Reviewerly/exHarmony huggingface dataset:
Spin-Boson Model as A Simulator of Non-Markovian Multiphoton Jaynes-Cummings Models

The paradigmatic spin-boson model considers a spin degree of freedom interacting with an environment typically constituted by a continuum of bosonic modes. This ubiquitous model is of relevance in a number of physical systems where, in general, one has neither control over the bosonic modes, nor the ability to tune distinct interaction mechanisms. Despite this apparent lack of control, we present a suitable transformation that approximately maps the spin-boson dynamics into that of a tunable multiphoton Jaynes-Cummings model undergoing dissipation. Interestingly, the latter model describes the coherent interaction between a spin and a single bosonic mode via the simultaneous exchange of n bosons per spin excitation. Resorting to the so-called reaction coordinate method, we identify a relevant collective bosonic mode in the environment, which is then used to generate multiphoton interactions following the proposed theoretical framework. Moreover, we show that spin-boson models featuring structured environments can lead to non-Markovian multiphoton Jaynes-Cummings dynamics. We discuss the validity of the proposed method depending on the parameters and analyse its performance, which is supported by numerical simulations. In this manner, the spin-boson model serves as a good analogue quantum simulator for the inspection and realization of multiphoton Jaynes-Cummings models, as well as the interplay of non-Markovian effects and, thus, as a simulator of light-matter systems with tunable interaction mechanisms.
Item Response Theory for Efficient Human Evaluation of Chatbots

Conversational agent quality is currently assessed using human evaluation, and often requires an exorbitant number of comparisons to achieve statistical significance.In this paper, we introduce Item Response Theory (IRT) for chatbot evaluation, using a paired comparison in which annotators judge which system responds better to the next turn of a conversation.IRT is widely used in educational testing for simultaneously assessing the ability of test takers and the quality of test questions.It is similarly well suited for chatbot evaluation since it allows the assessment of both models and the prompts used to evaluate them.We use IRT to efficiently assess chatbots, and show that different examples from the evaluation set are better suited for comparing highquality (nearer to human performance) than low-quality systems.Finally, we use IRT to reduce the number of evaluation examples assessed by human annotators while retaining discriminative power.
Unsupervised Natural Language Inference via Decoupled Multimodal Contrastive Learning

We propose to solve the natural language inference problem without any supervision from the inference labels via task-agnostic multimodal pretraining.Although recent studies of multimodal self-supervised learning also represent the linguistic and visual context, their encoders for different modalities are coupled.Thus they cannot incorporate visual information when encoding plain text alone.In this paper, we propose Multimodal Aligned Contrastive Decoupled learning (MACD) network.MACD forces the decoupled text encoder to represent the visual information via contrastive learning.Therefore, it embeds visual knowledge even for plain text inference.We conducted comprehensive experiments over plain text inference datasets (i.e.SNLI and STS-B).The unsupervised MACD even outperforms the fully-supervised BiLST-M and BiLSTM+ELMO on STS-B.
Detecting Media Bias in News Articles using Gaussian Bias Distributions

Media plays an important role in shaping public opinion.Biased media can influence people in undesirable directions and hence should be unmasked as such.We observe that featurebased and neural text classification approaches which rely only on the distribution of low-level lexical information fail to detect media bias.This weakness becomes most noticeable for articles on new events, where words appear in new contexts and hence their "bias predictiveness" is unclear.In this paper, we therefore study how second-order information about biased statements in an article helps to improve detection effectiveness.In particular, we utilize the probability distributions of the frequency, positions, and sequential order of lexical and informational sentence-level bias in a Gaussian Mixture Model.On an existing media bias dataset, we find that the frequency and positions of biased statements strongly impact article-level bias, whereas their exact sequential order is secondary.Using a standard model for sentence-level bias detection, we provide empirical evidence that article-level bias detectors that use second-order information clearly outperform those without.
SHACL Constraints with Inference Rules

The Shapes Constraint Language (SHACL) has been recently introduced as a W3C recommendation to define constraints that can be validated against RDF graphs. Interactions of SHACL with other Semantic Web technologies, such as ontologies or reasoners, is a matter of ongoing research. In this paper we study the interaction of a subset of SHACL with inference rules expressed in datalog. On the one hand, SHACL constraints can be used to define a "schema" for graph datasets. On the other hand, inference rules can lead to the discovery of new facts that do not match the original schema. Given a set of SHACL constraints and a set of datalog rules, we present a method to detect which constraints could be violated by the application of the inference rules on some graph instance of the schema, and update the original schema, i.e, the set of SHACL constraints, in order to capture the new facts that can be inferred. We provide theoretical and experimental results of the various components of our approach.
Recent Challenges in Recommender Systems: A Survey

The recent revolutionary technology transformations in the internet domain have enabled us to move from static web pages to ubiquitous computing web through social networking web. In return, this has enabled the recommender systems to leave their infancy and get matured while tackling the dynamic challenges arising for users. Recommender system anticipates user requirements before the user requires them. Recommender system in various domains proves its efficiency by providing appropriate recommendations according to the preferences of the users. It is a software solution in different online applications which helps the user to make appropriate decisions and also acts as a business tool in various domains. The proposed article covers the various types of recommender systems as well as the strategies and recent challenging research issues to improve the capabilities of recommender systems.
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation

Automated generation of medical reports that describe the findings in the medical images helps radiologists by alleviating their workload.Medical report generation system should generate correct and concise reports.However, data imbalance makes it difficult to train models accurately.Medical datasets are commonly imbalanced in their finding labels because incidence rates differ among diseases; moreover, the ratios of abnormalities to normalities are significantly imbalanced.We propose a novel reinforcement learning method with a reconstructor to improve the clinical correctness of generated reports to train the data-to-text module with a highly imbalanced dataset.Moreover, we introduce a novel data augmentation strategy for reinforcement learning to additionally train the model on infrequent findings.From the perspective of a practical use, we employ a Two-Stage Medical Report Generator (TS-MRGen) for controllable report generation from input images.TS-MRGen consists of two separated stages: an image diagnosis module and a data-to-text module.Radiologists can modify the image diagnosis module results to control the reports that the data-totext module generates.We conduct an experiment with two medical datasets to assess the data-to-text module and the entire two-stage model.Results demonstrate that the reports generated by our model describe the findings in the input image more correctly.
Regularizing Dialogue Generation by Imitating Implicit Scenarios

Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario.To enable responses that are more meaningful and context-specific, we propose to improve generative dialogue systems from the scenario perspective, where both dialogue history and future conversation are taken into account to implicitly reconstruct the scenario knowledge.More importantly, the conversation scenarios are further internalized using imitation learning framework, where the conventional dialogue model that has no access to future conversations is effectively regularized by transferring the scenario knowledge contained in hierarchical supervising signals from the scenario-based dialogue model, so that the future conversation is not required in actual inference.Extensive evaluations show that our approach significantly outperforms state-of-theart baselines on diversity and relevance, and expresses scenario-specific knowledge.
When Hearst Is not Enough: Improving Hypernymy Detection from Corpus with Distributional Models

We address hypernymy detection, i.e., whether an is-a relationship exists between words (x, y), with the help of large textual corpora.Most conventional approaches to this task have been categorized to be either pattern-based or distributional.Recent studies suggest that pattern-based ones are superior, if large-scale Hearst pairs are extracted and fed, with the sparsity of unseen (x, y) pairs relieved.However, they become invalid in some specific sparsity cases, where x or y is not involved in any pattern.For the first time, this paper quantifies the non-negligible existence of those specific cases.We also demonstrate that distributional methods are ideal to make up for patternbased ones in such cases.We devise a complementary framework, under which a patternbased and a distributional model collaborate seamlessly in cases which they each prefer.On several benchmark datasets, our framework achieves competitive improvements and the case study shows its better interpretability.
Characterizing the Stability of NISQ Devices

In this study, we focus on the question of stability of NISQ devices. The parameters that define the device stability profile are motivated by the work of DiVincenzo where the requirements for physical implementation of quantum computing are discussed. We develop the metrics and theoretical framework to quantify the DiVincenzo requirements and study the stability of those key metrics. The basis of our assessment is histogram similarity (in time and space). For identical experiments, devices which produce reproducible histograms in time, and similar histograms in space, are considered more reliable. To investigate such reliability concerns robustly, we propose a moment-based distance (MBD) metric. We illustrate our methodology using data collected from IBM's Yorktown device. Two types of assessments are discussed: spatial stability and temporal stability.
JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-modal Image Alignment of Large-Scale Pathological CT Scans

Multi-modal image registration is a challenging problem that is also an important clinical task for many real applications and scenarios. As a first step in analysis, deformable registration among different image modalities is often required in order to provide complementary visual information. During registration, semantic information is key to match homologous points and pixels. Nevertheless, many conventional registration methods are incapable in capturing high-level semantic anatomical dense correspondences. In this work, we propose a novel multi-task learning system, JSSR, based on an end-to-end 3D convolutional neural network that is composed of a generator, a registration and a segmentation component. The system is optimized to satisfy the implicit constraints between different tasks in an unsupervised manner. It first synthesizes the source domain images into the target domain, then an intra-modal registration is applied on the synthesized images and target images. The segmentation module are then applied on the synthesized and target images, providing additional cues based on semantic correspondences. The supervision from another fully-annotated dataset is used to regularize the segmentation. We extensively evaluate JSSR on a large-scale medical image dataset containing 1,485 patient CT imaging studies of four different contrast phases (i.e., 5,940 3D CT scans with pathological livers) on the registration, segmentation and synthesis tasks. The performance is improved after joint training on the registration and segmentation tasks by $$0.9\%$$ and $$1.9\%$$ respectively compared to a highly competitive and accurate deep learning baseline. The registration also consistently outperforms conventional state-of-the-art multi-modal registration methods.
An Example of Performance Comparison of Supervised Machine Learning Algorithms Before and After PCA and LDA Application: Breast Cancer Detection

Rapid and accurate diagnosis of common diseases such as breast cancer that is common among women is of great importance. While this determination is made by specialist doctors, studies are carried out with machine learning algorithms to help them. Machine learning algorithms make inferences from existing data and predict what is unknown. Supervised machine learning algorithms used in classification of categorical data and of new data are frequently used in today's problems. In this study, dataset with 357 malign and 212 benign created using the features of the University of Wisconsin by making some measurements in the mass images on the breast and the feature methods extracted from these features were used. After applying the necessary pre-processing and normalization steps for the dataset, the data was separated as training and test data and training was performed for six supervised machine learning algorithms (k-nearest neighbors algorithm, random forest algorithm, decision trees, naive bayes algorithm, support vector machines and logistic regression). In addition, the same operations were carried out by applying principal components analysis and linear discriminant analysis to the dataset. In this study, the accuracy values for all our models were increased after applying linear discriminant analysis and a success rate of 96.49% was achieved with Logistic Regression. It was aimed to select the appropriate algorithm and to obtain observation that will be the source of the next studies with the result to be obtained in this study.
VSQL: Variational Shadow Quantum Learning for Classification

Classification of quantum data is essential for quantum machine learning and near-term quantum technologies. In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL). Our method in particular utilizes the classical shadows of quantum data, which fundamentally represent the side information of quantum data with respect to certain physical observables. Specifically, we first use variational shadow quantum circuits to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task. We show that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training. Simultaneously, less noise will be introduced since fewer quantum gates are employed in such shadow circuits. Moreover, we show that the Barren Plateau issue, a significant gradient vanishing problem in quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the efficiency of VSQL in quantum classification via numerical experiments on the classification of quantum states and the recognition of multi-labeled handwritten digits. In particular, our VSQL approach outperforms existing variational quantum classifiers in the test accuracy in the binary case of handwritten digit recognition and notably requires much fewer parameters.
Truck Platooning Aided Secure Publish/Subscribe System Based on Smart Contract in Autonomous Vehicular Networks

The publish/subscribe system over the autonomous vehicular networks (AVNs) enables the content providers to obtain more profits from a flexible notification system. However, few of current works focus on flexible brokers suitable for AVNs. In this paper, we propose a truck platoon aided secure publish/subscribe system based on smart contract in AVNs. Firstly, the truck platoons are utilized to be the brokers of the publish/subscribe system to realize a more efficient and secure publish/subscribe system. Secondly, a vehicular blockchain is established for the publish/subscribe system to record the transactions between brokers and publishers/subscribers to guarantee the complete and safe implementation of the smart contracts. Next, a contract based on the principal-agent problem is designed between the publisher and broker to maximize their utilities and stimulate the broker to be more active in the contents subscribing service. Furthermore, a smart contract is also designed between the subscriber and broker by the cloud server to achieve higher utilities and match an appropriate broker for the subscriber by considering its type. Finally, we carry out extensive numerical results to evaluate the performances of the proposed contracts, while the results also demonstrate that the proposal outperforms the conventional schemes.
PointINet: Point Cloud Frame Interpolation Network

