Reviewerly/exHarmony huggingface dataset:
Auction-Based Learning for Question Answering over Knowledge Graphs

Knowledge graphs are graph-based data models which can represent real-time data that is constantly growing with the addition of new information. The question-answering systems over knowledge graphs (KGQA) retrieve answers to a natural language question from the knowledge graph. Most existing KGQA systems use static knowledge bases for offline training. After deployment, they fail to learn from unseen new entities added to the graph. There is a need for dynamic algorithms which can adapt to the evolving graphs and give interpretable results. In this research work, we propose using new auction algorithms for question answering over knowledge graphs. These algorithms can adapt to changing environments in real-time, making them suitable for offline and online training. An auction algorithm computes paths connecting an origin node to one or more destination nodes in a directed graph and uses node prices to guide the search for the path. The prices are initially assigned arbitrarily and updated dynamically based on defined rules. The algorithm navigates the graph from the high-price to the low-price nodes. When new nodes and edges are dynamically added or removed in an evolving knowledge graph, the algorithm can adapt by reusing the prices of existing nodes and assigning arbitrary prices to the new nodes. For subsequent related searches, the “learned” prices provide the means to “transfer knowledge” and act as a “guide”: to steer it toward the lower-priced nodes. Our approach reduces the search computational effort by 60% in our experiments, thus making the algorithm computationally efficient. The resulting path given by the algorithm can be mapped to the attributes of entities and relations in knowledge graphs to provide an explainable answer to the query. We discuss some applications for which our method can be used.
Automated identification of patient subgroups: A case-study on mortality of COVID-19 patients admitted to the ICU

- Subgroup discovery (SGD) is the automated splitting of the data into complex subgroups. Various SGD methods have been applied to the medical domain, but none have been extensively evaluated. We assess the numerical and clinical quality of SGD methods.- We applied the improved Subgroup Set Discovery (SSD++), Patient Rule Induction Method (PRIM) and APRIORI - Subgroup Discovery (APRIORI-SD) algorithms to obtain patient subgroups on observational data of 14,548 COVID-19 patients admitted to 73 Dutch intensive care units. Hospital mortality was the clinical outcome. Numerical significance of the subgroups was assessed with information-theoretic measures. Clinical significance of the subgroups was assessed by comparing variable importance on population and subgroup levels and by expert evaluation.- The tested algorithms varied widely in the total number of discovered subgroups (5-62), the number of selected variables, and the predictive value of the subgroups. Qualitative assessment showed that the found subgroups make clinical sense. SSD++ found most subgroups (n = 62), which added predictive value and generally showed high potential for clinical use. APRIORI-SD and PRIM found fewer subgroups (n = 5 and 6), which did not add predictive value and were clinically less relevant.- Automated SGD methods find clinical subgroups that are relevant when assessed quantitatively (yield added predictive value) and qualitatively (intensivists consider the subgroups significant). Different methods yield different subgroups with varying degrees of predictive performance and clinical quality. External validation is needed to generalize the results to other populations and future research should explore which algorithm performs best in other settings.
“Standard Text” Relational Classification Model Based on Concatenated Word Vector Attention and Feature Concatenation

The task of relation classification is an important pre-task in natural language processing tasks. Relation classification can provide a high-quality corpus for tasks such as machine translation, human–computer dialogue, and structured text generation. In the process of the digitalization of standards, identifying the entity relationship in the standard text is an important prerequisite for the formation of subsequent standard knowledge. Only by accurately labeling the relationship between entities can there be higher efficiency and accuracy in the subsequent formation of knowledge bases and knowledge maps. This study proposes a standard text relational classification model based on cascaded word vector attention and feature splicing. The model was compared and ablated on our labeled standard text Chinese dataset. At the same time, in order to prove the performance of the model, the above experiments were carried out on two general English datasets, SemEval-2010 Task 8 and KBP37. On standard text datasets and general datasets, the model proposed in this study achieved excellent results.
Hainbach and the Sound of Destruction

Hainbach is the stage name of the Berlin-based composer, lyricist, and live musician, Stefan Paul Goetsch, who is perhaps best known from his YouTube channel of the same name. Descriptions and classifications of Hainbach’s music invariably include references to the musician’s innovative and ‘experimental’ approach to music production and highlight his use of magnetic tape and obsolete tone generators, such as those used by the early proponents of electronic-based music. This article examines three of Hainbach’s recent electronic works: Tagwerk (2022), Landfill Totems (2019), and Destruction Loops (2019–2021), framing them as palimpsests of archived destruction that recall such wide-ranging works as the magnetic tape experiments of Alvin Lucier, the auto-destructive impulse and social engagement of German-born artist and activist Gustav Metzger, or even the ‘erasures’ of Robert Rauschenberg. In this article, I reveal the complex, seemingly contradictory, palimpsestuous structure of Hainbach’s works, where creation meets destruction, collage and décollage are co-planar, and the gestures of ‘play’—that creative act with destruction at its horizon—are ever present. Through play, Hainbach explores the creative potential of both sound and instrument, surveys their affordances, and courts creativity at the unpredictable intersection of serendipity and failure.
Reverse-engineering information presentations: recovering hierarchical grouping from layouts of visual elements

