Reviewerly/exHarmony huggingface dataset:
TEAR: Exploring Temporal Evolution of Adversarial Robustness for Membership Inference Attacks Against Federated Learning

Federated learning (FL) is a privacy-preserving machine learning paradigm that enables multiple clients to train a unified model without disclosing their private data. However, susceptibility to membership inference attacks (MIAs) arises due to the natural inclination of FL models to overfit on the training data during the training process, thereby enabling MIAs to exploit the subtle differences in the FL model’s parameters, activations, or predictions between the training and testing data to infer membership information. It is worth noting that most if not all existing MIAs against FL require access to the model’s internal information or modification of the training process, yielding them unlikely to be performed in practice. In this paper, we present with TEAR the first evidence that it is possible for an honest-but-curious federated client to perform MIA against an FL system, by exploring the Temporal Evolution of the Adversarial Robustness between the training and non-training data. We design a novel adversarial example generation method to quantify the target sample’s adversarial robustness, which can be utilized to obtain the membership features to train the inference model in a supervised manner. Extensive experiment results on five realistic datasets demonstrate that TEAR can achieve a strong inference performance compared with two existing MIAs, and is able to escape from the protection of two representative defenses.
An Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data

Outlier detection is an important task in the field of data mining and a highly active area of research in machine learning. In industrial automation, datasets are often high-dimensional, meaning an effort to study all dimensions directly leads to data sparsity, thus causing outliers to be masked by noise effects in high-dimensional spaces. The "curse of dimensionality" phenomenon renders many conventional outlier detection methods ineffective. This paper proposes a new outlier detection algorithm called EOEH (Ensemble Outlier Detection Method Based on Information Entropy-Weighted Subspaces for High-Dimensional Data). First, random secondary subsampling is performed on the data, and detectors are run on various small-scale sub-samples to provide diverse detection results. Results are then aggregated to reduce the global variance and enhance the robustness of the algorithm. Subsequently, information entropy is utilized to construct a dimension-space weighting method that can discern the influential factors within different dimensional spaces. This method generates weighted subspaces and dimensions for data objects, reducing the impact of noise created by high-dimensional data and improving high-dimensional data detection performance. Finally, this study offers a design for a new high-precision local outlier factor (HPLOF) detector that amplifies the differentiation between normal and outlier data, thereby improving the detection performance of the algorithm. The feasibility of this algorithm is validated through experiments that used both simulated and UCI datasets. In comparison to popular outlier detection algorithms, our algorithm demonstrates a superior detection performance and runtime efficiency. Compared with the current popular, common algorithms, the EOEH algorithm improves the detection performance by 6% on average. In terms of running time for high-dimensional data, EOEH is 20% faster than the current popular algorithms.
The Memorial Electronic Information Resource Dedicated to the Baikal Researcher M. M. Kozhov

Scientific libraries are intermediaries between science and society; they concentrate scientific and popular scientific information for a wide range of users. The information block: science – library – society works. Libraries solve the issues of book preservation, documentary, archival heritage of scientists through various forms of preservation and popularization of the achievements of regional science: work with the rare collection of scientific publications (study of marks, book plates, autographs, etc. on books), publication of articles about scientists, creation of bibliographic and biographic databases (DB) and other electronic information publications of own generation.Communicative interaction between libraries, science and society is constantly expanding, including various new components of the popularization of science among young people than attract young people to science and cause interest in the profession of a scientist. The article describes the work of the Central Scientific Library of the Irkutsk Scientific Center of the Siberian Branch of RAS in this direction on the example of the organization of electronic memorial library of Baikal scientist, Professor Mikhail Mikhailovich Kozhov.
Feature Transformation for Cross-domain Few-Shot Remote Sensing Scene Classification

Effectively classifying remote sensing scenes is still a challenge due to the increasing spatial resolution of remote imaging and large variances between remote sensing images. Existing research has greatly improved the performance of remote sensing scene classification (RSSC) in recent years. However, these methods are not applicable to cross-domain few-shot problems where target domain is with very limited training samples available and has a different data distribution from source domain. To improve the model’s applicability, we propose the feature-wise transformation module (FTM) in this paper. FTM transfers the feature distribution learned on source domain to that of target domain by a very simple affine operation with negligible additional parameters. Moreover, FTM can be effectively learned on target domain in the case of few training data available and is agnostic to specific network structures. Experiments on RSSC and land-cover mapping tasks verified its capability to handle cross-domain few-shot problems. By comparison with finetuning methods, FTM achieves better performance and possesses better transferability and fine-grained discriminability.
Do digital hugs work? Re-embodying our social lives online with digital tact

