Reviewerly/exHarmony huggingface dataset:
Sparse polynomial optimisation for neural network verification

The prevalence of neural networks in applications is expanding at an increasing rate. It is becoming clear that providing robust guarantees on systems that use neural networks is very important, especially in safety-critical applications. A trained neural network’s sensitivity to adversarial attacks is one of its greatest shortcomings. To provide robust guarantees, one popular method that has seen success is to describe and bound the activation functions in the neural network using equality and inequality constraints. However, there are numerous ways to form these bounds, providing a trade-off between conservativeness and complexity. We approach the problem from a different perspective, using sparse polynomial optimisation theory and Positivstellensatz, a key result in real algebraic geometry. The former exploits the natural cascading structure of the neural network using ideas from chordal sparsity while the latter tests the emptiness of a semi-algebraic set using algebra, to provide tight bounds. We show that bounds can be tightened significantly, whilst the computational time remains reasonable. We compare the solve times of different solvers and show how accuracy can be improved at the expense of increased computation time. In particular, we show that with the sparse polynomial framework the solve time and accuracy can be improved over other methods for neural network verification, for networks with ReLU, sigmoid and tanh activation functions.
The SG-CIM Entity Linking Method Based on BERT and Entity Name Embeddings

The current iteration of the SG-CIM (State Grid Common Information Model) requires manual extraction of entity attributes from texts such as design plans and reports. To address the problems of slow update time and the high error rate of manual iteration data, this paper presents an entity linking method combing deep learning and knowledge base. Firstly, the SG-CIM model is used to construct a knowledge base of grid data used as a vector embedding of entities; Secondly, the joint recognition model of BERT-CRF and BERT-ENE(BERT-Entity Name Embeddings) is used for named entity recognition, where the BERT-ENE model can be used for dictionary matching of entity descriptions in the knowledge base; Then BERT-based binary classification model to predict the candidate entities, select the entity with the highest probability as the result, realize the entity disambiguation of alternate entities and new entities; Finally add the found important new entities to the SG-CIM model to realize the automated iteration of SG-CIM model. According to the experimental findings, the Entity Linking approaches have an F1-score of over 80%.
Leveraging self-sovereign identity &amp; distributed ledger technology in renewable energy certificate ecosystems

Renewable Energy Certificates (RECs) are tradable units that represent the commodity in the form of environmental attributes generated for each unit of electricity produced by a renewable energy source. Furthermore, the energy sector's digitalization ushers in new crucial enablers like Distributed Ledger Technology (DLT), which may be used for REC record tracking and trading. Unfortunately, there are a number of outstanding issues such as the lack of a common standard for the representation, communication, and verification of REC, reliance on centralized entities, and others. In order to harness the true potential of energy DLT and REC, it is imperative to address these issues. In this visionary article, we propose a holistic approach which leverages a novel decentralized identity mechanism called Self-sovereign Identity (SSI) and DLT. We present its architecture, based on a rigorous threat model and requirement analysis as well as detailed use-cases to illustrate how the architecture can be used in different REC use-cases.
Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo

In recent years, deep learning-based approaches have shown great strength in multi-view stereo because of their outstanding ability to extract robust visual features. However, most learning-based methods need to build the cost volume and increase the receptive field enormously to get a satisfactory result when dealing with large-scale textureless regions, consequently leading to prohibitive memory consumption. To ensure both memory-friendly and textureless-resilient, we innovatively transplant the spirit of deformable convolution from deep learning into the traditional PatchMatch-based method. Specifically, for each pixel with matching ambiguity (termed unreliable pixel), we adaptively deform the patch centered on it to extend the receptive field until covering enough correlative reliable pixels (without matching ambiguity) that serve as anchors. When performing PatchMatch, constrained by the anchor pixels, the matching cost of an unreliable pixel is guaranteed to reach the global minimum at the correct depth and therefore increases the robustness of multi-view stereo significantly. To detect more anchor pixels to ensure better adaptive patch deformation, we propose to evaluate the matching ambiguity of a certain pixel by checking the convergence of the estimated depth as optimization proceeds. As a result, our method achieves state-of-the-art performance on ETH3D and Tanks and Temples while preserving low memory consumption.
Self-Correctable and Adaptable Inference for Generalizable Human Pose Estimation

