Reviewerly/exHarmony huggingface dataset:
A Novel Algorithm for Distributed Data Stream Using Big Data Classification Model

In order to solve the problem of real-time detection of power grid equipment anomalies, this paper proposes a data flow classification model based on distributed processing. In order to realize distributed processing of power grid data flow, a local node mining method and a global mining mode based on uneven data flow classification are designed. A data stream classification model based on distributed processing is constructed, then the corresponding data sequence is selected and formatted abstractly, and the local node mining method and global mining mode under this model are designed. In the local node miner, the block-to-block mining strategy is implemented by acquiring the current data blocks. At the same time, the expression and real-time maintenance of local mining patterns are completed by combining the clustering algorithm, thus improving the transmission rate of information between each node and ensuring the timeliness of the overall classification algorithm.
KASR: Knowledge-Aware Sequential Recommendation

The goal of sequential recommendations is to capture the transitions of users’ interests. Most existing methods utilize sequential neural networks to model interaction records, mapping items into latent vectors. Although such methods do explore the transitions of items in interaction sequences, they only capture the sequence dependencies of items, neglecting the deep semantic relevance between items. Such limited information contributes less to catching the complicated sequential behaviors of users accurately. In this paper, we propose a novel model Knowledge-Aware Sequential Recommendation (KASR), which captures sequence dependencies and semantic relevance of items simultaneously in an end-to-end manner. Specifically, we first convert the interaction records into a knowledge-transfer interaction sequence, which reflects the fine-grained transitions of users’ interests. Next, we further recursively aggregate information in the knowledge graph based on a specific relation attention network, to explicitly capture the high-order relevance between items. A knowledge-aware GRU is later introduced to explore the sequential and semantic relevance between items automatically. We have conducted extensive experiments on three real datasets, and the results demonstrate that our method outperforms the state-of-the-art models.
Adaptive Skill Acquisition in Hierarchical Reinforcement Learning

Reinforcement learning has become an established class of powerful machine learning methods operating online on sequential tasks by direct interaction with an environment instead of processing precollected training datasets. At the same time, the nature of many tasks with an inner hierarchical structure has evoked interest in hierarchical RL approaches that introduced the two-level decomposition directly into computational models. These methods are usually composed of lower-level controllers – skills – providing simple behaviors, and a high-level controller which uses the skills to solve the overall task. Skill discovery and acquisition remain principal challenges in hierarchical RL, and most of the relevant works have focused on resolving this issue by using pre-trained skills, fixed during the main learning process, which may lead to suboptimal solutions. We propose a universal pluggable framework of Adaptive Skill Acquisition (ASA), aimed to augment existing solutions by trying to achieve optimality. ASA can observe the high-level controller during its training and identify skills that it lacks to successfully learn the task. These missing skills are subsequently trained and integrated into the hierarchy, enabling better performance of the overall architecture. As we show in the pilot maze-type experiments, the identification of missing skills performs reasonably well, and embedding such skills into the hierarchy may significantly improve the performance of an overall model.
Strong polygamy and monogamy relations for multipartite quantum systems

Monogamy and polygamy are the most striking features of the quantum world. We investigate the monogamy and polygamy relations satisfied by all quantum correlation measures for arbitrary multipartite quantum states. By introducing residual quantum correlations, analytical polygamy inequalities are presented, which are shown to be tighter than the existing ones. Then, similar to polygamy relations, we obtain strong monogamy relations that are better than all the existing ones. Typical examples are presented for illustration.
Extremal elements of a sublattice of the majorization lattice and approximate majorization

Given a probability vector x with its components sorted in non-increasing order, we consider the closed ball with p ⩾ 1 formed by the probability vectors whose ℓp-norm distance to the center x is less than or equal to a radius . Here, we provide an order-theoretic characterization of these balls by using the majorization partial order. Unlike the case p = 1 previously discussed in the literature, we find that the extremal probability vectors, in general, do not exist for the closed balls with 1 < p < ∞. On the other hand, we show that is a complete sublattice of the majorization lattice. As a consequence, this ball also has extremal elements. In addition, we give an explicit characterization of those extremal elements in terms of the radius and the center of the ball. This allows us to introduce some notions of approximate majorization and discuss its relation with previous results of approximate majorization given in terms of the ℓ1-norm. Finally, we apply our results to the problem of approximate conversion of resources within the framework of quantum resource theory of nonuniformity.
Stationary Activations for Uncertainty Calibration in Deep Learning

