Reviewerly/exHarmony huggingface dataset:
Leveraging Lexical Semantic Information for Learning Concept-Based Multiple Embedding Representations for Knowledge Graph Completion

Knowledge graphs (KGs) are important resources for a variety of natural language processing tasks but suffer from incompleteness. To address this challenge, a number of knowledge graph completion (KGC) methods have been developed using low-dimensional graph embeddings. Most existing methods focus on the structured information of triples in encyclopaedia KG and maximize the likelihood of them. However, they neglect semantic information contained in lexical KG. To overcome this drawback, we propose a novel KGC method (named as TransC), that integrates the structured information in encyclopaedia KG and the entity concepts in lexical KG, which describe the categories of entities. Since all entities appearing in the head (or tail) position with the same relation have some common concepts, we introduce a novel semantic similarity to measure the distinction of entity semantics with the concept information. And then TransC utilizes concept-based semantic similarity of the related entities and relations to capture prior distributions of entities and relations. With the concept-based prior distributions, TransC generates multiple embedding representations of each entity in different contexts and estimates the posterior probability of entity and relation prediction. Experimental results demonstrate the efficiency of the proposed method on two benchmark datasets.
Mining Entity Synonyms with Efficient Neural Set Generation

Mining entity synonym sets (i.e., sets of terms referring to the same entity) is an important task for many entity-leveraging applications. Previous work either rank terms based on their similarity to a given query term, or treats the problem as a two-phase task (i.e., detecting synonymy pairs, followed by organizing these pairs into synonym sets). However, these approaches fail to model the holistic semantics of a set and suffer from the error propagation issue. Here we propose a new framework, named SynSetMine, that efficiently generates entity synonym sets from a given vocabulary, using example sets from external knowledge bases as distant supervision. SynSetMine consists of two novel modules: (1) a set-instance classifier that jointly learns how to represent a permutation invariant synonym set and whether to include a new instance (i.e., a term) into the set, and (2) a set generation algorithm that enumerates the vocabulary only once and applies the learned set-instance classifier to detect all entity synonym sets in it. Experiments on three real datasets from different domains demonstrate both effectiveness and efficiency of SynSetMine for mining entity synonym sets.
Synthesis of Supremal Successful Normal Actuator Attackers on Normal Supervisors

In this paper, we propose and develop an actuator attack model for discrete-event systems. We assume the actuator attacker partially observes the execution of the closed-loop system and eavesdrops the control commands issued by the supervisor. The attacker can modify each control command on a specified subset of attackable events. The goal of the actuator attacker is to remain covert until it can establish a successful attack and lead the attacked closed-loop system into generating certain damaging strings. We then present a characterization for the existence of a successful attacker and prove the existence of the supremal successful attacker, when both the supervisor and the attacker are normal. Finally, we present an algorithm to synthesize the supremal successful normal attackers.
STCS Lexicon: Spectral-Clustering-Based Topic-Specific Chinese Sentiment Lexicon Construction for Social Networks

A sentiment lexicon is an important foundation for social network sentiment analysis. However, in social networks, the sentiments of words vary with their topics of use, which leads to a problem in the construction of a sentiment lexicon. We propose a method for constructing a topic-specific sentiment lexicon, which comprises three following models: first, we propose a filtering text model, namely, FT model, to calculate the text influence value and obtain topic-specific hot comments as a preprocessing data set; second, in our proposed constructing sentiment relationship graph model, namely, CRM model, three factors, i.e., the base sentiment similarity, topic sentiment similarity, and synonym sentiment similarity between each pair of sentiment words, are proposed and calculated in our data set, and then we can obtain the factor of final sentiment similarity by adding the three values in proportion; and finally, we propose a spectral clustering model, namely, SC model, to cluster the sentiment words on the basis of a sentiment relationship graph for obtaining the topic-specific sentiment lexicon, namely, STCS lexicon. Experiments show that our method is simple, flexible, and efficient. It can solve the problem of topic-related sentiment words and, thus, improve the accuracy of the sentiment lexicon.
Improving Generative Visual Dialog by Answering Diverse Questions

Prior work on training generative Visual Dialog models with reinforcement learning (Das et al., 2017b) has explored a Q-BOT-A-BOT image-guessing game and shown that this 'self-talk' approach can lead to improved performance at the downstream dialogconditioned image-guessing task.However, this improvement saturates and starts degrading after a few rounds of interaction, and does not lead to a better Visual Dialog model.We find that this is due in part to repeated interactions between Q-BOT and A-BOT during selftalk, which are not informative with respect to the image.To improve this, we devise a simple auxiliary objective that incentivizes Q-BOT to ask diverse questions, thus reducing repetitions and in turn enabling A-BOT to explore a larger state space during RL i.e. be exposed to more visual concepts to talk about, and varied questions to answer.We evaluate our approach via a host of automatic metrics and human studies, and demonstrate that it leads to better dialog, i.e. dialog that is more diverse (i.e. less repetitive), consistent (i.e. has fewer conflicting exchanges), fluent (i.e. more humanlike), and detailed, while still being comparably image-relevant as prior work and ablations.
Desoiling Dataset: Restoring Soiled Areas on Automotive Fisheye Cameras

