Reviewerly/exHarmony huggingface dataset:
Bonsai: A Generalized Look at Dual Deduplication

Cloud Service Providers (CSPs) offer a vast amount of storage space at competitive prices to cope with the growing demand for digital data storage. Dual deduplication is a recent framework designed to improve data compression on the CSP while keeping clients' data private from the CSP. To achieve this, clients perform lightweight information-theoretic transformations to their data prior to upload. We investigate the effectiveness of dual deduplication, and propose an improvement for the existing state-of-the-art method. We name our proposal Bonsai as it aims at reducing storage fingerprint and improving scalability. In detail, Bonsai achieves (1) significant reduction in client storage, (2) reduction in total required storage (client + CSP), and (3) reducing the deduplication time on the CSP. Our experiments show that Bonsai achieves compression rates of 68\% on the cloud and 5\% on the client, while allowing the cloud to identify deduplications in a time-efficient manner. We also show that combining our method with universal compressors in the cloud, e.g., Brotli, can yield better overall compression on the data compared to only applying the universal compressor or plain Bonsai. Finally, we show that Bonsai and its variants provide sufficient privacy against an honest-but-curious CPS that knows the distribution of the Clients' original data.
A Survey on Image Deblurring

With the improvement of social life quality and the real needs of daily work, images are more and more all around us. Image blurring due to camera shake, human movement, etc. has become the key to affecting image quality. How to remove image blur and restore clear image has gradually become an important research direction in the field of computer vision. After more than half a century of unremitting efforts, the majority of scientific and technological workers have made fruitful progress in image deblurring. This article reviews the work of image deblurring and specifically introduces more classic image deblurring methods, which is helpful to understand current research and look forward to future trends. This article reviews the traditional image deblurring methods and depth-represented image deblurring methods, and comprehensively classifies and introduces the corresponding technical methods. This review can provide some guidance for researchers in the field of image deblurring, and at the same time facilitate their subsequent study and research.
Predicting Critical Phases from Entanglement Dynamics in XXZ Alternating Chain

The quantum XXZ spin model with alternating bond strengths under magnetic field has a rich equilibrium phase diagram which includes Haldane, Luttinger liquid, singlet, and paramagnetic phases. We show that the nearest neighbor bipartite and multipartite entanglement can detect quantum critical lines and phases in this model. We determine a region in parameter space in which the dynamical states, starting from the ground state of the Haldane (dimer) phase can create highly multipartite entangled states for any time period, thereby establishing it as a potential candidate for the implementation of quantum information tasks. We also exhibit that if the initial and evolved states are in two different phases, the nonanalytic behavior of multipartite entanglement and the rate function based on Loschmidt echo can signal quantum phase transition happened at zero temperature. In a similar spirit, we report that from the product state, the patterns of block entanglement entropy of the evolved state with time can also infer the phase transition at equilibrium.
Speech Pro Apps: An Innovative Approach in Enhancing Tertiary Students’ Presentation

In the present study, the results of an investigation of the use of Speech Pro in Communicative English classroom explored among ten learners to see whether using a mobile application affects the improvement of learners' oral presentation skills.The research respondents consisted of 10 Diploma students who enrolled in Communicative English course in a Polytechnic in Sarawak, Malaysia.This research uses three research instruments mainly, pre-test and post-test together with an Oral Presentation Rubric for the course.Based on the findings, the use of this application has helped learners to reduce their anxiety and increase their confidence and motivation levels as well as helped them to formulate a better-structured presentation.Students found the application helpful and can be applied in other subjects as well.
Fourier Document Restoration for Robust Document Dewarping and Recognition

State-of-the-art document dewarping techniques learn to predict 3-dimensional information of documents which are prone to errors while dealing with documents with irregular distortions or large variations in depth. This paper presents FDRNet, a Fourier Document Restoration Network that can restore documents with different distortions and improve document recognition in a reliable and simpler manner. FDRNet focuses on high-frequency components in the Fourier space that capture most structural information but are largely free of degradation in appearance. It dewarps documents by a flexible Thin-Plate Spline transformation which can handle various deformations effectively without requiring deformation annotations in training. These features allow FDRNet to learn from a small amount of simply labeled training images, and the learned model can dewarp documents with complex geometric distortion and recognize the restored texts accurately. To facilitate document restoration research, we create a benchmark dataset consisting of over one thousand camera documents with different types of geometric and photometric distortion. Extensive experiments show that FDRNet outperforms the state-of-the-art by large margins on both dewarping and text recognition tasks. In addition, FDRNet requires a small amount of simply labeled training data and is easy to deploy.
The Influence of Consumption Patterns, Nutritional Status, and Family Characteristics on Learning Achievement of Children at SD Inpres Mokdale in Rote Ndao District

