Reviewerly/exHarmony huggingface dataset:
The Computational Complexity of ReLU Network Training Parameterized by Data Dimensionality

Understanding the computational complexity of training simple neural networks with rectified linear units (ReLUs) has recently been a subject of intensive research. Closing gaps and complementing results from the literature, we present several results on the parameterized complexity of training two-layer ReLU networks with respect to various loss functions. After a brief discussion of other parameters, we focus on analyzing the influence of the dimension d of the training data on the computational complexity. We provide running time lower bounds in terms of W[1]-hardness for parameter d and prove that known brute-force strategies are essentially optimal (assuming the Exponential Time Hypothesis). In comparison with previous work, our results hold for a broad(er) range of loss functions, including lp-loss for all p ∈ [0, ∞]. In particular, we improve a known polynomial-time algorithm for constant d and convex loss functions to a more general class of loss functions, matching our running time lower bounds also in these cases.
Application of Certainty Factor Method to Identify Pests in Crystal Jamboo Plants

Indonesia is an agricultural country with agriculture as one of the main sources of livelihood and a source of income for the people of Indonesia. One of them is the agriculture sector of guava crystal, guava crystal is one of the fruit plants that are already popular in the community. One potential problem in the cultivation of guava crystal is the presence of pests. The presence of pests in crystal guava plants can interfere with the health side of this crystal herbal medicine plant, where pests can attack the leaves, stems to fruit on the guava plants so that they can affect development and losses in the agricultural sector. As is the case in the Yellow River Crystal Guava Plantation Tourism which is still very difficult to detect pests. The results obtained from the calculation of identification of pests are 96% shoots caterpillars, caterpillars 92%, fire caterpillars 52%, caterpillars caterpillar 96%, caterpillars 84%, keket caterpillars 84%, grasshoppers 96%, fruit borer beetles 92%, White Aphids 92%, Aphids 88%, Shield Aphids 84%, and Fruit Flies 96%. That indicates the accuracy of the certainty factor is over 80% accurate.
Coherence effects on estimating general sub-Rayleigh object distribution moments

Quantum Fisher information and signal-to-noise ratio bounds are derived for the estimation of moments of general partially coherent objects. Under an asymptotic analysis in the sub-Rayleigh regime, these bounds are shown to be less restrictive than those of the incoherent case. This leaves open the possibility of more accurately estimating object distribution moments when the object is partially coherent.
Health Data Privacy in the COVID-19 Pandemic Context: Discourses on HIPAA

Background: Considering the impacts of the COVID-19 pandemic on health service delivery, the US Office for Civil Rights (OCR) updated the policies on health data processing, and Health Insurance Portability and Accountability Act (HIPAA). Objectives: In this study, we investigated discourses on HIPAA in relation to COVID-19. Methods: Through a search of media sources in the Factiva database, relevant texts were identified. We applied a text mining approach to identify concepts and themes in these texts. Results: Our analysis revealed six central themes, namely, Health, HIPAA, Privacy, Security, Patients, and Need, as well as their associated concepts. Among these, Health was the most frequently discussed theme. It comprised concepts such as public, care, emergency, providers, telehealth, entity, use, discretion, OCR, Health and Human Services (HHS), enforcement, business, and services. Conclusion: Our discourse analysis of media outlets highlights the role of health data privacy law in the response to global public health emergencies and demonstrates how discourse analysis and computational methods can inform health data protection policymaking in the digital health era.
Anomaly Detection Based on Latent Feature Training in Surveillance Scenarios

Anomaly detection in videos is challenging due to the scarcity and variance in positive samples. Current anomaly detection methods can be categorized into reconstruction models and future frame prediction-based models. However, reconstruction models might be exceptionally adapted to abnormal events due to the learning capacity and generalization ability of deep neural networks, whereas prediction-based methods can be sensitive to noise. In this study, we propose an anomaly detection model based on the latent feature space, which combines advantages from both sides. We argue that the constraints in the latent feature space can promote reconstruction; moreover, the optical flow is also considered to introduce temporal information. We use SPyNet for accurate and efficient optical flow estimation. We extensively validate our method on the UCSD Ped1, UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets. The results demonstrated the feasibility of the proposed method and the benefit of utilizing information in the latent feature space.
Towards Open and Expandable Cognitive AI Architectures for Large-Scale Multi-Agent Human-Robot Collaborative Learning

