Reviewerly/exHarmony huggingface dataset:
Development of Online Learning Resources as a Support System Learning from Home at SLB LAB UM

Online getting to know assets are getting to know assets that may be accessed on line. The foundation for its improvement is the significance of getting to know assets to guide the getting to know manner from home. Researchers behavior studies and improvement of getting to know innovation structures or merchandise on web sites as getting to know assets to grow to be getting to know companions for Malang State University Special School of Autism Laboratory college students. The stage of getting to know assets that help college students is understood thru fabric professional validation, media validation, and scholar use testing. The improvement of web-primarily based totally on line getting to know assets for college kids withinside the Malang State University Autism Special School Laboratory consists of assessment, improvement, implementation, and evaluation. The outcomes of the product are to be had withinside the shape of web-primarily based totally on line getting to know assets. This internet site gives multimedia opportunities with a aggregate of text, pics and videos, in addition to reference content material withinside the shape of magazines and e-books.
Adaptive Discretization in Online Reinforcement Learning

Adaptive Discretization in Reinforcement Learning Performance guarantees for RL algorithms are typically for worst case instances, which are pathological by design and not observed in meaningful applications. Moreover, many domains (such as computer systems and networking applications) have large state-action spaces and require algorithms to execute with low latency. This phenomenon highlights a trifecta of goals for practical RL algorithms: low sample, storage, and computational complexity. In this work, we develop an algorithmic framework for nonparametric RL with data-driven adaptive discretization. Our framework has provably better sample, storage, and computational complexity than uniform discretization or kernel regression methods. Moreover, we highlight how the performance guarantees are min-max optimal with respect to a novel instance-specific complexity measure that captures structure in facility location and newsvendor models.
Ensemble Labeling towards Scientific Information Extraction (ELSIE)—Blob Extraction

Scientific publications constitute an extremely valuable repository of knowledge and collection of facts crucial to the advancement of science and development of applications, which grows as researchers learn from previous works and scientists use results in the literature to design and create. With the exponential growth of available publications, reading and extracting this wealth of information has become impractical for humans. Despite great progress in natural language processing, machine-learned solutions require large amounts of carefully annotated data for good performance. This is especially true in the context of accurately labeling and extracting complex scientific data. Towards our ultimate goal of extracting scientific facts from the literature, we first aim to identify blobs of text that contain all of the facts in a publication to be later automatically extracted or scrutinized by experts. Our previous work identified some facts missed by experts yet missed others due to the assumption that the target relation—here, a polymer and its glass transition temperature—would be contained within the same sentence. We set out to enhance our approximate labeling system to look back and ahead for missing information and successfully achieved 100% recall of scientific facts while reducing the full-text publication to 6% of its original size. Moreover, we assign confidence scores to sentences to further assist expert curators in identifying important sentences and facts locked in unstructured text.
Adversarially Perturbed Wavelet-based Morphed Face Generation

Morphing is the process of combining two or more subjects in an image in order to create a new identity which contains features of both individuals. Morphed images can fool Facial Recognition Systems (FRS) into falsely accepting multiple people, leading to failures in national security. As morphed image synthesis becomes easier, it is vital to expand the research community's available data to help combat this dilemma. In this paper, we explore combination of two methods for morphed image generation, those of geometric transformation (warping and blending to create morphed images) and photometric perturbation. We leverage both methods to generate high-quality adversarially perturbed morphs from the FERET, FRGC, and FRLL datasets. The final images retain high similarity to both input subjects while resulting in minimal artifacts in the visual domain. Images are synthesized by fusing the wavelet sub-bands from the two look-alike subjects, and then adversarially perturbed to create highly convincing imagery to deceive both humans and deep morph detectors.
Detection of Schools in Remote Sensing Images Based on Attention-Guided Dense Network

The detection of primary and secondary schools (PSSs) is a meaningful task for composite object detection in remote sensing images (RSIs). As a typical composite object in RSIs, PSSs have diverse appearances with complex backgrounds, which makes it difficult to effectively extract their features using the existing deep-learning-based object detection algorithms. Aiming at the challenges of PSSs detection, we propose an end-to-end framework called the attention-guided dense network (ADNet), which can effectively improve the detection accuracy of PSSs. First, a dual attention module (DAM) is designed to enhance the ability in representing complex characteristics and alleviate distractions in the background. Second, a dense feature fusion module (DFFM) is built to promote attention cues flow into low layers, which guides the generation of hierarchical feature representation. Experimental results demonstrate that our proposed method outperforms the state-of-the-art methods and achieves 79.86% average precision. The study proves the effectiveness of our proposed method on PSSs detection.
Federated Learning Framework with Straggling Mitigation and Privacy-Awareness for AI-based Mobile Application Services

