Reviewerly/exHarmony huggingface dataset:
Unlocking Blockchain Interconnectivity: Smart Contract-Driven Cross-Chain Communication

In recent years, blockchain technology has gained significant attention for its potential in various domains.However, the lack of interoperability between different blockchain platforms poses a significant challenge in meeting the demands of the modern world.To address this issue, our research focuses on unlocking blockchain interconnectivity through smart contract-driven cross-chain communication.We aim to contribute to the development of a model that enhances the functionality and usability of blockchain technology.To achieve interoperability, we explore various options and leverage the power of smart contracts.These contracts enable seamless communication and exchange of services between different blockchain platforms, as well as with legacy systems.By implementing our proposed model, we intend to bridge the gap between isolated blockchain systems, enabling them to work together efficiently and effectively.To validate the effectiveness of our model, we have conducted a comparative analysis with existing parent blockchain models for cross-chain communication.We have measured and compared their mean and standard deviation, which indicate the performance improvements achieved by our approach.The results demonstrate that our model outperforms the existing parent blockchain model, offering better cross-chain communication capabilities and showing a 42.27% improvement in efficiency as well.Through our research, we aim to contribute to the advancement of blockchain technology by addressing the critical issue of interoperability.By enabling seamless communication and collaboration between different blockchain platforms, our model has the potential to revolutionize various industries and unlock new opportunities for innovation and growth.
Deterministic Generation of Multipartite Entanglement via Causal Activation in the Quantum Internet

Entanglement represents ``\textit{the}'' key resource for several applications of quantum information processing, ranging from quantum communications to distributed quantum computing. Despite its fundamental importance, deterministic generation of maximally entangled qubits represents an on-going open problem. Here, we design a novel generation scheme exhibiting two attractive features, namely, i) deterministically generating different classes -- namely, GHZ-like, W-like and graph states -- of genuinely multipartite entangled states, ii) without requiring any direct interaction between the qubits. Indeed, the only necessary condition is the possibility of coherently controlling -- according to the indefinite causal order framework -- the causal order among the unitaries acting on the qubits. Through the paper, we analyze and derive the conditions on the unitaries for deterministic generation, and we provide examples for unitaries practical implementation. We conclude the paper by discussing the scalability of the proposed scheme to higher dimensional genuine multipartite entanglement (GME) states and by introducing some possible applications of the proposal for quantum networks.
3SNet: Semi-Anchor-Free 3D Object Detector With Slice Attention and Symmetric Features Propagation

This paper investigates 3D object detection from point clouds scene recovered by lidar and proposes a novel semi-anchor-free 3D object detector. Compared with the point-based method, the voxel-based method has more advantages in feature extraction and avoids the complicated neighborhood search. However, voxel-based methods usually require to preset a large number of anchors related to the objects to be detected, which brings the computational cost to the model inference. This paper introduces a framework with the semi-anchor-free mechanism that utilizes the voxel-based method to aid the learning of point features and achieves anchor-free performance in inference. Actually, feature learning still needs to be strengthened for point cloud encoding, and this paper designs a Directional Slice Attention to augment the discriminability of features. Additionally, the region of interest characterization based on Symmetric Feature Propagation is proposed in this paper to alleviate the obstacle brought by the incomplete scanning of objects in autonomous driving scenarios. Extensive experiments on both the KITTI dataset and Waymo Open dataset demonstrate the superiority of 3SNet over state-of-the-art methods.
Text Detection on Technical Drawings for the Digitization of Brown-field Processes

This paper addresses the issue of autonomously detecting text on technical drawings. The detection of text on technical drawings is a critical step towards autonomous production machines, especially for brown-field processes, where no closed CAD-CAM solutions are available yet. Automating the process of reading and detecting text on technical drawings reduces the effort for handling inefficient media interruptions due to paper-based processes, which are often today's quasi-standard in brown-field processes. However, there are no reliable methods available yet to solve the issue of automatically detecting text on technical drawings. The unreliable detection of the contents on technical drawings using classical detection and object character recognition (OCR) tools is mainly due to the limited number of technical drawings and the captcha-like structure of the contents. Text is often combined with unknown symbols and interruptions by lines. Additionally, due to intellectual property rights and technical know-how issues, there are no out-of-the box training datasets available in the literature to train such models. This paper combines a domain knowledge-based generator to generate realistic technical drawings with a state-of-the-art object detection model to solve the issue of detecting text on technical drawings. The generator yields artificial technical drawings in a large variety and can be considered as a data augmentation generator. These artificial drawings are used for training, while the model is tested on real data. The authors show that artificially generated data of technical drawings improve the detection quality with an increasing number of drawings. Moreover, the trained model can be used to reliably detect text on real technical drawings. The authors were able to show that the model performs better than the current state-of-the-art models. The code is available at: https://github.com/2Obe/Text-Detection-on-Technical-Drawings
Dhrushti-AI: A multi-screen multi-user eye-tracking system to understand the cognitive behavior of humans in process industries

