Reviewerly/exHarmony huggingface dataset:
Deep Learning Techniques Applied for Automatic Sentence Generation

Automatic sentence generation is an important problem in natural language processing that has many applications, including language translation, summarization, and chatbots. Deep learning techniques, such as recurrent neural networks (RNNs) and transformer models, have been shown to be effective in generating coherent and diverse sentences. Recurrent neural networks (RNNs) have been widely used in natural language processing tasks, including automatic text generation. However, the traditional RNN suffers from the vanishing gradient problem, which hinders the learning of long-term dependencies. To address this issue, long short-term memory (LSTM) and gated recurrent unit (GRU) models have been introduced that can selectively forget or update certain information in the hidden state. These models have been shown to improve the quality of automatically generated text by better capturing the long-term dependencies in the input data.
High-fidelity parallel entangling gates on a neutral-atom quantum computer

The ability to perform entangling quantum operations with low error rates in a scalable fashion is a central element of useful quantum information processing1. Neutral-atom arrays have recently emerged as a promising quantum computing platform, featuring coherent control over hundreds of qubits2,3 and any-to-any gate connectivity in a flexible, dynamically reconfigurable architecture4. The main outstanding challenge has been to reduce errors in entangling operations mediated through Rydberg interactions5. Here we report the realization of two-qubit entangling gates with 99.5% fidelity on up to 60 atoms in parallel, surpassing the surface-code threshold for error correction6,7. Our method uses fast, single-pulse gates based on optimal control8, atomic dark states to reduce scattering9 and improvements to Rydberg excitation and atom cooling. We benchmark fidelity using several methods based on repeated gate applications10,11, characterize the physical error sources and outline future improvements. Finally, we generalize our method to design entangling gates involving a higher number of qubits, which we demonstrate by realizing low-error three-qubit gates12,13. By enabling high-fidelity operation in a scalable, highly connected system, these advances lay the groundwork for large-scale implementation of quantum algorithms14, error-corrected circuits7 and digital simulations15.
Multiple Kernelized Correlation Filters (MKCF) for Extended Object Tracking Using <i>X</i>-Band Marine Radar Data

Conventional tracking filter in marine radar only concerns the position or shape of the object. When the target encounters the clutter interruption or ship occlusion, the tracker is easy to drift away due to the wrong target association. Since in short-range maritime surveillance, the X-band marine radar captures the object in an extended region with varying intensities, in this paper, combining position, shape, and appearance of the target together, multiple kernelized correlation filters (MKCF) are proposed to conduct a single object tracking in the real marine radar. By automatic initializing KCF on a target in different time steps and fusing these multiple KCFs via the maximum likelihood, the proposed tracker implicitly uses multiple instances of the target to improve the robustness of the long-term tracking. Bounding rectangles of the multiple trackers also enhance the reliability of ship segmentation via a voting procedure. In the real ship tracking experiment, the proposed tracker performs favorably against the conventional radar tracker and top-ranked visual tracker in the cases of clutter interruption, ship occlusions, and scale changing. We believe that our proposal sheds light on that the intensity distribution of the extended object could be valuable for target association in marine radar data. To encourage further research, our tracking framework is made open-source.
Improving Neural Language Modeling via Adversarial Training

Recently, substantial progress has been made in language modeling by using deep neural networks. However, in practice, large scale neural language models have been shown to be prone to overfitting. In this paper, we present a simple yet highly effective adversarial training mechanism for regularizing neural language models. The idea is to introduce adversarial noise to the output embedding layer while training the models. We show that the optimal adversarial noise yields a simple closed-form solution, thus allowing us to develop a simple and time efficient algorithm. Theoretically, we show that our adversarial mechanism effectively encourages the diversity of the embedding vectors, helping to increase the robustness of models. Empirically, we show that our method improves on the single model state-of-the-art results for language modeling on Penn Treebank (PTB) and Wikitext-2, achieving test perplexity scores of 46.01 and 38.07, respectively. When applied to machine translation, our method improves over various transformer-based translation baselines in BLEU scores on the WMT14 English-German and IWSLT14 German-English tasks.
Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning

When observing the actions of others, humans make inferences about why they acted as they did, and what this implies about the world; humans also use the fact that their actions will be interpreted in this manner, allowing them to act informatively and thereby communicate efficiently with others. Although learning algorithms have recently achieved superhuman performance in a number of two-player, zero-sum games, scalable multi-agent reinforcement learning algorithms that can discover effective strategies and conventions in complex, partially observable settings have proven elusive. We present the Bayesian action decoder (BAD), a new multi-agent learning method that uses an approximate Bayesian update to obtain a public belief that conditions on the actions taken by all agents in the environment. BAD introduces a new Markov decision process, the public belief MDP, in which the action space consists of all deterministic partial policies, and exploits the fact that an agent acting only on this public belief state can still learn to use its private information if the action space is augmented to be over all partial policies mapping private information into environment actions. The Bayesian update is closely related to the theory of mind reasoning that humans carry out when observing others' actions. We first validate BAD on a proof-of-principle two-step matrix game, where it outperforms policy gradient methods; we then evaluate BAD on the challenging, cooperative partial-information card game Hanabi, where, in the two-player setting, it surpasses all previously published learning and hand-coded approaches, establishing a new state of the art.
Inferring Concept Prerequisite Relations from Online Educational Resources

The Internet has rich and rapidly increasing sources of high quality educational content. Inferring prerequisite relations between educational concepts is required for modern large-scale online educational technology applications such as personalized recommendations and automatic curriculum creation. We present PREREQ, a new supervised learning method for inferring concept prerequisite relations. PREREQ is designed using latent representations of concepts obtained from the Pairwise Latent Dirichlet Allocation model, and a neural network based on the Siamese network architecture. PREREQ can learn unknown concept prerequisites from course prerequisites and labeled concept prerequisite data. It outperforms state-of-the-art approaches on benchmark datasets and can effectively learn from very less training data. PREREQ can also use unlabeled video playlists, a steadily growing source of training data, to learn concept prerequisites, thus obviating the need for manual annotation of course prerequisites.
Binary Genetic Swarm Optimization: A Combination of GA and PSO for Feature Selection

Abstract Feature selection (FS) is a technique which helps to find the most optimal feature subset to develop an efficient pattern recognition model under consideration. The use of genetic algorithm (GA) and particle swarm optimization (PSO) in the field of FS is profound. In this paper, we propose an insightful way to perform FS by amassing information from the candidate solutions produced by GA and PSO. Our aim is to combine the exploitation ability of GA with the exploration capacity of PSO. We name this new model as binary genetic swarm optimization (BGSO). The proposed method initially lets GA and PSO to run independently. To extract sufficient information from the feature subsets obtained by those, BGSO combines their results by an algorithm called average weighted combination method to produce an intermediate solution. Thereafter, a local search called sequential one-point flipping is applied to refine the intermediate solution further in order to generate the final solution. BGSO is applied on 20 popular UCI datasets. The results were obtained by two classifiers, namely, k nearest neighbors (KNN) and multi-layer perceptron (MLP). The overall results and comparisons show that the proposed method outperforms the constituent algorithms in 16 and 14 datasets using KNN and MLP, respectively, whereas among the constituent algorithms, GA is able to achieve the best classification accuracy for 2 and 7 datasets and PSO achieves best accuracy for 2 and 4 datasets, respectively, for the same set of classifiers. This proves the applicability and usefulness of the method in the domain of FS.
Exploring Smart Glasses for Augmented Reality: A Valuable and Integrative Tool in Precision Livestock Farming

The growing interest in Augmented Reality (AR) systems is becoming increasingly evident in all production sectors. However, to the authors’ knowledge, a literature gap has been found with regard to the application of smart glasses for AR in the agriculture and livestock sector. In fact, this technology allows farmers to manage animal husbandry in line with precision agriculture principles. The aim of this study was to evaluate the performances of an AR head-wearable device as a valuable and integrative tool in precision livestock farming. In this study, the GlassUp F4 Smart Glasses (F4SG) for AR were explored. Laboratory and farm tests were performed to evaluate the implementation of this new technology in livestock farms. The results highlighted several advantages of F4SG applications in farm activities. The clear and fast readability of the information related to a single issue, combined with the large number of readings that SG performed, allowed F4SG adoption even in large farms. In addition, the 7 h of battery life and the good quality of audio-video features highlighted their valuable attitude in remote assistance, supporting farmers on the field. Nevertheless, other studies are required to provide more findings for future development of software applications specifically designed for agricultural purposes.
A Hybrid Blockchain Architecture for Privacy-Enabled and Accountable Auctions

