Reviewerly/exHarmony huggingface dataset:
Recognition Method of Abnormal Data in Local Area Network Common Channel Based on Convolutional Neural Network

In order to strengthen the accuracy of the recognition of abnormal data in the local area network common channel and improve the recognition efficiency, the convolutional network method is applied to data recognition, and the recognition method of the abnormal data in the local area network common channel based on the convolutional neural network is studied. First, the convolutional neural network structure is analyzed and the three main parts of the convolutional neural network are outlined. Second, the optimization algorithm flow of the abnormal data recognition of the common channel of the local area network is designed, and the specific calculation steps of the data recognition are obtained. Finally, a comparative experiment is designed to compare the traditional recognition method of abnormal data based on data stream and the recognition method of abnormal data based on log analysis with the recognition method of abnormal data in local area network common channel based on convolutional neural network designed in this paper. In this way, the loss value and recognition accuracy data are obtained, and the optimal abnormal data recognition method is judged through the data.
As Easy as 1, 2, 3: Behavioural Testing of NMT Systems for Numerical Translation

Mistranslated numbers have the potential to cause serious effects, such as financial loss or medical misinformation.In this work we develop comprehensive assessments of the robustness of neural machine translation systems to numerical text via behavioural testing.We explore a variety of numerical translation capabilities a system is expected to exhibit and design effective test examples to expose system underperformance.We find that numerical mistranslation is a general issue: major commercial systems and state-of-the-art research models fail on many of our test examples, for high-and low-resource languages.Our tests reveal novel errors that have not previously been reported in NMT systems, to the best of our knowledge.Lastly, we discuss strategies to mitigate numerical mistranslation.
Investigations of gold-sulfide mineralization and microthermometry within quartz veins/veinlets in the Gharehchay area, south of Tikmehdash, East-Azarbaidjan province, NW Iran

Investigations of gold-sulfide mineralization and microthermometry within quartz veins/veinlets in the Gharehchay area, south of Tikmehdash, East-Azarbaidjan province, NW Iran
Exploring Discourse Structures for Argument Impact Classification

Discourse relations among arguments reveal logical structures of a debate conversation.However, no prior work has explicitly studied how the sequence of discourse relations influence a claim's impact.This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument.We further propose DISCOC to inject and fuse the sentence-level structural discourse information with contextualized features derived from large-scale language models.Experimental results and extensive analysis show that the attention and gate mechanisms that explicitly model contexts and texts can indeed help the argument impact classification task defined by Durmus et al. (2019), and discourse structures among the context path of the claim to be classified can further boost the performance.* This work was done when Xin Liu was an intern at Huawei Noah's Ark Lab.Thesis: Physical torture of prisoners is an acceptable interrogation tool. S1:Torture can help force prisoners to reveal information that could prevent attacks and save lives. O1:Torture is ineffective at getting prisoners to reveal desired information.
An efficient implementation of iterative partial transcription for the traveling salesman problem

Iterative Partial Transcription (IPT) is an important recombination operator for Traveling Salesman Problem (TSP), and is a key component in the LKH inexact solver for the TSP. IPT shares many characteristics with Partition Crossover for the TSP. However, the standard implementation of IPT searches for all common subchains of sizes from 4 to n/2 between two parent tours and thus has a time complexity of O(n2) in the worst case. In this paper, an efficient implementation of IPT is proposed that uses a special data structure called the Extended Edge Table (EET) to find the recombination components. We prove that the proposed technique is approximately equivalent to IPT and has a time complexity of O(n). The performance of the two implementations of IPT is compared based on some random and benchmark TSP instances to establish the efficiency of the proposed algorithm.
TempDiff: Temporal Difference-Based Feature Map-Level Sparsity Induction in CNNs with &lt;4% Memory Overhead

The diffusion of vision sensor nodes in a wide range of applications has given rise to higher computational demand at the edge of the Internet of Things (IoT). Indeed, in-node video sense-making has become essential in the form of high-level tasks such as object detection for visual monitoring, mitigating data deluge from the wireless network to the cloud storage level. In such applications, deep neural networks are well known to be a prime choice, in view of their performance and flexibility. However, such properties come at the cost of high computational requirements at inference time, which directly hamper power efficiency, lifetime and cost of self-powered edge devices. In this paper, a computationally-efficient inference technique is introduced to perform the ubiquitously required task of bounding box-based object detection. The proposed method leverages the correlation among frames in the temporal dimension, uniquely requires minor memory overhead for intermediate feature map storage and architectural changes, and does not require any retraining for immediate deployment in existing vision frameworks. The proposed method achieves 18.3% (35.8%) computation reduction at 3.3% (3.2%) memory overhead, and 3.8% (6.8%) accuracy drop in YOLOv1(VGG16) SSD(VGG16) neural networks under the CAMEL dataset.
On the Generalization of Figurative Language Detection: The Case of Irony and Sarcasm