Surround-view cameras became an integral part of autonomous driving setup. Being directly exposed to harsh environmental settings, they can get soiled easily. When cameras get soiled, the degradation of performance is usually more dramatic compared to other sensors. Having this on mind, we decided to design a dataset for measuring the performance degradation as well as to help constructing classifiers for soiling detection, or for trying to restore the soiled images, so we can increase the performance of the off-the-shelf classifiers. The proposed dataset contains 40+ approximately 1 minute long video sequences with paired image information of both clean and soiled nature. The dataset will be released as a companion to our recently published dataset [14] to encourage further research in this area. We constructed a CycleGAN architecture to produce de-soiled images and demonstrate 5% improvement in road detection and 3% improvement in detection of lanes and curbs.
User-independent optical path length compensation scheme with sub-nanosecond timing resolution for a 1 × N quantum key distribution network system

Quantum key distribution (QKD) networks constitute promising solutions for secure communication. Beyond conventional point-to-point QKD, we developed 1 × N QKD network systems with a sub-nanosecond resolution optical path length compensation scheme. With a practical plug-and-play QKD architecture and compact timing control modules based on a field-programmable gate array, we achieved long-term stable operation of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m1"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>×</mml:mo> <mml:mn>64</mml:mn> </mml:mrow> </mml:math> QKD network system. Using this architecture, 64 users can simultaneously share secret keys with one server, without using complex software algorithms and expensive hardware. We demonstrated the workings of a <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="m2"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>×</mml:mo> <mml:mn>4</mml:mn> </mml:mrow> </mml:math> QKD network system using the fiber network of a metropolitan area.
Analyzing Political Parody in Social Media

Parody is a figurative device used to imitate an entity for comedic or critical purposes and represents a widespread phenomenon in social media through many popular parody accounts.In this paper, we present the first computational study of parody.We introduce a new publicly available data set of tweets from real politicians and their corresponding parody accounts.We run a battery of supervised machine learning models for automatically detecting parody tweets with an emphasis on robustness by testing on tweets from accounts unseen in training, across different genders and across countries.Our results show that political parody tweets can be predicted with an accuracy up to 90%.Finally, we identify the markers of parody through a linguistic analysis.Beyond research in linguistics and political communication, accurately and automatically detecting parody is important to improving fact checking for journalists and analytics such as sentiment analysis through filtering out parodical utterances.
Scalable and Practical Natural Gradient for Large-Scale Deep Learning

Large-scale distributed training of deep neural networks results in models with worse generalization performance as a result of the increase in the effective mini-batch size. Previous approaches attempt to address this problem by varying the learning rate and batch size over epochs and layers, or ad hoc modifications of batch normalization. We propose scalable and practical natural gradient descent (SP-NGD), a principled approach for training models that allows them to attain similar generalization performance to models trained with first-order optimization methods, but with accelerated convergence. Furthermore, SP-NGD scales to large mini-batch sizes with a negligible computational overhead as compared to first-order methods. We evaluated SP-NGD on a benchmark task where highly optimized first-order methods are available as references: training a ResNet-50 model for image classification on ImageNet. We demonstrate convergence to a top-1 validation accuracy of 75.4 percent in 5.5 minutes using a mini-batch size of 32,768 with 1,024 GPUs, as well as an accuracy of 74.9 percent with an extremely large mini-batch size of 131,072 in 873 steps of SP-NGD.
Quantitative Measures of Background Parenchymal Enhancement Predict Breast Cancer Risk

Quantitative Measures of Background Parenchymal Enhancement Predict Breast Cancer RiskBethany L. Niell, MD, PhD1, Mahmoud Abdalah, PhD2, Olya Stringfield, PhD2, Natarajan Raghunand, PhD3, Dana Ataya, MD1, Robert Gillies, PhD3 and Yoganand Balagurunathan, PhD4,5Audio Available | Share Journal Club
Challenges Developing Deep Learning Algorithms in Cytology

