Reviewerly/exHarmony huggingface dataset:
Sentence Boundary Augmentation for Neural Machine Translation Robustness

Neural Machine Translation (NMT) models have demonstrated strong state of the art performance on translation tasks where well-formed training and evaluation data are pro-vided, but they remain sensitive to inputs that include errors of various types. Specifically, in the context of long-form speech translation systems, where the input transcripts come from Automatic Speech Recognition (ASR), the NMT models have to handle errors including phoneme substitutions, grammatical structure, and sentence boundaries, all of which pose challenges to NMT robustness. Through in-depth error analysis, we show that sentence boundary segmentation has the largest impact on quality, and we develop a simple data augmentation strategy to improve segmentation robustness.
An Efficient Flow Detection and Scheduling Method in Data Center Networks

Nowdays Datacenter networks (DCNs) scale is rapidly increasing because of the widely deployment of cloud services. Thus, a large amount of data needs to be frequently interchanged among thousands of servers. In this environment, a key and challenging issue is to keep the traffic loadbalanced. In this letter, we propose a port forwarding load-balanced scheduling (PFLBS) approach for Fat-tree based DCNs. Firstly, we define a port-based source-routing addressing scheme, which simplifies the switch's functionality and makes the routing lookup operation unnecessary. Secondly, we apply the addressing scheme to the end hosts and formulate the PFLBS problem. PFLBS propose an efficient algorithm for multipath selection to obtain optimal paths and designs a trigger mechanism to split large flow implement scheduling data flows dynamically. The experiment results indicate that our PFLBS approach has better performance compared with the methods ECMP and Hedera and decreases the flow completion time and improve the average throughput significantly.
Ajalon: Simplifying the authoring of wearable cognitive assistants

Summary Wearable Cognitive Assistance (WCA) amplifies human cognition in real time through a wearable device and low‐latency wireless access to edge computing infrastructure. It is inspired by, and broadens, the metaphor of GPS navigation tools that provide real‐time step‐by‐step guidance, with prompt error detection and correction. WCA applications are likely to be transformative in education, health care, industrial troubleshooting, manufacturing, assisted driving, and sports training. Today, WCA application development is difficult and slow, requiring skills in areas such as machine learning and computer vision that are not widespread among software developers. This paper describes Ajalon, an authoring toolchain for WCA applications that reduces the skill and effort needed at each step of the development pipeline. Our evaluation shows that Ajalon significantly reduces the effort needed to create new WCA applications.
Topology-Aware Graph Signal Sampling for Pooling in Graph Neural Networks

As a generalization of convolutional neural networks to graph-structured data, graph convolutional networks learn feature embeddings based on the information of each nodes local neighborhood. However, due to the inherent irregularity of such data, extracting hierarchical representations of a graph becomes a challenging task. Several pooling approaches have been introduced to address this issue. In this paper, we propose a novel topology-aware graph signal sampling method to specify the nodes that represent the communities of a graph. Our method selects the sampling set based on the local variation of the signal of each node while considering vertex-domain distances of the nodes in the sampling set. In addition to the interpretability of the sampled nodes provided by our method, the experimental results both on stochastic block models and real-world dataset benchmarks show that our method achieves competitive results compared to the state-of-the-art in the graph classification task.
Recovering Texture with a Denoising-Process-Aware LMMSE Filter

Image denoising methods generally remove not only noise but also fine-scale textures and thus degrade the subjective image quality. In this paper, we propose a method of recovering the texture component that is lost under a state-of-the-art denoising method called weighted nuclear norm minimization (WNNM). We recover the image texture with a linear minimum mean squared error estimator (LMMSE filter), which requires statistical information about the texture and noise. This requirement is the key problem preventing the application of the LMMSE filter for texture recovery because such information is not easily obtained. We propose a new method of estimating the necessary statistical information using Stein’s lemma and several assumptions and show that our estimated information is more accurate than the simple estimation in terms of the Fréchet distance. Experimental results show that our proposed method can improve the objective quality of denoised images. Moreover, we show that our proposed method can also improve the subjective quality when an additional parameter is chosen for the texture to be added.
QFL: Data-Driven Feedback Loop to Manage Quality in Agile Development