Operator performance is key to ensuring safety in process industries. Therefore, a comprehensive assessment of their performance is critical for smooth and efficient plant operation. Traditional performance assessments are not comprehensive as these ignore cognitive aspects of performance. On the other hand, while eye-tracking-based approaches do provide a cognitive assessment during operating training, their applicability in a real-time real setting is limited. Existing eye-tracking systems come with many constraints affecting users' mobility (restricted movement in all directions) in their environment. In addition, it is beyond the scope of current eye trackers to track multiple users working in an environment, such as in control rooms. Satisfying the above requirements makes these eye-trackers expensive. In this work, we demonstrate the capabilities of an in-house developed cost-effective eye-tracking system to track users' eye movement in an unconstrained environment while giving freedom of head movement. Human subject studies are conducted to compare operators' gaze patterns and the quality of data with that obtained using commercial eye trackers. The results generally agree with data quality obtained using commercial eye trackers. Hence, the performance of the developed eye-tracking system is comparable to existing commercial eye-trackers while overcoming their limitations, such as restricted user movement, single-user tracking, and high cost.
A Concept for Dynamic and Robust Machine Learning with Context Modeling for Heterogeneous Manufacturing Data

With the increasing amount of available and connected data sources, industrial automation applications such as condition monitoring of a production machine can be improved by considering various data. To gain insights from this data and make it useable, heterogeneous data has to be analyzed intensively. Limited machine learning approaches exist in industrial automation and manufacturing for analyzing data acquired from multiple sources. In this paper, first, a suitable concept for handling heterogeneous data from integration to analysis is presented as well as a multi-layer architecture for the concept's realization. The architecture encapsulates functionalities into the different layers and allows easy extendability and modifiability. Afterwards, a context modeling approach for managing heterogeneous data and existing approaches and algorithms for analyzing this data robustly and dynamically analyzing it are presented.
Construction and Embedding Representation of Relational Aggregation Hypergraphs in Cyber-Physical Systems

With the rapid development of cyber-physical systems (CPS), constructing information systems with high-quality services has become an urgent requirement in both academia and industry. Hypergraphs are widely used in graph data analysis tasks due to their wide node-aware range and reasonable interpretability. This article proposes a model known as the relation-aggregated hypergraph neural network, which aims to better describe and apply complex structures and relationships in the physical world, thereby improving the management efficiency and service quality of service recommendation in CPS. First, explicit and implicit hyperedges are constructed based on the explicit and implicit relationships between nodes, respectively. Second, a global attention mechanism is adopted to calculate the importance between neighbor nodes that share implicit hyperedges with the current node, generating a relation-aggregated hypergraph. Finally, by weighting the generated hypergraph association matrix and passing the aggregated features through the hypergraph neural network, we obtain the embedding representation of nodes. Node classification experiments were conducted on three public network datasets, and the accuracy rates were 83.7%, 88.8%, and 84.9%, respectively, which were higher than those of the baseline model.
Benchmarks for Indian Legal NLP: A Survey

Legal text is significantly different from English text (e.g. Wikipedia, News) used for training most natural language processing (NLP) algorithms. As a result, the state of the art algorithms (e.g. GPT-3, BERT derivatives), need additional effort (e.g. fine-tuning and further pre-training) to achieve optimal performance on legal text. Hence there is a need to create separate NLP data sets and benchmarks for legal text which are challenging and focus on tasks specific to legal systems. This will spur innovation in applications of NLP for legal text and will benefit AI community and legal fraternity. This paper focuses on an empirical review of the existing work in the use of NLP in Indian legal text and proposes ideas to create new benchmarks for Indian Legal NLP.
Causal modeling semantics for counterfactuals with disjunctive antecedents

Causal Modeling Semantics (CMS, e.g., [6], [22], [12]) is a powerful framework for evaluating counterfactuals whose antecedent is a conjunction of atomic formulas. We extend CMS to an evaluation of the probability of counterfactuals with disjunctive antecedents, and more generally, to counterfactuals whose antecedent is an arbitrary Boolean combination of atomic formulas. Our main idea is to assign a probability to a counterfactual at a causal model M as a weighted average of the probability of C in those submodels that truthmake A∨B [1], [3], [4]. The weights of the submodels are given by the inverse distance to the original model M, based on a distance metric proposed by Eva et al. [2]. Apart from solving a major problem in the epistemology of counterfactuals, our paper shows how work in semantics, causal inference and formal epistemology can be fruitfully combined.
Quantum chip design optimization and automation in superconducting coupler architecture

