Reviewerly/exHarmony huggingface dataset:
Hopfield-like network with complementary encodings of memories

We present a Hopfield-like autoassociative network for memories representing examples of concepts. Each memory is encoded by two activity patterns with complementary properties. The first is dense and correlated across examples within concepts, and the second is sparse and exhibits no correlation among examples. The network stores each memory as a linear combination of its encodings. During retrieval, the network recovers sparse or dense patterns with a high or low activity threshold, respectively. As more memories are stored, the dense representation at low threshold shifts from examples to concepts, which are learned from accumulating common example features. Meanwhile, the sparse representation at high threshold maintains distinctions between examples due to the high capacity of sparse, decorrelated patterns. Thus, a single network can retrieve memories at both example and concept scales and perform heteroassociation between them. We obtain our results by deriving macroscopic mean-field equations that yield capacity formulas for sparse examples, dense examples, and dense concepts. We also perform simulations that verify our theoretical results and explicitly demonstrate the capabilities of the network.
An improved radar clutter suppression by simple neural network

Abstract The presented paper is further focused on the presentation and subsequent assessment of utilising a proposed Neural Network (NN) with simple architecture in the role of a signal preprocessing algorithm for the Constant False Alarm Rate detector and the fixed threshold detector applied on a Range‐Doppler (RD) map with the aim of radar clutter impact reduction and minimisation of processing time. Based on a comparison of all tested algorithm results, it is possible to state that utilising the proposed NN with simple architecture led to reducing the impact of radar clutter when detecting radar targets on RD maps created from provided datasets. Comparing the mean processing time t mean values of all tested algorithms, the authors can state that employing the proposed NN in combination with the fixed threshold detector led to a significant improvement in the computation time needed for processing one RD map while preserving the suppression of radar clutter and detection of the radar targets.
HyperDiff: Computing Source Code Diffs at Scale

With the advent of fast software evolution and multistage releases, temporal code analysis is becoming useful for various purposes, such as bug cause identification, bug prediction or code evolution analysis. Temporal code analyses can consist in analyzing multiple Abstract Syntax Trees (ASTs) extracted from code evolutions, e.g. one AST for each commit or release. Core feature to temporal analysis is code differencing: the computation of the so-called Diff or edit script between two given versions of the code. However, jointly analyzing and computing the difference on thousands versions of code faces scalability issues. Mainly because of the cost of: 1) parsing the original and evolved code in two source and target ASTs; 2) wasting resources by not reusing intermediate computation results that can be shared between versions. This paper details a novel approach based on time-oriented data structures that makes code differencing scale up to large software codebases. In particular, we leverage on the HyperAST, a novel representation of code histories, to propose an incremental and memory efficient approach by lazifying the well known GumTree diffing algorithms, a mainstream code differencing algorithm and tool. We evaluated our approach on a curated list of 19 large software projects and compared it to GumTree. Our approach outperforms it in scalability both in time and memory. We observed an order-of-magnitude difference: 1) in CPU time from x1.2 to x12.7 for the total time of diff computation and up to x226 in intermediate phases of the diff computation, and 2) in memory footprint of x4.5 per AST node. The approach produced 99.3% of identical diffs with respect to GumTree.
BiasAsker: Measuring the Bias in Conversational AI System

Powered by advanced Artificial Intelligence (AI) techniques, conversational AI systems, such as ChatGPT, and digital assistants like Siri, have been widely deployed in daily life. However, such systems may still produce content containing biases and stereotypes, causing potential social problems. Due to modern AI techniques' data-driven, black-box nature, comprehensively identifying and measuring biases in conversational systems remains challenging. Particularly, it is hard to generate inputs that can comprehensively trigger potential bias due to the lack of data containing both social groups and biased properties. In addition, modern conversational systems can produce diverse responses (e.g., chatting and explanation), which makes existing bias detection methods based solely on sentiment and toxicity hardly being adopted. In this paper, we propose BiasAsker, an automated framework to identify and measure social bias in conversational AI systems. To obtain social groups and biased properties, we construct a comprehensive social bias dataset containing a total of 841 groups and 5,021 biased properties. Given the dataset, BiasAsker automatically generates questions and adopts a novel method based on existence measurement to identify two types of biases (i.e., absolute bias and related bias) in conversational systems. Extensive experiments on eight commercial systems and two famous research models, such as ChatGPT and GPT-3, show that 32.83% of the questions generated by BiasAsker can trigger biased behaviors in these widely deployed conversational systems. All the code, data, and experimental results have been released to facilitate future research.
A methodology for building domain ontology of cultural heritage

