Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Decentralized Model Aggregation for Cross-Silo Federated Learning in Industry 4.0

Traditional federated learning (FL) adopts a client-server architecture where FL clients (e.g., IoT edge devices) train a common global model with the help of a centralized orchestrator (cloud server). However, current approaches are moving away from centralized orchestration toward a decentralized one in order to fully adapt FL for a cross-silo configuration with multiple organizations acting as clients. State-of-the-art decentralized FL mechanisms make at least one of the following assumptions: 1) clients are trusted organizations and cannot inject low-quality model updates for aggregation and 2) client local models can be shared with other clients or a third party for verification of low-quality updates. This article proposes a Blockchain-based decentralized framework for scenarios where participatory organizations are believed to be fully capable of injecting low-quality model updates as they are not willing to expose their local models to any other entity for verification purpose. The proposed decentralized FL framework adopts a novel hierarchical network of aggregators with the ability to punish/reward organizations in proportion to their local model quality updates. The framework is flexible and unlike state-of-the-art solutions, prevents a single entity from possessing the aggregated model in any FL round of training. The proposed framework is tested with respect to off-chain and on-chain performance in two Industry 4.0 use cases: 1) predictive maintenance and 2) product visual inspection. A comparative evaluation against the state-of-the-art reveals the proposed framework’s utility in terms of minimizing model convergence time and latency while maximizing accuracy and throughput.
CAF-RRT<sup>*</sup>: A 2D Path Planning Algorithm Based on Circular Arc Fillet Method

To improve the previous versions of Rapidly-exploring Random Trees (RRT) algorithms, a novel algorithm based on Circular Arc Fillet (CAF-RRT*) for path planning problems in two-dimension workspaces is proposed.Firstly, it is implemented by combining Quick-RRT* with the bidirectional RRT algorithm, which can obtain an initially planned path.Secondly, the cost of the initial planned path cost is reduced by a proposed path optimization strategy based on the triangle rule.Finally, based on the circular arc fillet method, a path smoothing strategy is proposed to process the middle nodes of the path, while further reducing its path cost.The experimental results on MATLAB simulation platform show that compared with other RRT algorithms, the proposed algorithm can reduce the path length by about 2%, ensure the smoothness and security of the path, while increasing the running speed of the algorithm by about 95%, and the experimental results of the robot based on Robot Operating System (ROS) platform also prove the advantages of the algorithm.
Pre-Distribution of Entanglements in Quantum Networks

Quantum network communication is challenging, as the No-Cloning theorem in quantum regime makes many classical techniques inapplicable. For long-distance communication, the only viable approach is teleportation of quantum states, which requires a prior distribution of entangled pairs (EPs) of qubits. Establishment of EPs across remote nodes can incur significant latency due to the low probability of success of the underlying physical processes. To reduce EP generation latency, prior works have looked at selection of efficient entanglement-routing paths and simultaneous use of multiple such paths for EP generation.In this paper, we propose and investigate a complementary technique to reduce EP generation latency—to pre-distribute EPs over certain (pre-determined) pairs of network nodes; these pre-distributed EPs can then be used to generate EPs for the requested pairs, when needed, with lower generation latency. For such a pre-distribution approach to be most effective, we need to address an optimization problem of selection of node-pairs where the EPs should be pre-distributed to minimize the generation latency of expected EP requests, under a given cost constraint. In this paper, we appropriately formulate the above optimization problem and design two efficient algorithms, one of which is a greedy approach based on an approximation algorithm for a special case. Via extensive evaluations over the NetSquid simulator [1], we demonstrate the effectiveness of our approach and developed techniques; we show that our developed algorithms outperform a naive approach by up to an order of magnitude.
Harmony Search for Security Enhancement

The honeyword system can be considered a mechanism for detecting passwords aiming to develop hashed password security via creating a password cracking easier to be detected. A lot of false passwords come with true passwords for forming the false and true passwords for all users. When a hacker logs in and uses a honeyword, a silent alarm trigger illustrates that the honeyword system could be compromised. A lot of mechanisms of honeyword generating have been submitted and all of them have a defect in the generation, a support shortage for every honeyword characteristic, and a slew of honeyword complications. HSA (harmony search algorithm), a metaheuristic intelligence algorithm getting inspiration from music, can be utilized here for offering a way to generate honeyword. The suggested mechanism of honeyword generating is enhancing the generating, enhancing every honeyword characteristic, and addressing the shortcomings of each prior approach. This paper is showing many previous mechanisms of honeyword generating, clarifying the proposed one, discussing every experimental finding, and comparing the novel mechanism of honeyword generating with the ones before it
Tuning the Hyperparameters of Anytime Planning: A Metareasoning Approach with Deep Reinforcement Learning

