Reviewerly/exHarmony huggingface dataset:
Preference-based Evaluation Metrics for Web Image Search

Following the success of Cranfield-like evaluation approaches to evaluation in web search, web image search has also been evaluated with absolute judgments of (graded) relevance. However, recent research has found that collecting absolute relevance judgments may be difficult in image search scenarios due to the multi-dimensional nature of relevance for image results. Moreover, existing evaluation metrics based on absolute relevance judgments do not correlate well with search users' satisfaction perceptions in web image search.
A Graph-based Coarse-to-fine Method for Unsupervised Bilingual Lexicon Induction

Unsupervised bilingual lexicon induction is the task of inducing word translations from monolingual corpora of two languages.Recent methods are mostly based on unsupervised cross-lingual word embeddings, the key to which is to find initial solutions of word translations, followed by the learning and refinement of mappings between the embedding spaces of two languages.However, previous methods find initial solutions just based on word-level information, which may be (1) limited and inaccurate, and (2) prone to contain some noise introduced by the insufficiently pre-trained embeddings of some words.To deal with those issues, in this paper, we propose a novel graph-based paradigm to induce bilingual lexicons in a coarse-to-fine way.We first build a graph for each language with its vertices representing different words.Then we extract word cliques from the graphs and map the cliques of two languages.Based on that, we induce the initial word translation solution with the central words of the aligned cliques.This coarse-to-fine approach not only leverages clique-level information, which is richer and more accurate, but also effectively reduces the bad effect of the noise in the pre-trained embeddings.Finally, we take the initial solution as the seed to learn cross-lingual embeddings, from which we induce bilingual lexicons.Experiments show that our approach improves the performance of bilingual lexicon induction compared with previous methods.
ESPRIT: Explaining Solutions to Physical Reasoning Tasks

Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training.We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events.We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach.Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions.Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations.Dataset, code and documenta-
Sentiment Analysis Of Cyberbullying On Twitter Using SentiStrength

Cyberbullying is a form of bullying that takes place across virtually every social media platform. Twitter is a form of social media that allows users to exchange information. Bullying has been a growing problem on Twitter over the past few years. Sentiment analysis is done to identify the element of bullying in a tweet. Sentiments are divided into 3 classes, namely Bullying, Non-Bullying and neutral. There are three steps to classify cyberbullying i.e. collection of data set, preprocessing data, and classification process. This research used sentiStrength, an algorithm which uses a lexicon based approach. This SentiStrength lexicon contains the weight of its sentiment strength. The assessment results from 454 tweets data obtained 161 tweet non-bullying (35.4%), 87 tweet neutral (19.1%), and 206 tweet bullying (45.4%). This research produces an accuracy value of 60.5%.
I4R: Promoting Deep Reinforcement Learning by the Indicator for Expressive Representations

Learning expressive representations is always crucial for well-performed policies in deep reinforcement learning (DRL). Different from supervised learning, in DRL, accurate targets are not always available, and some inputs with different actions only have tiny differences, which stimulates the demand for learning expressive representations. In this paper, firstly, we empirically compare the representations of DRL models with different performances. We observe that the representations of a better state extractor (SE) are more scattered than a worse one when they are visualized. Thus, we investigate the singular values of representation matrix, and find that, better SEs always correspond to smaller differences among these singular values. Next, based on such observations, we define an indicator of the representations for DRL model, which is the Number of Significant Singular Values (NSSV) of a representation matrix. Then, we propose I4R algorithm, to improve DRL algorithms by adding the corresponding regularization term to enhance the NSSV. Finally, we apply I4R to both policy gradient and value based algorithms on Atari games, and the results show the superiority of our proposed method.
Image Quality Assessment Using Kernel Sparse Coding

One key in image quality assessment (IQA) is the design of image representations that can capture the changes of image structures caused by distortions. Recent studies show that sparse coding has emerged as a promising approach to analyzing image structures for IQA. However, existing sparse-coding-based IQA approaches use linear coding models, which ignore the nonlinearities of manifolds of image patches and thus cannot analyze complex image structures well. To overcome such a weakness, in this paper, we introduce nonlinear sparse coding to IQA. A kernel dictionary construction scheme is proposed, which combines analytic dictionaries and learnable dictionaries to guarantee both the stability and effectiveness of kernel sparse coding in the context of IQA. Built upon the kernel dictionary construction, an effective full-reference IQA metric is developed. Benefiting from the considerations on nonlinearities during sparse coding, the proposed IQA metric not only characterizes image distortions better, but also achieves improvement on the consistency with subjective perception, when compared to the metrics built upon linear sparse coding. Such benefits are demonstrated with the experimental results on eight benchmark datasets in terms of common criteria.
Mutual Information Loss in Pyramidal Image Processing

