Reviewerly/exHarmony huggingface dataset:
THE USE OF FACEBOOK FANPAGES BY POLISH UNIVERSITY LIBRARIES BEFORE AND DURING THE COVID-19 PANDEMIC. CONTENT ANALYSIS OF THE POSTS

Thesis/Objective – The main aim of the article was to present the re- sults of the analysis of the content of posts made available by eighteen Polish uni- versity libraries in the period immediately preceding and during the COVID-19 pandemic. Research methods – The mixed methods study involved the use of qualitative and quantitative approaches, with a commercial online tool the Karma Fanpage service for social media analytics monitoring being used to collect data from individual fanpages. In order to determine the subject matter of the posts, the author of the article used ten codes, to which he assigned each post. Results/ Conclusions – The study showed that all Polish university libraries (part of pub- lic universities) used Facebook as part of their daily activities, both before and during the pandemic. This included using if for marketing purposes, such as, promoting events they organized, and library offerings. In the period before the pandemic, the most frequent posts were related to various scientific, educational, and popularizing events. Most of events took place at the library building. The pandemic and the necessary precautions it provoked, made the remote services of libraries one of the key elements in their daily functioning. The greatest number of posts in the analyzed periods concerned library collections and services, main- ly those available online.
An analytical study of employee loyalty and corporate culture satisfaction assessment based on sentiment analysis

As an important factor related to the interests of enterprises, the attitude and behavior of employees are related to the company's survival and the realization of business objectives. However, in recent years, with the rapid development of emerging industries and industry changes, the turnover rate of employees in the whole enterprise has been greatly improved. Frequent turnover of personnel will have a great impact on the stability of the company, the competitiveness of the enterprise, and the operating cost. Employee loyalty and corporate culture satisfaction assessment is the core of sustainable corporate development, but most of the traditional analysis studies are based on the performance approach, using employee overtime, employee achievement, and company effectiveness as data sources, the former performance data is only a side view of the company's situation, the latter as the data source of this study is a one-sided and involute form, and the data is not stable. Therefore, this paper proposes an analysis of employee loyalty and corporate culture satisfaction assessment based on sentiment analysis, using association rules and sentiment lexicons from anonymous employee evaluations to analyze the actual data.
Scientific Journals on Library Science, Bibliography Science and Book Studies: History, Realities, Prospects

The article presents the presentations of the participants of the round table “Scientific Journals on Library Science, Bibliography Science and Book Studies: History, Realities, Prospects (to the 70th anniversary of the journal “Bibliotekovedenie” (Library Science)”, which took place on April 19, 2022 within the framework of the International Scientific and Practical Conference “Rumyantsev Readings — 2022”. The paper reflects the place and role of the professional scientific journals in the development of communications that contribute to the preservation of libraries as cultural heritage institutions and form the human capital and cultural potential of all generations of Russian citizens. The journal “Library Science”, established in 1952, became the nationwide scientific periodical of the library sector. Participants described in their speeches the characteristics of the publication activity of some Russian journals, professional periodicals of Belarus, Kazakhstan, Azerbaijan and the international scientific journal “Libri”. The authors highlighted the peculiarities of the creation of periodicals of the designated subject, their functioning, the management models used, the diversity of elements of publication policy and design: regional differences, typological characteristics of the founders, departmental affiliation, cultural traditions, the uniqueness of the historical conditions of creation, etc. The discussion resulted in unanimous recognition of the significant role of professional scientific periodicals in supporting the scientific infrastructure of libraries and providing methodological support of the sector, including research practices, as well as atmosphere of creativity and creative competencies. In the future, the journals are considered as points of growth of professional knowledge, interdisciplinary thinking and cultural communications, activators of the socio-cultural space.
Recognizing societal influences in earthquake geohazard risk perception with explainable AI while mitigating risks through improved seismic interpretation

As public awareness of climate-change-related weather events and temperature anomalies increases in the United States, citizens’ attitudes toward climate change should be studied, including the effects of climate mitigation strategies and their associated risks such as induced seismicity. Using social survey data and earthquake records, an explainable artificial intelligence method (SHAP) is employed to investigate factors that are important in explaining individual perceptions and assessments of future induced seismicity. Focusing on Oklahoma, where the majority (greater than 75%) of earthquakes are induced, SHAP reveals that personally experiencing earthquakes is a significant factor in the respondents’ past and future perception of earthquake frequency. Realizing that induced seismicity plays a strong role in individual perceptions of earthquake frequency, it is increasingly important to mitigate this geohazard, which is expected to increase with climate mitigation strategies, whether they are carbon capture and sequestration or geothermal in nature. To this end, seismic attribute methods to improve subsurface characterization, particularly for fluid migration pathway identification, are examined using data from a carbon sequestration project in northwest Montana in the Kevin Dome area. While broadband and multispectral coherence do not improve the identification of faults and fractures, in this case, aberrancy (the third derivative of structure) successfully highlights lineations because it is more susceptible to detecting flexures in seismic data. Based on this result, we strongly encourage interpreters to include aberrancy in their reservoir analysis to identify and mitigate fluid migration that may result in induced seismicity.
Generative Models: An Interdisciplinary Perspective