Abstract Visual elements in an information presentation are often spatially and semantically grouped hierarchically for effective message delivery. Studying the hierarchical grouping information can help researchers and designers better explore layout structures and understand design demographics. However, recovering hierarchical grouping is challenging due to a large number of possibilities for compositing visual elements into a single-page design. This paper introduces an automatic approach that takes the layout of visual elements as input and returns the hierarchical grouping as output. To understand information presentations, we first contribute a dataset of 23,072 information presentations with diverse layouts to the community. Next, we propose our technique with a Transformer-based model to predict relatedness between visual elements and a bottom-up algorithm to produce the hierarchical grouping. Finally, we evaluate our technique through a technical experiment and a user study with 30 designers. The results show that the proposed technique is promising.
A SAR Image-Despeckling Method Based on HOSVD Using Tensor Patches

Coherent imaging systems, such as synthetic aperture radar (SAR), often suffer from granular speckle noise due to inherent defects, which can make interpretation challenging. Although numerous despeckling methods have been proposed in the past three decades, SAR image despeckling remains a challenging task. With the extensive use of non-local self-similarity, despeckling methods under the non-local framework have become increasingly mature. However, effectively utilizing patch similarities remains a key problem in SAR image despeckling. This paper proposes a three-dimensional (3D) SAR image despeckling method based on searching for similar patches and applying the high-order singular value decomposition (HOSVD) theory to better utilize the high-dimensional information of similar patches. Specifically, the proposed method extends two-dimensional (2D) to 3D for SAR image despeckling using tensor patches. A new, non-local similar patch-searching measure criterion is used to classify the patches, and similar patches are stacked into 3D tensors. Lastly, the iterative adaptive weighted tensor cyclic approximation is used for SAR image despeckling based on the HOSVD method. Experimental results demonstrate that the proposed method not only effectively reduces speckle noise but also preserves fine details.
Graph Neural Network-Guided Contrastive Learning for Sequential Recommendation

Sequential recommendation uses contrastive learning to randomly augment user sequences and alleviate the data sparsity problem. However, there is no guarantee that the augmented positive or negative views remain semantically similar. To address this issue, we propose graph neural network-guided contrastive learning for sequential recommendation (GC4SRec). The guided process employs graph neural networks to obtain user embeddings, an encoder to determine the importance score of each item, and various data augmentation methods to construct a contrast view based on the importance score. Experimental validation is conducted on three publicly available datasets, and the experimental results demonstrate that GC4SRec improves the hit rate and normalized discounted cumulative gain metrics by 1.4% and 1.7%, respectively. The model can enhance recommendation performance and mitigate the data sparsity problem.
4DHumanOutfit: A multi-subject 4D dataset of human motion sequences in varying outfits exhibiting large displacements

We present a new dataset of densely sampled spatio-temporal 4D human motion data of different actors, outfits and motions. The dataset contains different actors wearing different outfits while performing different motions in each outfit. It samples the space of 4D human motion along 3 axes with identity, outfit and motion, therefore providing a cube of data where each identity is represented by a planar slice of the cube with all outfits and for all motions. The dataset has numerous potential applications for the processing and creation of digital humans including augmented reality, avatar creation and virtual try on. 4DHumanOutfit is released for research purposes at https://kinovis.inria.fr/4dhumanoutfit/. In addition to image data and 4D reconstructions, the dataset includes reference solutions for each axis. We present independent baselines along each axis that demonstrate the value of these reference solutions for evaluation tasks.
SOREC: Self-organizing and resource efficient clustered blockchain network

