Reviewerly/exHarmony huggingface dataset:
Mobile access and flexible search over encrypted cloud data in heterogeneous systems

Abstract Never before has data sharing been more convenient with the rapid popularity and wide adoption of cloud computing. However, mobile access and flexible search with security for outsourced data in heterogeneous systems are two major obstacles prior to practical deployment of cloud computing. To meet the requirements in secure mobile access and flexible search to sensitive data, this paper, for the first time, proposes a versatile primitive referred to as an identity based proxy re-encryption system with outsourced equality test (IBPRE-ET). This primitive allows a data owner in an identity based broadcast encryption system (IBBE) seamlessly to share his/her data with a data sharer in an identity based encryption system (IBE). Moreover, it supports a data sharer in an IBBE system to perform search functionality on ciphertexts related to a data sharer in an IBE system. We also formally prove that the proposed IBPRE-ET system is selective secure using a random oracle model. Meanwhile, the theoretic evaluation and experimental result demonstrate our scheme is practical in the heterogeneous system. Finally, we show an application of our IBPRE-ET to the collaborative office automation system.
Attention-guided Low-light Image Enhancement.

Low-light image enhancement is challenging in that it needs to consider not only brightness recovery but also complex issues like color distortion and noise, which usually hide in the dark. Simply adjusting the brightness of a low-light image will inevitably amplify those artifacts. To address this difficult problem, this paper proposes a novel end-to-end attention-guided method based on multi-branch convolutional neural network. To this end, we first construct a synthetic dataset with carefully designed low-light simulation strategies. The dataset is much larger and more diverse than existing ones. With the new dataset for training, our method learns two attention maps to guide the brightness enhancement and denoising tasks respectively. The first attention map distinguishes underexposed regions from well lit regions, and the second attention map distinguishes noises from real textures. With their guidance, the proposed multi-branch decomposition-and-fusion enhancement network works in an input adaptive way. Moreover, a reinforcement-net further enhances color and contrast of the output image. Extensive experiments on multiple datasets demonstrate that our method can produce high fidelity enhancement results for low-light images and outperforms the current state-of-the-art methods by a large margin both quantitatively and visually.
Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics

If we wish to embed assessment for accountability within instruction, we need to better understand the relative contribution of different types of learner data to statistical models that predict scores and discrete achievement levels on assessments used for accountability purposes. The present work scales up and extends predictive models of math test scores and achievement levels from existing literature and specifies six categories of models that incorporate information about student prior knowledge, socio-demographics, and performance within the MATHia intelligent tutoring system. Linear regression, ordinal logistic regression, and random forest regression and classification models are learned within each category and generalized over a sample of 23,000+ learners in Grades 6, 7, and 8 over three academic years in Miami-Dade County Public Schools. After briefly exploring hierarchical models of this data, we discuss a variety of technical and practical applications, limitations, and open questions related to this work, especially concerning to the potential use of instructional platforms like MATHia as a replacement for time- consuming standardized tests.
Identity-Based Auditing for Shared Cloud Data With Efficient and Secure Sensitive Information Hiding

The advent of cloud computing arouses the flourish of data sharing, promoting the development of research, especially in the fields of data analysis, artificial intelligence, etc. In order to address sensitive information hiding, auditing shared data efficiently and malicious manager preventing, we propose an identity-based auditing scheme for shared cloud data with a secure mechanism to hide sensitive information. This scheme provides a solution that allows users to share plaintext with researchers and keeps sensitive information invisible to the cloud and researchers at the same time. Besides, a formal security analysis is given to prove the strong security of the proposed scheme. Performance evaluation and experimental results demonstrate that our scheme is significantly more efficient over the existing scheme due to our novel mechanism for sensitive information hiding and simplifying signature algorithm. Compared to the existing approach to audit the integrity of shared data with sensitive information hiding, our scheme has desirable features and advantages as follow. Firstly, previous work has failed to construct a secure scheme to prevent malicious manager. We fill this gap and guarantee the integrity and authenticity of shared data. Secondly, our scheme constructs a novel system model to support high concurrency and massive data in the real scenario.
Flexible Representative Democracy: An Introduction with Binary Issues