Abstract Ontology plays a vital role in linking and publishing heritage data. However, the main difficulties of building ontology faced by heritage institutions are how the cultural heritage information is integrated into a fine-grained ontology. Therefore, following the ISO principles of Terminology (ISO 1087 and 704), this article proposed a term-concept-characteristic methodology that is user-friendly for cultural heritage experts to build a fine-grained ontology. This article aims to provide a method to integrate the fine-grained knowledge of cultural heritage into an ontology, especially when it is necessary to build a fine-grained ontology for particular purposes, such as knowledge-based terminological resources.
Empirical Analysis of a Segmentation Foundation Model in Prostate Imaging

Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fashion, which requires expensive labeled datasets. Recent advances in several machine learning domains, such as natural language generation have demonstrated the feasibility and utility of building foundation models that can be customized for various downstream tasks with little to no labeled data. This likely represents a paradigm shift for medical imaging, where we expect that foundation models may shape the future of the field. In this paper, we consider a recently developed foundation model for medical image segmentation, UniverSeg [6]. We conduct an empirical evaluation study in the context of prostate imaging and compare it against the conventional approach of training a task-specific segmentation model. Our results and discussion highlight several important factors that will likely be important in the development and adoption of foundation models for medical image segmentation.
Implementing two-qubit gates at the quantum speed limit

The speed of elementary quantum gates, particularly two-qubit gates, ultimately sets the limit on the speed at which quantum circuits can operate. In this work, we experimentally demonstrate commonly used two-qubit gates at nearly the fastest possible speed allowed by the physical interaction strength between two superconducting transmon qubits. We achieve this quantum speed limit by implementing experimental gates designed using a machine-learning-inspired optimal control method. Importantly, our method only requires the single-qubit drive strength to be moderately larger than the interaction strength to achieve an arbitrary two-qubit gate close to its analytical speed limit with high fidelity. Thus the method is applicable to a variety of platforms, including those with comparable single-qubit and two-qubit gate speeds, or those with always-on interactions. We expect our method to offer significant speedups for non-native two-qubit gates that are typically achieved with a long sequence of single-qubit and native two-qubit gates.
Contemporary Approaches in Evolving Language Models

This article provides a comprehensive survey of contemporary language modeling approaches within the realm of natural language processing (NLP) tasks. This paper conducts an analytical exploration of diverse methodologies employed in the creation of language models. This exploration encompasses the architecture, training processes, and optimization strategies inherent in these models. The detailed discussion covers various models ranging from traditional n-gram and hidden Markov models to state-of-the-art neural network approaches such as BERT, GPT, LLAMA, and Bard. This article delves into different modifications and enhancements applied to both standard and neural network architectures for constructing language models. Special attention is given to addressing challenges specific to agglutinative languages within the context of developing language models for various NLP tasks, particularly for Arabic and Turkish. The research highlights that contemporary transformer-based methods demonstrate results comparable to those achieved by traditional methods employing Hidden Markov Models. These transformer-based approaches boast simpler configurations and exhibit faster performance during both training and analysis. An integral component of the article is the examination of popular and actively evolving libraries and tools essential for constructing language models. Notable tools such as NLTK, TensorFlow, PyTorch, and Gensim are reviewed, with a comparative analysis considering their simplicity and accessibility for implementing diverse language models. The aim is to provide readers with insights into the landscape of contemporary language modeling methodologies and the tools available for their implementation.
Blockchain for regenerative built environment governance

Abstract Regenerative approaches have gained attention in the built environment, but remain highly conceptual. This position paper argues for new regenerative governance structures that consider data governance, reassess complex stakeholder interactions, and ensure the inclusivity of diverse values and ownership. It then presents early ideas on how blockchain technology could facilitate scalable socio-economic-ecologic interactions along three inquiries, giving practical examples. Overall, the paper aims to inspire and guide further research into the development of modern digital governance tools fostering a regenerative built environment.
Navigating Innovation: An Enhanced Business Value Calculator and Its Impact on Library Service Innovations for Entrepreneurs and Businesses

The article offers a thorough analysis of the Enhanced Business Value Calculator and how it could revolutionize library services designed for patrons, especially entrepreneurs. By letting them choose which services they have used and how often, the calculator calculates the economic value of services. This facilitates the observation of consumer usage trends by libraries, enabling them to make strategic decisions regarding service advancements. It gives libraries the capacity to close talent shortages, improve services, strategically adopt new technologies, spot opportunities for open innovation partnerships, encourage patron co-creation, and gain a better awareness of user demands, which allows them to better tailor their offerings. These breakthroughs subsequently become business prospects. The calculator serves as a driving force behind ongoing advancements in services.
An anti-maser for mode cooling of a microwave cavity