We introduce a new family of non-linear neural network activation functions that mimic the properties induced by the widely-used Matern family of kernels in Gaussian process (GP) models. This class spans a range of locally stationary models of various degrees of mean-square differentiability. We show an explicit link to the corresponding GP models in the case that the network consists of one infinitely wide hidden layer. In the limit of infinite smoothness the Matern family results in the RBF kernel, and in this case we recover RBF activations. Matern activation functions result in similar appealing properties to their counterparts in GP models, and we demonstrate that the local stationarity property together with limited mean-square differentiability shows both good performance and uncertainty calibration in Bayesian deep learning tasks. In particular, local stationarity helps calibrate out-of-distribution (OOD) uncertainty. We demonstrate these properties on classification and regression benchmarks and a radar emitter classification task.
MedFilter: Improving Extraction of Task-relevant Utterances through Integration of Discourse Structure and Ontological Knowledge

Information extraction from conversational data is particularly challenging because the task-centric nature of conversation allows for effective communication of implicit information by humans, but is challenging for machines.The challenges may differ between utterances depending on the role of the speaker within the conversation, especially when relevant expertise is distributed asymmetrically across roles.Further, the challenges may also increase over the conversation as more shared context is built up through information communicated implicitly earlier in the dialogue.In this paper, we propose the novel modeling approach MEDFILTER, which addresses these insights in order to increase performance at identifying and categorizing task-relevant utterances, and in so doing, positively impacts performance at a downstream information extraction task.We evaluate this approach on a corpus of nearly 7,000 doctor-patient conversations where MEDFILTER is used to identify medically relevant contributions to the discussion (achieving a 10% improvement over SOTA baselines in terms of area under the PR curve).Identifying task-relevant utterances benefits downstream medical processing, achieving improvements of 15%, 105%, and 23% respectively for the extraction of symptoms, medications, and complaints.
Quantum Sensing of Curvature

We address the problem of sensing the curvature of a manifold by performing measurements on a particle constrained to the manifold itself. In particular, we consider situations where the dynamics of the particle is quantum mechanical and the manifold is a surface embedded in the three-dimensional Euclidean space. We exploit ideas and tools from quantum estimation theory to quantify the amount of information encoded into a state of the particle, and to seek for optimal probing schemes, able to actually extract this information. Explicit results are found for a free probing particle and in the presence of a magnetic field. We also address precision achievable by position measurement, and show that it provides a nearly optimal detection scheme, at least to estimate the radius of a sphere or a cylinder.
Towards Interpretable Reasoning over Paragraph Effects in Situation

We focus on the task of reasoning over paragraph effects in situation, which requires a model to understand the cause and effect described in a background paragraph, and apply the knowledge to a novel situation.Existing works ignore the complicated reasoning process and solve it with a one-step "black box" model.Inspired by human cognitive processes, in this paper we propose a sequential approach for this task which explicitly models each step of the reasoning process with neural network modules.In particular, five reasoning modules are designed and learned in an end-to-end manner, which leads to a more interpretable model.Experimental results on the ROPES dataset demonstrate the effectiveness and explainability of our proposed approach.
Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

Previous work on adversarially robust neural networks for image classification requires large training sets and computationally expensive training procedures. On the other hand, few-shot learning methods are highly vulnerable to adversarial examples. The goal of our work is to produce networks which both perform well at few-shot classification tasks and are simultaneously robust to adversarial examples. We develop an algorithm, called Adversarial Querying (AQ), for producing adversarially robust meta-learners, and we thoroughly investigate the causes for adversarial vulnerability. Moreover, our method achieves far superior robust performance on few-shot image classification tasks, such as Mini-ImageNet and CIFAR-FS, than robust transfer learning.
SEERA: a software cost estimation dataset for constrained environments

The accuracy of software cost estimation depends on the relevancy of the cost estimation dataset, the quality of its data and its suitability for the targeted software development environment. Software development cost is impacted by technical, socio-economic and country-specific organizational and cultural environments. Current publicly available software cost estimation datasets represent environments of North America and Europe, thus limiting their application in technically and economically constrained software industries. In this paper we introduce the SEERA (Software enginEERing in SudAn) cost estimation dataset, a dataset of 120 software development projects representing 42 organizations in Sudan. The SEERA dataset contains 76 attributes and, unlike current cost estimation datasets, is augmented with metadata and the original raw data. This paper describes the data collection process, submitting organizations and project characteristics. In addition, we give a general analysis of the dataset projects to illustrate the impact of local factors on software project cost and compare the data quality of the SEERA dataset to public datasets from the PROMISE repository. The SEERA dataset fills a gap in the diversity of current cost estimation datasets and provides researchers with an opportunity to evaluate the generalization of previous and future cost estimation methods to constrained environments and to develop new techniques that are more suitable for these environments.
Facial Feature Extraction For Face Recognition

