Reviewerly/exHarmony huggingface dataset:
An Evaluation of Subword Segmentation Strategies for Neural Machine Translation of Morphologically Rich Languages

Byte-Pair Encoding (BPE) (Sennrich et al., 2016) has become a standard pre-processing step when building neural machine translation systems. However, it is not clear whether this is an optimal strategy in all settings. We conduct a controlled comparison of subword segmentation strategies for translating two low-resource morphologically rich languages (Swahili and Turkish) into English. We show that segmentations based on a unigram language model (Kudo, 2018) yield comparable BLEU and better recall for translating rare source words than BPE.
Impacts of Voice and Accountability upon Innovation in Pakistan: Evidence from ARDL and Maki Cointegration Approaches

Innovative capacity has a decisive and crucial role in determining who is prospering in the global arena. Innovation is crucial for value creation, high productivity, economic growth, employment, and sustainable development that benefit the consumers, firms, businesses, and economy as a whole. Several institutional, political, and social conditions lead to different responses of innovation. For instance, low voice and accountability may lead to low institutional setting, cronyism, and favoritism in allocation of resources, which can harm innovation. Similarly, the terrorist attack of 9/11 caused horrific scales of destructions, which caught the countries on various fronts such as economic, political, and technological. Further, sound infrastructure and savings rates are essential to enhance innovation. Applying the ARDL approach to cointegration, this study investigates the impact of voice and accountability, terrorism, infrastructure, and savings on innovation in Pakistan. The empirical evidence shows that low voice and accountability and terrorism have adverse impacts on innovation in the short- and long-run periods. Further, findings reveal that inadequate infrastructures and low savings adversely affect the innovation in both periods. Various robust checks such as Maki cointegration, Johansen cointegration test, fully modified ordinary least square, dynamic ordinary least square, and canonical cointegration regression corroborate the results. The findings of the study would be of substantial implications for government and policymakers.
New extremal binary self-dual codes of length 68 from generalized neighbors

In this work, we use the concept of distance between self-dual codes, which generalizes the concept of a neighbor for self-dual codes. Using the k-neighbors, we are able to construct extremal binary self-dual codes of length 68 with new weight enumerators. We construct 143 extremal binary self-dual codes of length 68 with new weight enumerators including 42 codes with γ=8 in their W68,2 and 40 with γ=9 in their W68,2. These examples are the first in the literature for these γ values. This completes the theoretical list of possible values for γ in W68,2.
The EM Algorithm gives Sample-Optimality for Learning Mixtures of Well-Separated Gaussians

We consider the problem of spherical Gaussian Mixture models with $k \geq 3$ components when the components are well separated. A fundamental previous result established that separation of $\Omega(\sqrt{\log k})$ is necessary and sufficient for identifiability of the parameters with polynomial sample complexity (Regev and Vijayaraghavan, 2017). In the same context, we show that $\tilde{O} (kd/\epsilon^2)$ samples suffice for any $\epsilon \lesssim 1/k$, closing the gap from polynomial to linear, and thus giving the first optimal sample upper bound for the parameter estimation of well-separated Gaussian mixtures. We accomplish this by proving a new result for the Expectation-Maximization (EM) algorithm: we show that EM converges locally, under separation $\Omega(\sqrt{\log k})$. The previous best-known guarantee required $\Omega(\sqrt{k})$ separation (Yan, et al., 2017). Unlike prior work, our results do not assume or use prior knowledge of the (potentially different) mixing weights or variances of the Gaussian components. Furthermore, our results show that the finite-sample error of EM does not depend on non-universal quantities such as pairwise distances between means of Gaussian components.
DeLighT: Very Deep and Light-weight Transformer

We introduce a very deep and light-weight transformer, DeLighT, that delivers similar or better performance than transformer-based models with significantly fewer parameters. DeLighT more efficiently allocates parameters both (1) within each Transformer block using DExTra, a deep and light-weight transformation and (2) across blocks using block-wise scaling, that allows for shallower and narrower DeLighT blocks near the input and wider and deeper DeLighT blocks near the output. Overall, DeLighT networks are 2.5 to 4 times deeper than standard transformer models and yet have fewer parameters and operations. Experiments on machine translation and language modeling tasks show that DeLighT matches the performance of baseline Transformers with significantly fewer parameters. On the WMT'14 En-Fr high resource dataset, DeLighT requires 1.8 times fewer parameters and 2 times fewer operations and achieves better performance (+0.4 BLEU score) than baseline transformers. On the WMT'16 En-Ro low resource dataset, DeLighT delivers similar performance with 2.8 times fewer parameters than baseline transformers.
TPM-FAIL: TPM meets Timing and Lattice Attacks

