Reviewerly/exHarmony huggingface dataset:
Action Machine: Toward Person-Centric Action Recognition in Videos

Existing RGB and CNN-based methods in video action recognition mostly do not distinguish human body from the environment, thus easily overfit the scenes and objects of training sets. In this work, we present a conceptually simple, general and high-performance framework for action recognition in videos, aiming at person-centric modeling. The method, called Action Machine, is based on person bounding boxes for instance-level action analysis. It extends the Inflated 3D ConvNet (I3D) by adding a branch for human pose estimation and a 2D CNN for pose-based action recognition. Action Machine can benefit from the multi-task training of action recognition and pose estimation, the fusion of predictions from RGB images and poses. Experiments results are provided on trimmed video action datasets, NTU RGB+D, Northwestern UCLA Multiview Action3D, MSR Daily Activity3D. Action Machine achieves superior performance and generalizes well across datasets.
Generating Image Description on Indonesian Language using Convolutional Neural Network and Gated Recurrent Unit

Recently, research on image captioning is to generate the proper description for an image given in English. No previous research has been found on image captioning to generating description in Bahasa Indonesia. In fact, quoted from Wikipedia, Bahasa Indonesia is spoken by 198.7 million people worldwide and ranked 10th for the most used languages. This paper focuses on developing a generative model connecting machine translation and computer vision to generate image description in Bahasa Indonesia. The model uses the pre-trained inception-v3 image embedding model stacked with Gated Recurrent Unit (GRU) layer. The proposed model has been trained and validated with the translated Flickr30K dataset and obtained BLEU-1, BLEU-2, BLEU-3, BLEU-4 score of 36, 17, 6, 2 respectively.
The Classification of Mammogram Using Convolutional Neural Network with Specific Image Preprocessing for Breast Cancer Detection

The incidence rate of breast cancer continued to rise in the last few decades. Current screening strategy of breast cancer is based on classic X-ray imaging. The sensitivity and specificity of the diagnosis are largely depend on the experiences of the radiologists, and uncertain diagnosis is quite frequent because of resolution limitations and the concerns of lawsuits arisen from wrong diagnosis or undetected lesions. The convolutional neural network is an effective technique for classification in deep learning model. In this study, we utilized median filter, contrast-limited adaptive histogram equalization, and data augmentation to preprocess over 9,000 mammograms, and trained a classified model by using convolutional neural network. The experiment results demonstrated that the accuracy of model with preprocessed images significantly outperformed the model without preprocessed images.
Evaluation measures for quantification: an axiomatic approach

Quantification is the task of estimating, given a set $$\sigma $$ of unlabelled items and a set of classes $${\mathcal {C}}=\{c_{1}, \ldots , c_{|{\mathcal {C}}|}\}$$, the prevalence (or "relative frequency") in $$\sigma $$ of each class $$c_{i}\in {\mathcal {C}}$$. While quantification may in principle be solved by classifying each item in $$\sigma $$ and counting how many such items have been labelled with $$c_{i}$$, it has long been shown that this "classify and count" method yields suboptimal quantification accuracy. As a result, quantification is no longer considered a mere byproduct of classification, and has evolved as a task of its own. While the scientific community has devoted a lot of attention to devising more accurate quantification methods, it has not devoted much to discussing what properties an evaluation measure for quantification (EMQ) should enjoy, and which EMQs should be adopted as a result. This paper lays down a number of interesting properties that an EMQ may or may not enjoy, discusses if (and when) each of these properties is desirable, surveys the EMQs that have been used so far, and discusses whether they enjoy or not the above properties. As a result of this investigation, some of the EMQs that have been used in the literature turn out to be severely unfit, while others emerge as closer to what the quantification community actually needs. However, a significant result is that no existing EMQ satisfies all the properties identified as desirable, thus indicating that more research is needed in order to identify (or synthesize) a truly adequate EMQ.
Twenty-five years of information extraction

Abstract Information extraction is the process of converting unstructured text into a structured data base containing selected information from the text. It is an essential step in making the information content of the text usable for further processing. In this paper, we describe how information extraction has changed over the past 25 years, moving from hand-coded rules to neural networks, with a few stops on the way. We connect these changes to research advances in NLP and to the evaluations organized by the US Government.
Quantum computational universality of hypergraph states with Pauli-X and Z basis measurements

