Reviewerly/exHarmony huggingface dataset:
Neural Decipherment via Minimum-Cost Flow: From Ugaritic to Linear B

In this paper we propose a novel neural approach for automatic decipherment of lost languages.To compensate for the lack of strong supervision signal, our model design is informed by patterns in language change documented in historical linguistics.The model utilizes an expressive sequence-to-sequence model to capture character-level correspondences between cognates.To effectively train the model in an unsupervised manner, we innovate the training procedure by formalizing it as a minimum-cost flow problem.When applied to the decipherment of Ugaritic, we achieve a 5.5% absolute improvement over state-of-the-art results.We also report the first automatic results in deciphering Linear B, a syllabic language related to ancient Greek, where our model correctly translates 67.3% of cognates. 1
GrapAL: Connecting the Dots in Scientific Literature

We introduce GrapAL (Graph database of Academic Literature), a versatile tool for exploring and investigating a knowledge base of scientific literature that was semiautomatically constructed using NLP methods.GrapAL fills many informational needs expressed by researchers.At the core of Gra-pAL is a Neo4j graph database with an intuitive schema and a simple query language.In this paper, we describe the basic elements of GrapAL, how to use it, and several use cases such as finding experts on a given topic for peer reviewing, discovering indirect connections between biomedical entities, and computing citation-based metrics.
A benefit-driven genetic algorithm for balancing privacy and utility in database fragmentation

In outsourcing data storage, privacy and utility are significant concerns. Techniques such as data encryption can protect the privacy of sensitive information but affect the efficiency of data usage accordingly. By splitting attributes of sensitive associations, database fragmentation can protect data privacy. In the meantime, data utility can be improved through grouping data of high affinity. In this paper, a benefit-driven genetic algorithm is proposed to achieve a better balance between privacy and utility for database fragmentation. To integrate useful fragmentation information in different solutions, a matching strategy is designed. Two benefit-driven operators for mutation and improvement are proposed to construct valuable fragments and rearrange elements. The experimental results show that the proposed benefit-driven genetic algorithm is competitive when compared with existing approaches in database fragmentation.
Interpretable Relative Squeezing bottleneck design for compact convolutional neural networks model

Convolutional neural networks (CNN) are mainly used for image recognition tasks. However, some huge models are infeasible for mobile devices because of limited computing and memory resources. In this paper, feature maps of DenseNet and CondenseNet are visualized. It could be observed that there are some feature channels in locked state and some have similar distribution property, which could be compressed further. Thus, in this work, a novel architecture — RSNet is introduced to improve the computing efficiency of CNNs. This paper proposes Relative-Squeezing (RS) bottleneck design, where the output is the weighted percentage of input channels. Besides, RSNet also contains multiple compression layers and learned group convolutions (LGCs). By eliminating superfluous feature maps, relative squeezing and compression layers only transmit the most significant features to the next layer. Less parameters are employed and much computation is saved. The proposed model is evaluated on three benchmark datasets: CIFAR-10, CIFAR-100 and ImageNet. Experiment results show that RSNet performs better with less parameters and FLOPs, compared to the state-of-the-art baseline, including CondenseNet, MobileNet and ShuffleNet.
Vocabulary Learning Through Mobile Apps: A Phenomenological Inquiry of Student Acceptance and Desired Apps Features

Vocabulary plays a great role in language learning as learners would face difficulties in language learning if they have insufficient vocabulary knowledge. The advent of new technologies has encouraged the development of mobile assisted language learning (MALL) and the increase in mobile apps for vocabulary learning. Nonetheless, it would seem that there is a dearth in research on the use of mobile apps for vocabulary learning especially in our local contexts. Capitalising on the strength of phenomenological inquiry and a model for technology acceptance, this study investigated several existing mobile apps for language learning. In particular, it looked into students’ acceptance and the features of mobile apps conducive for vocabulary learning. This study employed in-depth interviews and surveys as instruments. Using purposive sampling technique, thirty-three students were selected as participants to experience using mobile apps to learn vocabulary on self-directed basis for a specified duration. At the end of the trial stage, all participants responded to the surveys, and three were interviewed. The accounts given by participants indicated positive acceptance and several desired features. Interestingly, this study also revealed several challenges in learning vocabulary through mobile apps which should be the concern of both apps developers and language teachers.
Fuzzy Cognitive Modeling: Theoretical and Practical Considerations