We introduce Flexible Representative Democracy (FRD), a novel hybrid of Representative Democracy (RD) and Direct Democracy (DD), in which voters can alter the issue-dependent weights of a set of elected representatives. In line with the literature on Interactive Democracy, our model allows the voters to actively determine the degree to which the system is direct versus representative. However, unlike Liquid Democracy, FRD uses strictly non-transitive delegations, making delegation cycles impossible, preserving privacy and anonymity, and maintaining a fixed set of accountable elected representatives. We present FRD and analyze it using a computational approach with issues that are independent, binary, and symmetric; we compare the outcomes of various democratic systems using Direct Democracy with majority voting and full participation as an ideal baseline. We find through theoretical and empirical analysis that FRD can yield significant improvements over RD for emulating DD with full participation.
DatalogMTL: Computational Complexity and Expressive Power

We study the complexity and expressive power of DatalogMTL - a knowledge representation language that extends Datalog with operators from metric temporal logic (MTL) and which has found applications in ontology-based data access and stream reasoning. We establish tight PSpace data complexity bounds and also show that DatalogMTL extended with negation on input predicates can express all queries in PSpace; this implies that MTL operators add significant expressive power to Datalog. Furthermore, we provide tight combined complexity bounds for the forward-propagating fragment of DatalogMTL, which was proposed in the context of stream reasoning, and show that it is possible to express all PSpace queries in the fragment extended with the falsum predicate.
An efficient method for pruning redundant negative and positive association rules

One of the most important problems that occur when mining positive and negative association rules (PNARs) is that the number of mined PNARs is usually large, which increases the difficulties that users retrieve decision-information. Those methods that prune redundant positive association rules (PARs) don't work when negative association rules (NARs) are taken into consideration. So in this paper, we first analyze what kinds of PNARs are redundant and then propose a novel method called LOGIC by using logical reasoning to prune redundant PNARs. In addition, we combine correlation coefficient and multiple minimum confidences (mc) to ensure that the mined PNARs are strongly correlated and their number can be flexibly controlled. Experimental results show that our method can prune up to 81.6% redundant PNARs. To the best of our knowledge, LOGIC is the first method to prune redundant PNARs simultaneously.
Learning Hierarchical Symbolic Representations to Support Interactive Task Learning and Knowledge Transfer

Interactive Task Learning (ITL) focuses on learning the definition of tasks through online natural language instruction in real time. Learning the correct grounded meaning of the instructions is difficult due to ambiguous words, lack of common ground, and the presence of distractors in the environment and the agent’s knowledge. We present a learning strategy embodied in an ITL agent that interactively learns in one shot the meaning of task concepts for 40 games and puzzles in ambiguous scenarios. Our approach learns hierarchical symbolic representations of task knowledge rather than learning a mapping directly from perceptual representations. These representations enable the agent to transfer and compose knowledge, analyze and debug multiple interpretations, and communicate efficiently with the teacher to resolve ambiguity. We evaluate the efficiency of the learning by examining the number of words required to teach tasks across cases of no transfer, positive transfer, and interference from prior tasks. Our results show that the agent can correctly generalize, disambiguate, and transfer concepts within variations in language descriptions and world representations of the same task, and across variations in different tasks.
Statistical learning for semantic parsing: A survey

A long-term goal of Artificial Intelligence (AI) is to provide machines with the capability of understanding natural language. Understanding natural language may be referred as the system must produce a correct response to the received input order. This response can be a robot move, an answer to a question, etc. One way to achieve this goal is semantic parsing. It parses utterances into semantic representations called logical form, a representation of many important linguistic phenomena that can be understood by machines. Semantic parsing is a fundamental problem in natural language understanding area. In recent years, researchers have made tremendous progress in this field. In this paper, we review recent algorithms for semantic parsing including both conventional machine learning approaches and deep learning approaches. We first give an overview of a semantic parsing system, then we summary a general way to do semantic parsing in statistical learning. With the rise of deep learning, we will pay more attention on the deep learning based semantic parsing, especially for the application of Knowledge Base Question Answering (KBQA). At last, we survey several benchmarks for KBQA.
Computer-vision analysis reveals facial movements made during Mandarin tone production align with pitch trajectories

