Reviewerly/exHarmony huggingface dataset:
Coordinated Optimization Scheduling of Geo-Distributed Multiple Data Centers and Electricity Retailers Based on Cooperative Game Theory

As the number of internet data centers (IDC) increases, the energy consumption of modern data centers is growing rapidly, which takes an increasing part of the global electricity consumption. In implementation, to cover the electricity consumption and reduce the electricity bill, data centers are often deployed with renewable energy, energy storage system (ESS) and conventional generators, which has brought the characteristics of both flexible electricity load and micro grid to the data centers. Therefore, the data center micro grids can adjust its operations according to the real time electricity price and cut down the operational cost. However, the rising of electricity will force the data center to start up the conventional generators, which leads to the increment of operational cost. In view of this, we propose a coordinated operation scheduling method of multiple distributed data centers and electricity retailers based on cooperative game theory. In this method, a cooperative game union comprised of the data centers and the electricity retailers is established to minimize the total cost. And the extra income of the alliance is distributed using the Shapley value method. A simulation is launched on Gurobi software, and the results have illustrated that the proposed method can reduce the data center operational cost by 8.9% to 13.5%.
User-Distribution-Aware Federated Learning for Efficient Communication and Fast Inference

Deep learning as a service (DLaaS) that promotes deep learning-based applications by selling computing services from IT companies to end-users has introduced potential privacy leaks from users and cloud servers. Federated learning (FL) provides an emerging distributed paradigm that enables numerous users to collaboratively train deep-learning models while protecting user privacy and data security. However, many FL-related existing works only focus on improving communication bottlenecks due to frequent model parameter transmission, but ignore the performance degradation incurred by imbalanced user distribution and high inference latency due to the high complexity of deep-learning models in the emerging IoT-edge-cloud FL. In this paper, we propose an efficient user-distribution-aware hierarchical FL for communication-efficient training and fast inference in the IoT-edge-cloud DLaaS architecture. Specifically, we propose a user-distribution-aware hierarchical FL architecture to cope with the performance degradation owing to the imbalanced user distribution. The proposed architecture also features a lightweight deep neural network that adopts the designed lightweight fire modules as components and has a side branch for communication-efficient training and fast inference. Extensive experiments demonstrate that the proposed schemes significantly boost the accuracy by up to 67.12%, save 47.98% communication costs, and accelerate inference by up to 87.24× compared to benchmarking methods.
Guided Visual Analytics for Image Selection in Time and Space

Unexploded Ordnance (UXO) detection, the identification of remnant active bombs buried underground from archival aerial images, implies a complex workflow involving decision-making at each stage. An essential phase in UXO detection is the task of image selection, where a small subset of images must be chosen from archives to reconstruct an area of interest (AOI) and identify craters. The selected image set must comply with good spatial and temporal coverage over the AOI, particularly in the temporal vicinity of recorded aerial attacks, and do so with minimal images for resource optimization. This paper presents a guidance-enhanced visual analytics prototype to select images for UXO detection. In close collaboration with domain experts, our design process involved analyzing user tasks, eliciting expert knowledge, modeling quality metrics, and choosing appropriate guidance. We report on a user study with two real-world scenarios of image selection performed with and without guidance. Our solution was well-received and deemed highly usable. Through the lens of our task-based design and developed quality measures, we observed guidance-driven changes in user behavior and improved quality of analysis results. An expert evaluation of the study allowed us to improve our guidance-enhanced prototype further and discuss new possibilities for user-adaptive guidance.
Cryptocurrency Liquidity Forecasting

Market makers provide liquidity to small investors. To optimally provide liquidity and deal with potential demand spikes, it is important to know the liquidity required in advance. This paper describes an algorithm that attempts to predict the liquidity for a four-hour period using previous transaction data, return data, sentiment and macroeconomic data. One of the challenges to consider is the daily and weekly seasonality of order data. The main algorithm is based on LSTM, with data being pre-processed using a wavelet-based low-pass filter. As alternatives, SARIMAX and TBATS algorithms are considered, as they can also deal with seasonality. Experiments have shown that, while all three algorithms capture seasonality well, the LSTM-based algorithm outperforms the other two overall, mostly by dealing slightly better with unusual situations. In addition to that, an ensemble model was built that combines the three approaches using a ridge regression, boosting the performance further. Data for experiments was provided by Nuri, a market maker in Berlin, Germany.
Data Player: Automatic Generation of Data Videos with Narration-Animation Interplay

