Reviewerly/exHarmony huggingface dataset:
Machine Learning Meets Natural Language Processing -- The story so far

Natural Language Processing (NLP) has evolved significantly over the last decade. This paper highlights the most important milestones of this period while trying to pinpoint the contribution of each individual model and algorithm to the overall progress. Furthermore, it focuses on issues still remaining to be solved, emphasizing the groundbreaking proposals of Transformers, BERT, and all the similar attention-based models.
Multi-Task Learning for End-to-End ASR Word and Utterance Confidence with Deletion Prediction

Confidence scores are very useful for downstream applications of automatic speech recognition (ASR) systems. Recent works have proposed using neural networks to learn word or utterance confidence scores for end-to-end ASR. In those studies, word confidence by itself does not model deletions, and utterance confidence does not take advantage of word-level training signals. This paper proposes to jointly learn word confidence, word deletion, and utterance confidence. Empirical results show that multi-task learning with all three objectives improves confidence metrics (NCE, AUC, RMSE) without the need for increasing the model size of the confidence estimation module. Using the utterance-level confidence for rescoring also decreases the word error rates on Google's Voice Search and Long-tail Maps datasets by 3-5% relative, without needing a dedicated neural rescorer.
3D Adversarial Attacks Beyond Point Cloud

Recently, 3D deep learning models have been shown to be susceptible to adversarial attacks like their 2D counterparts. Most of the state-of-the-art (SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To reproduce these attacks in the physical scenario, a generated adversarial 3D point cloud need to be reconstructed to mesh, which leads to a significant drop in its adversarial effect. In this paper, we propose a strong 3D adversarial attack named Mesh Attack to address this problem by directly performing perturbation on mesh of a 3D object. In order to take advantage of the most effective gradient-based attack, a differentiable sample module that back-propagate the gradient of point cloud to mesh is introduced. To further ensure the adversarial mesh examples without outlier and 3D printable, three mesh losses are adopted. Extensive experiments demonstrate that the proposed scheme outperforms SOTA 3D attacks by a significant margin. We also achieved SOTA performance under various defenses. Our code is available at: https://github.com/cuge1995/Mesh-Attack.
Knowledge-Driven Architecture Composition: Assisting the System Integrator to Reuse Integration Knowledge

Semantic interoperability for web services is still a problem. Although decentralized solutions such as describing the integration context with a formal mapping language or using a web service description language exist, practitioners rely on implementing software adapters manually. For IoT and Web of Things systems, current scientific solutions fall short as changing them, once defined, requires strenuous effort. However, devices and thus, their interfaces change often in this class of system. This paper tackles the barrier of high formalization effort for mappings between required and provided interfaces. Therefore, we apply and evaluate a novel integration method for web service choreography. Our empirical experiment shows that this method lowers the integration time and number of errors by assisting the system integrator to reuse integration knowledge from previous integration cases.
An Intelligent Service Method for Grid Spatio-Temporal Big Data Based on Beidou

Various assets and operation-related data in the smart grid have their own time and space attributes. The Beidou Global Positioning System continues to mature and improve its technology. The positioning accuracy has greatly improved. In order to make better use of these spatio-temporal data and serve each link of the power system in the smart grid, an overall design method based on the Beidou grid spatio-temporal intelligent service system platform is proposed. The method integrates Beidou location service module, blockchain technology and Internet of Things access technology. The method takes the construction of spatiotemporal data bus and spatiotemporal service bus as the core, expounds the overall framework of data bus and the specific content of service bus. It provides an idea for the modeling and analysis of spatiotemporal data.
Bayesian Deep Active Learning for Medical Image Analysis

Deep Learning has achieved a state-of-the-art performance in medical imaging analysis but requires a large number of labelled images to obtain good adequate performance. However, such labelled images are costly to acquire in time, labour, and human expertise. We propose a novel practical Bayesian Active Learning approach using Dropweights and overall bias-corrected uncertainty measure to suggest which unlabelled image to annotate. Experiments were done on Brain Tumour MR images, Microscopic Cell Image classification, Fluoro-chromogenic cytokeratin-Ki-67 double staining cancer images and Retina fundus image segmentation tasks. We demonstrate that our active learning technique is equally successful or better than other existing active learning approaches in high dimensional data to reduce the image labelling effort significantly. We believe Bayesian deep active learning framework with very few annotated samples in a practical way will benefit clinicians to obtain fast and accurate image annotation with confidence.
Commodity Search Algorithm based on Ant Colony Algorithm

