Reviewerly/exHarmony huggingface dataset:
ORB: An Open Reading Benchmark for Comprehensive Evaluation of Machine Reading Comprehension.

Reading comprehension is one of the crucial tasks for furthering research in natural language understanding. A lot of diverse reading comprehension datasets have recently been introduced to study various phenomena in natural language, ranging from simple paraphrase matching and entity typing to entity tracking and understanding the implications of the context. Given the availability of many such datasets, comprehensive and reliable evaluation is tedious and time-consuming for researchers working on this problem. We present an evaluation server, ORB, that reports performance on seven diverse reading comprehension datasets, encouraging and facilitating testing a single model's capability in understanding a wide variety of reading phenomena. The evaluation server places no restrictions on how models are trained, so it is a suitable test bed for exploring training paradigms and representation learning for general reading facility. As more suitable datasets are released, they will be added to the evaluation server. We also collect and include synthetic augmentations for these datasets, testing how well models can handle out-of-domain questions.
Everybody's Talkin': Let Me Talk as You Want

We present a method to edit a target portrait footage by taking a sequence of audio as input to synthesize a photo-realistic video. This method is unique because it is highly dynamic. It does not assume a person-specific rendering network yet capable of translating arbitrary source audio into arbitrary video output. Instead of learning a highly heterogeneous and nonlinear mapping from audio to the video directly, we first factorize each target video frame into orthogonal parameter spaces, i.e., expression, geometry, and pose, via monocular 3D face reconstruction. Next, a recurrent network is introduced to translate source audio into expression parameters that are primarily related to the audio content. The audio-translated expression parameters are then used to synthesize a photo-realistic human subject in each video frame, with the movement of the mouth regions precisely mapped to the source audio. The geometry and pose parameters of the target human portrait are retained, therefore preserving the context of the original video footage. Finally, we introduce a novel video rendering network and a dynamic programming method to construct a temporally coherent and photo-realistic video. Extensive experiments demonstrate the superiority of our method over existing approaches. Our method is end-to-end learnable and robust to voice variations in the source audio.
Mining Maximal High Utility Itemsets on Dynamic Profit Databases

To overcome the limitation of high-utility itemset mining, more compact, lossless, and concise representations of high utility itemsets (HUIs) have been proposed in previous works, such as closed HUIs (CHUIs) or maximal HUIs (MHUIs). Focusing into MHUI mining, in this article, we present efficient approaches to directly mine MHUIs from transactional databases without generating any candidates. The proposed algorithms, which all execute in one phase, utilize many efficient data structures and pruning techniques such as EUCP combined with EUCS, CUIP combined with FUCS, and the P-set structure to significantly reduce the search space and remove nonpromising itemsets, thus, increase the performance of the MHUI mining process. Furthermore, while previous works assumed that the unit profit of items is fixed, which is not practical in many real-world applications, our work resolved this issue by applying a new utility calculation into the mining process to reflect the true nature of real-world databases, thus, generating more accurate results.
Modeling and Simulation as a Service infrastructure capabilities for discovery, composition and execution of simulation services

Modeling and Simulation as a Service (MSaaS) embodies the idea that simulations should be composed quickly for the task at hand from loosely coupled shared components, simulation services, in a cloud-based environment. These simulations are then offered, as composed simulation services, to human and technical consumers. Instrumental to this, is functionality that lets a simulation operator discover and compose simulation services and execute the composition. We describe this functionality in terms of what we call MSaaS infrastructure capabilities. Following the idea of stepwise refinement, the discovery and composition of simulation services can be done at design time using implementation-independent information about simulation services and at implementation time using implementation-specific information about simulation services. The execution environment can also be set up at design time and at implementation time. We therefore describe the MSaaS infrastructure capabilities in terms of how they are used on both implementation-independent and implementation-specific service information. By doing these elaborations, we intend to gain greater insight into how to perform simulation service discovery, composition, and execution. We conclude that although much of the required functionality for a MSaaS infrastructure is available through existing platforms and frameworks, it is necessary to offer this functionality as services, alongside (composed) simulation services, to fulfill the MSaaS vision.
Automatic exposure algorithms for digital photography

Abstract In this paper we deal with the problem of calculating Automatic Exposure (AE) in digital cameras. The main problem that often occurs when taking pictures is correct exposure setting. Typically, smartphones with built-in cameras, as well as “cheap” compact digital cameras do not offer possibility of manual exposure setting. The reason is that users do not have knowledge how to set the optimal exposure, or just simply do not want to do this. Therefore, it forces that user has to rely on automatic exposure algorithms implemented in the camera. Unfortunately, these algorithms often do not perform well what causes improperly exposed images. In this paper, new algorithms for automatic exposure are proposed with the special focus on minimizing overexposed areas in the images. We have implemented proposed algorithms and conducted experiments for their efficiency, comparing with some modern cameras or smartphones. Experimental verification (enhanced by statistical analysis) shows that proposed algorithms give statistically less overexposed areas than comparative AEs.
UR2KiD: Unifying Retrieval, Keypoint Detection, and Keypoint Description without Local Correspondence Supervision