The maser, a microwave (MW) analog of the laser, is a well-established method for generating and amplifying coherent MW irradiation with ultralow noise. This is usually accomplished by creating a state of population inversion between two energy levels separated by MW frequency. Thermodynamically, such a state corresponds to a small but negative temperature. The reverse condition, where only the lower energy level is highly populated, corresponds to a very low positive temperature. In this work, we experimentally demonstrate how to generate such a state in condensed matter at moderate cryogenic temperatures. This state is then used to efficiently remove microwave photons from a cavity, continuously cooling it, well below its ambient temperature. Such an “anti-maser” device could be extremely beneficial for applications that would normally require cooling to millikelvin temperatures to eliminate any MW photons. For instance, superconducting MW quantum circuits (such as qubits and amplifiers) could, with the use of this device, operate efficiently at liquid helium temperatures.
End-to-end image feature extraction - aggregation loop closure detection network for visual SLAM

Loop closure detection(LCD) is a key component in VSLAM systems to eliminate cumulative errors. We propose an end-to-end image feature extraction-aggregate LCD network, Res2Net-SE-NetVLAD, to extract discriminative multi-scale fusion features for VSLAM. In Res2Net-SE-NetVLAD, the deep learning network Res2Net is chosen as the backbone, and the channel attention mechanism SE-block is implemented to obtain multiple perceptual fields with different granularity. Based on this, the channel optimization module is used to quantify the feature maps from the channel level, and the NetVLAD layer is further fused to implement the scale feature extraction network Res2Net-SE-NetVLAD, for which end-to-end training can be performed to achieve LCD. The experimental results show that the proposed model outperforms other deep learning-based LCD methods in scenes with loop closure attributes.
Fitting oriented bounding box algorithm for accurate positioning of transformer wiring terminals

As an important connection component of the power grid, transformer wiring terminals need regular maintenance and repair. Some problems exist in traditional maintenance such as high labor cost, high operation risks, and low efficiency. Relying on the point cloud processing technique, this paper proposes a F-OBB (fitting oriented bounding box) algorithm based on surface normal estimation and applies it to the high-precision positioning of terminal blocks. Surface regions of the rectangular-like terminal are separated by the clustering method of region growing after filtering pre-processing. Then the optimal normal vector of each region is solved by the spherical coordinate system transformation and least-squares method to determine a bounding box to represent the target and obtain the positioning parameters (position and orientation). The practical test results show that the proposed method can generate bounding box highly close to the target, accurately extract the positioning parameters and keeps good robustness in various scenarios.
Spectrogram Dataset of Korean Smartphone Audio Files Forged Using the “Mix Paste” Command

This study focuses on the field of voice forgery detection, which is increasing in importance owing to the introduction of advanced voice editing technologies and the proliferation of smartphones. This study introduces a unique dataset that was built specifically to identify forgeries created using the “Mix Paste” technique. This editing technique can overlay audio segments from similar or different environments without creating a new timeframe, making it nearly infeasible to detect forgeries using traditional methods. The dataset consists of 4665 and 45,672 spectrogram images from 1555 original audio files and 15,224 forged audio files, respectively. The original audio was recorded using iPhone and Samsung Galaxy smartphones to ensure a realistic sampling environment. The forged files were created from these recordings and subsequently converted into spectrograms. The dataset also provided the metadata of the original voice files, offering additional context and information that could be used for analysis and detection. This dataset not only fills a gap in existing research but also provides valuable support for developing more efficient deep learning models for voice forgery detection. By addressing the “Mix Paste” technique, the dataset caters to a critical need in voice authentication and forensics, potentially contributing to enhancing security in society.
Bridging the Digital Divide

In light of the COVID-19 pandemic, this chapter examines the trend toward distance learning at African universities and draws attention to the resulting digital divide and inequalities. It explores the significance of China-Africa cooperation in overcoming these obstacles and advancing higher education in Africa. Access to technology, internet connectivity, and the adaptability of instructors and students to digital platforms are just a few of the challenges that are reviewed in this chapter. The subsequent section examines China's significant contributions to Africa's digital education landscape. This chapter examines the ways in which traditional and digital pedagogical practices have affected the learning and achievement of students in the wake of the pandemic, with a focus on equity, diversity, and social justice. The chapter finishes by questioning the long-term viability of digital teaching methods and highlighting the importance of a long-term China-Africa relationship for post-pandemic expansion of higher education in Africa.
Collaboration Analysis in Supply Chain 4.0 for Smart Businesses