Learning from Demonstration (LfD) constitutes one of the most robust methodologies for constructing efficient cognitive robotic systems. Despite the large body of research works already reported, current key technological challenges include those of multi-agent learning and long-term autonomy. Towards this direction, a novel cognitive architecture for multi-agent LfD robotic learning is introduced in this paper, targeting to enable the reliable deployment of open, scalable and expandable robotic systems in large-scale and complex environments. In particular, the designed architecture capitalizes on the recent advances in the Artificial Intelligence (AI) (and especially the Deep Learning (DL)) field, by establishing a Federated Learning (FL)-based framework for incarnating a multi-human multi-robot collaborative learning environment. The fundamental conceptualization relies on employing multiple AI-empowered cognitive processes (implementing various robotic tasks) that operate at the edge nodes of a network of robotic platforms, while global AI models (underpinning the aforementioned robotic tasks) are collectively created and shared among the network, by elegantly combining information from a large number of human-robot interaction instances. Regarding pivotal novelties, the designed cognitive architecture a) introduces a new FL-based formalism that extends the conventional LfD learning paradigm to support large-scale multi-agent operational settings, b) elaborates previous FL-based self-learning robotic schemes so as to incorporate the human in the learning loop and c) consolidates the fundamental principles of FL with additional sophisticated AI-enabled learning methodologies for modelling the multi-level inter-dependencies among the robotic tasks. The applicability of the proposed framework is explained using an example of a real-world industrial case study (subject to ongoing research activities) for agile production-based Critical Raw Materials (CRM) recovery.
End-to-End Deep One-Class Learning for Anomaly Detection in UAV Video Stream

In recent years, the use of drones for surveillance tasks has been on the rise worldwide. However, in the context of anomaly detection, only normal events are available for the learning process. Therefore, the implementation of a generative learning method in an unsupervised mode to solve this problem becomes fundamental. In this context, we propose a new end-to-end architecture capable of generating optical flow images from original UAV images and extracting compact spatio-temporal characteristics for anomaly detection purposes. It is designed with a custom loss function as a sum of three terms, the reconstruction loss (Rl), the generation loss (Gl) and the compactness loss (Cl) to ensure an efficient classification of the “deep-one” class. In addition, we propose to minimize the effect of UAV motion in video processing by applying background subtraction on optical flow images. We tested our method on very complex datasets called the mini-drone video dataset, and obtained results surpassing existing techniques’ performances with an AUC of 85.3.
Survey on Human Activity Recognition using Smartphone

The field of Human Activity Recognition (HAR) is an active research field in which methods are being developed to understand human behavior by interpreting features obtained from various sources, these activities can be recognized using interactive sensors that are affected by human movement.Sensor can embed elements within Smartphones or Personal Digital Assistants (PDAs).The great increase in smart phone users and the increase in the sensor ability of these smart phones, and users usually carry their smartphones with them.This fact makes HAR more important and accepted.In this survey, A number of previous studies were studied and analyzed, where we prepared a comparison of the research works conducted over the period 2010-2020 in human activity recognition using Smartphone sensors.Comparison charts highlight their most important aspects such as a type of sensor used, activities, sensor placement, HAR-system type (offline, online), computing device, classifier (type of algorithms) and system accuracy levels.
Towards an Efficient Integrated Distance and Blended Learning Model: How to Minimize the Impact of COVID-19 on Education

&lt;p&gt;&lt;span style="font-family: Times New Roman;"&gt;As governments and international bodies worldwide sought to prevent the spread of Covid-19, underdeveloped countries have been particularly challenged as they have little or no technological models and frameworks in place and the literature offers little guidance. The current research applied a quantitative approach to researching educational practitioners’ perceptions of e-learning acceptance, using a sample of 4,024 educators and students from a range of Kuwaiti educational institutions. Findings indicate that respondents were slightly supportive of e-learning adoption, with a majority acknowledging its advantages. However, respondents also mention the need for development in the technological competencies required to operate the proposed e-learning programs and a range of challenges that may impact e-learning. Hence, the authors propose a distance and blended learning model (DBLM) to enhance the implementation, utilisation, and evaluation of e-learning within the Kuwaiti educational system. Also, an e-learning teacher capability maturity model (eTCMM) was proposed to foster educators’ competencies and enable their active use of e-learning platforms. &lt;/span&gt;&lt;/p&gt;
FDRN: A fast deformable registration network for medical images

