Reviewerly/exHarmony huggingface dataset:
Path-Based Spectral Clustering: Guarantees, Robustness to Outliers, and Fast Algorithms

We consider the problem of clustering with the longest-leg path distance (LLPD) metric, which is informative for elongated and irregularly shaped clusters. We prove finite-sample guarantees on the performance of clustering with respect to this metric when random samples are drawn from multiple intrinsically low-dimensional clusters in high-dimensional space, in the presence of a large number of high-dimensional outliers. By combining these results with spectral clustering with respect to LLPD, we provide conditions under which the Laplacian eigengap statistic correctly determines the number of clusters for a large class of data sets, and prove guarantees on the labeling accuracy of the proposed algorithm. Our methods are quite general and provide performance guarantees for spectral clustering with any ultrametric. We also introduce an efficient, easy to implement approximation algorithm for the LLPD based on a multiscale analysis of adjacency graphs, which allows for the runtime of LLPD spectral clustering to be quasilinear in the number of data points.
Text classification based on gated recurrent unit combines with support vector machine

As the amount of unstructured text data that humanity produce largely and a lot of texts are grows on the Internet, so the one of the intelligent technique is require processing it and extracting different types of knowledge from it. Gated recurrent unit (GRU) and support vector machine (SVM) have been successfully used to Natural Language Processing (NLP) systems with comparative, remarkable results. GRU networks perform well in sequential learning tasks and overcome the issues of “vanishing and explosion of gradients in standard recurrent neural networks (RNNs) when captureing long-term dependencies. In this paper, we proposed a text classification model based on improved approaches to this norm by presenting a linear support vector machine (SVM) as the replacement of Softmax in the final output layer of a GRU model. Furthermore, the cross-entropy function shall be replaced with a margin-based function. Empirical results present that the proposed GRU-SVM model achieved comparatively better results than the baseline approaches BLSTM-C, DABN.
An error similarity-based error-compensation method for measurement in the nonuniform temperature field

Measurement error compensation is a key issue for the application of laser trackers in an unsteady environment, especially in the nonuniform temperature field. Aimed at reducing induced measurement uncertainty, a measurement error compensation method with error similarity analysis is proposed in this paper. The spatial similarity of measurement errors is qualitatively analyzed based on the mathematical model, and quantitative analysis is presented by means of simulation and semivariogram. We then model the error similarity, and an error-estimation model for target points is established with the radius basis function neural network and regularized extreme learning machine. The experiments are conducted to verify error similarity and the applicability and accuracy of the estimation model in practice. The results show that error similarity does exist in practical measurements, and the proposed method can reduce the absolute measurement error to less than 0.03 mm (). The method is applicable for measurements in different environments since the compensated point sets are almost coincident.
On Effectiveness of Adversarial Examples and Defenses for Malware Classification

Artificial neural networks have been successfully used for many different classification tasks including malware detection and distinguishing between malicious and non-malicious programs. Although artificial neural networks perform very well on these tasks, they are also vulnerable to adversarial examples. An adversarial example is a sample that has minor modifications made to it so that the neural network misclassifies it. Many techniques have been proposed, both for crafting adversarial examples and for hardening neural networks against them. Most previous work was done in the image domain. Some of the attacks have been adopted to work in the malware domain which typically deals with binary feature vectors. In order to better understand the space of adversarial examples in malware classification, we study different approaches of crafting adversarial examples and defense techniques in the malware domain and compare their effectiveness on multiple data sets.
Learning in the machine: To share or not to share?

Weight-sharing is one of the pillars behind Convolutional Neural Networks and their successes. However, in physical neural systems such as the brain, weight-sharing is implausible. This discrepancy raises the fundamental question of whether weight-sharing is necessary. If so, to which degree of precision? If not, what are the alternatives? The goal of this study is to investigate these questions, primarily through simulations where the weight-sharing assumption is relaxed. Taking inspiration from neural circuitry, we explore the use of Free Convolutional Networks and neurons with variable connection patterns. Using Free Convolutional Networks, we show that while weight-sharing is a pragmatic optimization approach, it is not a necessity in computer vision applications. Furthermore, Free Convolutional Networks match the performance observed in standard architectures when trained using properly translated data (akin to video). Under the assumption of translationally augmented data, Free Convolutional Networks learn translationally invariant representations that yield an approximate form of weight-sharing.
Features of Introducing Blockchain Technology in Digital Economy Developing Conditions in Uzbekistan

