Reviewerly/exHarmony huggingface dataset:
Measurement operator for quantum nondemolition measurements

We derive a measurement operator corresponding to a quantum nondemolition (QND) measurement of an atomic ensemble. The quantum measurement operator takes the form of a positive operator valued measure (POVM) and is valid for arbitrary interaction times, initial coherent state amplitudes, and final photon measurement outcomes. We analyze the dependence on various parameters and show that the effect of the QND measurement for short interaction times is to apply a Gaussian modulation of the initial state wavefunction. We derive approximate expressions for the POVM in various limits, such as the short interaction time regime and projective measurement limit. Several examples are shown, which show how spin squeezing and Schrodinger cat states can be generated using the measurement.
PoseGU: 3D human pose estimation with novel human pose generator and unbiased learning

3D pose estimation has recently gained substantial interests in computer vision domain. Existing 3D pose estimation methods have a strong reliance on large size well-annotated 3D pose datasets, and they suffer poor model generalization on unseen poses due to limited diversity of 3D poses in training sets. In this work, we propose PoseGU, a novel human pose generator that generates diverse poses with access only to a small size of seed samples, while equipping the Counterfactual Risk Minimization to pursue an unbiased evaluation objective. Extensive experiments demonstrate PoseGU outperforms almost all the state-of-the-art 3D human pose methods under consideration over three popular benchmark datasets. Empirical analysis also proves PoseGU generates 3D poses with improved data diversity and better generalization ability.
Teaching language processing with the PAMOJA framework

Existing educational tools for language processors have varying capabilities. There is no single tool that covers every aspect of language processors. As a result, there is a possibility that the educator and students will need multiple tools, switching between different specification notations, organization and interpretation of outputs, which could result in a steep learning curve. PAMOJA is a Java-based component framework with a broader scope — it supports construction of grammar-aware applications using the rapid application development style found in modern IDEs such as NetBeans. This paper investigated the possibility of using PAMOJA to develop educational tools for language processing courses. We conducted two major studies. The first study, summarized six design considerations, identified in the literature, for language processor educational tools. Then, we identified three example applications and demonstrated how to construct tools for them using PAMOJA. These include building and visualizing lexical scanners and parsers, as well as constructing a front-end for a software language, using a subset of Java language as an example. The second study evaluated the PAMOJA approach in relation to the identified design considerations, and by analyzing student and educator perceptions. The results seem to demonstrate a positive reception and acceptance, concluding that its application would facilitate the design of language processor teaching tools and learning language processors.
I am all EARS: Using open data and knowledge graph embeddings for music recommendations

Music streaming platforms offer music listeners an overwhelming choice of music. Therefore, users of streaming platforms need the support of music recommendation systems to find music that suits their personal taste. Currently, a new class of recommender systems based on knowledge graph embeddings promises to improve the quality of recommendations, in particular to provide diverse and novel recommendations. This paper investigates how knowledge graph embeddings can improve music recommendations. First, it is shown how a collaborative knowledge graph can be derived from open music data sources. Based on this knowledge graph, the music recommender system EARS (knowledge graph Embedding-based Artist Recommender System) is presented in detail, with particular emphasis on recommendation diversity and explainability. Finally, a comprehensive evaluation with real-world data is conducted, comparing of different embeddings and investigating the influence of different types of knowledge.
Elastic-net based robust extreme learning machine for one-class classification

The one-class extreme learning machine (OC-ELM) builds a classification model by learning samples of the known class to detect abnormal samples, and has the advantages of high learning speed and good generalization performance. However, OC-ELM with the square loss function is sensitive to outliers, which leads to poor robustness. To address this issue, an elastic-net based robust extreme learning machine for one-class classification (ER-OCELM) is proposed to achieve both excellent robustness and comparable sparsity. Correntropy loss function is utilized to limit the negative effects of outliers due to its property of bounded. With an elastic-net regularizer, automatic variable selection and continuous shrinkage can be performed simultaneously, and groups of correlated variables can be selected to make the model sparse. The corresponding optimization can be transformed into the linear equation system. We employ an iterative reweighted algorithm to obtain the optimal solution. In each iteration, the solution form of ER-OCELM is similar to that of ELM. Experiments on artificial and benchmark datasets verify that ER-OCELM has superior robustness and sparsity compared to many state-of-the-art methods.
Significance measures for rules in probabilistic-fuzzy inference systems based on fuzzy transforms

This paper defines a novel probabilistic-fuzzy inference system that considers fuzzy inputs and returns, as output, a probability distribution. In this way, it combines two different ways to represent uncertainty: the one modeled by fuzzy theory, that allows to represent reliable but vague information; and the one modeled by probability theory, that allows to represent undetermined but specific information. The novelty of this probabilistic-fuzzy inference system, with respect to the other existing in the literature, is that its inference engine combines quantile functions instead of distribution, probabilistic or density functions. Besides the formal definition of this novel kind of fuzzy inference systems, we propose: firstly, the construction of probabilistic-fuzzy rules by means of direct quantiles F-transforms; secondly, the definition of several significance measures for the obtained association rules; and finally, we present a set of experiments to validate all the assertions done throughout the paper.
LSIAN: Exploiting interval interests for session-based recommendation via sparse attention network

Session-based recommendation (SBR) recommends the next clicked item to anonymous users. Recent studies utilize graph attention networks for modeling. There exist various problems in these algorithms: First, most of these methods only consider the long-term interests of users, ignoring the short-term interest transfer of users; second, when the graph attention mechanism obtains item weights, irrelevant items are assigned weights, which reduces the weights of related items, resulting in insufficient weight discretization. Third, the last action is taken as the user's final preference, however, this assumption does not necessarily correspond to the user's actual interest. This paper proposes a long- and short-term interest-attention aware network (LSIAN) model. First, we propose a novel time-aware attention mechanism that learns users' short-term interests by considering context and time interval, and utilizes average pooling to represent users' long-term interests. Next, we introduce a context-based adaptive sparse attention mechanism to discover irrelevant items, down-weight irrelevant items through a dynamic loop, and reserve higher weights for related items. Furthermore, we introduce a gated fusion method adaptively integrate the user's long-term and short-term preferences, thereby alleviating the problem of excessive weighting of the last term. Experiments on two public datasets show that LSIAN outperforms state-of-the-art algorithms.
Entanglement dynamics and population inversion of a two-qubit system in two cavities coupled with optical fiber in the presence of two-photon transition

In this paper, the entanglement between two qubits that exist in two distinct cavities which are connected by an optical fiber is investigated by using the concurrence, while two-photon transitions and Kerr medium effect are also considered. Each cavity contains a qubit and a single-mode quantized field. The appearance of entanglement between the two qubits in the separate cavities originates from the presence of optical fiber. The obtained numerical results of the considered system show that, if the Kerr medium is large enough, the initial values of entanglement between the two qubits (which may have the maximum possible value, i.e. 1) can be approximately protected from the large amplitude fluctuations, so that acceptable stability is created. Also, qubit-field coupling and detuning effects are investigated and it is observed that symmetric coupling results in more stability of entanglement. Eventually, atomic population inversion is also studied and it is observed that it is controllable by considering different parameters.
An Improved Mixture Model of Gaussian Processes and Its Classification Expectation–Maximization Algorithm

The mixture of experts (ME) model is effective for multimodal data in statistics and machine learning. To treat non-stationary probabilistic regression, the mixture of Gaussian processes (MGP) model has been proposed, but it may not perform well in some cases due to the limited ability of each Gaussian process (GP) expert. Although the mixture of Gaussian processes (MGP) and warped Gaussian process (WGP) models are dominant and effective for non-stationary probabilistic regression, they may not be able to handle general non-stationary probabilistic regression in practice. In this paper, we first propose the mixture of warped Gaussian processes (MWGP) model as well as its classification expectation–maximization (CEM) algorithm to address this problem. To overcome the local optimum of the CEM algorithm, we then propose the split and merge CEM (SMC EM) algorithm for MWGP. Experiments were done on synthetic and real-world datasets, which show that our proposed MWGP is more effective than the models used for comparison, and the SMCEM algorithm can solve the local optimum for MWGP.
Data association for maneuvering targets through a combined siamese network and XGBoost model