Deformable image registration is a fundamental task in medical imaging. Due to the large computational complexity of deformable registration of volumetric images, conventional iterative methods usually face the tradeoff between the registration accuracy and the computation time in practice. In order to boost the performance of deformable registration in both accuracy and runtime, we propose a fast unsupervised convolutional neural network for deformable image registration.The proposed registration model FDRN possesses a compact encoder-decoder network architecture which employs a pair of fixed and moving images as input and outputs a three-dimensional displacement vector field (DVF) describing the offsets between the corresponding voxels in the fixed and moving images. In order to efficiently utilize the memory resources and enlarge the model capacity, we adopt additive forwarding instead of channel concatenation and deepen the network in each encoder and decoder stage. To facilitate the learning efficiency, we leverage skip connection within the encoder and decoder stages to enable residual learning and employ an auxiliary loss at the bottom layer with lowest resolution to involve deep supervision. Particularly, the low-resolution auxiliary loss is weighted by an exponentially decayed parameter during the training phase. In conjunction with the main loss in high-resolution grid, a coarse-to-fine learning strategy is achieved. Last but not least, we involve a proposed multi-label segmentation loss (SL) to improve the network performance in Dice score in case the segmentation prior is available. Comparing to the SL using average Dice score, the proposed SL does not require additional memory in the training phase and improves the registration accuracy efficiently.We evaluated FDRN on multiple brain MRI datasets from different aspects including registration accuracy, model generalizability, and model analysis. Experimental results demonstrate that FDRN performs better than the state-of-the-art registration method VoxelMorph by 1.46% in Dice score in LPBA40. In addition to LPBA40, FDRN obtains the best Dice and NCC among all the investigated methods in the unseen MRI datasets including CUMC12, MGH10, ABIDE, and ADNI by a large margin.The proposed FDRN provides better performance than the existing state-of-the-art registration methods for brain MR images by resorting to the compact autoencoder structure and efficient learning. Additionally, FDRN is a generalized framework for image registration which is not confined to a particular type of medical images or anatomy.
Voting with random classifiers (VORACE): theoretical and experimental analysis

Abstract In many machine learning scenarios, looking for the best classifier that fits a particular dataset can be very costly in terms of time and resources. Moreover, it can require deep knowledge of the specific domain. We propose a new technique which does not require profound expertise in the domain and avoids the commonly used strategy of hyper-parameter tuning and model selection. Our method is an innovative ensemble technique that uses voting rules over a set of randomly-generated classifiers. Given a new input sample, we interpret the output of each classifier as a ranking over the set of possible classes. We then aggregate these output rankings using a voting rule, which treats them as preferences over the classes. We show that our approach obtains good results compared to the state-of-the-art, both providing a theoretical analysis and an empirical evaluation of the approach on several datasets.
Mixed state entanglement classification using artificial neural networks

Abstract Reliable methods for the classification and quantification of quantum entanglement are fundamental to understanding its exploitation in quantum technologies. One such method, known as separable neural network quantum states (SNNS), employs a neural network inspired parameterization of quantum states whose entanglement properties are explicitly programmable. Combined with generative machine learning methods, this ansatz allows for the study of very specific forms of entanglement which can be used to infer/measure entanglement properties of target quantum states. In this work, we extend the use of SNNS to mixed, multipartite states, providing a versatile and efficient tool for the investigation of intricately entangled quantum systems. We illustrate the effectiveness of our method through a number of examples, such as the computation of novel tripartite entanglement measures, and the approximation of ultimate upper bounds for qudit channel capacities.
Avoiding Dense and Dynamic Obstacles in Enclosed Spaces: Application to Moving in Crowds

This article presents a closed-form approach to constraining a flow within a given volume and around objects. The flow is guaranteed to converge and to stop at a single fixed point. The obstacle avoidance problem is inverted to enforce that the flow remains enclosed within a volume defined by a polygonal surface. We formally guarantee that such a flow will never contact the boundaries of the enclosing volume or obstacles. It asymptotically converges toward an attractor. We further create smooth motion fields around obstacles with edges (e.g., tables). Both obstacles and enclosures may be time-varying, i.e., moving, expanding, and shrinking. The technique enables a robot to navigate within enclosed corridors while avoiding static and moving obstacles. It was applied on an autonomous robot (QOLO) in a static complex indoor environment and tested in simulations with dense crowds. The final proof of concept was performed in an outdoor environment in Lausanne. The QOLO-robot successfully traversed a marketplace in the center of town in the presence of a diverse crowd with a nonuniform motion pattern.
Revisiting data complexity metrics based on morphology for overlap and imbalance: snapshot, new overlap number of balls metrics and singular problems prospect