Abstract Measurement-based quantum computing is one of the most promising quantum computing models. Although various universal resource states have been proposed so far, it was open whether only two Pauli bases are enough for both of universal measurement-based quantum computing and its verification. In this paper, we construct a universal hypergraph state that only requires X and Z -basis measurements for universal measurement-based quantum computing. We also show that universal measurement-based quantum computing on our hypergraph state can be verified in polynomial time using only X and Z -basis measurements. Furthermore, in order to demonstrate an advantage of our hypergraph state, we construct a verifiable blind quantum computing protocol that requires only X and Z -basis measurements for the client.
On a generalization of planar functions

Planar functions have deep applications in different areas of mathematics. We give a generalization of planar functions, and we obtain construction and classification results concerning these new objects. More in detail, for a prime power $$q=p^r$$ and $$\beta \in {\mathbb {F}}_q{\setminus }\{0,1\}$$ , we call a function $$f : {\mathbb {F}}_q \rightarrow {\mathbb {F}}_q$$ $$\beta $$ -planar function in $${\mathbb {F}}_{q}$$ if for each $$\gamma \in {\mathbb {F}}_{q}$$ we have that $$f(x+\gamma )-\beta f(x)$$ permutes $${\mathbb {F}}_{q}$$ . In this work, we study $$\beta $$ -planar monomials: We provide some necessary conditions for general r, whereas for $$r\le 3$$ we construct examples of $$\beta $$ -planar monomials. Connections with algebraic curves are used to prove nonexistence results.
Fast Detection Method for Low-Observable Maneuvering Target via Robust Sparse Fractional Fourier Transform

In this letter, a novel fast detection algorithm, known as robust sparse fractional Fourier transform (RSFRFT), is proposed for low-observable maneuvering target detection in a clutter background. The discrete FRFT (DFRFT)-based detection method is time-consuming for large data volumes and the detection performance of sparse FRFT (SFRFT)-based algorithm will be significantly degraded in a heavy clutter background. Using two levels of detection, the defects of DFRFT and SFRFT algorithms are overcome using the proposed algorithm. The first-level detection is performed on the subsampled spectrum to estimate the target frequencies. The second-level detection is carried out after reconstruction for target detection. The simulation analysis and experiments using marine radar data show that the proposed method can achieve a good detection performance for low-observable maneuvering target detection in the clutter background with lower computational complexity.
DP-Siam: Dynamic Policy Siamese Network for Robust Object Tracking

Balancing the trade-off between real-time performance and accuracy in object tracking is a major challenge. In this paper, a novel dynamic policy gradient Agent-Environment architecture with Siamese network (DP-Siam) is proposed to train the tracker to increase the accuracy and the expected average overlap while performing in real-time. DP-Siam is trained offline with reinforcement learning to produce a continuous action that predicts the optimal object location. DP-Siam has a novel architecture that consists of three networks: an Agent network to predict the optimal state (bounding box) of the object being tracked, an Environment network to get the Q-value during the offline training phase to minimize the error of the loss function, and a Siamese network to produce a heat-map. During online tracking, the Environment network acts as a verifier to the Agent network action. Extensive experiments are performed on six widely used benchmarks: OTB2013, OTB50, OTB100, VOT2015, VOT2016 and VOT2018. The results show that DP-Siam significantly outperforms the current state-of-the-art trackers.
Chip-Scale Optical Matrix Computation for PageRank Algorithm

Matrix computations are indispensable tools in science and engineering, while the electronic matrix computations suffered from limited speed. Alternatively, the optical methods offered a high-speed solution. The optical matrix computation is impractical unless it is capable of extending to a large scale, reconfiguring for a general purpose and operating with high efficiency. Here, we report and experimentally demonstrate an optical matrix computing processor based on an integrated linear optical network. The proposed photonic processor is capable of performing fundamental matrix computations including XB = C, AB = X and AX = C, where A, B, C are known matrices, and X is the matrix to be solved. An optical PageRank algorithm is further demonstrated based on the matrix computing processor for the first time. Our demonstration offers an optical method to achieve matrix computations and PageRank algorithm. Meanwhile, it suggests great potential for chip-scale fully programmable matrix computations with self-configuring methods.
Contested energy futures, conflicted rewards? Examining low-carbon transition risks and governance dynamics in China's built environment

China's urbanisation has caused city populations to grow rapidly, boosting continuous development and scaling up the construction industry more intensely. The building sector is thus a key area to consider for climate change mitigation efforts. This study initially seeks to explore the development of a green transition pathway for the Chinese building sector, informed by national and local low-carbon policies and strategies, with specific references to Beijing and Shanghai. Acknowledging that the barriers and impacts of these policies have not been explored in depth and in consideration of the multiplicity of stakeholder views, we then set out to collect stakeholders’ perspectives of implementation and consequential risks associated with the envisaged transition and with the policies aiming to promote this transition. These concerns are evaluated in a multiple-criteria group decision making approach. By focusing on the resulting most critical implementation barriers, we then outline five plausible socioeconomic scenarios, against which we simulate the impacts of the considered policy strategies on the low-carbon transition of the Chinese built environment as well the extent of their key possible negative consequences, by means of fuzzy cognitive maps.
A Geometry-Inspired Decision-Based Attack