Data association plays an important role in forming target tracks when false alarms exist. Its accuracy is key to reducing the computational burden of the combinatorial explosion problem inherent to target tracking in environments where false alarms are densely distributed. Traditional methods are usually developed according to some assumed motion models and suffer performance degradation when there is mismatch between assumed models and the actual motion, which may often be the case for maneuvering targets. To cope with this problem, we propose a data-driven method that learns the association criteria directly from data. By use of the domain knowledge and a convolutional Siamese network, features of different attributes present in the observations are extracted. Then the features are fused through the use of XGBoost. Simulation results show that the proposed method performs better than the traditional model-based methods in correlating observations of maneuvering targets and avoiding correlations of false alarms. By visualizing the decision process of the feature fusion model, it is found that the proposed method learns from the data to make comprehensive use of multiple features and does not simply set hard thresholds for the features. The computational complexity is also analyzed both theoretically and experimentally.
Text characterization based on recurrence networks

Several complex systems are characterized by presenting intricate characteristics taking place at several scales of time and space. These multiscale characterizations are used in various applications, including better understanding diseases, characterizing transportation systems, and comparison between cities, among others. In particular, texts are also characterized by a hierarchical structure that can be approached by using multi-scale concepts and methods. The multiscale properties of texts constitute a subject worth further investigation. In addition, more effective approaches to text characterization and analysis can be obtained by emphasizing words with potentially more informational content. The present work aims at developing these possibilities while focusing on mesoscopic representations of networks. More specifically, we adopt an extension to the mesoscopic approach to represent text narratives, in which only the recurrent relationships among tagged parts of speech (subject, verb and direct object) are considered to establish connections among sequential pieces of text (e.g., paragraphs). The characterization of the texts was then achieved by considering scale-dependent complementary methods: accessibility, symmetry and recurrence signatures. In order to evaluate the potential of these concepts and methods, we approached the problem of distinguishing between literary genres (fiction and non-fiction). A set of 300 books organized into the two genres was considered and were compared by using the aforementioned approaches. All the methods were capable of differentiating to some extent between the two genres. The accessibility and symmetry reflected the narrative asymmetries, while the recurrence signature provided a more direct indication about the non-sequential semantic connections taking place along the narrative.
Applications of Genetic Algorithm with Integrated Machine Learning

The Meta heuristic algorithms are the higher level technique which helps to find the best feasible solution out of all possible solution of an optimization problem. There are various different types of meta heuristic algorithms like Ant Colony Optimization (ACO), Genetic Algorithm, Simulated Annealing, Particle Swarm Optimization, etc. Genetic Algorithm is a search-based optimization technique based on the biological principle of Genetics and adaptation. It is a meta-heuristic approach which is used to solve complex combinatorial problem. The integration of Genetic algorithm with machine learning will be helpful in solving unconstrained and constrained optimization problem. The various genetic operator like selection operator, mutation and cross-over are discussed which will be helpful in knowing how these operators significantly improves State Space search. In this paper the various applications of Genetic algorithms which can be used in machine learning has been discussed. In this paper the author discussed how the significance of Genetic algorithm will be improved while solving complex optimization problem in machine learning. In this paper, flow diagram of Genetic Algorithms has been discussed which will ease the understanding of complex optimization problem like 0–1 Knapsack, Traveling Salesman Problem, etc. In this paper a comparative analysis between traditional algorithm and genetic algorithm has been done on the basis of parameters like flow of control, state space search, Complication, Preconditions, CPU utilization etc. The various limitations of Genetic Algorithms in solving problems with optimal solutions has also been discussed.
Development of a Blockchain-based Platform to Simplify the Sharing of Patient Data

Although blockchain technology was first created to manage financial ledgers, it has lately found use in a wide range of industries, including healthcare. Research innovation in this area will be boosted by the sharing of healthcare data. Nevertheless, patients who disclose their medical records face several privacy and security concerns. Here, the authors propose a blockchain-dependent system architectural design to demonstrate the prospect of blockchain technology in supporting (i) personal and examined medical data exchange and (ii) medical data access authorization processing.
AI Enabled NLP based Text to Text Medical Chatbot

Usually, people are not conscious of the several treatments or manifestations of a particular illness. They usually have to go to the hospital for minor health problems, which needs an extra amount of time. Also, answering phone calls for complaints is quite hectic. However, this matter can be fixed by utilizing a medical Chatbot called MedBot, which can give proper advice on leading a healthy lifestyle. The basic idea is to construct a healthcare chatbot (MedBot) based on Artificial Intelligence and Natural Language Processing, which can identify the illness and provide necessary information about it prior to consulting or visiting a doctor, thereby making the MedBot more reachable and reducing healthcare costs. Some of these chatbots act as virtual medical assistants, teaching patients regarding their sickness and motivating them to have better health. A text-to-text medical Chatbot involves users in an online conversation about their medical issues and offers a range of personalized diagnoses depending on the symptoms that have been presented. The MedBot interacts with potential patients who come to the application.[6]
Multi-User Matching and Resource Allocation in Vision Aided Communications

