Reviewerly/exHarmony huggingface dataset:
Bayesian Optimization of Composite Functions

We consider optimization of composite objective functions, i.e., of the form $f(x)=g(h(x))$, where $h$ is a black-box derivative-free expensive-to-evaluate function with vector-valued outputs, and $g$ is a cheap-to-evaluate real-valued function. While these problems can be solved with standard Bayesian optimization, we propose a novel approach that exploits the composite structure of the objective function to substantially improve sampling efficiency. Our approach models $h$ using a multi-output Gaussian process and chooses where to sample using the expected improvement evaluated on the implied non-Gaussian posterior on $f$, which we call expected improvement for composite functions (\ei). Although \ei cannot be computed in closed form, we provide a novel stochastic gradient estimator that allows its efficient maximization. We also show that our approach is asymptotically consistent, i.e., that it recovers a globally optimal solution as sampling effort grows to infinity, generalizing previous convergence results for classical expected improvement. Numerical experiments show that our approach dramatically outperforms standard Bayesian optimization benchmarks, reducing simple regret by several orders of magnitude.
Predicting Word Concreteness and Imagery

Concreteness of words has been studied extensively in psycholinguistic literature.A number of datasets has been created with average values for perceived concreteness of words.We show that we can train a regression model on these data, using word embeddings and morphological features.We evaluate the model on 7 publicly available datasets and show that concreteness and imagery values can be predicted with high accuracy.Furthermore, we analyse typical contexts of abstract and concrete words and review the potentials of concreteness prediction for image annotation.
Counting Complexity for Reasoning in Abstract Argumentation

In this paper, we consider counting and projected model counting of extensions in abstract argumentation for various semantics. When asking for projected counts we are interested in counting the number of extensions of a given argumentation framework while multiple extensions that are identical when restricted to the projected arguments count as only one projected extension. We establish classical complexity results and parameterized complexity results when the problems are parameterized by treewidth of the undirected argumentation graph. To obtain upper bounds for counting projected extensions, we introduce novel algorithms that exploit small treewidth of the undirected argumentation graph of the input instance by dynamic programming (DP). Our algorithms run in time double or triple exponential in the treewidth depending on the considered semantics. Finally, we take the exponential time hypothesis (ETH) into account and establish lower bounds of bounded treewidth algorithms for counting extensions and projected extension.
Night Person Re-Identification and a Benchmark

Person re-identification is an important problem in computer vision fields due to its widely application. However, most of existing person re-identification methods are evaluated in daytime scenarios which is still far from real applications. In this paper, we pay attention to the night scenario person re-identification problem which most of works are not focused on. For this purpose, we contribute a large and real-scenario person re-identification dataset for night scenario named KnightReid, which aims to bridge the gap between theoretical research and practical application. To the best of our knowledge, the KnightReid dataset is the first night scenario dataset for the person re-identification which distinguishes existing works. Furthermore, by carefully examining the properties of night scenario data, we propose to combine image denoising networks with common used person re-identification networks to adapt to this kind of problem. Besides, we provide a comprehensive benchmark result that is evaluated on the dataset. The extensive experiments convince the effectiveness of the proposed model.
NEO Blockchain and Smart Contracts

In Chapter 1 , I covered the NEO proof of stake (PoS) blockchain consensus mechanism. In Chapter 2 , you created a NEO bookkeeping node on AWS Ubuntu and learned how to request a consensus authority certificate and get elected as a bookkeeper.
Sharing Experience in Multitask Reinforcement Learning

In multitask reinforcement learning, tasks often have sub-tasks that share the same solution, even though the overall tasks are different. If the shared-portions could be effectively identified, then the learning process could be improved since all the samples between tasks in the shared space could be used. In this paper, we propose a Sharing Experience Framework (SEF) for simultaneously training of multiple tasks. In SEF, a confidence sharing agent uses task-specific rewards from the environment to identify similar parts that should be shared across tasks and defines those parts as shared-regions between tasks. The shared-regions are expected to guide task-policies sharing their experience during the learning process. The experiments highlight that our framework improves the performance and the stability of learning task-policies, and is possible to help task-policies avoid local optimums.
An approach to improve the performance of the earthquake early warning system for the 2018 Hualien earthquake in Taiwan

