutch><French language pairs. This revisited version of the corpus, which we dub Dutch Parallel Corpus 2.0, is dynamic in nature, and contains 2.75 million words at the time of writing. The corpus is sentence-aligned, lemmatized and POS-tagged using the state-of-the-art natural language processing toolkit Stanza. Compared to its predecessor, the Dutch Parallel Corpus 2.0 contains more metadata about the translators (e.g. gender, education, experience) and the translation projects (e.g. L1/L2 translation, software used, degree and type of revision), next to the traditional metadata about the texts themselves (e.g. source and target language, intended audience, intended goal, register). The availability of an extensive set of metadata is considered the main asset of this corpus, together with a more principled and flexible register classification, thus stimulating corpus-based translation scholars to answer more refined research questions about the linguistic and contextual factors that shape translated texts, and ultimately fostering ideas and theories about the social and cognitive processes involved in translation performance. The corpus is freely available for research purposes via https://www.dpc2.ugent.be/.
Attention-based Unsupervised Keyphrase Extraction and Phrase Graph for COVID-19 Medical Literature Retrieval

Searching, reading, and finding information from the massive medical text collections are challenging. A typical biomedical search engine is not feasible to navigate each article to find critical information or keyphrases. Moreover, few tools provide a visualization of the relevant phrases to the query. However, there is a need to extract the keyphrases from each document for indexing and efficient search. The transformer-based neural networks—BERT has been used for various natural language processing tasks. The built-in self-attention mechanism can capture the associations between words and phrases in a sentence. This research investigates whether the self-attentions can be utilized to extract keyphrases from a document in an unsupervised manner and identify relevancy between phrases to construct a query relevancy phrase graph to visualize the search corpus phrases on their relevancy and importance. The comparison with six baseline methods shows that the self-attention-based unsupervised keyphrase extraction works well on a medical literature dataset. This unsupervised keyphrase extraction model can also be applied to other text data. The query relevancy graph model is applied to the COVID-19 literature dataset and to demonstrate that the attention-based phrase graph can successfully identify the medical phrases relevant to the query terms.
Exploiting Probabilistic Siamese Visual Tracking with a Conditional Variational Autoencoder

Visual tracking is a fundamental capability for robots tasked with humans and environment interaction. However, state-of-the-art visual tracking methods are still prone to failures and are imprecise when applied to challenging stereos, and their results are generally confidence agonistic. These methods depend on an embedded deep learning model to provide deterministic features or regression maps. A deterministic output with low confidence can result in disastrous consequences and lacks evidence needed for subsequent operations. Moreover, training data ambiguities or noise in the observations (so-called data uncertainty) can also lead to inherent uncertainty. In this paper, we focus on exploiting probabilistic Siamese visual tracking with a conditional variational autoencoder (CVAE). First, we build a bridge between the Siamese architecture and the CVAE and propose a novel Bayesian visual tracking method. Second, the proposed method generates a complete probability distribution that enables the production of multiple plausible tracking outputs. Third, CVAE conditioned by ground truth data encodes a low-dimensional latent space and conducts noise-injection training to prevent overfitting. Our proposed tracking method outperformed the state-of-the-art trackers on the VOT2016, VOT2018 and TColor-128 datasets.