Trusted Platform Module (TPM) serves as a hardware-based root of trust that protects cryptographic keys from privileged system and physical adversaries. In this work, we perform a black-box timing analysis of TPM 2.0 devices deployed on commodity computers. Our analysis reveals that some of these devices feature secret-dependent execution times during signature generation based on elliptic curves. In particular, we discovered timing leakage on an Intel firmware-based TPM as well as a hardware TPM. We show how this information allows an attacker to apply lattice techniques to recover 256-bit private keys for ECDSA and ECSchnorr signatures. On Intel fTPM, our key recovery succeeds after about 1,300 observations and in less than two minutes. Similarly, we extract the private ECDSA key from a hardware TPM manufactured by STMicroelectronics, which is certified at Common Criteria (CC) EAL 4+, after fewer than 40,000 observations. We further highlight the impact of these vulnerabilities by demonstrating a remote attack against a StrongSwan IPsec VPN that uses a TPM to generate the digital signatures for authentication. In this attack, the remote client recovers the server's private authentication key by timing only 45,000 authentication handshakes via a network connection. The vulnerabilities we have uncovered emphasize the difficulty of correctly implementing known constant-time techniques, and show the importance of evolutionary testing and transparent evaluation of cryptographic implementations. Even certified devices that claim resistance against attacks require additional scrutiny by the community and industry, as we learn more about these attacks.
Communication Cost Aware Resource Efficient Load Balancing (CARELB) Framework for Cloud Datacenter

Background: Load balancing of communication-intensive applications, allowing efficient resource utilization and minimization of power consumption is a challenging multi-objective virtual machine (VM) placement problem. The communication among inter-dependent VMs, raises network traffic, hampers cloud client’s experience and degrades overall performance, by saturating the network. Introduction: Cloud computing has become an indispensable part of Information Technology (IT), which supports the backbone of digitization throughout the world. It provides shared pool of IT resources, which are: always on, accessible from anywhere, at anytime and delivered on demand, as a service. The scalability and pay-per-use benefits of cloud computing has driven the entire world towards on-demand IT services that facilitates increased usage of virtualized resources. The rapid growth in the demands of cloud resources has amplified the network traffic in and out of the datacenter. Cisco Global Cloud Index predicts that by the year 2021, the network traffic among the devices within the datacenter will grow at Compound Annual Growth Rate (CAGR) of 23.4% Methods: To address these issues, a communication cost aware and resource efficient load balancing (CARE-LB) framework is presented, that minimizes communication cost, power consumption and maximize resource utilization. To reduce the communication cost, VMs with high affinity and inter-dependency are intentionally placed closer to each other. The VM placement is carried out by applying the proposed integration of Particle Swarm Optimization and non-dominated sorting based Genetic Algorithm i.e. PSOGA algorithm encoding VM allocation as particles as well as chromosomes. Results: The performance of proposed framework is evaluated by the execution of numerous experiments in the simulated datacenter environment and it is compared with the state-of-the-art methods like, Genetic Algorithm, First-Fit, Random-Fit and Best-Fit heuristic algorithms. The experimental outcome reveals that the CARE-LB framework improves 11% resource utilization, minimize 4.4% power consumption, 20.3% communication cost with reduction of execution time up to 49.7% over Genetic Algorithm based Load Balancing framework. Conclusion: The proposed CARE-LB framework provides promising solution for faster execution of data-intensive applications with improved resource utilization and reduced power consumption. Discussion: In the observed simulation, we analyze all the three objectives, after execution of the proposed multi-objective VM allocations and results are shown in Table 4. To choose the number of users for analysis of communication cost, the experiments are conducted with different number of users. For instance, for 100 VMs we choose 10, 20,...,80 users, and their request for VMs (number of VMs and type of VMs) are generated randomly, such that the total number of requested VMs do not exceed number of available VMs.
Spatio-Temporal Consistency and Negative Label Transfer for 3D Freehand US Segmentation

The manual segmentation of multiple organs in 3D ultrasound (US) sequences and volumes towards their quantitative analysis is very expensive and time-consuming. Fully supervised segmentation methods still require the collection of large volumes of annotated data while unlabeled images are abundant. In this work, we propose a novel semi-automatic deep learning approach modeled as a weak-label learning problem: given a few 2-D annotations for selected slices, the goal is to propagate the masks to the entire sequence. To this end, we make use of both positive and negative constraints induced by incomplete labels to penalize the segmentation loss function. Our model is composed of one encoder and two decoders to model the segmentation and an auxiliary reconstruction task. Moreover, we consider the spatio-temporal information by deploying a Convolutional Long Short Term Memory module. Our findings suggest that the reconstruction decoder and the Spatio-temporal information lead to a better geometrical estimation of the mask shape. We apply the model to the task of low-limb muscle segmentation in a dataset of 44 patients and 6160 images.
An ASP-Based Approach to Counterfactual Explanations for Classification

