Reviewerly/exHarmony huggingface dataset:
Constructing Envelopes: How Institutional Custodians Can Tame Disruptive Algorithms

The infusion of algorithms into organizational fields—accelerated by advances in artificial intelligence—can have disruptive effects that trigger defensive responses. One important response involves establishing a boundary around an algorithm to delimit its interactions with its environment—in engineering terms, constructing an "envelope." Yet, we know little about the process through which such envelopes are constructed. We address this issue by exploring how institutional custodians construct envelopes around disruptive algorithms. We empirically examine custodians' responses to the high-frequency trading algorithms that disrupted the field of U.S. securities trading, focusing on the years 2009–2016. Our inductive analysis shows that custodians created an envelope with interconnected normative, governance, and practice "layers" that jointly constrained high-frequency trading. Each layer emerged as custodians "coupled" one element of the field (e.g., its values) to one aspect of the disruptive algorithms (e.g., their impacts). Our study contributes to research on the social dynamics of algorithms by generating novel theory of how envelopes around algorithms are constructed, and to research on institutional custodianship by highlighting the constructing of envelopes as a custodial response to a wide range of threats—including, but not restricted to, disruptive algorithms.
Leveraging Coverless Image Steganography to Hide Secret Information by Generating Anime Characters Using GAN

There has been a growing need for secure communication in recent years, particularly in the digital realm. Researchers have developed various text-hiding methods to address this need, but these methods often suffer from limitations such as low embedding capacity or high embedding distortion. However, current methods for information hiding require a large image database or have a low capacity for hiding information, which makes them impractical. To address these issues, we propose a coverless information-hiding method that generates anime characters through generative adversarial networks (GANs). This method converts secret information into an attribute label set for anime characters and uses the label set to generate anime characters directly. The quality of the generated anime characters was improved by the super-resolution GAN (SRGAN) model. The resulting images were used to communicate secret information on the digital channel. Numerous experiments were conducted to evaluate the performance of the proposed framework, including hiding capacity, image clarity, robustness and security. Results showed that the proposed framework outperforms existing text-hiding methods in terms of hiding capacity and robustness while maintaining image clarity and computation time. In conclusion, our proposed framework provides a secure and efficient solution for text encryption and decryption in feasible computation time using anime characters generated by a GAN. The framework has a high embedding capacity and low embedding distortion, making it a promising solution for secure communication in the digital world.
MPFC-Net: A multi-perspective feature compensation network for medical image segmentation

Image segmentation is one of the vital steps in medical image analysis. The segmentation precision and delicacy of targets affect the diagnosis accuracy to a great extent. Deep learning-based technology is currently a hot research direction in the field of medical image analysis. Compared with manual labeling, deep convolutional neural networks (DCNN) can extract valuable deep features that are difficult for humans to identify. However, some existing DCNN methods tend to only focus on extracting deep features and ignore the semantics loss in the process of continuous pooling and convolution. In addition, medical image datasets are characterized by small-scale, mass noise, and imperceptible differences between foreground and background. How to construct sufficient contextual dependency and feature compensation from multiple perspectives to improve the segmentation performance is still faced with great challenges. In this paper, we propose a deep attention network with multi-perspective feature compensation, called Multi-Perspective Feature Compensation Network (MPFC-Net), to supplement semantics loss during continuous pooling and convolution as much as possible and establish strong contextual dependency. Merits of MPFC-Net are as follows: (1) Serializing Multi-scale Aggregation Blocks (SMAB) are constructed to extract features with different scale receptive fields, capture semantics loss in convolutions, and enhance the nonlinear fitting capability of the network. (2) Space and Channel Compound Multidimensional Interaction Attention (SCMI Attention) is designed to consider the weights of feature maps of space and channel and build long-term information interactions between different dimensions. (3) Multi-Perspective Feature Compensation (MPFC) is proposed to establish dense context interaction and compensate for semantics loss in continuous pooling and convolution. (4) Deep Feature Reuse (DFR) module is adopted to strengthen the propagation and advanced features reuse. We conduct extensive experiments on the 2018 Data Science Bowl (2018DSB), the ISIC-2018 Challenge (ISIC2018), the JSUAH-Cerebellum, and the DRIVE dataset, and the results show that MPFC-Net achieves competitive performance with simultaneous increases in various metrics.
Is the Era of Broadband Seismometry Coming to an End?

