Reviewerly/exHarmony huggingface dataset:
Compressing Neural Machine Translation Models with 4-bit Precision

Quantization is one way to compress Neural Machine Translation (NMT) models, especially for edge devices.This paper pushes quantization from 8 bits, seen in current work on machine translation, to 4 bits.Instead of fixed-point quantization, we use logarithmic quantization since parameters are skewed towards zero.We then observe that quantizing the bias terms in this way damages quality, so we leave them uncompressed.Bias terms are a tiny fraction of the model so the impact on compression rate is minimal.Retraining is necessary to preserve quality, for which we propose to use an error-feedback mechanism that treats compression errors like noisy gradients.We empirically show that NMT models based on the Transformer or RNN architectures can be compressed up to 4-bit precision without any noticeable quality degradation.Models can be compressed up to binary precision, albeit with lower quality.The RNN architecture appears more robust towards compression, compared to the Transformer.
Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning

We consider a general asynchronous Stochastic Approximation (SA) scheme featuring a weighted infinity-norm contractive operator, and prove a bound on its finite-time convergence rate on a single trajectory. Additionally, we specialize the result to asynchronous $Q$-learning. The resulting bound matches the sharpest available bound for synchronous $Q$-learning, and improves over previous known bounds for asynchronous $Q$-learning.
Removing Backdoor-Based Watermarks in Neural Networks with Limited Data

Deep neural networks have been widely applied and achieved great success in various fields. As training deep models usually consumes massive data and computational resources, trading the trained deep models is highly demanded and lucrative nowadays. Unfortunately, the naive trading schemes typically involves potential risks related to copyright and trustworthiness issues, e.g., a sold model can be illegally resold to others without further authorization to reap huge profits. To tackle this problem, various watermarking techniques are proposed to protect the model intellectual property, amongst which the backdoor-based watermarking is the most commonly-used one. However, the robustness of these watermarking approaches is not well evaluated under realistic settings, such as limited in-distribution data availability and agnostic of watermarking patterns. In this paper, we benchmark the robustness of watermarking, and propose a novel backdoor-based watermark removal framework using limited data, dubbed WILD. The proposed WILD removes the watermarks of deep models with only a small portion of training data, and the output model can perform the same as models trained from scratch without watermarks injected. In particular, a novel data augmentation method is utilized to mimic the behavior of watermark triggers. Combining with the distribution alignment between the normal and perturbed (e.g., occluded) data in the feature space, our approach generalizes well on all typical types of trigger contents. The experimental results demonstrate that our approach can effectively remove the watermarks without compromising the deep model performance for the original task with the limited access to training data.
A Tool-Based Perspective on Software Code Maintainability Metrics: A Systematic Literature Review

Software maintainability is a crucial property of software projects. It can be defined as the ease with which a software system or component can be modified to be corrected, improved, or adapted to its environment. The software engineering literature proposes many models and metrics to predict the maintainability of a software project statically. However, there is no common accordance with the most dependable metrics or metric suites to evaluate such nonfunctional property. The goals of the present manuscript are as follows: (i) providing an overview of the most popular maintainability metrics according to the related literature; (ii) finding what tools are available to evaluate software maintainability; and (iii) linking the most popular metrics with the available tools and the most common programming languages. To this end, we performed a systematic literature review, following Kitchenham’s SLR guidelines, on the most relevant scientific digital libraries. The SLR outcome provided us with 174 software metrics, among which we identified a set of 15 most commonly mentioned ones, and 19 metric computation tools available to practitioners. We found optimal sets of at most five tools to cover all the most commonly mentioned metrics. The results also highlight missing tool coverage for some metrics on commonly used programming languages and minimal coverage of metrics for newer or less popular programming languages. We consider these results valuable for researchers and practitioners who want to find the best selection of tools to evaluate the maintainability of their projects or to bridge the discussed coverage gaps for newer programming languages.
Securing Branch Predictors with Two-Level Encryption

Modern processors rely on various speculative mechanisms to meet performance demand. Branch predictors are one of the most important micro-architecture components to deliver performance. However, they have been under heavy scrutiny because of recent side-channel attacks. Branch predictors are indexed using the PC and recent branch histories. An adversary can manipulate these parameters to access and control the same branch predictor entry that a victim uses. Recent Spectre attacks exploit this to set up speculative-execution-based security attacks. In this article, we aim to mitigate branch predictor side-channels using two-level encryption. At the first level, we randomize the set-index by encrypting the PC using a per-context secret key. At the second level, we encrypt the data in each branch predictor entry. While periodic key changes make the branch predictor more secure, performance degradation can be significant. To alleviate performance degradation, we propose a practical set update mechanism that also considers parallelism in multi-banked branch predictors. We show that our mechanism exhibits only 1.0% and 0.2% performance degradation while changing keys every 10K and 50K cycles, respectively, which is much lower than other state-of-the-art approaches.
Joint Self-Attention and Multi-Embeddings for Chinese Named Entity Recognition

