Reviewerly/exHarmony huggingface dataset:
Certification information on trustworthy digital repository websites: A content analysis

In 1996, an international group of representatives from national archives and libraries, universities, industry, publishing offices, and other government and private sector organizations first articulated the need for certified Trustworthy Digital Repositories (TDRs). Henceforth, multiple standards for TDRs have developed worldwide and their reviewers provide third party audit of digital repositories. Even though hundreds of repositories are currently certified, we do not know if audit and certification of TDRs actually matters. For example, we do not know if digital repositories are actually better at preserving digital information after certification than they were before. Additionally, we do not know if TDRs preserve digital information better than their counterparts, although TDR standards definitely promulgate this assumption. One way of assessing whether audit and certification of TDRs matters is to study its impact on TDRs’ stakeholders (e.g., funders, data producers, data consumers). As an initial critical step forward, this study examines what certification-related information repositories actually include on their websites since repository websites provide a means of disseminating information. Using findings from a content analysis of 91 TDR-certified repository websites, this research examines: 1) written statements about TDR status, 2) the presence of TDR seals and their location, 3) whether the seals hyperlink to additional certification information, 4) the extent to which the certification process is explained, and 5) whether audit reports are shared. Nearly three-fourths of the repository websites provide TDR status statements and put seals in one or more places; nearly 60% post audit reports and link seals to additional certification information; and over one-third explain the certification process. Directions for future research and practical application of the results are discussed.
Path planning and robust fuzzy output-feedback control for unmanned ground vehicles with obstacle avoidance

Obstacle avoidance strategy is important to ensure the driving safety of unmanned ground vehicles. In the presence of static and moving obstacles, it is challenging for the unmanned ground vehicles to plan and track the collision-free paths. This paper proposes an obstacle avoidance strategy consists of the path planning and the robust fuzzy output-feedback control. A path planner is formed to generate the collision-free paths that avoid static and moving obstacles. The quintic polynomial curves are employed for path generation considering computational efficiency and ride comfort. Then, a robust fuzzy output-feedback controller is designed to track the planned paths. The Takagi–Sugeno (T–S) fuzzy modeling technique is utilized to handle the system variables when forming the vehicle dynamic model. The robust output-feedback control approach is used to track the planned paths without using the lateral velocity signal. The proposed obstacle avoidance strategy is validated in CarSim ® simulations. The simulation results show the unmanned ground vehicle can avoid the static and moving obstacles by applying the designed path planning and robust fuzzy output-feedback control approaches.
An Embarrassingly Simple Model for Dialogue Relation Extraction

Dialogue relation extraction (RE) is to predict the relation type of two entities mentioned in a dialogue. In this paper, we propose a simple yet effective model named SimpleRE for the RE task. SimpleRE captures the interrelations among multiple relations in a dialogue through a novel input format named BERT Relation Token Sequence (BRS). In BRS, multiple [CLS] tokens are used to capture possible relations between different pairs of entities mentioned in the dialogue. A Relation Refinement Gate (RRG) is then designed to extract relation-specific semantic representation in an adaptive manner. Experiments on the DialogRE dataset show that SimpleRE achieves the best performance, with much shorter training time. Further, SimpleRE outperforms all direct baselines on sentence-level RE without using external resources.
IoT-Based HVAC Monitoring System for Smart Factory

This paper presents an Internet of Things (IoT) Based HVAC monitoring platform for smart factory. A system model of energy-efficient Heating, Ventilation and Air Conditioning (HVAC) management in smart factory, which is useful for monitoring, controlling and saving energy. The hardware implementation was done using prototype built with Raspberry pi model B, temperature sensor, humidity sensor, current sensor and Micro-controller to gather and monitor data related to the HVAC for a typical smart factory. The system can collect local/raw data needed for the analysis using an off-the-shelf Big data analytics software. MATLAB pattern recognition toolbox was used to test, train and vaildate the behaviour of data collected and processed using R programming software. The paper presents a promising insights into the combined usage of IoT technologies, Big data analytics and neural network to solve future generation smart factory problems of HVAC management.
AI Based Feature Extraction Through Content Based Image Retrieval

Therapeutic pictures are progressively being utilized inside human services for conclusion, arranging treatment, controlling treatment and checking sickness movement. In reality, helpful imaging prevalently shapes vague, missing, dubious, essential, clashing, dull restricting, contorted data additionally, information has a strong fundamental character. The proposed approach can be used to achieve the accuracy by using the artificial intelligence techniques wherein the disease level is identified by comparing it with the artificial intelligence data. The two fold merit of this system is it provides better accuracy and also determines all the possibilities of spreading of the disease including the various stages of the disease. This research work also represents new automated strategies of the division and arrangement of therapeutic pictures utilizing computerized reasoning, i.e., delicate processing strategies, data combination and particular area information. Promising outcomes demonstrate the predominance of the delicate processing and information based approach over best customary systems as far as division mistakes. The arrangement of various structures is made by executing rules obtained by both space literature and by medical experts.
Quantum speedup in adaptive boosting of binary classification

