Reviewerly/exHarmony huggingface dataset:
Light Field Neural Rendering

Classical light field rendering for novel view synthesis can accurately reproduce view-dependent effects such as reflection, refraction, and translucency, but requires a dense view sampling of the scene. Methods based on geometric reconstruction need only sparse views, but cannot accurately model non-Lambertian effects. We introduce a model that combines the strengths and mitigates the limitations of these two directions. By operating on a four-dimensional representation of the light field, our model learns to represent view-dependent effects accurately. By enforcing geometric constraints during training and inference, the scene geometry is implicitly learned from a sparse set of views. Concretely, we introduce a two-stage transformer-based model that first aggregates features along epipolar lines, then aggregates features along reference views to produce the color of a target ray. Our model outperforms the state-of-the-art on multiple forward-facing and 360° datasets, with larger margins on scenes with severe view-dependent variations. Code and results can be found at light-field-neural-rendering.github. io.
Probabilistic error cancellation with sparse Pauli–Lindblad models on noisy quantum processors

Noise in quantum computers can result in biased estimates of physical observables. Accurate bias-free estimates can be obtained using probabilistic error cancellation, an error-mitigation technique that effectively inverts well-characterized noise channels. Learning correlated noise channels in large quantum circuits, however, has been a major challenge and has severely hampered experimental realizations. Our work presents a practical protocol for learning and inverting a sparse noise model that is able to capture correlated noise and scales to large quantum devices. These advances allow us to demonstrate probabilistic error cancellation on a superconducting quantum processor, thereby providing a way to measure noise-free observables at larger circuit volumes. Probabilistic error cancellation could improve the performance of quantum computers without the prohibitive overhead of fault-tolerant error correction. The method has now been demonstrated on a device with 20 qubits.
Timeline-Based Space Operations Scheduling with External Constraints

We describe a timeline-based scheduling algorithm developed for mission operations of the EO-1 earth observing satellite. We first describe the range of operational constraints for operations focusing on maneuver and thermal constraints that cannot be modeled in typical planner/schedulers. We then describe a greedy heuristic scheduling algorithm and compare its performance to both the prior scheduling algorithm - documenting an over 50% increase in scenes scheduled with estimated value of millions of dollars US. We also compare to a relaxed optimal scheduler showing that the greedy scheduler produces schedules with scene count within 15% of an upper bound on optimal schedules.
An evaluation for VR glasses system user experience: The influence factors of interactive operation and motion sickness

As a new type of Virtual Reality (VR) headset, VR glasses rise rapidly in a number of areas. It's essential to understand the importance of user experience (UX) on VR glasses design. This study aimed to develop questionnaires for evaluating VR glasses' UX, as well as to investigate the relationship between various UX variables. With using lab-based usability tests, this study analyzed participants' self-reports and performance based on testing eight VR glasses and seven mobile applications. A nine-item questionnaire and a ten-item questionnaire were successfully developed to measure VR glasses systems' UX quality in terms of hardware and application, respectively. Within a proposed UX evaluation framework, the perceived UX quality relative to VR glasses hardware emerged as a core predictor in predicting interactive operation performance, whereas the application UX perception was a significant predictor of motion sickness.
Central Bank Digital Currency: optimization of the currency system and its issuance design

Central Bank Digital Currency (CBDC) contributes to optimizing payment functions of fiat money, reducing reliance on payment services provided by the private sector, alleviating regulatory burdens and pressure on the central bank, and strengthening the authority of fiat money. Moreover, issuance of CBDC helps to address dilemmas of modern monetary policies, including inefficiencies in policy transmission, difficulties in countercyclical control, flow of currency away from the real economy to the virtual economy and inadequate management of policy expectations. This paper proposes a CBDC issuance framework based on forward contingencies. The incorporation of time, sector, and loan rate contingencies in the activation of CBDC will realize real-time transmission of monetary policy, enable targeted supply of money and prevent the currency from circulating beyond the real economy. The economic state contingency makes it possible to exercise countercyclical control of currency. The embedment of these contingencies also enables currency to perform the function of forward guidance.
Hybridizing Evolutionary Computation and Deep Neural Networks: An Approach to Handwriting Recognition Using Committees and Transfer Learning