The automatic detection of figurative language, such as irony and sarcasm, is one of the most challenging tasks of Natural Language Processing (NLP). In this paper, we investigate the generalization capabilities of figurative language detection models, focusing on the case of irony and sarcasm. Firstly, we compare the most promising approaches of the state of the art. Then, we propose three different methods for reducing the generalization errors on both in- and out-domain scenarios.
MetricOpt: Learning to Optimize Black-Box Evaluation Metrics

We study the problem of directly optimizing arbitrary non-differentiable task evaluation metrics such as misclassification rate and recall. Our method, named MetricOpt, operates in a black-box setting where the computational details of the target metric are unknown. We achieve this by learning a differentiable value function, which maps compact task-specific model parameters to metric observations. The learned value function is easily pluggable into existing optimizers like SGD and Adam, and is effective for rapidly finetuning a pre-trained model. This leads to consistent improvements since the value function provides effective metric supervision during finetuning, and helps to correct the potential bias of loss-only supervision. MetricOpt achieves state-of-the-art performance on a variety of metrics for (image) classification, image retrieval and object detection. Solid benefits are found over competing methods, which often involve complex loss design or adaptation. MetricOpt also generalizes well to new tasks and model architectures.
Automatic-differentiated Physics-Informed Echo State Network (API-ESN)

We propose the Automatic-differentiated Physics-Informed Echo State Network (API-ESN). The network is constrained by the physical equations through the reservoir’s exact time-derivative, which is computed by automatic differentiation. As compared to the original Physics-Informed Echo State Network, the accuracy of the time-derivative is increased by up to seven orders of magnitude. This increased accuracy is key in chaotic dynamical systems, where errors grow exponentially in time. The network is showcased in the reconstruction of unmeasured (hidden) states of a chaotic system. The API-ESN eliminates a source of error, which is present in existing physics-informed echo state networks, in the computation of the time-derivative. This opens up new possibilities for an accurate reconstruction of chaotic dynamical states.
Oriented Object Detection For Remote Sensing Images Based On Weakly Supervised Learning

Object detection of remote sensing images (RSIs) is an active yet challenging task because of the complex appearance of ground objects and the particular imaging views. One of the difficulties in RSI object detection is the orientation variation, where the objects could take on arbitrary orientations due to the birdview shot from high altitudes. For oriented object detection, existing methods rely on largescale dense oriented annotations for training deep networks under full supervision, which are resource-intensive. To address this problem, (a) we propose a kind of weakly supervised oriented object detection method in this paper. With only the horizontal-object supervision, we rotate object proposals via an angle search strategy to align them as horizontally as possible and detect the oriented objects just like the horizontal ones. We aim to mine more oriented objects and thus can train the Rotational RCNN framework. Experimental results demonstrate that our method can achieve significant performance improvement on the oriented object detection and outperforms the state-of-the-art methods.
Clustering Nico Nico Douga Videos by Using the Distribution of Time-Synchronyzed Comments

Nico Nico Douga (NND) is one of the most famous social media platforms for sharing videos in Japan. There are 17 million videos posted to NND, for which users search by using keyword search and related video recommendation. However, it is difficult for the users to find interesting videos because videos generally are associated with only short texts and a few tags. In this paper, we present a method for clustering videos in NND by using the distribution of time-synchronized comments. Our method regards a video as the set of the users commenting on it and enables the clustering of videos based on users' shared interests. In our experiment, we applied the proposed method to videos posted to NND, and evaluated our method by quantitatively comparing it with existing text-based methods and by qualitatively performing the subjective evaluation of clustering results. In the result of the quantitative evaluation, the proposed method showed a higher score of normalized mutual information than the existing methods when results of text-based methods and category metadata were used as correct results. The experimental results of the qualitative evaluation showed that the proposed method was as good as or better than the existing text-based methods.
RASSF1A Suppression as a Potential Regulator of Mechano-Pathobiology Associated with Mammographic Density in BRCA Mutation Carriers