One indicator of an individual's wellbeing is related to the nutritional status. The status is influenced by consumption patterns that can be determined by family conditions. Good nutritional status has a positive impact on learning achievement. The purpose of this study is to find whether there is a link between consumption pattern, nutritional status, and family characteristics on academic achievement. This research used an analytical survey method with a cross-sectional study design. The sample consisted of 118 children in grades 4 and 5 at SD Inpres Mokdale, Rote Ndao District, East Nusa Tenggara. Data analysis used the chi-square test with α=0,05. The results showed that there was no influence between parental education, parental work, family size and expenses on students' learning achievement. Consumption patterns and nutritional status also had no influence on the achievement. This research suggested that learning achievement can be influenced by other factors besides biological and family factors such as psychological factors, school environment, and the community support around the children.
2nd Place Solution to Facebook AI Image Similarity Challenge Matching  Track

This paper presents the 2nd place solution to the Facebook AI Image Similarity Challenge : Matching Track on DrivenData. The solution is based on self-supervised learning, and Vision Transformer(ViT). The main breaktrough comes from concatenating query and reference image to form as one image and asking ViT to directly predict from the image if query image used reference image. The solution scored 0.8291 Micro-average Precision on the private leaderboard.
DriverGym: Democratising Reinforcement Learning for Autonomous Driving

Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an open-source platform capable of training and effectively validating the RL policies on real-world data. We propose DriverGym, an open-source OpenAI Gym-compatible environment specifically tailored for developing RL algorithms for autonomous driving. DriverGym provides access to more than 1000 hours of expert logged data and also supports reactive and data-driven agent behavior. The performance of an RL policy can be easily validated on real-world data using our extensive and flexible closed-loop evaluation protocol. In this work, we also provide behavior cloning baselines using supervised learning and RL, trained in DriverGym. We make DriverGym code, as well as all the baselines publicly available to further stimulate development from the community.
Sweedler Theory of Monads

Abstract Monad-comonad interaction laws are a mathematical concept for describing communication protocols between effectful computations and coeffectful environments in the paradigm where notions of effectful computation are modelled by monads and notions of coeffectful environment by comonads. We show that monad-comonad interaction laws are an instance of measuring maps from Sweedler theory for duoidal categories whereby the final interacting comonad for a monad and a residual monad arises as the Sweedler hom and the initial residual monad for a monad and an interacting comonad as the Sweedler copower. We then combine this with a (co)algebraic characterization of monad-comonad interaction laws to derive descriptions of the Sweedler hom and the Sweedler copower in terms of their coalgebras resp. algebras.
The Development and Prospect of Code Clone

The application of code clone technology accelerates code search, improves code reuse efficiency, and assists in software quality assessment and code vulnerability detection. However, the application of code clones also introduces software quality issues and increases the cost of software maintenance. As an important research field in software engineering, code clone has been extensively explored and studied by researchers, and related studies on various sub-research fields have emerged, including code clone detection, code clone evolution, code clone analysis, etc. However, there lacks a comprehensive exploration of the entire field of code clone, as well as an analysis of the trend of each sub-research field. This paper collects related work of code clones in the past ten years. In summary, the contributions of this paper mainly include: (1) summarize and classify the sub-research fields of code clone, and explore the relative popularity and relation of these sub-research fields; (2) analyze the overall research trend of code clone and each sub-research field; (3) compare and analyze the difference between academy and industry regarding code clone research; (4) construct a network of researchers, and excavate the major contributors in code clone research field; (5) The list of popular conferences and journals was statistically analyzed. The popular research directions in the future include clone visualization, clone management, etc. For the clone detection technique, researchers can optimize the scalability and execution efficiency of the method, targeting particular clone detection tasks and contextual environments, or apply the technology to other related research fields continuously.
AC-VC: Non-parallel Low Latency Phonetic Posteriorgrams Based Voice  Conversion

