Reviewerly/exHarmony huggingface dataset:
Deep Learning Aided Method for Automatic Modulation Recognition

Automatic modulation recognition (AMR) is considered one of most important techniques in the non-cooperative wireless communication systems. Traditional algorithms, e.g., support vector machine (SVM) based on high order cumulants (HOC), are hard to achieve the reliable performance. In this paper, we propose an effective AMR algorithm based on deep learning (DL) with capabilities of automatically extracting representative and effective features. Our proposed method resorts to in-phase and quadrature (IQ) samples which are IQ components of received baseband signal, respectively. We adopt convolutional neural networks (CNN) and recurrent neural networks (RNN) to classify six types of signal modulations over additive white Gaussian noise (AWGN) channel and Rayleigh fading channel, respectively. Simulation results show that DL-AMR is much better than traditional algorithms under two fading channels.
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling

The visual storytelling (VST) task aims at generating a reasonable and coherent paragraph-level story with the image stream as input. Different from caption that is a direct and literal description of image content, the story in the VST task tends to contain plenty of imaginary concepts that do not appear in the image. This requires the AI agent to reason and associate with the imaginary concepts based on implicit commonsense knowledge to generate a reasonable story describing the image stream. Therefore, in this work, we present a commonsense-driven generative model, which aims to introduce crucial commonsense from the external knowledge base for visual storytelling. Our approach first extracts a set of candidate knowledge graphs from the knowledge base. Then, an elaborately designed vision-aware directional encoding schema is adopted to effectively integrate the most informative commonsense. Besides, we strive to maximize the semantic similarity within the output during decoding to enhance the coherence of the generated text. Results show that our approach can outperform the state-of-the-art systems by a large margin, which achieves a 29\% relative improvement of CIDEr score. With additional commonsense and semantic-relevance based objective, the generated stories are more diverse and coherent.
Trajectory Planning for a Tractor with Multiple Trailers in Extremely Narrow Environments: A Unified Approach

Trajectory planning for a tractor-trailer vehicle is challenging because the vehicle kinematics consists of underactuated and nonholonomic constraints that are highly coupled. Prevalent sampling-based or search-based planners suitable for rigid-body vehicles are not capable of handling the tractor-trailer vehicle cases. This work aims to deal with generic n-trailer cases in the tiny environments. To this end, an optimal control problem is formulated, which is beneficial in being accurate, straightforward, and unified. An adaptively homotopic warm-starting approach is proposed to facilitate the numerical solution process of the formulated optimal control problem. Compared with the existing sequential warm starting strategies, our proposal can adaptively define the subproblems with the purpose of making the gaps between adjacent subproblems “pleasant” for the solver. Unification and efficiency of the proposed adaptively homotopic warm-starting approach have been investigated in several extremely tiny scenarios. Our planner finds solutions that other existing planners cannot. Online planning opportunities are briefly discussed as well.
Underestimation-Assisted Global-Local Cooperative Differential Evolution and the Application to Protein Structure Prediction

Various mutation strategies show distinct advantages in differential evolution (DE). The cooperation of multiple strategies in the evolutionary process may be effective. This article presents an underestimation-assisted global and local cooperative DE to simultaneously enhance the effectiveness and efficiency. In the proposed algorithm, two phases, namely, the global exploration and the local exploitation, are performed in each generation. In the global phase, a set of trial vectors is produced for each target individual by employing multiple strategies with strong exploration capability. Afterward, an adaptive underestimation model with an self-adapted slope control parameter is proposed to evaluate these trial vectors, the best of which is selected as the candidate. In the local phase, the better-based strategies guided by individuals that are better than the target individual are designed. For each individual accepted in the global phase, multiple trial vectors are generated by using these strategies and filtered by the underestimation value. The cooperation between the global and local phases includes two aspects. First, both of them concentrate on generating better individuals for the next generation. Second, the global phase aims to locate promising regions quickly while the local phase serves as a local search for enhancing convergence. Moreover, a simple mechanism is designed to determine the parameter of DE adaptively in the searching process. Finally, the proposed approach is applied to predict the protein 3-D structure. The experimental studies on classical benchmark functions, CEC test sets, and protein structure prediction problem show that the proposed approach is superior to the competitors.
Simple and Effective Noisy Channel Modeling for Neural Machine Translation