In this paper, we explore how three related tasks, namely keypoint detection, description, and image retrieval can be jointly tackled using a single unified framework, which is trained without the need of training data with point to point correspondences. By leveraging diverse information from sequential layers of a standard ResNet-based architecture, we are able to extract keypoints and descriptors that encode local information using generic techniques such as local activation norms, channel grouping and dropping, and self-distillation. Subsequently, global information for image retrieval is encoded in an end-to-end pipeline, based on pooling of the aforementioned local responses. In contrast to previous methods in local matching, our method does not depend on pointwise/pixelwise correspondences, and requires no such supervision at all i.e. no depth-maps from an SfM model nor manually created synthetic affine transformations. We illustrate that this simple and direct paradigm, is able to achieve very competitive results against the state-of-the-art methods in various challenging benchmark conditions such as viewpoint changes, scale changes, and day-night shifting localization.
Chatbots and virtual assistant in Indian banks

This paper discusses the adoption of chatbots and virtual Assistants by different category of banks (private sector banks and public sector banks) in India.The research paper presents a brief introduction of banking industry in India, history, characteristics, and architecture of chatbots and virtual assistants.The research paper also included basic features, way to connect, services offered, accuracy, technology providers of chatbots and virtual assistants implemented by Indian banks.Research paper concluded that Indian banks are aggressively investing in chatbots and virtual assistant technology but features are limited.Most of the questions answered by chatbots/ virtual assistant are routine for which data is already available on websites of the banks.Secondly, awareness of chatbots/ virtual assistants is very low among the customers and employees of the banks.It necessitates enhancement of existing capabilities of chatbots/ virtual assistant deployed by Indian banks and awareness with respect to usefulness among employees and customers
When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions

Wireless systems are vulnerable to various attacks such as jamming and eavesdropping due to the shared and broadcast nature of wireless medium. To support both attack and defense strategies, machine learning (ML) provides automated means to learn from and adapt to wireless communication characteristics that are hard to capture by hand-crafted features and models. This article discusses motivation, background, and scope of research efforts that bridge ML and wireless security. Motivated by research directions surveyed in the context of ML for wireless security, ML-based attack and defense solutions and emerging adversarial ML techniques in the wireless domain are identified along with a roadmap to foster research efforts in bridging ML and wireless security.
Zeroth-Order Algorithms for Nonconvex Minimax Problems with Improved Complexities

In this paper, we study zeroth-order algorithms for minimax optimization problems that are nonconvex in one variable and strongly-concave in the other variable. Such minimax optimization problems have attracted significant attention lately due to their applications in modern machine learning tasks. We first consider a deterministic version of the problem. We design and analyze the Zeroth-Order Gradient Descent Ascent (\texttt{ZO-GDA}) algorithm, and provide improved results compared to existing works, in terms of oracle complexity. We also propose the Zeroth-Order Gradient Descent Multi-Step Ascent (\texttt{ZO-GDMSA}) algorithm that significantly improves the oracle complexity of \texttt{ZO-GDA}. We then consider stochastic versions of \texttt{ZO-GDA} and \texttt{ZO-GDMSA}, to handle stochastic nonconvex minimax problems. For this case, we provide oracle complexity results under two assumptions on the stochastic gradient: (i) the uniformly bounded variance assumption, which is common in traditional stochastic optimization, and (ii) the Strong Growth Condition (SGC), which has been known to be satisfied by modern over-parametrized machine learning models. We establish that under the SGC assumption, the complexities of the stochastic algorithms match that of deterministic algorithms. Numerical experiments are presented to support our theoretical results.
Rapid Collision Detection for Multicopter Trajectories

We present a continuous-time collision detection algorithm for quickly detecting whether certain polynomial trajectories in time intersect with convex obstacles. The algorithm is used in conjunction with an existing multicopter trajectory generation method to achieve rapid, obstacle-aware motion planning in environments with both static convex obstacles and dynamic convex obstacles whose boundaries do not rotate. In general, this problem is difficult because the presence of convex obstacles makes the feasible space of trajectories nonconvex. The performance of the algorithm is benchmarked using Monte Carlo simulations, and experimental results are presented that demonstrate the use of the method to plan collision-free multicopter trajectories in milliseconds in environments with both static and dynamic obstacles.
Schools That Promote the Improvement of Academic Performance and the Success of All Students