Anytime planning algorithms often have hyperparameters that can be tuned at runtime to optimize their performance. While work on metareasoning has focused on when to interrupt an anytime planner and act on the current plan, the scope of metareasoning can be expanded to tuning the hyperparameters of the anytime planner at runtime. This paper introduces a general, decision-theoretic metareasoning approach that optimizes both the stopping point and hyperparameters of anytime planning. We begin by proposing a generalization of the standard meta-level control problem for anytime algorithms. We then offer a meta-level control technique that monitors and controls an anytime algorithm using deep reinforcement learning. Finally, we show that our approach boosts performance on a common benchmark domain that uses anytime weighted A* to solve a range of heuristic search problems and a mobile robot application that uses RRT* to solve motion planning problems.
Neural Myerson auction for truthful and energy-efficient autonomous aerial data delivery

A successful deployment of drones provides an ideal solution for surveillance systems.Using drones for surveillance can provide access to areas that may be difficult or impossible to reach by humans or in-land vehicles gathering images or video recordings of a specific target in their coverage.Therefore, we introduces a data delivery drone to transfer collected surveillance data in harsh communication conditions.This paper proposes a Myerson auction-based asynchronous data delivery in an aerial distributed data platform in surveillance systems taking battery limitation and long flight constraints into account.In this paper, multiple delivery drones compete to offer data transfer to a single fixed-location surveillance drone.Our proposed Myerson auction based algorithm, which uses the truthful second-price auction (SPA) as a baseline, is to maximize the seller's revenue while meeting several desirable properties, i.e., individual rationality and incentive compatibility while pursuing truthful operations.On top of this SPA-based operations, a deep learning based framework is additionally designed for delivery performance improvements.
Hey ASR System! Why Aren’t You More Inclusive?

Speech is the fundamental means of communication between humans. The advent of AI and sophisticated speech technologies have led to the rapid proliferation of human-to-computer-based interactions, fueled primarily by Automatic Speech Recognition (ASR) systems. ASR systems normally take human speech in the form of audio and convert it into words, but for some users, it cannot decode the speech, and any output text is filled with errors that are incomprehensible to the human reader. These systems do not work equally for everyone and actually hinder the productivity of some users. In this paper, we present research that addresses ASR biases against gender, race, and the sick and disabled, while exploring studies that propose ASR debiasing techniques for mitigating these discriminations. We also discuss techniques for designing a more accessible and inclusive ASR technology. For each approach surveyed, we also provide a summary of the investigation and methods applied, the ASR systems and corpora used, and the research findings, and highlight their strengths and/or weaknesses. Finally, we propose future opportunities for Natural Language Processing researchers to explore in the next level creation of ASR technologies.
Blockchain Based Adaptive Resource Allocation in Cloud Computing

Cloud computing is breakthrough technology with applications in education, industry and research.Implementing a cloud environment, however, depends on virtualization, parallel computing, resource management, service-oriented architecture, and distributed computing.The efficiency of cloud computing depends on effective resource allocation (RA).Conventional RA, from resource requests to price settlement, is handled entirely by cloud service providers (CSPs), which make it difficult for end users (EUs) to view details relating to resource availability and price.Hence, an alternative technique is required to provide an efficient RA between EU's.In this work, the blockchain (BC) technique is used for RA between the EU's without the intervention of CSP.The BC provides decentralized and secured communication between the EU's without any inconsistency.Based on demand, RA is carried out in two different ways, fixed size and variable size.In a fixed size RA, each user utilizes an equal quantity of resources at a particular time but in a variable size RA each user utilizes the varying quantity of resources based on demand.Given that the RA, chosen depends on resource availability and demand, the proposed work employs hybrid RA (Adaptive Resource Allocation) schemes such as fixed-size adaptive RA (FSARA) and variable-sized adaptive RA (VSARA).The simulation results show the comparative analysis of the proposed and existing RA techniques.When compared to existing RA techniques like optimal, greedy and iterative techniques, the proposed technique HIGHLIGHTS• The permissioned blockchain is used for resource allocation and monitoring.• To reduce storage size, partial data is stored in the onchain blocks instead of entire data.• The EU's based Fixed and variable size resource allocation techniques are proposed.
Category-Level Adversaries for Outdoor LiDAR Point Clouds Cross-Domain Semantic Segmentation

