Reviewerly/exHarmony huggingface dataset:
WAM-studio, a Digital Audio Workstation (DAW) for the Web

This paper presents WAM Studio, an open source, online Digital Audio Workstation (DAW) that takes advantages of several W3C Web APIs, such as Web Audio, Web Assembly, Web Components, Web Midi, Media Devices etc. It also uses the Web Audio Modules proposal that has been designed to facilitate the development of inter-operable audio plugins (effects, virtual instruments, virtual piano keyboards as controllers etc.) and host applications. DAWs are feature-rich software and therefore particularly complex to develop in terms of design, implementation, performances and ergonomics. Very few commercial online DAWs exist today and the only open-source examples lack features (no support for inter-operable plugins, for example) and do not take advantage of the recent possibilities offered by modern W3C APIs (e.g. AudioWorklets/Web Assembly). WAM Studio was developed as an open-source technology demonstrator with the aim of showcasing the potential of the web platform, made possible by these APIs. The paper highlights some of the difficulties we encountered (i.e limitations due to the sandboxed and constrained environments that are Web browsers, latency compensation etc.). An online demo, as well as a GitHub repository for the source code are available.
Towards Timeline Generation with Abstract Meaning Representation

Timeline summarization (TLS) is a challenging research task that requires researchers to distill extensive and intricate temporal data into a concise and easily comprehensible representation. This paper proposes a novel approach to timeline summarization using Abstract Meaning Representations (AMRs), a graphical representation of the text where the nodes are semantic concepts and the edges denote relationships between concepts. With AMR, sentences with different wordings, but similar semantics, have similar representations. To make use of this feature for timeline summarization, a two-step sentence selection method that leverages features extracted from both AMRs and the text is proposed. First, AMRs are generated for each sentence. Sentences are then filtered out by removing those with no named-entities and keeping the ones with the highest number of named-entities. In the next step, sentences to appear in the timeline are selected based on two scores: Inverse Document Frequency (IDF) of AMR nodes combined with the score obtained by applying a keyword extraction method to the text. Our experimental results on the TLS-Covid19 test collection demonstrate the potential of the proposed approach.
Computing and Visualizing Agro-Meteorological Parameters based on an Observational Weather Knowledge Graph

Linked-data principles are more and more adopted to integrate and publish semantically described open data using W3C standards resulting in a large amount of available resources [7]. In particular, meteorological sensor data have been uplifted into public RDF graphs, such as WeKG-MF which offers access to a large set of meteorological variables described through spatial and temporal dimensions. Nevertheless, these resources include huge numbers of raw observations that are tedious to be explored and reused by lay users. In this paper, we leverage WeKG-MF to compute important agro-meteorological parameters and views with SPARQL queries. As a result, we deployed a LOD platform as a web application to allow users to navigate, consume and produce linked datasets of agro-meterological parameters calculated on-the-fly.
A context-enhanced Dirichlet model for online clustering in short text streams

Online clustering of short text streams has become significant due to the popularity of news and social media platforms. The objective of online clustering is to maintain active topics (clusters) by automatically detecting new topics and forgetting outdated ones. Most existing approaches exploit static and high dimensional semantic term representation of the text to enhance the clustering quality. While these approaches use inference procedures that depend on a fixed batch size to reduce the number of clusters related to a given topic and bring it closer to the actual number of topics. This paper proposes a non-parametric Dirichlet model with episodic inference (EINDM) to cluster the evolving short text stream by introducing a window-based low-dimensional semantic term representation which captures the contextual relationships between words. In addition, an episodic inference procedure is introduced to reduce the cluster sparsity in the model. Furthermore, a novel “word specificity” measure is proposed based on neighborhood terms for evolving contexts for individual terms. Extensive empirical evaluation demonstrates that EINDM yields the best performance, in terms of NMI, homogeneity, and cluster purity, compared to recent state-of-the-art clustering models.
Eagle-YOLO: An Eagle-Inspired YOLO for Object Detection in Unmanned Aerial Vehicles Scenarios