Visual perception is an effective way to obtain the spatial characteristics of wireless channels and to reduce the overhead for communications system. A critical problem for the visual assistance is that the communications system needs to match the radio signal with the visual information of the corresponding user, i.e., to identify the visual user that corresponds to the target radio signal from all the environmental objects. In this paper, we propose a user matching method for environment with a variable number of objects. Specifically, we apply 3D detection to extract all the environmental objects from the images taken by multiple cameras. Then, we design a deep neural network (DNN) to estimate the location distribution of users by the images and beam pairs at multiple moments, and thereby identify the users from all the extracted environmental objects. Moreover, we present a resource allocation method based on the taken images to reduce the time and spectrum overhead compared to traditional resource allocation methods. Simulation results show that the proposed user matching method outperforms the existing methods, and the proposed resource allocation method can achieve 92% transmission rate of the traditional resource allocation method but with the time and spectrum overhead significantly reduced.
Knowledge Enhanced Semantic Communication Receiver

In recent years, with the rapid development of deep learning and natural language processing technologies, semantic communication has become a topic of great interest in the field of communication. Although existing deep learning-based semantic communication approaches have shown many advantages, they still do not make sufficient use of prior knowledge. Moreover, most existing semantic communication methods focus on the semantic encoding at the transmitter side, while we believe that the semantic decoding capability of the receiver should also be concerned. In this letter, we propose a knowledge enhanced semantic communication framework in which the receiver can more actively utilize the facts in the knowledge base for semantic reasoning and decoding, on the basis of only affecting the parameters rather than the structure of the neural networks at the transmitter side. Specifically, we design a transformer-based knowledge extractor to find relevant factual triples for the received noisy signal. Extensive simulation results on the WebNLG dataset demonstrate that the proposed receiver yields superior performance on top of the knowledge graph enhanced decoding.
Aggregated Zero-Knowledge Proof and Blockchain-Empowered Authentication for Autonomous Truck Platooning

Platooning technologies enable trucks to drive cooperatively and automatically, providing benefits including less fuel consumption, greater road capacity, and safety. This paper introduces an aggregated zero-knowledge proof and blockchain-empowered system for privacy-preserving identity verification in the mixed fleet platooning environment. The correctness proof and the security analysis of the proposed authentication scheme are provided, highlighting its increased security and fast performance in comparison to a single-proof design. The blockchain performs the role of verifier within the authentication scheme, reducing unnecessary communication overhead. Moreover, the blockchain improves system resilience by providing fault tolerance to the decentralized verification process. Platooning records are stored directly on the digital ledger to guarantee data immutability and integrity, while the programmable access control policies ensure data privacy. The experimental results demonstrate that the proposed approach can perform authentication on the order of milliseconds, regardless of the number of proofs, highlighting feasibility for real-world deployment in truck platooning.
Multi-speaker Multi-style Speech Synthesis with Timbre and Style Disentanglement

Disentanglement of a speaker's timbre and style is very important for style transfer in multi-speaker multi-style text-to-speech (TTS) scenarios. With the disentanglement of timbres and styles, TTS systems could synthesize expressive speech for a given speaker with any style which has been seen in the training corpus. However, there are still some shortcomings with the current research on timbre and style disentanglement. The current method either requires single-speaker multi-style recordings, which are difficult and expensive to collect, or uses a complex network and complicated training method, which is difficult to reproduce and control the style transfer behavior. To improve the disentanglement effectiveness of timbres and styles, and to remove the reliance on single-speaker multi-style corpus, a simple but effective timbre and style disentanglement method is proposed in this paper. The FastSpeech2 network is employed as the backbone network, with explicit duration, pitch, and energy trajectory to represent the style. Each speaker's data is considered as a separate and isolated style, then a speaker embedding and a style embedding are added to the FastSpeech2 network to learn disentangled representations. Utterance level pitch and energy normalization are utilized to improve the decoupling effect. Experimental results demonstrate that the proposed model could synthesize speech with any style seen during training with high style similarity while maintaining very high speaker similarity.