Reviewerly/exHarmony huggingface dataset:
What company do words keep? Revisiting the distributional semantics of J.R. Firth &amp; Zellig Harris

The power of word embeddings is attributed to the linguistic theory that similar words will appear in similar contexts.This idea is specifically invoked by noting that "you shall know a word by the company it keeps," a quote from British linguist J.R. Firth who, along with his American colleague Zellig Harris, is often credited with the invention of "distributional semantics."While both Firth and Harris are cited in all major NLP textbooks and many foundational papers, the content and differences between their theories is seldom discussed.Engaging in a close reading of their work, we discover two distinct and in many ways divergent theories of meaning.One focuses exclusively on the internal workings of linguistic forms, while the other invites us to consider words in new company-not just with other linguistic elements, but also in a broader cultural and situational context.Contrasting these theories from the perspective of current debates in NLP, we discover in Firth a figure who could guide the field towards a more culturally grounded notion of semantics.We consider how an expanded notion of "context" might be modeled in practice through two different strategies: comparative stratification and syntagmatic extension.
Blockchain Assisted Federated Learning for Enabling Network Edge Intelligence

The recently emerging federated learning (FL) exploits massive data stored at multiple user nodes to train a global optimal learning model without leaking the privacy of user data. However, it is still inadequate to learn the global model safely at the centralized aggregator, which is an essential part for the traditional FL architecture. Specifically, when using FL in radio access networks to enable edge intelligence, it is difficult for a central server, which belongs to a third party, to guarantee its credibility. Moreover, because the central server may cause a single point of failure, its reliability is also difficult to guarantee. Besides, a malicious participating node of FL may send ill parameters for model aggregation. In this article, we develop a blockchain assisted federated learning (BC-FL) framework, with aim to overcome the single point of failure caused by central server. Meanwhile, we propose to use blockchain to implement auditing of individual involved nodes to ensure the reliability of learning process. To avoid privacy leakage during the audit process to the greatest extent, we design a matching audit mechanism to realize efficient random matching audit process. A cryptocurrency free delegated byzantine fault tolerant (CF-DBFT) consensus mechanism is also designed to realize the low-latency distributed consensus of all nodes in the FL proces. We apply the proposed BC-FL framework to resolve the computing resource allocation problem at the edger servers in MEC network. Simulation results demonstrate the effectiveness and performance superiority of the proposed BC-FL framework. Compared with legacy FL algorithm, the serving time of MEC servers and utilization of computing resource are increased by 35 percent and 48 percent respectively under our proposed BC-FL algorithm.
Target-Guided Dialogue Response Generation Using Commonsense and Data Augmentation

Target-guided response generation enables dialogue systems to smoothly transition a conversation from a dialogue context toward a target sentence.Such control is useful for designing dialogue systems that direct a conversation toward specific goals, such as creating nonobtrusive recommendations or introducing new topics in the conversation.In this paper, we introduce a new technique for target-guided response generation, which first finds a bridging path of commonsense knowledge concepts between the source and the target, and then uses the identified bridging path to generate transition responses.Additionally, we propose techniques to re-purpose existing dialogue datasets for target-guided generation.Experiments reveal that the proposed techniques outperform various baselines on this task.Finally, we observe that the existing automated metrics for this task correlate poorly with human judgement ratings.We propose a novel evaluation metric that we demonstrate is more reliable for target-guided response evaluation.Our work generally enables dialogue system designers to exercise more control over the conversations that their systems produce. 1 Commonsense-Guided Response GenerationWe frame the target-guided response generation task as follows.Given a conversation context c Context: i like the sand on my feet Target: my puppy is called georgie.GPT-2: My mom likes the water.Multigen: My pet is the gecko.CODA: My dog walks along the beach with sand.CODA-Path: sand is at location beach belongs to walk is desired by puppy Context: my favorite city is seattle.Target: i ride my bicycle
Why No Reference Metrics for Image and Video Quality Lack Accuracy and Reproducibility

This article provides a comprehensive overview of no reference (NR) metrics for image quality analysis (IQA) and video quality analysis (VQA).We examine 26 independent evaluations of NR metrics (previously published) and analyze 32 NR metrics on six IQA datasets and six VQA datasets (new results).Where NR metric developers claim Pearson correlation values between 0.66 and 0.99, our measurements range from 0.0 to 0.63.None of the NR metrics we analyzed are accurate enough to be deployed by industry.Performance evaluations that indicate otherwise are based on insufficient data and highly inaccurate.We will examine development strategies, tools, datasets, root cause analysis, and our baseline metric for collaboration, Sawatch.
Fine-tuning BERT Models for Summarizing German Radiology Findings

