Reviewerly/exHarmony huggingface dataset:
Anomaly Detection in Cloud Computing using Knowledge Graph Embedding and Machine Learning Mechanisms

Abstract The orchestration of cloud computing infrastructures is challenging, considering the number, heterogeneity and dynamicity of the involved resources, along with the highly distributed nature of the applications that use them for computation and storage. Evidently, the volume of relevant monitoring data can be significant, and the ability to collect, analyze, and act on this data in real time is critical for the infrastructure’s efficient use. In this study, we introduce a novel methodology that adeptly manages the diverse, dynamic, and voluminous nature of cloud resources and the applications that they support. We use knowledge graphs to represent computing and storage resources and illustrate the relationships between them and the applications that utilize them. We then train GraphSAGE to acquire vector-based representations of the infrastructures’ properties, while preserving the structural properties of the graph. These are efficiently provided as input to two unsupervised machine learning algorithms, namely CBLOF and Isolation Forest, for the detection of storage and computing overusage events, where CBLOF demonstrates better performance across all our evaluation metrics. Following the detection of such events, we have also developed appropriate re-optimization mechanisms that ensure the performance of the served applications. Evaluated in a simulated environment, our methods demonstrate a significant advancement in anomaly detection and infrastructure optimization. The results underscore the potential of this closed-loop operation in dynamically adapting to the evolving demands of cloud infrastructures. By integrating data representation and machine learning methods with proactive management strategies, this research contributes substantially to the field of cloud computing, offering a scalable, intelligent solution for modern cloud infrastructures.
Conway Numbers – Formal Introduction

Summary Surreal numbers, a fascinating mathematical concept introduced by John Conway, have attracted considerable interest due to their unique properties. In this article, we formalize the basic concept of surreal numbers close to the original Conway’s convention in the field of combinatorial game theory. We define surreal numbers with the pre-order in the Mizar system which satisfy the following condition: x ⩽ y iff L x ≪ { y } Λ { x } ≪ R y .
CRUD Operation On WordPress Custom Post Type (CPT) From C# Over REST API

Purpose: WordPress is a popular content management system. Around 45% of the internet's websites are powered by WordPress. Its main feature is posts. The contents are stored as posts. WordPress uses a database to store posts and configurations. Its REST API interface is robust and feature-rich. We can efficiently save the data from external environments into the database over the REST API as a post. However, standard post types provide enormous amounts of data. Low-speed systems and real-time processing can create a bottleneck. The data flow can be optimized using custom post type (CPT). Here, we describe how to create a custom field using available plugins. We make a REST API client using C# language. From the client application, we execute CRUD operation. The complete project code is freely available on GitHub. Design/Methodology/Approach: There are several ways to test WordPress code. Instead of testing in live WordPress website, the best way is to work in local website. It is not harmful anymore because code is deploying in local only. to install WordPress locally, we use the “LOCAL” application. Using this approach, we can eliminate the server hiring cost for the experiment. after that inside the local application, we install WordPress Website. Then create Custom route to navigate our code. For client side, we make a client application in C# with GUI for better operation. Findings/Result: We ran the project for a long time with practical examples without any issues. This architecture can be used to store IoT data inside the cloud. For the POST data is stored in the “wp_Posts” table where the additional CPT field is kept inside the “wp_postmeta” table, a couple of extra transactions that might cause data flow delays. Originality/Value: WordPress database generally stores external data using post type. Here, we are using an approach beyond general usage. Even C# is not used for this purpose. The other more high-level languages are available to do our task effectively. However, the researchers from the dot net framework domain who want to keep data in the WordPress database as a custom post type can get this as a practical reference. Paper Type: Experimental-based Research.
A Covariance Matching-Based Adaptive Measurement Differencing Kalman Filter for INS’s Error Compensation

In this study, a covariance matching-based adaptive measurement differencing Kalman filter (AMDKF) for the case of time-correlated measurement errors is proposed. The solution to the state estimation problem involves deriving a filter that accounts for measurement differences. Specifically, the measurement noise in the generated measurements is assumed to be correlated with the process noise. To address this issue in the context of correlated process and measurement noise, we propose an adaptive measurement differencing Kalman filter that is robust to measurement faults. We also evaluate the robustness of the suggested AMDKF through an analysis. When noise increment type sensor faults are present in the time-correlated inertial navigation systems (INS) measurements, the states of a multi-input/output aircraft model were estimated using both the previously developed measurement differencing Kalman filter (MDKF) and the suggested AMDKF and the results were compared.
Academic Payment Tokenization: An Online Payment System for Academia Utilizing Non-Fungible Tokens and Permissionless Blockchain

