Reviewerly/exHarmony huggingface dataset:
Hierarchical Task Planning from Object Goal State for Human-Assist Robot

Task planning is a very important ability for human-assist robots that are used for helping humans with their housework. Given a complex and unseen task, these robots must plan and take appropriate actions according to the situation at that time in the human living environment. In this work, we focus on a method for task planning to learn a policy to hierarchically decompose a given task from the object goal state and the observation at that time. In the learning-based design of a policy for task planning, the optimal policy can be acquired from demonstrations without requiring to explicitly define the precondition. And we think that Hierarchical policy, in which a given task is decomposed into sub-tasks, makes it easier to learn the policy because the number of choices of actions becomes smaller by decomposing a given task into sub-tasks. Existing methods to hierarchically decompose a given task require the demonstration of the task to be executed, but our method does not require a demonstration for unseen task. In order to enable to hierarchically decompose the task from the object goal state, we split the model into a module to decompose the task and a module to predict the argument which is necessary for the action, and use the hierarchical structure of the task in predicting the argument. Having learned the policy, we carry out the execution of the task. We demonstrate the effectiveness of our method on the BlockStacking and Table-Arrange tasks.
Structured Binary Neural Networks for Image Recognition

In this paper, we propose to train binarized convolutional neural networks (CNNs) that are of significant importance for deploying deep learning to mobile devices with limited power capacity and computing resources. Previous works on quantizing CNNs often seek to approximate the floating-point information of weights and/or activations using a set of discrete values. Such methods, termed value approximation here, typically are built on the same network architecture of the full-precision counterpart. Instead, we take a new "structured approximation" view for network quantization — it is possible and valuable to exploit flexible architecture transformation when learning low-bit networks, which can achieve even better performance than the original networks in some cases. In particular, we propose a "group decomposition" strategy, termed GroupNet, which divides a network into desired groups. Interestingly, with our GroupNet strategy, each full-precision group can be effectively reconstructed by aggregating a set of homogeneous binary branches. We also propose to learn effective connections among groups to improve the representation capability. To improve the model capacity, we propose to dynamically execute sparse binary branches conditioned on input features while preserving the computational cost. More importantly, the proposed GroupNet shows strong flexibility for a few vision tasks. For instance, we extend the GroupNet for accurate semantic segmentation by embedding the rich context into the binary structure. The proposed GroupNet also shows strong performance on object detection. Experiments on image classification, semantic segmentation, and object detection tasks demonstrate the superior performance of the proposed methods over various quantized networks in the literature. Moreover, the speedup and runtime memory cost evaluation comparing with related quantization strategies is analyzed on GPU platforms, which serves as a strong benchmark for further research.
A novel method to estimate Height, Weight and Body Mass Index from face images

Body Mass Index (BMI) is the most commonly used tool to evaluate an individual's health. It is used to classify a person as underweight, healthy weight, overweight or obese. BMI is co-related with body fat and is a vital indicator of possible diseases that can transpire with higher body fat ranges. Higher body fat is prevalent these days with a higher calorie diet and a low physical activity lifestyle. On the other end of the spectrum, Adult malnutrition is more common and widespread than we are conscious of these days. The BMI can be used as a measure of adult nutritional status, both of individuals and of communities. Given that people have less time in their busy life and most people dont own a weighing machine and/or a measuring tape, we propose a time and cost efficient method of estimating Height, Weight and BMI from a persons face. In this paper, we propose a novel model using Convolution Neural Networks (CNN) and Artificial Neural Networks (ANN). We start by detecting the face from an image using the Viola-Jones algorithm. The image is fed to the Feature Extractor model. The extracted features are passed to an Artificial Neural Network (ANN) model which gives the predicted Height, Weight and BMI values. We have evaluated our model on the Reddit-HWBMI dataset and Face-to-BMI dataset. We propose a novel dataset, the Reddit-HWBMI dataset which contains 982 subjects with their corresponding Height, Weight, BMI, Gender and Age. The best performance for BMI was given by the XceptionNet model when used as a Feature Extractor. The XceptionNet also performed best for weight, whereas VGG-Face (Resnet model) performed slightly better than XceptionNet for height.
Clear-sky direct normal irradiance estimation based on adjustable inputs and error correction