Writing the conclusion section of radiology reports is essential for communicating the radiology findings and its assessment to physician in a condensed form.In this work, we employ a transformer-based Seq2Seq model for generating the conclusion section of German radiology reports.The model is initialized with the pretrained parameters of a German BERT model and fine-tuned in our downstream task on our domain data.We proposed two strategies to improve the factual correctness of the model.In the first method, next to the abstractive learning objective, we introduce an extraction learning objective to train the decoder in the model to both generate one summary sequence and extract the key findings from the source input.The second approach is to integrate the pointer mechanism into the transformer-based Seq2Seq model.The pointer network helps the Seq2Seq model to choose between generating tokens from the vocabulary or copying parts from the source input during generation.The results of the automatic and human evaluations show that the enhanced Seq2Seq model is capable of generating human-like radiology conclusions and that the improved models effectively reduce the factual errors in the generations despite the small amount of training data.
Implementing B-Spline Path Planning Method Based on Roundabout Geometry Elements

This paper presents a parametric B-spline-based path planning approach for roundabout exit traverse planners to handle successively generated trajectories. A roundabout is a complex driving scene because of its large curvature and the varying intersection leg angles. A few studies focus on the relationship between roundabout intersection leg angle and path performance. This paper discusses B-spline advantages to curve road conditions and the impact of roundabout geometry elements on path planning performance. Knot vectors are defined, and control points are calculated to meet the requirements for generating paths that fit in different roundabout direction maneuvers. Four countries&#x2019; roundabout design standards are summarized with their unique geometry elements. Additionally, the Stanley tracking controller was used to calculate the steering behavior. The impacts of maneuver directions and intersection angles are discussed. It is explained stepwise how the maneuver system framework was built and what notable impacts the geometric structure of the roundabout has on the paths.
DCSO: towards an ontology for machine-actionable data management plans

The concept of Data Management Plan (DMP) has emerged as a fundamental tool to help researchers through the systematical management of data. The Research Data Alliance DMP Common Standard (DCS) working group developed a set of universal concepts characterising a DMP so it can be represented as a machine-actionable artefact, i.e., machine-actionable Data Management Plan (maDMP). The technology-agnostic approach of the current maDMP specification: (i) does not explicitly link to related data models or ontologies, (ii) has no standardised way to describe controlled vocabularies, and (iii) is extensible but has no clear mechanism to distinguish between the core specification and its extensions.This paper reports on a community effort to create the DMP Common Standard Ontology (DCSO) as a serialisation of the DCS core concepts, with a particular focus on a detailed description of the components of the ontology. Our initial result shows that the proposed DCSO can become a suitable candidate for a reference serialisation of the DMP Common Standard.
Runtime analysis of competitive co-evolutionary algorithms for maximin optimisation of a bilinear function

Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by pathological behaviour, such as loss of gradient, relative over-generalisation, and mediocre objective stasis. It is an open challenge to develop a theory that can predict when co-evolutionary algorithms find solutions efficiently and reliably.
Continuous-Variable Quantum Key Distribution Without Synchronized Clocks

It is well known that accurate clock synchronization is the backbone of quantum information processing tasks. Continuous-variable quantum key distribution (CV QKD), when proposed, benefited from its natural synchronization signals, i.e., local oscillator (LO), which provides a precise and automatic clock synchronization without a special clock board. The application of LO, however, incurs security loopholes, exposing the system to the risk of being attacked. We resolve this unsafe synchronization by proposing a virtual clock synchronization method for the CV QKD protocol without sending a real LO. The proposed method, which is based on the interpolation algorithm, extracts the clock information from the measured quadrature of the quantum signal rather than the classical LO. By formulating the measured quadrature as the synchronization signal, Bob recovers the clock information without both security loopholes and a specific clock board. Numerical results test the robustness of our method and show its potential in a real CV QKD implementation.
GPJax: A Gaussian Process Framework in JAX

Gaussian processes (GPs, Rasmussen & Williams, 2006) are Bayesian nonparametric models that have been successfully used in applications such as geostatistics (Matheron, 1963), Bayesian optimisation (Mockus et al., 1978), and reinforcement learning (Deisenroth & Rasmussen, 2011).GPJax is a didactic GP library targeted at researchers who wish to develop novel GP methodology.The scope of GPJax is to provide users with a set of composable objects for constructing GP models that closely resemble the underlying maths that one would write on paper.Furthermore, by the virtue of being written in JAX (Bradbury et al., 2018), GPJax natively supports CPUs, GPUs and TPUs through efficient compilation to XLA, automatic differentiation and vectorised operations.Consequently, GPJax provides a modern GP package that can effortlessly be tailored, extended and interleaved with other libraries to meet the individual needs of researchers and scientists.
Between surveillance and technological solutionism: A critique of privacy-preserving apps for COVID-19 contact-tracing