Gaussian and Laplacian pyramids have long been important for image analysis and compression. More recently, multiresolution pyramids have become an important component of machine learning and deep learning for image analysis and image recognition. Constructing Gaussian and Laplacian pyramids consists of a series of filtering, decimation, and differencing operations, and the quality indicator is usually mean squared reconstruction error in comparison to the original image. We present a new characterization of the information loss in a Gaussian pyramid in terms of the change in mutual information. More specifically, we show that one half the log ratio of entropy powers between two stages in a Gaussian pyramid is equal to the difference in mutual information between these two stages. We show that this relationship holds for a wide variety of probability distributions and present several examples of analyzing Gaussian and Laplacian pyramids for different images.
Improving the Gating Mechanism of Recurrent Neural Networks

Gating mechanisms are widely used in neural network models, where they allow gradients to backpropagate more easily through depth or time. However, their saturation property introduces problems of its own. For example, in recurrent models these gates need to have outputs near 1 to propagate information over long time-delays, which requires them to operate in their saturation regime and hinders gradient-based learning of the gate mechanism. We address this problem by deriving two synergistic modifications to the standard gating mechanism that are easy to implement, introduce no additional hyperparameters, and improve learnability of the gates when they are close to saturation. We show how these changes are related to and improve on alternative recently proposed gating mechanisms such as chrono initialization and Ordered Neurons. Empirically, our simple gating mechanisms robustly improve the performance of recurrent models on a range of applications, including synthetic memorization tasks, sequential image classification, language modeling, and reinforcement learning, particularly when long-term dependencies are involved.
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent

Contextualized language modeling using deep Transformer networks has been applied to a variety of natural language processing tasks with remarkable success. However, we find that these models are not a panacea for a question-answering dialogue agent corpus task, which has hundreds of classes in a long-tailed frequency distribution, with only thousands of data points. Instead, we find substantial improvements in recall and accuracy on rare classes from a simple one-layer RNN with multi-headed self-attention and static word embeddings as inputs. While much research has used attention weights to illustrate what input is important for a task, the complexities of our dialogue corpus offer a unique opportunity to examine how the model represents what it attends to, and we offer a detailed analysis of how that contributes to improved performance on rare classes. A particularly interesting phenomenon we observe is that the model picks up implicit meanings by splitting different aspects of the semantics of a single word across multiple attention heads.
Exploring Bilingual Parallel Corpora for Syntactically Controllable Paraphrase Generation

Paraphrase generation is of great importance to many downstream tasks in natural language processing. Recent efforts have focused on generating paraphrases in specific syntactic forms, which, generally, heavily relies on manually annotated paraphrase data that is not easily available for many languages and domains. In this paper, we propose a novel end-to-end framework to leverage existing large-scale bilingual parallel corpora to generate paraphrases under the control of syntactic exemplars. In order to train one model over the two languages of parallel corpora, we embed sentences of them into the same content and style spaces with shared content and style encoders using cross-lingual word embeddings. We propose an adversarial discriminator to disentangle the content and style space, and employ a latent variable to model the syntactic style of a given exemplar in order to guide the two decoders for generation. Additionally, we introduce cycle and masking learning schemes to efficiently train the model. Experiments and analyses demonstrate that the proposed model trained only on bilingual parallel data is capable of generating diverse paraphrases with desirable syntactic styles. Fine-tuning the trained model on a small paraphrase corpus makes it substantially outperform state-of-the-art paraphrase generation models trained on a larger paraphrase dataset.
G2RL: Geometry-Guided Representation Learning for Facial Action Unit Intensity Estimation