Fuzzy cognitive maps (FCMs) are knowledge-based neural systems comprised of causal relations and well-defined neural concepts. Since their inception three decades ago, FCMs have been used to model a myriad of problems. Despite the research progress achieved in this field, FCMs are still surrounded by important misconceptions that hamper their competitiveness in several scenarios. In this paper, we discuss some theoretical and practical issues to be taken into account when modeling FCM-based systems. Such issues range from the causality fallacy and the timing component to limited prediction horizon imposed by the network structure. The conclusion of this paper is that the FCM’s theoretical underpinnings need to be revamped in order to overcome these limitations. Closing the gap between FCMs and other neural network models seems to be the right path in that journey.
Bi-GANs-ST for Perceptual Image Super-Resolution

Image quality measurement is a critical problem for image super-resolution (SR) algorithms. Usually, they are evaluated by some well-known objective metrics, e.g., PSNR and SSIM, but these indices cannot provide suitable results in accordance with the perception of human being. Recently, a more reasonable perception measurement has been proposed in [1], which is also adopted by the PIRM-SR 2018 challenge. In this paper, motivated by [1], we aim to generate a high-quality SR result which balances between the two indices, i.e., the perception index and root-mean-square error (RMSE). To do so, we design a new deep SR framework, dubbed Bi-GANs-ST, by integrating two complementary generative adversarial networks (GAN) branches. One is memory residual SRGAN (MR-SRGAN), which emphasizes on improving the objective performance, such as reducing the RMSE. The other is weight perception SRGAN (WP-SRGAN), which obtains the result that favors better subjective perception via a two-stage adversarial training mechanism. Then, to produce final result with excellent perception scores and RMSE, we use soft-thresholding method to merge the results generated by the two GANs. Our method performs well on the perceptual image super-resolution task of the PIRM 2018 challenge. Experimental results on five benchmarks show that our proposal achieves highly competent performance compared with other state-of-the-art methods.
ConcurORAM: High-Throughput Stateless Parallel Multi-Client ORAM

ConcurORAM is a parallel, multi-client oblivious RAM (ORAM) that eliminates waiting for concurrent stateless clients and allows overall throughput to scale gracefully, without requiring trusted third party components (proxies) or direct inter-client coordination.A key insight behind ConcurORAM is the fact that, during multi-client data access, only a subset of the concurrently-accessed server-hosted data structures require access privacy guarantees.Everything else can be safely implemented as oblivious data structures that are later synced securely and efficiently during an ORAM "eviction".Further, since a major contributor to latency is the eviction -in which client-resident data is reshuffled and reinserted back encrypted into the main server database -ConcurORAM also enables multiple concurrent clients to evict asynchronously, in parallel (without compromising consistency), and in the background without having to block ongoing queries.As a result, throughput scales well with increasing number of concurrent clients and is not significantly impacted by evictions.For example, about 65 queries per second can be executed in parallel by 30 concurrent clients, a 2x speedup over the state-ofthe-art [13].The query access time for individual clients increases by only 2x when compared to a single-client deployment.
Learning Neurosymbolic Generative Models via Program Synthesis

Significant strides have been made toward designing better generative models in recent years. Despite this progress, however, state-of-the-art approaches are still largely unable to capture complex global structure in data. For example, images of buildings typically contain spatial patterns such as windows repeating at regular intervals; state-of-the-art generative methods can't easily reproduce these structures. We propose to address this problem by incorporating programs representing global structure into the generative model---e.g., a 2D for-loop may represent a configuration of windows. Furthermore, we propose a framework for learning these models by leveraging program synthesis to generate training data. On both synthetic and real-world data, we demonstrate that our approach is substantially better than the state-of-the-art at both generating and completing images that contain global structure.
Learning Linear Dynamical Systems with Semi-Parametric Least Squares

We analyze a simple prefiltered variation of the least squares estimator for the problem of estimation with biased, semi-parametric noise, an error model studied more broadly in causal statistics and active learning. We prove an oracle inequality which demonstrates that this procedure provably mitigates the variance introduced by long-term dependencies. We then demonstrate that prefiltered least squares yields, to our knowledge, the first algorithm that provably estimates the parameters of partially-observed linear systems that attains rates which do not not incur a worst-case dependence on the rate at which these dependencies decay. The algorithm is provably consistent even for systems which satisfy the weaker marginal stability condition obeyed by many classical models based on Newtonian mechanics. In this context, our semi-parametric framework yields guarantees for both stochastic and worst-case noise.
Head Mounted Display Interaction Evaluation: Manipulating Virtual Objects in Augmented Reality