We propose answer-set programs that specify and compute counterfactual interventions as a basis for causality-based explanations to decisions produced by classification models. They can be applied with black-box models and models that can be specified as logic programs, such as rule-based classifiers. The main focus is on the specification and computation of maximum responsibility causal explanations. The use of additional semantic knowledge is investigated.
A quantum encryption design featuring confusion, diffusion, and mode of operation

Abstract Quantum cryptography—the application of quantum information processing and quantum computing techniques to cryptography has been extensively investigated. Two major directions of quantum cryptography are quantum key distribution (QKD) and quantum encryption, with the former focusing on secure key distribution and the latter focusing on encryption using quantum algorithms. In contrast to the success of the QKD, the development of quantum encryption algorithms is limited to designs of mostly one-time pads (OTP) that are unsuitable for most communication needs. In this work we propose a non-OTP quantum encryption design utilizing a quantum state creation process to encrypt messages. As essentially a non-OTP quantum block cipher the method stands out against existing methods with the following features: 1. complex key-ciphertext relation (i.e. confusion) and complex plaintext-ciphertext relation (i.e. diffusion); 2. mode of operation design for practical encryption on multiple blocks. These features provide key reusability and protection against eavesdropping and standard cryptanalytic attacks.
Research on and Application of Fault Disposal in Flexible Interconnection Distribution Network

The application of flexible DC technology in distribution network can realize closed-loop operation of multiple feeders and flexible regulation of power flow. It provides a new solution for large-scale distributed power friendly access and high reliability power supply. This paper introduces the model and control method of back-to-back flexible DC in detail. According to the operation characteristics of the flexible interconnected distribution network, a cooperative fault recovery strategy among the master station, flexible DC control system and protection devices is proposed. The power supply capacity analysis model of flexible DC device with passive load is established. Finally, based on the actual situation of the flexible DC in Suzhou, a typical fault is taken as an example to elaborate the fault disposal process. This paper provides references and experiences for the operation of other AC-DC hybrid distribution networks.
Digital Forensics of Scanned QR Code Images for Printer Source Identification Using Bottleneck Residual Block

With the rapid development of information technology and the widespread use of the Internet, QR codes are widely used in all walks of life and have a profound impact on people’s work and life. However, the QR code itself is likely to be printed and forged, which will cause serious economic losses and criminal offenses. Therefore, it is of great significance to identify the printer source of QR code. A method of printer source identification for scanned QR Code image blocks based on convolutional neural network (PSINet) is proposed, which innovatively introduces a bottleneck residual block (BRB). We give a detailed theoretical discussion and experimental analysis of PSINet in terms of network input, the first convolution layer design based on residual structure, and the overall architecture of the proposed convolution neural network (CNN). Experimental results show that the proposed PSINet in this paper can obtain extremely excellent printer source identification performance, the accuracy of printer source identification of QR code on eight printers can reach 99.82%, which is not only better than LeNet and AlexNet widely used in the field of digital image forensics, but also exceeds state-of-the-art deep learning methods in the field of printer source identification.
Accuracy Versus Simplification in an Approximate Logic Neural Model

An approximate logic neural model (ALNM) is a novel single-neuron model with plastic dendritic morphology. During the training process, the model can eliminate unnecessary synapses and useless branches of dendrites. It will produce a specific dendritic structure for a particular task. The simplified structure of ALNM can be substituted by a logic circuit classifier (LCC) without losing any essential information. The LCC merely consists of the comparator and logic NOT, AND, and OR gates. Thus, it can be easily implemented in hardware. However, the architecture of ALNM affects the learning capacity, generalization capability, computing time and approximation of LCC. Thus, a Pareto-based multiobjective differential evolution (MODE) algorithm is proposed to simultaneously optimize ALNM’s topology and weights. MODE can generate a concise and accurate LCC for every specific task from ALNM. To verify the effectiveness of MODE, extensive experiments are performed on eight benchmark classification problems. The statistical results demonstrate that MODE is superior to conventional learning methods, such as the backpropagation algorithm and single-objective evolutionary algorithms. In addition, compared against several commonly used classifiers, both ALNM and LCC are capable of obtaining promising and competitive classification performances on the benchmark problems. Besides, the experimental results also verify that the LCC obtains the faster classification speed than the other classifiers.
Multi-threaded memory efficient crossover in C++ for generational genetic programming