In classical machine learning, a set of weak classifiers can be adaptively combined for improving the overall performance, a technique called adaptive boosting (or AdaBoost). However, constructing a combined classifier for a large data set is typically resource consuming. Here we propose a quantum extension of AdaBoost, demonstrating a quantum algorithm that can output the optimal strong classifier with a quadratic speedup in the number of queries of the weak classifiers. Our results also include a generalization of the standard AdaBoost to the cases where the output of each classifier may be probabilistic. We prove that the query complexity of the non-deterministic classifiers is the same as those of deterministic classifiers, which may be of independent interest to the classical machine-learning community. Additionally, once the optimal classifier is determined by our quantum algorithm, no quantum resources are further required. This fact may lead to applications on near term quantum devices.
Neural Machine Translation Doesn’t Translate Gender Coreference Right Unless You Make It

Neural Machine Translation (NMT) has been shown to struggle with grammatical gender that is dependent on the gender of human referents, which can cause gender bias effects. Many existing approaches to this problem seek to control gender inflection in the target language by explicitly or implicitly adding a gender feature to the source sentence, usually at the sentence level. In this paper we propose schemes for incorporating explicit word-level gender inflection tags into NMT. We explore the potential of this gender-inflection controlled translation when the gender feature can be determined from a human reference, or when a test sentence can be automatically gender-tagged, assessing on English-to-Spanish and English-to-German translation. We find that simple existing approaches can over-generalize a gender-feature to multiple entities in a sentence, and suggest effective alternatives in the form of tagged coreference adaptation data. We also propose an extension to assess translations of gender-neutral entities from English given a corresponding linguistic convention, such as a non-binary inflection, in the target language.
An Overview of Blockchain Consensus Algorithms: Comparison, Challenges and Future Directions

Like any other distributed system, the Blockchain technology relies on consensus algorithms in order to reach agreement and secure its network. Over the past few years, several kinds of consensus algorithms were created in the Blockchain ecosystem. In this paper, we present some main consensus algorithms used by the Blockchain technology. We also provide a comparison summary of their advantages and weaknesses as well as in which Blockchain type can be used. Furthermore, we present some challenges and possible directions related to the trendiest domains. With this work, for a given situation, we hope to help to choose the right algorithm to use. In addition, we hope to give directions to new possible algorithms propositions.
Distributed Methods for Reinforcement Learning Survey

Distributed methods have become an important tool to address the issue of high computational requirements for reinforcement learning. With this survey, we present several distributed methods including multi-agent schemes, synchronous and asynchronous parallel systems, as well as population-based approaches. We introduce the general principle and problem formulation, and discuss the historical development of distributed methods. We also analyze technical challenges, such as process communication and memory requirements, and give an overview of different application areas.
Joint tracking and classification of multiple extended targets via the PHD filter and star-convex RHM

For joint tracking and classification (JTC) of multiple extended targets in the presence of clutter and detection uncertainty, this paper proposes a recursive algorithm based on the probability hypothesis density (PHD) filter and star-convex random hypersurface model (RHM), resulting in the JTC-RHM-PHD filter. By modeling the extent state via the star-convex RHM, the JTC-RHM-PHD filter can classify the extended targets using the feature information of size and shape, instead of the size information only in the random matrix model (RMM)-based JTC method. To integrate the prior class-dependent information into the tracking procedure and obtain the recursive expressions of the JTC-RHM-PHD filter, the relationship between the instantaneous extent state and prior class-dependent information is first constructed. Then, the target state is remodeled by two vectors: one for the kinematic state and another for the extent state. The kinematic state is estimated via a Kalman-like filter. For the extent state, the measurement-based extent state and class-based extent state are obtained via the sensor measurements and prior class-dependent information, respectively. The two kinds of extent states are used to determine the target class. At last, a Gamma-Gaussian-Gaussian mixture implementation of the JTC-RHM-PHD filter is presented. Simulation results indicate that: (1) Compared with the RMM-based JTC method, the proposed JTC-RHM-PHD filter can correctly classify multiple extended targets with similar sizes. (2) Compared with the RHM-PHD filter, the proposed JTC-RHM-PHD filter has superior tracking performance with a similar algorithm complexity.
Critical and prospective analysis of online education in pandemic and post-pandemic contexts