A central challenge in human pose estimation, as well as in many other machine learning and prediction tasks, is the generalization problem. The learned network does not have the capability to characterize the prediction error, generate feedback information from the test sample, and correct the prediction error on the fly for each individual test sample, which results in degraded performance in generalization. In this work, we introduce a self-correctable and adaptable inference (SCAI) method to address the generalization challenge of network prediction and use human pose estimation as an example to demonstrate its effectiveness and performance. We learn a correction network to correct the prediction result conditioned by a fitness feedback error. This feedback error is generated by a learned fitness feedback network which maps the prediction result to the original input domain and compares it against the original input. Interestingly, we find that this self-referential feedback error is highly correlated with the actual prediction error. This strong correlation suggests that we can use this error as feedback to guide the correction process. It can be also used as a loss function to quickly adapt and optimize the correction network during the inference process. Our extensive experimental results on human pose estimation demonstrate that the proposed SCAI method is able to significantly improve the generalization capability and performance of human pose estimation.
Asynchronous blockchain‐based federated learning for tokenized smart power contract of heterogeneous networked microgrid system

Abstract In a networked microgrid system (NMS), various heterogeneous microgrids are interconnected. A networked microgrid system facilitates a new kind of physical design that provides numerous advantages such as distributed economic optimization, reliability, resiliency, and focusing on distributed generations and customers. Designing the secure and privacy‐protected smart power contract between electricity suppliers and consumers, considered as agents, of different microgrids, is a challenging task in the networked‐ microgrid system. Each microgrid implements a heterogeneous or isomorphic blockchain based platform. The blockchain interoperability, inherently, presents in different blockchains implemented by various microgrids. This paper reviews the interoperability issues and smart contract designs in blockchain‐based systems and proposes new mechanisms to cater blockchain interoperability challenges to facilitate the design of secure and seamless smart contracts among different blockchains of microgrids. A network hub of heterogeneous blockchains of network microgrids has been proposed. A methodology has been developed to transfer tokens between interoperable blockchains. A distributed identity‐based microgrid (DIBM) scheme is incorporated to make the networked microgrid system secure and trustworthy. This paper suggests an effective consensus protocol for cross‐chain architecture that improves the tokenization system and smart power contract designs. Asynchronous blockchain based federated learning for peer‐to‐peer smart power exchange has been implemented in learning process of interoperable and heterogeneous blockchain based network hub of microgrid. For simulation purposes, MATLAB and python programming have been used with real‐time data of microgrids.
Comparison of machine learning models applied on anonymized data with different techniques

Anonymization techniques based on obfuscating the quasi-identifiers by means of value generalization hierarchies are widely used to achieve preset levels of privacy. To prevent different types of attacks against database privacy it is necessary to apply several anonymization techniques beyond the classical k-anonymity or l-diversity. However, the application of these methods is directly connected to a reduction of their utility in prediction and decision making tasks. In this work we study four classical machine learning methods currently used for classification purposes in order to analyze the results as a function of the anonymization techniques applied and the parameters selected for each of them. The performance of these models is studied when varying the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> for k-anonymity and additional tools such as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\ell}-diversity$</tex> , t-closeness and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">${\delta}-disclosure\ privacy$</tex> are also deployed on the well-known adult dataset.
Enhancing Engagement through Digital Cultural Heritage: A Case Study about Senior Citizens using a Virtual Reality Museum

As the use of Virtual Reality (VR) increases, museums have been using it to create simulations of their artefact collections. However, the level of accessibility, inclusiveness, and engagement of these simulations with senior citizens has been understudied. To address the problem, this case study presents the design of the "Pop-up VR Museum", a VR experience based on cultural heritage artefacts from the Design Museum in Helsinki that attempts to engage with audiences of wide age ranges. Users can interact with virtual artefacts and listen to stories contributed by different communities. The Pop-up VR Museum has been tested with 254 users at the museum and taken to several elderly care homes. Evaluation is based on users' gameplay data and their responses to post-experience questionnaires. Results indicate some variation in types of engagement based on users' age groups. Despite potential limitations, this study provides valuable insights for other museums to create inclusive VR experiences.
Towards Transparent Deep Image Aesthetics Assessment with Tag-based Content Descriptors