Previous work on neural noisy channel modeling relied on latent variable models that incrementally process the source and target sentence.This makes decoding decisions based on partial source prefixes even though the full source is available.We pursue an alternative approach based on standard sequence to sequence models which utilize the entire source.These models perform remarkably well as channel models, even though they have neither been trained on, nor designed to factor over incomplete target sentences.Experiments with neural language models trained on billions of words show that noisy channel models can outperform a direct model by up to 3.2 BLEU on WMT'17 German-English translation.We evaluate on four language-pairs and our channel models consistently outperform strong alternatives such right-to-left reranking models and ensembles of direct models. 1
Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration

In multi-turn dialogue, utterances do not always take the full form of sentences.These incomplete utterances will greatly reduce the performance of open-domain dialogue systems.Restoring more incomplete utterances from context could potentially help the systems generate more relevant responses.To facilitate the study of incomplete utterance restoration for open-domain dialogue systems, a large-scale multi-turn dataset Restoration-200K 1 is collected and manually labeled with the explicit relation between an utterance and its context.We also propose a "pick-andcombine" model to restore the incomplete utterance from its context.Experimental results demonstrate that the annotated dataset and the proposed approach significantly boost the response quality of both single-turn and multiturn dialogue systems.
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network

Despite the wide success of deep neural networks (DNN), little progress has been made on end-to-end unsupervised outlier detection (UOD) from high dimensional data like raw images. In this paper, we propose a framework named E^3Outlier, which can perform UOD in a both effective and end-to-end manner: First, instead of the commonly-used autoencoders in previous end-to-end UOD methods, E^3Outlier for the first time leverages a discriminative DNN for better representation learning, by using surrogate supervision to create multiple pseudo classes from original unlabelled data. Next, unlike classic UOD that utilizes data characteristics like density or proximity, we exploit a novel property named inlier priority to enable end-to-end UOD by discriminative DNN. We demonstrate theoretically and empirically that the intrinsic class imbalance of inliers/outliers will make the network prioritize minimizing inliers' loss when inliers/outliers are indiscriminately fed into the network for training, which enables us to differentiate outliers directly from DNN's outputs. Finally, based on inlier priority, we propose the negative entropy based score as a simple and effective outlierness measure. Extensive evaluations show that E^3Outlier significantly advances UOD performance by up to 30% AUROC against state-of-the-art counterparts, especially on relatively difficult benchmarks.
Detection of genuine tripartite entanglement by multiple sequential observers

We explore the possibility of multiple usage of a single genuine entangled state by considering a scenario consisting of three spin-$\frac{1}{2}$ particles shared between Alice, Bob, and multiple Charlies. Alice performs measurements on the first particle, Bob performs measurements on the second particle, and multiple Charlies perform measurements on the third particle sequentially. Here the choice of measurement settings of each Charlie is independent and uncorrelated with the choices of measurement settings and outcomes of the previous Charlies. In this scenario, we investigate whether more than one Charlie can detect genuine tripartite entanglement, and we answer this question affirmatively. In order to probe genuine entanglement, we use correlation inequalities the violations of which certify genuine tripartite entanglement in a device-independent way. We extend our investigation by using appropriate genuine tripartite entanglement witness operators. Using each of these different tools for detecting genuine tripartite entanglement, we find out the maximum number of Charlies who can detect genuine entanglement in the above scenario.
A Decoupled Trajectory Planning Framework Based on the Integration of Lattice Searching and Convex Optimization

This paper presents a decoupled trajectory planning framework based on the integration of lattice searching and convex optimization for autonomous driving in structured environments.For a 3D trajectory planning problem with timestamps information, due to the presence of multiple kinds of constraints, the feasible domain is non-convex, so it is easy to fall into local optimum for trajectory planning.And the solution space of this problem is so enormous that it is difficult to identify an optimal solution in polynomial time.To address this non-convex problem, and to improve the convergence speed of an optimization process, the approach based on lattice searching is adopted in consideration of the ability to discretize driving environments and reduce the solution space.And the resulting path generated by lattice searching typically lies in the neighborhood of the global optimum.But this solution is neither spatiotemporally smooth nor globally optimal, so it is generally called the rough solution.For this reason, a subsequent nonlinear optimization process is introduced to refine the rough trajectory (combined by path and speed).The proposed framework is implemented and evaluated through simulations in various challenging scenarios in this paper.The simulation results verify that the trajectory planner can generate high-quality trajectories, and the execution time is also acceptable.
Sequential Neural Networks as Automata