By linking conceptual theories with observed data, generative models can support reasoning in complex situations. They have come to play a central role both within and beyond statistics, providing the basis for power analysis in molecular biology, theory building in particle physics, and resource allocation in epidemiology, for example. We introduce the probabilistic and computational concepts underlying modern generative models and then analyze how they can be used to inform experimental design, iterative model refinement, goodness-of-fit evaluation, and agent based simulation. We emphasize a modular view of generative mechanisms and discuss how they can be flexibly recombined in new problem contexts. We provide practical illustrations throughout, and code for reproducing all examples is available at https://github.com/krisrs1128/generative_review . Finally, we observe how research in generative models is currently split across several islands of activity, and we highlight opportunities lying at disciplinary intersections.
Generalized Wigner-Yanase skew information and the affiliated inequality

A family of skew information quantities is obtained, in which the well-known Wigner-Yanase skew information and quantum Fisher information stand as special cases. A transparent proof of convexity of the generalized skew information is given, implying a simple proof of the Wigner-Yanase-Dyson conjecture. We find in this work an exact skew information inequality for qubit system, which may regard as the information counterpart of the uncertainty relation. A lower bound for generalized skew information of a pair of incompatible observables in arbitrary dimension and also the upper bound for qubit system are achieved.
Bayesian inference for pseudo-Poisson data

The bivariate pseudo-Poisson model discussed in this paper is one in which one marginal density and the conditional density of the other variable are both of the Poisson form. Such models find application for modelling bivariate count data with usually positive correlation. In the present note, we examine Bayesian estimation of the unknown parameters of bivariate pseudo-Poisson models utilizing independent gamma priors for the parameters as well as pseudo-gamma priors. Possible conjugacy is investigated in both full and submodels. In some special subcases, conjugate priors can be identified. A simulation study is included to illustrate the performance of Bayesian parameter estimates using a variety of priors, both informative and non-informative. The techniques are illustrated by re-analysing two well-known bivariate count data sets.
Encryption chain based on measurement result and its applications on semi-quantum key distribution protocol

This study proposes a new encoding method, also known as an encryption chain based on the measurement result. Then, using the encryption chain to propose a unitary-operation-based semi-quantum key distribution protocol (SQKD) protocol. In the existing SQKD protocols, semi-quantum environments adopt a round-trip transmission strategy. In round-trip transmission, the classical participant must resend the received photons to the quantum participant after implementing local operations. Therefore, round-trip transmissions are vulnerable to Trojan horse attacks. Hence, the classical participant must be equipped with a photon number splitter and an optical wavelength filter device against Trojan horse attacks. This is illogical for semi-quantum environments because the burden on the classical participant is significantly increased as it involves the prevention of Trojan horse attacks. The proposed SQKD protocol is congenitally immune to Trojan horse attacks and involves no extra hardware because it is designed based on a one-way transmission as opposed to a round-trip transmission. When compared to the existing SQKD protocols, the proposed SQKD protocol provides the best qubit efficiency, and classical participants only require two quantum capabilities, which enhance its practicability. Moreover, the proposed SQKD protocol is free from collective attacks, Trojan horse attacks, and intercept-resend attacks. Thus, the proposed scheme is more efficient and practical than the existing SQKD protocols.
Panoptic blind image inpainting

In autonomous driving, scene understanding is a critical task in recognizing the driving environment or dangerous situations. Here, a variety of factors, including foreign objects on the lens, cloudy weather, and light blur, often reduce the accuracy of scene recognition. In this paper, we propose a new blind image inpainting model that accurately reconstructs images in a real environment where there is no ground truth for restoration. To this end, we first introduce a panoptic map to represent content information in detail and design an encoder-decoder structure to predict the panoptic map and the corrupted region mask. Then, we construct an image inpainting model that utilizes the information of the predicted map. Lastly, we present a mask refinement process to improve the accuracy of map prediction. To evaluate the effectiveness of the proposed model, we compared the restoration results of various inpainting methods on the cityscapes and coco datasets. Experimental results show that the proposed model outperforms other blind image inpainting models in terms of L1/L2 losses, PSNR and SSIM, and achieves similar performance to other image inpainting techniques that utilize additional information.
Towards combining commonsense reasoning and knowledge acquisition to guide deep learning