This study aims to review collaboration in Supply Chain 4.0. For this objective, dimensions and outcomes of collaboration are analyzed based on businesses. An overview of the different dimensions and outcomes of collaboration was created in Industry 4.0 platform. Research data was obtained from literature review and then refined according to SC experts. The findings show that collaboration in Supply Chain 4.0, with the activation of dimensions including information and communication management, resource planning in the production and logistics process, collaborative decisions for business goals, technology and business development, motivational alignment, performance evaluation in integrated processes, marketing and customer orientation can lead to sustainable performance. In this study, a comprehensive approach is made to accessing collaboration concept among business partners in Industry 4.0 by creating a new conceptual framework to achieve economic, social, and environmental performance.
Teaching With Immersive Virtual Reality

Immersive virtual reality (iVR), an emerging technology, is starting to lead and shape the future of education. Yet, due to technical, financial, or pedagogical reasons, this novel technology has been seldom applied directly in classroom teaching by language learners and teachers. The present study attempts to contribute a qualitative understanding of language learning in the iVR environment from trainee teachers' perspectives. These trainee teachers, armed with basic pedagogical knowledge, designed and practiced teaching with a commercial VR platform. Through analysis of interviews and reflective journals, insights were gathered from the participants who organized teaching in a completely new setting. Findings suggested a great line of contributing and hindering factors of using iVR in language education from teachers' perspectives. Implications are discussed at the end of the article to guide future learning and teaching with immersive technology.
Closest Lattice Point Decoding for Multimode Gottesman-Kitaev-Preskill Codes

Quantum error correction (QEC) plays an essential role in fault-tolerantly realizing quantum algorithms of practical interest. Among different approaches to QEC, encoding logical quantum information in harmonic oscillator modes has been shown to be promising and hardware efficient. In this work, we study multimode Gottesman-Kitaev-Preskill (GKP) codes, encoding a qubit in many oscillators, through a lattice perspective. In particular, we implement a closest point decoding strategy for correcting random Gaussian shift errors. For decoding a generic multimode GKP code, we first identify its corresponding lattice followed by finding the closest lattice point in its symplectic dual lattice to a candidate shift error compatible with the error syndrome. We use this method to characterize the error-correction capabilities of several known multimode GKP codes, including their code distances and fidelities. We also perform numerical optimization of multimode GKP codes up to ten modes and find three instances (with three, seven, and nine modes) with better code distances and fidelities compared to the known GKP codes with the same number of modes. While exact closest point decoding incurs exponential time cost in the number of modes for general unstructured GKP codes, we give several examples of structured GKP codes (i.e., of the repetition-rectangular GKP code types) where the closest point decoding can be performed exactly in linear time. For the surface-GKP code, we show that the closest point decoding can be performed exactly in polynomial time with the help of a minimum-weight-perfect-matching algorithm (MWPM). We show that this MWPM closest point decoder improves both the fidelity and the noise threshold of the surface-GKP code to 0.602 compared to the previously studied MWPM decoder assisted by log-likelihood analog information, which yields a noise threshold of 0.599.11 MoreReceived 19 April 2023Accepted 30 October 2023DOI:https://doi.org/10.1103/PRXQuantum.4.040334Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum error correctionQuantum information processing with continuous variablesSurface code quantum computingQuantum Information, Science & Technology
KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition

Feature learning is a widely used method for large-scale face recognition tasks. Recently, large-margin softmax loss methods have demonstrated significant improvements in deep face recognition. However, these methods typically propose fixed positive margins to enforce intra-class compactness and inter-class diversity, without considering imbalanced learning issues that arise due to different learning difficulties or the number of training samples available in each class. This overlook not only compromises the efficiency of the learning process but, more critically, the generalization capability of the resultant models. To address this problem, we introduce a novel adaptive strategy called KappaFace, which modulates the relative importance of each class based on its learning difficulty and imbalance. Drawing inspiration from the von Mises-Fisher distribution, KappaFace increases the margin values for the challenging or underrepresented classes and decreases that of more well-represented classes. Comprehensive experiments across eight cutting-edge baselines and nine well-established facial benchmark datasets strongly confirm the advantage of our method. Notably, we observed an enhancement of up to 0.5% on the verification task when evaluated on the IJB-B/C datasets. In conclusion, KappaFace offers a novel solution that effectively tackles imbalanced learning in deep face recognition tasks and establishes a new baseline.