Facial action unit (AU) intensity estimation aims to measure the intensity of different facial muscle movements. The external knowledge such as AU co-occurrence relationship is typically leveraged to improve performance. However, the AU characteristics may vary among individuals due to different physiological structures of human faces. To this end, we propose a novel geometry-guided representation learning (G2RL) method for facial AU intensity estimation. Specifically, our backbone model is based on a heatmap regression framework, where the produced heatmaps reflect rich information associated with AU intensities and their spatial distributions. Besides, we incorporate the external geometric knowledge into the backbone model to guide the training process via a learned projection matrix. The experimental results on two benchmark datasets demonstrate that our method is comparable with the state-of-the-art approaches, and validate the effectiveness of incorporating external geometric knowledge for facial AU intensity estimation.
Typechecking Java Protocols with [St]Mungo

This is a tutorial paper on [St]Mungo, a toolchain based on multiparty session types and their connection to typestates for safe distributed programming in Java language. The StMungo (“Scribble-to-Mungo”) tool is a bridge between multiparty session types and typestates. StMungo translates a communication protocol, namely a sequence of sends and receives of messages, given as a multiparty session type in the Scribble language, into a typestate specification and a Java API skeleton. The generated API skeleton is then further extended with the necessary logic, and finally typechecked by Mungo. The Mungo tool extends Java with (optional) typestate specifications. A typestate is a state machine specifying a Java object protocol, namely the permitted sequence of method calls of that object. Mungo statically typechecks that method calls follow the object’s protocol, as defined by its typestate specification. Finally, if no errors are reported, the code is compiled with javac and run as standard Java code. In this tutorial paper we give an overview of the stages of the [St]Mungo toolchain, starting from Scribble communication protocols, translating to Java classes with typestates, and finally to typechecking method calls with Mungo. We illustrate the [St]Mungo toolchain via a real-world case study, the HTTP client-server request-response protocol over TCP. During the tutorial session, we will apply [St]Mungo to a range of examples having increasing complexity, with HTTP being one of them.
MEC‐enabled video streaming in device‐to‐device networks

By offloading video streaming from the centralised cloud to the edge, mobile edge computing (MEC) servers offer new opportunities for real-time video transmission. Deploying on the edge of users can ensure low latency transmission, however, the limited storage and computing ability cannot adapt to the currently used video transmission technologies such as video transcoding or simulcast. To solve this problem, a more flexible video transmission architecture needs to be considered. Under this motivation, the authors propose a device-to-device (D2D) assisted video streaming scheme, which fuses the technical advantages of MEC and scalable video coding. Specifically, they first construct a novel architecture for delay-sensitive live video streaming services in edge-enabled wireless heterogeneous networks named MEC-enabled goodput-aware (MEGA) model. Then they present a mathematical formulation for optimising the aggregation goodput performance of video traffic including both cellular and D2D links. Finally, they derive a three-step solution based on a distributed heuristic algorithm. Numerical simulation results show that MEGA outperforms existing models in terms of goodput, end-to-end delay, effective loss rate, and users' quality-of-experience.
Acehnese traditional clothing recognition based on augmented reality using hybrid tracking method

Aceh's traditional clothing is cultural heritage in Indonesia. In today's, the problem faced is the lack of media to introduce cultural heritage in Aceh. Therefore was formed a media which could recognition Acehnese traditional clothing using Augmented Reality technology. Augmented Reality technology is used to users can add virtual objects to real environments. In this research, the method used is the Hybrid Tracking method, a Hybrid Tracking method is a combination tracking system of two or more tracking techniques, hybrid tracking is used to get a better tracking system and to get tracking speeds based on better distances. The results showed that the reading of markers by the cameras was greatly influenced by distance, light intensity, marker angle, and size of the device's RAM. The closest distance from the marker to the camera that can display 3D objects is 20 cm while the farthest distance cannot display 3D objects is 100 cm. And the angle of reading the marker is only around 0° an angle of 45°. The average distance that can detect markers is 0.5 m, the average time that can be detected is 0.068 sec, and the average speed obtained is 7.505 m / sec.
Staying at the Edge of Privacy: Edge Computing and Impersonal Extraction

From self-driving cars to smart city sensors, billions of devices will be connected to networks in the next few years. These devices will collect vast amounts of data which needs to be processed in real-time, overwhelming centralized cloud architectures. To address this need, the industry seeks to process data closer to the source, driving a major shift from the cloud to the ‘edge.’ This article critically investigates the privacy implications of edge computing. It outlines the abilities introduced by the edge by drawing on two recently published scenarios, an automated license plate reader and an ethnic facial detection model. Based on these affordances, three key questions arise: what kind of data will be collected, how will this data be processed at the edge, and how will this data be ‘completed’ in the cloud? As a site of intermediation between user and cloud, the edge allows data to be extracted from individuals, acted on in real-time, and then abstracted or sterilized, removing identifying information before being stored in conventional data centers. The article thus argues that edge affordances establish a fundamental new ‘privacy condition’ while sidestepping the safeguards associated with the ‘privacy proper’ of personal data use. Responding effectively to these challenges will mean rethinking person-based approaches to privacy at both regulatory and citizen-led levels.
Multitask Multisource Deep Correlation Filter for Remote Sensing Data Fusion

