Reviewerly/exHarmony huggingface dataset:
Extended K alman Filter for Bearings Only T racking

Target tracking using bearings-only measurements in passive mode operation of sonar is a crucial issue of underwater tracking. Target motion in underwater scenario is analyzed using bearings-only measurements and calculating parameters like range, course and speed of the target. This is called Target Motion Analysis (TMA). TMA process is highly non-linear as the measurements chosen are nonlinearly related to the selected target state vector and the traditional, optimal linear Kalman filter will not be appropriate to use. It is presumed that the target is moving in straight line path with constant velocity, so Extended Kalman Filter (EKF) is proposed in this paper. The algorithm is simulated for several scenarios using MATLAB. Monte-Carlo runs are performed to evaluate the capability of the algorithm.
Multiperiod Unmanned Aerial Vehicles Path Planning With Dynamic Emergency Priorities for Geohazards Monitoring

Advances in unmanned aerial vehicle (UAV) technology provide an opportunity for geohazards monitoring in harsh environments, thereby reducing man-hours and corresponding risks. Due to the limited duration, UAV can hardly visit all the scattered geohazard sites (GSs) in one trip. Therefore, GSs with higher emergency priorities should be monitored first in earlier trips. In this article, we formulate a multiperiod UAVs path planning problem with dynamic emergency priorities for monitoring GSs, in which the emergency levels of GSs change dynamically according to the latest disaster information. An effective solution strategy for this problem selects GSs that must be monitored and accesses more optional GSs within the limited UAV's duration time, after distinguishing them according to emergency priorities. We therefore propose a heuristic algorithm based on the adaptive large neighborhood search. We finally present a real-world case for demonstration and validation.
Smart and Secure CAV Networks Empowered by AI-Enabled Blockchain: The Next Frontier for Intelligent Safe Driving Assessment

Securing safe-driving for connected and autonomous vehicles (CAVs) continues to be a widespread concern despite various sophisticated functions delivered by artificial intelligence for in-vehicle devices.Besides, diverse malicious network attacks become ubiquitous along with the worldwide implementation of the Internet of Vehicles, which exposes a range of reliability and privacy threats for managing data in CAV networks.Combined with the fact that the capability of existing CAVs in handling intensive computation tasks is limited, this implies a need for designing an efficient assessment system to guarantee autonomous driving safety without compromising data security.Motivated by this, in this article, we propose a novel framework, namely Blockchain-enabled intElligent Safe-driving assessmenT (BEST), that offers a smart and reliable approach for conducting safe driving supervision while protecting vehicular information.Specifically, a promising solution that exploits a long short-term memory model is introduced to assess the safety level of the moving CAVs.Then, we investigate how a distributed blockchain obtains adequate trustworthiness and robustness for CAV data by adopting a byzantine fault tolerance-based delegated proofof-stake consensus mechanism.Simulation results demonstrate that our presented BEST gains better data credibility with a higher prediction accuracy for vehicular safety assessment when compared with existing schemes.Finally, we discuss several open challenges that need to be addressed in future CAV networks.
Blockchain and the transformation of customer co-creation

Purpose The blockchain represents a seminal paradigm shift, likely to radically transform business in the future. While the paradigm associated with the World Wide Web and Big Data is focused on the “sharing of information,” the paradigm associated with blockchain is focused on the “sharing of assets.” Intellectual assets are among the most valuable of assets, and customer co-creation is a key approach for creating new value for firms. This paper aims to draw on blockchain-centric logic to develop an initial theoretical framework, with managerial recommendations, for the use of blockchain in customer co-creation. Design/methodology/approach Building upon established indigenous theory development and inductive realist approaches, the authors develop an original two-step methodology to create the initial theoretical framework. This methodology, involving foundational premises and propositions, is ideal for relatively new areas of research and is well suited to serve as a relatively faster catalyst for future research. Findings Despite the substantial potential impact of blockchain in innovation, no theoretical foundation for blockchain in customer co-creation exists. To fill this gap, the authors present an initial theoretical framework, using blockchain-centric logic in customer co-creation. The proposed theoretical framework highlights how key prerequisites in customer co-creation, including trust, security, transparency, identity and immutability, can be enhanced with blockchain-centric logic. Originality/value It is hoped that the initial theoretical framework, based on blockchain-centric logic, can contribute to future academic research on blockchain in customer co-creation and help practitioners better exploit the blockchain in co-creation. Directions for future research, the larger agenda for this paper, are presented in the conclusion.
A Distributed Ledger-Based Automated Marketplace for the Decentralized Trading of Renewable Energy in Smart Grids