In the Crowd Intelligent Trading Network (CIbTN), intelligent individuals connect with each other and make up a distributed network. Each intelligent individual store commodity information in local node. They realize the transmission and sharing of information through addressing and routing circle of friends. The purpose of this paper is to design a commodity search algorithm based on ant colony optimization (ACO) to realize searching commodity information resources efficiently, quickly and cheaply in the network. This paper designs a commodity search algorithm based on ant colony optimization. It defines the commodity information keyword pheromone and heuristic function calculation and uses the ant colony optimization's transfer probability and pheromone update mechanism to design the rules of commodity information resource search between nodes in the network, so as to reduce the blindness of search. The network environment is simulated based on Peersim simulation software, and the algorithm designed in this paper is programmed by Java language. The paper's algorithm has innovations in pheromone definition, heuristic factor setting, and pheromone update strategy. Its advantages in search success rate, average product matching degree, and number of returned product information resources can efficiently and quickly complete commodity information search.
WikiTalkEdit: A Dataset for modeling Editors’ behaviors on Wikipedia

This study introduces and analyzes Wik-iTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling.The dataset comprises dialog triplets from the Wikipedia Talk pages, and editing actions on the corresponding articles being discussed.The exchanges occur between two turn-taking individuals and span all of Wikipedia.We show how the data supports the classic understanding of style matching, where positive emotion and the use of first-person pronouns predict a positive emotional change in a Wikipedia contributor.However, they do not predict editorial behavior.On the other hand, feedback invoking evidentiality and criticism, and references to Wikipedia's community norms, is more likely to persuade the contributor to perform edits but is less likely to lead to a positive emotion.We developed baseline classifiers trained on pretrained RoBERTa features that can predict editorial change with an F 1 score of .54,as compared to an F 1 score of .66 for predicting emotional change.A diagnostic analysis of persisting errors is also provided.We conclude with possible applications and recommendations for future work.
Enabling On-Device Self-Supervised Contrastive Learning With Selective Data Contrast

After a model is deployed on edge devices, it is desirable for these devices to learn from unlabeled data to continuously improve accuracy. Contrastive learning has demonstrated its great potential in learning from unlabeled data. However, the online input data are usually none independent and identically distributed (non-iid) and storages of edge devices are usually too limited to store enough representative data from different data classes. We propose a framework to automatically select the most representative data from the unlabeled input stream, which only requires a small data buffer for dynamic learning. Experiments show that accuracy and learning speed are greatly improved.
Semantic Frame Forecast

This paper introduces semantic frame forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story.Prior work focused on predicting the immediate future of a story, such as one to a few sentences ahead.However, when novelists write long stories, generating a few sentences is not enough to help them gain high-level insight to develop the follow-up story.In this paper, we formulate a long story as a sequence of "story blocks," where each block contains a fixed number of sentences (e.g., 10, 100, or 200).This formulation allows us to predict the follow-up story arc beyond the scope of a few sentences.We represent a story block using the term frequencies (TF) of semantic frames in it, normalized by each frame's inverse document frequency (IDF).We conduct semantic frame forecast experiments on 4,794 books from the Bookcorpus and 7,962 scientific abstracts from CODA-19, with block sizes ranging from 5 to 1,000 sentences.The results show that automated models can forecast the follow-up story blocks better than the random, prior, and replay baselines, indicating the task's feasibility.We also learn that the models using the frame representation as features outperform all the existing approaches when the block size is over 150 sentences.The human evaluation also shows that the proposed frame representation, when visualized as word clouds, is comprehensible, representative, and specific to humans.Our code is available at: https://github.com/appleternity/FrameForecasting.
Don’t Just Blame Over-parametrization for Over-confidence: Theoretical Analysis of Calibration in Binary Classification

Modern machine learning models with high accuracy are often miscalibrated -- the predicted top probability does not reflect the actual accuracy, and tends to be over-confident. It is commonly believed that such over-confidence is mainly due to over-parametrization, in particular when the model is large enough to memorize the training data and maximize the confidence. In this paper, we show theoretically that over-parametrization is not the only reason for over-confidence. We prove that logistic regression is inherently over-confident, in the realizable, under-parametrized setting where the data is generated from the logistic model, and the sample size is much larger than the number of parameters. Further, this over-confidence happens for general well-specified binary classification problems as long as the activation is symmetric and concave on the positive part. Perhaps surprisingly, we also show that over-confidence is not always the case -- there exists another activation function (and a suitable loss function) under which the learned classifier is under-confident at some probability values. Overall, our theory provides a precise characterization of calibration in realizable binary classification, which we verify on simulations and real data experiments.
Security Analysis Using Deep Learning in IoT and Intelligent Transport System