Deep neural networks have recently achieved tremendous success in image classification. Recent studies have however shown that they are easily misled into incorrect classification decisions by adversarial examples. Adversaries can even craft attacks by querying the model in black-box settings, where no information about the model is released except its final decision. Such decision-based attacks usually require lots of queries, while real-world image recognition systems might actually restrict the number of queries. In this paper, we propose qFool, a novel decision-based attack algorithm that can generate adversarial examples using a small number of queries. The qFool method can drastically reduce the number of queries compared to previous decision-based attacks while reaching the same quality of adversarial examples. We also enhance our method by constraining adversarial perturbations in low-frequency subspace, which can make qFool even more computationally efficient. Altogether, we manage to fool commercial image recognition systems with a small number of queries, which demonstrates the actual effectiveness of our new algorithm in practice.
Wiretap Code Design by Neural Network Autoencoders

In industrial machine type communications, an increasing number of wireless devices communicate under reliability, latency, and confidentiality constraints, simultaneously. From information theory, it is known that wiretap codes can asymptotically achieve reliability (vanishing block error rate (BLER) at the legitimate receiver Bob) while also achieving secrecy (vanishing information leakage (IL) to an eavesdropper Eve). However, under finite block length, there exists a tradeoff between the BLER at Bob and the IL at Eve. In this work, we propose a flexible wiretap code design for degraded Gaussian wiretap channels under finite block length, which can change the operating point on the Pareto boundary of the tradeoff between BLER and IL given specific code parameters. To attain this goal, we formulate a multi-objective programming problem, which takes the BLER at Bob and the IL at Eve into account. During training, we approximate the BLER by the mean square error and the IL by schemes based on Jensen's inequality and the Taylor expansion and then solve the optimization problem by neural network autoencoders. Simulation results show that the proposed scheme can find codes outperforming polar wiretap codes (PWC) with respect to both BLER and IL simultaneously. We show that the codes found by the autoencoders could be implemented with real modulation schemes with only small losses in performance.
Image captioning via hierarchical attention mechanism and policy gradient optimization

Automatically generating the descriptions of an image, i.e., image captioning, is an important and fundamental topic in artificial intelligence, which bridges the gap between computer vision and natural language processing. Based on the successful deep learning models, especially the CNN model and Long Short Term Memories (LSTMs) with attention mechanism, we propose a hierarchical attention model by utilizing both of the global CNN features and the local object features for more effective feature representation and reasoning in image captioning. The generative adversarial network (GAN), together with a reinforcement learning (RL) algorithm, is applied to solve the exposure bias problem in RNN-based supervised training for language problems. In addition, through the automatic measurement of the consistency between the generated caption and the image content by the discriminator in the GAN framework and RL optimization, we make the finally generated sentences more accurate and natural. Comprehensive experiments show the improved performance of the hierarchical attention mechanism and the effectiveness of our RL-based optimization method. Our model achieves state-of-the-art results on several important metrics in the MSCOCO dataset, using only greedy inference.
A New Knowledge Distillation for Incremental Object Detection

Nowadays, the Convolutional Neural Network is successfully applied to the images object detection. When new classes of object emerges, it is popular to adapt the convolutional neural network based detection model through a retraining process with the new classes of samples. Unfortunately, the adapted model can only detect the new classes of objects, but cannot identify the old classes of objects, which is called catastrophic forgetting, also occurring in incremental classification tasks. Knowledge distillation has achieved good results in incremental learning for classification tasks. Due to the dual tasks within object detection, object classification and location at the same time, a straightforward migration of knowledge distillation method cannot provide a satisfactory result in incremental learning for object detection tasks. Hence, this paper propose a new knowledge distillation for incremental object detection, which introduces a new object detection distillation loss, a loss not only for classification results but also for location results of the predicted bounding boxes, not only for all final detected regions of interest but also for all intermediate regions proposal. Furthermore, to avoid forgetting learned knowledge from old datasets, this paper not only employs hint learning to retain the characteristic information of the initial model, but also innovatively uses confidence loss to extract the confidence information of the initial model. A series of experiment results on the PASCAL VOC 2007 dataset verify the effectiveness of the proposed method.
Multimodal Data Enhanced Representation Learning for Knowledge Graphs