With the emergence of Industry 4.0, the features of the technologies that are utilized in broad range of areas are obliged to change according to the new necessities. Especially, the network requirements have to be adopted accordingly in terms of efficiency, transparency, faster pay-outs and asset security. The blockchain technology promises to fulfill the requirements these key aspects of networking impose with the advantages it provides. However, the technology has certain limitations regarding its scalability and its transaction throughput rate. The advantages in security provided by blockchain comes at a cost in terms of scalability and transaction throughput rate. In this paper, a novel decentralized and distributed network architecture is presented. With its novel approach the proposed architecture aims to address these issues with network scalability and to provide an increased transaction throughput rate. Without introducing any centralization with its clusters, the proposed architecture allows the network to utilize its resources much more efficiently and effectively, which allows the participants to focus their efforts on mining, increasing the performance of the network. The architecture also proposes a novel communication handling promoting bulk communications and random workload distribution over the entire network to reduce the bottlenecking that occurs on the peers. A comparison to other state-of-the-art works, namely Bitcoin [1] and Community Clustering [2], is also presented in this paper. The comparison of the collected data shows that the proposed architecture in this paper is able to reduce the overall network latency and provide an increase in the transaction throughout of the network.
An optimized hybrid deep learning model based on word embeddings and statistical features for extractive summarization

Extractive summarization has recently gained significant attention as a classification problem at the sentence level. Most current summarization methods rely on only one way of representing sentences in a document (i.e., extracted features, word embeddings, BERT embeddings). However, classification performance and summary generation quality will be improved if we combine two ways of representing sentences. This paper presents a novel extractive text summarization method based on word embeddings and statistical features of a single document. Each sentence is encoded using a Convolutional Neural Network (CNN) and a Feed-Forward Neural Network (FFNN) based on word embeddings and statistical features. CNN and FFNN outputs are concatenated to classify the sentence using a Multilayer Perceptron (MLP). In addition, hybrid model parameters are optimized by the KerasTuner optimization technique to determine the most efficient hybrid model. The proposed method was evaluated on the standard Newsroom dataset. Experiments show that the proposed method effectively captures the document's semantic and statistical information and outperforms deep learning, machine learning, and state-of-the-art approaches with scores of 78.64, 74.05, and 72.08 for ROUGE-1 ROUGE-2, and ROUGE-L, respectively.
A cybernetic participatory approach for policy system of systems mapping: Case study of inclusive economies

Traditional participatory systems modelling demands synchronous time from many experts and face-to-face interaction. This is not always feasible (e.g., during a pandemic) and can restrict which participants can be included. There are additional limitations in the effectiveness of physical paper-based modelling when handling large complex systems with numerous variables and links between them. The key challenge facing practitioners is then how can we retain the benefits of traditional participatory modelling whilst exploiting the advantages of new technologies? This paper contributes to development of an original systematic methodology inspired by Cybernetic principles. The proposed method, referred to as 5X – standing for Expose, Explore, Exploit, Explain, and Expand – offers a fully virtual co-produced environment for iterative cycles of stakeholder engagement and feedback before, during and after workshops, leading to developing more confidence in systems mapping, and promoting knowledge across policy areas. A primary application of the proposed method in a real policy setting illustrates its capability to generate a shared policy understanding of a complex inclusive economy system, where there is conflicting or dispersed knowledge about system structure, refine this understanding through online feedback channels and dynamic visualisations, and transfer this understanding to wider policy and academic partners.
Learning from hybrid labels with partial labels via hybrid-grained contrast regularization

Learning from hybrid labels is suitable for dealing with the real-world scenario, where the labels of the training dataset include fine-grained labels and coarse-grained labels. Unfortunately, obtaining fine-grained labels with strong supervision information is difficult. Compared with the more difficult-to-obtain fine-grained labels, it is labor-saving and efficient for the annotator to remove several impossible options to obtain partial labels, which also provide more information than coarse-grained labels. Nonetheless, the goal of existing methods of PLL is only to learn from partially labeled data but cannot utilize fine-grained labels, coarse-grained labels, and partial labels to train an ordinary supervised classifier. In this paper, we propose a novel setting called learning from hybrid labels with partial labels (LHLP) and propose a method to learn a classifier for identifying difficult-to-recognize images by using hybrid labels with partial labels. Specifically, we drive hybrid-grained contrast regularization (HGCR) for exploring the relationship between sample labels, which uses fine-grained label information and coarse-grained label information to disambiguate labels respectively. Extensive experiments on benchmark validate the effectiveness of the proposed method HGCR in LHLP, compare to the state-of-art methods on the CIFAR-100, Kuzushiji-MNIST and Fashion-MNIST datasets, our method achieves promising results.
A Binary Characterization Method for Shape Convexity and Applications