The article presents blockchain technology for developing national digital economy, efficiency of digitalization and basic principles of digitalization, and a set of blocks requiring material resources. The purpose of the article is to study features of digital economy development and propose application of blockchain technology in national economy sphere. Research methods: The instrumental and methodical apparatus of research based on application, in framework of a systematic approach, general scientific research methods. Implementing blockchain technology in financial system at micro-macro degrees, insurance system, system of government, electron trade system, industrial system, intellectual property account system, education system, health care system was analyzed in this article. Organizing in perfect way the operations to implement the digital economy in Uzbekistan, and develop and establish large financial projects at the state level, moreover, illustrated in details importance of using blockchain technology in national economy industries while developing and establishing large financial projects at the state level.
Human Action Recognition Based on Supervised Class-specific Dictionary Learning with Deep Convolutional Neural Network Features

Human action recognition under complex environment is a challenging work. Recently, sparse representation has achieved excellent results of dealing with human action recognition problem under different conditions. The main idea of sparse representation classification is to construct a general classification scheme where the training samples of each class can be considered as the dictionary to express the query class, and the minimal reconstruction error indicates its corresponding class. However, how to learn a discriminative dictionary is still a difficult work. In this work, we make two contributions. First, we build a new and robust human action recognition framework by combining one modified sparse classification model and deep convolutional neural network (CNN) features. Secondly, we construct a novel classification model which consists of the representation-constrained term and the coefficients incoherence term. Experimental results on benchmark datasets show that our modified model can obtain competitive results in comparison to other state-of-the-art models.
Evaluating Machines by their Real-World Language Use

There is a fundamental gap between how humans understand and use language -- in open-ended, real-world situations -- and today's NLP benchmarks for language understanding. To narrow this gap, we propose to evaluate machines by their success at real-world language use -- which greatly expands the scope of language tasks that can be measured and studied. We introduce TuringAdvice, a new challenge for language understanding systems. Given a complex situation faced by a real person, a machine must generate helpful advice. We make our challenge concrete by introducing RedditAdvice, a dataset and leaderboard for measuring progress. Though we release a training set with 600k examples, our evaluation is dynamic, continually evolving with the language people use: models must generate helpful advice for recently-written situations. Empirical results show that today's models struggle at our task, even those with billions of parameters. The best model, a finetuned T5, writes advice that is at least as helpful as human-written advice in only 9% of cases. This low performance reveals language understanding errors that are hard to spot outside of a generative setting, showing much room for progress.
A novel movies recommendation algorithm based on reinforcement learning with DDPG policy

Purpose English original movies played an important role in English learning and communication. In order to find the required movies for us from a large number of English original movies and reviews, this paper proposed an improved deep reinforcement learning algorithm for the recommendation of movies. In fact, although the conventional movies recommendation algorithms have solved the problem of information overload, they still have their limitations in the case of cold start-up and sparse data. Design/methodology/approach To solve the aforementioned problems of conventional movies recommendation algorithms, this paper proposed a recommendation algorithm based on the theory of deep reinforcement learning, which uses the deep deterministic policy gradient (DDPG) algorithm to solve the cold starting and sparse data problems and uses Item2vec to transform discrete action space into a continuous one. Meanwhile, a reward function combining with cosine distance and Euclidean distance is proposed to ensure that the neural network does not converge to local optimum prematurely. Findings In order to verify the feasibility and validity of the proposed algorithm, the state of the art and the proposed algorithm are compared in indexes of RMSE, recall rate and accuracy based on the MovieLens English original movie data set for the experiments. Experimental results have shown that the proposed algorithm is superior to the conventional algorithm in various indicators. Originality/value Applying the proposed algorithm to recommend English original movies, DDPG policy produces better recommendation results and alleviates the impact of cold start and sparse data.
Fair and Privacy-Respecting Bitcoin Payments for Smart Grid Data