The accurate estimation of direct normal irradiance (DNI) under clear sky conditions plays an important role in the concentrated solar thermal plant. A hybrid model with adjustable inputs is proposed to calculate the clear-sky DNI, including a base clear-sky model and an error-correction model. The base clear-sky model is able to estimate the clear-sky DNI at any place with only the local date and location information, and the error-correction model serves as a supplementary to improve the calculating accuracy with available meteorological data. The error-correction model effectively integrates a linear part and a nonlinear part, and its inputs are adjustable according to the available meteorological observations. Several experiments have been conducted to evaluate the performance of the proposed model with data from three observation stations provided by the National Renewable Energy Laboratory open database. The results show that the hybrid model is able to provide great improvement over the base clear-sky model with 28%–70% on normalized root mean square error, and it also performs better than those using a linear or nonlinear error correction model. It is concluded that the performance of the hybrid model is comparable with other published methods in calculating the clear-sky DNI with concrete statistics.
Multi-head attention model for aspect level sentiment analysis

Aspect level sentiment classification task requires topical polarity classification for different description aspect. There is a polysemy in the same vocabulary, and the emotional polarity is different for different objects. Word embedding can capture semantic information but cannot adapt to the po lysemy. Attention mechanism has achieved good performance in the above tasks; however, it is only able to get the degree of association between words and unable to get detailed descriptions. In this paper, the ELMOs model is used to adjust the polysemy of the word. The Transformer model is used to extract the features with the highest degree of relevance to the target object for emotional polarity classification. Our work contribution is to overcome the polysemy interference, and use the attention mechanism to model the network relationship between words, so that the model can extract important classification features according to different target words. Experiments on laptop and restaurant datasets demonstrate that our approach achieves a new state-of-the-art performance on a few benchmarks.
The CNN-Corpus

This paper details the features and the methodology adopted in the construction of the CNN-corpus, a test corpus for single document extractive text summarization of news articles. The current version of the CNN-corpus encompasses 3,000 texts in English, and each of them has an abstractive and an extractive summary. The corpus allows quantitative and qualitative assessments of extractive summarization strategies.
Comparing CNNs for non-conventional traffic participants

This paper investigates performance of three state-of-the-art pretrained Convolutional Neural Network (CNN) models in terms of accuracy and latency for on and off-road obstacle detection in context of autonomous vehicle in Indian road. We investigated performance of Mask R-CNN, RetinaNet, and YOLOv3 on publicly available Indian road dataset. We evaluated accuracy and latency of these models on novel classes of objects such as animals, autorickshaws, caravan. Our results show that accuracy of Mask R-CNN is significantly higher than YOLOv3 and RetinaNet. We have also found Yolov3 is significantly higher than RetinaNet. We have also tested latency of the CNN models and found that latency of YOLOv3 is significantly lower than other two models and RetinaNet is significantly faster than Mask R-CNN. Finally, we have proposed an expert system to integrate environment parameters inside car along with outside car obstacles detected by YOLOv3 to estimate cognitive load of co-passengers of autonomous vehicle.
Uncertainty Estimation for Black-Box Classification Models: A Use Case for Sentiment Analysis

With the advent of new pre-trained word embedding models like ELMO, GPT or BERT, that leverage transfer-learning to deliver high-quality prediction systems, natural language processing (NLP) methods are reaching or even overtaking human baselines in some applications. The basic principle of these successful models is to train a model to solve a given NLP task, mainly Language Modelling, using significant volumes of data like the whole Wikipedia. The model is then fine-tuned to solve another NLP task, requiring fewer domain-specific data to achieve state-of-the-art accuracies. The method proposed in the present work assists the practitioner in evaluating the quality of the transferred classification models when applied to new data domains. In this case, we consider the original model as a black box. No matter how complex the original model may be, the method only requires access to the output layer to train a measure of the uncertainty associated with the predictions of the original model. This measure of uncertainty is a measure of how well the black-box model accommodates to the new data. Later on, we show how a rejection system can use this uncertainty to improve its accuracy, effectively enabling the practitioner to find the best trade-off between the quality of the model and the number of rejected cases.
Value-Dependent Information-Flow Security on Weak Memory Models

Weak memory models implemented on modern multicore processors are known to affect the correctness of concurrent code. They can also affect whether or not it is secure. This is particularly the case in programs where the security levels of variables are value-dependent, i.e., depend on the values of other variables. In this paper, we illustrate how instruction reordering allowed by contemporary multicore processors leads to vulnerabilities in such programs, and present a compositional, timing-sensitive information-flow logic which can be used to detect such vulnerabilities. The logic allows step-local reasoning (one instruction at a time) about a thread's security by tracking information about dependencies between instructions which guarantee their order of occurrence. Program security can then be established from individual thread security using rely/guarantee reasoning.
Position-Aware Deep Character-Level CTR Prediction for Sponsored Search

