Reviewerly/exHarmony huggingface dataset:
Research on Uyghur-Chinese Neural Machine Translation Based on the Transformer at Multistrategy Segmentation Granularity

In recent years, machine translation based on neural networks has become the mainstream method in the field of machine translation, but there are still challenges of insufficient parallel corpus and sparse data in the field of low resource translation. Existing machine translation models are usually trained on word-granularity segmentation datasets. However, different segmentation granularities contain different grammatical and semantic features and information. Only considering word granularity will restrict the efficient training of neural machine translation systems. Aiming at the problem of data sparseness caused by the lack of Uyghur-Chinese parallel corpus and complex Uyghur morphology, this paper proposes a multistrategy segmentation granular training method for syllables, marked syllable, words, and syllable word fusion and targets traditional recurrent neural networks and convolutional neural networks; the disadvantage of the network is to build a Transformer Uyghur-Chinese Neural Machine Translation model based entirely on the multihead self-attention mechanism. In CCMT2019, dimension results on Uyghur-Chinese bilingual datasets show that the effect of multiple translation granularity training method is significantly better than the rest of granularity segmentation translation systems, while the Transformer model can obtain higher BLEU value than Uyghur-Chinese translation model based on Self-Attention-RNN.
Bidirectionally Densifying LSH Sketches with Empty Bins

As an efficient tool for approximate similarity computation and search, Locality Sensitive Hashing (LSH) has been widely used in many research areas including databases, data mining, information retrieval, and machine learning. Classical LSH methods typically require to perform hundreds or even thousands of hashing operations when computing the LSH sketch for each input item (e.g., a set or a vector); however, this complexity is still too expensive and even impractical for applications requiring processing data in real-time. To address this issue, several fast methods such as OPH and BCWS have been proposed to efficiently compute the LSH sketches; however, these methods may generate many sketches with empty bins, which may introduce large errors for similarity estimation and also limit their usage for fast similarity search. To solve this issue, we propose a novel densification method, i.e., BiDens. Compared with existing densification methods, our BiDens is more efficient to fill a sketch's empty bins with values of its non-empty bins in either the forward or backward directions. Furthermore, it also densifies empty bins to satisfy the densification principle (i.e., the LSH property). Theoretical analysis and experimental results on similarity estimation, fast similarity search, and kernel linearization using real-world datasets demonstrate that our BiDens is up to 106 times faster than state-of-the-art methods while achieving the same or even better accuracy.
An Area Efficient VLSI Architecture for 1-D and 2-D Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT)

Several discrete wavelet transform architectures have already been introduced due to their wide range of applications in the field of image, video and speech signal processing. In this paper, an area efficient VLSI architecture for 2-dimensional discrete wavelet transform (DWT) and inverse discrete wavelet transform (IDWT) have been proposed. Theoretically, the hardware and timing analysis of existing and proposed architectures are presented. The proposed and existing several architectures are also simulated and synthesized for virtex-4 FPGA device. Performances of all architectures are observed in terms of slice, slice flip-flops, clock frequency using Xilinx ISE Design Suite 14.7 Synthesis tool. Also the number of slice LUTs, slice registers, clock frequency, delay and power of proposed DWT and IDWT architectures are found using virtex-7 FPGA device family.
Posterior Promoted GAN with Distribution Discriminator for Unsupervised Image Synthesis

Sufficient real information in generator is a critical point for the generation ability of GAN. However, GAN and its variants suffer from lack of this point, resulting in brittle training processes. In this paper, we propose a novel variant of GAN, Posterior Promoted GAN (P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN), which promotes generator with the real information in the posterior distribution produced by discriminator. In our frame-work, different from other variants of GAN, the discriminator maps images to a multivariate Gaussian distribution and extracts real information. The generator employs the real information by AdaIN and a latent code regularizer. Besides, reparameterization trick and pretraining is applied to guarantee a stable training process in practice. The convergence of P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN is theoretically proved. Experimental results on typical high-dimensional multi-modal datasets demonstrate that P <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> GAN has achieved comparable results with the state-of-the-art variants of GAN on unsupervised image synthesis.
A tutorial on capturing mental representations through drawing and crowd-sourced scoring