Deep learning approaches for Image Aesthetics Assessment (IAA) have shown promising results in recent years, but the internal mechanisms of these models remain unclear. Previous studies have demonstrated that image aesthetics can be predicted using semantic features, such as pre-trained object classification features. However, these semantic features are learned implicitly, and therefore, previous works have not elucidated what the semantic features are representing. In this work, we aim to create a more transparent deep learning framework for IAA by introducing explainable semantic features. To achieve this, we propose Tag-based Content Descriptors (TCDs), where each value in a TCD describes the relevance of an image to a human-readable tag that refers to a specific type of image content. This allows us to build IAA models from explicit descriptions of image contents. We first propose the explicit matching process to produce TCDs that adopt predefined tags to describe image contents. We show that a simple MLP-based IAA model with TCDs only based on predefined tags can achieve an SRCC of 0.767, which is comparable to most state-of-the-art methods. However, predefined tags may not be sufficient to describe all possible image contents that the model may encounter. Therefore, we further propose the implicit matching process to describe image contents that cannot be described by predefined tags. By integrating components obtained from the implicit matching process into TCDs, the IAA model further achieves an SRCC of 0.817, which significantly outperforms existing IAA methods. Both the explicit matching process and the implicit matching process are realized by the proposed TCD generator. To evaluate the performance of the proposed TCD generator in matching images with predefined tags, we also labeled 5101 images with photography-related tags to form a validation set. And experimental results show that the proposed TCD generator can meaningfully assign photography-related tags to images.
Customer Complaints-Based Water Quality Analysis

Social media has become a useful instrument and forum for expressing worries about various difficulties and day-to-day concerns. The pertinent postings containing people’s complaints about water quality as an additional source of information can be automatically acquired/retrieved and analyzed using natural language processing and machine learning approaches. In this paper, we search social media for a water quality analysis and propose a scalable messaging system for quality-related issues to the subscribers. We classify the WaterQualityTweets dataset, our newly collected collection, in two phases. In the first phase, tweets are classified into two classes (water quality-related or not). In the second phase, water quality-related issues are classified into four classes (color, illness, odor/taste, and unusual state). The best performance results are BERT and CNN, respectively, for binary and multi-class classification. Also, these issues are sent to different subscribers via a topic-based system with their location and timing information. Depending on the topics that online users are interested in, some information spreads faster than others. In our dataset, we also predict the information diffusion to understand water quality issues’ spreading. The time and effort required for manual comments obtained through crowd-sourcing techniques will significantly decline as a result of this automatic analysis of water quality issues.
Large Language Models as Optimizers

Optimization is ubiquitous. While derivative-based algorithms have been powerful tools for various problems, the absence of gradient imposes challenges on many real-world applications. In this work, we propose Optimization by PROmpting (OPRO), a simple and effective approach to leverage large language models (LLMs) as optimizers, where the optimization task is described in natural language. In each optimization step, the LLM generates new solutions from the prompt that contains previously generated solutions with their values, then the new solutions are evaluated and added to the prompt for the next optimization step. We first showcase OPRO on linear regression and traveling salesman problems, then move on to prompt optimization where the goal is to find instructions that maximize the task accuracy. With a variety of LLMs, we demonstrate that the best prompts optimized by OPRO outperform human-designed prompts by up to 8% on GSM8K, and by up to 50% on Big-Bench Hard tasks. Code at https://github.com/google-deepmind/opro.
The Effects of Neocolonialism on Africa’s Development

This article examines the effects of neocolonialism on Africa’s development and explores potential strategies to counter its negative impact. The legacy of colonialism has cast a long shadow over the continent, with neocolonial practices posing significant obstacles to sustained socio-economic growth and self-determination. This study aims to extensively understand neocolonialism’s multifaceted impacts on Africa’s economic growth and development by employing a qualitative approach and analyzing secondary data sources. The analysis highlights the intricate dynamics of neocolonial control in Africa, including economic dominance, political interference, cultural subjugation, and power imbalances. Moreover, it sheds light on the role of foreign aid and trade, multinational corporations, and international financial institutions in perpetuating neocolonial practices. These means and mechanisms enable the former colonial powers to exert indirect influence over Africa’s economic and political landscape, impeding genuine progress. Findings reveal that Africa’s nominal and flag independence following decolonization has not translated into genuine autonomy and self-sufficiency. Instead, the continent continues to face economic dependence, corruption, and stunted development due to neocolonial exploitation. The exploitation of Africa’s vast natural and human resources, unequal trade relationships, and support for authoritarian regimes have all contributed to the continent’s ongoing struggles. To address these challenges, the article proposes tentative strategies and solutions. These include promoting fair trade practices, empowering African nations to regain control over their resources and economies, promoting regional integration and cooperation, strengthening governance and institutions, and fostering a more equitable global economic order. Africa can achieve genuine economic growth, social progress, and self-determination by countering neocolonial practices and fostering sustainable development. Acknowledging the complex web of neocolonial dynamics is crucial for formulating effective strategies and policies to dismantle neocolonial structures and promote sustainable development.
Noise-robust ground state energy estimates from deep quantum circuits