In this article, we present DPTS, a data payment and transfer the scheme that uses bitcoin payments to reward users for detailed electricity measurements they submit to a utility provider (UP). DPTS emphasizes both privacy and fairness of transactions; not only it allows participants to earn bitcoins in a way that cannot be linked to their actions or identities but also ensures that data are delivered if and only if an appropriate payment is received. While DPTS is described in the smart grid setting, the protocol can also be applied in other areas where incentives are used to increase user participation. One such important area is participatory or crowdsensing, where individuals use their smartphones to report sensed data back to a campaign administrator and obtain a reward for it. DPTS allows users to enjoy the benefits of participation without compromising anonymity. The proposal is coupled with a security analysis showing the privacy-preserving character of the system along with an efficiency analysis demonstrating the feasibility of our approach.
Developing a strategy to improve data sharing in health research: A mixed-methods study to identify barriers and facilitators

Background: Data sharing presents new opportunities across the spectrum of research and is vital for science that is open, where data are easily discoverable, accessible, intelligible, reproducible, replicable and verifiable. Despite this, it is yet to become common practice. Global efforts to develop practical guidance for data sharing and open access initiatives are underway, however evidence-based studies to inform the development and implementation of effective strategies are lacking. Objective: This study sought to determine the barriers and facilitators to data sharing among health researchers and to identify the target behaviours for designing a behaviour change intervention strategy. Method: Data were drawn from a cross-sectional survey of data management practices among health researchers from one Australian research institute. Determinants of behaviour were theoretically derived using well-established behavioural models. Results: Data sharing practices have been described for 77 researchers, and 6 barriers and 4 facilitators identified. The primary barriers to data sharing included perceived negative consequences and lack of competency to share data. The primary facilitators to data sharing included trust in others using the data and social influence related to public benefit. Intervention functions likely to be most effective at changing target behaviours were also identified. Conclusion: Results of this study provide a theoretical and evidence-based process to understand the behavioural barriers and facilitators of data sharing among health researchers. Implications: Designing interventions that specifically address target behaviours to promote data sharing are important for open researcher practices.
Par3DNet: Using 3DCNNs for Object Recognition on Tridimensional Partial Views

Deep learning-based methods have proven to be the best performers when it comes to object recognition cues both in images and tridimensional data. Nonetheless, when it comes to 3D object recognition, the authors tend to convert the 3D data to images and then perform their classification. However, despite its accuracy, this approach has some issues. In this work, we present a deep learning pipeline for object recognition that takes a point cloud as input and provides the classification probabilities as output. Our proposal is trained on synthetic CAD objects and is able to perform accurately when fed with real data provided by commercial sensors. Unlike most approaches, our method is specifically trained to work on partial views of the objects rather than on a full representation, which is not the representation of the objects as captured by commercial sensors. We trained our proposal with the ModelNet10 dataset and achieved a 78.39 % accuracy. We also tested it by adding noise to the dataset and against a number of datasets and real data with high success.
A GENETIC ALGORITHM FOR MULTI-OBJECTIVE RECONFIGURATION OF BALANCED AND UNBALANCED DISTRIBUTION SYSTEMS IN FUZZY FRAMEWORK

The use of multi-objective network in crisis identification and management operations is quite evident due to the aggregation of collected information and the instantaneous information of the region and the geographical area of operation on forest experts, crisis management, officers and intelligence and security managers. The existence of restrictions on energy consumption and the challenges of scientists and researchers have led to the idea of networking several multifunctional networks, using various sensors simultaneously and researching and developing algorithms and communication protocols to reduce energy consumption. The clustering technique of network components and sensors uses the genetic algorithm of relative justice to select the main network cluster and finally, after aggregating the information, directs them to the operating station. The operating station also relays the information to the main station, which compresses the data and reduces battery consumption on the multi-purpose network. In this research paper, we want to use a multi-objective network that plays the role of a direct workstation and a multi-objective network that plays the role of a client in the field of identification and relief by combining fuzzy logic and genetic algorithm in identifying and performing operations. Improve relief and rescue.
An Investigation of Design Constraints in the Process of Converting Face-to-face course into Online Course