Blockchain has recently emerged as an important tool that can enable critical distributed applications without requiring centralized trust. For example, public blockchains have been used to enable many different cryptocurrencies. Unfortunately, existing public blockchains and smart contracts deployed on them may disclose sensitive information. Although there is some ongoing work that leverage advanced cryptography to address some of these sensitive information leakage issues, they require significant changes to existing and popular blockchains such as Ethereum and are usually computationally expensive. On the other hand, private blockchains have been proposed to allow more efficient and privacy-preserving data sharing among pre-approved group of nodes/participants. Although private blockchains address some of the privacy challenges by allowing sensitive data to be only seen by the select group of participants, they do not allow public accountability of transactions since transactions are approved by known set of users, and cannot be accessed publicly. Given these observations, one natural question that arise is, can we leverage both public and private blockchain infrastructures to enable efficient, privacy enhancing and accountable applications? In this work, we try to address this challenge in the context of digital auctions. Mainly, we propose a novel hybrid blockchain architecture that combines private and public blockchains to allow sensitive bids to be opened on a private blockchain so that only the auctioneer can learn the bids, and no one else. At the same time, we leverage public blockchains to make the auction winner announcement, and payments accountable. Furthermore, using smart contracts deployed on public blockchain, we show how to incentivize truthful behavior among the auction participants. Our extensive empirical results show that this architecture is more efficient in terms of run time and monetary cost compared to pure public blockchain based auction implementations.
State representation modeling for deep reinforcement learning based recommendation

Reinforcement learning techniques have recently been introduced to interactive recommender systems to capture the dynamic patterns of user behavior during the interaction with recommender systems and perform planning to optimize long-term performance. Most existing research work focuses on designing policy and learning algorithms of the recommender agent but seldom cares about the state representation of the environment, which is indeed essential for the recommendation decision making. In this paper, we first formulate the interactive recommender system problem with a deep reinforcement learning recommendation framework. Within this framework, we then carefully design four state representation schemes for learning the recommendation policy. Inspired by recent advances in feature interaction modeling in user response prediction, we discover that explicitly modeling user–item interactions in state representation can largely help the recommendation policy perform effective reinforcement learning. Extensive experiments on four real-world datasets are conducted under both the offline and simulated online evaluation settings. The experimental results demonstrate the proposed state representation schemes lead to better performance over the state-of-the-art methods.
Reconstructing Quantum States With Quantum Reservoir Networks

Reconstructing quantum states is an important task for various emerging quantum technologies. The process of reconstructing the density matrix of a quantum state is known as quantum state tomography. Conventionally, tomography of arbitrary quantum states is challenging as the paradigm of efficient protocols has remained in applying specific techniques for different types of quantum states. Here, we introduce a quantum state tomography platform based on the framework of reservoir computing. It forms a quantum neural network and operates as a comprehensive device for reconstructing an arbitrary quantum state (finite-dimensional or continuous variable). This is achieved with only measuring the average occupation numbers in a single physical setup, without the need of any knowledge of optimum measurement basis or correlation measurements.
Evolver: A Deep Learning Processor With On-Device Quantization–Voltage–Frequency Tuning

When deploying deep neural networks (DNNs) onto deep learning processors, we usually exploit mixed-precision quantization and voltage-frequency scaling to make tradeoffs among accuracy, latency, and energy. Conventional methods usually determine the quantization-voltage-frequency (QVF) policy before DNNs are deployed onto local devices. However, they are difficult to make optimal customizations for local user scenarios. In this article, we solve the problem by enabling on-device QVF tuning with a new deep learning processor architecture Evolver. Evolver has a QVF tuning mode to deploy DNNs with local customizations before normal execution. In this mode, Evolver uses reinforcement learning to search the optimal QVF policy based on direct hardware feedbacks from the chip itself. After that, Evolver runs the newly quantized DNN inference under the searched voltage and frequency. To improve the performance and energy efficiency of both training and inference, we introduce bidirectional speculation and runtime reconfiguration techniques into the architecture. To the best of our knowledge, Evolver is the first deep learning processor that utilizes on-device QVF tuning to achieve both customized and optimal DNN deployment.
Re-examining the Role of Schema Linking in Text-to-SQL