In the lead up to fault tolerance, the utility of quantum computing will be determined by how adequately the effects of noise can be circumvented in quantum algorithms. Hybrid quantum-classical algorithms such as the variational quantum eigensolver (VQE) have been designed for the short-term regime. However, as problems scale, VQE results are generally scrambled by noise on present-day hardware. While error mitigation techniques alleviate these issues to some extent, there is a pressing need to develop algorithmic approaches with higher robustness to noise. Here, we explore the robustness properties of the recently introduced quantum computed moments (QCM) approach to ground state energy problems, and show through an analytic example how the underlying energy estimate explicitly filters out incoherent noise. Motivated by this observation, we implement QCM for a model of quantum magnetism on IBM Quantum hardware to examine the noise-filtering effect with increasing circuit depth. We find that QCM maintains a remarkably high degree of error robustness where VQE completely fails. On instances of the quantum magnetism model up to 20 qubits for ultra-deep trial state circuits of up to 500 CNOTs, QCM is still able to extract reasonable energy estimates. The observation is bolstered by an extensive set of experimental results. To match these results, VQE would need hardware improvement by some 2 orders of magnitude on error rates.
Immersive Display Design Based on Deep Learning Intelligent VR Technology

Introducing deep learning into smart VR devices can make them iteratively upgraded and allow users to have a better immersive design experience. This study analyzes and processes the data of visual interaction screens based on image difference prediction computation established by deep learning to build an image prediction model. After the definition of VR technology is clarified, the first VR devices and the mainstream devices today are introduced. After adding the extensions to image difference and difference prediction, the final image prediction computation model for the immersive display design screen is established. This experiment uses the image difference prediction model to perform the removal of redundant pixels from multiple screenshots of the display device, and multiple determinations of the color display, and based on the data of the acquisition points on the initial color, which eventually leads to a quality level improvement of the display screen effect. More polygonal modeling was added to make the display of clothes and props more realistic. The specular reflections are also no longer mirror mapped but are the result of real-time algorithm production images. The final results of the questionnaire distributed showed that 83% of the users were very satisfied with the immersive display screen effect.
Digital Comic Strips as Multi-Modal Text for Learning Interpersonal and Transactional Text in Junior High School: A Content Analysis

This research aimed to analyze the content of the digital comic strips for learning interpersonal and transactional text at Junior High School levels. The research was conducted based on the research questions: (1) How is the suitability of the digital comic strips used for grade 8 Junior High School in terms of the content of the interpersonal and transactional text? (2) How do the digital comic strips meet the criteria of digital learning material? The research used qualitative content analysis the instrument of which was adapted from the Indonesia Minister of Education and Culture Indonesia Syllabus and three Digital Learning Material Framework. The data was sourced from the digital comic strips uploaded on social media and comic strips web. It was revealed that 95% of all of the digital comic strips already have the language features that the Indonesia Education and Culture Minister stated on the syllabus, also accomplish the digital material learning criteria from accessibility and content by The National Center for Accessible Educational Resources (AEM), PRIMO (Peer-Reviewed Instructional Materials Online) Selection Criteria, and Petri Nokelainen. The findings revealed promising future to use digital comic strips as an instructional reading materials.
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection

This paper presents a novel framework, named Global-Local Correspondence Framework (GLCF), for visual anomaly detection with logical constraints. Visual anomaly detection has become an active research area in various real-world applications, such as industrial anomaly detection and medical disease diagnosis. However, most existing methods focus on identifying local structural degeneration anomalies and often fail to detect high-level functional anomalies that involve logical constraints. To address this issue, we propose a two-branch approach that consists of a local branch for detecting structural anomalies and a global branch for detecting logical anomalies. To facilitate local-global feature correspondence, we introduce a novel semantic bottleneck enabled by the visual Transformer. Moreover, we develop feature estimation networks for each branch separately to detect anomalies. Our proposed framework is validated using various benchmarks, including industrial datasets, Mvtec AD, Mvtec Loco AD, the logical dataset DigitAnatomy, and the newly proposed Mvtec AAD dataset. Experimental results show that our method outperforms existing methods, particularly in detecting logical anomalies.