Predicting the click-through rate of an advertisement is a critical component of online advertising platforms. In sponsored search, the click-through rate estimates the probability that a displayed advertisement is clicked by a user after she submits a query to the search engine. Commercial search engines typically rely on machine learning models trained with a large number of features to make such predictions. This inevitably requires a lot of engineering efforts to define, compute, and select the appropriate features. In this paper, we propose two novel approaches (one working at character level and the other working at word level) that use deep convolutional neural networks to predict the click-through rate of a query-advertisement pair. Specifically, the proposed architectures consider as input only the textual content appearing in a query-advertisement pair and the page position at which the advertisement appears on the search result page of the query, and produce as output a click-through rate prediction. By comparing the character-level model with the word-level model, we show that language representation can be learnt from scratch at character level when trained on enough data. Through extensive experiments using billions of query-advertisement pairs of a popular commercial search engine, we demonstrate that both approaches significantly outperform a baseline model built on well-selected text features and a state-of-the-art word2vec-based approach. We also show the importance of the position feature in the proposed approaches in improving the prediction accuracy. When combining the predictions of the deep models introduced in this study with the prediction of the model in production of the same commercial search engine, we significantly improve the accuracy and the calibration of the click-through rate prediction of the production system. We also show the potential of leveraging the CTR prediction of the proposed deep learning models for query-ad relevance modeling and query-ad matching tasks in sponsored search.
Attention based Residual-Time Delay Neural Network for Indian Language Identification

India is a multilingual society having more than 1600 languages. Most of these languages are having an overlapping set of phonemes. This makes developing language identification (LID) framework difficult for Indian languages. In this paper, the above challenge is addressed using phonetic features. To model the temporal variations in phonetic features, attention based residual-time delay neural network (RES-TDNN) is proposed. This network effectively captures long-range temporal dependencies through TDNN and attention mechanism. The proposed network has been evaluated on IIITH-ILSC database using phonetic and acoustic features. The database consists of 22 official Indian languages and Indian English. Attention based RES-TDNN outperformed the other state-of-the-art networks such as deep neural network, long short-term memory network and produced an equal error rate of 9.46%. Further, the fusion of shifted delta cepstral and phonetic features have improved the performance.
Movie Genre in Multi-label Classification Using Semantic Extraction from Only Movie Poster

In this paper, we present the framework, SEMPD (the Semantic Extraction of Movie poster based on fundamental of poster Design) for multi-label genre classification in the state of insufficient data, included only movie poster. In order to get manageable semantic tags, we use a combination of fundamental movie poster design in order to understand the result of movie genre. With the collection of a movie poster, we decompose into twelve meaningful features. Then, eight-teen genres are classified by using multi-label prediction algorithms. The results proved that our semantic features perform better than without any information. The experiment has shown that applying Multinomial Naïve Bayes with Label Power Set can perform Jaccard score 41.78% compared with baseline 1.11% without any meaningful information. On the other hand, a movie poster can be collected with more manageable features in order to enhance multi-label genre classification.
Blockchain-enabled workflow management system for fine-grained resource sharing in E-commerce logistics

E-commerce logistics is on the rise with the rapid development of logistics industry. Facing the fierce competition, the improvement of logistics service becomes necessity for service providers to gain competitiveness in the market. However, the heterogeneous requirements from different service demanders place great challenges for the E-commerce logistics service providers. The most puzzled one is to prevent fragmentation of logistics resources to serve different demanders (customers). This phenomenon of resource fragmentation is aggravated by the implementation of ogies or management requirements. It restricts the liquidity of logistics resource to serve different kinds of demanders. Hence, to break this limitation and achieve central coordination of logistics resources to satisfy heterogeneous requests from demanders becomes an imperative requirement for E-commerce logistics service provider. Therefore, this paper proposed a blockchain-enabled workflow management system (BCWMS) to centrally share heterogeneous logistics resources with different customers. Three key innovative technologies are involved to empower BCWMS. The first one is gateway technology introduced to realize the UPnP (Universal Plug and Play) management of heterogeneous logistics resources. Secondly, to effectively and efficiently coordinate logistics resources, a workflow operating platform is designed to suit different business logic. Additionally, a resource blockchain is designed with the integration of blockchain technology with agent technology to guarantee the data reliability to provide accurate front-line resource execution data for different customers to conduct higher decision-makings.
A Comparison of Performance Metrics with Severely Imbalanced Network Security Big Data