On 6 February 2018, a damaging earthquake with a magnitude of 6.4 on the moment magnitude scale occurred off the eastern coast of Taiwan near Hualien, causing the collapse of buildings and human casualties.About 17 s after the occurrence of the earthquake, an earthquake alarm was issued by the earthquake early warning (EEW) system operated by the Central Weather Bureau of Taiwan (CWB).During the Hualien earthquake sequence (including foreshocks, mainshock, and aftershocks), the EEW system sent 44 warnings without any false alarms.However, one earthquake with magnitude larger than 5.0 was missed by the EEW system because the multiple earthquakes occurred in a short period causing the EEW system to be unable to correctly distinguish multiple P-wave arrivals.In this study, we proposed an approach that instead uses the centroid of the triggered stations as an effective epicenter and then accordingly calculates magnitude and intensity.The proposed method has been implemented in the new EEW system and was tested during the period of the Hualien earthquake sequence.The results show that the proposed method can shorten the processing time by an average of 4.7 s as compared to the existing EEW system and can detect all large events without being affected by the multiple concurrent events.In addition, the predicted intensities from the new EEW system were similar to the original intensities.
Hyper-parameter Tuning under a Budget Constraint

Hyper-parameter tuning is of crucial importance for real-world machine learning applications. While existing works mainly focus on speeding up the tuning process, we propose to study the problem of hyper-parameter tuning under a budget constraint, which is a more realistic scenario in developing large-scale systems. We formulate the task into a sequential decision making problem and propose a solution, which uses a Bayesian belief model to predict future performances, and an action-value function to plan and select the next configuration to run. With long term prediction and planning capability, our method is able to early stop unpromising configurations, and adapt the tuning behaviors to different constraints. Experiment results show that our method outperforms existing algorithms, including the-state-of-the-art one, on real-world tuning tasks across a range of different budgets.
The Efficacy of MALL Instruction in Business English Learning

&lt;p&gt;&lt;strong&gt; &lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;T&lt;/strong&gt;he existing technological expansion has transformed pervasive revolutions in modern civilization, with continuing innovation of services and products. Nowadays people are having different channels of communication. Currently, mobile learning (M-learning) is being used extensively in teaching and learning foreign languages which further widens the mobility of learners. The usage of mobile instruction can lead learners towards a real interruption to learn effectively when used inappositely. The current study is an attempt to explore the potential usage of M-Learning in English for specific purpose (ESP) classes. For this intention smartphone were used in the ESP context for teaching Business English at the College of Business Administration at a public sector university in Saudi Arabia. A questionnaire was distributed to 21 participants of the present study after a 5-week exposure to M-learning activities in the formal classroom. The findings revealed that ESP learners acknowledge M-learning as a prompt source of feedback in ESP. The results of this study also indicate that incorporation of M-learning in ESP learning with conventional classroom can support learners to develop their learning proficiency. Moreover, learners showed significantly positive attitude in mobile instruction in a formal classroom.&lt;/p&gt;
Deterministic bidirectional quantum-controlled teleportation with six-qubit maximally entangled state

Each of three nodes in a quantum network has two qubits. The total six qubits are in a maximally entangled state [Helwig et al., Phys. Rev. A 86, 052335 (2012)]. Using such an entangled state as quantum channel, we put forward three deterministic bidirectional quantum-controlled teleportation (BQCT) schemes. To be specific, BQCT can be realized between any two nodes in a deterministic manner with another as the control. Alternatively, the BQCT capacity of such state in the given qubit distribution is thus essentially revealed by virtue of the schemes.
Arbitrary reconfiguration of universal silicon photonic circuits by bacteria foraging algorithm to achieve reconfigurable photonic digital-to-analog conversion

Reconfigurable/reprogrammable universal silicon photonic circuits represent a paradigm shift in designing photonic devices. However, it is very challenging to perform adaptive arbitrary reconfiguration when the high-dimensional solution of phase distribution cannot be explicitly determined, especially when there are random initial phase errors, which hinder the implementation of novel potential functions in universal circuits. This work presents an arbitrary black-box reconfiguration for universal circuits with random phase errors by a bacteria-foraging algorithm and unlocks a novel function of arbitrary-port-and-arbitrary-bit-resolution reconfigurable 6-bit photonic digital-to-analog conversion. This work offers a general and efficient method to ease multipurpose reconfiguration for universal silicon photonic circuits.
Realization of arbitrary state-transfer via superadiabatic passages in a superconducting circuit