Nowadays, traditional payment methods such as cash, cheques, credit/debit cards, and bank transfers are commonly used for financial transactions across a wide range of industries, including healthcare, real estate, supply chain, gaming, music, finance, retail, agriculture, tourism, pharmaceuticals, and the education sector too. However, these methods often involve transaction fees, potential delays, and security concerns. These methods lack transparency and traceability features, making tracking, authenticating, and verifying payments and their associated records difficult. To overcome these issues, we propose a Non-Fungible Token (NFT) based online payment system that leverages blockchain technology (BT) features and security to revolutionize financial expenses in the education sector. We also present wallet generation, fee tokenization and NFT generation processes. These processes utilize Ethereum and an interplanetary file system (IPFS) to store metadata information associated with fee payment details securely. We use Remix IDE, MetaMask wallet, and Sepolia test network to deploy and analyze the proposed system's results. We have also used the JMeter simulation toolkit to conduct comprehensive experiments to evaluate the performance of the proposed system in terms of throughput, latency, and response time. Our assessment identifies the increase in performance parameters with the number of users. This initiative will positively contribute towards addressing the issues of financial transactions in the education sector.
Understanding and Optimizing Workloads for Unified Resource Management in Large Cloud Platforms

To fully utilize computing resources, cloud providers such as Google and Alibaba choose to co-locate online services with batch processing applications in their data centers. By implementing unified resource management policies, different types of complex computing jobs request resources in a consistent way, which can help data centers achieve global optimal scheduling and provide computing power with higher quality. To understand this new scheduling paradigm, in this paper, we first present an in-depth study of Alibaba's unified scheduling workloads. Our study focuses on the characterization of resource utilization, the application running performance, and scheduling scalability. We observe that although computing resources are significantly over-committed under unified scheduling, the resource utilization in Alibaba data centers is still low. In addition, existing resource usage predictors tend to make severe overestimations. At the same time, tasks within the same application behave fairly consistently, and the running performance of tasks can be well-profiled with respect to resource contention on the corresponding physical host.
Do you trust ChatGPTs? Effects of the ethical and quality issues of generative AI on travel decisions

This study investigated the impact of ChatGPT's recommendation quality and ethical concerns on travelers' acceptance, satisfaction, and perceived trustworthiness. Results showed that when quality and ethical concerns were prominent, acceptance of and satisfaction with ChatGPT's recommendations decreased significantly, and the negative effects were mediated by perceived trustworthiness. This study also identified that message framing containing ChatGPT's errors, and the information types delivered by ChatGPT, acted as moderators of the positive effect of its recommendations. These findings underscore the significance of addressing ethical and quality concerns in using AI (Artificial intelligence)-powered chatbots, with implications for AI acceptance and satisfaction.
Jurisdiction over Transnational Quantum Networks

The international quantum communication networks develop; governance and legal jurisdiction remain unclear due to jurisdictional gaps in existing legal frameworks. Analysis of applicable accords, state practice, court judgments and domestic law related to digital infrastructure reveals minimal current governance explicitly addressing jurisdiction over global quantum systems. Results indicate specialized multilateral treaties are necessary to establish acceptable jurisdiction given quantum computing's novel abilities allowing exponentially scalable computing, cryptographically secured data transfers and precision metrology. Centralized supranational authority and distributed governance models reflect policy trade-offs for advancing global networks. Like historical technologies such as aviation and the Internet requiring years of legal development to mature, progressing quantum networks need focused creation of an internationally harmonized legal framework to balance security, sovereignty and innovation across a landscape holding tremendous transformative potential.
Text Summarization Techniques Using Natural Language Processing: A Systematic Literature Review

In recent years, data has been growing rapidly in almost every domain. Due to this excessiveness of data, there is a need for an automatic text summarizer that summarizes long and numerical data especially textual data without losing its content. Text summarization has been under research for decades and researchers used different summarization methods by using natural language processing and combining various algorithms. This paper presents a systematic literature review by showing a survey of text summarization methods and explains the accuracy of these methods used for text summarization. The paper first introduced some concepts of extractive and abstractive text summarization and also define how deep learning models can be used for the improvement of text summarization. This paper aims to identify the current utilization of text summarization in different application domains. Different methodologies are discussed for text summarization. To carry out this SLR, twenty-four published articles have been chosen carefully for this domain. Moreover, it discusses issues and challenges which are investigated in different application domains using text summarization methods. Lastly, the existing work of different researchers has been carried out for further discussion.
Focus on Your Target: A Dual Teacher-Student Framework for Domain-adaptive Semantic Segmentation