Humanity, prior to the COVID-19 pandemic, was already in the process of a gradual cultural transition between modernity and postmodernity. The rapid pace of scientific and technological advances has led humans towards a progressive adaptation, as they develop new ways to confront and to solve their problems. In the exceptional context of the pandemic, people and institutions have faced a sudden need to readapt their ways of life and their means of communication. In education, the need to migrate from traditional classrooms to online systems was urgent, and the shift required digital tools and resources to support teaching. This theoretical article aims to analyze the disruption in the educational context and to detail the adaptations that may contribute to the effectiveness of educational processes. Moreover, it discusses the pedagogical, methodological, didactic, and evaluative aspects of the change in education. The article also proposes some guidelines for learning on both synchronous and asynchronous platforms. The trends associated with these changes are analyzed in order to offer a perspective on education in the post-pandemic world.
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms

Federated learning is typically approached as an optimization problem, where the goal is to minimize a global loss function by distributing computation across client devices that possess local data and specify different parts of the global objective. We present an alternative perspective and formulate federated learning as a posterior inference problem, where the goal is to infer a global posterior distribution by having client devices each infer the posterior of their local data. While exact inference is often intractable, this perspective provides a principled way to search for global optima in federated settings. Further, starting with the analysis of federated quadratic objectives, we develop a computation- and communication-efficient approximate posterior inference algorithm—federated posterior averaging (FedPA). Our algorithm uses MCMC for approximate inference of local posteriors on the clients and efficiently communicates their statistics to the server, where the latter uses them to refine a global estimate of the posterior mode. Finally, we show that FedPA generalizes federated averaging (FedAvg), can similarly benefit from adaptive optimizers, and yields state-of-the-art results on four realistic and challenging benchmarks, converging faster, to better optima.
Federated Learning with Communication Delay in Edge Networks

Federated learning has received significant attention as a potential solution for distributing machine learning (ML) model training through edge networks. This work addresses an important consideration of federated learning at the network edge: communication delays between the edge nodes and the aggregator. A technique called FedDelAvg (federated delayed averaging) is developed, which generalizes the standard federated averaging algorithm to incorporate a weighting between the current local model and the delayed global model received at each device during the synchronization step. Through theoretical analysis, an upper bound is derived on the global model loss achieved by FedDelAvg, which reveals a strong dependency of learning performance on the values of the weighting and learning rate. Experimental results on a popular ML task indicate significant improvements in terms of convergence speed when optimizing the weighting scheme to account for delays.
Deep Networks and the Multiple Manifold Problem

We study the multiple manifold problem, a binary classification task modeled on applications in machine vision, in which a deep fully-connected network is trained to separate two low-dimensional submanifolds of the unit sphere. We provide an analysis of the one-dimensional case, proving for a simple manifold configuration that when the network depth L is large relative to certain geometric and statistical properties of the data, the network width n grows as a sufficiently large polynomial in L, and the number of i.i.d. samples from the manifolds is polynomial in L, randomly-initialized gradient descent rapidly learns to classify the two manifolds perfectly with high probability. Our analysis demonstrates concrete benefits of depth and width in the context of a practically-motivated model problem: the depth acts as a fitting resource, with larger depths corresponding to smoother networks that can more readily separate the class manifolds, and the width acts as a statistical resource, enabling concentration of the randomly-initialized network and its gradients. The argument centers around the neural tangent of Jacot et al. and its role in the nonasymptotic analysis of training overparameterized networks; to this literature, we contribute essentially optimal rates of concentration for the tangent kernel of deep fully-connected ReLU networks, requiring width n≥Lpoly(d0) to achieve uniform concentration of the initial kernel over a d0-dimensional submanifold of the unit sphere Sn0−1, and a nonasymptotic framework for establishing generalization of networks trained in the NTK regime with structured data. The proof makes heavy use of martingale concentration to optimally treat statistical dependencies across layers of the initial random network. This approach should be of use in establishing similar results for other network architectures.
Video Source Identification Algorithm Based on 3D Geometric Transformation

Digital video has become one of the most preferred ways for people to share information. Considering people tend to release illegal information in anonymous way, the problem of video source identification attracts more and more attention as an important part of multimedia forensics. The Photo-Response Non-Uniformity (PRNU) based algorithm shows to be a promising solution for the problem of video source identification. However, it is necessary to make a geometric transformation for testing PRNU noise to align it with the reference noise, due to the effect of video stabilization. This paper analyzes the three-dimensional (3D) characteristics of camera jitters and studies how to estimate the parameters of 3D geometric transformation when aligning PRNU noises between reference and test. In the algorithm design, quaternion is used to transform PRNU noise image in 3D space, and 15 rotation axes of 3D space are estimated for experiments. 162 videos of 9 smart phones were tested. Most of the videos got a higher peak to correlation energy (PCE) value by using this algorithm, and showed better results when applied to videos with complex texture. The experiment part also records the geometric transformation parameters of the PRNU noise which need to map from image domain to video domain.
Roman Urdu toxic comment classification