We propose and demonstrate experimentally the arbitrary state-transfer in a qubit by using a superadiabatic approach in a superconducting circuit. We encode the qubit in a time-dependent dark state generated by an applied microwave field, speeding up the adiabatic evolution by transitionless quantum driving algorithms. This approach is realized experimentally in a qutrit system, which consists of a qubit and an ancillary level. Furthermore, we analyze the robustness of the implementation and measure the fidelity of transfer operation by using randomized benchmarking technique.
Customizing Object Detectors for Indoor Robots

Object detection models based on convolutional neural networks (CNNs) demonstrate impressive performance when trained on large-scale labeled datasets. While a generic object detector trained on such a dataset performs adequately in applications where the input data is similar to user photographs, the detector performs poorly on small objects, particularly ones with limited training data or imaged from uncommon viewpoints. Also, a specific room will have many objects that are missed by standard object detectors, frustrating a robot that continually operates in the same indoor environment.This paper describes a system for rapidly creating customized object detectors. Data is collected from a quadcopter that is teleoperated with an interactive interface. Once an object is selected, the quadcopter autonomously photographs the object from multiple viewpoints to collect data to train DUNet (Dense Upscaled Network), our proposed model for learning customized object detectors from scratch given limited data. Our experiments compare the performance of learning models from scratch with DUNet vs. fine tuning existing state of the art object detectors, both on our indoor robotics domain and on standard datasets.
SoCRATES

Test smells are indications of poorly designed unit tests. Previous studies have demonstrated their negative impact on test understanding and maintenance. Moreover, surveys show that developers are not able to identify test smells, hindering optimal software quality. Automated tools can aid developers to handle these issues and detect test smells in the early stage of software development. However, few tools are publicly available and all of them target JUnit -- the most popular testing framework in Java. To overcome these limitations, we propose SoCRATES. This fully automated tool is able to identify six test smells in Scala Test which is the most prevalent testing framework in Scala. An empirical investigation on 164 Scala projects shows that our tool is able to reach a high precision without sacrificing recall. Moreover, the results show that Scala projects have a lower diffusion than Java projects. We make SoCRATES publicly available as an IntelliJ IDEA plugin, as well as an open-source project in order to facilitate the detection of test smells.
MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization

Tremendous amount of parameters make deep neural networks impractical to be deployed for edge-device-based real-world applications due to the limit of computational power and storage space. Existing studies have made progress on learning quantized deep models to reduce model size and energy consumption, i.e. converting full-precision weights ($r$'s) into discrete values ($q$'s) in a supervised training manner. However, the training process for quantization is non-differentiable, which leads to either infinite or zero gradients ($g_r$) w.r.t. $r$. To address this problem, most training-based quantization methods use the gradient w.r.t. $q$ ($g_q$) with clipping to approximate $g_r$ by Straight-Through-Estimator (STE) or manually design their computation. However, these methods only heuristically make training-based quantization applicable, without further analysis on how the approximated gradients can assist training of a quantized network. In this paper, we propose to learn $g_r$ by a neural network. Specifically, a meta network is trained using $g_q$ and $r$ as inputs, and outputs $g_r$ for subsequent weight updates. The meta network is updated together with the original quantized network. Our proposed method alleviates the problem of non-differentiability, and can be trained in an end-to-end manner. Extensive experiments are conducted with CIFAR10/100 and ImageNet on various deep networks to demonstrate the advantage of our proposed method in terms of a faster convergence rate and better performance. Codes are released at: \texttt{https://github.com/csyhhu/MetaQuant}
Representation of Constituents in Neural Language Models: Coordination Phrase as a Case Study

Aixiu An, Peng Qian, Ethan Wilcox, Roger Levy. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.
Setoid Type Theory—A Syntactic Translation

We introduce setoid type theory, an intensional type theory with a proof-irrelevant universe of propositions and an equality type satisfying function extensionality, propositional extensionality and a definitional computation rule for transport. We justify the rules of setoid type theory by a syntactic translation into a pure type theory with a universe of propositions. We conjecture that our syntax is complete with regards to this translation.
Image-Dehazing Method Based on the Fusion Coding of Contours and Colors