Convexity prior is one of the main cues for human vision and shape completion with important applications in image processing and computer vision. This paper provides a computable characterization method for shape convexity, and illustrates its applications in image segmentation and convex hull computation. We prove that the convexity of a region is equivalent to a series of quadratic inequality constraints on its indicator function. By incorporating this result, models are proposed for image segmentation with convexity prior and convex hull computation of clean and noisy data sets, respectively. Then, these models are summarized into a unified optimization problem on binary function(s) with the quadratic inequality constraints. Numerical method is proposed by linearizing the quadratic constraints. The linearization problem is solved by a proximal alternating direction method of multipliers, the convergence of which is guaranteed under some proper conditions. Numerical experiments demonstrate the efficiency and effectiveness of the proposed methods for image segmentation and convex hull computation.
Blockchain Based Electronic Voting System

In today’s digital environment, the voting system has moved from paper based to a digital system. A digital e-voting system has many properties such as transparency, decentralization, irreversibility, and non-repudiation. The growth in the digital e-voting system raises many security and transparency issues. In this paper, we used the blockchain technology in the digital electronic voting system to solve the security issues and ful?ll the system requirements. It offers new opportunities to deploy a secure e-voting system in any organization or country. The solution is far better as compared to other solutions because it is a decentralized system, containing the results in the form of bit-coins, having different locations. We will also analyze the security of our proposed voting system, which shows our protocol is more secure as compared to other solutions. The paper proposes a novel electronic voting system based on block chain that addresses some of the limitations in existing systems and evaluates some of the popular blockchain frameworks for the purpose of constructing a blockchain based e-voting system. In particular, we evaluate the potential of distributed ledger technologies through the description of a case study namely, the process of an election, and the implementation of a blockchain based application, which improves the security and decreases the cost of hosting a nation wide election.
EDBC Algorithm used for Content-Based Image Retrieval

The tremendous increase and om- nipresent accessibility of graphic documents on the network led to the high interest in research on content-based image retrieval (CBIR). This has ce- mented the approach for a massive sum of innovative procedures and schemes, and growing curiosity in allied fields to upkeep such projects. Existing associ- ated theories include efficient Content-based Image Retrieval (CBIR) frame by enacting the content- based image, K-means and hybrid clustering is func- tional over combined lineament vector of information images, texture features. In similar cases it is tight in expressing the user’s semantic Intention knowledge to permit information distribution and reuse, models ought to be managed within repositories, where they might be retrieved upon users’ queries. There is still a lack of adequate tools for incisive/handling visual content. In this paper, a novel algorithm Efficient Density-based Clustering Algorithm (EDBC) is sug- gested for content-based image retrieval technique that will enhance scalability and lower maintenance costs significantly, enhance the efficacy of software development.
The dynamic versus the stable team: The unspoken question in large‐scale agile development

Summary The importance of the team, its internal dynamics, and its performance are widely recognized within the software engineering community. While popular frameworks identify wholeness, stability over time, and smallness as important factors, they offer little guidance on how to form teams that achieve these three characteristics. The objective of this study is to investigate how these team characteristics interact in large‐scale software development contexts, particularly focusing on the impact of stable and dynamic teaming approaches. This was done through a multivocal study of literature, followed by individual semi‐structured interviews with 19 engineers from two companies and validation workshops with an additional two companies from unrelated industry segments. The study results show that the question of stable versus dynamic approaches to forming software engineering teams is largely unaddressed in industry, with stable teams representing a habitual default option. Meanwhile, both stable and dynamic teams clearly have respective strengths and weaknesses, calling for careful consideration of the most suitable approach in any given situation. To support such consideration, this paper presents a model of how team stability, wholeness, and smallness interact. This model is found relevant, accurate, generalizable, and useful by practitioners.
Out‐of‐distribution detection based on multi‐classifiers

Abstract Existing out‐of‐distribution detection models rely on the prediction of a single classifier and are sensitive to classifier bias, making it difficult to discriminate similar feature out‐of‐distribution data. This article proposed a multi‐classifier‐based model and two strategies to enhance the performance of the model. The model first trains several different base classifiers and obtains the predictions of the test data on each base classifier, then uses cross‐entropy to calculate the dispersion between these predictions, and finally uses the dispersion as a metric to identify the out‐of‐distribution data. A large scatter implies inconsistency in the predictions of the base classifier, and the greater the probability of belonging to the out‐of‐distribution data. The first strategy is applied in the training process of the model to increase the difference between base classifiers by using various scales of Label smoothing regularisation. The second strategy is applied to the inference process of the model by changing the mean and variance of the activations in the neural network to perturb the inference results of the test data. These two strategies can effectively amplify the discrepancy in the dispersion of the in‐distribution and out‐of‐distribution data. The experimental results show that the method in this article can effectively improve the performance of the model in the detection of different types of out‐of‐distribution data, improve the robustness of deep neural networks (DNN) in the face of unknown classes, and promote the application of DNN in systems and engineering with high security requirements.