Neuroevolution is the field of study that uses evolutionary computation in order to optimize certain aspect of the design of neural networks, most often its topology and hyperparameters. The field was introduced in the late-1980s, but only in the latest years the field has become mature enough to enable the optimization of deep learning models, such as convolutional neural networks. In this paper, we rely on previous work to apply neuroevolution in order to optimize the topology of deep neural networks that can be used to solve the problem of handwritten character recognition. Moreover, we take advantage of the fact that evolutionary algorithms optimize a population of candidate solutions, by combining a set of the best evolved models resulting in a committee of convolutional neural networks. This process is enhanced by using specific mechanisms to preserve the diversity of the population. Additionally, in this paper, we address one of the disadvantages of neuroevolution: the process is very expensive in terms of computational time. To lessen this issue, we explore the performance of topology transfer learning: whether the best topology obtained using neuroevolution for a certain domain can be successfully applied to a different domain. By doing so, the expensive process of neuroevolution can be reused to tackle different problems, turning it into a more appealing approach for optimizing the design of neural networks topologies. After evaluating our proposal, results show that both the use of neuroevolved committees and the application of topology transfer learning are successful: committees of convolutional neural networks are able to improve classification results when compared to single models, and topologies learned for one problem can be reused for a different problem and data with a good performance. Additionally, both approaches can be combined by building committees of transferred topologies, and this combination attains results that combine the best of both approaches.
Light-efficient augmented reality display with steerable eyebox

We present a novel head-mounted display setup that uses the pinhole imaging principle coupled with a low-latency dynamic pupil follower.A transmissive LCD is illuminated by a single LED backlight.LED illumination is focused onto the viewer's pupil to form an eyebox smaller than the average human pupil, thereby creating a pinhole display effect where objects at all distances appear in focus.Since nearly all the light is directed to the viewer's pupil, a single low-power LED for each primary color with 0.42 lumens total output is sufficient to create a bright and full-color display of 360 cd/m 2 luminance.In order to follow the viewer's pupil, the eyebox needs to be steerable.We achieved a dynamic eyebox using an array of LEDs that is coupled with a real-time pupil tracker.The entire system is operated at 11 msec motion-to-photon latency, which meets the demanding requirements of the real-time pupil follower system.Experimental results effectively demonstrated our headmounted pinhole display with 37° FOV and very high light efficiency, equipped with a pupil follower with low motion-to-photon latency.
Adversarial Network Compression

Neural network compression has recently received much attention due to the computational requirements of modern deep models. In this work, our objective is to transfer knowledge from a deep and accurate model to a smaller one. Our contributions are threefold: (i) we propose an adversarial network compression approach to train the small student network to mimic the large teacher, without the need for labels during training; (ii) we introduce a regularization scheme to prevent a trivially-strong discriminator without reducing the network capacity and (iii) our approach generalizes on different teacher-student models. In an extensive evaluation on five standard datasets, we show that our student has small accuracy drop, achieves better performance than other knowledge transfer approaches and it surpasses the performance of the same network trained with labels. In addition, we demonstrate state-of-the-art results compared to other compression strategies.
On Recommendation of Learning Objects Using Felder-Silverman Learning Style Model

The e-learning recommender system in learning institutions is increasingly becoming the preferred mode of delivery, as it enables learning anytime, anywhere. However, delivering personalised course learning objects based on student's preferences is still a challenge. Current mainstream recommendation algorithms, such as the Collaborative Filtering (CF) and Content-Based Filtering (CBF), deal with only two types of entities, namely users and items with their ratings. However, these methods do not pay attention to student's preferences, such as learning styles, which are especially important for the accuracy of course learning objects prediction or recommendation. Moreover, several recommendation techniques experience cold-start and rating sparsity problems. To address the challenge of improving the quality of recommender systems, in this paper a novel recommender algorithm for machine learning is proposed, which combines students actual rating with their learning styles to recommend personalised course learning objects (LOs). Various recommendation techniques are considered in an experimental study investigating the best technique to use in predicting student ratings for e-learning recommender systems. We use the Felder-Silverman Learning Styles Model (FSLSM) to represent both the student learning styles and the learning object profiles. The predicted ratings are compared with the actual student ratings to determine the accuracy of the recommendation techniques, using the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. This approach has been experimented on 80 students for an online course created in the MOODLE Learning Management System. The results of the experiment show that the best recommendation technique is our proposed hybrid recommendation algorithm that combines the collaborative filtering and the content-based filtering techniques to enhance the accuracy of the predictions, and solves the cold-start and the rating sparsity problems using the FSLSM representations of the student learning styles and the learning object profiles.
Keywords-Guided Abstractive Sentence Summarization