Data Science and Machine Learning have become fundamental assets for companies and research institutions alike. As one of its fields, supervised classification allows for class prediction of new samples, learning from given training data. However, some properties can cause datasets to be problematic to classify. In order to evaluate a dataset a priori, data complexity metrics have been used extensively. They provide information regarding different intrinsic characteristics of the data, which serve to evaluate classifier compatibility and a course of action that improves performance. However, most complexity metrics focus on just one characteristic of the data, which can be insufficient to properly evaluate the dataset towards the classifiers' performance. In fact, class overlap, a very detrimental feature for the classification process (especially when imbalance among class labels is also present) is hard to assess. This research work focuses on revisiting complexity metrics based on data morphology. In accordance to their nature, the premise is that they provide both good estimates for class overlap, and great correlations with the classification performance. For that purpose, a novel family of metrics has been developed. Being based on ball coverage by classes, they are named after Overlap Number of Balls. Finally, some prospects for the adaptation of the former family of metrics to singular (more complex) problems are discussed.
Practical Transformer-based Multilingual Text Classification

Transformer-based methods are appealing for multilingual text classification, but common research benchmarks like XNLI (Conneau et al., 2018) do not reflect the data availability and task variety of industry applications.We present an empirical comparison of transformer-based text classification models in a variety of practical monolingual and multilingual pretraining and fine-tuning settings.We evaluate these methods on two distinct tasks in five different languages.Departing from prior work, our results show that multilingual language models can outperform monolingual ones in some downstream tasks and target languages.We additionally show that practical modifications such as task-and domain-adaptive pretraining and data augmentation can improve classification performance without the need for additional labeled data.
Learning Management Systems in Academic and Corporate Distance Education

Present-day development of information technologies is making deeper and deeper incursions into all spheres of human activity, both social and economic. It is only natural that they greatly influence the educational industry, changing the approaches, methods and forms of education. Currently e-learning and distance technologies are being actively introduced into both academic and corporate education, as complementary to traditional forms. The efforts of teachers are aimed at finding effective models of blended learning. All this determines the relevance of the present study.&#x0D; The abstracted article is aimed at developing frameworks of a distance learning model based on the use of modern learning management systems.&#x0D; To obtain thorough and reliable results the authors make use of two research approaches: acmeological and competence-based. They review the features, the scope and use of the existing LMS platforms and work out tools for the development of e-learning system. In addition, they assess the compliance of e-learning tools with the preferences of X, Y and Z generation users.&#x0D; The outcome of the research is the development of a universal competency that can be applied to the study of modern methods and approaches with reference to both academic teaching and learning, and to corporate training. It is quite apparent that pros and cons of the learning management system and its possibilities should be carefully studied and thoughtfully used. The effective e-learning requires constant monitoring of equipment, proper work of host servers and considering user-friendliness of the platforms. This is related to the fact that public educational organizations, including schools, colleges and universities, especially in low developed countries, prefer traditional teaching methods and are slowly moving to innovative models.&#x0D; The research findings can be viewed as a step forward in creating user-friendly and flexible e-learning platforms, suitable for separate users, institutions and businesses. They can be applied for further assessment of existing learning models, especially for the younger generation, and for research and development of new strategies and methods of using learning management systems and platforms.
MetaXL: Meta Representation Transformation for Low-resource Cross-lingual Learning

The combination of multilingual pre-trained representations and cross-lingual transfer learning is one of the most effective methods for building functional NLP systems for lowresource languages.However, for extremely low-resource languages without large-scale monolingual corpora for pre-training or sufficient annotated data for fine-tuning, transfer learning remains an under-studied and challenging task.Moreover, recent work shows that multilingual representations are surprisingly disjoint across languages (Singh et al., 2019), bringing additional challenges for transfer onto extremely low-resource languages.In this paper, we propose MetaXL, a meta-learning based framework that learns to transform representations judiciously from auxiliary languages to a target one and brings their representation spaces closer for effective transfer.Extensive experiments on real-world low-resource languages -without access to large-scale monolingual corpora or large amounts of labeled data -for tasks like crosslingual sentiment analysis and named entity recognition show the effectiveness of our approach.Code for MetaXL is publicly available at github.com/microsoft/MetaXL.