We present a prototype of a decentralized power trading system based on the use of distributed ledger technology. This sort of efficient, decentralized marketplace is needed to empower prosumers and make them first-class members of a smart, decentralized power grid in order to drive further renewable energy adoption. Unlike the bulk of previous work in this field, we focus on private permissioned distributed ledgers rather than conventional blockchains. The proposed solution is entirely independent of cryptocurrency, with an explicit design capability of being adapted piecemeal without any fundamental changes to the present regulatory environment. To be economical, efficient, and scalable, our prototype is based on a lean, Corda-based private permissioned distributed ledger. It allows for instant, automatic bidding on and trading of ‘power promises’ and the robust implementation of short-term, small-scale liquid electrical power futures. We demonstrate that the prototype performs well and presents several clear advantages over existing solutions based on conventional blockchains. Therefore, the proposed approach represents a promising, robust solution to the smart grid decentralized power trading problem.
Approximating Perfect Recall when Model Checking Strategic Abilities: Theory and Applications

The model checking problem for multi-agent systems against specifications in the alternating-time temporal logic AT L, hence AT L∗ , under perfect recall and imperfect information is known to be undecidable. To tackle this problem, in this paper we investigate a notion of bounded recall under incomplete information. We present a novel three-valued semantics for AT L∗ in this setting and analyse the corresponding model checking problem. We show that the three-valued semantics here introduced is an approximation of the classic two-valued semantics, then give a sound, albeit partial, algorithm for model checking two-valued perfect recall via its approximation as three-valued bounded recall. Finally, we extend MCMAS, an open-source model checker for AT L and other agent specifications, to incorporate bounded recall; we illustrate its use and present experimental results.
Reducing Circuit Depth in Adaptive Variational Quantum Algorithms via Effective Hamiltonian Theories

The electronic structure is an anticipated application for quantum computers. However, quantum circuits required to represent the highly entangled electronic wave functions within the variational quantum eigensolver (VQE) framework are far beyond the capacity of current quantum devices. To adapt the VQE algorithms to near-term quantum hardware, it has been suggested to incorporate a part of the electronic correlation into an effective Hamiltonian, leaving the wave function in a less entangled form. We propose a new scheme to construct the effective Hamiltonian with the transformation in the form of a product of linear combinations of excitation operators. This new scheme promises a quadratic multiplicative growth of the effective Hamiltonian. We integrate this effective Hamiltonian method into the adaptive VQE algorithms to maintain constant-size quantum circuits. The new computational scheme is assessed by performing numerical simulations for small molecules. A milli-Hartree accuracy in a minimal basis is achieved with a much shallower circuit depth.
Label Semantic Aware Pre-training for Few-shot Text Classification

In text classification tasks, useful information is encoded in the label names.Label semantic aware systems have leveraged this information for improved text classification performance during fine-tuning and prediction.However, use of label-semantics during pre-training has not been extensively explored.We therefore propose Label Semantic Aware Pre-training (LSAP) to improve the generalization and data efficiency of text classification systems.LSAP incorporates label semantics into pre-trained generative models (T5 in our case) by performing secondary pre-training on labeled sentences from a variety of domains.As domaingeneral pre-training requires large amounts of data, we develop a filtering and labeling pipeline to automatically create sentence-label pairs from unlabeled text.We perform experiments on intent (ATIS, Snips, TOPv2) and topic classification (AG News, Yahoo!Answers).LSAP obtains significant accuracy improvements over state-of-the-art models for few-shot text classification while maintaining performance comparable to state of the art in high-resource settings.
Interpretable Bias Mitigation for Textual Data: Reducing Genderization in Patient Notes While Maintaining Classification Performance

Medical systems in general, and patient treatment decisions and outcomes in particular, can be affected by bias based on gender and other demographic elements. As language models are increasingly applied to medicine, there is a growing interest in building algorithmic fairness into processes impacting patient care. Much of the work addressing this question has focused on biases encoded in language models—statistical estimates of the relationships between concepts derived from distant reading of corpora. Building on this work, we investigate how differences in gender-specific word frequency distributions and language models interact with regards to bias. We identify and remove gendered language from two clinical-note datasets and describe a new debiasing procedure using BERT-based gender classifiers. We show minimal degradation in health condition classification tasks for low- to medium-levels of dataset bias removal via data augmentation. Finally, we compare the bias semantically encoded in the language models with the bias empirically observed in health records. This work outlines an interpretable approach for using data augmentation to identify and reduce biases in natural language processing pipelines.
Alleviating Cold-start Problem in CTR Prediction with A Variational Embedding Learning Framework

We propose a general Variational Embedding Learning Framework (VELF) for alleviating the severe cold-start problem in CTR prediction. VELF addresses the cold start problem via alleviating over-fits caused by data-sparsity in two ways: learning probabilistic embedding, and incorporating trainable and regularized priors which utilize the rich side information of cold start users and advertisements (Ads). The two techniques are naturally integrated into a variational inference framework, forming an end-to-end training process. Abundant empirical tests on benchmark datasets well demonstrate the advantages of our proposed VELF. Besides, extended experiments confirmed that our parameterized and regularized priors provide more generalization capability than traditional fixed priors.
Improving understandability of feature contributions in model-agnostic explainable AI tools