&lt;b&gt;&lt;i&gt;Background:&lt;/i&gt;&lt;/b&gt; The incorporation of digital pathology into routine pathology practice is becoming more widespread. Definite advantages exist with respect to the implementation of artificial intelligence (AI) and deep learning in pathology, including cytopathology. However, there are also unique challenges in this regard. &lt;b&gt;&lt;i&gt;Summary:&lt;/i&gt;&lt;/b&gt; This review discusses cytology-specific challenges, including the need to implement digital cytology prior to AI; the large file sizes and increased acquisition times for whole slide images in cytology; the routine use of multiple stains, such as Papanicolaou and Romanowsky stains; the lack of high-quality annotated datasets on which to train algorithms; and the considerable computer resources required, in terms of both computer infrastructure and skilled personnel, for computing and storage of data. Global concerns regarding AI that are certainly applicable to cytology include the need for model validation and continued quality assurance, ethical issues such as the use of patient data in developing algorithms, the need to develop regulatory frameworks regarding what type of data can be utilized and ensuring cybersecurity during data collection and storage, and algorithm development. &lt;b&gt;&lt;i&gt;Key Messages:&lt;/i&gt;&lt;/b&gt; While AI will likely play a role in cytology practice in the future, applying this technology to cytology poses a unique set of challenges. A broad understanding of digital pathology and algorithm development is desirable to guide the development of algorithms, as well as the need to be cognizant of potential pitfalls to avoid when incorporating the technology in practice.
Improving Opus Low Bit Rate Quality with Neural Speech Synthesis

The voice mode of the Opus audio coder can compress wideband speech at bit rates ranging from 6 kb/s to 40 kb/s. However, Opus is at its core a waveform matching coder, and as the rate drops below 10 kb/s, quality degrades quickly. As the rate reduces even further, parametric coders tend to perform better than waveform coders. In this paper we propose a backward-compatible way of improving low bit rate Opus quality by re-synthesizing speech from the decoded parameters. We compare two different neural generative models, WaveNet and LPCNet. WaveNet is a powerful, high-complexity, and high-latency architecture that is not feasible for a practical system, yet provides a best known achievable quality with generative models. LPCNet is a low-complexity, low-latency RNN-based generative model, and practically implementable on mobile phones. We apply these systems with parameters from Opus coded at 6 kb/s as conditioning features for the generative models. A listening test shows that for the same 6 kb/s Opus bit stream, synthesized speech using LPCNet clearly outperforms the output of the standard Opus decoder. This opens up ways to improve the decoding quality of existing speech and audio waveform coders without breaking compatibility.
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack

Adversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models.Though raised great concerns, such adversarial attacks can be leveraged to estimate the robustness of NLP models.Compared with the adversarial example generation in continuous data domain (e.g., image), generating adversarial text that preserves the original meaning is challenging since the text space is discrete and non-differentiable.To handle these challenges, we propose a target-controllable adversarial attack framework T3, which is applicable to a range of NLP tasks.In particular, we propose a tree-based autoencoder to embed the discrete text data into a continuous representation space, upon which we optimize the adversarial perturbation.A novel tree-based decoder is then applied to regularize the syntactic correctness of the generated text and manipulate it on either sentence (T3(SENT)) or word (T3(WORD)) level.We consider two most representative NLP tasks: sentiment analysis and question answering (QA).Extensive experimental results and human studies show that T3 generated adversarial texts can successfully manipulate the NLP models to output the targeted incorrect answer without misleading the human.Moreover, we show that the generated adversarial texts have high transferability which enables the black-box attacks in practice.Our work sheds light on an effective and general way to examine the robustness of NLP models.Our code is publicly available at
On Learning Universal Representations Across Languages

Recent studies have demonstrated the overwhelming advantage of cross-lingual pre-trained models (PTMs), such as multilingual BERT and XLM, on cross-lingual NLP tasks. However, existing approaches essentially capture the co-occurrence among tokens through involving the masked language model (MLM) objective with token-level cross entropy. In this work, we extend these approaches to learn sentence-level representations, and show the effectiveness on cross-lingual understanding and generation. We propose Hierarchical Contrastive Learning (HiCTL) to (1) learn universal representations for parallel sentences distributed in one or multiple languages and (2) distinguish the semantically-related words from a shared cross-lingual vocabulary for each sentence. We conduct evaluations on two challenging cross-lingual tasks, XTREME and machine translation. Experimental results show that the HiCTL outperforms the state of the art XLM-R by an absolute gain of 1.3% accuracy on XTREME as well as achieves substantial improvements of +1.7~+3.6 BLEU on both the high-resource and low-resource English-X translation tasks over strong baselines.
An unsupervised domain adaptation scheme for single-stage artwork recognition in cultural sites