The dehazing of images shot in fog is a hot spot in the study of computer vision.Unlike dehazing methods, which use an atmospheric scattering model, the method proposed here is based on fusion coding of contours and colors.It simulates the characteristics of visual perception in blurred scenes and balances the amount of color information in foggy images by actively fusing the contour features, thus preventing issues that often arise in dehazing, such as distortion or halo effects.First, the method constructs a contour feature extractor and extracts the contour features of the image, enhancing their weight in feature coding, and then it constructs a low-level feature-coding region to extract colors while adding the contours to fusion code the contours and colors.Then, the an advanced semantic coding region is constructed with dilated convolution residual blocks to deeply analyze the semantic information from the back propagation.Finally, after the fusion of the outputs of the low-level feature coding and the middle and final outputs of the advanced semantic coding, the method decodes the contours and colors in several layers of a convolutional neural network.This paper discriminates the results of dehazing and the sample labels using the discriminator net, composed of several convolution layers, and then intensifies and generates the network's dehazing ability while improving the discrimination ability of the discriminator net.Synthetic and natural foggy images are chosen as experimental objects, and the results of the novel methods are compared with those of currently available methods.The results show that, unlike other methods, this method produces good results, is robust, addresses issues like color distortion and halos, and dehazes images with natural saturability and sharpness, providing new potential for study of image dehazing.
Spectral reflectance recovery using optimal illuminations

The spectral reflectance of objects provides intrinsic information on material properties that have been proven beneficial in a diverse range of applications, e.g., remote sensing, agriculture and diagnostic medicine, to name a few. Existing methods for the spectral reflectance recovery from RGB or monochromatic images either ignore the effect from the illumination or implement/optimize the illumination under the linear representation assumption of the spectral reflectance. In this paper, we present a simple and efficient convolutional neural network (CNN)-based spectral reflectance recovery method with optimal illuminations. Specifically, we design illumination optimization layer to optimally multiplex illumination spectra in a given dataset or to design the optimal one under physical restrictions. Meanwhile, we develop the nonlinear representation for spectral reflectance in a data-driven way and jointly optimize illuminations under this representation in a CNN-based end-to-end architecture. Experimental results on both synthetic and real data show that our method outperforms the state-of-the-arts and verifies the advantages of deeply optimal illumination and nonlinear representation of the spectral reflectance.
A Virtual Reality Framework for Training Incident First Responders and Digital Forensic Investigators

This paper presents the basic functionalities of a virtual reality framework developed for training first responders and digital forensic investigators. The framework is divided into two modules: training and evaluation. The framework provides a variety of functionalities and behaviors that can be assigned to virtual objects and allows training and evaluation of crime scenes to be easily customized. During the training module, an individual can be trained to perform various procedures, pipelines, and task execution sequences. After the training is completed, the individual can then be immersed in and interact with a virtual reality crime scene by collecting as much evidence as possible. This process allows the system to evaluate the trainee’s performance and the training process in general. In the evaluation process, the developed framework captures several types of information regarding the individual’s performance, including missing evidence, the task execution sequence, and the task completion time. The collected data can then be provided to the trainee or supervisor for evaluation. A simple customizable scoring method was also developed and incorporated into the developed framework to provide immediate performance feedback to the trainee.
Security by Design in Software Engineering

Security is a non-functional requirement difficult-to-handle during software development. However, it appears to be common in software engineering, that security is taken care of during the design- and test-phase only. If security is neglected during the implementation phase, flaws will be introduced. Those may be - if at all - found during testing where the cost-to-fix is higher as if found during the implementation phase. Hence, this research proposal suggests to investigate the extent to which code analysis tools can be used as a step towards continuous security inspection in software engineering projects. By automating security testing in development flaws can be found as soon as they are introduced. This could greatly reduce the cost to fix flaws and help building more secure software.
Mover Position Detection for PMSLM Based on Line-Scanning Fence Pattern and Subpixel Polynomial Fitting Algorithm

The article presents a rapid and precision position detection method for permanent magnet synchronous linear motors (PMSLM) based on digital image measurement and line-scanning photography. An exclusive fence pattern is designed as the target image for image measurement method, according to the motion feature of PMSLM. A line scan camera is installed on the mover to record image sequences instantaneously with the movement of linear motor, and from which the pixel displacement can be obtained using an image matching algorithm. To further improve the measurement accuracy, a subpixel polynomial fitting algorithm is proposed based on the line-scanning fence patterns. Finally, the displacement can be calculated according to system magnification parameters. Compared with the traditional image measurement method based on the area-array speckle pattern, both the cost and time delay of the proposed method are reduced greatly by employing line-scanning photography. The detection accuracy is also improved by four times. To improve the measurement robustness, an assessment index of fence pattern is proposed to optimize the fence image. The measurement error and robustness are analyzed in theory and simulation, and it demonstrates that the fence pattern shows stronger resistance to scanning deviation than speckle pattern. A mover position detection platform is established, and the experimental results show that the proposed method can achieve position estimation with average error of 0.005 mm under different working conditions.