Data visualizations and narratives are often integrated to convey data stories effectively. Among various data storytelling formats, data videos have been garnering increasing attention. These videos provide an intuitive interpretation of data charts while vividly articulating the underlying data insights. However, the production of data videos demands a diverse set of professional skills and considerable manual labor, including understanding narratives, linking visual elements with narration segments, designing and crafting animations, recording audio narrations, and synchronizing audio with visual animations. To simplify this process, our paper introduces a novel method, referred to as Data Player, capable of automatically generating dynamic data videos with narration-animation interplay. This approach lowers the technical barriers associated with creating data videos rich in narration. To enable narration-animation interplay, Data Player constructs references between visualizations and text input. Specifically, it first extracts data into tables from the visualizations. Subsequently, it utilizes large language models to form semantic connections between text and visuals. Finally, Data Player encodes animation design knowledge as computational low-level constraints, allowing for the recommendation of suitable animation presets that align with the audio narration produced by text-to-speech technologies. We assessed Data Player's efficacy through an example gallery, a user study, and expert interviews. The evaluation results demonstrated that Data Player can generate high-quality data videos that are comparable to human-composed ones.
Explore Your Network in Minutes: A Rapid Prototyping Toolkit for Understanding Neural Networks with Visual Analytics

Neural networks attract significant attention in almost every field due to their widespread applications in various tasks. However, developers often struggle with debugging due to the black-box nature of neural networks. Visual analytics provides an intuitive way for developers to understand the hidden states and underlying complex transformations in neural networks. Existing visual analytics tools for neural networks have been demonstrated to be effective in providing useful hints for debugging certain network architectures. However, these approaches are often architecture-specific with strong assumptions of how the network should be understood. This limits their use when the network architecture or the exploration goal changes. In this paper, we present a general model and a programming toolkit, Neural Network Visualization Builder (NNVisBuilder), for prototyping visual analytics systems to understand neural networks. NNVisBuilder covers the common data transformation and interaction model involved in existing tools for exploring neural networks. It enables developers to customize a visual analytics interface for answering their specific questions about networks. NNVisBuilder is compatible with PyTorch so that developers can integrate the visualization code into their learning code seamlessly. We demonstrate the applicability by reproducing several existing visual analytics systems for networks with NNVisBuilder. The source code and some example cases can be found at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/sysuvis/NVB</uri> .
UEE-Delay Balanced Online Resource Optimization for Cooperative MEC-enabled Task Offloading in Dynamic Vehicular Networks

Mobile edge computing (MEC), pushing the centralized cloud computing, storage, and communication capability to the edge close to vehicular terminals, is proposed as a promising solution to support computation-intensive and delay-sensitive services. This paper proposes a cooperative MEC-enabled task offloading framework where the computational task of each vehicle is divided and computed by multiple collaborative MECs located on the roadside. However, existing MEC-enabled offloading research is based on offline settings or static networks and fails to address the dynamic communication environments. These dynamic environments involve variations in temporality (real-time channel state) and spatiality (uncertain data-queue backlogs as vehicles pass through different coverage areas of MECs). In the dynamic vehicular networks, the degradation of utility energy efficiency (UEE) and time delay are inevitable and significantly impacted. To tackle this issue, we propose an online dynamic scheme to solve the problem of maximizing UEE while meeting time delay constraints. We then introduce a novel online dynamic optimization algorithm based on Lyapunov optimization theory to adaptively create strategies for task offloading and communication resource allocation in parallel. Numerical simulations demonstrate that the proposed algorithm achieves a balance between UEE and delay, striking a flexible tradeoff by tuning the control parameter V. Furthermore, the results confirm that the proposed algorithm outperforms baseline algorithms in terms of real-time communication and transmission capability.
Shortest Paths Publishing With Differential Privacy

The growing prevalence of graphs representations in our society has led to a corresponding rise in the publishing of graphs by researchers and organizations. To protect the privacy, it is important to ensure that graphs including sensitive data are not disclosed. Since the weight of edges could be utilized to infer confidential information, the graph should be privately published to avoid ethical and legal issues. In this paper, we propose a novel method for privately publishing shortest paths while preserving the privacy of sensitive edge weights in graph. Specifically, we divide the edge weights into internal and external edges based on their edge betweenness centrality. Then, we give two different differentially private algorithms to perturb edge weights based on the distinction between internal and external edges, respectively. To reduce the error ratios between differentially private shortest paths and real shortest paths, we employ edge betweenness centrality to search for the shortest path, which is closest to the true one. Our experimental results show that our mechanisms can effectively reduce the error in the average shortest path distance by 1.1% for large graphs, while for the shortest path change rate, our mechanisms can reduce it by 8.3%.
Dynamic Point-Pixel Feature Alignment for Multi-modal 3D Object Detection