Recognizing artworks in a cultural site using images acquired from the user's point of view (First Person Vision) allows to build interesting applications for both the visitors and the site managers. However, current object detection algorithms working in fully supervised settings need to be trained with large quantities of labeled data, whose collection requires a lot of times and high costs in order to achieve good performance. Using synthetic data generated from the 3D model of the cultural site to train the algorithms can reduce these costs. On the other hand, when these models are tested with real images, a significant drop in performance is observed due to the differences between real and synthetic images. In this study we consider the problem of Unsupervised Domain Adaptation for object detection in cultural sites. To address this problem, we created a new dataset containing both synthetic and real images of 16 different artworks. We hence investigated different domain adaptation techniques based on one-stage and two-stage object detector, image-to-image translation and feature alignment. Based on the observation that single-stage detectors are more robust to the domain shift in the considered settings, we proposed a new method which builds on RetinaNet and feature alignment that we called DA-RetinaNet. The proposed approach achieves better results than compared methods on the proposed dataset and on Cityscapes. To support research in this field we release the dataset at the following link https://iplab.dmi.unict.it/EGO-CH-OBJ-UDA/ and the code of the proposed architecture at https://github.com/fpv-iplab/DA-RetinaNet.
Racist and Sexist Figleaves 1

Jennifer Saul articulates the notion of a figleaf, which is an utterance that blocks the inference (that would otherwise be warranted) that the speaker has violated the relevant social norm. Examples include, “I have great respect for women, but…” or “I have many Black friends….”
MonoGRNet: A General Framework for Monocular 3D Object Detection

Detecting and localizing objects in the real 3D space, which plays a crucial role in scene understanding, is particularly challenging given only a monocular image due to the geometric information loss during imagery projection. We propose MonoGRNet for the amodal 3D object detection from a monocular image via geometric reasoning in both the observed 2D projection and the unobserved depth dimension. MonoGRNet decomposes the monocular 3D object detection task into four sub-tasks including 2D object detection, instance-level depth estimation, projected 3D center estimation and local corner regression. The task decomposition significantly facilitates the monocular 3D object detection, allowing the target 3D bounding boxes to be efficiently predicted in a single forward pass, without using object proposals, post-processing or the computationally expensive pixel-level depth estimation utilized by previous methods. In addition, MonoGRNet flexibly adapts to both fully and weakly supervised learning, which improves the feasibility of our framework in diverse settings. Experiments are conducted on KITTI, Cityscapes and MS COCO datasets. Results demonstrate the promising performance of our framework in various scenarios.
Differential diagnosis of breast cancer assisted by S-Detect artificial intelligence system

<abstract> <sec><title>Objective</title> Traditional breast ultrasound relies too much on the operation skills of diagnostic doctors, and the repeatability in different doctors was low. This study aimed to evaluate the assistant diagnostic value of S-Detect artificial intelligence (AI) system in differentiating benign from malignant breast masses. </sec> <sec><title>Methods</title> The ultrasound images of 40 patients who underwent ultrasound examination in our hospital were collected. The conventional ultrasound images, elastic images, and S-Detect mode of breast lesions were analyzed. The breast imaging reporting and data system recommended by the American Society of Radiology (BI-RADS) classification for each breast mass was evaluated both by the doctor and AI. The receiver operator characteristics (ROC) curves were drawn to compare the diagnostic efficiency. </sec> <sec><title>Result</title> Among the 40 lesions, 16 were benign, and 24 were malignant. The S-Detect AI system had a high diagnostic efficiency for malignant mass, with sensitivity, specificity, and accuracy of 95.8%, 93.8%, and 89.6%. The accuracy of AI was higher than the elastic image and then than the conventional gray-scale image. With the assistance of the S-Detect AI system, the accuracy of BI-RADS classification was improved significantly. </sec> <sec><title>Conclusion</title> The S-Detect AI system will enhance breast cancer diagnostic accuracy and improve ultrasound examination quality. </sec> </abstract>
CacheTrack-YOLO: Real-Time Detection and Tracking for Thyroid Nodules and Surrounding Tissues in Ultrasound Videos

To accurately detect and track the thyroid nodules in a video is a crucial step in the thyroid screening for identification of benign and malignant nodules in computer-aided diagnosis (CAD) systems. Most existing methods just perform excellent on static frames selected manually from ultrasound videos. However, manual acquisition is labor-intensive work. To make the thyroid screening process in a more natural way with less labor operations, we develop a well-designed framework suitable for practical applications for thyroid nodule detection in ultrasound videos. Particularly, in order to make full use of the characteristics of thyroid videos, we propose a novel post-processing approach, called Cache-Track, which exploits the contextual relation among video frames to propagate the detection results into adjacent frames to refine the detection results. Additionally, our method can not only detect and count thyroid nodules, but also track and monitor surrounding tissues, which can greatly reduce the labor work and achieve computer-aided diagnosis. Experimental results show that our method performs better in balancing accuracy and speed.