C++ code snippets from a multi-core parallel memory-efficient crossover for genetic programming are given here . They may be adapted for separate generation evolutionary algorithms where large chromosomes or small RAM require no more than M + 2 x nthreads simultaneously active individuals.
200×200 µm<sup>2</sup> structured light source

3D structured illumination is important in high-speed 3D metrology where beam patterns are roughly categorized into multi-dot and fringe patterns. For example, large-scale multi-dot patterns are utilized for facial recognition in an iPhone X on the basis of an active stereo method, while fringe patterns are utilized in Grey code patterns or fringe projection profilometry including Fourier transform profilometry and the phase shifting profilometry, which is suitable for high-resolution measurement. Among these applications, the light sources include a combination of vertical-cavity surface-emitting lasers (VCSELs) and diffractive optical elements (DOEs), a projector, and so on. Recently, we demonstrated static arbitrary two-dimensional beam patterns without a zero-order beam from needle-tip sized integrable spatial-phase-modulating surface-emitting lasers (iPMSELs). Due to their compactness (they are one order of magnitude smaller than DOE), surface-emitting device, lack of zero-order beam, and ease of switching the beam patterns electrically, iPMSELs will be suitable as an ultra-compact light source for 3D metrology that not only downsizes the conventional light source but also contributes to 3D inspections in narrow spaces such as dental and endoscope examinations. In this context, we have examined two beam patterns (multi-dot and fringe) both without a zero-order beam by using the iPMSELs. In the former, we have demonstrated projection of large-scale dot patterns of more than 10,000 points, which is the same order of magnitude as points in a practical device from a 200×200-µm 2 emitter. Since the emitter has approximately 1 mega scattering points, this structure enables 1-mega-pixel images in the wavenumber space, which are comparable to the images of a typical projector emitting several-mega-pixel images from several tens of centimeters. In the latter, we successfully shifted the fringe patterns, which is vital to applying the phase shifting profilometry, despite the superposition of the conjugate ±1st order beam patterns.
Security Introduction Framework for Operational Technologies and Applying to Industrial Control System

Business owners of control systems have to ensure their system operations are protected from various security threats. Especially for control systems in critical infrastructures, business owners should deploy security countermeasures that fulfill mission assurance to continue their business function. The purpose of this paper is to introduce security measures that enable business continuation along with satisfying mission assurance. The proposed security introduction framework in this paper enables business owners of control systems to obtain exhaustive security measures from the viewpoint of technical, operational, and management aspects with reasonable effort. The proposed security introduction framework can be applied to a wide variety of control systems.
WITHDRAWN: Smart rural financial innovation based on 5G network and internet of things

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn at the request of the Editor in Chief. Subsequent to acceptance of this special issue paper by the responsible Guest Editor Sundhararajan Mahalingam, the integrity and rigor of the peer-review process was investigated and confirmed to fall beneath the high standards expected by Microprocessors & Microsystems. There are also indications that much of the Special Issue includes unoriginal and heavily paraphrased content. Due to a configuration error in the editorial system, unfortunately the Editor in Chief did not receive these papers for approval as per the journal's standard workflow.
Combining Event Semantics and Degree Semantics for Natural Language Inference

In formal semantics, there are two well-developed semantic frameworks: event semantics, which treats verbs and adverbial modifiers using the notion of event, and degree semantics, which analyzes adjectives and comparatives using the notion of degree. However, it is not obvious whether these frameworks can be combined to handle cases in which the phenomena in question are interacting with each other. Here, we study this issue by focusing on natural language inference (NLI). We implement a logic-based NLI system that combines event semantics and degree semantics and their interaction with lexical knowledge. We evaluate the system on various NLI datasets containing linguistically challenging problems. The results show that the system achieves high accuracies on these datasets in comparison with previous logic-based systems and deep-learning-based systems. This suggests that the two semantic frameworks can be combined consistently to handle various combinations of linguistic phenomena without compromising the advantage of either framework.
Deep Reinforcement Learning for Robot Batching Optimization and Flow Control

Robot batching is an optimization problem found in many industrial applications. Current state-of-the-art approaches utilize a combination of heuristic based parameters and statistical analysis. This approach necessitates many tunable parameters, which again provides challenges when delivering systems to new customers. We challenge current state-of-the-art in statistical approaches by presenting a novel application of a policy gradient method for a Deep Reinforcement Learning (DRL/RL) agent. We have developed a Unity simulation framework of an existing robot-batching cell, on which a RL agent is able to successfully train and obtain a policy for performing robot batching, using a tabula rasa approach. The trained agent is capable of packaging 47.86% of 1218 total batches within the prescribed tolerances, with a positive give-away of 8.76%. The application of DRL in performing robot batching is to the authors knowledge the first of its kind.