We study unsupervised domain adaptation (UDA) for semantic segmentation. Currently, a popular UDA framework lies in self-training which endows the model with two-fold abilities: (i) learning reliable semantics from the labeled images in the source domain, and (ii) adapting to the target domain via generating pseudo labels on the unlabeled images. We find that, by decreasing/increasing the proportion of training samples from the target domain, the ‘learning ability’ is strengthened/weakened while the ‘adapting ability’ goes in the opposite direction, implying a conflict between these two abilities, especially for a single model. To alleviate the issue, we propose a novel dual teacher-student (DTS) framework and equip it with a bidirectional learning strategy. By increasing the proportion of target-domain data, the second teacher-student model learns to ‘Focus on Your Target’ while the first model is not affected. DTS is easily plugged into existing self-training approaches. In a standard UDA scenario (training on synthetic, labeled data and real, unlabeled data), DTS shows consistent gains over the baselines and sets new state-of-the-art results of 76.5% and 75.1% mIoUs on GTAv→Cityscapes and SYNTHIA→Cityscapes, respectively. The implementation is available at https://github.com/xinyuehuo/DTS.
RecRecNet: Rectangling Rectified Wide-Angle Images by Thin-Plate Spline Model and DoF-based Curriculum Learning

The wide-angle lens shows appealing applications in VR technologies, but it introduces severe radial distortion into its captured image. To recover the realistic scene, previous works devote to rectifying the content of the wide-angle image. However, such a rectification solution inevitably distorts the image boundary, which changes related geometric distributions and misleads the current vision perception models. In this work, we explore constructing a win-win representation on both content and boundary by contributing a new learning model, i.e., Rectangling Rectification Network (RecRecNet). In particular, we propose a thin-plate spline (TPS) module to formulate the nonlinear and non-rigid transformation for rectangling images. By learning the control points on the rectified image, our model can flexibly warp the source structure to the target domain and achieves an end-to-end unsupervised deformation. To relieve the complexity of structure approximation, we then inspire our RecRecNet to learn the gradual deformation rules with a DoF (Degree of Freedom)-based curriculum learning. By increasing the DoF in each curriculum stage, namely, from similarity transformation (4-DoF) to homography transformation (8-DoF), the network is capable of investigating more detailed deformations, offering fast convergence on the final rectangling task. Experiments show the superiority of our solution over the compared methods on both quantitative and qualitative evaluations. The code and dataset are available at https://github.com/KangLiao929/RecRecNet.
Bridging Cross-task Protocol Inconsistency for Distillation in Dense Object Detection

Knowledge distillation (KD) has shown potential for learning compact models in dense object detection. However, the commonly used softmax-based distillation ignores the absolute classification scores for individual categories. Thus, the optimum of the distillation loss does not necessarily lead to the optimal student classification scores for dense object detectors. This cross-task protocol inconsistency is critical, especially for dense object detectors, since the foreground categories are extremely imbalanced. To address the issue of protocol differences between distillation and classification, we propose a novel distillation method with cross-task consistent protocols, tailored for the dense object detection. For classification distillation, we address the cross-task protocol inconsistency problem by formulating the classification logit maps in both teacher and student models as multiple binary-classification maps and applying a binary-classification distillation loss to each map. For localization distillation, we design an IoU-based Localization Distillation Loss that is free from specific network structures and can be compared with existing localization distillation losses. Our proposed method is simple but effective, and experimental results demonstrate its superiority over existing methods. Code is available at https://github.com/TinyTigerPan/BCKD.
Conformal Off-Policy Evaluation in Markov Decision Processes

Reinforcement Learning aims at identifying and evaluating efficient control policies from data. In many real-world applications, the learner is not allowed to experiment and cannot gather data in an online manner (this is the case when experimenting is expensive, risky or unethical). For such applications, the reward of a given policy (the target policy) must be estimated using historical data gathered under a different policy (the behavior policy). Most methods for this learning task, referred to as Off-Policy Evaluation (OPE), do not come with accuracy and certainty guarantees. We present a novel OPE method based on Conformal Prediction that outputs an interval containing the true reward of the target policy with a prescribed level of certainty. The main challenge in OPE stems from the distribution shift due to the discrepancies between the target and the behavior policies. We propose and empirically evaluate different ways to deal with this shift. Some of these methods yield conformalized intervals with reduced length compared to existing approaches, while maintaining the same certainty level.
E-Voting System in a Higher Education Institution