Unsupervised domain adaptation (UDA) is a low-cost way to deal with the lack of annotations in a new domain. For outdoor point clouds in urban transportation scenes, the mismatch of sampling patterns and the transferability difference between classes make cross-domain segmentation extremely difficult. To overcome these challenges, we propose a category-level adversarial framework. Firstly, we propose a multi-scale domain conditioned block that facilitates to extract the critical low-level domain-dependent knowledge and reduce the domain gap caused by distinct LiDAR sampling patterns. Secondly, we make full use of multiple representation forms (i.e., point-based sets and voxel-based cells) and utilize the prediction consistency between the two forms to measure how well each point is semantically aligned. The model then focuses on the poorly-aligned points without affecting the well-aligned points. Experimental results on three autonomous driving point cloud datasets show that the proposed method outperforms existing methods by a large margin, especially on the low-beam to high-beam cross-domain segmentation task.
Dual Attention Network Approaches to Face Forgery Video Detection

Forged videos are commonly spread online.Most have malicious content and cause serious information security problems.The most critical issue in deepfake detection is the identification of traces of tampering in fake videos.This study designs a Dual Attention Forgery Detection Network (DAFDN), which embeds a spatial reduction attention block (SRAB) and a forgery feature attention module (FFAM) to the backbone network.DAFDN embeds the two proposed attention mechanisms and enables the convolution neural network to extract peculiar traces left by images' warping.This study uses two benchmark datasets, DFDC and FaceForensics++, to compare the performance of the proposed DAFDN with other methods.The results show that the proposed DAFDN mechanism achieves AUC scores of 0.911 and 0.945 in the datasets DFDC and FaceForensics++, respectively.These results are better than those of previously developed methods, such as XceptionNet and EfficientNet-related methods.
Compressing and Querying Integer Dictionaries Under Linearities and Repetitions

We revisit the fundamental problem of compressing an integer dictionary that supports efficient rank and select operations by exploiting simultaneously two kinds of regularities arising in real data: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">repetitiveness</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">approximate linearity</i> . We attack this problem by proposing two novel compressed indexing approaches that extend the classic Lempel-Ziv compression scheme and the more recent block tree data structure with new algorithms and data structures that allow them to also capture regularities in terms of the approximate linearity in the data. Finally, we corroborate these theoretical results with a wide set of experiments on real and synthetic datasets, which allow us to show that our approaches achieve new interesting space-time trade-offs that characterise them as more robust in most practical scenarios compared to the known data structures that exploit only one of the two regularities.
Pentingnya Peran Orang Tua dalam Meningkatkan Prestasi Belajar Siswa

ABSTRACT&#x0D; The purpose of this study was to determine the role of parents in guiding or fostering students, to find out what factors support and hinder the role of parents in improving student achievement, and to determine the role of parents in improving student achievement. This type of research is a field research with a qualitative approach, the data obtained through the methods of observation, documentation, and interviews. The results showed that the role of parents in improving student achievement in the family has been going quite well. The role of parents of students, among others; 1) Parents have created a comfortable and peaceful atmosphere in the home environment, creating good communication between parents and children; 2) Parents of students can condition learning activities and understand students' circumstances. Problems faced by parents at home in improving learning achievement, including; Limited facilities, low interest in children's learning. Relationships and the surrounding environment. Parents try to improve student achievement by providing direction, supervision, and coaching to improve children's achievement; 2) Provide facilities to improve children's achievement; 3) Parents involve their children in activities related to their children's learning, such as tutoring or tutoring; 4) Controlling children studying at home and familiarizing children with discipline with time in doing all activities. In conclusion, the role of parents in improving the learning achievement of grade 3 SD Muhammadiyah Tangerang is very good, covering several aspects, both the role and educational and social background of parents. The role and attention of parents is one of the important factors that can affect children's learning achievement.&#x0D; &#x0D; Keywords: Student Achievement, Role of Parents.
Anti-Asian Hate Speech Detection via Data Augmented Semantic Relation Inference

With the spreading of hate speech on social media in recent years, automatic detection of hate speech is becoming a crucial task and has attracted attention from various communities. This task aims to recognize online posts (e.g., tweets) that contain hateful information. The peculiarities of languages in social media, such as short and poorly written content, lead to the difficulty of learning semantics and capturing discriminative features of hate speech. Previous studies have utilized additional useful resources, such as sentiment hashtags, to improve the performance of hate speech detection. Hashtags are added as input features serving either as sentiment-lexicons or extra context information. However, our close investigation shows that directly leveraging these features without considering their context may introduce noise to classifiers. In this paper, we propose a novel approach to leverage sentiment hashtags to enhance hate speech detection in a natural language inference framework. We design a novel framework SRIC that simultaneously performs two tasks: (1) semantic relation inference between online posts and sentiment hashtags, and (2) sentiment classification on these posts. The semantic relation inference aims to encourage the model to encode sentiment-indicative information into representations of online posts. We conduct extensive experiments on two real-world datasets and demonstrate the effectiveness of our proposed framework compared with state-of-the-art representation learning models.
Analysis and Prediction of Question Topic Popularity in Community Q&amp;A Sites: A Case Study of Quora

