Reviewerly/exHarmony huggingface dataset:
Study of entanglement in symmetric multi-quDits systems through information diagrams

Along this paper, we analyze the entanglement properties of symmetric multi-quDits systems in a special type of states created from the generalization to U(D) of the usual spin coherent states. By means of parity operators, we define what we call multicomponent Schrödinger cat states as parity adapted coherent states. Introducing the tool of information diagrams, i.e. representations of pairs of entropy measures, we analyze the correlation structure of this type of states and their M-wise reduced density matrices.
Multi-Resolution and Semantic-Aware Bidirectional Adapter for Multi-Scale Object Detection

Scale variation presents a significant challenge in object detection. To address this, multi-level feature fusion techniques have been proposed, exemplified by methods such as the feature pyramid network (FPN) and its extensions. Nonetheless, the input features provided to these methods and the interaction among features across different levels are limited and inflexible. In order to fully leverage the features of multi-scale objects and amplify feature interaction and representation, we introduce a novel and efficient framework known as a multi-resolution and semantic-aware bidirectional adapter (MSBA). Specifically, MSBA comprises three successive components: multi-resolution cascaded fusion (MCF), a semantic-aware refinement transformer (SRT), and bidirectional fine-grained interaction (BFI). MCF adaptively extracts multi-level features to enable cascaded fusion. Subsequently, SRT enriches the long-range semantic information within high-level features. Following this, BFI facilitates ample fine-grained interaction via bidirectional guidance. Benefiting from the coarse-to-fine process, we can acquire robust multi-scale representations for a variety of objects. Each component can be individually integrated into different backbone architectures. Experimental results substantiate the superiority of our approach and validate the efficacy of each proposed module.
Design of a data processing method for the farmland environmental monitoring based on improved Spark components

With the popularization of big data technology, agricultural data processing systems have become more intelligent. In this study, a data processing method for farmland environmental monitoring based on improved Spark components is designed. It introduces the FAST-Join (Join critical filtering sampling partition optimization) algorithm in the Spark component for equivalence association query optimization to improve the operating efficiency of the Spark component and cluster. The experimental results show that the amount of data written and read in Shuffle by Spark optimized by the FAST-join algorithm only accounts for 0.958 and 1.384% of the original data volume on average, and the calculation speed is 202.11% faster than the original. The average data processing time and occupied memory size of the Spark cluster are reduced by 128.22 and 76.75% compared with the originals. It also compared the cluster performance of the FAST-join and Equi-join algorithms. The Spark cluster optimized by the FAST-join algorithm reduced the processing time and occupied memory size by an average of 68.74 and 37.80% compared with the Equi-join algorithm, which shows that the FAST-join algorithm can effectively improve the efficiency of inter-data table querying and cluster computing.
Identification of commonalities across different languages

This article fulfills the need for quantifiable, physical, common characteristics across different languages, which is needed to support the theory that humans use domain-general neurocognitive machinery to acquire, process, and produce language. It is shown that four different languages—English, German, Slovak and Japanese—contain linguistic chunks characterized by at least one redundancy, degeneracy, pluripotentiality, or modularity (R, D, P, or M, respectively) trait, following precedent from other fields of signal investigation. It is first established that language can be regarded as a set of signals between individuals within a complex adaptive system framework and that all signals in all signaling systems exhibit R, D, P, and/or M traits. It is then shown that linguistic chunks can be regarded as individual signals and that the chunks examined from the aforementioned languages express at least one R, D, P, and/or M trait . The present contribution thereby indicates the potential provision of a new source of data for quantifying some of the pressures involved in language production and processing, and the work concludes by assessing the value of the present work for further investigation in related fields.
A Unified Vendor-Agnostic Solution for Big Data Stream Processing in a Multi-Cloud Environment

The field of cloud computing has witnessed tremendous progress, with commercial cloud providers offering powerful distributed infrastructures to small and medium enterprises (SMEs) through their revolutionary pay-as-you-go model. Simultaneously, the rise of containers has empowered virtualisation, providing orchestration technologies for the deployment and management of large-scale distributed systems across different geolocations and providers. Big data is another research area which has developed at an extraordinary pace as industries endeavour to discover innovative and effective ways of processing large volumes of structured, semi-structured, and unstructured data. This research aims to integrate the latest advances within the fields of cloud computing, virtualisation, and big data for a systematic approach to stream processing. The novel contributions of this research are: (1) MC-BDP, a reference architecture for big data stream processing in a containerised, multi-cloud environment; (2) a case study conducted with the Estates and Sustainability departments at Leeds Beckett University to evaluate an MC-BDP prototype within the context of energy efficiency for smart buildings. The study found that MC-BDP is scalable and fault-tolerant across cloud environments, key attributes for SMEs managing resources under budgetary constraints. Additionally, our experiments on technology agnosticism and container co-location provide new insights into resource utilisation, cost implications, and optimal deployment strategies in cloud-based big data streaming, offering valuable guidelines for practitioners in the field.
Graph reasoning over explicit semantic relation