Model-agnostic explainable AI tools explain their predictions by means of 'local' feature contributions. We empirically investigate two potential improvements over current approaches. The first one is to always present feature contributions in terms of the contribution to the outcome that is perceived as positive by the user ("positive framing"). The second one is to add "semantic labeling", that explains the directionality of each feature contribution ("this feature leads to +5% eligibility"), reducing additional cognitive processing steps. In a user study, participants evaluated the understandability of explanations for different framing and labeling conditions for loan applications and music recommendations. We found that positive framing improves understandability even when the prediction is negative. Additionally, adding semantic labels eliminates any framing effects on understandability, with positive labels outperforming negative labels. We implemented our suggestions in a package ArgueView[11].
How to Guide Task-oriented Chatbot Users, and When: A Mixed-methods Study of Combinations of Chatbot Guidance Types and Timings

The popularity of task-oriented chatbots is constantly growing, but smooth conversational progress with them remains profoundly challenging. In recent years, researchers have argued that chatbot systems should include guidance for users on how to converse with them. Nevertheless, empirical evidence about what to place in such guidance, and when to deliver it, has been lacking. Using a mixed-methods approach that integrates results from a between-subjects experiment and a reflection session, this paper compares the effectiveness of eight combinations of two guidance types (example-based and rule-based) at four guidance timings (service-onboarding, task-intro, after-failure, and upon-request), as measured by users' task performance, improvement on subsequent tasks, and subjective experience. It establishes that each guidance type and timing has particular strengths and weaknesses, thus that each type/timing combination has a unique impact on performance metrics, learning outcomes, and user experience. On that basis, it presents guidance-design recommendations for future task-oriented chatbots.
“Alexa, Do You Want to Build a Snowman?” Characterizing Playful Requests to Conversational Agents

Conversational Agents (CAs) such as Apple's Siri and Amazon's Alexa are well-suited for task-oriented interactions ("Call Jason"), but other interaction types are often beyond their capabilities. One notable example is playful requests: for example, people ask their CAs personal questions ("What's your favorite color?") or joke with them, sometimes at their expense ("Find Nemo"). Failing to recognize playfulness causes user dissatisfaction and abandonment, destroying the precious rapport with the CA.
Weighted programming: a programming paradigm for specifying mathematical models

We study weighted programming, a programming paradigm for specifying mathematical models. More specifically, the weighted programs we investigate are like usual imperative programs with two additional features: (1) nondeterministic branching and (2) weighting execution traces. Weights can be numbers but also other objects like words from an alphabet, polynomials, formal power series, or cardinal numbers. We argue that weighted programming as a paradigm can be used to specify mathematical models beyond probability distributions (as is done in probabilistic programming). We develop weakest-precondition- and weakest-liberal-precondition-style calculi à la Dijkstra for reasoning about mathematical models specified by weighted programs. We present several case studies. For instance, we use weighted programming to model the ski rental problem — an optimization problem. We model not only the optimization problem itself, but also the best deterministic online algorithm for solving this problem as weighted programs. By means of weakest-precondition-style reasoning, we can determine the competitive ratio of the online algorithm on source code level.
Deep Learning Meets Software Engineering: A Survey on Pre-Trained Models of Source Code

Recent years have seen the successful application of deep learning to software engineering (SE). In particular, the development and use of pre-trained models of source code has enabled state-of-the-art results to be achieved on a wide variety of SE tasks. This paper provides an overview of this rapidly advancing field of research and reflects on future research directions.
DialFRED: Dialogue-Enabled Agents for Embodied Instruction Following

Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively. We present DialFRED, a dialogue-enabled embodied instruction following benchmark based on the ALFRED benchmark. DialFRED allows an agent to actively ask questions to the human user; the additional information in the user's response is used by the agent to better complete its task. We release a human-annotated dataset with 53K task-relevant questions and answers and an oracle to answer questions. To solve DialFRED, we propose a questioner-performer framework wherein the questioner is pre-trained with the human-annotated data and fine-tuned with reinforcement learning. We make DialFRED publicly available and encourage researchers to propose and evaluate their solutions to building dialog-enabled embodied agents.
Duration Controllable Voice Conversion via Phoneme-Based Information Bottleneck