Augmented Reality (AR) is getting close to real use cases, which is driving the creation of innovative applications and the unprecedented growth of Head-Mounted Display (HMD) devices in consumer availability. However, at present there is a lack of guidelines, common form factors and standard interaction paradigms between devices, which has resulted in each HMD manufacturer creating their own specifications. This paper presents the first experimental evaluation of two AR HMDs evaluating their interaction paradigms, namely we used the HoloLens v1 (metaphoric interaction) and Meta2 (isomorphic interaction). We report on precision, interactivity and usability metrics in an object manipulation task-based user study. 20 participants took part in this study and significant differences were found between interaction paradigms for translation tasks, where the isomorphic mapped interaction outperformed the metaphoric mapped interaction in both time to completion and accuracy, while the contrary was found for the resize task. From an interaction perspective, the isomorphic mapped interaction (using the Meta2) was perceived as more natural and usable with a significantly higher usability score and a significantly lower task-load index. However, when task accuracy and time to completion is key mixed interaction paradigms need to be considered.
SAND/3: SDN-Assisted Novel QoE Control Method for Dynamic Adaptive Streaming over HTTP/3

Dynamic Adaptive Streaming over HTTP (DASH) is a widely used standard for video content delivery. Video traffic, most of which is generated from mobile devices, is shortly to become the most significant part of Internet traffic. Current DASH solutions only consider either client- or server-side optimization, leaving other components in DASH (e.g., at the transport layer) to default solutions that cause a performance bottleneck. In that regard, although it is assumed that HTTP must be necessarily transported on top of TCP, with the latest introduction of HTTP/3, it is time to re-evaluate its effects on DASH. The most substantial change in HTTP/3 is having Quick UDP Internet Connections (QUIC) as its primary underlying transport protocol. However, little is still know about the effects on standard DASH client-based adaption algorithms when exposed to the future HTTP/3. In this paper, we present SAND/3, an SDN (Software Defined Networking)-based Quality of Experience (QoE) control method for DASH over HTTP/3. Since the official deployment of HTTP/3 has not been released yet, we used the current implementation of Google QUIC. Preliminary results show that, by applying SAND/3, which combines information from different layers orchestrated by SDN to select the best QoE, we can obtain steadier media throughput, reduce the number of quality shifts in at least 40%, increase the amount downloaded content at least 20%, and minimize video interruptions compared to the current implementations regardless of the client adaption algorithm.
A Theory of State Abstraction for Reinforcement Learning

Reinforcement learning presents a challenging problem: agents must generalize experiences, efficiently explore the world, and learn from feedback that is delayed and often sparse, all while making use of a limited computational budget. Abstraction is essential to all of these endeavors. Through abstraction, agents can form concise models of both their surroundings and behavior, supporting effective decision making in diverse and complex environments. To this end, the goal of my doctoral research is to characterize the role abstraction plays in reinforcement learning, with a focus on state abstraction. I offer three desiderata articulating what it means for a state abstraction to be useful, and introduce classes of state abstractions that provide a partial path toward satisfying these desiderata. Collectively, I develop theory for state abstractions that can 1) preserve near-optimal behavior, 2) be learned and computed efficiently, and 3) can lower the time or data needed to make effective decisions. I close by discussing extensions of these results to an information theoretic paradigm of abstraction, and an extension to hierarchical abstraction that enjoys the same desirable properties.
Deep Learning-Based Luma and Chroma Fractional Interpolation in Video Coding

Motion compensated prediction is one of the essential methods to reduce temporal redundancy in inter coding. The target of motion compensated prediction is to predict the current frame from the list of reference frames. Recent video coding standards commonly use interpolation filters to obtain sub-pixel for the best matching block located in the fractional position of the reference frame. However, the fixed filters are not flexible to adapt to the variety of natural video contents. Inspired by the success of Convolutional Neural Network (CNN) in super-resolution, we propose CNN-based fractional interpolation for Luminance (Luma) and Chrominance (Chroma) components in motion compensated prediction to improve the coding efficiency. Moreover, two syntax elements indicate interpolation methods for the Luminance and Chrominance components, have been added to bin-string and encoded by CABAC using regular mode. As a result, our proposal gains 2.9%, 0.3%, 0.6% Y, U, V BD-rate reduction, respectively, under low delay P configuration.
Post-incident audits on cyber insurance discounts

We introduce a game-theoretic model to investigate the strategic interaction between a cyber insurance policyholder whose premium depends on her self-reported security level and an insurer with the power to audit the security level upon receiving an indemnity claim. Audits can reveal fraudulent (or simply careless) policyholders not following reported security procedures, in which case the insurer can refuse to indemnify the policyholder. However, the insurer has to bear an audit cost even when the policyholders have followed the prescribed security procedures. As audits can be expensive, a key problem insurers face is to devise an auditing strategy to deter policyholders from misrepresenting their security levels to gain a premium discount. This decision-making problem was motivated by conducting interviews with underwriters and reviewing regulatory filings in the US; we discovered that premiums are determined by security posture, yet this is often self-reported and insurers are concerned by whether security procedures are practised as reported by the policyholders. To address this problem, we model this interaction as a Bayesian game of incomplete information and devise optimal auditing strategies for the insurers considering the possibility that the policyholder may misrepresent her security level. To the best of our knowledge, this work is the first theoretical consideration of post-incident claims management in cyber security. Our model captures the trade-off between the incentive to exaggerate security posture during the application process and the possibility of punishment for non-compliance with reported security policies. Simulations demonstrate that common sense techniques are not as efficient at providing effective cyber insurance audit decisions as the ones computed using game theory.
Towards Efficient Convolutional Neural Networks Through Low-Error Filter Saliency Estimation