Multi-hop reasoning over language or graphs represents a significant challenge in contemporary research, particularly with the reliance on deep neural networks. These networks are integral to text reasoning processes, yet they present challenges in extracting and representing domain or commonsense knowledge, and they often lack robust logical reasoning capabilities. To address these issues, we introduce an innovative text reasoning framework. This framework is grounded in the use of a semantic relation graph and a graph neural network, designed to enhance the model’s ability to encapsulate knowledge and facilitate complex multi-hop reasoning. Our framework operates by extracting knowledge from a broad range of texts. It constructs a semantic relationship graph based on the logical relationships inherent in the reasoning process. Beginning with the core question, the framework methodically deduces key knowledge, using it as a guide to iteratively establish a complete evidence chain, thereby determining the final answer. Leveraging the advanced reasoning capabilities of the graph neural network, this approach is adept at multi-hop logical reasoning. It demonstrates strong performance in tasks like machine reading comprehension and question answering, while also clearly delineating the path of logical reasoning.
Anonymity preserving lightweight authentication protocol for resource-limited wireless sensor networks

Wireless sensor networks have been deployed in areas such as healthcare, military, transportation and home automation to collect data and forward it to remote users for further processing. Since open wireless communication channels are utilized for data transmissions, the exchanged messages are vulnerable to various threats such as eavesdropping and message falsifications. Therefore, many security solutions have been introduced to address these challenges. However, the resource-constrained nature of the sensor nodes makes it inefficient to deploy the conventional security schemes which require long keys for improved security. Therefore, lightweight authentication protocols have been presented. Unfortunately, majority of these schemes are still insecure while others incur relatively higher energy, computation, communication and storage complexities. In this paper, a protocol that deploys only lightweight one-way hashing and exclusive OR operations is presented. Its formal security analysis using Real-or Random (ROR) model demonstrates its capability to uphold the security of the derived session keys. In addition, its semantic security evaluation shows that it offers user privacy, anonymity, untraceability, authentication, session key agreement and key secrecy. Moreover, it is shown to resist attacks such as side-channeling, physical capture, eavesdropping, offline guessing, spoofing, password loss, session key disclosure, forgery and impersonations. In terms of performance, it has relatively lower communication overheads and improves the computation costs and supported security characteristics by 31.56% and 33.33% respectively.
YOLOv5-TS: Detecting traffic signs in real-time

Traffic sign detection plays a vital role in assisted driving and automatic driving. YOLOv5, as a one-stage object detection solution, is very suitable for Traffic sign detection. However, it suffers from the problem of false detection and missed detection of small objects. To address this issue, we have made improvements to YOLOv5 and subsequently introduced YOLOv5-TS in this work. In YOLOv5-TS, a spatial pyramid with depth-wise convolution is proposed by replacing maximum pooling operations in spatial pyramid pooling with depth-wise convolutions. It is applied to the backbone to extract multi-scale features at the same time prevent feature loss. A Multiple Feature Fusion module is proposed to fuse multi-scale feature maps multiple times with the purpose of enhancing both the semantic expression ability and the detail expression ability of feature maps. To improve the accuracy in detecting small even extra small objects, a specialized detection layer is introduced by utilizing the highest-resolution feature map. Besides, a new method based on k-means++ is proposed to generate stable anchor boxes. The experiments on the data set verify the usefulness and effectiveness of our work.
Integrating Earth Observation with Stream Health and Agricultural Activity

The overall health of streams, including their surrounding urban or agricultural areas, is inextricably linked to general ecological balance and public health (physical and mental well-being). This study aims to contribute to the monitoring of rural or suburban areas adjacent to streams. Specifically, low-cost and rapid ground and Earth observation techniques were used to (a) obtain a rapid assessment of stream soil and water patterns, (b) create a database of selected parameters for the study area that can be used for future comparisons, and (c) identify soil variability in agricultural fields adjacent to streams and determine soil zones that will enable the rational use of inputs (water, fertilisers, and pesticides). Robust techniques from related fields of topography, geology, geophysics, and remote sensing were combined using GIS for two selected areas (I and II) in Heraklion, central Crete (Greece) in the eastern Mediterranean. Our results indicate that area I (east of Heraklion) is under pressure only in its coastal part, most probably due to urbanisation (land change). The agricultural fields of area II (west of Heraklion) show normal values for the distribution of electrical conductivity and magnetic susceptibility and present spatial variability indicating intra-parcel zones. Intra-parcel variability of the conductivity and magnetic susceptibility should be considered in future cropping and environmental management.
Fedisp: an incremental subgradient-proximal-based ring-type architecture for decentralized federated learning