Due to the spread of COVID-19, universities have postponed opening of classes and decided to provide online courses instead of face-to-face course to prevent the spread of COVID-19 Many professors are asked to convert their existing face-to-face courses to into online courses There has been a demand for converting face-to-face courses to into online courses In the process, professors are voicing the difficulty of converting courses Despite the ever-increasing demand for converting face-to-face course into online course, it is hard to find a study that explored course format change The purpose of this study is to identify design constraints that professors face in the process of converting existing face-to-face courses to online This study chose a case study as a methodology and the subjects of the study are four professors who have experience converting face-to-face courses into online Professor interviews and course materials were collected as data and analyzed using comparative analysis methods As a result of study, the identified design constraints are 1) the necessity to redesign the course structure, 2) the nonconformity of existing teaching strategies, 3) the low level of online learning readiness of professors and learners, 4) the limited understanding and support of universities for online courses, and 5) the difficulty of designing out of class activities Designing online courses is a much more complex design activity than simply adding new communication tool to existing face-to-face course This study proposes the role of a university and a professor for a successful course format change
PneumoVolley: Pressure-based Haptic Feedback on the Head through Pneumatic Actuation

Haptic Feedback brings immersion and presence in Virtual Reality (VR) to the next level. While research proposes the usage of various tactile sensations, such as vibration or ultrasound approaches, the potential applicability of pressure feedback on the head is still underexplored. In this paper, we contribute concepts and design considerations for pressure-based feedback on the head through pneumatic actuation. As a proof-of-concept implementing our pressure-based haptics, we further present PneumoVolley: A VR experience similar to the classic Volleyball game but played with the head. In an exploratory user study with 9 participants, we evaluated our concepts and identified a significantly increased involvement compared to a no-haptics baseline along with high realism and enjoyment ratings using pressure-based feedback on the head in VR.
High-dimensional Robust Mean Estimation via Gradient Descent

We study the problem of high-dimensional robust mean estimation in the presence of a constant fraction of adversarial outliers. A recent line of work has provided sophisticated polynomial-time algorithms for this problem with dimension-independent error guarantees for a range of natural distribution families. In this work, we show that a natural non-convex formulation of the problem can be solved directly by gradient descent. Our approach leverages a novel structural lemma, roughly showing that any approximate stationary point of our non-convex objective gives a near-optimal solution to the underlying robust estimation task. Our work establishes an intriguing connection between algorithmic high-dimensional robust statistics and non-convex optimization, which may have broader applications to other robust estimation tasks.
2D Bidirectional Gated Recurrent Unit Convolutional Neural Networks for End-to-End Violence Detection in Videos

Abnormal behavior detection, action recognition, fight and violence detection in videos is an area that has attracted a lot of interest in recent years. In this work, we propose an architecture that combines a Bidirectional Gated Recurrent Unit (BiGRU) and a 2D Convolutional Neural Network (CNN) to detect violence in video sequences. A CNN is used to extract spatial characteristics from each frame, while the BiGRU extracts temporal and local motion characteristics using CNN extracted features from multiple frames. The proposed end-to-end deep learning network is tested in three public datasets with varying scene complexities. The proposed network achieves accuracies up to 98%. The obtained results are promising and show the performance of the proposed end-to-end approach.
Theory-driven statistical modeling for semantics and pragmatics: A case study on grammatically generated implicature readings

Computational probabilistic modeling is increasingly popular in linguistics, but its relationship with linguistic theory is ambivalent. We argue here for the potential benefit of theory-driven statistical modeling, based on a case study situated at the semantics-pragmatics interface. Using data from a novel experiment, we employ Bayesian model comparison to evaluate the predictive adequacy of four models that differ in the extent to and manner in which grammatically generated candidate readings are taken into account in four probabilistic pragmatic models of utterance and interpretation choice. The data provide strong evidence for the idea that the full range of potential readings made available by recently popular grammatical approaches to scalar-implicature computation might be needed, and that classical Gricean reasoning may help manage the manifold ambiguity introduced by grammatical approaches to these. The case study thereby shows a way of bridging linguistic theory and empirical data with the help of probabilistic pragmatic modeling as a linking function.
Automatic Building and Labeling of HD Maps with Deep Learning

In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and labeling high-definition (HD) maps accurately and efficiently. Today, the process of creating HD maps requires a lot of human input, which takes time and is prone to errors. In this paper, we propose a novel method capable of generating labelled HD maps from raw sensor data. We implemented and tested our methods on several urban scenarios using data collected from our test vehicle. The results show that the proposed deep learning based method can produce highly accurate HD maps. This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicles.