Reviewerly/exHarmony huggingface dataset:
Reconstructing Continuous Light Field From Single Coded Image

We propose a method for reconstructing a continuous light field of a target scene from a single observed image. Our method takes the best of two worlds: joint aperture-exposure coding for compressive light-field acquisition, and a neural radiance field (NeRF) for view synthesis. Joint aperture-exposure coding implemented in a camera enables effective embedding of 3-D scene information into an observed image, but in previous works, it was used only for reconstructing discretized light-field views. NeRF-based neural rendering enables high quality view synthesis of a 3-D scene from continuous viewpoints, but when only a single image is given as the input, it struggles to achieve satisfactory quality. Our method integrates these two techniques into an efficient and end-to-end trainable pipeline. Trained on a wide variety of scenes, our method can reconstruct continuous light fields accurately and efficiently without any test time optimization. To our knowledge, this is the first work to bridge two worlds: camera design for efficiently acquiring 3-D information and neural rendering.
Comparison of Quantum Simulators for Variational Quantum Search: A Benchmark Study

Simulating quantum circuits using classical computers can accelerate the development and validation of quantum algorithms. Our newly developed algorithm, variational quantum search (VQS), has shown an exponential advantage over Grover's algorithm in the range from 5 to 26 qubits, in terms of circuit depth, for searching unstructured databases. We need to further validate the VQS for more than 26 qubits. Numerous simulators have been developed. However, it is not clear which simulator is most suitable for executing VQS with many qubits. To solve this issue, we implement a typical quantum circuit used in VQS on eight mainstream simulators. Results show that the time and memory required by most simulators increase exponentially with the number of qubits and that Pennylane with GPU and Qulacs are the most suitable simulators for executing VQS efficiently. Our results aid researchers in selecting suitable quantum simulators without the need for exhaustive implementation, and we have made our codes available for community contributions.
Situational Question Answering over Commonsense Knowledge Using Memory Nets

In this paper we extend the idea of Embodied Question Answering (EQA), which is a rather novel research direction. EQA provides a way to evaluate the behavior and reasoning of a mobile robotic platform based by a well defined question answering problem. The task is to respond to requests by exploring the environment for required information. Questions are usually related to object properties or object conditions. To enforce a robotic agent to answer questions beyond object properties or conditions, we deal with Situational Question Answering (SQA). The difference to EQA is that SQA requires context-relevant commonsense reasoning. We show the SQA performance of a knowledge graph complemented by inference mechanisms with transparent, human-understandable explanations. In particular, we combine a set of facts with basic knowledge about the world, a situational memory and commonsense understanding. As simple interface between natural language input and the knowledge graph, we propose a Semantics Abstraction Layer (SAL). The SAL is designed in a way that reasoning functions can be executed hierarchically to allow for modular processing chains. We evaluate our system on a set of object related questions first (similar to EQA) and, second, on a set of questions that requires commonsense information about tools, actions and objects. For the latter evaluation, we compared our method against human performance, which is simulated by a mental exercise, similar to the urn problem. The human answers are drawn from a distribution based on real annotated data including 20 subjects.
Speech-to-Speech Translation with Discrete-Unit-Based Style Transfer

Direct speech-to-speech translation (S2ST) with discrete self-supervised representations has achieved remarkable accuracy, but is unable to preserve the speaker timbre of the source speech during translation. Meanwhile, the scarcity of high-quality speaker-parallel data poses a challenge for learning style transfer between source and target speech. We propose an S2ST framework with an acoustic language model based on discrete units from a self-supervised model and a neural codec for style transfer. The acoustic language model leverages self-supervised in-context learning, acquiring the ability for style transfer without relying on any speaker-parallel data, thereby overcoming the issue of data scarcity. By using extensive training data, our model achieves zero-shot cross-lingual style transfer on previously unseen source languages. Experiments show that our model generates translated speeches with high fidelity and style similarity. Audio samples are available at http://stylelm.github.io/ .
Role of Metaverse in the Fourth Industrial Revolution for Providing Customer Experiences

The ongoing digital revolution is poised to bring many changes to the culture, society, and economies of nations to elevate human standards by providing advanced digital products and services. The Metaverse is a vision shared by many industries based on the fourth industrial revolution, which predicts that the next stage of computer interaction will be on the internet in a single, shared, immersive, and persistent 3D virtual space where humans interact as avatars in real-time to perform experiential activities and develop mutual relationships. Metaverse activities could include watching movies, attending concerts, working in offices, conducting collaborative meetings in halls, shopping, traveling to tourist places, and undergoing advanced training by cognitively experimenting with risky driving, etc. All of this could be made possible by integrating different technologies such as Artificial Intelligence, Big Data Analytics, Blockchain Technology, Crypto-Currencies, Distributed Ledger Technology, Internet of Things, and Machine Learning, along with Augmented Reality and Virtual Reality to benefit Metaverse as a platform business model. However, while offering these innovative customer experiences, building the Metaverse has to address many challenges related to security, privacy, and legal issues. The objective of this paper is to explore the concepts of 4IR, Metaverse, and Digital Twin primarily as a part of the smart city development phenomenon so that governments can have command and control of the human universe while providing innovative user experiences in the Metaverse.
Racing Control Variable Genetic Programming for Symbolic Regression

Symbolic regression, as one of the most crucial tasks in AI for science, discovers governing equations from experimental data. Popular approaches based on genetic programming, Monte Carlo tree search, or deep reinforcement learning learn symbolic regression from a fixed dataset. They require massive datasets and long training time especially when learning complex equations involving many variables. Recently, Control Variable Genetic Programming (CVGP) has been introduced which accelerates the regression process by discovering equations from designed control variable experiments. However, the set of experiments is fixed a-priori in CVGP and we observe that sub-optimal selection of experiment schedules delay the discovery process significantly. To overcome this limitation, we propose Racing Control Variable Genetic Programming (Racing-CVGP), which carries out multiple experiment schedules simultaneously. A selection scheme similar to that used in selecting good symbolic equations in the genetic programming process is implemented to ensure that promising experiment schedules eventually win over the average ones. The unfavorable schedules are terminated early to save time for the promising ones. We evaluate Racing-CVGP on several synthetic and real-world datasets corresponding to true physics laws. We demonstrate that Racing-CVGP outperforms CVGP and a series of symbolic regressors which discover equations from fixed datasets.
VoicePAT: An Efficient Open-source Evaluation Toolkit for Voice Privacy Research

Speaker anonymization is the task of modifying a speech recording such that the original speaker cannot be identified anymore. Since the first Voice Privacy Challenge in 2020, along with the release of a framework, the popularity of this research topic is continually increasing. However, the comparison and combination of different anonymization approaches remains challenging due to the complexity of evaluation and the absence of user-friendly research frameworks. We therefore propose an efficient speaker anonymization and evaluation framework based on a modular and easily extendable structure, almost fully in Python. The framework facilitates the orchestration of several anonymization approaches in parallel and allows for interfacing between different techniques. Furthermore, we propose modifications to common evaluation methods which improves the quality of the evaluation and reduces their computation time by 65 to 95%, depending on the metric. Our code is fully open source.
Design and Implementation of a Novel Image Encryption System

The field of image encryption is studied to encrypt and hide the image effectively, and the most critical thing is whether the image can be restored without any loss of details or distortion. Classical cryptography provides good encryption and decryption algorithms for one-dimensional data streams, encrypting plaintext data into ciphertext data for the purpose of confidentiality. Although we can consider the image data as a one-dimensional data stream and use traditional encryption algorithms for encryption, these algorithms often ignore some special properties of digital images such as two-dimensional self-similarity, large data volume, etc. Moreover, traditional encryption algorithms can hardly meet the real-time requirements in network transmission. Therefore, we improve the traditional encryption techniques for digital images, and process the images by means of Arnold matrix transform, two-dimensional logistic chaos mapping, and DWT and DCT transforms, and experimentally verify that the method has faster and lower loss degree, which confirms the feasibility of its application in the field of professional encryption.
The Efficiency of Feynman's Quantum Computer

Feynman's circuit-to-Hamiltonian construction enables the mapping of a quantum circuit to a time-independent Hamiltonian. Here we investigate the efficiency of Feynman's quantum computer by analysing the time evolution operator $e^{-i\hat{H}t}$ for Feynman's clock Hamiltonian $\hat{H}$. A general formula is established for the probability, $P_k(t)$, that the desired computation is complete at time $t$ for a quantum computer which executes an arbitrary number $k$ of operations. The optimal stopping time, denoted by $\tau$, is defined as the time of the first local maximum of this probability. We find numerically that there is a linear relationship between this optimal stopping time and the number of operations, $\tau = 0.50 k + 2.37$. Theoretically, we corroborate this linear behaviour by showing that at $\tau = \frac{1}{2} k + 1$, $P_k(\tau)$ is approximately maximal. We also establish a relationship between $\tau$ and $P_k(\tau)$ in the limit of a large number $k$ of operations. We show analytically that at the maximum, $P_k(\tau)$ behaves like $k^{-2/3}$. This is further proven numerically where we find the inverse cubic root relationship $P_k(\tau) = 6.76 \; k^{-2/3}$. This is significantly more efficient than paradigmatic models of quantum computation.
MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep Learning

Advancements in deep learning-based 3D object detection necessitate the availability of large-scale datasets. However, this requirement introduces the challenge of manual annotation, which is often both burdensome and time-consuming. To tackle this issue, the literature has seen the emergence of several weakly supervised frameworks for 3D object detection which can automatically generate pseudo labels for unlabeled data. Nevertheless, these generated pseudo labels contain noise and are not as accurate as those labeled by humans. In this paper, we present the first approach that addresses the inherent ambiguities present in pseudo labels by introducing an Evidential Deep Learning (EDL) based uncertainty estimation framework. Specifically, we propose MEDL-U, an EDL framework based on MTrans, which not only generates pseudo labels but also quantifies the associated uncertainties. However, applying EDL to 3D object detection presents three primary challenges: (1) relatively lower pseudolabel quality in comparison to other autolabelers; (2) excessively high evidential uncertainty estimates; and (3) lack of clear interpretability and effective utilization of uncertainties for downstream tasks. We tackle these issues through the introduction of an uncertainty-aware IoU-based loss, an evidence-aware multi-task loss function, and the implementation of a post-processing stage for uncertainty refinement. Our experimental results demonstrate that probabilistic detectors trained using the outputs of MEDL-U surpass deterministic detectors trained using outputs from previous 3D annotators on the KITTI val set for all difficulty levels. Moreover, MEDL-U achieves state-of-the-art results on the KITTI official test set compared to existing 3D automatic annotators.
A Linear Combination-based Method to Construct Proxy Benchmarks for Big Data Workloads

During early stages of CPU design, benchmarks can only run on simulators to evaluate CPU performance. However, most big data benchmarks are too huge at code size scale, which causes them to be unable to finish running on simulators at an acceptable time cost. Moreover, big data benchmarks usually need complex software stacks to support their running, which is hard to be ported on simulators. Proxy benchmarks, without long running times and complex software stacks, have the same micro-architectural metrics as real benchmarks, which means they can represent real benchmarks' micro-architectural characteristics. Therefore, proxy benchmarks can replace real benchmarks to run on simulators to evaluate the CPU performance. The biggest challenge is how to guarantee that the proxy benchmarks have exactly the same micro-architectural metrics as real benchmarks when the number of micro-architectural metrics is very large. To deal with this challenge, we propose a linear combination-based proxy benchmark generation methodology that transforms this problem into solving a linear equation system. We also design the corresponding algorithms to ensure the linear equation is astringency, which means that although sometimes the linear equation system doesn't have a unique solution, the algorithm can find the best solution by the non-negative least square method. We generate fifteen proxy benchmarks and evaluate their running time and accuracy in comparison to corresponding real benchmarks for Mysql and RockDB. On the typical Intel Xeon platform, the average running time is 1.62s, and the average accuracy of every micro-architectural metric is over 92%, while the longest running time of real benchmarks is nearly 4 hours. We also conduct two case studies that demonstrate that our proxy benchmarks are consistent with real benchmarks both before and after prefetch or Hyper-Threading is turned on.
Replay to Remember: Continual Layer-Specific Fine-Tuning for German Speech Recognition

Abstract While Automatic Speech Recognition (ASR) models have shown significant advances with the introduction of unsupervised or self-supervised training techniques, these improvements are still only limited to a subsection of languages and speakers. Transfer learning enables the adaptation of large-scale multilingual models to not only low-resource languages but also to more specific speaker groups. However, fine-tuning on data from new domains is usually accompanied by a decrease in performance on the original domain. Therefore, in our experiments, we examine how well the performance of large-scale ASR models can be approximated for smaller domains, with our own dataset of German Senior Voice Commands (SVC-de), and how much of the general speech recognition performance can be preserved by selectively freezing parts of the model during training. To further increase the robustness of the ASR model to vocabulary and speakers outside of the fine-tuned domain, we apply Experience Replay [20] for continual learning. By adding only a fraction of data from the original domain, we are able to reach Word-Error-Rates (WERs) below 5% on the new domain, while stabilizing performance for general speech recognition at acceptable WERs.
Optimizing Dynamic Predictions from Joint Models using Super Learning

Joint models for longitudinal and time-to-event data are often employed to calculate dynamic individualized predictions used in numerous applications of precision medicine. Two components of joint models that influence the accuracy of these predictions are the shape of the longitudinal trajectories and the functional form linking the longitudinal outcome history to the hazard of the event. Finding a single well-specified model that produces accurate predictions for all subjects and follow-up times can be challenging, especially when considering multiple longitudinal outcomes. In this work, we use the concept of super learning and avoid selecting a single model. In particular, we specify a weighted combination of the dynamic predictions calculated from a library of joint models with different specifications. The weights are selected to optimize a predictive accuracy metric using V-fold cross-validation. We use as predictive accuracy measures the expected quadratic prediction error and the expected predictive cross-entropy. In a simulation study, we found that the super learning approach produces results very similar to the Oracle model, which was the model with the best performance in the test datasets. All proposed methodology is implemented in the freely available R package JMbayes2.
Speak While You Think: Streaming Speech Synthesis During Text Generation

Large Language Models (LLMs) demonstrate impressive capabilities, yet interaction with these models is mostly facilitated through text. Using Text-To-Speech to synthesize LLM outputs typically results in notable latency, which is impractical for fluent voice conversations. We propose LLM2Speech, an architecture to synthesize speech while text is being generated by an LLM which yields significant latency reduction. LLM2Speech mimics the predictions of a non-streaming teacher model while limiting the exposure to future context in order to enable streaming. It exploits the hidden embeddings of the LLM, a by-product of the text generation that contains informative semantic context. Experimental results show that LLM2Speech maintains the teacher's quality while reducing the latency to enable natural conversations.
Value-Oriented Language Education in the Modern World: Modern Challenges and Critical Issues of Higher School Linguodidactics

This paper critically reflects on the perspectives of developing value- oriented communicative education through co-learned languages and cultures which is designed with the view to the sociocultural context of human existence in a postglobal world burdened with geopolitical confrontation, political and economic conflicts, proxy wars, endless fake information attacks on the individual’s world vision. It gives an insight into the transdisciplinary basis for implementing an axiological approach to value-oriented language-and culture education as an outcome of the author’s transdisciplinary analysis of the Humanities researches and didactic reading of some draft documents related to Russia’s policy on preserving its traditional spiritual and moral values in modern society. It also discusses the purposes of introducing problem-oriented educational environment that may give Russian students an opportunity to master university disciplines in an innovative way in the classroom, and outside the university classroom, to be much involved into public speaking activities on critical issues of modern life and its values, as well as into research cooperation and science promotion in innovative areas of scientific knowledge. The reader’s attention is also drawn to some ways of creating a value-oriented and culture-bound research environment that helps to develop students’ competences needed to be able to act as a promoter of the dialogue of cultures and civilizations in their professional life, able to preserve cultural heritage and counteract modern cultural vandalism, cultural imperialism and cancel culture whenever she or he comes across it.
When matter and information merge into “Quantum”

Despite only being its 3rd edition, the QUANTUM MATTER international conference & expo (QUANTUMatter) has already become a reference event for communities involved in the study of quantum materials, quantum information and quantum technologies at large. This article reports on this meeting, which took place in Madrid, in June 2023, with 420 participants and keynote speakers from all areas of quantum technologies and quantum materials. The authors present a report on the Quantum Matter 2023 international conference and expo that took place in Madrid, on June 2023.
SlowFast Network for Continuous Sign Language Recognition

The objective of this work is the effective extraction of spatial and dynamic features for Continuous Sign Language Recognition (CSLR). To accomplish this, we utilise a two-pathway SlowFast network, where each pathway operates at distinct temporal resolutions to separately capture spatial (hand shapes, facial expressions) and dynamic (movements) information. In addition, we introduce two distinct feature fusion methods, carefully designed for the characteristics of CSLR: (1) Bi-directional Feature Fusion (BFF), which facilitates the transfer of dynamic semantics into spatial semantics and vice versa; and (2) Pathway Feature Enhancement (PFE), which enriches dynamic and spatial representations through auxiliary subnetworks, while avoiding the need for extra inference time. As a result, our model further strengthens spatial and dynamic representations in parallel. We demonstrate that the proposed framework outperforms the current state-of-the-art performance on popular CSLR datasets, including PHOENIX14, PHOENIX14-T, and CSL-Daily.
TRACKING FUNDS? USAGE USING BLOCKCHAIN TECHNOLOGY

Corruption in Government agencies is quite common and not traceable. Funds used for different Government projects are not utilized optimally, rather these funds are used in illegal or personal expenses of corrupt entities present in the organization. This results in the loss of common people as they don’t actually get what they were promised. Blockchain is a distributed, decentralized ledger which stores records in the form chain of blocks which are cryptograhically secure. The use of Blockchain in Government fund allocation system can eliminate corruption as there will be transparency, and we will be able to track the usage of funds easily.