We study the problem of generating a summary for a given sentence. Existing researches on abstractive sentence summarization ignore that keywords in the input sentence provide significant clues for valuable content, and humans tend to write summaries covering these keywords. In this paper, we propose an abstractive sentence summarization method by applying guidance signals of keywords to both the encoder and the decoder in the sequence-to-sequence model. A multi-task learning framework is adopted to jointly learn to extract keywords and generate a summary for the input sentence. We apply keywords-guided selective encoding strategies to filter source information by investigating the interactions between the input sentence and the keywords. We extend pointer-generator network by a dual-attention and a dual-copy mechanism, which can integrate the semantics of the input sentence and the keywords, and copy words from both the input sentence and the keywords. We demonstrate that multi-task learning and keywords-oriented guidance facilitate sentence summarization task, achieving better performance than the competitive models on the English Gigaword sentence summarization dataset.
Predicting Breast Cancer in Chinese Women Using Machine Learning Techniques: Algorithm Development

Background Risk-based breast cancer screening is a cost-effective intervention for controlling breast cancer in China, but the successful implementation of such intervention requires an accurate breast cancer prediction model for Chinese women. Objective This study aimed to evaluate and compare the performance of four machine learning algorithms on predicting breast cancer among Chinese women using 10 breast cancer risk factors. Methods A dataset consisting of 7127 breast cancer cases and 7127 matched healthy controls was used for model training and testing. We used repeated 5-fold cross-validation and calculated AUC, sensitivity, specificity, and accuracy as the measures of the model performance. Results The three novel machine-learning algorithms (XGBoost, Random Forest and Deep Neural Network) all achieved significantly higher area under the receiver operating characteristic curves (AUCs), sensitivity, and accuracy than logistic regression. Among the three novel machine learning algorithms, XGBoost (AUC 0.742) outperformed deep neural network (AUC 0.728) and random forest (AUC 0.728). Main residence, number of live births, menopause status, age, and age at first birth were considered as top-ranked variables in the three novel machine learning algorithms. Conclusions The novel machine learning algorithms, especially XGBoost, can be used to develop breast cancer prediction models to help identify women at high risk for breast cancer in developing countries.
A Survey on Internet of Things: Applications, Recent Issues, Attacks, and Security Mechanisms

The Internet of Things (IoT) is a series of recording devices, mechanical and automated machines, documents, creatures, or individuals that have one of the user identifiers and the ability to substitute information over a network without requiring human-to-human connections. The IoT technology is used in different applications to transfer information between two or more stations or devices using actuators/sensors. The IoT has many fields of application, including smart home, healthcare, transport and logistics, and industry. Each application gives highlights and important and explicit administrations. This paper provides an overview of IoT applications, discussing their current problems and potential challenges. In addition, it focuses on issues and challenges that arise for future IoT systems including enormous scaling, planning and needs, generating information and big data, strength, frankness, resource management, privacy, and security. The secure data transformation is more problematic in ongoing research. So, this survey presents security attacks and different security mechanisms involved in IoT applications including authentication, encryption, blockchain, and trust management. Blockchain with IoT applications provides good secure data transfer compared to other security mechanisms. So, this survey mainly concentrates on the blockchain with IoT applications and its latest issues. The objective of this paper is to analyze the issues and challenges of IoT applications, eight research topics in existing systems, security attacks and their countermeasures, and different security mechanisms confronting the IoT.
Learning-Based Intent-Aware Task Offloading for Air-Ground Integrated Vehicular Edge Computing

Existing task offloading mechanisms are developed on some single and rigid quality of service (QoS) performance metrics, which is widely apart from satisfying the true intent of a user vehicle (UV), thereby resulting in low quality of experience (QoE), large queuing latency, and poor reliability. There is an unprecedented demand for an intent-aware task offloading strategy that provides improved QoE and guarantees reliability. In this paper, we develop a novel task offloading framework for air-ground integrated vehicular edge computing (AGI-VEC), which is called the learning-based Intent-aware Upper Confidence Bound (IUCB) algorithm. IUCB enables a UV to learn the long-term optimal task offloading strategy while satisfying the long-term ultra-reliable low-latency communication (URLLC) constraints in a best effort way under information uncertainty. IUCB can achieve three-dimension intent awareness including QoE awareness, URLLC awareness, and trajectory similarity awareness. Simulation results demonstrate that IUCB significantly outperforms existing EMM, sleeping-UCB, and UCB mechanisms in terms of QoE, end-to-end delay, queuing delay, throughput, and times of task offloading failure.
Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification

Kernel methods have been successfully applied to the areas of pattern recognition and data mining.In this paper, we mainly discuss the issue of propagating labels in kernel space.A Kernel-Induced Label Propagation (Kernel-LP) framework by mapping is proposed for high-dimensional data classification using the most informative patterns of data in kernel space.The essence of Kernel-LP is to perform joint label propagation and adaptive weight learning in a transformed kernel space.That is, our Kernel-LP changes the task of label propagation from the commonly-used Euclidean space in most existing work to kernel space.The motivation of our Kernel-LP to propagate labels and learn the adaptive weights jointly by the assumption of an inner product space of inputs, i.e., the original linearly inseparable inputs may be mapped to be separable in kernel space.Kernel-LP is based on existing positive and negative LP model, i.e. the effects of negative label information are integrated to improve the label prediction power.Also, Kernel-LP performs adaptive weight construction over the same kernel space, so it can avoid the tricky process of choosing the optimal neighborhood size suffered in traditional criteria.Two novel and efficient out-ofsample approaches for our Kernel-LP to involve new test data are also presented, i.e., (1) direct kernel mapping and (2) kernel mapping-induced label reconstruction, both of which purely depend on the kernel matrix between training set and testing set.Owing to the kernel trick, our algorithms will be applicable to handle the high-dimensional real data.Extensive results on real datasets demonstrate the effectiveness of our approach.
Self-improving system integration: Mastering continuous change

The research initiative “self-improving system integration” (SISSY) was established with the goal to master the ever-changing demands of system organisation in the presence of autonomous subsystems, evolving architectures, and highly-dynamic open environments. It aims to move integration-related decisions from design-time to run-time, implying a further shift of expertise and responsibility from human engineers to autonomous systems. This introduces a qualitative shift from existing self-adaptive and self-organising systems, moving from self-adaptation based on predefined variation types, towards more open contexts involving novel autonomous subsystems, collaborative behaviours, and emerging goals. In this article, we revisit existing SISSY research efforts and establish a corresponding terminology focusing on how SISSY relates to the broad field of integration sciences. We then investigate SISSY-related research efforts and derive a taxonomy of SISSY technology. This is concluded by establishing a research road-map for developing operational self-improving self-integrating systems.
Exploring Cross-sentence Contexts for Named Entity Recognition with BERT

Named entity recognition (NER) is frequently addressed as a sequence classification task with each input consisting of one sentence of text. It is nevertheless clear that useful information for NER is often found also elsewhere in text. Recent self-attention models like BERT can both capture long-distance relationships in input and represent inputs consisting of several sentences. This creates opportunities for adding cross-sentence information in natural language processing tasks. This paper presents a systematic study exploring the use of cross-sentence information for NER using BERT models in five languages. We find that adding context as additional sentences to BERT input systematically increases NER performance. Multiple sentences in input samples allows us to study the predictions of the sentences in different contexts. We propose a straightforward method, Contextual Majority Voting (CMV), to combine these different predictions and demonstrate this to further increase NER performance. Evaluation on established datasets, including the CoNLL’02 and CoNLL’03 NER benchmarks, demonstrates that our proposed approach can improve on the state-of-the-art NER results on English, Dutch, and Finnish, achieves the best reported BERT-based results on German, and is on par with other BERT-based approaches in Spanish. We release all methods implemented in this work under open licenses.
Optronic convolutional neural networks of multi-layers with different functions executed in optics for image classification

Although deeper convolutional neural networks (CNNs) generally obtain better performance on classification tasks, they incur higher computation costs. To address this problem, this study proposes the optronic convolutional neural network (OPCNN) in which all computation operations are executed in optics, and data transmission and control are executed in electronics. In OPCNN, we implement convolutional layers with multi input images by the lenslet 4 f system, downsampling layers by optical-strided convolution and obtaining nonlinear activation by adjusting the camera’s curve and fully connected layers by optical dot product. The OPCNN demonstrates good performance on the classification tasks in simulations and experiments and achieves better performance than other current optical convolutional neural networks by comparison due to the more complex architecture. The scalability of OPCNN contributes to building deeper networks when facing complicated datasets.