Cloud computing is an embryonic and vast field. It is cost effective, flexible and customized solution provided to customers according to their needs. Due to cloud computing customers need of having their own data centers storages and services became truncated. Cloud service providers serves customers with infrastructure, storages, security, computation resources and fault rectifications. Due to the customized and flexible in nature cloud computing created many security concerns meanwhile security is the first requirement of customer. Storages, infrastructure, software and network need security measures. In this paper we deliver different defense techniques by using which cloud computing infrastructure could be made secure. It includes different protocols, encryption techniques, and hardware and software solutions for different level of security.
A Study on the Allowance for Bad Debts due to K-IFRS No-1109 Adoption: Focusing on Conservatism and Value Relevance

In 2018, K-IFRS No. 1109 (Financial Instrument) was Introduced. It was that firms were forced to allocate additional their allowance for bad debts. So This study analyze the change of allowance for bad debts after the government introduced K-IFRS No. 1109 (Financial Instrument). We also investigated whether this change can have an effect on firm
Particle Swarm Based Optimal Key Generation: Block Chain Assisted Secure Information Sharing in Supply Chain Management

The supply chain is essentially a network of businesses and people, who are involved in manufacturing or delivering products and services. As businesses have enlarged, the supply chains have become more complex, making them difficult to manage and optimize. In this scenario, “Blockchain” has emerged as a technology that helps in the efficient management of these complicated supply chains. In this research work, the block chain assisted “Supply Chain Management (SCM)” is introduced. With the intention of enhancing the security of the proposed SCM, a new privacy preservation approach is adopted, where the “Data Sanitization and Data Restoration” takes place with the optimally selected key. More importantly, in both the processes, the optimal key is generated via a standard “Particle Swarm Optimization Algorithm (PSO)”. The proposed secures blockchain based PSCM model is validated by comparing it over the traditional models in terms of attack analysis.
Hiding secret message using matrix reordering and image blocking

Image Steganography is a solitary concept of hiding a data inside the image. As there is an expansion of interconnected networks so users exchange their information over the channels but transmission is hardly secure. So there is a concept of Steganography which is a proficiency of deceptive transmission of secret deets concealed inside other media. This paper proposes a new model in which capacity of data embedding is increased by using LSB2z algorithm and security is also increased by using Matrix Reordering and Image Blocking.
An Embedding of ReLU Networks and an Analysis of Their Identifiability

Neural networks with the Rectified Linear Unit (ReLU) nonlinearity are described by a vector of parameters $\theta$, and realized as a piecewise linear continuous function $R_{\theta}: x \in \mathbb R^{d} \mapsto R_{\theta}(x) \in \mathbb R^{k}$. Natural scalings and permutations operations on the parameters $\theta$ leave the realization unchanged, leading to equivalence classes of parameters that yield the same realization. These considerations in turn lead to the notion of identifiability -- the ability to recover (the equivalence class of) $\theta$ from the sole knowledge of its realization $R_{\theta}$. The overall objective of this paper is to introduce an embedding for ReLU neural networks of any depth, $\Phi(\theta)$, that is invariant to scalings and that provides a locally linear parameterization of the realization of the network. Leveraging these two key properties, we derive some conditions under which a deep ReLU network is indeed locally identifiable from the knowledge of the realization on a finite set of samples $x_{i} \in \mathbb R^{d}$. We study the shallow case in more depth, establishing necessary and sufficient conditions for the network to be identifiable from a bounded subset $\mathcal X \subseteq \mathbb R^{d}$.
Direction-aware Feature-level Frequency Decomposition for Single Image Deraining

We present a novel direction-aware feature-level frequency decomposition network for single image deraining. Compared with existing solutions, the proposed network has three compelling characteristics. First, unlike previous algorithms, we propose to perform frequency decomposition at feature-level instead of image-level, allowing both low-frequency maps containing structures and high-frequency maps containing details to be continuously refined during the training procedure. Second, we further establish communication channels between low-frequency maps and high-frequency maps to interactively capture structures from high-frequency maps and add them back to low-frequency maps and, simultaneously, extract details from low-frequency maps and send them back to high-frequency maps, thereby removing rain streaks while preserving more delicate features in the input image. Third, different from existing algorithms using convolutional filters consistent in all directions, we propose a direction-aware filter to capture the direction of rain streaks in order to more effectively and thoroughly purge the input images of rain streaks. We extensively evaluate the proposed approach in three representative datasets and experimental results corroborate our approach consistently outperforms state-of-the-art deraining algorithms.
Recognition of user activity with a combined image and accelerometer wearable sensor

