Reviewerly/exHarmony huggingface dataset:
Video Saliency Detection via Graph Clustering With Motion Energy and Spatiotemporal Objectness

We present a novel, robust estimation method to distinguish salient objects from complicated, dynamic backgrounds in videos. In this method, we propose a novel approach to model motion energy based on motion magnitude, motion orientation, gradient flow field, and spatial gradient of the video frame. Furthermore, an effective spatiotemporal objectness map is also proposed to estimate a compact object-like region in the current video frame leveraging both the objectness proposals and the saliency map of the previous frame. Then the current video frame is oversegmented into the granularity of superpixels using the simple linear iterative clustering algorithm. Each superpixel is designated as a node of a graph. The similarity between adjacent superpixels will be assigned as the weight of an edge that connects these two nodes. The feature values of motion energy and spatiotemporal objectness within each superpixel will be averaged respectively, and used to graphically cluster similar superpixels to form the detected salient object. Extensive experiments comparing this proposed new method against twelve existing salient object detection (SOD) methods have been performed using the benchmark datasets unconstrained video saliency detection and densely annotated video segmentation. Superior performance of this proposed SOD method has been observed through three well-known performance metrics: precision-recall curves, F-measure curves, and the mean absolute error.
Towards a Deep and Unified Understanding of Deep Neural Models in NLP

We define a unified information-based measure to provide quantitative explanations on how intermediate layers of deep Natural Language Processing (NLP) models leverage information of input words. Our method advances existing explanation methods by addressing issues in coherency and generality. Explanations generated by using our method are consistent and faithful across different timestamps, layers, and models. We show how our method can be applied to four widely used models in NLP and explain their performances on three real-world benchmark datasets.
Learning Perceptually-Aligned Representations via Adversarial Robustness.

An important goal in deep learning is to learn versatile, high-level feature representations of input data. However, standard networks' representations seem to possess shortcomings that, as we illustrate, prevent them from fully realizing this goal. In this work, we show that robust optimization can be re-cast as a tool for enforcing priors on the features learned by deep neural networks. It turns out that representations learned by robust models address the aforementioned shortcomings and make significant progress towards learning a high-level encoding of inputs. In particular, these representations are approximately invertible, while allowing for direct visualization and manipulation of salient input features. More broadly, our results indicate adversarial robustness as a promising avenue for improving learned representations. Our code and models for reproducing these results is available at this https URL .
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation

Unsupervised bilingual word embedding (UBWE), together with other technologies such as back-translation and denoising, has helped unsupervised neural machine translation (UNMT) achieve remarkable results in several language pairs.In previous methods, UBWE is first trained using nonparallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT.That is, the training of UBWE and UNMT are separate.In this paper, we first empirically investigate the relationship between UBWE and UNMT.The empirical findings show that the performance of UNMT is significantly affected by the performance of UBWE.Thus, we propose two methods that train UNMT with UBWE agreement.Empirical results on several language pairs show that the proposed methods significantly outperform conventional UNMT.
Detection of Bots and Cyborgs in Twitter: A Study on the Chilean Presidential Election in 2017

The increase of content in social networks, and specially its use in the political environment, has led to the creation and proliferation of autonomous entities commonly known as bots. Bots are programms that performs an automated task over the internet. In this study these entities were initially detected based on a manual analysis carried out on the activity produced by electoral candidates in Chile during the presidential election in year 2017. As a result of this, the need to identify these accounts in an automatic way arose, in order to asses the impact of these accounts in the social network activity during the presidential election in 2017. Various features were extracted in order to train Machine Learning algorithms for the automatic classification task using a set of publicly available data, and other semi-automatic approaches. The models obtain over $$80\%$$ in the training stage, but less than $$60\%$$ in the testing stage, thus encouraging us to continue to work in other types of representations and models in order to improve the results.
Left-to-Right Dependency Parsing with Pointer Networks

Daniel Fernández-González, Carlos Gómez-Rodríguez. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.
Particle Swarm Optimization-Based Deep Neural Network for Digital Modulation Recognition