Object detection in images taken by unmanned aerial vehicles (UAVs) is drawing ever-increasing research interests. Due to the flexibility of UAVs, their shooting altitude often changes rapidly, which results in drastic changes in the scale size of the identified objects. Meanwhile, there are often many small objects obscured from each other in high-altitude photography, and the background of their captured images is also complex and variable. These problems lead to a colossal challenge with object detection in UAV aerial photography images. Inspired by the characteristics of eagles, we propose an Eagle-YOLO detection model to address the above issues. First, according to the structural characteristics of eagle eyes, we integrate the Large Kernel Attention Module (LKAM) to enable the model to find object areas that need to be focused on. Then, in response to the eagle’s characteristic of experiencing dramatic changes in its field of view when swooping down to hunt at high altitudes, we introduce a large-sized feature map with rich information on small objects into the feature fusion network. The feature fusion network adopts a more reasonable weighted Bi-directional Feature Pyramid Network (Bi-FPN). Finally, inspired by the sharp features of eagle eyes, we propose an IoU loss named Eagle-IoU loss. Extensive experiments are performed on the VisDrone2021-DET dataset to compare it with the baseline model YOLOv5x. The experiments showed that Eagle-YOLO outperformed YOLOv5x by 2.86% and 4.23% in terms of the mAP and AP50, respectively, which demonstrates the effectiveness of Eagle-YOLO for object detection in UAV aerial image scenes.
Temporal Ordinance Mining for Event-Driven Social Media Reaction Analytics

As a growing number of policies are adopted to address the substantial rise in urbanization, there is a significant push for smart governance, endowing transparency in decision-making and enabling greater public involvement. The thriving concept of smart governance goes beyond just cities, ultimately aiming at a smart planet. Ordinances (local laws) affect our life with regard to health, business, etc. This is particularly notable during major events such as the recent pandemic, which may lead to rapid changes in ordinances, pertaining for instance to public safety, disaster management, and recovery phases. However, many citizens view ordinances as impervious and complex. This position paper proposes a research agenda enabling novel forms of ordinance content analysis over time and temporal web question answering (QA) for both legislators and the broader public. Along with this, we aim to analyze social media posts so as to track the public opinion before and after the introduction of ordinances. Challenges include addressing concepts changing over time and infusing subtle human reasoning in mining, which we aim to address by harnessing terminology evolution methods and commonsense knowledge sources, respectively. We aim to make the results of the historical ordinance mining and event-driven analysis seamlessly accessible, relying on a robust semantic understanding framework to flexibly support web QA.
Hierarchical few-shot learning with feature fusion driven by data and knowledge

Few-shot learning (FSL) aims to use only a few samples to learn a model and utilizes that model to identify unseen classes. Recent, metric-based feature fusion methods mainly focus on the fusion of inter-layer features and show superior performance in solving FSL problems. However, due to the data scarcity in FSL, existing methods still face severe challenges in obtaining high-quality sample features for the improvement of classification performance. In this paper, we propose a hierarchical metric FSL model with comprehensive feature fusion driven by data and knowledge (HFFDK), which is based on intra-layer channel-feature and hierarchical class structure perspectives. First, we utilize the network hierarchy to construct an intra-layer channel feature fusion, which transfers the intra-layer fused features of the higher layer to the lower layer. The model can extract high-quality sample features in a data-driven manner. Moreover, we focus on different levels of granularity to obtain various levels of information, while hierarchical class structures can provide both coarse- and fine-grained information in a knowledge-driven manner. Then, we utilize the coarse-grained information to assist fine-grained recognition. Finally, we optimize hierarchical FSL with coarse- and fine-grained relational constraints and similarity measures among samples. Experiments on four benchmark datasets show that HFFDK achieves state-of-the-art performance.
Signal Novelty Detection as an Intrinsic Reward for Robotics

In advanced robot control, reinforcement learning is a common technique used to transform sensor data into signals for actuators, based on feedback from the robot's environment. However, the feedback or reward is typically sparse, as it is provided mainly after the task's completion or failure, leading to slow convergence. Additional intrinsic rewards based on the state visitation frequency can provide more feedback. In this study, an Autoencoder deep learning neural network was utilized as novelty detection for intrinsic rewards to guide the search process through a state space. The neural network processed signals from various types of sensors simultaneously. It was tested on simulated robotic agents in a benchmark set of classic control OpenAI Gym test environments (including Mountain Car, Acrobot, CartPole, and LunarLander), achieving more efficient and accurate robot control in three of the four tasks (with only slight degradation in the Lunar Lander task) when purely intrinsic rewards were used compared to standard extrinsic rewards. By incorporating autoencoder-based intrinsic rewards, robots could potentially become more dependable in autonomous operations like space or underwater exploration or during natural disaster response. This is because the system could better adapt to changing environments or unexpected situations.
Revival patterns for Dirac cat states in a constant magnetic field