Named Entity Recognition (NER) is a fundamental task in Natural Language Processing (NLP), but it remains more challenging in Chinese due to the particularity and complexity of Chinese. Traditional Chinese Named Entity Recognition (Chinese NER) methods require cumbersome feature engineering and domain-specific knowledge to achieve high performance. In this paper, we propose a simple yet effective neural network framework for Chinese NER, named A-NER. A-NER is the first Bidirectional Gated Recurrent Unit - Conditional Random Field (BiGRU-CRF) model that combines self-attention mechanism with multi-embeddings technology. It can extract richer linguistic information of characters from different granularities (e.g., radical, character, word) and find the correlations between characters in the sequence. Moreover, A-NER does not rely on any external resources and hand-crafted features. The experimental results show that our model outperforms (or approaches) existing state-of-the-art methods on different domain datasets.
On post-processing in the quantum algorithm for computing short discrete logarithms

Abstract We revisit the quantum algorithm for computing short discrete logarithms that was recently introduced by Ekerå and Håstad. By carefully analyzing the probability distribution induced by the algorithm, we show its success probability to be higher than previously reported. Inspired by our improved understanding of the distribution, we propose an improved post-processing algorithm that is considerably more efficient, enables better tradeoffs to be achieved, and requires fewer runs, than the original post-processing algorithm. To prove these claims, we construct a classical simulator for the quantum algorithm by sampling the probability distribution it induces for given logarithms. This simulator is in itself a key contribution. We use it to demonstrate that Ekerå–Håstad achieves an advantage over Shor, not only in each individual run, but also overall, when targeting cryptographically relevant instances of RSA and Diffie–Hellman with short exponents.
Optimization of light-dependent resistor sensor for the application of solar energy tracking system

Optimization of cadmium sulfide light-dependent resistor (CdS-LDR) sensor is one of the suitable circuit elements to be used as the sun-pointing sensor. The sun-pointing sensor is used in solar energy tracking systems to capture maximum power by photovoltaic (PV) cells or systems at the time of uniform or partial irradiance of the sun and effect of shade during clouds. PV cells or modules’ generating power is affected due to partial shading. The PV cells are able to produce maximum power, as long as the sun’s irradiance is projected perpendicular to the panel’s surface. Optimization of light-dependent resistor CdS-LDR sensor is designed based on the intensity of the sun. A relation is obtained between intensity and photo-resistance of CdS-LDR sensor with coefficient of regression ( $${R}^{2}$$ ) value of 0.99. Maximum intensity of the sun is captured using CdS-LDR sensor which is designed with characteristic curve of intensity and photo-resistance using power algorithm. Minimum dull intensity of the sunlight is used to place the panel in initial position. In addition to that cloud effects with sudden transients are also analyzed using temperature humidity index term. The power gained by optimized dual axis solar tracking (DAST) system is 50.63% with reference to fixed panel. The generated power of DAST system is also analyzed under shading effect of cloud. Data are collected for every 2–3 s of time interval from 6:32:00 to 17:38:33 using real-time virtual instrumentation Parallax Data Acquisition tool.
PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data

The 3D volumetric shape of the heart’s left ventricle (LV) myocardium (MYO) wall provides important information for diagnosis of cardiac disease and invasive procedure navigation. Many cardiac image segmentation methods have relied on detection of region-of-interest as a pre-requisite for shape segmentation and modeling. With segmentation results, a 3D surface mesh and a corresponding point cloud of the segmented cardiac volume can be reconstructed for further analyses. Although state-of-the-art methods (e.g., U-Net) have achieved decent performance on cardiac image segmentation in terms of accuracy, these segmentation results can still suffer from imaging artifacts and noise, which will lead to inaccurate shape modeling results. In this paper, we propose a PC-U net that jointly reconstructs the point cloud of the LV MYO wall directly from volumes of 2D CT slices and generates its segmentation masks from the predicted 3D point cloud. Extensive experimental results show that by incorporating a shape prior from the point cloud, the segmentation masks are more accurate than the state-of-the-art U-Net results in terms of Dice’s coefficient and Hausdorff distance. The proposed joint learning framework of our PC-U net is beneficial for automatic cardiac image analysis tasks because it can obtain simultaneously the 3D shape and segmentation of the LV MYO walls.
The Digital Transformation and Disruption in Business Models of the Banks under the Impact of FinTech and BigTech