Modulation recognition is a major task in many wireless communication systems including cognitive radio and signal reconnaissance. The diversification of modulation schemes and the increased complexity of the channel environment put higher requirements on the correct identification of modulated signals. Deep learning (DL) is considered as a potential solution to solve these problems due to the superior big data processing and classification capabilities. This paper proposes an efficient digital modulation recognition method based on deep neural network (DNN) model. Furthermore, we present the particle swarm optimization (PSO) algorithm to optimize the number of hidden layer nodes of the DNN so as to solve the problem that the traditional DNN is trapped in local minimum values and the number of hidden layer nodes needs selecting manually. In this paper, we utilize the proposed PSO-DNN method to learn characteristics extracted from the modulated signal added by additive white Gaussian noise (AWGN) and to train the network, which can improve the performance of recognition under the condition of low signal-to-noise ratio (SNR). The experimental results demonstrate that the recognition rate on this algorithm has improved by 9.4% and 8.8% compared with methods that adopt conventional DNN and support vector machine (SVM) when SNR equals 0 and 1 dB, respectively. Besides, another experiment compared with the genetic algorithm (GA) also proves that our proposed algorithm is more effective in optimizing the DNN. The proposed method is easy to be implemented so that it has a broad development prospect in modulation recognition.
Physical layer identification of LoRa devices using constellation trace figure

LoRa wireless technology is a revolutionary wireless network access technology with a wide application prospect. An identification method for Lora devices based on physical layer fingerprinting is proposed to provide identities for authentication. Contrary to previous works, a differential constellation trace figure is established from the radio frequency (RF) fingerprinting features of LoRa devices, which transforms the feature matching to the image recognition. A classification method based on Euclidean distance of clustering center of LoRa signal is performed to analyze the differential constellation trace figure. The experimental results show that six LoRa transmission modules can be recognized accurately, and even in a low signal-to-noise ratio (SNR) environment, the different LoRa devices can still be distinguished and identified effectively.
An Autonomous T-Intersection Driving Strategy Considering Oncoming Vehicles Based on Connected Vehicle Technology

Autonomous driving strategies for intersection scenarios are challenging due to the varying traffic conditions of oncoming vehicles. Based on the connected vehicle technology, this article proposes an autonomous T-intersection driving strategy considering the oncoming vehicles for motion-planning and path following. A finite-state machine (FSM) is developed in the motion planner to decide the driving strategies considering the oncoming vehicles. Information pieces from the connected vehicle, specifically vehicle position and speed, are selected and effectively utilized to construct the temporal windows that manage the driving states transition of the FSM. Speed profiles in different driving states are modified for collision avoidance. Then, a path-following controller based on the back-stepping method is designed to track the planned path and speed simultaneously. The proposed strategy is validated by both simulation and experimental investigations. The results show the controlled vehicle can safely and quickly pass through the intersection using the proposed driving strategy that avoids possible collisions with the oncoming vehicles.
CU-Net: Cascaded U-Net with Loss Weighted Sampling for Brain Tumor Segmentation

This paper proposes a novel cascaded U-Net for brain tumor segmentation. Inspired by the distinct hierarchical structure of brain tumor, we design a cascaded deep network framework, in which the whole tumor is segmented firstly and then the tumor internal substructures are further segmented. Considering that the increase of the network depth brought by cascade structures leads to a loss of accurate localization information in deeper layers, we construct between-net connections to link features at the same resolution and transmit the detailed information from shallow layers to the deeper layers. Then we present a loss weighted sampling (LWS) scheme to eliminate the issue of imbalanced data. Experimental results on the BraTS 2017 dataset show that our framework outperforms the state-of-the-art segmentation algorithms, especially in terms of segmentation sensitivity.
Situational systematicity: A role for schema in understanding the differences between abstract and concrete concepts

Abstract concepts differ from concrete concepts in several ways. Here, we focus on what we refer to as situational systematicity: The objects and relations that constitute an abstract concept (e.g., justice) are more dispersed through space and time than are those that typically constitute a concrete concept (e.g., chair); a larger set of objects and relations constitute an abstract concept than a concrete one; and exactly which objects and relations constitute a concept is more context-dependent for abstract concepts. We thus refer to abstract concepts as having low situational systematicity. We contend that situational systematicity, rather than abstractness per se, is a critical determinant of the cognitive, behavioural, and neural phenomena associated with concepts. Further, viewing concepts as schema provides insight into (i) the situation-based dynamics of concept learning and representation and (ii) the functional significance of the brain regions and their interactions that comprise the schema control network.
Aspect-Aware Multimodal Summarization for Chinese E-Commerce Products