In this article, we examine the rise of contact-tracing apps during the first 2 years of the COVID-19 pandemic as a new form of technological solutionism – a technological or techno-social fix that can be deployed at national scale in response to an urgent, supranational problem. A dystopian view saw the rapid development and proliferation of COVID-19 contact-tracing apps as a vanguard technology for surveillance. Expediently deployed as a technological fix to the pandemic, contact-tracing was seen to threaten to transform a state of emergency into a state of exception, under which accepted or constitutional laws and norms might be suspended. Here, we extend early critiques of the contact-tracing app as a ‘technofix’ to argue the growing intervention of global technology corporations in digital governance and affairs of national sovereignty throughout the COVID-19 pandemic represents a new frontier of state–industrial surveillance that exploits people’s pre-investment in and dependence on technology corporations. We exemplify this with the ‘technofix’ of the Google–Apple Exposure Notification (GAEN) framework and critically examine the notion of a decentralised and privacy-preserving Bluetooth-based contact-tracing framework proposed by global technology corporations that may threaten state sovereignty when determining public health responses to current or future crises.
Contrastive semantic similarity learning for image captioning evaluation

Automatically evaluating the quality of image captions can be very challenging since human language is quite flexible that there can be various expressions for the same meaning. Most current captioning metrics rely on token-level matching between candidate caption and the ground truth label sentences. It usually neglects the sentence-level information. Motivated by the auto-encoder mechanism and contrastive representation learning advances, we propose a learning-based metric I2CE (Intrinsic Image Captioning Evaluation). For learning the evaluation metric, we develop three progressive model structures capturing the sentence level representations–single branch model, dual branches model, and triple branches model. For evaluation of the proposed metric, we select one automatic captioning model and collect human scores on the quality of the generated captions. We introduce a statistical test on the correlation between human scores and metric scores. Our proposed metric I2CE achieves the Spearman correlation value of 51.42, which is better than the score of 41.95 achieved by one recently proposed BERT-based metric. The result is also better than the conventional rule-based metrics. Extensive results on the Composite-coco dataset and PASCAL-50S also validate the effectiveness of our proposed metric. The proposed metric could serve as a novel indicator of the intrinsic information between captions, which complements the existing ones.
Robustness of a teleported state influenced by dipole interaction and magnetic field under intrinsic decoherence

A pair of entangled states is teleported through a class of a two-qubit XYZ-Heisenberg chain model in the presence of dipole–dipole interaction and magnetic field. The exact solution of the teleportation channel is obtained via using the intrinsic decoherence master equation. The dynamics of the teleported entanglement, quantum steering, and degree of non-locality are studied. It is found that the setting parameter of the input state plays a central role in the robustness of quantum correlations, where these correlations increase as the input state exists maximally entangled state. The increase in strength of the channel’s parameters restrains the three kinds of quantum correlations, where the small values of dipole–dipole, magnetic field, and decoherence parameters improve the quantum correlation of teleported state. The hierarchy of the three quantum correlations is satisfied for small values of channel parameters, while entanglement, steering, and non-locality are significantly identical at large values of these parameters.
Siamese networks with an online reweighted example for imbalanced data learning

One key challenging problem in data mining and decision-making is to establish a decision support system based on unbalanced datasets. In this study, we propose a novel algorithm to handle unbalanced learning problems that integrates the advantages of Siamese convolutional neural networks (SCNN) and the online reweighted example (ORE) algorithm into a unified method. First, the SCNN model is established for learning and extracting deep feature features at different levels. Second, the ORE algorithm is used to address the problem of data with a class-imbalanced distribution. Compared with baseline approaches, the experimental results show that our proposed method substantially enhances the performance of both within-project defect prediction and cross-project defect prediction.
Knowledge-Infused Learning: A Sweet Spot in Neuro-Symbolic AI

Deep learning has revolutionized the artificial intelligence (AI) landscape by enhancing machine capabilities to understand data-dependant relationships. On the other hand, knowledge may not directly correlate or depend on the data but represents facts that are true. Combining knowledge with the data-driven deep learning techniques improves upon what can be learned from data alone, resulting in improved performance with reduced training, user-level explainability, modeling uncertainty in deep learning, achieving context-sensitivity, and better control over the behavior of AI systems such as to assure the safety or avoid toxic behavior. We refer to the approach of combining various types of explicit knowledge as knowledge-infused learning (KiL). Knowledge infusion brings symbolic AI into data-driven AI, giving us a class of neuro-symbolic AI methods. The work on KiL has already developed a suite of context-adaptive algorithms that infuses various knowledge into deep learning methods in various ways, broadly categorized as a shallow infusion, semi-deep infusion, and deep infusion. This special issue allows interdisciplinary researchers and practitioners from diverse fields such as natural language processing, recommender systems, and computer vision to contribute their research on the infusion of external and expert-curated knowledge in data-driven learning methodologies for consistency and robustness in outcomes.
Heterogenous vehicle routing: comparing parameter tuning using genetic algorithm and bayesian optimization