Abstract The explosive development of artificial intelligence, machine learning and big data methods in the last 10 years has been felt in the financial-banking field which has subjected to profound changes aimed at determining an unprecedented increase in the efficiency and profitability of the businesses they carry out. The tendencies of applying the concepts coming from AI, together with the continuous increase of the volume, complexity and variety of the data that the banks collect, store and process have acquired the generic names of FinTech, respectively BigTech. Five main areas exist where Fintechs and Bigtechs can provide improvements in business models for the banks: introducing specialized platforms, covering neglected customer segments, improving customer selection, reduction of the operating costs of the banks, and optimization of the business processes of the banks. We will present some of these improvements, and then we will show how the business models of the banks dramatically transform under the influence of these changes.
Multi-Cell Mobile Edge Coded Computing: Trading Communication and Computing for Distributed Matrix Multiplication

A multi-cell mobile edge computing network is studied, in which each user wishes to compute the product of a user-generated data matrix with a network-stored matrix through data uploading, distributed edge computing, and output downloading. Assuming randomly straggling edge servers, this paper investigates the interplay among upload, compute, and download times in high signal-to-noise ratio regimes. A policy based on cascaded coded computing and on coordinated and cooperative interference management in uplink and downlink is proposed and proved to be approximately optimal for sufficiently large upload times. By investing more time in uplink transmission, the policy creates data redundancy at the edge nodes to reduce both computation times by coded computing, and download times via transmitter cooperation. Moreover, it allows computing times to be traded for download times.
Ontology Modularization with OAPT

Abstract Ontologies are the backbone of the Semantic Web. As a result, the number of existing ontologies and the number of topics covered by them has increased considerably. With this, reusing these ontologies becomes preferable to constructing new ontologies from scratch. However, a user might be interested in a part and/or a set of parts of a given ontology, only. Therefore, ontology modularization, i.e., splitting up an ontology into smaller parts that can be independently used, becomes a necessity. In this paper, we introduce a new approach to partition ontology based on the seeding-based scheme, which is developed and implemented through the Ontology Analysis and Partitioning Tool ( OAPT ). This tool proceeds according to the following methodology: first, before a candidate ontology is partitioned, OAPT optionally analyzes the input ontology to determine, if this ontology is worth considering using a predefined set of criteria that quantify the semantic and structural richness of the ontology. After that, we apply the seeding-based partitioning algorithm to modularize it into a set of modules. To decide upon a suitable number of modules that will be generated by partitioning the ontology, we provide the user a recommendation based on an information theoretic model selection method. We demonstrate the effectiveness of the OAPT tool and validate the performance of the partitioning approach by conducting an extensive set of experiments. The results prove the quality and the efficiency of the proposed tool.
Infinite-fold enhancement in communications capacity using pre-shared entanglement

Pre-shared entanglement can significantly boost communication rates in the regime of high thermal noise, and a low-brightness transmitter. In this regime, the ratio between the entanglement-assisted capacity and the Holevo capacity, the maximum reliable-communication rate permitted by quantum mechanics without any pre-shared entanglement as a resource, is known to scale as log(1/N̅ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> ), where N̅ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</sub> ≪ 1 is the mean transmitted photon number per mode. This is especially promising in enabling a large boost to radio-frequency communications in the weak-transmit-power regime, by exploiting pre-shared optical-frequency entanglement, e.g., distributed by the quantum internet. In this paper, we propose a structured design of a quantum transmitter and receiver that leverages continuous-variable pre-shared entanglement from a downconversion source, which can harness this purported infinite-fold capacity enhancement- a problem that has been open for over a decade. Its implication to the breaking of the well-known square root law for covert communications, with entanglement assistance, is discussed.
Multimodal interaction design and application in augmented reality for chemical experiment

Augmented reality classrooms have become an interesting research topic in the field of education, but there are some limitations. Firstly, most researchers use cards to operate experiments, and a large number of cards cause difficulty and inconvenience for users. Secondly, most users conduct experiments only in the visual modal, and such single-modal interaction greatly reduces the users' real sense of interaction. In order to solve these problems, we propose the Multimodal Interaction Algorithm based on Augmented Reality (ARGEV), which is based on visual and tactile feedback in Augmented Reality. In addition, we design a Virtual and Real Fusion Interactive Tool Suite (VRFITS) with gesture recognition and intelligent equipment. The ARGVE method fuses gesture, intelligent equipment, and virtual models. We use a gesture recognition model trained by a convolutional neural network to recognize the gestures in AR, and to trigger a vibration feedback after a recognizing a fivefinger grasp gesture. We establish a coordinate mapping relationship between real hands and the virtual model to achieve the fusion of gestures and the virtual model. The average accuracy rate of gesture recognition was 99.04%. We verify and apply VRFITS in the Augmented Reality Chemistry Lab (ARCL), and the overall operation load of ARCL is thus reduced by 29.42%, in comparison to traditional simulation virtual experiments. We achieve real-time fusion of the gesture, virtual model, and intelligent equipment in ARCL. Compared with the NOBOOK virtual simulation experiment, ARCL improves the users' real sense of operation and interaction efficiency.
Low cost flex powered gesture detection system and its applications