We present an abstractive summarization system that produces summary for Chinese e-commerce products. This task is more challenging than general text summarization. First, the appearance of a product typically plays a significant role in customers' decisions to buy the product or not, which requires that the summarization model effectively use the visual information of the product. Furthermore, different products have remarkable features in various aspects, such as “energy efficiency” and “large capacity” for refrigerators. Meanwhile, different customers may care about different aspects. Thus, the summarizer needs to capture the most attractive aspects of a product that resonate with potential purchasers. We propose an aspect-aware multimodal summarization model that can effectively incorporate the visual information and also determine the most salient aspects of a product. We construct a large-scale Chinese e-commerce product summarization dataset that contains approximately 1.4 million manually created product summaries that are paired with detailed product information, including an image, a title, and other textual descriptions for each product. The experimental results on this dataset demonstrate that our models significantly outperform the comparative methods in terms of both the ROUGE score and manual evaluations.
RKA Security for Identity-Based Signature Scheme

Related-key attack (RKA) is a kind of side-channel attack considered for kinds of cryptographic primitives, such as public key encryption, digital signature, pseudorandom functions etc. However, we note that the RKA-security seems to be not considered for identity-based signature (IBS), which is an important primitive for identity-based cryptography and proposed by Shamir in 1984. In this paper, for the first time, we introduce the RKA security into IBS schemes and try to define the security model for it. More specifically, we consider the RKA occurs in the users' signing key or the master key of the key-generation center (KGC), which derives two kinds of RKA securities for IBS. Meanwhile, we illustrate that the most efficient Schnorr-like IBS scheme proposed by Galindo and Garcia is RKA-insecure by launching a simple RKA. However, a slight modification of it yields a RKA-secure IBS scheme, for which we give the detailed security proof in the random oracle. Finally, the performance analysis shows that the modified scheme is still extremely efficient but has higher security.
Application of Augmented Reality (AR) Technologies in inhouse Logistics

As a new information technology, mobile augmented reality (AR) technology has attracted more and more attention in recent years. Mobile AR uses mobile terminal, with the help of entity object recognition, obtains virtual information through calculation or in database, and superimposes virtual information on live video to realize the "enhancement" of virtual information to reality. In order to explore the application of AR technology in inhouse logistics, this paper analyses the principle and technical characteristics of AR technology. By analysing the application status of AR technology in inhouse logistics, the application advantages of AR technology in inhouse logistics are obtained. This paper summarizes 36 cases of AR technology application in inhouse logistics, summarizes and analyses the application prospect of AR technology in inhouse logistics. The functions of AR technology in the process of receiving, storage, sorting, transportation, inventory and planning are put forward. It provides theoretical guidance for the practical application of AR technology in inhouse logistics.
Personalization of End-to-End Speech Recognition on Mobile Devices for Named Entities

We study the effectiveness of several techniques to personalize end-to-end speech models and improve the recognition of proper names relevant to the user. These techniques differ in the amounts of user effort required to provide supervision, and are evaluated on how they impact speech recognition performance. We propose using keyword-dependent precision and recall metrics to measure vocabulary acquisition performance. We evaluate the algorithms on a dataset that we designed to contain names of persons that are difficult to recognize. Therefore, the baseline recall rate for proper names in this dataset is very low: 2.4%. A data synthesis approach we developed brings it to 48.6%, with no need for speech input from the user. With speech input, if the user corrects only the names, the name recall rate improves to 64.4%. If the user corrects all the recognition errors, we achieve the best recall of 73.5%. To eliminate the need to upload user data and store personalized models on a server, we focus on performing the entire personalization workflow on a mobile device.
New developments on EDR (Event Data Recorder) for automated vehicles