Editorial| February 08, 2024 Early Publication Is the Era of Broadband Seismometry Coming to an End? Horst Rademacher Horst Rademacher * 1Seismology Laboratory (retired), University of California, Berkeley, California, U.S.A.2Now at, GEO Enterprise Orinda, Orinda, California, U.S.A. *Corresponding author: horst@berkeley.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Horst Rademacher * 1Seismology Laboratory (retired), University of California, Berkeley, California, U.S.A.2Now at, GEO Enterprise Orinda, Orinda, California, U.S.A. *Corresponding author: horst@berkeley.edu Publisher: Seismological Society of America First Online: 08 Feb 2024 Online ISSN: 1938-2057 Print ISSN: 0895-0695 © Seismological Society of America Seismological Research Letters (2024) https://doi.org/10.1785/0220240007 Article history First Online: 08 Feb 2024 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Horst Rademacher; Is the Era of Broadband Seismometry Coming to an End?. Seismological Research Letters 2024; doi: https://doi.org/10.1785/0220240007 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search It was more than fifty years ago, that seismologists in what was then Czechoslovakia deployed the first experimental broadband feedback seismometers in seismic stations in Poland and in Czechoslovakia (Plešinger and Horalek, 1976). Based on their experience Erhard Wielandt and his student Gunar Streckeisen at the Eidgenössische Technische Hochschule in Zürich, Switzerland, developed the first commercial broadband instrument, dubbed STS‐1 (Wielandt and Streckeisen, 1982). For a detailed overview of the history of the development of broadband seismometers see Rademacher (2023). Several of these new STS‐1 seismometers were first deployed at the Seismologisches Zentralobservatorium Gräfenberg in... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
Node-personalized multi-graph convolutional networks for recommendation

Graph neural networks have revealed powerful potential in ranking recommendation. Existing methods based on bipartite graphs for ranking recommendation mainly focus on homogeneous graphs and usually treat user and item nodes as the same kind of nodes, however, the user-item bipartite graph is always heterogeneous. Additionally, various types of nodes have varying effects on recommendations, and a good node representation can be learned by successfully differentiating the same type of nodes. In this paper, we develop a node-personalized multi-graph convolutional network (NP-MGCN) for ranking recommendation. It consists of a node importance awareness block, a graph construction module, and a node information propagation and aggregation framework. Specifically, a node importance awareness block is proposed to encode nodes using node degree information to highlight the differences between nodes. Subsequently, the Jaccard similarity and co-occurrence matrix fusion graph construction module is devised to acquire user-user and item-item graphs, enriching correlation information between users and between items. Finally, a composite hop node information propagation and aggregation framework, including single-hop and double-hop branches, is designed. The high-order connectivity is used to aggregate heterogeneous information for the single-hop branch, while the multi-hop dependency is utilized to aggregate homogeneous information for the double-hop branch. It makes user and item node embedding more discriminative and integrates the different nodes’ heterogeneity into the model. Experiments on several datasets manifest that NP-MGCN achieves outstanding recommendation performance than existing methods.
ImGame Project

The study presents the authors' research for the purpose of designing ImGame, a virtual environment inviting users to playfully learn about the concept of immersion and its historical antecedents. The authors describe ImGame's current pre-production stage and examine the basic characteristics of the feeling of immersion. They intend to deepen the current understanding of the immersive experience in art, taking into account its broad cultural connotations. The article suggests that the aesthetics of immersion can be classified into two psychological modes that have not been explicitly defined in the discourse of immersivity: a calm reflection and one of awe. From the technical standpoint, the project offers a simple handling of triggering animations and events of the game as well as storing of their state in order to create gamification elements, interactions for quizzes and other activities for ImGame or any other game using the a-frame framework to create WebXR experiences.
A Literature Survey on Transaction and Phishing URL Fraud Detection System

The literature survey provides a comprehensive overview of the complexities surrounding cryptocurrencies, focusing on fraud detection and regulation within the global financial system. It traces the historical evolution of monetary systems, the emergence of cryptocurrencies, and regulatory approaches. Significant findings include the application of machine learning algorithms like LGBM and random forest in Ethereum fraud detection, and the effectiveness of unsupervised learning for anomaly mining in Bitcoin transactions. Novel frameworks for fraud detection through ensemble stacking models are also highlighted. The survey underscores the need for effective approaches to combat fraudulent activities within blockchain platforms, such as Ponzi schemes and phishing scams. Proposed solutions utilize methodologies like graph neural networks and ensemble learning, exhibiting high accuracy. Regulatory measures, classification technique refinement, and future research directions are emphasized to enhance fraud detection models. Overall, the survey illuminates the potential impact of cryptocurrencies on the global financial system, the challenges of unregulated circulation, and promising advancements in fraud detection and regulatory efforts within the digital economy
A two-stream deep model for automated ICD-9 code prediction in an intensive care unit