This work proposes a novel framework to address straggling and privacy issues for federated learning (FL)-based mobile application services, considering limited computing/communications resources at mobile users (MUs)/mobile application provider (MAP), privacy cost, the rationality and incentive competition among MUs in contributing data to the MAP. Particularly, the MAP first determines a set of the best MUs for the FL process based on MUs' provided information/features. Then, each selected MU can encrypt part of local data and upload the encrypted data to the MAP for an encrypted training process, in addition to the local training process. For that, the selected MU can propose a contract to the MAP according to its expected local and encrypted data. To find optimal contracts that can maximize utilities while maintaining high learning quality of the system, we develop a multi-principal one-agent contract-based problem considering the MUs' privacy cost, the MAP's limited computing resources, and asymmetric information between the MAP and MUs. Experiments with a real-world dataset show that our framework can speed up training time up to 49% and improve prediction accuracy up to 4.6 times while enhancing network's social welfare up to 114% under the privacy cost consideration compared with those of baseline methods.
An Energy-Efficient Edge Computing Paradigm for Convolution-Based Image Upsampling

State-of-the-art deep learning solutions for image upsampling are currently trained using either resize or sub-pixel convolution to learn kernels that generate high fidelity images with minimal artifacts. However, performing inference with these learned convolution kernels requires memory-intensive feature map transformations that dominate time and energy costs in real-time applications. To alleviate this pressure on memory bandwidth, we propose a novel energy-efficient edge computing paradigm that confines the use of resize or sub-pixel convolution to training in the cloud by transforming learned convolution kernels to deconvolution kernels before deploying them for inference as a functionally equivalent deconvolution. These kernel transformations, intended as a one-time cost when shifting from training to inference, enable a systems designer to use each algorithm in their optimal context by preserving the image fidelity learned when training in the cloud while minimizing data transfer penalties during inference at the edge. We compare the inference properties of these convolution-based image upsampling algorithms and introduce a novel deconvolution inference algorithm, which we refer to as REVD2. To demonstrate the benefits of our approach, we upsample images selected from the BSD300 dataset using a pre-trained single-image super resolution network provided by the PyTorch model zoo. Using quantitative models of incurred time and energy costs to analyze this deep neural network, we estimate that using REVD2 for inference at the edge improves system latency by 2.1x or 2.8x and energy efficiency by 2.1x or 2.7x when respectively compared to sub-pixel or resize convolution counterparts.
Reducing the Breach between Simulated and Real Data for Top View Images

The advances in Artificial Intelligence have provided powerful tools for perception in road applications, such as Traffic Monitoring or Autonomous vehicles. However, detecting and classifying objects from top-view or aerial imagery becomes more challenging, especially in urban cities; due to the variation in size and scale of the objects; in addition to the lack of the datasets that contain information about the object classes and their 3D positions. This paper introduces a novel algorithm to refine the semantic images captured from CARLA simulator; refining the semantic images, creating a new dataset with more classes of the objects, and calculating its 3D bounding box positions. The proposed method and dataset have been validated by training a CNN - based 2D object detector, and inferring with real aerial images from VisDrone-2019, and from fixed infrastructure cameras i.e. cameras mounted on Traffic lights. The obtained results illustrates the improvements of using the proposed dataset in the object detection process, with real top-view images in complex environments.
HetMAML

Most of existing gradient-based meta-learning approaches to few-shot learning assume that all tasks have the same input feature space. However, in the real world scenarios, there are many cases that the input structures of tasks can be different, that is, different tasks may vary in the number of input modalities or data types. Existing meta-learners cannot handle the heterogeneous task distribution (HTD) as there is not only global meta-knowledge shared across tasks but also type-specific knowledge that distinguishes each type of tasks. To deal with task heterogeneity and promote fast within-task adaptions for each type of tasks, in this paper, we propose HetMAML, a task-heterogeneous model-agnostic meta-learning framework, which can capture both the type-specific and globally shared knowledge and can achieve the balance between knowledge customization and generalization. Specifically, we design a multi-channel backbone module that encodes the input of each type of tasks into the same length sequence of modality-specific embeddings. Then, we propose a task-aware iterative feature aggregation network which can automatically take into account the context of task-specific input structures and adaptively project the heterogeneous input spaces to the same lower-dimensional embedding space of concepts. Our experiments on six task-heterogeneous datasets demonstrate that HetMAML successfully leverages type-specific and globally shared meta-parameters for heterogeneous tasks and achieves fast within-task adaptions for each type of tasks.
Preserving Privacy in the Globalized Smart Home: The SIFIS-Home Project

This article introduces the Secure Interoperable Full-Stack Internet of Things for Smart Home (SIFIS-Home) project, which defines and enforces privacy and security policies in a smart home environment. SIFIS-Home allows users to control data privacy and developers to provide privacy-aware services to mitigate the privacy risk of a globalized smart home by leveraging the concept of the smart home cyberperimeter.
Bitcoin Price Prediction: A Deep Learning Approach

The world first decentralized currency - cryptocurrency brings us the new era of a mode of exchange. It has been included many technologies like bitcoin, ethereum, and hyper ledger. Due to its rising popularity and demand, people are often curious to know the future price of these coins to make a good deal with them. The future price of bitcoin would help investors as well as corporate to get an overview of the demand and role in the economy. Many researchers have investigated various solutions that will predict the future price of bitcoin. But, the solutions achieved low accuracy. This paper main aim is to propose a prediction model that will predict the future price of bitcoin. The model is based on the deep learning approaches. The proposed model included four different deep learning models. These models are Long Short-Term Memory (LSTM), Bidirectional LSTM, Gated Recurrent Unit (GRU), and Bidirectional GRU. The performance of the prediction models is computed and it found Bi-GRU gave the best-predicted results.
Intersection of AI and Blockchain Technology: Concerns and Prospects

Artificial Intelligence (AI) and blockchain are two major technologies that emphasize the break through innovations in various industries. Every technology has its own benefits in spite of its technical complexity for building advanced business applications. Combination of AI and blockchain has led to the restructuring of architectural changes to meet the present industrial demands of globalized financial markets, Internet of things, intelligent business data models and smart medical applications. Alliance of AI and blockchain technologies give rise to decentralized AI which enables machines to understand and take decisions on reliable and secured data independently by avoiding the involvement of any intermediaries in the course of action. In this paper, we review the intersection junctures, benefits and support provided by AI and blockchain. We also shed light on tools and latest technologies that has emerged as a result of intersection between these two technologies.
Dysarthric Speech Augmentation Using Prosodic Transformation and Masking for Subword End-to-end ASR

End-to-end speech recognition systems are effective, but in order to train an end-to-end model, a large amount of training data is needed. For applications such as dysarthric speech recognition, we do not have sufficient data. In this paper, we propose a specialized data augmentation approach to enhance the performance of an end-to-end dysarthric ASR based on sub-word models. The proposed approach contains two methods, including prosodic transformation and time-feature masking. Prosodic transformation modifies the speaking rate and pitch of normal speech to control prosodic characteristics such as loudness, intonation, and rhythm. Using time and feature masking, we apply a mask to the Mel Frequency Cepstral Coefficients (MFCC) for robustness-focused augmentation. Results show that augmenting normal speech with prosodic transformation plus masking decreases CER by 5.4% and WER by 5.6%, and the further addition of dysarthric speech masking decreases CER by 11.3% and WER by 11.4%.
Influential Prototypical Networks for Few Shot Learning: A Dermatological Case Study

Prototypical network (PN) is a simple yet effective few shot learning strategy. It is a metric-based meta-learning technique where classification is performed by computing Euclidean distances to prototypical representations of each class. Conventional PN attributes equal importance to all samples and generates prototypes by simply averaging the support sample embeddings belonging to each class. In this work, we propose a novel version of PN that attributes weights to support samples corresponding to their influence on the support sample distribution. Influence weights of samples are calculated based on maximum mean discrepancy (MMD) between the mean embeddings of sample distributions including and excluding the sample. Comprehensive evaluation of our proposed influential PN (IPNet) is performed by comparing its performance with other baseline PNs on three different benchmark dermatological datasets. IPNet outperforms all baseline models with compelling results across all three datasets and various N -way, K-shot classification tasks. Findings from cross-domain adaptation experiments further establish the generalizability of IPNet.
An Augmented Reality Application to Enhance the Shopping Experience of Electronic Appliances

As a consequence of the Covid-19 pandemic, the retail industry has altered customer behaviour and conventional wisdom. People in India still prefer buying products offline due to their ‘try before you buy’ way of thinking. This scenario can be changed through a tech-based solution. Augmented Reality can bridge the gap between online and offline shopping. Users can superimpose the desired electronic product sitting at home. They can also ensure how the product looks at their house. Users can also adjust the size and rotate the object to get a sense of assurance. This paper will explain marker-based and markerless techniques used in our system to place the realistic 3D model and to give a virtual try-on experience. This system will help businesses grow despite challenges caused by Covid-19. As a result, people will be in a position to maintain social distance, but using Augmented Reality technologies they will also be virtually close.
UI-FAME

This paper describes a Java-based forgetting system UI-FAME for creating views of ontologies. In relational databases, a view is a subset of the database, while in ontologies, a view is more than a subset; it contains not only axioms that are contained in the original ontology, but also newly-derived axioms that are entailed by the original ontology. Forgetting is a form of non-standard reasoning that can be used to create views of ontologies by eliminating from the ontologies a set of concept and role names, namely the forgetting signature, while keeping all logical consequences over the names in the remaining signature. We compared UI-FAME with publicly accessible forgetting systems, namely LETHE and an early prototypical version of UI-FAME, over the 'AL'-TBox fragment of 494 ontologies taken from the Oxford Ontology Library. The results showed that UI-FAME had better success rates than LETHE and its prototypical version, and outsped LETHE by a large margin. UI-FAME has been integrated as back-end support in Babylon Health's (a leading digital healthcare company based in London) knowledge base (formed by the merger and alignment of medical ontologies from different sources) interfaces for their main concerns of ontology analysis and keeping track of the changes in different versions of its knowledge base.
IoT Blockchain Data Veracity with Data Loss Tolerance

Recent years have witnessed the advancement of the Internet of Things (IoT) and its emergence as a technology that could revolutionize many businesses. It helps considerably in creating data-driven business models with the insights it provides. IoT systems are deployed in data collection, monitor processes, provide insights and allow businesses to make data-driven productivity improvements. However, IoT systems are often experiencing data loss due to inevitable failures ranging from devices, networks, to the application layer, especially in scarce infrastructure resources environments. Data loss might be unrecoverable in many circumstances. As such, this research presents a blockchain based IoT model (framework) with the aim of circumventing data loss. We envisioned IoT blockchain technology in enhancing data veracity with data loss tolerance. That is, to have blockchain enhancing the IoT data veracity by leveraging on the features existed in its peer-to-peer network (P2P) and distributed ledger storage technology (DLT). Additionally, the edge computing of IoT blockchain technology is also conceptually workable; with intelligent small computing resources, it opens up a new era of bringing the intelligence of data collection, connectivity, computation and storage into the edge/device layer. A novel IoT blockchain strength monitoring system is also been studied to further enhance data veracity; this is achieved through a capacitance monitoring on the IoT blockchain system. The empirical results show that the proposed IoT blockchain with a strength monitoring model can alleviate data loss and thus enhance data veracity with data loss tolerance.
Fine and Coarse Granular Argument Classification before Clustering

Computational argumentation and especially argument mining together with retrieval enjoys increasing popularity. In contrast to standard search engines that focus on finding documents relevant to a query, argument retrieval aims at finding the best supporting and attacking premises given a query claim, e.g., from a predefined collection of arguments. Here, a claim is the central part of an argument representing the standpoint of a speaker with the goal to persuade the audience, and a premise serves as evidence to the claim. In addition to the actual retrieval process, existing work has focused on (1) classifying polarities of arguments into supporting or opposing, (2) classifying arguments by their frames (such as economic or environmental), and (3) clustering similar arguments by their meaning to avoid repetitions in the result list. For experiments, either hand-made argument collections or arguments extracted from debate portals were used. In this paper, we extend existing work on argument clustering, making the following contributions: First, we introduce a novel pipeline for clustering arguments. While previous work classified arguments either by polarity, frame, or meaning, our pipeline incorporates these three, allowing a more systematic presentation of arguments. Second, we introduce a new dataset consisting of 365 argument graphs accompanying more than 11,000 high-quality arguments that, contrary to previous datasets, have been generated, displayed, and verified by journalists and were published in newspapers. A thorough evaluation with this dataset provides a first baseline for future work.
Stable Allocation of Services in Public Cloud Manufacturing Platforms: A Game Theory View

With unbounding the manufacturing resources and capabilities from shopfloors and being available as on-demand services for everyone who needs, the service allocation problem becomes critical. Despite of private Cloud Manufacturing platforms in which the service allocation problem is an optimization matching problem, in public Cloud Manufacturing platforms the service allocation problem is a matching game between manufacturing providers and consumers. This is a complex form of games that has two sides, one side is the players that has manufacturing resources and capabilities and the other side is the players who want to receives such services. In this paper, we present a matching game model for public Cloud Manufacturing platforms, and discusses the stable solution of the game using Deferred Acceptance algorithm. Then, we examine the model using a case study from automotive spare parts manufacturing in Iran which was the main motivation of this study. The novelty of the contribution of this paper is in development of a stable and optimal matching mechanism for public platforms.