Detection of small or distant objects is a major challenge in 3D object detection in autonomous driving either through RGB images or LiDAR point clouds. Despite the growing popularity of sensor fusion in this task, existing fusion methods have not adequately taken into account the challenges associated with 3D small object detection, such as semantic misalignment of small objects caused by occlusion and calibration errors. To address this issue, we propose DPPFA-Net for multi-modal 3D small object detection by introducing Memory-based Point-Pixel Fusion (MPPF) modules, Deformable Point-Pixel Fusion (DPPF) modules, and Semantic Alignment Evaluator (SAE) modules. More concretely, the proposed MPPF module automatically performs intra-modal and cross-modal feature interactions. The intra-modal interaction reduces sensitivity to noise points, while the explicit cross-modal feature interaction based on the memory bank facilitates easier network learning and enables a more comprehensive and discriminative feature representation. The DPPF module establishes interactions exclusively with key position pixels based on a sampling strategy. This design not only guarantees a low computational complexity but also enables adaptive fusion functionality, especially beneficial for high-resolution images. The SAE module guarantees semantic alignment of the fused features, thereby enhancing the robustness and reliability of the fusion process. Furthermore, we construct a simulated multi-modal noise dataset, which enables quantitative analysis of the robustness of multi-modal methods under varying degrees of multi-modal noise. Extensive experiments on the KITTI benchmark and challenging multi-modal noisy cases show that DPPFA-Net achieves a new state-of-the-art, highlighting its effectiveness in detecting small objects. Our proposed method is compared to the first place on the KITTI leaderboard and achieves better performance by 2.07%, 6.52%, 7.18%, and 6.22% of the Average Precision on the varying degrees of multi-modal noise cases.
Efficient Key-Escrow-Free and Vehicle-Revocable Data Sharing Protocol for Vehicular Ad Hoc Network

Vehicular ad hoc network (VANET), as an important part of intelligent transportation system, attracts more and more attention since it can provide communication about traffic and vehicle conditions, and optimize the transportation. It is noted that it also faces a series of security challenges. One of core issues is how to achieve secure data sharing and access control. Ciphertext-policy attribute-based encryption (CP-ABE) technique is often used to achieve these goals simultaneously. However, in the execution process of applying CP-ABE to VANET, there are two crucial issues that need to be resolved. One is key-escrow issue, which means that all vehicles’ secret keys will be leaked once if the central attribute authority (CAA) is compromised, and another is the revocation issue of departing vehicles from current region. In order to resolve these two issues simultaneously, in this paper, we propose an efficient data sharing protocol for VANET with the key-escrow-free and vehicle-revocable properties. More specifically, we design a sub-protocol between CAA and vehicles, which interactively issues secret keys for these vehicles. But CAA does not know any user’s true secret key, which guarantees that it is still secure even if CAA is compromised. In addition, we additionally introduce an entity named group manager, which manages and updates the unrevoked users in the current group. In order to improve the efficiency of the whole system, we adopt the online/offline encryption method for data owner and outsourced decryption technique for the resource-constraint data users. Finally, the performance analysis show that our proposed protocol is competitive in the phases of online encryption, key generation and CAA-setup, and thus is useful in the practical applications of VANET.
PCAM: Optimal Pricing Scheme for Rendering Camera-Network-as-a-Service

This work proposes an optimal pricing scheme, named PCAM, for Camera-Network-as-a-Service (CNaaS) platform to characterize the complex monetary transactions among the participating actors. In CNaaS, a camera-network service provider (CNSP) delivers different applications to multiple end-users (EUs) in the form of Virtual Camera Networks (VCNs) in exchange for usage-based service charge. On the other hand, the camera network owners (CNOs) rent camera nodes to the CNSP to create the VCNs in exchange for a rental fee. Each of these actors utilize heterogeneous resources with different procurement, deployment, and maintenance costs. Further, EUs prefer to spend minimum price while the CNOs expect to fetch maximum rents. Therefore, it is necessary to design a suitable pricing scheme to provide affordable charges to the EUs while also ensuring optimum profits for both the CNSP and the CNOs. To achieve these objectives, we design PCAM to ensure fair transactions among the actors by optimizing the price of the nodes and VCN. Extensive simulations and numerical analysis show that PCAM increases the profit of the CNSP by 77.70%, reduces the expenditure of the EUs by 33.25%, and improves average network lifetime by 84.6%, when incorporated in the traditional CNaaS architecture.
Detecção de Indisponibilidades em Aplicações Web