A hand gesture is a non-verbal means of communication involving motion of fingers to convey information. Hand gesture are used in sign language and are a way of communication for deaf and mute people. Common public most often is unable to understand the sign language. So, in this paper we aim to provide an electronic method for hand gesture recognition which is cost effective, this system makes use of flex sensors, on-board gyroscope and accelerometer. A flex sensor works on the principle of change in the internal resistance to detect the angle made by the user's finger at any given time. The flexes made by hand in different combinations amount to a gesture and this gesture can be converted into speech signals or as a text display to aid physically challenged people. A smart glove is designed which has an Arduino circuit which is equipped with the custom-made flex sensors, an on-board gyroscope and an accelerometer, out of which the last two components are used to supplement the gestures detected by a flex sensor.
3D Auto-Segmentation of Mandibular Condyles

Temporomandibular joints (TMJ) like a hinge connect the jawbone to the skull. TMJ disorders could cause pain in the jaw joint and the muscles controlling jaw movement. However, the disease cannot be diagnosed until it becomes symptomatic. It has been shown that bone resorption at the condyle articular surface is already evident at initial diagnosis of TMJ Osteoarthritis (OA). Therefore, analyzing the bone structure will facilitate the disease diagnosis. The important step towards this analysis is the condyle segmentation. This article deals with a method to automatically segment the temporomandibular joint condyle out of cone beam CT (CBCT) scans. In the proposed method we denoise images and apply 3D active contour and morphological operations to segment the condyle. The experimental results show that the proposed method yields the Dice score of 0.9461 with the standards deviation of 0.0888 when it is applied on CBCT images of 95 patients. This segmentation will allow large datasets to be analyzed more efficiently towards data sciences and machine learning approaches for disease classification.
Optimal Accelerated Variance Reduced EXTRA and DIGing for Strongly Convex and Smooth Decentralized Optimization

We study stochastic decentralized optimization for the problem of training machine learning models with large-scale distributed data. We extend the famous EXTRA and DIGing methods with accelerated variance reduction (VR), and propose two methods, which require the time of $O((\sqrt{n\kappa_s}+n)\log\frac{1}{\epsilon})$ stochastic gradient evaluations and $O(\sqrt{\kappa_b\kappa_c}\log\frac{1}{\epsilon})$ communication rounds to reach precision $\epsilon$, where $\kappa_s$ and $\kappa_b$ are the stochastic condition number and batch condition number for strongly convex and smooth problems, $\kappa_c$ is the condition number of the communication network, and $n$ is the sample size on each distributed node. Our stochastic gradient computation complexity is the same as the single-machine accelerated variance reduction methods, such as Katyusha, and our communication complexity is the same as the accelerated full batch decentralized methods, such as MSDA, and they are both optimal. We also propose the non-accelerated VR based EXTRA and DIGing, and provide explicit complexities, for example, the $O((\kappa_s+n)\log\frac{1}{\epsilon})$ stochastic gradient computation complexity and the $O((\kappa_b+\kappa_c)\log\frac{1}{\epsilon})$ communication complexity for the VR based EXTRA. The two complexities are also the same as the ones of single-machine VR methods, such as SAG, SAGA, and SVRG, and the non-accelerated full batch decentralized methods, such as EXTRA, respectively.
Classification accuracy of basketball simulation training system based on sensor fusion and Bayesian algorithm

As a pattern recognition application direction, human body posture recognition provides decision-making basis for human body behavior pattern analysis of human-computer intelligent interactive control. Therefore, in a complete human-computer intelligent interaction system, human body posture recognition is a necessary link that can complete the human body’s behavioral characterization and make humanized decision-making. This paper studies the athlete’s posture recognition algorithm based on multi-sensor method and completes the whole process from data acquisition to data processing and model algorithm construction and verification. Moreover, this paper designs experiments to verify the model’s recognition results for athletes, and discusses the results, and analyzes the advantages and disadvantages of the model in this experiment. In addition, this study takes basketball action as an example to take identification analysis. The results show that the proposed method has certain practical effects and can provide theoretical reference for subsequent related research.