High mammographic density (MD) increases breast cancer (BC) risk and creates a stiff tissue environment. BC risk is also increased in BRCA1/2 gene mutation carriers, which may be in part due to genetic disruption of the tumour suppressor gene Ras association domain family member 1 (RASSF1A), a gene that is also directly regulated by tissue stiffness. High MD combined with BRCA1/2 mutations further increase breast cancer risk, yet BRCA1/2 mutations alone or in combination do not increase MD. The molecular basis for this additive effect therefore remains unclear. We studied the interplay between MD, stiffness, and BRCA1/2 mutation status in human mammary tissue obtained after prophylactic mastectomy from women at risk of developing BC. Our results demonstrate that RASSF1A expression increased in MCF10DCIS.com cell cultures with matrix stiffness up until ranges corresponding with BiRADs 4 stiffnesses (~16 kPa), but decreased in higher stiffnesses approaching malignancy levels (&gt;50 kPa). Similarly, higher RASSF1A protein was seen in these cells when co-cultivated with high MD tissue in murine biochambers. Conversely, local stiffness, as measured by collagen I versus III abundance, repressed RASSF1A protein expression in BRCA1, but not BRCA2 gene mutated tissues; regional density as measured radiographically repressed RASSF1A in both BRCA1/2 mutated tissues. The combinatory effect of high MD and BRCA mutations on breast cancer risk may be due to RASSF1A gene repression in regions of increased tissue stiffness.
Real-time Small-size Pixel Target Perception Algorithm Based on Embedded System for Smart City

With the continuous development of technology in the artificial intelligence era, smart city applications based on high-performance servers in large data centers have penetrated all walks of life. However, the current mainstream smart city application model is only data collection on the device side, and then calculations and inferences in the data center. Data transmission is difficult to achieve the real-time performance of the system, resulting in poor effects in many smart city applications. The intelligent perception for smart city is required to perceive the whole urban environment comprehensively. Among them, small-size pixel target detection and recognition is particularly critical. To this end, a real-time small-size pixel target perception algorithm based on embedded system for smart city is proposed, which uses lightweight neural networks and model pruning optimization to realize terminal intelligence for smart city applications, and integrates traditional machine learning filtering algorithms for improving the detection speed and the accuracy. The experimental results of the method show that the real-time performance and the accuracy of detection are greatly improved for different sizes of small-size pixel targets.
Lung Field Segmentation of X-ray Images by Normalized Gradient Gaussian Filter and Snake Segmentation

Scientists have considered the programmed segmentation of lung areas as a fundamental pre-processing undertaking when examining pulmonary radiographs (CXRs) obviously. In this paper, a method for lung field segmentation is proposed. This is performed on MATLAB image processing coding. It improves the speed of processing and detects abnormality in the lung area. The method used is Normalized Gradient Gaussian Filter and Snake segmentation is combined to improve the performance of edge detector.
MultiLogVC: Efficient Out-of-Core Graph Processing Framework for Flash Storage

Graph analytics are at the heart of a broad range of applications such as drug discovery, page ranking, transportation systems, and recommendation models. When graph size exceeds the available memory size in a computing node, out-of-core graph processing is needed. For the widely used out-of-core graph processing systems, the graphs are stored and accessed from a long latency SSD storage, which becomes a significant performance bottleneck. To tackle this long latency this work exploits the key insight that that nearly all graph algorithms have a dynamically varying number of active vertices that must be processed in each iteration. However, existing graph processing frameworks, such as GraphChi, load the entire graph in each iteration even if a small fraction of the graph is active. This limitation is due to the structure of the graph storage used by these systems. In this work, we propose to use a compressed sparse row (CSR) based graph storage that is more amenable for selectively loading only a few active vertices in each iteration. However, CSR based graph processing suffers from random update propagation to many target vertices. To solve this challenge, we propose to use a multi-log update mechanism that logs updates separately, rather than directly update the active edges and vertices in a graph. The multi-log system maintains a separate log per each vertex interval (a group of vertices). This separation enables efficient processing of all updates bound to each vertex interval by just loading the corresponding log. Further, by logging all the updates associated with a vertex interval in one contiguous log this approach reduces read amplification since all the pages in the log will be processed in the next iteration without wasted page reads. Over the current state of the art out-of-core graph processing framework, our evaluation results show that the MultiLogVC framework improves performance by up to 17.84×, 1.19×, 1.65×, 1.38×, 3.15×, and 6.00× for the widely used breadth-first search, pagerank, community detection, graph coloring, maximal independent set, and random-walk applications, respectively.
Multiscale Hierarchical Image Decomposition and Refinements: Qualitative and Quantitative Results