Abstract With the upcoming new legislative rules in the EU on Event Data Recorder beginning 2022 the question is whether the discussed data base is sufficient for the needs of clarifying accidents involving automated vehicles. Based on the reconstruction of real accidents including vehicles with ADAS combined with specially designed crash tests a broader data base than US EDR regulation (NHTSA 49 CFR Part 563.7) is proposed. The working group AHEAD, to which the authors contribute, has already elaborated a data model that fits the needs of automated driving. The structure of this data model is shown. Moreover, the special benefits of storing internal video or photo feeds form the vehicle camera systems combined with object data is illustrated. When using a sophisticate 3D measurement method of the accident scene the videos or photos can also serve as a control instance for the stored vehicle data. The AHEAD Data Model enhanced with the storage of the video and photo feeds should be considered in the planned roadmap of the Informal Working Group (IWG) on EDR/ DSSAD (Data Storage System for Automated Driving) reporting to UNECE WP29. Also, a data access over the air using technology already applied in China for electric vehicles called Real Time Monitoring would allow a quantum leap in forensic accident reconstruction.
Single-/Multi-Source Cross-Lingual NER via Teacher-Student Learning on Unlabeled Data in Target Language

To better tackle the named entity recognition (NER) problem on languages with little/no labeled data, cross-lingual NER must effectively leverage knowledge learned from source languages with rich labeled data.Previous works on cross-lingual NER are mostly based on label projection with pairwise texts or direct model transfer.However, such methods either are not applicable if the labeled data in the source languages is unavailable, or do not leverage information contained in unlabeled data in the target language.In this paper, we propose a teacher-student learning method to address such limitations, where NER models in the source languages are used as teachers to train a student model on unlabeled data in the target language.The proposed method works for both single-source and multi-source crosslingual NER.For the latter, we further propose a similarity measuring method to better weight the supervision from different teacher models.Extensive experiments for 3 target languages on benchmark datasets well demonstrate that our method outperforms existing state-of-theart methods for both single-source and multisource cross-lingual NER.
Quantum-enhanced interferometry with large heralded photon-number states

Quantum phenomena such as entanglement can improve fundamental limits on the sensitivity of a measurement probe. In optical interferometry, a probe consisting of N entangled photons provides up to a N enhancement in phase sensitivity compared to a classical probe of the same energy. Here, we employ high-gain parametric down-conversion sources and photon-number-resolving detectors to perform interferometry with heralded quantum probes of sizes up to N = 8 (i.e. measuring up to 16-photon coincidences). Our probes are created by injecting heralded photon-number states into an interferometer, and in principle provide quantum-enhanced phase sensitivity even in the presence of significant optical loss. Our work paves the way towards quantum-enhanced interferometry using large entangled photonic states.
A Contour Angle Orientation for Power Equipment Infrared and Visible Image Registration

Automatic registration for infrared, and visible images of power equipment has become a challenging work in intelligent diagnosis system of the power grid. Existing registration methods usually fail in accurately aligning power equipment infrared, and visible images because of resolutions, spectrums, and viewpoints differences. To solve this problem, we propose a novel main orientation of feature points named contour angle orientation (CAO), and describe an automatic infrared, and visible image registration method named CAO-Coarse to Fine (CAO-C2F). CAO is based on the contour feature of images, and invariant to images viewpoints, and scales differences. C2F is a feature matching method to obtain correct matches. Our proposed CAO-C2F method includes four steps. First, feature points in contours are extracted by the curvature scale space (CSS) corner detector based on local, and global curvature. Second, the CAO of each feature point is computed as the main orientation. Third, modified scale-invariant feature transform (SIFT) descriptors on the main orientations are extracted, and matched by bilateral matching. Finally, accurate matches are obtained by applying the C2F method. Registration experiments on a self-established images dataset show our proposed CAO-C2F method accurately aligns images, and outperforms other state-of-arts in terms of precision, recall, and root-mean-square error.
Real Time Multi-Scale Facial Mask Detection and Classification Using Deep Transfer Learning Techniques

In the era of deep learning, object detection plays an influential role for many industries Detecting minute things are very much essential without human intervention especially at large scale industries In this paper we have proposed multiple approaches for Multi-scale facial mask real time detection and classification for the hospital industry, crowd surveillance in the streets and malls are more useful in this COVID-19 Pandemic Situation In our approach we have implemented two different detection models which are FMY3 using Yolov3 Algorithm and FMNMobile using NASNetMobile and Resnet_SSD300 Algorithms and used two different face mask dataset with 680 and 1400 images respectively We have analyzed both the models by computing various probabilistic accuracy measures and achieved the 34% Mean Average Precision (mAP) and 91 7% Recall rate on FMY3 Model and achieved the 98% and 99% of accuracy and recall rate on FMNMobile Model Finally we have shown results of various face mask detections from both the models © 2020, World Academy of Research in Science and Engineering All rights reserved