This work attempts to explain the types of computation that neural networks can perform by relating them to automata.We first define what it means for a real-time network with bounded precision to accept a language.A measure of network memory follows from this definition.We then characterize the classes of languages acceptable by various recurrent networks, attention, and convolutional networks.We find that LSTMs function like counter machines and relate convolutional networks to the subregular hierarchy.Overall, this work attempts to increase our understanding and ability to interpret neural networks through the lens of theory.These theoretical insights help explain neural computation, as well as the relationship between neural networks and natural language grammar.
Collaborative and geometric multi-kernel learning for multi-class classification

The multi-class classification is the problem of classifying the sample into one of three or more classes. In this paper, we propose an algorithm named collaborative and geometric multi-kernel learning (CGMKL) to classify multi-class data into corresponding class directly. The CGMKL uses the Multiple Empirical Kernel Learning (MEKL) to map the sample into multiple kernel spaces, and then trains the softmax function in each kernel space. To realize the collaborative learning, one regularization term, which controls the consistent outputs of samples in different kernel spaces, provides the complementary information. Moreover, another regularization term exhibits the classification result with a geometric feature by reducing the within-class distance of the outputs of samples. Extensive Experiments on the multi-class data sets validate the effectiveness of the CGMKL.
Artificial Potential Field Algorithm Implementation for Quadrotor Path Planning

Potential field algorithm introduced by Khatib is well-known in path planning for robots.The algorithm is very simple yet provides real-time path planning and effective to avoid robot's collision with obstacles.The purpose of the paper is to implement and modify this algorithm for quadrotor path planning.The conventional potential method is firstly applied to introduce challenging problems, such as not reachable goals due to local minima solutions or nearby obstacles (GNRON).This will be solved later by proposed modified algorithms.The first proposed modification is by adding virtual force to the repulsive potential force to prevent local minima solutions.Meanwhile, the second one is to prevent GNRON issue by adding virtual force and considering quadrotor's distance to goal point on the repulsive potential force.The simulation result shows that the second modification is best applied to environment with GNRON issue whereas the first one is suitable only for environment with local minima traps.The first modification is able to reach goals in six random tests with local minima environment.Meanwhile, the second one is able to reach goals in six random tests with local minima environment, six random tests with GNRON environment, and six random tests with both local minima and GNRON environment.
Deep Learning-Based Technology in Responses to the Joint Call for Proposals on Video Compression With Capability Beyond HEVC

Deep learning has achieved great success in the past decade, especially in the fields of computer vision and image processing. After witnessing such success, video coding experts are motivated to consider whether deep learning can also benefit video coding, and if so, they seek to discover why and how. Indeed, a number of research studies have been conducted to explore deep learning for image and video coding, which has been an active and fast-growing research area especially since the year 2015. These prior arts can be divided into two categories: new coding schemes that are built solely upon deep networks (deep schemes), and deep network-based coding tools that are embedded into traditional coding schemes (deep tools). Moreover, in the responses to the joint call for proposals on video compression with capability beyond High Efficiency Video Coding (HEVC), a number of deep tools have been proposed, and some of them are further studied for the upcoming Versatile Video Coding (VVC). In this paper, we summarize the ongoing efforts in the Joint Video Experts Team about the proposed deep tools, and we discuss several promising tools in much detail, including neural network-based intra prediction, convolutional neural network (CNN) based in-loop filtering, and CNN-based block-adaptive-resolution coding. A series of experimental results are provided to demonstrate the capability of these tools in achieving higher compression efficiency than the VVC or HEVC anchor. These results shed light on the promising direction of deep learning-based future video coding, towards which a lot of open problems call for further study.
Secure Authentication and Group Key Distribution Scheme for WBANs Based on Smartphone ECG Sensor