The multiscale hierarchical decomposition method (MHDM) proposed in [E. Tadmor, S. Nezzar, and L. Vese, Multiscale Model. Simul., 2 (2004), pp. 554--579] has been proven very appropriate for denoising images with features at different scales and for scale separation. Extensions of it to image deblurring or to time-dependent settings [E. Tadmor and P. Athavale, Inverse Probl. Imaging, 35 (2009), pp. 693--710] have also been considered, showing convergence properties and more applications. The recent paper [K. Modin, A. Nachman, and L. Rondi, Adv. Math., 346 (2019), pp. 1009--1066] fills in further qualitative results even for nonlinear problems and introduces a tighter version of MHDM with better convergence properties. The contribution of the present work is as follows. First, we derive novel error estimates for MHDM and its tighter version. Second, we provide rules for early stopping of the algorithms in the case of perturbed data, while still ensuring stable approximations of the true image. Last but not least, we propose a refined version of the tighter MHDM, which allows recovering structured images and promotes different features of the components, as compared to the entire image. The theoretical results are validated by numerous numerical experiments for image denoising and deblurring, which also assess the analyzed methods in terms of rate of convergence, stopping rule, and quality of restoration.
Natural Language Processing in Strategy and Implementation

This chapter discusses the concept of natural language processing (NLP), specifically how the NLP is applied. The chapter further outlines the typical functions of the NLP. In addition, it explores the NLP in strategy and strategy implementation. The NLP is a branch of computer science, AI and linguistics that focuses on the interaction between computer programs and human language. There are three functions of the NLP, namely, machine translation (MT), text summarisation and sentiment analysis (SA). MT is a branch of computer linguistics that analyses the use of computerised tools to translate the context derived from this, from one language to the next. The language that is being translated is the human language. Automatic text summarisation refers to a process of bringing about a succinct and meaningful summary of the text. SA is a way of finding out the polarity or strength of the opinion that is expressed in written text. The opinion could be either positive or negative. MT has three major approaches, namely, rule-based MT, statistical MT and neural MT. A rule-based system would typically require the expert to know both the source and the target languages. This knowledge is then used to develop syntactic, semantic and morphological rules to achieve the translation. The statistical approach uses statistical models based on the analysis of bilingual text corpora. The neural MT approach uses neural networks to achieve machine translation. There are two broad categories of text summarisation approaches, namely, extractive summarisation and abstractive summarisation. Furthermore, there are four popular types of SA, namely, the fine-grained SA, emotion detection, aspect-based SA and multilingual SA. The firm that deploys the NLP and ML in Big Data Analytics would gain crucial and better insights, setting the firm apart from its competitors.
Sentiments expressed in <scp>YouTube</scp> public awareness campaigns: stroke

Abstract Social media (YouTube) has been used to educate the public on stroke signs and the need to contact emergency hotline. We perform sentiment analysis to explore the emotional response to the 2006 North American and contrast this with the five Australian campaigns (2009–2015). The positive sentiment in the North American campaign provides insights for developing material for public health campaigns.
42‐1: Invited Paper: Perceptual Quality Performance for Automotive Display Requirement

A new image quality enhancement technology in terms of perceptual performance without increasing luminance specification values is introduced. Various perceptual factors such as brightness, local contrast, sharpness at high frequency image, and overall colorfulness of image are improved when optical specification values of display are fixed. Three Plastic OLED (POLED) samples were prepared to compare image quality levels between conventional and enhanced one, and a series of visual assessment has been carried out at different ambient conditions (5,000 lx, and 45,000 lx) in order to verify the performance of the developed technology. The luminance values of proposed and conventional samples were ranged from 800 to 1,000 cd/m 2 . The results show that the proposed technology outperforms conventional ones, even though the luminance value of conventional sample was higher than that of proposed technology.
Understanding Dynamics of Nonlinear Representation Learning and Its Application

Representations of the world environment play a crucial role in artificial intelligence. It is often inefficient to conduct reasoning and inference directly in the space of raw sensory representations, such as pixel values of images. Representation learning allows us to automatically discover suitable representations from raw sensory data. For example, given raw sensory data, a deep neural network learns nonlinear representations at its hidden layers, which are subsequently used for classification (or regression) at its output layer. This happens implicitly during training through minimizing a supervised or unsupervised loss. In this letter, we study the dynamics of such implicit nonlinear representation learning. We identify a pair of a new assumption and a novel condition, called the on-model structure assumption and the data architecture alignment condition. Under the on-model structure assumption, the data architecture alignment condition is shown to be sufficient for the global convergence and necessary for global optimality. Moreover, our theory explains how and when increasing network size does and does not improve the training behaviors in the practical regime. Our results provide practical guidance for designing a model structure; for example, the on-model structure assumption can be used as a justification for using a particular model structure instead of others. As an application, we then derive a new training framework, which satisfies the data architecture alignment condition without assuming it by automatically modifying any given training algorithm dependent on data and architecture. Given a standard training algorithm, the framework running its modified version is empirically shown to maintain competitive (practical) test performances while providing global convergence guarantees for deep residual neural networks with convolutions, skip connections, and batch normalization with standard benchmark data sets, including MNIST, CIFAR-10, CIFAR-100, Semeion, KMNIST, and SVHN.