Knowledge graph, or knowledge base, plays an important role in a variety of applications in the field of artificial intelligence. In both research and application of knowledge graph, knowledge representation learning is one of the fundamental tasks. Existing representation learning approaches are mainly based on structural knowledge between entities and relations, while knowledge among entities per se is largely ignored. Though a few approaches integrated entity knowledge while learning representations, these methods lack the flexibility to apply to multimodalities. To tackle this problem, in this paper, we propose a new representation learning method, TransAE, by combining multimodal autoencoder with TransE model, where TransE is a simple and effective representation learning method for knowledge graphs. In TransAE, the hidden layer of autoencoder is used as the representation of entities in the TransE model, thus it encodes not only the structural knowledge, but also the multimodal knowledge, such as visual and textural knowledge, into the final representation. Compared with traditional methods based on only structural knowledge, TransAE can significantly improve the performance in the sense of link prediction and triplet classification. Also, TransAE has the ability to learn representations for entities out of knowledge base in zero-shot. Experiments on various tasks demonstrate the effectiveness of our proposed TransAE method.
Social Media as an Alternative to Surveys of Opinions About the Economy

There is interest in using social media content to supplement or even substitute for survey data. In one of the first studies to test the feasibility of this idea, O’Connor, Balasubramanyan, Routledge, and Smith report reasonably high correlations between the sentiment of tweets containing the word “jobs” and survey-based measures of consumer confidence in 2008–2009. Other researchers report a similar relationship through 2011, but after that time it is no longer observed, suggesting such tweets may not be as promising an alternative to survey responses as originally hoped. But, it’s possible that with the right analytic techniques, the sentiment of “jobs” tweets might still be an acceptable alternative. To explore this, we first classify “jobs” tweets into categories whose content is either related to employment or not, to see whether sentiment of the former correlates more highly with a survey-based measure of consumer sentiment. We then compare the relationship when sentiment is determined with traditional dictionary-based methods versus newer machine learning-based tools developed for Twitter-like texts. We calculated daily sentiment in three different ways and used a measure of association less sensitive to outliers than correlation. None of these approaches improved the size of the relationship in the original or more recent data. We found that the many micro-decisions these analyses require, such as the size of the smoothing interval and the length of the lag between the two series, can significantly affect the outcomes. In the end, despite the earlier promise of tweets as an alternative to survey responses, we find no evidence that the original relationship in these data was more than a chance occurrence.
Healthcare Data Security Technology: HIPAA Compliance

Information technology (IT) plays an increasingly important and prominent role in the health sector. Data security is more important than ever to the healthcare industry and in world in general. The number of data breaches compromising confidential healthcare data is on the rise. For data security, cloud computing is very useful for securing data. Due to data storage issue, there is a need to use the electronic communication, and a number of methods have been developed for data security technology. Health Insurance Portability and Accountability Act (HIPAA) is one of the methods that can help in healthcare research. On stored database of patient in hospital or clinic, we can develop a conservational and analytical method so as to keep the medical records of the patients in a well-preserved and adequate environment. The method includes the improvement of working possibilities by delivering all the details necessary for the patient. All the information must be identified clearly. The protection of the privacy of the patients and the security of their information are the most imperative obstacles to obtain their intakes when considering the adoption of useful health data in the electronic field of healthcare industries.
Emotion analysis of Arabic tweets using deep learning approach

Abstract Nowadays, sharing moments on social networks have become something widespread. Sharing ideas, thoughts, and good memories to express our emotions through text without using a lot of words. Twitter, for instance, is a rich source of data that is a target for organizations for which they can use to analyze people’s opinions, sentiments and emotions. Emotion analysis normally gives a more profound overview of the feelings of an author. In Arabic Social Media analysis, nearly all projects have focused on analyzing the expressions as positive, negative or neutral. In this paper we intend to categorize the expressions on the basis of emotions, namely happiness, anger, fear, and sadness. Different approaches have been carried out in the area of automatic textual emotion recognition in the case of other languages, but only a limited number were based on deep learning. Thus, we present our approach used to classify emotions in Arabic tweets. Our model implements a deep Convolutional Neural Networks (CNN) trained on top of trained word vectors specifically on our dataset for sentence classification tasks. We compared the results of this approach with three other machine learning algorithms which are SVM, NB and MLP. The architecture of our deep learning approach is an end-to-end network with word, sentence, and document vectorization steps. The deep learning proposed approach was evaluated on the Arabic tweets dataset provided by SemiEval for the EI-oc task, and the results-compared to the traditional machine learning approaches-were excellent.