System of face recognition techniques plays a crucial role in life, and Face recognition is one among the biometric techniques used for identification of humans. among which it's utilized in security protection and protection systems (electronic door through the face) and in advertisements, the foremost famous of which is Facebook, which uses face recognition technology in terms of garments and hairstyle this technique has advantages that are easy to use, accurate and unnecessary to the human factor. this technique has been programmed through algorithms and high algorithms to research the position of the top and therefore the background image, and a few define some areas of the face consistent with the programmed algorithms and the most idea of our work is to extract the features from the face by using some techniques like (HOG, LBP, PCA, SURF, HAARIS) Then, the KNN algorithm is applied to seek out the similarity ratio between the training image that stored within the system and testing image. Our dataset contained about 100 images (60 training images, 40 testing image) of various people and when applying the techniques, we embraced that the hog algorithm is that the most effective compared to other algorithms with a hit rate of quite 85%.
Secure Log Scheme for Cloud Forensics

Nowadays organization use the cloud infrastructure to store data as no local setup needed in the user system. To retrieve files from the cloud, the user needs internet service. When the internet comes into the picture, a lot of attacks happens on the cloud, and to detect & prevent those attacks, cloud forensics is used. Secure user log file on the cloud is also important as the cloud log contains valuable information which helps in forensics investigation. Previously designed logging systems have some drawbacks for providing security to the cloud user. The existing system provides security on user files which is uploaded by the user or they provide login authentication of the user. In this secure logging, the scheme is provided by encrypting cloud logs (sensitive information of the user) using encryption techniques and it identifies DDoS (distributed denial of service) assault on the cloud framework. It is distinguished through investigating accessible cloud logs in the cloud server. Encryption algorithms will be utilized to build the security of the logging system and to keep up secrecy and protection of client information.
Deep active inference agents using Monte-Carlo methods

Active inference is a Bayesian framework for understanding biological intelligence. The underlying theory brings together perception and action under one single imperative: minimizing free energy. However, despite its theoretical utility in explaining intelligence, computational implementations have been restricted to low-dimensional and idealized situations. In this paper, we present a neural architecture for building deep active inference agents operating in complex, continuous state-spaces using multiple forms of Monte-Carlo (MC) sampling. For this, we introduce a number of techniques, novel to active inference. These include: i) selecting free-energy-optimal policies via MC tree search, ii) approximating this optimal policy distribution via a feed-forward `habitual' network, iii) predicting future parameter belief updates using MC dropouts and, finally, iv) optimizing state transition precision (a high-end form of attention). Our approach enables agents to learn environmental dynamics efficiently, while maintaining task performance, in relation to reward-based counterparts. We illustrate this in a new toy environment, based on the dSprites data-set, and demonstrate that active inference agents automatically create disentangled representations that are apt for modeling state transitions. In a more complex Animal-AI environment, our agents (using the same neural architecture) are able to simulate future state transitions and actions (i.e., plan), to evince reward-directed navigation - despite temporary suspension of visual input. These results show that deep active inference - equipped with MC methods - provides a flexible framework to develop biologically-inspired intelligent agents, with applications in both machine learning and cognitive science.
Lemmatization for Ancient Greek

Abstract This article presents the result of accuracy tests for currently available Ancient Greek lemmatizers and recently published lemmatized corpora. We ran a blinded experiment in which three highly proficient readers of Ancient Greek evaluated the output of the CLTK lemmatizer, of the CLTK backoff lemmatizer, and of GLEM , together with the lemmatizations offered by the Diorisis corpus and the Lemmatized Ancient Greek Texts repository. The texts chosen for this experiment are Homer, Iliad 1.1–279 and Lysias 7. The results suggest that lemmatization methods using large lexica as well as part-of-speech tagging—such as those employed by the Diorisis corpus and the CLTK backoff lemmatizer—are more reliable than methods that rely more heavily on machine learning and use smaller lexica.
Optimising the management of mining waste by means Sentinel-2 imagery: a case study in Joda West Iron and Manganese Mine (India)

A smart economy minimizes the production of waste from mining activities and reuses waste as a potential resource, with the goal of moving towards a near-zero waste society. This paper presents integrated multidisciplinary methodology in order to optimise the management of mining waste. The test site is the FeeMn mine in Odisha (India). The mining waste present in the mine has been collected and afterwards X-Ray Powder Diffraction, X-Ray Fluorescence and spectral signatures analysis have been performed for mineralogical, chemical and spectral characterization of the materials. Finally, the classification and mapping of the characterized mining waste was carried out by Sentinel-2A image.
Text Zoning and Classification for Job Advertisements in German, French and English