The internet's growth transformed business services but also brought challenges with failures in Information Systems. Stable operations require continuous monitoring aligned with ITIL. This study suggests a novel method, applying data mining for rapid failure detection. It evaluates real-time web analytics data to swiftly identify critical web app failures. Preprocessing and hyperparameters were optimized using hyperopt for improve accuracy. Seven models were made; LSTM excelled, validating its effectiveness in differentiating failures from successes.
NLP and Archaeology: A View from a Digital Archive

The Archaeology Data Service (ADS) has been experimenting with Natural Language Processing (NLP) methodologies for over 12 years. As an accredited digital repository, the focus has been to explore how NLP techniques can be used to augment any basic digital object’s metadata and to begin to facilitate increased human and machine access. Thus, the words used within the ADS archive catalogue and archaeological reports have added value; they provide detail, context and understanding, but conversely, they can also be ambiguous. The NLP techniques studied go beyond allowing a user to search a PDF file, to building a classification for the user, and then continuing to improve the rules behind the method(s). While these experiments solidified our view that NLP has an important role to play in our core services, our ability to implement them in a robust way has remained elusive. This chapter presents our journey from an archaeological perspective, being useful to both researchers who wish to engage with NLP methodologies in Social Sciences and Humanities, while also giving the point of view of a trusted digital repository. Also, it reports ADS efforts to implement NLP within our collections, discussing why it remains elusive and future challenges.
A Nearest Neighbor-Based Hamiltonian Clustering Algorithm

Unsupervised and semi-supervised clustering algorithms are used to draw conclusions out of unlabeled data. Since not all data in real life is labeled, it is essential for modern clustering algorithms to be powerful. In this article, we have used the nearest neighbor-based Hamiltonian path to create a clustering algorithm. As far as we know, the approach is a never-seen method and is based on grouping nodes that are closer to each other based on two thresholds, namely the distance to the previous node in the path, and the distance to the centroid of the previous clustered points. We have applied the algorithm on several synthetic and real datasets, against many widely known unsupervised and semi-supervised algorithms such as KMeans, Spectral clustering, Gaussian Mixture, and others. The approach was able to give good results compared to different clustering algorithms in terms of purity and mutual information metrics with an average improvement of 7% and 13% respectively across synthetic and real data.
Toward Understanding the Impact of Demographic Factors on Cybersecurity Awareness in the Moroccan Context

The main aim of this study is to assess the level of cybersecurity awareness (CA) among employees in the public sector of Morocco and further examine the impact of various demographic factors on this awareness. To achieve this, a quantitative research method was employed, utilizing an online survey to collect and analyze data from 244 public sector employees. In the present study, three hypotheses were formulated and tested using different statistical methods to determine whether there are any relationships between CA in terms of knowledge and behavior and demographic factors (e.g., gender, age, and educational level). The results revealed a statistically significant correlation between gender and CA, while no significant impact of age or educational level on CA was observed.
Kulturna diplomatija i saradnja Biblioteka sa društvom na primeru Biblioteke grada Beograda

Satisfying various cultural needs, education (lifelong learning, information and media literacy), information are the main tasks of public libraries, and Belgrade City Library, as the largest and most influential public library in Serbia and Southeast Europe, are already traditional activities, which implements in a way that expands its field of activity: increasing the number of users, increasing the number of innovative programs, technical and IT modernization space ... The vision of the librarians of the Library of the City of Belgrade: a modern, adaptable library that follows all the needs of its users and the wider local community has become a mission that is realized every day as its lasting benefit
Međubibliotečka pozajmica u Narodnoj biblioteci Užice - analiza poslovanja 2009-2019

The paper describes the development of interlibrary loan in the Public Library of Užice. The period 2009-2019 was analyzed, which according to all statistical data is the most successful in the business of this service. With the interlibrary loan, libraries are connected, cooperation is improved and possible omissions in the procurement policy are pointed out. By establishing a single Central Electronic Catalog of Serbia on the COBISS IT platform, librarians and users are able to search the funds of all libraries participating in this project and to obtain the desired information in a faster and easier way