Filter saliency based channel pruning is a state-of-the-art method for deep convolutional neural network compression and acceleration. This channel pruning method ranks the importance of individual filter by estimating its impact of each filter’s removal on the training loss, and then remove the least important filters and fine-tune the remnant network. In this work, we propose a systematic channel pruning method that significantly reduces the estimation error of filter saliency. Different from existing approaches, our method largely reduces the magnitude of parameters in a network by introducing alternating direction method of multipliers (ADMM) into the pre-training procedure. Therefore, the estimation of filter saliency based on Taylor expansion is significantly improved. Extensive experiments with various benchmark network architectures and datasets demonstrate that the proposed method has a much improved unimportant filter selection capability and outperform state-of-the-art channel pruning method.
What’s Wrong with Hebrew NLP? And How to Make it Right

For languages with simple morphology, such as English, automatic annotation pipelines such as spaCy or Stanford's CoreNLP successfully serve AI/DS projects in academia and the industry.For many morphologically-rich languages (MRLs), similar pipelines show suboptimal performance that limits their applicability for text analysis in research and commerical use.The suboptimal performance is mainly due to errors in early morphological disambiguation decisions, which cannot be recovered later in the pipeline, yielding incoherent annotations on the whole.In this paper we describe the design and use of the ONLP suite, a joint morpho-syntactic parsing framework for processing Modern Hebrew texts.The joint inference over morphology and syntax substantially limits error propagation, and leads to high accuracy.ONLP provides rich and expressive output which already serves diverse academic and commercial needs.Its accompanying demo further serves educational activities, introducing Hebrew NLP intricacies to researchers and non-researchers alike.
Hate Speech Identification using the Hate Codes for Indonesian Tweets

The hate speech has become the major source of negativity spread in all over the social media. As the social media becomes aware of this issue, they gradually build several new regulations to handle the spread of hate speech e.g. by automatically blocking or suspending the accounts or posts containing hate speech. However, the social media users have become more creative in expressing the hate speech. To avoid the social media regulations regarding the hate speech, users usually use some special codes to interact with each other. This study aims to utilize the hate codes to identify the hate speech on the social media data. We used the Indonesian tweets as the dataset. We utilized Logistic Regression, Support Vector Machine, Naïve Bayes, and Random Forest Decision Tree as the classifiers. The highest F-Measure score for the hate speech identification was 80.71% by using the hate code feature combined with Logistic Regression as the classifier.
Cross-lingual Transfer Learning with Data Selection for Large-Scale Spoken Language Understanding

A typical cross-lingual transfer learning approach boosting model performance on a resource-poor language is to pre-train the model on all available supervised data from another resource-rich language.However, in large-scale systems, this leads to high training times and computational requirements.In addition, characteristic differences between the source and target languages raise a natural question of whether source-language data selection can improve the knowledge transfer.In this paper, we address this question and propose a simple but effective language model based source-language data selection method for cross-lingual transfer learning in largescale spoken language understanding.The experimental results show that with data selection i) the source data amount and hence training speed is reduced significantly and ii) model performance is improved.
EXTENSIVE READING IN INDONESIAN SCHOOLS: A SUCCESSFUL STORY

The low level of literacy of the Indonesian students has become a major nation-wide concern in Indonesia. The 2015 PISA (Program for International Student Assessment) result showed that Indonesian students’ literacy level in English was one of the lowest in the world. Gerakan Literasi Nasional (GLN) was established in 2016 as one of the possible solutions to increase the Indonesian students’ literacy level. The extensive reading programs done in the schools and in the universities described here is in line with GLN’s vision and mission, in that the Extensive Reading programs and activities play a tremendous role in the schools’ and communities’ effort to create more established, well-planned actions to increase the students’ literacy level. Therefore, this paper will discuss two objectives: (1) describing steps to establish a successful Extensive Reading program at Indonesian schools and (2) reporting factors that contribute to the success of an extensive reading program. The findings of this research revealed how extensive reading activities supported the success of GLN through the creation of extensive reading programs within and outside the curriculum, and how schools and home literacy culture could be contributing factors to the success of this program.