This paper presents AC-VC (Almost Causal Voice Conversion), a phonetic posteriorgrams based voice conversion system that can perform any-to-many voice conversion while having only 57.5 ms future look-ahead. The complete system is composed of three neural networks trained separately with non-parallel data. While most of the current voice conversion systems focus primarily on quality irrespective of algorithmic latency, this work elaborates on designing a method using a minimal amount of future context thus allowing a future real-time implementation. According to a subjective listening test organized in this work, the proposed AC-VC system achieves parity with the non-causal ASR-TTS baseline of the Voice Conversion Challenge 2020 in naturalness with a MOS of 3.5. In contrast, the results indicate that missing future context impacts speaker similarity. Obtained similarity percentage of 65% is lower than the similarity of current best voice conversion systems.
FreeTalky: Don't Be Afraid! Conversations Made Easier by a Humanoid Robot using Persona-based Dialogue

We propose a deep learning-based foreign language learning platform, named FreeTalky, for people who experience anxiety dealing with foreign languages, by employing a humanoid robot NAO and various deep learning models. A persona-based dialogue system that is embedded in NAO provides an interesting and consistent multi-turn dialogue for users. Also, an grammar error correction system promotes improvement in grammar skills of the users. Thus, our system enables personalized learning based on persona dialogue and facilitates grammar learning of a user using grammar error feedback. Furthermore, we verified whether FreeTalky provides practical help in alleviating xenoglossophobia by replacing the real human in the conversation with a NAO robot, through human evaluation.
Data mining method of false transaction in webcast platform based on Cluster Learning

In order to improve the authenticity of transactions on the webcast platform, it is necessary to mine the false transaction data in the webcast platform. However, the traditional false transaction data mining methods have the problem of low feature extraction accuracy and mining accuracy of false transaction data. Therefore, a false transaction data mining method on the webcast platform based on cluster learning is proposed. Firstly, the transaction data of the webcast platform is collected through the network sensor; Secondly, the features of false transaction data on webcast platform are extracted by clustering learning. Finally, the false transaction data is mined by genetic algorithm. Experimental results show that this method can effectively extract the characteristics of false transaction data, and the accuracy of false transaction data mining is significantly improved.
Identifying Terms and Conditions Important to Consumers using  Crowdsourcing

Terms and conditions (T&Cs) are pervasive on the web and often contain important information for consumers, but are rarely read. Previous research has explored methods to surface alarming privacy policies using manual labelers, natural language processing, and deep learning techniques. However, this prior work used pre-determined categories for annotations, and did not investigate what consumers really deem as important from their perspective. In this paper, we instead combine crowdsourcing with an open definition of "what is important" in T&Cs. We present a workflow consisting of pairwise comparisons, agreement validation, and Bradley-Terry rank modeling, to effectively establish rankings of T&C statements from non-expert crowdworkers on this open definition, and further analyzed consumers' preferences. We applied this workflow to 1,551 T&C statements from 27 e-commerce websites, contributed by 3,462 unique crowd workers doing 203,068 pairwise comparisons, and conducted thematic and readability analysis on the statements considered as important/unimportant. We found that consumers especially cared about policies related to after-sales and money, and tended to regard harder-to-understand statements as more important. We also present machine learning models to identify T&C clauses that consumers considered important, achieving at best a 92.7% balanced accuracy, 91.6% recall, and 89.2% precision. We foresee using our workflow and model to efficiently and reliably highlight important T&Cs on websites at a large scale, improving consumers' awareness
Analyzing and Improving Fault Tolerance of Learning-Based Navigation Systems

Learning-based navigation systems are widely used in autonomous applications, such as robotics, unmanned vehicles and drones. Specialized hardware accelerators have been proposed for high-performance and energy-efficiency for such navigational tasks. However, transient and permanent faults are increasing in hardware systems and can catastrophically violate tasks safety. Meanwhile, traditional redundancy-based protection methods are challenging to deploy on resource-constrained edge applications. In this paper, we experimentally evaluate the resilience of navigation systems with respect to algorithms, fault models and data types from both RL training and inference. We further propose two efficient fault mitigation techniques that achieve 2x success rate and 39% quality-of-flight improvement in learning-based navigation systems.
A Novel Tropical Geometry-based Interpretable Machine Learning Method:  Application in Prognosis of Advanced Heart Failure

A model's interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to investigate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, the proposed method was applied to a clinical application that identified heart failure patients that would benefit from advanced therapies such as heart transplant or durable mechanical circulatory support. Experimental results show that the proposed network achieved great performance on the classification tasks. In addition to learning humanly understandable rules from the dataset, existing fuzzy domain knowledge can be easily transferred into the network and used to facilitate model training. From our results, the proposed model and the ability of learning existing domain knowledge can significantly improve the model generalizability. The characteristics of the proposed network make it promising in applications requiring model reliability and justification.
Privacy Breaches in Smart Contract due to Solidity Language and its Protection