The COVID-19 pandemic led to social restrictions that often prevented us from hugging the ones we love. This absence helped some realize just how important these interactions are to our sense of care and connection. Many turned to digitally mediated social interactions to address these absences, but often unsatisfactorily. Some theorists might blame this on the disembodied character of our digital spaces, e.g., that interpersonal touch is excluded from our lives online. However, others continued to find care and connection in their digitally mediated interactions despite not being able to touch. Inspired by such contrasting cases, we ask if 'digital hugs' can work? We use the Mixed Reality Interaction Matrix to examine hugging as a social practice. This leads us to several claims about the nature of our embodied social interactions and their digital mediation: (1) all social interaction is mediated; (2) all virtual experiences are embodied; (3) technology has become richer and more supportive of embodiment; and (4) expertise plays a role. These claims help make the case that quality social connections online are substantially dependent upon the dynamic skilful resourcing of multiple mediating components, what we term digital tact. By introducing and developing this concept, we hope to contribute to a better understanding of our digital embodied sociality and the possibilities for caring connections online.
Financial development and economic growth in Ethiopia: Is there a causal link?

The relationship between financial development and economic growth has been widely debated in the economics literature, but the results have been inconsistent and vary between the short and long run. In this study, we investigate the causal relationship between financial development and economic growth in Ethiopia using annual data from 1980 to 2021. We employ the Toda-Yamamoto causality test and the nonlinear autoregressive distributed lag (NARDL) modeling framework to analyze the data. Our results show that none of the variables are stationary at the level, but after applying first differences, all variables become stationary. Using the Toda-Yamamoto causality test, we find no causality running from financial development to economic growth, but there is evidence of reverse causality from economic growth to financial development. Furthermore, the NARDL model results suggest that economic growth drives financial development, and the relationship between financial development and economic growth in Ethiopia is nonlinear and asymmetric. Specifically, neither positive nor negative shocks to economic growth affect financial development in the short run, but both affect it in the long run and in joint short run and long run effects. We conclude from our study that financial development may not guarantee economic growth without building better institutions and following sound and stable fiscal policies. Consequently, constructing an effective economic growth strategy that maintains financial development is crucial. Our findings have significant implications for policymakers, academics, and investors and underscore the importance of informed decision-making based on a thorough understanding of the relationship between financial development and economic growth.
NTRU in Quaternion Algebras of Bounded Discriminant

The NTRU assumption provides one of the most prominent problems on which to base post-quantum cryptography. Because of the efficiency and security of NTRU-style schemes, structured variants have been proposed, using modules. In this work, we create a structured form of NTRU using lattices obtained from orders in cyclic division algebras of index 2, that is, from quaternion algebras. We present a public-key encryption scheme, and show that its public keys are statistically close to uniform. We then prove IND-CPA security of a variant of our scheme when the discriminant of the quaternion algebra is not too large, assuming the hardness of Learning with Errors in cyclic division algebras.
Smooth and collision-free path planning for holonomic mobile robot based RRT-Connect

Abstract Robot navigation is a crucial aspect of any mobile robot system. Many path planners generate a path that is zigzag in nature and contains many sharp or angular turns. Such courses are not adequate for a mobile robot to follow as it has to slow down and then speed up at these sharp turns. Thus, the integration of the smoothing function is a vital aspect to generate smooth and continuous paths. Moreover, a collision check after path smoothing needs to be accounted for a possible collision with obstacles surrounding. Herein, a path pruning algorithm is utilized to reduce the total path waypoints. Then a piecewise cubic B-spline smoothing function is applied to ensure the path is smooth and continuous. In addition, a post-smoothing collision checking and improvement algorithm based on mid-point insertion for the collide segments is proposed. Experiments and comparisons with the geometric RRT-Connect path planner have shown that the inclusion of a path pruning algorithm, smoothing function and post-smoothing collision check generates better results with reduced path length.
Dynamic Access Control Through Cryptography in Cloud

In recent days, enabling encrypted access control to hosted data is attractive to organizations and many users in untrusted cloud. However, an efficient encrypted progressive access control system designing in the cloud remains a challenge. This paper proposes DAC in cryptographic, for effective access control that provides practical encryption in the system. DAC in cryptography removes permission by designate to update the encrypted data in cloud. DAC in cryptography includes file encryption with a well- formed key list that document a file key and a set of lock keys. A dedicated administrator is required to replace a new revocation key by comparing the previous key. Revocation requires a dedicated administrator to upload a new revocation key to encrypt files with the new encryption level and update the encryption key list accordingly in cloud. DAC in cryptography proposes a three-key technique to limit the key list size and later on encryption. As a result, change in access control is enforced using DAC in cryptography. This improves efficiency by not requiring high decryption or re-encryption and to upload or re-upload of large quantity of data by the administrator and assures security by revoking permissions immediately. It demonstrates the construction safety and efficiency using formalization framework and system implementations.
Video Scene Detection Using Transformer Encoding Linker Network (TELNet)