Severe class imbalance between the majority and minority classes in large datasets can prejudice Machine Learning classifiers toward the majority class. Our work uniquely consolidates two case studies, each utilizing three learners implemented within an Apache Spark framework, six sampling methods, and five sampling distribution ratios to analyze the effect of severe class imbalance on big data analytics. We use three performance metrics to evaluate this study: Area Under the Receiver Operating Characteristic Curve, Area Under the Precision-Recall Curve, and Geometric Mean. In the first case study, models were trained on one dataset (POST) and tested on another (SlowlorisBig). In the second case study, the training and testing dataset roles were switched. Our comparison of performance metrics shows that Area Under the Precision-Recall Curve and Geometric Mean are sensitive to changes in the sampling distribution ratio, whereas Area Under the Receiver Operating Characteristic Curve is relatively unaffected. In addition, we demonstrate that when comparing sampling methods, borderline-SMOTE2 outperforms the other methods in the first case study, and Random Undersampling is the top performer in the second case study.
Latent Attribute Based Hierarchical Decoder for Neural Machine Translation

Neural machine translation (NMT) has achieved state-of-the-art performance in many translation tasks. However, because the computational cost increases with the size of the search space for predicting the target words, the translation quality of NMT is constrained by the limited vocabulary. To alleviate this problem, we propose a novel dynamic hierarchical decoder for NMT to utilize all of the target words in the training and decoding process. In the proposed model, a target word is represented by two latent attribute vectors rather than a word vector. The model is trained to dynamically put together those words that share similar linguistic attributes. The prediction of a target word is, therefore, turned into the prediction of attribute vectors, where the $\mathrm{softmax}$ functions are performed at the attribute level. This greatly reduces the model size and the decoding time. Our experimental results demonstrate that the proposed model significantly outperforms the NMT baselines in both Chinese-English and English-German translation tasks.
Enhancing Text Mining Using Deep Learning Models

Text mining, a section of the synthetic intelligence, is gaining grounds nowadays in terms of the applications in business and analysis. Varied sectors and domains across industries understand the potential of text mining in gaining information, mining helpful data and in enhancing the choice creating method in terms of speed and potency. In today's world, where the web area is overflowing with data, text mining technology and solutions will persuade be the turning purpose. Most of the recent researches conducted during this space focused principally on advanced or the hybrid of deep neural networks so as to induce economical and higher results. Functioning on such serious models isn't solely time taking however conjointly needs the usage of heap of resources. Therefore, to get the comparable results only basic deep learning models have been used in order to minimize the model's complexity and computational cost. For the same, RNN and LSTM based models have been used and accuracy of proposed models have been enhanced by by varying the hyper parameters and using Glove word embeddings. Moreover, a labeled dataset named Sentiment5k has also been created using semi-supervised learning approach for evaluating the performance of RNN & LSTM based models.
Some new forms of permutation polynomials based on the AGW criterion

In this paper, we propose several classes of permutation polynomials over F22m of the form x+(Trmn(x)k+δ)s and x+(Trmn(x)k1+δ1)s1+(Trmn(x)k2+δ2)s2 based on the AGW criterion.
TEE-Based Distributed Watchtowers for Fraud Protection in the Lightning Network

The Lightning Network is a payment channel network built on top of the cryptocurrency Bitcoin. It allows Bitcoin to scale by performing transactions off-chain to reduce load on the blockchain. Malicious payment channel participants can try to commit fraud by closing channels with outdated balances. The Lightning Network allows resolving this dispute on the blockchain. However, this mechanism forces the channels' participants to watch the blockchain in regular intervals. It has been proposed to offload this monitoring duty to a third party, called a watchtower. However, existing approaches for watchtowers do not scale as they have storage requirements linear in the number of updates in a channel. In this work, we propose TEE Guard, a new architecture for watchtowers that leverages the features of Trusted Execution Environments to build watchtowers that require only constant memory and are thus able to scale. We show that TEE Guard is deployable because it can run with the existing Bitcoin and Lightning Network protocols. We also show that it is economically viable for a third party to provide watchtower services. As a watchtower needs to be trusted to be watching the blockchain, we also introduce a mechanism that allows customers to verify that a watchtower has been running continuously.
Security of Verifiable Threshold Quantum Secret Sharing With Sequential Communication

A verifiable (t, n) threshold quantum secret sharing scheme with sequential communication was proposed recently. In this work, we analyze its security and then give two new participant attacks. Using the first participant attack, the first participant can obtain the dealer's secrets by himself with nonzero probability without being detected. Using the second participant attack, a dishonest participant can gain access to the dealer's secrets by himself in the secret reconstruction phase while he can make the other participants recover false secrets instead of the real ones without being detected. Furthermore, we present an effective way to prevent these attacks.