<h2>Abstract</h2> Assigning medical codes for patients is essential for healthcare organizations, not only for billing purposes but also for maintaining accurate records of patients' medical histories and analyzing the outputs of certain procedures. Due to the abundance of disease codes, it can be laborious and time-consuming for medical specialists to manually assign these codes to each procedure. To address this problem, we discuss the automatic prediction of ICD-9 codes, the most popular and widely accepted system of medical coding. We introduce a two-stream deep learning framework specifically designed to analyze multi-modal data. This framework is applied to the extensive and publicly available MIMIC-III dataset, enabling us to leverage both numerical and text-based data for improved ICD-9 code prediction. Our system uses text representation models to understand the text-based medical records; the Gated Recurrent Unit (GRU) to model the numerical health records; and fuses these two streams to automatically predict the ICD-9 codes used in the intensive care unit. We discuss the preprocessing and classification methods and demonstrate that our proposed two-stream model outperforms other state-of-the-art studies in the literature.
A novel IoT trust model leveraging fully distributed behavioral fingerprinting and secure delegation

The pervasiveness and high number of Internet of Things (IoT) applications in people’s daily lives make this context a very critical attack surface for cyber threats. The high heterogeneity of involved entities, both in terms of hardware and software characteristics, does not allow the definition of uniform, global, and efficient security solutions. Therefore, researchers have started to investigate novel mechanisms, in which a super node (a gateway, a hub, or a router) analyzes the interactions of the target node with other peers in the network, to detect possible anomalies. The most recent of these strategies base such an analysis on the modeling of the fingerprint of a node behavior in an IoT; nevertheless, existing solutions do not cope with the fully distributed nature of the referring scenario. In this paper, we try to provide a contribution in this setting, by designing a novel and fully distributed trust model exploiting point-to-point devices’ behavioral fingerprints, a distributed consensus mechanism, and Blockchain technology. In our solution we tackle the non-trivial issue of equipping smart things with a secure mechanism to evaluate, also through their neighbors, the trustworthiness of an object in the network before interacting with it. Beyond the detailed description of our framework, we also illustrate the security model associated with it and the tests carried out to evaluate its correctness and performance.
Customized Video Summarization with Thumbnail Containers and 2D CNN

This project focuses on acquiring customized video summaries using thumbnail container-based summarization framework and 2D CNN model to select and extract specific features from thumbnails. This framework creates a custom keyshot summary for two or more concurrent users by leveraging the computing power of the user’s device. The most advanced methods that collect and analyze complete video data to create video summaries consume a lot of computing power. In this context, we use the thumbnail containers framework which implements light thumbnails to manage the complex detection of events. This minimizes computational complexity and increases communication and storage performance by overcoming computational and privacy issues in resource constrained end-user environments. We aim on developing a user interactive customized video summarization tool which will be trained utilizing diverse datasets leading to the generation of personalized video summaries for feature-length videos.
Case-based selection of explanation methods for neural network image classifiers

Deep learning is especially remarkable in terms of image classification. However, the outcomes of models are not explainable to users due to their complex nature, having an impact on the users’ trust in the provided classifications. To solve this problem, several explanation techniques have been proposed, but they greatly depend on the nature of the images being classified and the users’ perception of the explanations. In this work, we present Case-Based Reasoning as a learning-based solution to the problem of selecting the best explanation method for the image classifications obtained by models. We propose the elicitation of a case base that reflects the human perception of the quality of the explanations and how to reuse this knowledge to select the best explanation approach for a given image classification.
Constructing MRD codes by switching

Abstract Maximum rank‐distance (MRD) codes are (not necessarily linear) maximum codes in the rank‐distance metric space on ‐by‐ matrices over a finite field . They are diameter perfect and have the cardinality if . We define switching in MRD codes as the replacement of special MRD subcodes by other subcodes with the same parameters. We consider constructions of MRD codes admitting switching, such as punctured twisted Gabidulin codes and direct‐product codes. Using switching, we construct a huge class of MRD codes whose cardinality grows doubly exponentially in if the other parameters (, the code distance) are fixed. Moreover, we construct MRD codes with different affine ranks and aperiodic MRD codes.
An enhanced multilevel secure data dissemination approximate solution for future networks