Using computer-vision and image processing techniques, we aim to identify specific visual cues as induced by facial movements made during Mandarin tone production and examine how they are associated with each of the four Mandarin tones. Audio-video recordings of 20 native Mandarin speakers producing Mandarin words involving the vowel /3/ with each of the four tones were analyzed. Four facial points of interest were detected automatically: medial point of left eyebrow, nose tip (proxy for head movement), and midpoints of the upper and lower lips. The detected points were then automatically tracked in the subsequent video frames. Critical features such as the distance, velocity, and acceleration describing local facial movements with respect to the resting face of each speaker were extracted from the positional profiles of each tracked point. Analysis of variance and feature importance analysis based on random forest were performed to examine the significance of each feature for representing each tone and how well these features can individually and collectively characterize each tone. Results suggest alignments between articulatory movements and pitch trajectories, with downward or upward head and eyebrow movements following the dipping and rising tone trajectories respectively, lip closing movement being associated with the falling tone, and minimal movements for the level tone.
Collaborative Filtering Recommendation Algorithm for Heterogeneous Data Mining in the Internet of Things

With the popularization of Internet of Things (IOT) technology, a large number of multi-source heterogeneous data are constantly generated and collected by cloud platforms, which indicates that the problem of large data in IOT has become increasingly prominent, especially for massive tags and information in IOT which is urgent to use appropriate data mining algorithms to mine the value of these data. A collaborative filtering recommendation algorithm based on multi-information source fusion (CFR-MIF) is proposed where a feature vector and time weight function are introduced to improve the accuracy of top-N recommendation. It can conveniently and effectively process the IoT data, and furthermore integrate, manage and store the massive data collected from different industries and data formats. Besides, It also provides data mining services in the whole IoT realizing prediction and decision-making, which reverses control these sensor networks, so as to control the movement and development process of objective in the Internet of Things. The experimental results based on DeviceLens 1M data set show that the proposed algorithm greatly improves the accuracy of recommendation results, recall rate and F1 value compared with other advanced algorithms.
Deanonymizing Cryptocurrency With Graph Learning: The Promises and Challenges

The world economy is embracing the next generation currency, i.e., cryptocurrencies, which dates back to 2009 when Satoshi Nakamoto made Bitcoin publicly available. Rooted from the nature of decentralization and anonymity of blockchain, the cryptocurrencies have, unfortunately, been leveraged for illicit activities by the criminals. The good news is that typical cryptocurrencies, such as Bitcoin, have to publicly publish their transactions, known as a graph, to retain their ultimate goal of trustless and decentralized transaction verification, which lends law enforcement a means to deanonymizing cryptocurrencies. At meantime, graph learning is an extremely powerful tool to extract the latent features of each vertex in a graph to fulfill various tasks, such as, classifying graph vertices. In this work, we discuss the promises and challenges of exploiting graph learning to deanonymizing cryptocurrencies, which can aid the cyberfighters to circumvent cryptocurrency-based illicit activities.
A GMM-Based Segmentation Method for the Detection of Water Surface Floats

Gaussian Mixture Model (GMM) is a widely used approach for the background subtraction and the moving objects detection. However, the classical GMM probably detects incorrectly and cannot deal with the shadows with a pixel-level and time-domain classification, and thus it cannot monitor the water surface floats effectively. To solve this problem, an improved GMM-based automatic segmentation method (IGASM) is proposed to detect the water surface floats in this paper, where the background updating strategy is improved to segment the water surface floats more effectively. Besides, the GMM results are mapped into an HSV color space, and a light-shadow discriminant function is applied to solve the problems of light and shadow. Then, a morphological method is used to smooth the extracted foregrounds. Finally, Graph Cuts algorithm is applied to optimized the segmentation results according to the spatial information of video images. Experimental results demonstrate that IGASM can detect the water surface floats quickly and accurately, and the influences of light, shadows and ripples of water surface can be eliminated as much as possible.
Deep Learning for Semantic Segmentation on Minimal Hardware

Deep learning has revolutionised many fields, but it is still challenging to transfer its success to small mobile robots with minimal hardware. Specifically, some work has been done to this effect in the RoboCup humanoid football domain, but results that are performant and efficient and still generally applicable outside of this domain are lacking. We propose an approach conceptually different from those taken previously. It is based on semantic segmentation and does achieve these desired properties. In detail, it is being able to process full VGA images in real-time on a low-power mobile processor. It can further handle multiple image dimensions without retraining, it does not require specific domain knowledge to achieve a high frame rate and it is applicable on a minimal mobile hardware.
Machine Translation for Machines: the Sentiment Classification Use Case

We propose a neural machine translation (NMT) approach that, instead of pursuing adequacy and fluency ("human-oriented" quality criteria), aims to generate translations that are best suited as input to a natural language processing component designed for a specific downstream task (a "machine-oriented" criterion).Towards this objective, we present a reinforcement learning technique based on a new candidate sampling strategy, which exploits the results obtained on the downstream task as weak feedback.Experiments in sentiment classification of Twitter data in German and Italian show that feeding an English classifier with machine-oriented translations significantly improves its performance.Classification results outperform those obtained with translations produced by general-purpose NMT models as well as by an approach based on reinforcement learning.Moreover, our results on both languages approximate the classification accuracy computed on gold standard English tweets.
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations

Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.In real multiparty conversations, we can observe who is speaking, but the addressee information is not always explicit.In this paper, we aim to tackle the challenge of identifying all the missing addressees in a conversation session.To this end, we introduce a novel who-to-whom (W2W) model which models users and utterances in the session jointly in an interactive way.We conduct experiments on the benchmark Ubuntu Multi-Party Conversation Corpus and the experimental results demonstrate that our model outperforms baselines with consistent improvements.
3D measurement method based on S-shaped segmental phase encoding

Phase-based encoding has good immunity and robustness due to its insensitivity to ambient light, camera noise, and surface reflectance of measured objects. However, the number of code words is limited using traditional phase encoding method. In order to solve this problem, this paper proposes a new approach named S-shaped segmental phase encoding for 3D measurement. Specifically, by using a segmentation mechanism, the proposed method can, firstly, expand the range of code words whose number is limited using traditional phase encoding method. Secondly, the measurement accuracy can be improved. In this paper, Gray code is used to encode the phase encoding segment number, such that the fringe order judgment accuracy can be guaranteed. The experimental results demonstrated more accurate and efficient 3D shape measurement than the traditional methods, especially regarding the measurement of complex and isolated objects.
Developing insights from social media using semantic lexical chains to mine short text structures

Social media is increasingly being used for communication by individuals and organizations. Social media stores vast amounts of publicly available data that provides a rich source of information and insights. Often, social media users can easily infer meaning from short text such as microblogs and Facebook posts because they understand the context and terminology used. Although automated data-mining can be effective for gaining insights from text data, a significant challenge is to accurately infer meaning from social media text derived from a single social media account. This is difficult because social media communication uses very short, or sparse, text, which yields a relatively small sample of usable words for analysis. Furthermore, interpreting the contextual meaning from a relatively small set of words is challenging. This research proposes a methodology for extracting semantic lexical chains from frequently occurring words in a single social media account and using these chains to mine short text structures to infer the overall themes of the user. The methodology is based on a proposed clustering algorithm and illustrated with examples from Facebook posts. The algorithm is tested and illustrated by comparing it to existing work and further applying it to a variety of news posts. This methodology could be useful for gaining decision-making insights from social media, or other online forms with short or sparse text.
Transformer-based Automatic Post-Editing Model with Joint Encoder and Multi-source Attention of Decoder

This paper describes POSTECH’s submission to the WMT 2019 shared task on Automatic Post-Editing (APE). In this paper, we propose a new multi-source APE model by extending Transformer. The main contributions of our study are that we 1) reconstruct the encoder to generate a joint representation of translation (mt) and its src context, in addition to the conventional src encoding and 2) suggest two types of multi-source attention layers to compute attention between two outputs of the encoder and the decoder state in the decoder. Furthermore, we train our model by applying various teacher-forcing ratios to alleviate exposure bias. Finally, we adopt the ensemble technique across variations of our model. Experiments on the WMT19 English-German APE data set show improvements in terms of both TER and BLEU scores over the baseline. Our primary submission achieves -0.73 in TER and +1.49 in BLEU compare to the baseline.
Cross-lingual Semantic Specialization via Lexical Relation Induction

Semantic specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints.However, this technique cannot be leveraged in many languages, because their structured external resources are typically incomplete or non-existent.To bridge this gap, we propose a novel method that transfers specialization from a resource-rich source language (English) to virtually any target language.Our specialization transfer comprises two crucial steps: 1) Inducing noisy constraints in the target language through automatic word translation; and 2) Filtering the noisy constraints via a state-of-the-art relation prediction model trained on the source language constraints.This allows us to specialize any set of distributional vectors in the target language with the refined constraints.We prove the effectiveness of our method through intrinsic word similarity evaluation in 8 languages, and with 3 downstream tasks in 5 languages: lexical simplification, dialog state tracking, and semantic textual similarity.The gains over the previous state-of-art specialization methods are substantial and consistent across languages.Our results also suggest that the transfer method is effective even for lexically distant source-target language pairs.Finally, as a by-product, our method produces lists of WordNet-style lexical relations in resource-poor languages.