LiDAR point cloud streams are usually sparse in time dimension, which is limited by hardware performance. Generally, the frame rates of mechanical LiDAR sensors are 10 to 20 Hz, which is much lower than other commonly used sensors like cameras. To overcome the temporal limitations of LiDAR sensors, a novel task named Point Cloud Frame Interpolation is studied in this paper. Given two consecutive point cloud frames, Point Cloud Frame Interpolation aims to generate intermediate frame(s) between them. To achieve that, we propose a novel framework, namely Point Cloud Frame Interpolation Network (PointINet). Based on the proposed method, the low frame rate point cloud streams can be upsampled to higher frame rates. We start by estimating bi-directional 3D scene flow between the two point clouds and then warp them to the given time step based on the 3D scene flow. To fuse the two warped frames and generate intermediate point cloud(s), we propose a novel learning-based points fusion module, which simultaneously takes two warped point clouds into consideration. We design both quantitative and qualitative experiments to evaluate the performance of the point cloud frame interpolation method and extensive experiments on two large scale outdoor LiDAR datasets demonstrate the effectiveness of the proposed PointINet. Our code is available at https://github.com/ispc-lab/PointINet.git.
Deep Learning Based, Real-Time Object Detection for Autonomous Driving

One of the active research topics that maintains its popularity in the field of Computer Vision is the problem of object detection in autonomous cars. Since object detection is a difficult problem, high performance solutions do not work very quickly. Similarly, real-time solutions make compromise on performance. However, due to the nature of autonomous driving, object detection systems must perform in real time and high performance. In this study, Tiny YOLOv3, one of the most successful object detection architectures, was combined with one of the classical object tracking methods, the Kalman filter. A small and real-time object detection system, which increases the model's accuracy without losing its speed, is proposed.
Digital Contact Tracing Based on a Graph Database Algorithm for Emergency Management During the COVID-19 Epidemic: Case Study

Background The COVID-19 epidemic is still spreading globally. Contact tracing is a vital strategy in epidemic emergency management; however, traditional contact tracing faces many limitations in practice. The application of digital technology provides an opportunity for local governments to trace the contacts of individuals with COVID-19 more comprehensively, efficiently, and precisely. Objective Our research aimed to provide new solutions to overcome the limitations of traditional contact tracing by introducing the organizational process, technical process, and main achievements of digital contact tracing in Hainan Province. Methods A graph database algorithm, which can efficiently process complex relational networks, was applied in Hainan Province; this algorithm relies on a governmental big data platform to analyze multisource COVID-19 epidemic data and build networks of relationships among high-risk infected individuals, the general population, vehicles, and public places to identify and trace contacts. We summarized the organizational and technical process of digital contact tracing in Hainan Province based on interviews and data analyses. Results An integrated emergency management command system and a multi-agency coordination mechanism were formed during the emergency management of the COVID-19 epidemic in Hainan Province. The collection, storage, analysis, and application of multisource epidemic data were realized based on the government’s big data platform using a centralized model. The graph database algorithm is compatible with this platform and can analyze multisource and heterogeneous big data related to the epidemic. These practices were used to quickly and accurately identify and trace 10,871 contacts among hundreds of thousands of epidemic data records; 378 closest contacts and a number of public places with high risk of infection were identified. A confirmed patient was found after quarantine measures were implemented by all contacts. Conclusions During the emergency management of the COVID-19 epidemic, Hainan Province used a graph database algorithm to trace contacts in a centralized model, which can identify infected individuals and high-risk public places more quickly and accurately. This practice can provide support to government agencies to implement precise, agile, and evidence-based emergency management measures and improve the responsiveness of the public health emergency response system. Strengthening data security, improving tracing accuracy, enabling intelligent data collection, and improving data-sharing mechanisms and technologies are directions for optimizing digital contact tracing.
The effect of Using Flipbook-Based E-modules on Student Learning Outcomes

The creative innovation in education in the era of revolution 4.0 is the use of electronic-based teaching materials as a source of student learning. This study aims to examine the effect of using a flipbook-based e-module on transverse wave material with a one group pretest posttest design research design. The research method that has been used is quasi-experimental research design with one group pretest posttest design. The research sample was students of Sriwijaya University Physics Education. Data were analyzed using N-gain analysis, normality test, homogeneity test and Paired Sample T-test. The results showed that the posttest test results were higher than the pretest tests with an average of 33.28, while the normality test results obtained pretest data of 0.026 and posttest data of 0.257. Finally, the N-gain test results were obtained at 0.466 in the moderate category, it was concluded that there was an effect of using flipbook-based e-modules on dynamic electricity on student learning outcomes, so the use of flipbook-based e-modules was effectively used as teaching material on student learning outcomes.