Heterogeneous vehicles (e.g., unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV)) are best suited for surveillance application over large areas. UAVs are fast, but fuel limited, while, UGVs have a larger fuel capacity, but are relatively slow. When UAVs are combined with UGVs they can provide larger coverage at a relatively fast speed. The UAV may also be recharged on the UGV as needed. The resulting route optimization problem is computationally complex, but may be solved relatively fast using heuristics. In this paper, we solve for a mission route using a two-level optimization; (1) the UGV route is assigned using heuristics with free parameters, (2) the UAV route is solved using a vehicle routing problem formulation with capacity constraints, time windows, and dropped visits. However, this open-loop two-level optimization may yield non-optimal solutions or fail completely because of poor choice of UGV parameters. Our primary objective is to explore closed loop optimization where the free parameters of the UGV routes are optimized using Bayesian optimization and Genetic algorithms. Our results show that both methods produce good quality solutions, but bayesian optimization is computationally more efficient than genetic algorithm.
Learning Deep Binary Descriptors via Bitwise Interaction Mining

In this paper, we propose a GraphBit method to learn unsupervised deep binary descriptors for efficient image representation. Conventional binary representation learning methods directly quantize each element according to the threshold without considering the quantization ambiguousness. The elements near the boundary dubbed as "ambiguous bits" fail to collect effective information for reliable binarization and are sensitive to noise that causes reversed bits. We argue that there are implicit inner relationships among bits in binary descriptors called bitwise interaction, where the related bits can provide extra instruction as prior knowledge for ambiguousness reduction. Specifically, we design a deep reinforcement learning model to learn the structure of the graph for bitwise interaction mining, and the uncertainty of binary codes is reduced by maximizing the mutual information with input and related bits. Consequently, the ambiguous bits receive additional instruction from the graph for reliable binarization. Moreover, we further present a differentiable search method (GraphBit+) that mines the bitwise interaction in continuous space, so that the heavy search cost caused by the training difficulties in reinforcement learning is significantly reduced. Since the GraphBit and GraphBit+ methods learn fixed bitwise interaction which is suboptimal for various input, the inaccurate instruction from the fixed bitwise interaction cannot effectively decrease the ambiguousness of binary descriptors. To address this, we further propose the unsupervised binary descriptor learning method via dynamic bitwise interaction mining (D-GraphBit), where a graph convolutional network called GraphMiner reasons the optimal bitwise interaction for each input sample. Extensive experimental results on the CIFAR-10, NUS-WIDE, ImageNet-100, Brown and HPatches datasets demonstrate the efficiency and effectiveness of the proposed GraphBit, GraphBit+ and D-GraphBit.
Lightweight Single Image Super-Resolution with Selective Channel Processing Network

With the development of deep learning, considerable progress has been made in image restoration. Notably, many state-of-the-art single image super-resolution (SR) methods have been proposed. However, most of them contain many parameters, which leads to a significant amount of calculation consumption in the inference phase. To make current SR networks more lightweight and resource-friendly, we present a convolution neural network with the proposed selective channel processing strategy (SCPN). Specifically, the selective channel processing module (SCPM) is first designed to dynamically learn the significance of each channel in the feature map using a channel selection matrix in the training phase. Correspondingly, in the inference phase, only the essential channels indicated by the channel selection matrixes need to be further processed. By doing so, we can significantly reduce the parameters and the calculation consumption. Moreover, the differential channel attention (DCA) block is proposed, which takes into consideration the data distribution of the channels in feature maps to restore more high-frequency information. Extensive experiments are performed on the natural image super-resolution benchmarks (i.e., Set5, Set14, B100, Urban100, Manga109) and remote-sensing benchmarks (i.e., UCTest and RESISCTest), and our method achieves superior results to other state-of-the-art methods. Furthermore, our method keeps a slim size with fewer than 1 M parameters, which proves the superiority of our method. Owing to the proposed SCPM and DCA, our SCPN model achieves a better trade-off between calculation cost and performance in both general and remote-sensing SR applications, and our proposed method can be extended to other computer vision tasks for further research.