As one of the crucial components in the emerging internet of things (IoT), wireless body area networks (WBANs) is capable of monitoring vital physiological and behavioral information of users through wearable sensors, offering a new paradigm for the next-generation healthcare systems.However, due to the inherent open wireless communicating characteristics, security and privacy issues for WBANs communication remain unsolved.Note that the deployed WBANs sensors are resource-restrained entities, which restricts its wide applications in medical environment.In this case, effective authentication scheme with efficient group key management strategy is of great significance.Moreover, with comparatively large computation ability and storage capacity, smartphone is able to perform as the vital data processing gateway for WBANs, especially in the upcoming 5G network implementation with superior transmission quality and speed.Furthermore, the WBAN sensors are responsible for continuous physiological monitoring, where the acquired biometric features could be adopted to the authentication process.For the above consideration, a secure certificateless biometric authentication and group key management for WBAN scenarios is proposed in this paper.In our design, user's smartphone takes the role of personal controller (PC) in traditional WBANs structure.The representative features of the gathered electrocardiogram (ECG) records are applied as the distinctive biometric parameter during authentication procedure.Hence efficient authentication towards participating sensors is enabled.Subsequently, fast group key management among all validated sensors is presented, where small modification is required for dynamic key updating mechanism in sensor side.Security analysis indicates that the proposed protocol can achieve desired security properties and provide resistance to various attacks.Performance analysis demonstrates that the proposed protocol is efficient compared with the state-of-the-art WBAN authentication schemes.
Differential Privacy for Data and Model Publishing of Medical Data

Combining medical data and machine learning has fully utilized the value of medical data. However, medical data contain a large amount of sensitive information, and the inappropriate handling of data can lead to the leakage of personal privacy. Thus, both publishing data and training data in machine learning may reveal the privacy of patients. To address the above issue, we propose two effective approaches. One combines a differential privacy and decision tree (DPDT) approach to provide strong privacy guarantees for publishing data, which establishes a weight calculation system based on the classification and regression tree (CART) method and takes weights as a new element of differential privacy to participate in privacy protection and reduce the negative impact of differential privacy on data availability. Another uses the differentially private mini-batch gradient descent algorithm (DPMB) to provide strong protection for training data; it tracks the privacy loss and allows the model to satisfy differential privacy in the process of gradient descent to prevent attackers from invading personal privacy with the training data. It is worth mentioning that, in this paper, we adopt the data processed by DPDT as the training data of DPMB to further strengthen the privacy of data.
Deep Unbiased Embedding Transfer for Zero-Shot Learning

Zero-shot learning aims to recognize objects which do not appear in the training dataset. Previous prevalent mapping-based zero-shot learning methods suffer from the projection domain shift problem due to the lack of image classes in the training stage. In order to alleviate the projection domain shift problem, a deep unbiased embedding transfer (DUET) model is proposed in this paper. The DUET model is composed of a deep embedding transfer (DET) module and an unseen visual feature generation (UVG) module. In the DET module, a novel combined embedding transfer net which integrates the complementary merits of the linear and nonlinear embedding mapping functions is proposed to connect the visual space and semantic space. What's more, the end-to-end joint training process is implemented to train the visual feature extractor and the combined embedding transfer net simultaneously. In the UVG module, a visual feature generator trained with a conditional generative adversarial framework is used to synthesize the visual features of the unseen classes to ease the disturbance of the projection domain shift problem. Furthermore, a quantitative index, namely the score of resistance on domain shift (ScoreRDS), is proposed to evaluate different models regarding their resistance capability on the projection domain shift problem. The experiments on five zero-shot learning benchmarks verify the effectiveness of the proposed DUET model. As demonstrated by the qualitative and quantitative analysis, the unseen class visual feature generation, the combined embedding transfer net and the end-to-end joint training process all contribute to alleviating projection domain shift in zero-shot learning.
Personalized Question Routing via Heterogeneous Network Embedding

Question Routing (QR) on Community-based Question Answering (CQA) websites aims at recommending answerers that have high probabilities of providing the “accepted answers” to new questions. The existing question routing algorithms simply predict the ranking of users based on query content. As a consequence, the question raiser information is ignored. On the other hand, they lack learnable scoring functions to explicitly compute ranking scores.To tackle these challenges, we propose NeRank that (1) jointly learns representations of question content, question raiser, and question answerers by a heterogeneous information network embedding algorithm and a long short-term memory (LSTM) model. The embeddings of the three types of entities are unified in the same latent space, and (2) conducts question routing for personalized queries, i.e., queries with two entities (question content, question raiser), by a convolutional scoring function taking the learned embeddings of all three types of entities as input. Using the scores, NeRank routes new questions to high-ranking answerers that are skillfulness in the question domain and have similar backgrounds to the question raiser.Experimental results show that NeRank significantly outperforms competitive baseline question routing models that ignore the raiser information in three ranking metrics. In addition, NeRank is convergeable in several thousand iterations and insensitive to parameter changes, which prove its effectiveness, scalability, and robustness.