Abstract Superconducting coupler architecture demonstrates great potential for scalable and high-performance quantum processors, yet how to design efficiently and automatically ‘Qubit–Coupler–Qubit (QCQ)’ of high performance from the layout perspective remains obscure. In this work, this issue is studied for the first time resulting in three key findings. Firstly, we acquire the crucial zero-coupling condition that is only dependent on the geometric design of the layout. Secondly, the upper bound of the qubit–qubit effective coupling is found as <?CDATA $0.0822~\omega_l/\beta_s^2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0.0822</mml:mn> <mml:mtext> </mml:mtext> <mml:msub> <mml:mi>ω</mml:mi> <mml:mi>l</mml:mi> </mml:msub> <mml:mrow> <mml:mo>/</mml:mo> </mml:mrow> <mml:msubsup> <mml:mi>β</mml:mi> <mml:mi>s</mml:mi> <mml:mn>2</mml:mn> </mml:msubsup> </mml:math> which surprisingly depends only on the artificially pre-decided quantities <?CDATA $\omega_l, \beta_s$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mi>ω</mml:mi> <mml:mi>l</mml:mi> </mml:msub> <mml:mo>,</mml:mo> <mml:msub> <mml:mi>β</mml:mi> <mml:mi>s</mml:mi> </mml:msub> </mml:math> instead of specific layouts. Thirdly, we propose an optimal layout design procedure to reach the very upper bound, leading to efficient and high-performance layout design. The effectiveness of the procedure has been demonstrated scrupulously using electromagnetic simulation experiments. As a stirring application, we report a state-of-the-art 3202 um long-range and scalable QCQ layout that is especially crucial to quantum error correction. Our work provides practical guides to optimize the performance of the existing coupler architecture, find out novel layouts, and further advance the progress of quantum chip design automation.
Two-layer accumulated quantized compression for communication-efficient federated learning: TLAQC

Abstract Federated learning enables multiple nodes to perform local computations and collaborate to complete machine learning tasks without centralizing private data of nodes. However, the frequent model gradients upload/download operations required by the framework result in high communication costs, which have become the main bottleneck for federated learning as deep models scale up, hindering its performance. In this paper, we propose a two-layer accumulated quantized compression algorithm (TLAQC) that effectively reduces the communication cost of federated learning. TLAQC achieves this by reducing both the cost of individual communication and the number of global communication rounds. TLAQC introduces a revised quantization method called RQSGD, which employs zero-value correction to mitigate ineffective quantization phenomena and minimize average quantization errors. Additionally, TLAQC reduces the frequency of gradient information uploads through an adaptive threshold and parameter self-inspection mechanism, further reducing communication costs. It also accumulates quantization errors and retained weight deltas to compensate for gradient knowledge loss. Through quantization correction and two-layer accumulation, TLAQC significantly reduces precision loss caused by communication compression. Experimental results demonstrate that RQSGD achieves an incidence of ineffective quantization as low as 0.003% and reduces the average quantization error to 1.6 × $${10}^{-5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow> <mml:mn>10</mml:mn> </mml:mrow> <mml:mrow> <mml:mo>-</mml:mo> <mml:mn>5</mml:mn> </mml:mrow> </mml:msup> </mml:math> . Compared to full-precision FedAVG, TLAQC compresses uploaded traffic to only 6.73% while increasing accuracy by 1.25%.
Dual-attention Network for View-invariant Action Recognition

Abstract View-invariant action recognition has been widely researched in various applications, such as visual surveillance and human–robot interaction. However, view-invariant human action recognition is challenging due to the action occlusions and information loss caused by view changes. Modeling spatiotemporal dynamics of body joints and minimizing representation discrepancy between different views could be a valuable solution for view-invariant human action recognition. Therefore, we propose a D ual- A ttention Net work (DANet) aims to learn robust video representation for view-invariant action recognition. The DANet is composed of relation-aware spatiotemporal self-attention and spatiotemporal cross-attention modules. The relation-aware spatiotemporal self-attention module learns representative and discriminative action features. This module captures local and global long-range dependencies, as well as pairwise relations among human body parts and joints in the spatial and temporal domains. The cross-attention module learns view-invariant attention maps and generates discriminative features for semantic representations of actions in different views. We exhaustively evaluate our proposed approach on the NTU-60, NTU-120, and UESTC large-scale challenging datasets with multi-type evaluation metrics including Cross-Subject, Cross-View, Cross-Set, and Arbitrary-view. The experimental results demonstrate that our proposed approach significantly outperforms state-of-the-art approaches in view-invariant action recognition.
Shor–Laflamme distributions of graph states and noise robustness of entanglement