This paper introduces a transformer encoding linker network (TELNet) for automatically identifying scene boundaries in videos without prior knowledge of their structure. Videos consist of sequences of semantically related shots or chapters, and recognizing scene boundaries is crucial for various video processing tasks, including video summarization. TELNet utilizes a rolling window to scan through video shots, encoding their features extracted from a fine-tuned 3D CNN model (transformer encoder). By establishing links between video shots based on these encoded features (linker), TELNet efficiently identifies scene boundaries where consecutive shots lack links. TELNet was trained on multiple video scene detection datasets and demonstrated results comparable to other state-of-the-art models in standard settings. Notably, in cross-dataset evaluations, TELNet demonstrated significantly improved results (F-score). Furthermore, TELNet's computational complexity grows linearly with the number of shots, making it highly efficient in processing long videos.
The Metaverse Era: The Fourth Transformation in the Age of Internet Communication

We discuss the issues of the virtual realmVirtual realm. AsInternet virtual worldsVirtual world conceptualized by humans using digital technologyDigital technologies, the metaverseMetaverse that can map, transcend, and interact with the real world, but it cannot be separated from the physical realmPhysical realm. The development of metaverse-related technologiesMetaverse-related technologies has reached a critical point of large-scale application, but the metaverse is still in in the “social + gaming” stage. 2021 became a crossroad for the virtualization of human societyVirtualization of human society, which has led to a wide range of business possibilities. Users of the metaverseMetaverse can act in both physical and virtual realmsVirtual realm. User behaviorsUser behavior in the phygital realityPhygital reality environment, such as online communities or online games, are common with user behaviorUser behavior in the metaverseMetaverse. In the future, the metaverseMetaverse, matured as a virtual realm, will provide a new world for human communication and marketing. In this chapter, we describe in detail about Second LifeSecond Life, Horizon WorldsHorizon Worlds, RobloxRoblox, Mesh for TeamsMesh for Teams, and HoloBoardHoloBoard as cases of communication in the metaverseMetaverse era. The logic of communication in the virtual realmVirtual realm and its potentiality are is also described.
More user engagement, better for libraries: Proposing a framework for social Media integration in libraries

The integration of social media into libraries has become a popular worldwide trend, offering the potential to improve services and increase user participation. This literature review explores the benefits, challenges, and barriers experienced by libraries during social media integration. A comprehensive search was conducted, and the literature was synthesized by breaking it down into various titles, findings, and key takeaways. The findings reveal that social media integration in libraries is beneficial and increases user engagement. However, financial constraints, lack of competent personnel, and lack of policies were identified as barriers to successful integration. Based on these findings, a conceptual framework for successfully integrating social media into libraries is proposed that involves four steps: 1) study, 2) adoption, 3) implementation, and 4) interaction that comprehend identifying library goals, assessing resources, developing a social media strategy, creating and implementing policies, and evaluating performance that will lead to enhanced user engagement.
Excitatory/inhibitory balance emerges as a key factor for RBN performance, overriding attractor dynamics

Reservoir computing provides a time and cost-efficient alternative to traditional learning methods.Critical regimes, known as the "edge of chaos," have been found to optimize computational performance in binary neural networks. However, little attention has been devoted to studying reservoir-to-reservoir variability when investigating the link between connectivity, dynamics, and performance. As physical reservoir computers become more prevalent, developing a systematic approach to network design is crucial. In this article, we examine Random Boolean Networks (RBNs) and demonstrate that specific distribution parameters can lead to diverse dynamics near critical points. We identify distinct dynamical attractors and quantify their statistics, revealing that most reservoirs possess a dominant attractor. We then evaluate performance in two challenging tasks, memorization and prediction, and find that a positive excitatory balance produces a critical point with higher memory performance. In comparison, a negative inhibitory balance delivers another critical point with better prediction performance. Interestingly, we show that the intrinsic attractor dynamics have little influence on performance in either case.
GOLDCASE: A Generic Ontology Layer for Data Catalog Semantics