When we draw, we are depicting a rich mental representation reflecting a memory, percept, schema, imagination, or feeling. In spite of the abundance of data created by drawings, drawings are rarely used as an output measure in the field of psychology, due to concerns about their large variance and their difficulty of quantification. However, recent work leveraging pen-tracking, computer vision, and online crowd-sourcing has revealed new ways to capture and objectively quantify drawings, to answer a wide range of questions across fields of psychology. Here, I present a tutorial on modern methods for drawing experiments, ranging from how to quantify pen-and-paper type studies, up to how to administer a fully closed-loop online experiment. I go through the concrete steps of designing a drawing experiment, recording drawings, and objectively quantifying them through online crowd-sourcing and computer vision methods. Included with this tutorial are code examples at different levels of complexity and tutorials designed to teach basic lessons about web architecture and be useful regardless of skill level. I also discuss key methodological points of consideration, and provide a series of potential jumping points for drawing studies across fields in psychology. I hope this tutorial will arm more researchers with the skills to capture these naturalistic snapshots of a mental image.
Compositional Semantics for Probabilistic Programs with Exact Conditioning

We define a probabilistic programming language for Gaussian random variables with a first-class exact conditioning construct. We give operational, denotational and equational semantics for this language, establishing convenient properties like exchangeability of conditions. Conditioning on equality of continuous random variables is nontrivial, as the exact observation may have probability zero; this is Borel's paradox. Using categorical formulations of conditional probability, we show that the good properties of our language are not particular to Gaussians, but can be derived from universal properties, thus generalizing to wider settings. We define the Cond construction, which internalizes conditioning as a morphism, providing general compositional semantics for probabilistic programming with exact conditioning.
CoStar: a verified ALL(*) parser

Parsers are security-critical components of many software systems, and verified parsing therefore has a key role to play in secure software design. However, existing verified parsers for context-free grammars are limited in their expressiveness, termination properties, or performance characteristics. They are only compatible with a restricted class of grammars, they are not guaranteed to terminate on all inputs, or they are not designed to be performant on grammars for real-world programming languages and data formats.
Crossing the chasm between ontology engineering and application development: A survey

The adoption of Knowledge Graphs (KGs) by public and private organizations to integrate and publish data has increased in recent years. Ontologies play a crucial role in providing the structure for KGs, but are usually disregarded when designing Application Programming Interfaces (APIs) to enable browsing KGs in a developer-friendly manner. In this paper we provide a systematic review of the state of the art on existing approaches to ease access to ontology-based KG data by application developers. We propose two comparison frameworks to understand specifications, technologies and tools responsible for providing APIs for KGs. Our results reveal several limitations on existing API-based specifications, technologies and tools for KG consumption, which outline exciting research challenges including automatic API generation, API resource path prediction, ontology-based API versioning, and API validation and testing.
PSRR-MaxpoolNMS: Pyramid Shifted MaxpoolNMS with Relationship Recovery

Non-maximum Suppression (NMS) is an essential post-processing step in modern convolutional neural networks for object detection. Unlike convolutions which are inherently parallel, the de-facto standard for NMS, namely GreedyNMS, cannot be easily parallelized and thus could be the performance bottleneck in convolutional object detection pipelines. MaxpoolNMS is introduced as a parallelizable alternative to GreedyNMS, which in turn enables faster speed than GreedyNMS at comparable accuracy. However, MaxpoolNMS is only capable of replacing the GreedyNMS at the first stage of two-stage detectors like Faster-RCNN. There is a significant drop in accuracy when applying MaxpoolNMS at the final detection stage, due to the fact that MaxpoolNMS fails to approximate GreedyNMS precisely in terms of bounding box selection. In this paper, we propose a general, parallelizable and configurable approach PSRR-MaxpoolNMS, to completely replace GreedyNMS at all stages in all detectors. By introducing a simple Relationship Recovery module and a Pyramid Shifted MaxpoolNMS module, our PSRR-MaxpoolNMS is able to approximate GreedyNMS more precisely than MaxpoolNMS. Comprehensive experiments show that our approach outperforms MaxpoolNMS by a large margin, and it is proven faster than GreedyNMS with comparable accuracy. For the first time, PSRR-MaxpoolNMS provides a fully parallelizable solution for customized hardware design, which can be reused for accelerating NMS everywhere.
Test case prioritization using test case diversification and fault-proneness estimations

Regression testing activities greatly reduce the risk of faulty software release. However, the size of the test suites grows throughout the development process, resulting in time-consuming execution of the test suite and delayed feedback to the software development team. This has urged the need for approaches such as test case prioritization (TCP) and test-suite reduction to reach better results in case of limited resources. In this regard, proposing approaches that use auxiliary sources of data such as bug history can be interesting. We aim to propose an approach for TCP that takes into account test case coverage data, bug history, and test case diversification. To evaluate this approach we study its performance on real-world open-source projects. The bug history is used to estimate the fault-proneness of source code areas. The diversification of test cases is preserved by incorporating fault-proneness on a clustering-based approach scheme. The proposed methods are evaluated on datasets collected from the development history of five real-world projects including 357 versions in total. The experiments show that the proposed methods are superior to coverage-based TCP methods. The proposed approach shows that improvement of coverage-based and fault-proneness-based methods is possible by using a combination of diversification and fault-proneness incorporation.
Efficiently Mastering the Game of NoGo with Deep Reinforcement Learning Supported by Domain Knowledge

Computer games have been regarded as an important field of artificial intelligence (AI) for a long time. The AlphaZero structure has been successful in the game of Go, beating the top professional human players and becoming the baseline method in computer games. However, the AlphaZero training process requires tremendous computing resources, imposing additional difficulties for the AlphaZero-based AI. In this paper, we propose NoGoZero+ to improve the AlphaZero process and apply it to a game similar to Go, NoGo. NoGoZero+ employs several innovative features to improve training speed and performance, and most improvement strategies can be transferred to other nonspecific areas. This paper compares it with the original AlphaZero process, and results show that NoGoZero+ increases the training speed to about six times that of the original AlphaZero process. Moreover, in the experiment, our agent beat the original AlphaZero agent with a score of 81:19 after only being trained by 20,000 self-play games’ data (small in quantity compared with 120,000 self-play games’ data consumed by the original AlphaZero). The NoGo game program based on NoGoZero+ was the runner-up in the 2020 China Computer Game Championship (CCGC) with limited resources, defeating many AlphaZero-based programs. Our code, pretrained models, and self-play datasets are publicly available. The ultimate goal of this paper is to provide exploratory insights and mature auxiliary tools to enable AI researchers and computer-game communities to study, test, and improve these promising state-of-the-art methods at a much lower cost of computing resources.
ILCOC: An Incremental Learning Framework based on Contrastive One-class Classifiers

In the class incremental learning, the number of classes to be handled dynamically raises with the number of considered tasks. The main challenge of this learning schema is catastrophic forgetting, that is the performance degradation on old tasks after learning new tasks. Existing incremental learning algorithms generally choose to train a multi-class classifier (e.g. softmax classifier), which learns a decision boundary to divide the feature space into several parts. Therefore, when new data arrive, the learned boundary will be updated and thus may cause forgetting. Compared with multi-class classifiers, a one-class classifier focuses on characterizing the distribution of a single class. As a result, the decision boundary learned for each category is tighter and does not change during learning new tasks. Inspired by this characteristic of one-class classifier, we propose a novel Incremental Learning framework based on Contrastive One-class Classifiers (ILCOC) to avoid catastrophic forgetting. Specifically, we train a specific one-class classifier for each category and parallelly use them to achieve incremental multi-class recognition. Besides, we design a scale-boundary loss, a classifier-contrastive loss and a negative-suppression loss to strengthen the comparability of classifiers outputs and the discrimination ability of each one-class classifier. We evaluate ILCOC on MNIST, CIFAR-10 and Tiny-ImageNet datasets, and the experimental results show that ILCOC achieves state-of-the-art performance. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Design of a Chatbot to Assist the Elderly

Nowadays, conversational agents are solutions that can provide a highly valuable addition to the existing healthcare services to assist the elderly in following their care plans and gradually changing adverse patterns of behaviour. Nevertheless, the development of a conversation agent in the healthcare domain presents several technical, design and linguistic challenges. In our paper, we describe a chatbot conversing with elderly persons, with age-related problems. Charlie, the name of our chatbot, has been designed to provide the elderly with companionship through innovative strategies based on gamification, active notifications, and promotion of self-compassion that can be explored for preventive mental healthcare. Moreover, Charlie can be used to monitor meaningful or anomalous situations that can affect aged people. To specify these situations in the paper we describe a web application that enables medical assistants and relatives to create rules that depend on data gathered by Charlie such as the number of required news, followed tips, completed games, but also biometrical data such as the number of steps made during the day, burned calories or the number of hours of sleep. To help caregivers in creating these rules, we are studying how to endow our web app with a recommendation service for predicting recurring anomalous situations and, accordingly, provide suggestions on which characteristics the elderly will have to change to improve their lifestyle.
High Utility Pattern Mining Distributed Algorithm Based on Spark RDD

Kumar, Sunil Mohbey, Krishna KumarHigh utility pattern identification is a key pattern mining method that extracts items with more utility than the mentioned utility threshold limit. The demand for Mining of high utility items has grown in the last few years. It addresses frequent pattern identification barriers, which considers the only frequency of itemset as a measure to find patterns. Several techniques for high utility pattern identification were designed. However, such methods are effective for smaller data sizes and operate on an Independent system. Individual machines can not process massive transaction data files. To deal with that problem, a distributed approach is proposed for High utility mining and represented as Dis-EFIM. It performs partitioned mining tasks on a cluster of multiple nodes. Extensive experiments on various real-time datasets are performed with the proposed Dis-EFIM and found that Dis-EFIM is more efficient than current cutting-edge techniques EFIM and PHUI-Miner.
Continual learning in cross-modal retrieval

Multimodal representations and continual learning are two areas closely related to human intelligence. The former considers the learning of shared representation spaces where information from different modalities can be compared and integrated (we focus on cross-modal retrieval between language and visual representations). The latter studies how to prevent forgetting a previously learned task when learning a new one. While humans excel in these two aspects, deep neural networks are still quite limited. In this paper, we propose a combination of both problems into a continual cross-modal retrieval setting, where we study how the catastrophic interference caused by new tasks impacts the embedding spaces and their cross-modal alignment required for effective retrieval. We propose a general framework that decouples the training, indexing and querying stages. We also identify and study different factors that may lead to forgetting, and propose tools to alleviate it. We found that the indexing stage pays an important role and that simply avoiding reindexing the database with updated embedding networks can lead to significant gains. We evaluated our methods in two image-text retrieval datasets, obtaining significant gains with respect to the fine tuning baseline.
Interpretable Robust Feature Selection via Joint -Norms Minimization

Dimension reduction is a hot topic in data processing field. The challenge lies in how to find a suitable feature subset in low-dimensional space to accurately summarize the important information in high-dimensional space, rather than redundant information or noise. This requires the proposed model to reasonably explain the importance of features and be robust to noise. In order to solve this problem, this paper proposes an interpretable robust feature selection method, in which both the reconstruction error term and the regularization term are constrained by -norm. The reconstruction error term can capture samples corroded by noise, while the regular term automatically finds a group of discriminative features on relatively clean samples. Experimental results show the effectiveness of the proposed method, especially on noise data sets.
Smartphone Security Using Swipe Behavior-based Authentication

Most smartphone users prefer easy and convenient authentication without remembering complicated passwords or drawing intricate patterns. Preferably, after one-time authentication, there is no verification of the user's authenticity. Therefore, security and privacy against unauthorized users is a crucial research area. Behavioral authentication is an emerging security technique that is gaining attention for its uniqueness and transparency. In this paper, a behavior-based authentication system is built using swipe movements to continuously authenticate the user after one-time traditional authentication. The key feature is the selection of an optimal feature set for the swipe movement. Five machine learning classifiers are used, of which random forest is selected based on the best values of accuracy and F-measure. A real-time system is developed by shifting all of the computational power to a cloud server to overcome the smartphone's computational limitations. The system is tested on three smartphones, and it is found that a minimum of seven swipes is sufficient to check user authenticity. In our experiments, the proposed feature set performs better than a state-of-the-art feature set.
The Design Space of Emergent Scheduling for Distributed Execution Frameworks

Distributed Execution Frameworks (DEFs) such as Apache Spark have become ubiquitous as a solution for the execution of user-defined jobs to process terabytes of data across hundreds of nodes. One of the key costs of DEFs is scheduling of which parts of each job are placed on each host; better scheduling decisions provide lower overall execution time for each job, more efficient resource usage, and reduced energy consumption. Existing DEFs use a static approach to scheduling, either with a single generalised scheduler which aims to be a good fit for most workloads, or with a special-purpose scheduler which is tuned to optimise for a particular kind of workload. In both cases the scheduling implementation is fixed at design-time such that the DEF is unable to adjust to the actual characteristics of workloads that arrive at deployment time. In this paper we introduce an emergent scheduler for Distributed Execution Frameworks. This scheduler can be composed and re-composed at runtime from a set of different building blocks, allowing the system to dynamically provide the benefits of differing scheduling policies over time depending on the actual properties of incoming workloads - with improved performance and resource usage. In this paper we present the overall design of our emergent scheduler, we discuss the theoretical design space of different scheduling approaches, and we examine a specific research question to determine the correlation between workload properties and scheduling performance for different scheduler implementations. Our results are based on a real implementation of our emergent DEF running across multiple hosts in a real datacentre, and our implementation is made available as open-source software.