With the increasing popularity of user-generated content on social media, the number of toxic texts is also on the rise. Such texts cause adverse effects on users and society at large, therefore, the identification of toxic comments is a growing need of the day. While toxic comment classification has been studied for resource-rich languages like English, no work has been done for Roman Urdu despite being a widely used language on social media in South Asia. This paper addresses the challenge of Roman Urdu toxic comment detection by developing a first-ever large labeled corpus of toxic and non-toxic comments. The developed corpus, called RUT (Roman Urdu Toxic), contains over 72 thousand comments collected from popular social media platforms and has been labeled manually with a strong inter-annotator agreement. With this dataset, we train several classification models to detect Roman Urdu toxic comments, including classical machine learning models with the bag-of-words representation and some recent deep models based on word embeddings. Despite the success of the latter in classifying toxic comments in English, the absence of pre-trained word embeddings for Roman Urdu prompted to generate different word embeddings using Glove, Word2Vec and FastText techniques, and compare them with task-specific word embeddings learned inside the classification task. Finally, we propose an ensemble approach, reaching our best F1-score of 86.35%, setting the first-ever benchmark for toxic comment classification in Roman Urdu.
Self-training For Few-shot Transfer Across Extreme Task Differences

Most few-shot learning techniques are pre-trained on a large, labeled “base dataset”. In problem domains where such large labeled datasets are not available for pre-training (e.g., X-ray, satellite images), one must resort to pre-training in a different “source” problem domain (e.g., ImageNet), which can be very different from the desired target task. Traditional few-shot and transfer learning techniques fail in the presence of such extreme differences between the source and target tasks. In this paper, we present a simple and effective solution to tackle this extreme domain gap: self-training a source domain representation on unlabeled data from the target domain. We show that this improves one-shot performance on the target domain by 2.9 points on average on the challenging BSCD-FSL benchmark consisting of datasets from multiple domains.
High-resolution sequential thermal fringe projection technique for fast and accurate 3D shape measurement of transparent objects

Three-dimensional (3D) shape measurement systems based on diffuse reflection of projected structured light do not deliver reliable data when measuring glossy, transparent, absorbent, or translucent objects. In recent years, we have developed a method based on stereo recording with infrared cameras and projection of areal aperiodic sinusoidal thermal patterns to detect such objects. However, the measurements took longer than 10 s, up to minutes; moreover, the measurement accuracy was improvable. Now, we have succeeded in both drastically reducing measurement time and significantly increasing measurement quality. This finally provides a technique for reliably measuring transparent objects, e.g., in series production. We demonstrate measurement examples achieved within 1 s and with 3D standard deviations less than 10 µm.
Rock Burst Evaluation Using the CRITIC Algorithm-Based Cloud Model

Under high-stress conditions, rock burst disasters can significantly impact underground civil engineering construction. For underground metal mines, rock burst evaluations and prevention during mining have become major research topics, and the prediction and prevention of rock burst must be based on the study of rocks and rock burst tendencies. To further prevent the risk of geological disasters and provide timely warnings, a finite-interval cloud model based on the CRITIC algorithm is proposed in this paper to address the uncertainty of rock burst evaluation, the complexity under multi-factor interactions, and the correlations between factors, and it then realizes a preliminary qualitative judgment of rock burst disasters. This paper selects the uniaxial compressive strength σ c ( I 1 ), ratio of the uniaxial compressive strength to the tensile strength σ c /σ t (brittleness coefficient, I 2 ), elastic deformation energy index W et ( I 3 ), ratio of the maximum tangential stress to the uniaxial compressive strength σ θ / σ c (stress coefficient, I 4 ) of the rock, depth of the roadway H ( I 5 ), and integrity coefficient of the rock mass K v ( I 6 ) as indicators for rock burst propensity predictions. The CRITIC algorithm is used to consider the relationships between the evaluation indicators, and it is combined with an improved cloud model to verify 20 groups of learning samples. The calculation results obtained by the prediction method are basically consistent with the actual situation. The validity of the model is tested, and then the model is applied to the Dongguashan Copper Mine in Tongling, Anhui Province, China, for rock burst evaluation.