This paper presents the main points and stages in the design and development of an electronic voting system and processing of its results in a higher education institution are presented. The main tasks and requirements of the designed system are described. The base model, information flows, relational scheme of the database, and the block diagram of the functional modules of the system are presented. The system automates the creation and processing of ballots in different variants, considering the specifics of elections in a higher education institution: election of governing bodies, election of student representatives, decision-making on the educational process, and management of the institution. The key functionalities of the system solve some problems encountered in existing e-voting systems. It provides accountability in voting and ensures anonymity in the voting process, protection from hacker attacks, reliability, and uptime in operation. It allows voter identity verification and voting by authorized persons. Includes simplified voting procedure and result processing. The processing of results is transparent and fair. It has an intuitive and easy-to-use program interface.
Incentive Attacks on DAG-Based Blockchains with Random Transaction Selection

Several blockchain consensus protocols proposed to use of Directed Acyclic Graphs (DAGs) to solve the limited processing throughput of traditional single-chain Proof-of-Work (PoW) blockchains. Many such protocols utilize a random transaction selection (RTS) strategy (e.g., PHANTOM, GHOSTDAG, SPECTRE, Inclusive, and Prism) to avoid transaction duplicates across parallel blocks in DAG and thus maximize the network throughput. However, previous research has not rigorously examined incentive-oriented greedy behaviors when transaction selection deviates from the protocol. In this work, we first perform a generic game-theoretic analysis abstracting several DAG-based blockchain protocols that use the RTS strategy, and we prove that such a strategy does not constitute a Nash equilibrium. Next, to validate the conclusions from game theoretic analysis, we perform simulations, confirming that greedy actors who do not follow the RTS strategy can profit more than honest miners and harm the processing throughput of the protocol. Finally, we show that greedy miners are incentivized to form a shared mining pool to increase their profits. This undermines the decentralization and degrades the design of the protocols in question.
Factors of realization and differentiation scientific potencial of young scientists

In domestic and foreign scientists’ research the scientific potential is investigated thro-ugh a series of economic indicators, but social and cultural aspects are not revealed. Scien-tists’ interest focuses primarily on the resource component of the scientific potential expres-sed through quantitative indexes (funding of science, number of academic staff involved in economics, the volume of scientific &amp; technical work, etc.). The analyses of the current scientific literature have found that the concept of scientific potential with respect to young scientists has not been sufficiently studied.
Setting the First Few Syntactic Parameters - A Computational Analysis

We consider the process by which the syntactic parameters of human language are set. Previous work has shown that for natural languages there can be no instant “automatic” triggering of parameters because the trigger properties in natural languages are often deep properties, not recognizable without parsing the input sentence. There are parametric algorithms that learn by parsing, but they are inefficient because they do not respect the Parametric Principle: they evaluate millions of grammars, rather than establishing the values of a few dozen parameters. They do so because they cannot tell in advance which input sentences are pertinent to which parameters, and because they have no protection against misleaming due to parametric ambiguity of the input. There is one model that does implement the Parametric Principle. This is the Structural Triggers Learner (STL). For an STL, a parameter value and its trigger are one and the same thing; they are what we call a structural trigger or tree let (a subtree or in the limiting case a single feature). These structural triggers are made available by UG and adopted into the learner’s grammar just in case they prove essential for parsing input sentences. This permits efficient recognition of the parameter values entailed by input sentences and allows the learner to avoid errors by discarding ambiguous input. However, the high degree of ambiguity inherent in natural language impedes learning even for this efficient system. An STL must wait a long time between unambiguous inputs. As we explain, this problem is particularly acute in the early stages of learning. In this paper we give a computational analysis of the performance of an STL. We then identify an important factor - the parametric expression rate - that holds promise of a solution to this early learning problem.
A new trainable trajectory formation system for facial animation

A new trainable trajectory formation system for facial animation is here proposed that dissociates parametric spaces and methods for movement planning and execu-tion.Movement planning is achieved by HMM-based trajectory formation.Move-ment execution is performed by concatenation of multirepresented diphones.Movement planning ensures that the essential visual characteristics of visemes are reached (lip closing for bilabials, rounding and opening for palatal fricatives, etc) and that appropriate coarticulation is planned.Movement execution grafts phonetic details and idiosyncratic articulatory strategies (dissymetries, importance of jaw movements, etc) to the planned gestural score.
AIIDE 2012 StarCraft Competition

In 2012, AIIDE will host the Third Annual StarCraft AI Competition. Participants are given the task of building the best performing AI system for the popu- lar real-time strategy game StarCraft Brood War, which was released by Blizzard Entertainment in 1998. The goals of the competition are to provide a testbed for real-time AI systems and to promote game AI research by exhibiting AI techniques such as scripting, planning, optimization, spatial reasoning, and opponent modeling in a fast-paced popular video game.