Considering the parity symmetry related to the Dirac equation, the interplay between energy localization and the temporal evolution of parity-defined quantum superpositions is investigated for fermions in a magnetic field. The unitary evolution of Dirac cat states is obtained by initializing either even or odd principal quantum numbers in the equivalent harmonic oscillator basis in relativistic Landau levels. Quantum operators feature well-defined selection rules for states thus identified, exhibiting a permanent revival structure. Our analysis is specialized for the survival probability function and for the expectation values of spinor matrix operators, which are identified as quantifiers of spin-parity correlations encoded in Dirac bispinors. In such a context, the time-evolving quantum state also imprints a signature on the energy expansion. Namely, frequencies associated with revivals are doubled for each revival order, being observed up to a so-called super revival timescale. Results show that Dirac cat states exhibit a fractional revival structure, which works as a probe of suppressions and regenerations of intrinsic correlations driven by the discrete spin-parity degrees of freedom of Dirac bispinors.
Cross Border E-Commerce Uses Blockchain Technology to Solve Payment Risks

The exponential expansion of addressing payment risks through E-Commerce Apps is mostly attributable to technical developments in this area. Although the blockchain was first created to support Bitcoin, its potential applications outside that digital currency are currently being explored. The adoption of blockchain technology may reduce the time it takes to complete a transaction, provide a more secure platform, and eliminate payment risk. Before this change, businesses were forced to go outside their borders for a satisfactory answer to the problem of payment risks. Blockchain technology has allowed us to overcome this obstacle. DTA-KNN is used as a cross-border model in this study. To do this, KNN is used to analyse data from neighbouring nations, and DTA is used to choose the most appropriate international payment gateway. To evaluate the efficacy of the suggested model, it is put up against well-established algorithms like DTA and KNN. The findings demonstrate that the hybrid model is more accurate than the other two separately.
The pysat ecosystem

The Python Satellite Data Analysis Toolkit (pysat) is an open source package that implements a general data analysis workflow for arbitrary data sets, providing a consistent manner for obtaining, managing, analysing, and processing data, including modelled and observational ground and space-based data sets for the space sciences. Pysat enables systematic and individual treatment of data as well as simplifies rigorous data access and use, allowing larger-scale scientific efforts including machine learning, data assimilation, and constellation instrumentation processing. Since the start of its development pysat has evolved into an ecosystem, separating general file and data handling functionality from both individual data set support and generalized data analysis. This design choice ensures that the core pysat package has only the necessary functionality required to provide data management services for the wider development community. The shift of data and analysis support to ecosystem packages makes it easier for the community to contribute to, as well as use, the full array of features and data sources enabled by pysat. Pysat’s ease of use, and generality, supports adoption outside of professional science to include industry, citizen science, and education.
Rough Guides and Carbon Ruins

This essay asks scholars to overcome the disciplinary divides between the humanities on the one side and the social sciences on the other in order to imagine hopeful future alternatives to petromodernity. This collaboration can only be effective if both sides are open to it, which is why I suggest leveraging speculative design as a means to bridging the disciplines. Speculative design not only unlocks avenues for generating impact outside academia that funding institutions demand but also allows for meta-theoretical self-reflection. I present two examples of such design fictions: a vision of a largely decarbonized European city in the mid-2040s and a future museum in which traces of late twentieth-/early twenty-first-century oil culture are on display. These two examples showcase how methods and theories from the arts and humanities can be applied in "the real world" to reconfigure imaginaries.This article was published open access under a CC BY licence: https://creativecommons.org/licences/by/4.0.
GAMBD: Generating adversarial malware against MalConv

Deep learning technology has been widely used in malware detection in recent years. Although malware detection models based on deep learning are very effective in malware detection tasks, deep learning models are very vulnerable to adversarial attacks by adversarial examples. Adversarial malware can fool deep-learning malware detection models into classifying it as benign software. Due to the structural and discrete properties of Portable Executable (PE) files, it is very difficult and time-consuming to generate adversarial malware. Therefore, this paper proposes GAMBD, a gradient-based adversarial malware generative approach that can quickly generate powerful adversarial malware. Compared to existing methods, GAMBD has a higher success rate and takes less time to generate adversarial malware. Through experimental verification, GAMBD can achieve a success rate of 100% adversarial attack and the average time spent is only 2.8s, which is more than 30 times shorter than that of existing methods.
Multi-domain clustering pruning: Exploring space and frequency similarity based on GAN