In emerging markets such as Vietnam, Human activity recognition is to identify the type of human activity from the received signal stream (image, accelerometer, etc.). This is a topic that has received a lot of attention from researchers and has opened up a new trend in robotics interaction in recent times. The activity recognition problem can be classified in two different ways depending on whether the type of sensor is used to collect activity signals or machine learning models for recognition. Authors use combination of methods: Experimental research and Test evaluation through survey. Authors also perform intensive research on previous related studies.The research of the topic is expected to be the foundation for feature selection orientation when building a recognition system through accelerometer, gyroscope and image sensors. And it is  expected to be the foundation for feature selection orientation when building recognition systems through accelerometer, gyroscope and image sensors.
Sentiment Analysis of Public Opinions on the Higher Education Expansion Policy in China

Drawing on sentiment analysis, this study explores public opinions on the higher education expansion policy that was specifically implemented by China’s government to navigate graduate employment difficulties against the impact of COVID-19. The results indicated that the overall degree of acceptance of the expansion plan was highly positive, but some people expressed negative opinions and concerns about over-education and deferral of employment pressure. The results also suggested that the government is expected to deal with the balance between higher education expansion and graduate employment difficulties by prioritizing domestic graduate employment rather than opening up permanent resident applications for foreigners, allocating a regionally balanced expansion quota, covering social science disciplines, and creating more employment opportunities. The findings provide important suggestions for policymakers to improve policy practice and offer a referable sample for other countries in their management of graduate employment issues influenced by COVID-19.
State space structure of tripartite quantum systems

State space structure of tripartite quantum systems is analyzed. In particular, it has been shown that the set of states separable across all the three bipartitions [say, ${\mathcal{B}}^{int}(ABC)$] is a strict subset of the set of states having positive partial transposition across the three bipartite cuts [say, ${\mathcal{P}}^{int}(ABC)$] for all the tripartite Hilbert spaces ${\mathbb{C}}_{A}^{{d}_{1}}\ensuremath{\bigotimes}{\mathbb{C}}_{B}^{{d}_{2}}\ensuremath{\bigotimes}{\mathbb{C}}_{C}^{{d}_{3}}$ with $min{{d}_{1},{d}_{2},{d}_{3}}\ensuremath{\ge}2$. The claim is proved by constructing a state belonging to the set ${\mathcal{P}}^{int}(ABC)$ but not to ${\mathcal{B}}^{int}(ABC)$. For ${({\mathbb{C}}^{d})}^{\ensuremath{\bigotimes}3}$ with $d\ensuremath{\ge}3$, the construction follows from a specific type of multipartite unextendible product bases. However, such a construction is not possible for ${({\mathbb{C}}^{2})}^{\ensuremath{\bigotimes}3}$ since bipartite systems ${\mathbb{C}}^{2}\ensuremath{\bigotimes}{\mathbb{C}}^{n}$ do not allow any unextendible product bases for arbitrary $n$ [Phys. Rev. Lett. 82, 5385 (1999)]. For the three-qubit system, we therefore come up with a different construction.
Multiscale Laplacian Learning

Machine learning methods have greatly changed science, engineering, finance, business, and other fields. Despite the tremendous accomplishments of machine learning and deep learning methods, many challenges still remain. In particular, the performance of machine learning methods is often severely affected in case of diverse data, usually associated with smaller data sets or data related to areas of study where the size of the data sets is constrained by the complexity and/or high cost of experiments. Moreover, data with limited labeled samples is a challenge to most learning approaches. In this paper, the aforementioned challenges are addressed by integrating graph-based frameworks, multiscale structure, modified and adapted optimization procedures and semi-supervised techniques. This results in two innovative multiscale Laplacian learning (MLL) approaches for machine learning tasks, such as data classification, and for tackling diverse data, data with limited samples and smaller data sets. The first approach, called multikernel manifold learning (MML), integrates manifold learning with multikernel information and solves a regularization problem consisting of a loss function and a warped kernel regularizer using multiscale graph Laplacians. The second approach, called the multiscale MBO (MMBO) method, introduces multiscale Laplacians to a modification of the famous classical Merriman-Bence-Osher (MBO) scheme, and makes use of fast solvers for finding the approximations to the extremal eigenvectors of the graph Laplacian. We demonstrate the performance of our methods experimentally on a variety of data sets, such as biological, text and image data, and compare them favorably to existing approaches.
Topological Mapping based Failure Recovery in Multi-domain Quantum Key Distribution Networks

This paper proposes a topological mapping based failure recovery algorithm in multi-domain quantum key distribution networks. Simulation results show that the algorithm can achieve good performance in terms of success rate of service recovery under multi-concurrent failures.