Abstract Algorithms based on deep network models are being used for many pattern recognition and decision-making tasks in robotics and AI. Training these models requires a large labeled dataset and considerable computational resources, which are not readily available in many domains. Also, it is difficult to explore the internal representations and reasoning mechanisms of these models. As a step towards addressing the underlying knowledge representation, reasoning, and learning challenges, the architecture described in this paper draws inspiration from research in cognitive systems. As a motivating example, we consider an assistive robot trying to reduce clutter in any given scene by reasoning about the occlusion of objects and stability of object configurations in an image of the scene. In this context, our architecture incrementally learns and revises a grounding of the spatial relations between objects and uses this grounding to extract spatial information from input images. Non-monotonic logical reasoning with this information and incomplete commonsense domain knowledge is used to make decisions about stability and occlusion. For images that cannot be processed by such reasoning, regions relevant to the tasks at hand are automatically identified and used to train deep network models to make the desired decisions. Image regions used to train the deep networks are also used to incrementally acquire previously unknown state constraints that are merged with the existing knowledge for subsequent reasoning. Experimental evaluation performed using simulated and real-world images indicates that in comparison with baselines based just on deep networks, our architecture improves reliability of decision making and reduces the effort involved in training data-driven deep network models.
Real-time spatial registration for 3D human atlas

The human body is made up of about 37 trillion cells (adults). Each cell has its own unique role and is affected by its neighboring cells and environment. The NIH Human BioMolecular Atlas Program (HuBMAP) aims at developing a 3D atlas of human body consisting of organs, vessels, tissues to singe cells with all 3D spatially registered in a single 3D human atlas using tissues obtained from normal individuals across a wide range of ages. A critical step of building the atlas is to register 3D tissue blocks in real-time to the right location of a human organ, which itself consists of complex 3D sub-structures. The complexity of the 3D organ model, e.g., 35 meshes for a typical kidney, poses a significant computational challenge for the registration. In this paper, we propose a comprehensive framework TICKET (TIssue bloCK rEgisTration) to support tissue block registration for 3D human atlas, including (1) 3D mesh pre-processing, (2) spatial queries on intersection relationship and (3) intersection volume computation between organs and tissue blocks. To minimize search space and computation cost, we develop multi-level indexing at both the anatomical structure level and mesh level, and utilize OpenMP for parallel computing. Considering cuboid based shape of the tissue block, we propose an efficient voxelization-based method to estimate the intersection volume. Our experiments demonstrate that the proposed framework is efficient and practical. TICKET is being integrated into the HuBMAP CCF registration portal [1].
Improving Replay-Based Continual Semantic Segmentation with Smart Data Selection

Abhstract- Continual learning for Semantic Segmentation (CSS) is a rapidly emerging field, in which the capabilities of the segmentation model are incrementally improved by learning new classes or new domains. A central challenge in Continual Learning is overcoming the effects of catastrophic forgetting, which refers to the sudden drop in accuracy on previously learned tasks after the model is trained on new classes or domains. In continual classification this challenge is often overcome by replaying a small selection of samples from previous tasks, however replay is rarely considered in CSS. Therefore, we investigate the influences of various replay strategies for semantic segmentation and evaluate them in class- and domain-incremental settings. Our findings suggest that in a class-incremental setting, it is critical to achieve a uniform distribution for the different classes in the buffer to avoid a bias towards newly learned classes. In the domainincremental setting, it is most effective to select buffer samples by uniformly sampling from the distribution of learned feature representations or by choosing samples with median entropy. Finally, we observe that the effective sampling methods help to decrease the representation shift significantly in early layers, which is a major cause of forgetting in domain-incremental learning.
High-Speed Recognition of Pedestrians out of Blind Spot with Pre-detection of Potentially Dangerous Regions

This paper presents a novel approach for the early detection of pedestrians crossing out of blind spots. We used a monocular camera mounted at the front of a vehicle and developed an algorithm for detecting blind spots caused by obstacles blocking the view of the driver, based on the depth information obtained from a monocular depth estimation before the vehicle approaches a potentially dangerous region. Unlike other studies in which pedestrians were searched for over the entire image, our blind spot pre-detection method allows for a considerably smaller search area for pedestrian detection, which can significantly reduce the time latency of detecting unexpected pedestrians. We evaluated the proposed method on test videos with complex pedestrian and street scenes. The experimental results revealed that the performance of the method in detecting blind spots and sudden appearance of pedestrians is good, and the reaction latency for such emergency situations is significantly reduced by applying the pre-detection method. Our method can be applied in advanced driver-assistance systems and reduce accident rate caused by sudden pedestrian crossing.
Mobility-Aware Computation Offloading and Blockchain-based Handover in Vehicular Edge Computing Networks