The Shor-Laflamme distribution (SLD) of a quantum state is a collection of local unitary invariants that quantify $k$-body correlations. We show that the SLD of graph states can be derived by solving a graph-theoretical problem. In this way, the mean and variance of the SLD are obtained as simple functions of efficiently computable graph properties. Furthermore, this formulation enables us to derive closed expressions of SLDs for some graph state families. For cluster states, we observe that the SLD is very similar to a binomial distribution, and we argue that this property is typical for graph states in general. Finally, we derive an SLD-based entanglement criterion from the purity criterion and apply it to derive meaningful noise thresholds for entanglement. Our new entanglement criterion is easy to use and also applies to the case of higher-dimensional qudits. In the bigger picture, our results foster the understanding both of quantum error-correcting codes, where a closely related notion of Shor-Laflamme distributions plays an important role, and of the geometry of quantum states, where Shor-Laflamme distributions are known as sector length distributions.
Netgraph: Publication-quality Network Visualisations in Python

The empirical study and scholarly analysis of networks have increased manifold in recent decades, fuelled by the new prominence of network structures in our lives (the web, social networks, artificial neural networks, ecological networks, etc.) and the data available on them.While there are several comprehensive Python libraries for network analysis such as NetworkX (Hagberg et al., 2008), igraph (Csardi & Nepusz, 2006), and graph-tool (Peixoto, 2014), their inbuilt visualisation capabilities lag behind specialised software solutions such as Graphviz (Ellson et al., 2002), Cytoscape (Shannon et al., 2003), and Gephi (Bastian et al., 2009).However, although Python bindings for these applications exist in the form of PyGraphviz, py4cytoscape, and GephiStreamer, respectively, their outputs are not manipulable Python objects, which restricts customisation, limits their extensibility, and prevents a seamless integration within a wider Python application. SummaryNetgraph is a Python library that aims to complement the existing network analysis libraries with publication-quality visualisations within the Python ecosystem.To facilitate a seamless integration, Netgraph supports a variety of input formats, including NetworkX, igraph, and graph-tool Graph objects.At the time of writing, Netgraph provides the following node layout algorithms:
shmem4py: OpenSHMEM for Python

shmem4py brings the Partitioned Global Address Space (PGAS) programming model to Python by exposing the functionality of the OpenSHMEM Application Programming Interface (API) specification.The feature set includes one-sided communication, shared memory access, atomic memory operations, and collective operations.The Python implementation of shmem4py emphasizes using NumPy arrays, providing convenient access to the symmetric memory allocations central to OpenSHMEM's programming model.Thanks to Python's versatility and OpenSHMEM implementations' focus on performance, shmem4py offers a seamless experience on a variety of hardware, from laptops to supercomputers, and for a wide range of applications and users.shmem4py API grounds in OpenSHMEM 1.5 specification; however, it also supports legacy 1.4 implementations.
Amplitude-controllable microwave pulse generator using single-flux-quantum pulse pairs for qubit control

Abstract To achieve large-scale quantum processors, cryogenic quantum-bit (qubit) interface circuits that can control qubits inside a dilution refrigerator are indispensable. Superconducting single-flux quantum (SFQ) circuits are a promising building block for qubit interface circuits because SFQ circuits can operate with high clock frequencies and low power dissipation. In the present study, we developed an SFQ-based qubit interface circuit that we refer to as a pulse-pair microwave pulse generator (PP-MPG). The PP-MPG can generate microwave pulses for qubit control, with the microwave amplitude controlled based on the delay between paired SFQ pulses. By finely adjusting the delay with delay controlling circuits, the microwave amplitude can be controlled over a wide range. We fabricated a PP-MPG chip and tested it at 4.2 K, showing that the microwave amplitude can be controlled over a range of approximately 30 dB. Our results show that the PP-MPG has the potential for use in future large-scale quantum processors.
RiverText: A Python Library for Training and Evaluating Incremental Word Embeddings from Text Data Streams

Word embeddings have become essential components in various information retrieval and natural language processing tasks, such as ranking, document classification, and question answering. However, despite their widespread use, traditional word embedding models present a limitation in their static nature, which hampers their ability to adapt to the constantly evolving language patterns that emerge in sources such as social media and the web (e.g., new hashtags or brand names). To overcome this problem, incremental word embedding algorithms are introduced, capable of dynamically updating word representations in response to new language patterns and processing continuous data streams.