Abstract Federated learning (FL) represents a promising distributed machine learning paradigm for resolving data isolation due to data privacy concerns. Nevertheless, most vanilla FL algorithms, which depend on a server, encounter the problem of reliability and a high communication burden in real cases. Decentralized federated learning (DFL) that does not follow the star topology faces the challenges of weight divergence and inferior communication efficiency. In this paper, a novel DFL framework called federated incremental subgradient-proximal (FedISP) is proposed that utilizes the incremental method to perform model updates to alleviate weight divergence. In our setup, multiple clients are distributed in a ring topology and communicate in a cyclic manner, which significantly mitigates the communication load. A convergence guarantee is given under the convex condition to demonstrate the impact of the learning rate on our algorithms, which further improves the performance of FedISP. Extensive experiments on benchmark datasets validate the effectiveness of the proposed approach in both independent and identically distributed (IID) and non-IID settings while illustrating the advantages of the FedISP algorithm in achieving model consensus and saving communication costs.
Reframing Sound Shapes in Spectromorphological Composition: Notating perspectival space through spherical, Euclidean and Cartesian-coordinate systems

This paper examines Smalley’s preliminary taxonomy of the sound shape and the subsequent application of graphical notation in electroacoustic music. It will demonstrate ways in which spatial categorisations of the morphological sound shape have remained relatively untouched in academia, despite a codependency of frequency, space and time. Theoretical examples and existing visualisations of the sound shape will be considered as a starting point, to determine why the holistic visualisation of space is warranted. A notational system addressing the codependency between spatial and spectral sound shapes will be presented, with reference to its context in Cartesian-coordinate sound environments. This method of electroacoustic notation will incorporate the visualisation of Smalley’s categorisation of spatial sound shapes and ideas of spatial gesture , texture and distribution within Smalley’s composed and listening spaces . This visualisation and notation of composed and listening spaces will demonstrate that audio technologies are imperative drivers in the future analysis and understanding of the sound shape. It will measure the modulation of spatial sound shape properties for Cartesian ( height, width, depth ) and spherical ( azimuth and altitude ) across linear temporality, to better represent the complete form of Smalley’s sound shape. This spatial notation will aid the rounded visualisation of Smalley’s morphology , motion , texture , gesture , structure and form . Use of this notational framework will illustrate ways in which a new tool to score electroacoustic sound shapes can inform new practices in computer music composition.
Robust LS-QSVM Implementation via Efficient Matrix Factorization and Eigenvalue Estimation

Traditional SVM and LSSVM face challenges in handling large datasets due to the exponential increase in computational complexity. Quantum Support Vector Machines (QSVM) based on the HHL algorithm offer a potential solution, but practical implementation in the Noisy Intermediate-Scale Quantum(NISQ) era remains limited. Our work Least Square-QSVM with Ridge Regression(LS-QSVM-RR) addresses these limitations by efficiently decomposing the LSSVM coefficient matrix and encoding it into quantum circuits via a matrix factorization algorithm. We also leverage quantum circuit properties to compute optimal ridge regression constraints by efficient eigenvalue estimation, achieving a balance between prediction accuracy and robustness in QSVM. Our contributions demonstrate the potential of Variational Quantum Algorithm(VQA)-based QSVM simulations and provide insights into handling large condition numbers in LSSVM coefficient matrices.
Matrix Contrastive Learning for Short Text Clustering

Recently, many studies have combined contrastive learning with clustering to address this issue and achieved excellent clustering results. However, traditional contrastive learning methods suffer from class conflict. We propose a new framework called Matrix Contrastive Learning (MCL) for text clustering to address this issue. Firstly, data augmentation techniques are utilized to generate pairs of positive and negative instances for all anchor examples. These pairs are mapped into a feature space, where the rows of the matrix represent soft labels for individual instances, and the columns represent cluster representations. We perform contrastive learning at both the instance and cluster levels using these rows and columns. To further improve the cluster allocation in unsupervised clustering tasks and alleviate the class conflict problem caused by instance-level contrastive learning in unsupervised conditions, the K-Nearest Neighbors algorithm is used to filter out negative instances. We conducted extensive experiments on eight challenging text datasets and compared MCL with six existing clustering methods. The results show that MCL significantly outperforms the competing methods. The code is available at https://github.com/2251821381/MCL .
Accurate Latency Prediction of Deep Learning Model Inference Under Dynamic Runtime Resource

Accurate prediction of inference time can effectively accelerate model design and deployment in neural architecture search (NAS) algorithms, and provide hints for process scheduling in intelligent systems. Most of current latency prediction methods assume that all hardware resources are dominated by a single inference task. Due to the neglect of inter-process resource contention, these methods will fail in multi-tasking parallel scenarios. As an attempt to fill this gap, a hardware-aware prediction method based on double feature embedding (HADE) for deep learning model inference time is proposed. In HADE, operator-wise switchable Multilayer Perceptron and GNN-based graph embedding are used to encode node features, which are generated by unified nodes vectorization. Then, relying on the Resource Aware Latency (RAL) formula based on processor modeling techniques, the model inference time under dynamic runtime resource can be predicted. The experiments show that the accuracy of standard inference time prediction can reach 96.4%, and of resource dependent inference time can reach 79.7%.
Sharpness-Aware Minimization for Out-of-Distribution Generalization

Machine learning models often suffer from a significant decline in performance when they encounter out-of-distribution (OOD) data that differs from the training distribution. The distribution shift can be broadly categorized into diversity shift and correlation shift. While seeking a flat minima in optimization has been shown to improve a neural network's generalization performance with the assumption of independent and identical distribution (IID), it also has been shown to be an effective strategy for improving OOD generalization. However, previous studies potentially focused on addressing diversity shift, leaving the relationship between flat minima and correlation shift unresolved. To address the issue, we propose Sharpness-aware Invariant Risk Minimization (SIRM) as a novel approach to enhance generalization under correlation shift. Our method combines two parts: (1) Invariant risk minimization (IRM), which learns invariant relationships across multiple training environments, and (2) Sharpness-aware minimization (SAM), which finds a flat minima. Our analysis reveals that IRM does not guarantee flat minima and SAM does not improve the generalization in OOD. Moreover, we also analyze the relationship between flat minima and OOD data under correlation shift. Through extensive experiments conducted on image classification datasets, we demonstrate that our proposed method outperforms other methods with a competitive margin.
An Adaptive Auxiliary Training Method of Autoencoders and Its Application in Anomaly Detection

In various applications of autoencoders, an auxiliary subnetwork is used to improve the performance of a neural network with an autoencoder as the key component. For the specific task of anomaly detection, we have observed that in certain cases, when the reconstruction performance reaches a high level, the auxiliary subnetwork becomes ineffective in further improving the autoencoder's performance. This phenomenon results in oscillation and degradation of the overall system. To address this issue, we propose an adaptive auxiliary training method (AAT) that ensures continuous improvement in the autoencoder's reconstruction performance throughout the entire training procedure. AAT enhances the monitoring of the autoencoder's training, enabling adaptive adjustment of the training strategy without a validation set. Additionally, an anomaly detection scheme is devised based on the proposed adaptive auxiliary training method. Experimental results on multiple datasets prove that the proposed methods produce autoencoders with better reconstruction and detection performances comparing to the state-of-the-art (SOTA) methods.
Visual Navigation of Target-Driven Memory-Augmented Reinforcement Learning

Compared to visual navigation methods based on reinforcement learning that rely on auxiliary information such as depth images, semantic segmentation, object detection, and relational graphs, methods that solely utilize RGB images do not require additional equipment and have better flexibility. However, these methods often suffer from underutilization of RGB image information, resulting in poor generalization performance of the model. To address this limitation, we present the Target-Driven Memory-Augmented (TDMA) framework. This framework utilizes an external memory to store fused Target-Scene features obtained from the observed and target images. To capture and leverage long-term dependencies within this stored data, we employ the Transformer model to process historical information. Additionally, we introduce a self-attention sub-layer in the Decoder section of the Transformer to enhance the model's focus on similar regions between the observed and target images. Experimental evaluations conducted on the AI2-THOR dataset demonstrate that our proposed method achieves an 8% improvement in success rate and a 16% improvement in success weighted by path length compared to methods in the same experimental setup.
A Memory Optimization Method for Distributed Training

In recent years, with the continuous development of artificial intelligence technology, the complexity of deep learning algorithms and the scale of model training is also increasing. A series of efficient pipelined parallel training methods emerged to improve the training speed and accuracy. Distributed training becomes an effective way to train large-scale models. To solve this problem, we propose an efficient pipeline-parallel training optimization method. Our approach processes small batches of data in parallel through multiple compute nodes in a pipelined manner. We propose a prefix sum partition algorithm to realize a balanced partition and save the memory of computing resources. At the same time, we also design a clock optimization strategy to limit the number of weight version generations to ensure the model's accuracy. Compared with the current famous pipeline parallel frameworks, our method can achieve about 2 times training acceleration, save about 30% of memory consumption, and improve the model accuracy by about 10% compared with PipeDream.