Abstract - Smart contracts are essentially agreements between two parties, and the information included within them must be kept private and not shared with the outside community. Simultaneously, the most typical smart contract systems’ public nature limits the meaning of contracts. Smart contract confidentiality problems are a major impediment to their widespread adoption. A viable technique to protecting sensitive information is to hide it with cryptographic primitives or a hashing algorithm, however, hashing is irreversible, thus to address these issues, we first observed the privacy leakage of public smart contracts in this study. We next introduced our technique for privacy-preserving smart contracts, which includes encrypted user output values and defines holders of private values to prevent unintentional data leakage.
Practical cognitive speech compression

This paper presents a new neural speech compression method that is practical in the sense that it operates at low bitrate, introduces a low latency, is compatible in computational complexity with current mobile devices, and provides a subjective quality that is comparable to that of standard mobile-telephony codecs. Other recently proposed neural vocoders also have the ability to operate at low bitrate. However, they do not produce the same level of subjective quality as standard codecs. On the other hand, standard codecs rely on objective and short-term metrics such as the segmental signal-to-noise ratio that correlate only weakly with perception. Furthermore, standard codecs are less efficient than unsupervised neural networks at capturing speech attributes, especially long-term ones. The proposed method combines a cognitive-coding encoder that extracts an interpretable unsupervised hierarchical representation with a multi stage decoder that has a GAN-based architecture. We observe that this method is very robust to the quantization of representation features. An AB test was conducted on a subset of the Harvard sentences that are commonly used to evaluate standard mobile-telephony codecs. The results show that the proposed method outperforms the standard AMR-WB codec in terms of delay, bitrate and subjective quality.
SpeechMoE2: Mixture-of-Experts Model with Improved Routing

Mixture-of-experts based acoustic models with dynamic routing mechanisms have proved promising results for speech recognition. The design principle of router architecture is important for the large model capacity and high computational efficiency. Our previous work SpeechMoE only uses local grapheme embedding to help routers to make route decisions. To further improve speech recognition performance against varying domains and accents, we propose a new router architecture which integrates additional global domain and accent embedding into router input to promote adaptability. Experimental results show that the proposed SpeechMoE2 can achieve lower character error rate (CER) with comparable parameters than SpeechMoE on both multi-domain and multi-accent task. Primarily, the proposed method provides up to 1.6% - 4.8% relative CER improvement for the multidomain task and 1.9% - 17.7% relative CER improvement for the multi-accent task respectively. Besides, increasing the number of experts also achieves consistent performance improvement and keeps the computational cost constant.
A benchmark with decomposed distribution shifts for 360 monocular depth estimation

In this work we contribute a distribution shift benchmark for a computer vision task; monocular depth estimation. Our differentiation is the decomposition of the wider distribution shift of uncontrolled testing on in-the-wild data, to three distinct distribution shifts. Specifically, we generate data via synthesis and analyze them to produce covariate (color input), prior (depth output) and concept (their relationship) distribution shifts. We also synthesize combinations and show how each one is indeed a different challenge to address, as stacking them produces increased performance drops and cannot be addressed horizontally using standard approaches.
Reinforced Meta Active Learning

In stream-based active learning, the learning procedure typically has access to a stream of unlabeled data instances and must decide for each instance whether to label it and use it for training or to discard it. There are numerous active learning strategies which try to minimize the number of labeled samples required for training in this setting by identifying and retaining the most informative data samples. Most of these schemes are rule-based and rely on the notion of uncertainty, which captures how small the distance of a data sample is from the classifier's decision boundary. Recently, there have been some attempts to learn optimal selection strategies directly from the data, but many of them are still lacking generality for several reasons: 1) They focus on specific classification setups, 2) They rely on rule-based metrics, 3) They require offline pre-training of the active learner on related tasks. In this work we address the above limitations and present an online stream-based meta active learning method which learns on the fly an informativeness measure directly from the data, and is applicable to a general class of classification problems without any need for pretraining of the active learner on related tasks. The method is based on reinforcement learning and combines episodic policy search and a contextual bandits approach which are used to train the active learner in conjunction with training of the model. We demonstrate on several real datasets that this method learns to select training samples more efficiently than existing state-of-the-art methods.