The movement for effective schools and school improvement has enjoyed a long history, at both the theoretical and practical level. The contextual variables focused on the educational process of the classrooms have been identified in numerous investigations, concluding that the improvement of academic performance is a key element of the movement. The main objective of this research is focused on verifying the effectiveness of the treatment based on collaborative/cooperative learning methodologies and projects to improve the linguistic and mathematical competence as an enhancing element of academic performance. The sample consists of 228 students belonging to two public schools located in the city of Murcia (Spain), selected through judgmental or discretionary sampling. A quasi-experimental design with pretest and post-test and control group was employed, verifying the effectiveness of the treatment, and how it influences the improvement of the academic performance of the students in the experimental group. It concludes by pointing out the importance of learning strategies and applied teaching methodologies, understanding both within the conglomerate of process factors in the improvement of academic performance.
K-means-SMOTE for handling class imbalance in the classification of diabetes with C4.5, SVM, and naive Bayes

The occurrence of imbalanced class in a dataset causes the classification results to tend to the class with the largest amount of data (majority class). A sampling method is needed to balance the minority class (positive class) so that the class distribution becomes balanced and leading to better classification results. This study was conducted to overcome imbalanced class problems on the Indian Pima diabetes illness dataset using k-means-SMOTE. The dataset has 268 instances of the positive class (minority class) and 500 instances of the negative class (majority class). The classification was done by comparing C4.5, SVM, and naïve Bayes while implementing k-means-SMOTE in data sampling. Using k-means-SMOTE, the SVM classification method has the highest accuracy and sensitivity of 82 % and 77 % respectively, while the naive Bayes method produces the highest specificity of 89 %.
Estimating Solar Insolation and Power Generation of Photovoltaic Systems Using Previous Day Weather Data

Day-ahead predictions of solar insolation are useful for forecasting the energy production of photovoltaic (PV) systems attached to buildings, and accurate forecasts are essential for operational efficiency and trading markets. In this study, a multilayer feed-forward neural network-based model that predicts the next day’s solar insolation by taking into consideration the weather conditions of the present day was proposed. The proposed insolation model was employed to estimate the energy production of a real PV system located in South Korea. Validation research was performed by comparing the model’s estimated energy production with the measured energy production data collected during the PV system operation. The accuracy indices for the optimal model, which included the root mean squared error, mean bias error, and mean absolute error, were 1.43 kWh/m 2 /day, −0.09 kWh/m 2 /day, and 1.15 kWh/m 2 /day, respectively. These values indicate that the proposed model is capable of producing reasonable insolation predictions; however, additional work is needed to achieve accurate estimates for energy trading.
Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective

This paper considers learning deep features from long-tailed data. We observe that in the deep feature space, the head classes and the tail classes present different distribution patterns. The head classes have a relatively large spatial span, while the tail classes have significantly small spatial span, due to the lack of intra-class diversity. This uneven distribution between head and tail classes distorts the overall feature space, which compromises the discriminative ability of the learned features. Intuitively, we seek to expand the distribution of the tail classes by transferring from the head classes, so as to alleviate the distortion of the feature space. To this end, we propose to construct each feature into a "feature cloud". If a sample belongs to a tail class, the corresponding feature cloud will have relatively large distribution range, in compensation to its lack of diversity. It allows each tail sample to push the samples from other classes far away, recovering the intra-class diversity of tail classes. Extensive experimental evaluations on person re-identification and face recognition tasks confirm the effectiveness of our method.
Acceleration for Compressed Gradient Descent in Distributed and Federated Optimization