Network compression plays an important role in accelerating deep neural networks, especially in the application of edge devices such as unmanned cars and drones. Recently, pruning-based methods have been improved significantly, but they still suffer from low efficiency because most of them only pay attention to feature similarities in the space domain. In this paper, we propose a multi-domain structured pruning method based on clustering (MDCP) which seamlessly integrates sufficient information extraction and knowledge distillation within a GAN-based framework, to address these aforementioned limitations. Specifically, 1) to exhaustively analyze the features for reasonable pruning, we perform pruning taking both space and frequency information into account, which considering the spectral statistics to produce a more accurate pruning map; 2) to tackle the distance distortion problem caused by feature insufficiency, we further propose a clustering-based measurement mechanism to acquire pruning guidance under extreme conditions; 3) to avoid the dependence on labels and fine-tuning in previous works, a generative adversarial mechanism with two label-level losses is introduced, which further ensures the pruning efficiency and accuracy. Such a multi-domain clustering-based framework along with an adversarial and contrastive learning pattern significantly improves the pruning quality. Comprehensive experiments conducted on four benchmarks demonstrate that our MDCP method performs favorably against existing competitors. Notably, for CIFAR-10 dataset, our method on ResNet-110 outperforms the former state-of-the-art method (94.33%) in terms of top-1 accuracy (94.61%) and achieves a maximum parameter pruning rate (73.33%).
Image Segmentation Based on the Hybrid Bias Field Correction

Image segmentation is the foundation for analyzing and understanding high-level images. How to effectively segment an intensity inhomogeneous image into several meaningful regions in terms of human visual perception and ensure that the segmented regions are consistent at different resolutions is still a very challenging task. In order to describe the structure information of the intensity inhomogeneous efficiently, this paper proposes a novel hybrid bias field correction model by decoupling the multiplicative bias field and the additive bias field. These kinds of bias fields are assumed to be smooth, so can employ the Sobolev space W1,2 to feature them and use a constraint to the multiplicative bias field. Since the proposed model is a constrained optimization problem, we use the Lagrangian multiplier method to transform it into an unconstrained optimization problem, and then the alternating direction method can be used to solve it. In addition, we also discuss some mathematical properties of our proposed model and algorithm. Numerical experiments on the natural images and the medical images demonstrate performance improvement over several state-of-the-art models.
Application of real-time data processing system of Internet of Things based on blockchain technology in the financial field of Yangtze River Delta urban agglomeration

After more than 40 years of long-term development, as a pioneer of China's reform and opening up, the Yangtze River Delta region has made tremendous economic progress and strong scientific and technological innovation strength, which has made the economy flourish and scientific and technological innovation strength strong. However, in the process of financial growth in the Yangtze River Delta, there are also financial structures that do not match the optimization of industrial structure, thus hindering the quality and efficiency of industrial structure optimization, thus affecting the strategic function of industrial development. For the development of the financial field, this paper applies the real-time data processing system of the Internet of Things based on blockchain technology to analyze the financial industry of the Yangtze River Delta urban agglomeration. The Internet of Things, based on the technology of real-time data processing system, can store different types of financial assets. These assets are a common component of supporting the application of the Internet of Things. They mix and build a powerful back-end process that can support the data processing application of the Internet of Things. Therefore, the Internet of Things data network and other information are stored in the data structure, so that data analysis and consolidation can be carried out with consensus, Thus, the processing efficiency of relevant financing data in the Yangtze River Delta region has been greatly improved, and the development process of the financial sector in the region has been effectively accelerated. Therefore, the research on this issue will enable the financial industry to give full play to its potential as a major component of national economic development, so as to maximize the improvement of the industrial structure and promote the development of blockchain and Internet of Things technologies.
TANGO: A temporal spatial dynamic graph model for event prediction

The event prediction task learns the dynamic changes of past events and infers the upcoming ones. This task helps people to understand how events evolve in our real world and make rapid, accurate, and efficient reactions in emergencies. The complex relation of events and entities (events participants) changes dynamically over time. The current solutions of event prediction have not fully exploited such event temporal dependency and entity relation dependency. In addition, an event may relate to a considerable number of entities. Among such related entities, only a few of them are the key participants in driving the evolution of the event. Previous works fail to emphasize such key entities and limit the non-related entities. In this paper, we introduce a novel gating and attention mechanism and propose a novel Temporal spAtial dyNamic Graph mOdel (TANGO) that is composed of a graph model (based on Graph Convolutional Network with gated and attention mechanisms) and a sequential model (based on Temporal Convolutional Network). TANGO is able to model the event temporal dependency and entity relation dependency simultaneously; learn the representation of events relations; control the aggregation of relational information among entities with varying degrees of mutual influence; support long effective historical sizes. We demonstrate the validity and effectiveness of our approach on three different datasets (i.e., ICEWS18, GDELT, and ICEWS14). The result shows almost 6.2% MRR improvement and 3.9% Hits@10 improvement over the previous state-of-the-art.