As a novel computing paradigm, vehicular edge computing (VEC) provides additional computing capacity to connected automated vehicles by deploying resources of computing and storage on base stations or roadside units. Vehicles migrate their tasks to VEC servers for execution through computation offloading (CO) to improve processing efficiency. However, the high-speed movement of vehicles causes handover among multiple VEC servers while raising the security issue of data sharing. In this paper, we design a mobility-aware CO and blockchain-based handover architecture to reduce the latency and improve the security of vehicular CO. A CO decision problem with models of mobility, CO, and blockchain-based handover is proposed to optimize the offloading decision of vehicles. Further, we transform this optimization into a Markov decision process (MDP) and construct a multi-agent deep reinforcement learning (MADRL) algorithm to solve it. The effectiveness and performance of the proposed method are verified by simulations.
Efficient Model Evidence Computation in Tree-structured Factor Graphs

Model evidence is a fundamental performance measure in Bayesian machine learning as it represents how well a model fits an observed data set. Since model evidence is often an intractable quantity, the literature often resorts to computing instead the Bethe Free Energy (BFE), which for cyclefree models is a tractable upper bound on the (negative log-) model evidence. In this paper, we propose a different and faster evidence computation approach by tracking local normalization constants of sum-product messages, termed scale factors. We tabulate scale factor update rules for various elementary factor nodes and by experimental validation we verify the correctness of these update rules for models involving both discrete and continuous variables. We show how tracking scale factors leads to performance improvements compared to the traditional BFE computation approach.
Refined LSTM Network for Sensor-based Human Activity Recognition in Real World Scenario

Sensor-based identification of human actions is an essential field of study in ubiquitous computing. This aims to facilitate the assessment or understanding of current occurrences and their context based on sensor signals. Activity recognition is employed in surveillance systems, patient health monitoring, and many other systems involving the interaction between human and intelligent wearable devices, including smartphones and smartwatches. The primary objective of this study work is to identify human behavior in the actual world. We proposed an improved long short-term memory network called RLSTM that uses a squeeze-and-excitation module to efficiently identify human actions and enhance action identification systems’ interpretation. A publicly available real-world dataset known as REALWORLD16 was used to train and validate the model five times to analyze the proposed network. The proposed RLSTM achieved the highest accuracy of 98.04% and F1-score of 97.76%, as determined by several investigations.
Hierarchical motion planning system with consideration of the dynamic lane-changing behaviour

In this paper, a hierarchical motion planning framework is proposed to solve complex navigation problem in realistic dynamic traffic environments. Firstly, according to the possible collision risk in the lane changing process, a safe lane-changing (LC) model with consideration of the emergency braking of the preceding car is established. Then a straight-line approximation method is proposed to convert the nonlinear constraints into linear constraints, which effectively improves the solution efficiency. Secondly, the quintic B-spline curve method and the quadratic programming (QP) method are integrated to screen out the optimal LC trajectory, with consideration of driving safety, traveling comfort and efficiency. Thirdly, a decision-making strategy is proposed to decide whether the trajectory should be re-planned according to the real-time prediction of the surrounding environments, and decide the optimal lane. Finally, a model predictive control (MPC) path tracker is proposed to track the planned trajectory. The CarSim-Simulink joint simulation verify the effectiveness of the proposed motion planning framework.
A Novel Framework for Data Trading Markets based on Blockchain-enabled Federated Learning

In recent years, with the continued technical advances and increasing prosperity of digital economy ecosystems, the value of data has been widely recognized, leading to a huge potential demand of establishing data markets for sharing and trading private data resources. The existing efforts of centralized data trading systems, however, cannot completely solve the longstanding issues of data security and privacy leakage, which motivates our work. In this paper, we designed a novel framework for data trading marketplace based on blockchain and federated learning, aiming at combining their technical advantages and solving the key issue of secured and decentralized data trading with privacy protection. Specially, we proposed a six-layered conceptual model for data markets, and each layer is focused on the key elements and technologies in digital assets, technical architecture, scheduling algorithms, incentive mechanisms, trading models and application scenarios, respectively. We also discuss the potential applications of our framework in areas including intelligent transportation and Internet of vehicles. Our work is targeted to offer useful references and guidance for designing a decentralized, secured and trusted data trading market, and also promoting the development of digital economy and industries.