In existing sophisticated text-to-SQL models, schema linking is often considered as a simple, minor component, belying its importance.By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking.We also build a simple BERT-based baseline, called Schema-Linking SQL (SLSQL) to perform a data-driven study.We find when schema linking is done well, SLSQL demonstrates good performance on Spider despite its structural simplicity.Many remaining errors are attributable to corpus noise.This suggests schema linking is the crux for the current textto-SQL task.Our analytic studies provide insights on the characteristics of schema linking for future developments of text-to-SQL tasks.
Fast and Robust Symmetric Image Registration Based on Distances Combining Intensity and Spatial Information

Intensity-based image registration approaches rely on similarity measures to guide the search for geometric correspondences with high affinity between images. The properties of the used measure are vital for the robustness and accuracy of the registration. In this study a symmetric, intensity interpolation-free, affine registration framework based on a combination of intensity and spatial information is proposed. The excellent performance of the framework is demonstrated on a combination of synthetic tests, recovering known transformations in the presence of noise, and real applications in biomedical and medical image registration, for both 2D and 3D images. The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK). The method is also empirically shown to have a low computational cost, making it practical for real applications. Source code is available.
Keystroke Dynamics based Hybrid Nanogenerators for Biometric Authentication and Identification using Artificial Intelligence

Cyberattack is one of the severe threats in the digital world as it encompasses everything related to personal information, health, finances, intellectual properties, and even national security. Password-based authentication is the most practiced authentication system, however, is vulnerable to several attacks such as dictionary attack, shoulder surfing attack, and guessing attack. Here, a new keystroke dynamics-based hybrid nanogenerator for biometric authentication and identification integrated with artificial intelligence (AI) is reported. Keystroke dynamics offer behavioral and contextual information that can distinguish and authorize the individuals based on their typing rhythms. The hybrid electromagnetic-triboelectric nanogenerators/sensors efficiently convert the keystroke mechanical energy into electrical signals, which are fed into an artificial neural network based AI system. The self-powered hybrid sensors-based biometric authentication system integrated with a neural network achieves an accuracy of 99% and offers a promising hybrid security layer against password vulnerability.
EBSR: Feature Enhanced Burst Super-Resolution with Deformable Alignment

We propose a novel architecture to handle the problem of multi-frame super-resolution (MFSR). The proposed framework is known as Enhanced Burst Super-Resolution (EBSR), which divides the MFSR problem into three parts: alignment, fusion, and reconstruction. We propose a Feature Enhanced Pyramid Cascading and Deformable convolution (FEPCD) module to align multiple low-resolution burst images in the feature level. And then the aligned features are fused by a Cross Non-Local Fusion (CNLF) module. Finally, the SR image is reconstructed by the Long Range Concatenation Network (LRCN). In addition, we build a cascading residual pathway structure (CR) to improve the performance. We conduct several experiments to analyze and demonstrate these modules. Our EBSR model won the champion in the real track and second place in the synthetic track in the NTIRE21 Burst Super-Resolution Challenge.
Multi-objective workflow scheduling based on genetic algorithm in cloud environment

In recent years, cloud computing plays a crucial role in many real applications. Thus, how to solve workflow scheduling problems, i.e., allocating and scheduling different resources, under the cloud computing environment becomes more important. Although some evolutionary algorithms (EAs) can solve workflow scheduling problems with a small scale, they show some disadvantages on larger scale workflow applications. In this paper, a multi-objective genetic algorithm (MOGA) is applied to optimize workflow scheduling problems. To enhance the search efficiency, this study proposes an initialization scheduling sequence scheme, in which each task’s data size is considered when initializing its virtual machine (VM) instance. Relying on the initial scheduling sequence, a proper trade-off between the makespan and the energy consumption, which are two optimization objectives in this study, can be achieved. In the early evolution stage, traditional crossover and mutate operators are performed to keep the population’s exploration. On the contrary, the longest common subsequence (LCS) of multiple elite individuals, which can be regarded as a favorable gene block, is saved during the later evolution stage. Based on the LCS, the probability of some favorable gene blocks being destroyed will be reduced when performing the crossover operator and the mutate operator. Hence, the integration of the LCS in GA can satisfy different requirements in different evolution stages, and then to attain a balance between the exploration and the exploitation. Extensive experimental results verify that the proposed GA combined with LCS, named as GALCS in this paper, can find a better Pareto front than the ordinary GA as well as other state-of-the-art algorithms. Furthermore, effectivenesses of the new proposed strategies are also verified by a set of experiments.