Several voice conversion (VC) methods using a simple autoencoder with a carefully designed information bottleneck have recently been studied.In general, they extract content information from a given speech through the information bottleneck between the encoder and the decoder, providing it to the decoder along with the target speaker information to generate the converted speech.However, their performance is highly dependent on the downsampling factor of an information bottleneck.In addition, such frame-by-frame conversion methods cannot convert speaking styles associated with the length of utterance, such as the duration.In this paper, we propose a novel duration controllable voice conversion (DCVC) model, which can transfer the speaking style and control the speed of the converted speech through a phoneme-based information bottleneck.The proposed information bottleneck does not need to find an appropriate downsampling factor, achieving a better audio quality and VC performance.In our experiments, DCVC outperformed the baseline models with a 3.78 MOS and a 3.83 similarity score.It can also smoothly control the speech duration while achieving a 39.35x speedup compared with a Seq2seq-based VC in terms of the inference speed.
MRI‐based prostate and dominant lesion segmentation using cascaded scoring convolutional neural network

Abstract Purpose Dose escalation to dominant intraprostatic lesions (DILs) is a novel treatment strategy to improve the treatment outcome of prostate radiation therapy. Treatment planning requires accurate and fast delineation of the prostate and DILs. In this study, a 3D cascaded scoring convolutional neural network is proposed to automatically segment the prostate and DILs from MRI. Methods and materials The proposed cascaded scoring convolutional neural network performs end‐to‐end segmentation by locating a region‐of‐interest (ROI), identifying the object within the ROI, and defining the target. A scoring strategy, which is learned to judge the segmentation quality of DIL, is integrated into cascaded convolutional neural network to solve the challenge of segmenting the irregular shapes of the DIL. To evaluate the proposed method, 77 patients who underwent MRI and PET/CT were retrospectively investigated. The prostate and DIL ground truth contours were delineated by experienced radiologists. The proposed method was evaluated with fivefold cross‐validation and holdout testing. Results The average centroid distance, volume difference, and Dice similarity coefficient (DSC) value for prostate/DIL are 4.3 ± 7.5/3.73 ± 3.78 mm, 4.5 ± 7.9/0.41 ± 0.59 cc, and 89.6 ± 8.9/84.3 ± 11.9%, respectively. Comparable results were obtained in the holdout test. Similar or superior segmentation outcomes were seen when compared the results of the proposed method to those of competing segmentation approaches. Conclusions The proposed automatic segmentation method can accurately and simultaneously segment both the prostate and DILs. The intended future use for this algorithm is focal boost prostate radiation therapy.
Quantum coherence

Quantum coherence is not only a fundamental concept of quantum mechanics, but also an important physical resource for quantum information processing. Along with the formulation of the resource theoretic framework of quantum coherence, the quantification of coherence is still one of the recent research focuses. Quantum coherence is also very fragile, and the environmental noise usually induces a system to decohere. Hence it is also an important subject to make clear the dynamical behavior and to seek a flexible way of preserving quantum coherence of an open quantum system. Besides, there are many potential applications of quantum coherence in quantum many-body system, quantum thermodynamics, quantum biology and other related fields. We review in this paper the resource theoretic framework for quantifying coherence and the relevant quantum coherence measures defined within this framework which includes the relative entropy of coherence, the &lt;i&gt;l&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt; norm of coherence, the entanglement-based measure of coherence, the convex roof measure of coherence, and the robustness of coherence. We also review the dynamical behaviors of quantum coherence for certain open quantum systems, the coherence generating and breaking power of typical quantum channels, and the freezing phenomenon of quantum coherence. Moreover, we exemplify applications of quantum coherence in Deutsch-Jozsa algorithm, Grover search algorithms, and the study of quantum phase transitions in multipartite systems. We hope that these results may provide not only an overview of the relevant field, but also an outlook of the future research direction of this exciting field.
SAL – A Lightweight Symmetric Cipher for Internet-of-Things

The modern era of computing demands high-speed performance and maximizing efficiency for commercial applications and business modules. Embedded systems and devices are unlike traditional computer systems. The computing power of the embedded device is very limited to perform a specific task, it consumes little hardware footprint and operates at electronic high speed by minimizing clock cycles and memory management. Embedded security issues and security challenges are a major concern resisting against advanced cryptanalytic attacks. In this research, we propose a new lightweight cryptographic algorithm SAL (Secure Advanced Lightweight). The algorithm is based on NIST (National Institute of Standards and Technology) standards and guidelines in design implementation of SAL to secure lower end of the device spectrum are internet of things (IoT), wireless sensors network (WSNs), aggregation network, pervasive devices, Radio frequency identification (RFID) tags, and embedded devices. SAL operates on the 64-bit block with a key size of 64-bit to 128- bit for 14 rounds of Feistel function. The internal structure of SAL powered with tiny 16-bit S-box using composite field arithmetic (CFA) technology. Our experimental results prove SAL has high efficiency and high throughput to achieve optimum performance when compared with standard cryptographic algorithms