We present experiments to structure job ads into text zones and classify them into professions, industries and management functions, thereby facilitating social science analyses on labor marked demand.Our main contribution are empirical findings on the benefits of contextualized embeddings and the potential of multi-task models for this purpose.With contextualized in-domain embeddings in BiLSTM-CRF models, we reach an accuracy of 91% for token-level text zoning and outperform previous approaches.A multi-tasking BERT model performs well for our classification tasks.We further compare transfer approaches for our multilingual data.
Optimal Private Median Estimation under Minimal Distributional Assumptions

We study the fundamental task of estimating the median of an underlying distribution from a finite number of samples, under pure differential privacy constraints. We focus on distributions satisfying the minimal assumption that they have a positive density at a small neighborhood around the median. In particular, the distribution is allowed to output unbounded values and is not required to have finite moments. We compute the exact, up-to-constant terms, statistical rate of estimation for the median by providing nearly-tight upper and lower bounds. Furthermore, we design a polynomial-time differentially private algorithm which provably achieves the optimal performance. At a technical level, our results leverage a Lipschitz Extension Lemma which allows us to design and analyze differentially private algorithms solely on appropriately defined typical instances of the samples.
Non-Convex SGD Learns Halfspaces with Adversarial Label Noise

We study the problem of agnostically learning homogeneous halfspaces in the distribution-specific PAC model. For a broad family of structured distributions, including log-concave distributions, we show that non-convex SGD efficiently converges to a solution with misclassification error $O(\opt)+\eps$, where $\opt$ is the misclassification error of the best-fitting halfspace. In sharp contrast, we show that optimizing any convex surrogate inherently leads to misclassification error of $\omega(\opt)$, even under Gaussian marginals.
Multi-task causal learning with Gaussian processes

This paper studies the problem of learning the correlation structure of a set of intervention functions defined on the directed acyclic graph (DAG) of a causal model. This is useful when we are interested in jointly learning the causal effects of interventions on different subsets of variables in a DAG, which is common in field such as healthcare or operations research. We propose the first multi-task causal Gaussian process (GP) model, which we call DAG-GP, that allows for information sharing across continuous interventions and across experiments on different variables. DAG-GP accommodates different assumptions in terms of data availability and captures the correlation between functions lying in input spaces of different dimensionality via a well-defined integral operator. We give theoretical results detailing when and how the DAG-GP model can be formulated depending on the DAG. We test both the quality of its predictions and its calibrated uncertainties. Compared to single-task models, DAG-GP achieves the best fitting performance in a variety of real and synthetic settings. In addition, it helps to select optimal interventions faster than competing approaches when used within sequential decision making frameworks, like active learning or Bayesian optimization.
Multi-view Consistency Loss for Improved Single-Image 3D Reconstruction of Clothed People

We present a novel method to improve the accuracy of the 3D reconstruction of clothed human shape from a single image. Recent work has introduced volumetric, implicit and model-based shape learning frameworks for reconstruction of objects and people from one or more images. However, the accuracy and completeness for reconstruction of clothed people is limited due to the large variation in shape resulting from clothing, hair, body size, pose and camera viewpoint. This paper introduces two advances to overcome this limitation: firstly a new synthetic dataset of realistic clothed people, 3DVH; and secondly, a novel multiple-view loss function for training of monocular volumetric shape estimation, which is demonstrated to significantly improve generalisation and reconstruction accuracy. The 3DVH dataset of realistic clothed 3D human models rendered with diverse natural backgrounds is demonstrated to allows transfer to reconstruction from real images of people. Comprehensive comparative performance evaluation on both synthetic and real images of people demonstrates that the proposed method significantly outperforms the previous state-of-the-art learning-based single image 3D human shape estimation approaches achieving significant improvement of reconstruction accuracy, completeness, and quality. An ablation study shows that this is due to both the proposed multiple-view training and the new 3DVH dataset. The code and the dataset can be found at the project website: https://akincaliskan3d.github.io/MV3DH/.
Low-Power Hardware Implementation for Parametric Rectified Linear Unit Function

PReLU (Parametric ReLU) function is an advanced ReLU (Rectified Linear Unit) for improving the accuracy of the neural network. However, up to now, there is no special attention to the hardware design of PReLU function. Based on the characteristics of PReLU function, in this paper, we propose a dedicated multiplier for PReLU function. With this dedicated multiplier, we develop a low-power PReLU hardware implementation. Experimental results show that the proposed approach can significantly reduce power consumption with small accuracy loss.