Background: Quality requirements (QRs) describe desired system qualities, playing an important role in the success of software projects. In the context of agile software development (ASD), where the main objective is the fast delivery of functionalities, QRs are often ill-defined and not well addressed during the development process. Software analytics tools help to control quality though the measurement of quality-related software aspects to support decision-makers in the process of QR management. Aim: The goal of this research is to explore the benefits of integrating a concrete software analytics tool, Q-Rapids Tool, to assess software quality and support QR management processes. Method: In the context of a technology transfer project, the Softeam company has integrated Q-Rapids Tool in their development process. We conducted a series of workshops involving Softeam members working in the Modelio product development. Results: We present the Quality Feedback Loop (QFL) process to be integrated in software development processes to control the complete QR life-cycle, from elicitation to validation. As a result of the implementation of QFL in Softeam, Modelio's team members highlight the benefits of integrating a data analytics tool with their project planning tool and the fact that project managers can control the whole process making the final decisions. Conclusions: Practitioners can benefit from the integration of software analytics tools as part of their software development toolchain to control software quality. The implementation of QFL promotes quality in the organization and the integration of software analytics and project planning tools also improves the communication between teams.
A Hierarchical Sequence-to-Sequence Model for Korean POS Tagging

Part-of-speech (POS) tagging is a fundamental task in natural language processing. Korean POS tagging consists of two subtasks: morphological analysis and POS tagging. In recent years, scholars have tended to use the seq2seq model to solve this problem. The full context of a sentence is considered in these seq2seq-based Korean POS tagging methods. However, Korean morphological analysis relies more on local contextual information, and in many cases, there exists one-to-one matching between morpheme surface form and base form. To make better use of these characteristics, we propose a hierarchical seq2seq model. In our model, the low-level Bi-LSTM encodes the syllable sequence, whereas the high-level Bi-LSTM models the context information of the whole sentence, and the decoder generates the morpheme base form syllables as well as the POS tags. To improve the accuracy of the morpheme base form recovery, we introduced the convolution layer and the attention mechanism to our model. The experimental results on the Sejong corpus show that our model outperforms strong baseline systems in both morpheme-level F1-score and eojeol-level accuracy, achieving state-of-the-art performance.
Replay-Attack Detection Using Features With Adaptive Spectro-Temporal Resolution

Variable-resolution processing aims to improve the feature representation ability by enlarging the local discriminative details. In previous anti-spoofing studies, different phones and frequency regions were both proven to have various levels of sensitivity to replay distortion. In this paper, an adaptive spectro-temporal resolution is proposed to obtain the optimal scale in the feature space: the frequency resolution is adaptive to frequency discrimination, while the temporal resolution is adaptive to continuous phones. In the process, phone-frequency F-ratio analysis is applied to investigate the sensitivity divergences to replay distortion among phones and frequencies. Then, attentive filters are designed to automatically adapt to the phone-frequency discrimination. Validation experiments for the proposed method are conducted on two well-acknowledged magnitude and phase features. A comparative analysis on the ASVspoof 2017 V2.0 database demonstrates that our proposed adaptive spectro-temporal resolution method attains considerably higher error reduction rates than the approaches involving the corresponding original resolution features.
Design and Implementation of Lightweight Dynamic Elliptic Curve Cryptography Using Schoof’s Algorithm

Security and efficiency for resource constrained devices using lightweight cryptography techniques is still a problem. It is important to make these devices secure from the intruders and communicate with them securely. Elliptic Curve Cryptography (ECC) comes out to be a major contender to solve this problem as it provides the security using smaller key length. Since the conventional ECC works on predefined and fixed curves, the elliptic curves are analysed thoroughly since the emergence of ECC. This paper, proposes an implementation of dynamic elliptic curves which are secure and feasible for Internet of Things (IoT) devices and other devices which require lightweight cryptography for security. The experimental results show the feasibility and efficiency of the algorithm used to generate dynamic curve for cryptography. Also, a new curve will be generated at each runtime. It would be nearly impossible for intruders to exploit and retract information from the devices. Hence, the proposed technique is more secure and practically efficient for resource constrained devices.
Fast Inverse Mapping of Face GANs

Generative adversarial networks (GANs) synthesize realistic images from random latent vectors. While many studies have explored various training configurations and architectures for GANs, the problem of inverting the generator of GANs has been inadequately investigated. We train a ResNet architecture to map given faces to latent vectors that can be used to generate faces nearly identical to the target. We use a perceptual loss to embed face details in the recovered latent vector while maintaining visual quality using a pixel loss. The vast majority of studies on latent vector recovery are very slow and perform well only on generated images. We argue that our method can be used to determine a fast mapping between real human faces and latent-space vectors that contain most of the important face style details. At last, we demonstrate the performance of our approach on both real and generated faces.
High-quality Frame Recurrent Video De-raining with Multi-contextual Adversarial Network

In this article, we address the problem of rain-streak removal in the videos. Unlike the image, challenges in video restoration comprise temporal consistency besides spatial enhancement. The researchers across the world have proposed several effective methods for estimating the de-noised videos with outstanding temporal consistency. However, such methods also amplify the computational cost due to their larger size. By way of analysis, incorporating separate modules for spatial and temporal enhancement may require more computational resources. It motivates us to propose a unified architecture that directly estimates the de-rained frame with maximal visual quality and minimal computational cost. To this end, we present a deep learning-based Frame-recurrent Multi-contextual Adversarial Network for rain-streak removal in videos. The proposed model is built upon a Conditional Generative Adversarial Network (CGAN)-based framework where the generator model directly estimates the de-rained frame from the previously estimated one with the help of its multi-contextual adversary. To optimize the proposed model, we have incorporated the Perceptual loss function in addition to the conventional Euclidean distance. Also, instead of traditional entropy loss from the adversary, we propose to use the Euclidean distance between the features of de-rained and clean frames, extracted from the discriminator model as a cost function for video de-raining. Various experimental observations across 11 test sets, with over 10 state-of-the-art methods, using 14 image-quality metrics, prove the efficacy of the proposed work, both visually and computationally.
A Novel Gaze Gesture Sensor for Smart Glasses Based on Laser Self-Mixing

The integration of gaze gesture sensors in next-generation smart glasses will improve usability and enable new interaction concepts. However, consumer smart glasses place additional requirements to gaze gesture sensors, such as a low power consumption, high integration capability and robustness to ambient illumination. We propose a novel gaze gesture sensor based on laser feedback interferometry (LFI), which is capable to measure the rotational velocity of the eye as well as the sensor’s distance towards the eye. This sensor delivers a unique and novel set of features with an outstanding sample rate allowing to not only predict a gaze gesture but also to anticipate it. To take full advantage of the unique sensor features and the high sampling rate, we propose a novel gaze symbol classification algorithm based on single sample. At a mean F1-score of 93.44 %, our algorithms shows exceptional classification performance.
Stochastic Label Refinery: Toward Better Target Label Distribution

This paper proposes a simple yet effective strategy for improving deep supervised learning, named Stochastic Label Refinery (SLR), by refining training labels to more informative labels. When training a neural network, target distributions (or ground-truth) are typically “hard”, which means the target label of each category consists of only 0 and 1. However, the fixed “hard” target distributions do not capture association between categories or that between objects. In this study, instead of using the hard target distributions, we iteratively generate “soft” target label distributions for training the neural networks, which leads to better performances. The soft target distributions are obtained via an Expectation-Maximization (EM) iteration, where the “true” target distributions and the learned models are regarded as hidden variables. In E step, the models are optimized to approximate the target distributions on stochastic splits of training data; In M step, the target distributions are updated with predicted pseudo-label on leave-out splits. Extensive experiments on classification and ordinal regression tasks, empirically prove that the refined target distribution consistently leads to considerable performance improvements even applied on competitive baselines. Notably, in DeepDR 2020 Diabetic Retinopathy Grading (DeepDRiD) challenge, our method improves the quadratic weighted kappa on official validation set from 0.8247 to 0.8348 and achieves a state-of-the-art score on online test set. The proposed SLR technique is easy to implement and practically applicable.
Object-Oriented Relational Distillation for Object Detection

Object detection models have achieved increasingly better performance based on more complex architecture designs, but the heavy computation limits their further widespread application on the devices with insufficient computational power. To this end, we propose a novel Object-Oriented Relational Distillation (OORD) method that drives small detection models to have an effective performance like large detection models with constant efficiency. Here, we introduce to distill relative relation knowledge from teacher/large models to student/small models, which promotes the small models to learn better soft feature representation by the guiding of large models. OORD consists of two parts, i.e., Object Extraction (OE) and Relation Distillation (RD). OE extracts foreground features to avoid background feature interference, and RD distills the relative relations between the foreground features through graph convolution. Related experiments conducted on various kinds of detection models show the effectiveness of OORD, which improves the performance of the small model by nearly 10% without additional inference time cost.
CASNet: Common Attribute Support Network for image instance and panoptic segmentation

Instance segmentation and panoptic segmentation is being paid more and more attention in recent years. In comparison with bounding box based object detection and semantic segmentation, instance segmentation can provide more analytical results at pixel level. Given the insight that pixels belonging to one instance have one or more common attributes of current instance, we bring up an one-stage instance segmentation network named Common Attribute Support Network (CASNet), which realizes instance segmentation by predicting and clustering common attributes. CASNet is designed in the manner of fully convolutional and can implement training and inference from end to end. And CASNet manages predicting the instance without overlaps and holes, which problem exists in most of current instance segmentation algorithms. Furthermore, it can be easily extended to panoptic segmentation through minor modifications with little computation overhead. CASNet builds a bridge between semantic and instance segmentation from finding pixel class ID to obtaining class and instance ID by operations on common attribute. Through experiment for instance and panoptic segmentation, CASNet gets mAP 32.8% and PQ 59.0% on Cityscapes validation dataset by joint training, and mAP 36.3% and PQ 66.1% by separated training mode. For panoptic segmentation, CASNet gets state-of-the-art performance on the Cityscapes validation dataset.
Training Logical Neural Networks by Primal–Dual Methods for Neuro-Symbolic Reasoning

Parametrized machine learning models for inference often include non-linear and nonconvex constraints over the parameters and meta-parameters. Training these models to convergence is in general difficult, and naive methods such as projected gradient descent or grid search are not easily able to enforce the functional constraints. This work explores the optimization of a constrained neural network (familiar from machine learning but with parameter constraints), in the service of neuro-symbolic logical reasoning. Logical neural networks (LNNs) provide a well-justified, interpretable example of training under non-trivial constraints. In this paper, we propose a unified framework for solving this nonlinear programming problem by leveraging primal-dual optimization methods, and quantify the corresponding convergence rate to the Karush-Kuhn-Tucker (KKT) points of this problem. Extensive numerical results on both a toy example and training an LNN over real datasets validate the efficacy of the method.
A Self-supervised GAN for Unsupervised Few-shot Object Recognition

This paper addresses unsupervised few-shot object recognition, where all training images are unlabeled, and test images are divided into queries and a few labeled support images per object class of interest. The training and test images do not share object classes. We extend the vanilla GAN with two loss functions, both aimed at self-supervised learning. The first is a reconstruction loss that enforces the discriminator to reconstruct the probabilistically sampled latent code which has been used for generating the “fake” image. The second is a triplet loss that enforces the discriminator to output image encodings that are closer for more similar images. Evaluation, comparisons, and detailed ablation studies are done in the context of few-shot classification. Our approach significantly outperforms the state of the art on the Mini-Imagenet and Tiered-Imagenet datasets.
Quantifying Novice Behavior, Experience, and Mental Effort in Code Puzzle Pathways

Code puzzles are an increasingly popular approach to introducing programming to young learners. Today, code puzzles are predominantly introduced through static puzzle sequences with increasing difficulty. However, adaptive systems in other domains have improved learning efficiency. This paper takes a step towards developing adaptive code puzzle systems based on controlling learners’ cognitive load. We conducted a study comparing static code puzzle pathways and adaptive pathways that predict cognitive load on future puzzles. While the trialled adaptive recommendation policy did not result in better learning, our findings point us towards a different policy which may have a greater effect on learner experience. In addition, we identify predictors of student dropout, and use our experimental data to quantify learners’ puzzle-solving experiences into 7 principal component properties and use these factors to suggest approaches for future adaptive systems.
Bank Account Classification for Gambling Transactions

Financial services provided by banks are often misused for money laundering. Gambling is a criminal offense in the jurisdiction of the Republic of Indonesia. The increase in the number of customers is a challenge for the Bank to supervise the financial transactions. Based on the previous research, classification in machine learning often used to identify money laundering in the banking industry. However, there are no studies about identifying misuse of accounts in gambling activities using machine learning. This research used real financial transaction data using the SLR, experimental, and semi structured interviews with several Subject Matter Expertise. The results of this study indicate the classification with ensemble algorithms such as LightGBM can identify gambling accounts. Based on the evaluation results of classification performance with LightGBM, this model has the best performance compared to other ensemble models at a precision of 97.26%. These findings can be used as a basis for automatic reporting of suspicious financial transactions to financial regulatory.
An Improved Numerical DBSCAN Algorithm Based on Non-IIDness Learning

In clustering algorithm research, objects, attributes and other aspects of data sets are usually considered to be independent and identically distributed; that is, each object is assumed to be an independent and uniformly distributed individual with no impacts between objects. However, objects in real life are often neither independently nor identically distributed; that is, they are non-IID, leading to a complex coupling relationship between objects, and objects interact with each other. The results of a clustering algorithm under an independent and identical distribution may be incomplete or even misleading. To make the results of the DBSCAN algorithm as accurate as possible, an improved numerical DBSCAN algorithm based on non-IIDness learning is proposed in this paper. The algorithm calculates the coupling relationship between objects to obtain the potential relationship between objects and determines the parameters <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Eps</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MinPts</i> by the distribution characteristics of the data. Experiments on large-scale real and synthetic data sets show that the algorithm achieves a higher accuracy than the original DBSCAN algorithm and the main algorithms that improved upon it.