Sensitive data, such as financial, personal, or classified governmental information, must be protected throughout its cycle. This paper studies the problem of safeguarding transmitted data based on data categorization techniques. This research aims to use a novel routine as a new meta-heuristic to enhance a novel data categorization based-traffic classification technique where private data is classified into multiple confidential levels. As a result, two packets belonging to the same confidentiality level cannot be transmitted through two routers simultaneously, ensuring a high data protection level. Such a problem is determined by a non-deterministic polynomial-time hardness (NP-hard) problem; therefore, a scheduling algorithm is applied to minimize the total transmission time over the two considered routers. To measure the proposed scheme’s performance, two types of distribution, uniform and binomial distributions used to generate packets transmission time datasets. The experimental result shows that the most efficient algorithm is the Best-Random Algorithm ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M1"><mml:mover accent="true"><mml:mrow><mml:mi>B</mml:mi> <mml:mi>R</mml:mi></mml:mrow> <mml:mo>˜</mml:mo></mml:mover></mml:math> ), recording 0.028 s with an average gap of less than 0.001 in 95.1% of cases compared to all proposed algorithms. In addition, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M2"><mml:mover accent="true"><mml:mrow><mml:mi>B</mml:mi> <mml:mi>R</mml:mi></mml:mrow> <mml:mo>˜</mml:mo></mml:mover></mml:math> is compared to the best-proposed algorithm in the literature which is the Modified decreasing Estimated-Transmission Time algorithm ( MDETA ). The results show that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="M3"><mml:mover accent="true"><mml:mrow><mml:mi>B</mml:mi> <mml:mi>R</mml:mi></mml:mrow> <mml:mo>˜</mml:mo></mml:mover></mml:math> is the best one in 100% of cases where MDETA reaches the best results in only 48%.
Genetic link between Mississippi Valley-type (MVT) Pb–Zn mineralization and hydrocarbon accumulation in the Niujiaotang ore field, SW China

The Niujiaotang MVT Pb-Zn ore field is located at the southwestern end of the West Hunan-East Guizhou (WHEG) metallogenic belt, which overlaps spatially with the Majiang paleo-oil reservoir. The Pb-Zn mineralization is closely associated with oil-gas accumulation processes. However, the genetic link between the paleo-oil reservoir and the Pb-Zn deposits remains unclear. In this paper, we present new findings of bitumen (paleo-oil reservoir) and its features in the deposits, and apply various isotopic geochemical methods and 2D seismic data to elucidate the source rock and coupling relationship of the paleo-oil reservoir and the Pb-Zn deposits. This study reveals that the ore has a simple mineral composition, mainly comprising sphalerite, pyrite, and dolomite. Solid bitumen occurs widely in the ore and the host dolostone, and coexists with minerals from the mineralization stage. Laser Raman spectroscopy shows that the bitumen in the wall rocks of the Chengong deposit and the Danzhai paleo-oil reservoir are both overmature, and their carbon isotope compositions (δ13CPDB = –29.17 ‰ – –31.20 ‰, average –30.10 ‰, n = 6) and initial 187Os/188Os ratio (1.08±0.19) suggest that they have the Lower Cambrian source rock similar to the Majiang paleo-oil reservoir. The δ34S value of pyrite in the bitumen from the wall rocks in the Chengong deposit ranges from +28.15 ‰ to +44.12 ‰ (average +33.28 ‰, n = 18), which is consistent with the sulfur isotope composition of the ore sulfide reported previously, indicating that they both derived from sulfate in the Middle-Upper Cambrian strata. Bitumen Re-Os and sphalerite Rb-Sr isotopic dating indicate that oil reservoir accumulation in the ore-hosting strata preceded Pb-Zn mineralization, occurring at 424±160 Ma (MSWD = 1.3) and 398±4.7 Ma (MSWD = 0.66), respectively. The initial 87Sr/86Sr ratio of sphalerite is 0.709983±0.000027, which, after comparison and in combination with previous studies, suggests that the metallogenic metals mainly originated from the underlying strata and basement. 2D seismic data show that the Zaolou fault (F2) that controls the mineralization is a deep fault that extends to the basin basement, and acts as a migration pathway for hydrocarbon fluids and ore-forming materials. We propose that liquid hydrocarbons played a significant role in Pb-Zn mineralization, acting as reductants to reduce sulfate through the thermochemical sulfate reduction (TSR) reaction and provide abundant reduced sulfur (H2S/HS−) for Pb-Zn mineralization. Finally, we present a comprehensive model of hydrocarbon accumulation and mineralization in the Niujiaotang ore field.
The role of digital skills in the acceptance of artificial intelligence

Purpose The service industry is facing the huge impact of digital transformation, in which artificial intelligence (AI) plays one of the most important roles. This study aims to expand the understanding of the AI acceptance framework and confirm whether consumers’ digital skills have a moderating effect on the research model. Design/methodology/approach Hypotheses were tested using a data set of 1,641 individuals. Partial least squares structural equation modeling and multi-group analysis were used to estimate the model. Findings The results indicate that antecedent factors influence consumers’ willingness to use AI devices in services. The two groups of different digitally savvy respondents differ because the influence of anthropomorphism, social influence and hedonic motivation on respondents’ perceived efforts to use AI devices in service delivery depends on respondents’ digital skills. Originality/value The novel contribution of this study is reflected in a comprehensive model that explains the moderating effect of individual digital skills on willingness to use AI devices. The attitudes of experienced and digitally skilled consumers are valuable and highlight some important theoretical, practical implications and future lines of research.