With the amount of remote sensing data increasing at an extremely fast pace, machine learning-based technique has been shown to perform superiorly in many applications. However, most of the existing methods in the real-time application are based on single modal image data. Although a few approaches use the different source images to represent the object via a fusion scheme, it may not be appropriate for multimodality information processing. In addition, these methods hardly benefit from the end-to-end network training due to the limitations of implementation difficulty and computational cost. In this article, we propose a multitask multisource information fusion method in the deep learning and correlation filter frameworks, which is applied to the fields of tracking and remote sensing data processing. The contribution of individual layers from different source data inside the deep network model is considered as a task. The proposed method can employ interdependencies among different sources data and tasks to learn deep network parameters and filters jointly to improve the performance. Second, we present an effective object appearance selection scheme to adaptively capture the object appearance changes via an effective deep learning network, then integrating information from different modalities to achieve information fusion. Different source information can provide robust performance from different aspects with complementary properties. Third, we further extend the proposed approach to the field of remote sensing for semantic labeling. The layers’ sensitivity is utilized to verify the robustness of different classes. Extensively experiments on five benchmarks show that the proposed approach performs favorably against the state-of-the-arts.
APPLYING THE CBB21 PHASE 2 METHOD FOR SECURING TWITTER ANALYSED DATA

In this paper, we show that the power function of sub E-function f n (x) is sub E-function.Furthermore, we establish some new integral inequalities of Hadamard type involving sub E-functions and concave E-functions.
Causal Knowledge Extraction through Large-Scale Text Mining

In this demonstration, we present a system for mining causal knowledge from large corpuses of text documents, such as millions of news articles. Our system provides a collection of APIs for causal analysis and retrieval. These APIs enable searching for the effects of a given cause and the causes of a given effect, as well as the analysis of existence of causal relation given a pair of phrases. The analysis includes a score that indicates the likelihood of the existence of a causal relation. It also provides evidence from an input corpus supporting the existence of a causal relation between input phrases. Our system uses generic unsupervised and weakly supervised methods of causal relation extraction that do not impose semantic constraints on causes and effects. We show example use cases developed for a commercial application in enterprise risk management.
Image Captioning using Deep Learning

Image captioning is a complicated research area of Arti cial Intel-ligence (AI) which requires a functional and robust model that generates a caption for any image. Image captioning is a fundamental task which requires not only semantic understanding of images but also the interactions between the objects present in the image.Another task is to understand the visual lan-guage dynamics and to translate these relations into sensible captions . In this paper , we have proposed an architecture employing the use of multilayer Convolutional Neural Network (CNN) which is used for image processing and extracting features from the image. It generates an embedding which repre-sents the image which is passed to Long Short TermMemory (LSTM) to accurately structure meaningful sentences using the generated keywords .We showcase the accuracy of our model using Flickr30K dataset which contains 31,783 images with 5 captions for each image.We show that our model gives better results using the bleu metric.
Different Strategies of Fitting Logistic Regression for Positive and Unlabelled Data

In the paper we revisit the problem of fitting logistic regression to positive and unlabelled data. There are two key contributions. First, a new light is shed on the properties of frequently used naive method (in which unlabelled examples are treated as negative). In particular we show that naive method is related to incorrect specification of the logistic model and consequently the parameters in naive method are shrunk towards zero. An interesting relationship between shrinkage parameter and label frequency is established. Second, we introduce a novel method of fitting logistic model based on simultaneous estimation of vector of coefficients and label frequency. Importantly, the proposed method does not require prior estimation, which is a major obstacle in positive unlabelled learning. The method is superior in predicting posterior probability to both naive method and weighted likelihood method for several benchmark data sets. Moreover, it yields consistently better estimator of label frequency than other two known methods. We also introduce simple but powerful representation of positive and unlabelled data under Selected Completely at Random assumption which yields straightforwardly most properties of such model.