Due to the high communication cost in distributed and federated learning problems, methods relying on compression of communicated messages are becoming increasingly popular. While in other contexts the best performing gradient-type methods invariably rely on some form of acceleration/momentum to reduce the number of iterations, there are no methods which combine the benefits of both gradient compression and acceleration. In this paper, we remedy this situation and propose the first accelerated compressed gradient descent (ACGD) methods. In the single machine regime, we prove that ACGD enjoys the rate $O\Big((1+\omega)\sqrt{\frac{L}{\mu}}\log \frac{1}{\epsilon}\Big)$ for $\mu$-strongly convex problems and $O\Big((1+\omega)\sqrt{\frac{L}{\epsilon}}\Big)$ for convex problems, respectively, where $\omega$ is the compression parameter. Our results improve upon the existing non-accelerated rates $O\Big((1+\omega)\frac{L}{\mu}\log \frac{1}{\epsilon}\Big)$ and $O\Big((1+\omega)\frac{L}{\epsilon}\Big)$, respectively, and recover the optimal rates of accelerated gradient descent as a special case when no compression ($\omega=0$) is applied. We further propose a distributed variant of ACGD (called ADIANA) and prove the convergence rate $\widetilde{O}\Big(\omega+\sqrt{\frac{L}{\mu}}+\sqrt{\big(\frac{\omega}{n}+\sqrt{\frac{\omega}{n}}\big)\frac{\omega L}{\mu}}\Big)$, where $n$ is the number of devices/workers and $\widetilde{O}$ hides the logarithmic factor $\log \frac{1}{\epsilon}$. This improves upon the previous best result $\widetilde{O}\Big(\omega + \frac{L}{\mu}+\frac{\omega L}{n\mu} \Big)$ achieved by the DIANA method of Mishchenko et al. (2019). Finally, we conduct several experiments on real-world datasets which corroborate our theoretical results and confirm the practical superiority of our accelerated methods.
Obstacle Magnification for 2-D Collision and Occlusion Avoidance of Autonomous Multirotor Aerial Vehicles

Collision or occlusion avoidance is one of the most important functions of autonomous unmanned vehicles when maneuvering or conducting missions, including monitoring or following targets in environments with various obstacles such as trees and buildings. While considering the minimum distance to obstacles or approximating the shape of obstacles is convenient, incorporating the shape of the original obstacles, sometimes, can provide more effective strategies for collision and occlusion avoidance of obstacles with complex shapes or arrangements. This article presents an obstacle magnification algorithm that magnifies but retains the original shape of the detected obstacles. Two simple parameters can adjust the position and magnification of the virtual obstacles. The simulation and outdoor experimental results for the obstacle magnification, implemented on an onboard embedded system, demonstrate the effectiveness of this concept, which should be useful in various applications for small autonomous multirotor aircrafts.
A Survey of Approximate Quantile Computation on Large-Scale Data

As data volume grows extensively, data profiling helps to extract metadata of large-scale data.However, one kind of metadata, order statistics, is difficult to be computed because they are not mergeable or incremental.Thus, the limitation of time and memory space does not support their computation on large-scale data.In this paper, we focus on an order statistic, quantiles, and present a comprehensive analysis of studies on approximate quantile computation.Both deterministic algorithms and randomized algorithms that compute approximate quantiles over streaming models or distributed models are covered.Then, multiple techniques for improving the efficiency and performance of approximate quantile algorithms in various scenarios, such as skewed data and high-speed data streams, are presented.Finally, we conclude with coverage of existing packages in different languages and with a brief discussion of the future direction in this area.
Multi-Wavelet Residual Dense Convolutional Neural Network for Image Denoising

The neural networks with large receptive field show excellent fitting ability and have been successfully applied in image denoising, but with a difficulty to reduce the computational overhead while acquiring good denoising performance.Here we choose a representative of the above networks named multiwavelet convolutional neural network (MWCNN) as the backbone.To obtain a better tradeoff between the denoising performance and computation speed, we propose to adopt residual dense blocks (RDBs) in each layer of the MWCNN.We call this scheme multi-wavelet residual dense convolutional neural network (MWRDCNN).Benefitting from the applied short-term residual learning strategy, it can increase the learning efficiency.Besides, since we use a hierarchical structure to build our network, the adopted RDBs in different layers are helpful for extracting more object details in different scales.Both horizontal and vertical comparison experiments have been performed to demonstrate the effectiveness of this network in image denoising.The results also show that our MWRDCNN takes much shorter time than other RDBbased networks to extract more features from adjacent layers and is good at handling the images which are badly corrupted by the noise.Thereby, it is a successful attempt to make full use of the advantages of multiple networks without any conflicts.
High-fidelity light-shift gate for clock-state qubits

To date, the highest fidelity quantum logic gates between two qubits have been achieved with variations on the geometric-phase gate in trapped ions, with the two leading variants being the Molmer-Sorensen gate and the light-shift (LS) gate. Both of these approaches have their respective advantages and challenges. For example, the latter is technically simpler and is natively insensitive to optical phases, but it has not been made to work directly on a clock-state qubit. We present a new technique for implementing the LS gate that combines the best features of these two approaches: By using a small ($\sim {\rm MHz}$) detuning from a narrow (dipole-forbidden) optical transition, we are able to operate an LS gate directly on hyperfine clock states, achieving gate fidelities of $99.74(4)\%$ using modest laser power at visible wavelengths. Current gate infidelities appear to be dominated by technical noise, and theoretical modeling suggests a path towards gate fidelity above $99.99\%$.