Data catalogs automatically collect metadata from distributed data sources and provide a unified and easily accessible view on the data. Many existing data catalog tools focus on the automatic collection of technical metadata (e.g., from a data dictionary) into a central repository. The functionality of annotating data with semantics (i.e., its meaning) in these tools is often not expressive enough to model complex real-world scenarios. In this paper, we propose a generic ontology layer (GOLDCASE), which maps the semantics of data in form of a high-expressive data model to the technical metadata provided by a data catalog. Hence, we achieve the following advantages: 1) users have access to an understandable description of the data objects, their relationships, and their semantics in the domain-specific data model. 2) GOLDCASE maps this knowledge directly to the metadata provided by data catalog tools and thus enables their reuse. 3) The ontology layer is machine-readable, which greatly improves automatic evaluation and data exchange. This is accompanied by improved FAIRness of the overall system. We implemented the approach at PIERER Innovation GmbH on top of an Informatica Enterprise Data Catalog to show and evaluate its applicability.
A Residual UNet Denoising Network Based on Multi-Scale Feature Extraction and Attention-Guided Filter

In order to obtain high-quality images, it is very important to remove noise effectively and retain image details reasonably. In this paper, we propose a residual UNet denoising network that adds the attention-guided filter and multi-scale feature extraction blocks. We design a multi-scale feature extraction block as the input block to expand the receiving domain and extract more useful features. We also develop the attention-guided filter block to hold the edge information. Further, we use the global residual network strategy to model residual noise instead of directly modeling clean images. Experimental results show our proposed network performs favorably against several state-of-the-art models. Our proposed model can not only suppress the noise more effectively, but also improve the sharpness of the image.
AdaptMVSNet: Efficient Multi-View Stereo with adaptive convolution and attention fusion

Multi-View Stereo (MVS) is a crucial technique for reconstructing the geometric structure of a scene, given the known camera parameters. Previous deep learning-based MVS methods have mainly focused on improving the reconstruction quality but overlooked the running efficiency during the actual algorithm deployment. For example, deformable convolutions have been introduced to improve the accuracy of the reconstruction results further, however, its inability for parallel optimization caused low inference speed. In this paper, we propose AdaptMVSNet which is device-friendly and reconstruction-efficient, while preserving the original results. To this end, adaptive convolution is introduced to significantly improve the efficiency in speed and metrics compared to current methods. In addition, an attention fusion module is proposed to blend features from adaptive convolution and the feature pyramid network. Our experiments demonstrate that our proposed approach achieves state-of-the-art performance and is almost 2× faster than the recent fastest MVS method. We will release our source code.
Contextual attribute‐based access control scheme for cloud storage using blockchain technology

Abstract Access control of data that are outsourced to cloud storage is a challenging problem because data owners lose direct control over outsourced data. Attribute‐based encryption (ABE) is a potential cryptographic solution to provide confidentiality and flexible sharing of these outsourced data. However, the traditional ABE schemes do not meet the need of the current dynamic environment where data access not only considers the user's static and inherent attributes but also takes the user's contextual information such as location and time of access. This paper presents an improved ABE scheme using blockchain technology that can handle the frequently changing location and time attributes of data users efficiently, leading to support for fine‐grained access control of cloud storage embedding contextual information in the access policy of ABE. A prototype implementation of the proposed ABE scheme using solidity on the Ethereum platform and the experimental evaluation in terms of performance and execution cost shows a promising result.
Screen content video quality assessment based on spatiotemporal sparse feature

In recent years, the explosive growth of application scenarios have occurred in screen content videos (SCVs), in which the SCVs are unavoidably suffered from the quality degradation and the video quality assessment (VQA) of the SCVs becomes very essential. In view of this, a full-reference VQA algorithm, called the spatiotemporal sparse feature-based model (SSFM) is proposed in this paper, aiming to give a precise and efficient quality evaluation about the distorted SCVs. Note that the SCVs are full of edge information which the human eyes are highly sensitive to, and the sparse coding can provide accurate quantitative predictions which are consistent with the perception resulted from the cerebral cortex in various receptive field models of the visual cortex. With these considerations, three dimensional Difference of Gaussian (3D-DOG) filter and 3D Sparse Dictionary are developed to extract multi-scale spatiotemporal features and obtain spatiotemporal sparse features respectively, from the reference and distorted SCVs. Based on these features, the spatiotemporal sparse feature similarity can be measured and followed by generating the quality scores of the distorted SCVs under evaluation. Compared to other classic and state-of-the-art image/video quality evaluation metrics, the experimental results of the proposed SSFM on the screen content video database (SCVD) are more consistent with the perceived evaluation of SCVs according to the human visual system (HVS).