In the past few years, Quora a community-driven social platform for question and answering, has grown exponentially from a small community of users into one of the largest and reliable source of Q&amp;A on the Internet. Quora has a built-in social structure integrated to its backbone; users can follow each other, follow question, topics etc. Apart from the social connections that Quora provides, it has developed a knowledge base nicely organized via hierarchy and relatedness of topics. In this paper, we consider a massive dataset of more than four years and analyze the dynamics of topical growth over time; how various factors affect the popularity of a topic or its acceptance in Q&amp;A community. We also propose a regression model to predict the popularity of the topics and discuss the important discriminating features. We achieve a high prediction accuracy (correlation coefficient ~0.773) with low root mean square error (~1.065). We further categorize thetopics into a few broad classes by implementing a simple Latent Dirichlet Allocation (LDA) model on the question texts associated with the topics. In comparison to the data sample with no categorization, this stratification of the topics enhances the prediction accuracies for several categories. However, for certain categories there seems to a slight decrease in the accuracy values and we present an in-depth discussion analyzing the cause for the same pointing out potential ways for improvement. We believe that this thorough measurement study will have a direct application to a service like recommending trending topics in Quora.
Pelatihan Aplikasi Microsoft Word, Microsoft Excel dan Power Point Pada siswa-siswi di SMPN 4 Kutacane

Campus Teaching Activities 3 is a service activity for the world of education. Campus Teaching 3 program in 2022 focuses on literacy, numeracy, administration and technology adaptation of students carried out in isolated, impacted and disadvantaged schools. The purpose of the Teaching Campus activity is (1) Knowing the factors causing the low reading interest of students at school (2) Improving the literacy and numeracy culture in schools (3) assisting school administration (4) students have a responsibility to assist learning activities and help familiarize themselves with technology. The type of research is qualitative with case study method. The subjects adopted in this study were all students of SMP Negeri 4 Kutacane, Mbarung, Babussalam District, Southeast Aceh Regency, Aceh Province due to the students' low understanding of Microsoft Word, Microsoft Excel and Power Point applications. Data collection techniques used in this study were observation, interviews and documentation. The results of the research are (1) Can find out the cause of the students' low understanding of Microsoft Word, Microsoft Excel and Power Point applications, circumstances factors, interest and willingness factors, family environmental factors and school environmental factors. (2) Improving students' understanding of Microsoft Word, Microsoft Excel and Power Point applications with technology adaptation activities at SMP Negeri 4 Kutacane, Mbarung, Babussalam District, Southeast Aceh District, Aceh Province.
Resolving Misconceptions about the Plans of Agents via Theory of Mind

For a plan to achieve some goal -- to be valid -- a set of sufficient and necessary conditions must hold. In dynamic settings, agents (including humans) may come to hold false beliefs about these conditions and, by extension, about the validity of their plans or the plans of other agents. Since different agents often believe different things about the world and about the beliefs of other agents, discrepancies may occur between agents' beliefs about the validity of plans. In this work, we explore how agents can use their Theory of Mind to resolve such discrepancies by communicating and/or acting in the environment. We appeal to an epistemic logic framework to allow agents to reason over other agents' nested beliefs, and demonstrate how epistemic planning tools can be used to resolve discrepancies regarding plan validity in a number of domains. Our work shows promise for human decision support as demonstrated by a user study that showcases the ability of our approach to resolve misconceptions held by humans.
Data Mining Techniques for Web Mining: A Survey

The data mining (DM) is the computational process that consists of searching, extracting, and analyzing patterns in large data sets, including methods at the intersection of artificial intelligence, machine learning, statistics, and database schemes. Specifically, its primary goal is to extract information from a raw data set and transform it into an expected structure for further use. Moreover, an evolving perspective of DM is web mining (WM), which refers to the whole of DM and related routines. It is used to discover and extract information from web records and services automatically, that is, WM’s purpose is to obtain valuable data from the World Wide Web. Due to its importance, a survey about DM techniques in WM is necessary, as performed in this paper.
Modeling musician diffraction and absorption for artificially excited clarinet directivity measurements

Directivity measurements of musical instruments have many applications in musical, audio, and architectural acoustics. Typical measurement methods include artificially excited instruments and instruments played by live musicians. While recent advances in directivity measurement techniques enable higher resolutions for played instruments, the results are still limited in bandwidth and repeatability compared with directivity results from artificially excited instruments. However, artificially excited instruments typically neglect musician diffraction and absorption. This work compares possible approaches for representing musician diffraction in artificially excited clarinet measurements to improve their directivity results for room simulations or auralizations.