Reviewerly/exHarmony huggingface dataset:
Detection of Motor Seizures and Falls in Mobile Application using Machine Learning Classifiers

We have developed a healthcare mobile application, for human activity recognition, monitoring of well-being and detection of individuals going towards a health hazard based on the data collected from sensors embedded in mobile phones and wearables. The data from sensors are processed within the mobile application to detect and classify different Activities of Daily Living. The developed framework is used to collect data in an unconstraint environment from individuals suffering from neurological disorders. The data is further tested using signal processing and machine learning algorithms. Results of in-app processing and classification are stored in a dedicated mobile database for later reference and analysis. This paper shows that statistical and Machine Learning methods can also be used within a mobile application for classification of ADLs. MyNeuroHealth has been designed in accordance with the scale of the prevalence of neurological disorders among the general population of developing countries and has become more relevant in COVID-19 pandemic as it offers real-time nonintrusive monitoring. Results show that MyNeuroHealth can detect and classify Motor Seizures and falls with an accuracy of 99%. The app is also able to detect if a patient had stumbled or fallen due to any reason and notifies caregiver accordingly.
The ICARUS Ontology

The management of aviation data is a great challenge in the aviation industry, as they are complex and can be derived from heterogeneous data sources. To handle this challenge, ontologies can be applied to facilitate the modelling of the data across multiple data sources. This paper presents an aviation domain ontology, the ICARUS ontology, which aims at facilitating the semantic description and integration of information resources that represent the various assets of the ICARUS platform and their use. To present the functionality and usability of the proposed ontology, we present the results of querying the ontology using SPARQL queries through three use case scenarios. As shown from the evaluation, the ICARUS ontology enables the integration and reasoning over multiple sources of heterogeneous aviation-related data, the semantic description of metadata produced by ICARUS, and their storage in a knowledge-base which is dynamically updated and provides access to its contents via SPARQL queries.
Domain Specific Knowledge Graphs as a Service to the Public

Web and mobile technologies enable ubiquitous access to information. Yet, it is getting harder, even for subject matter experts, to quickly identify quality, trustworthy, and reliable content available online through search engines powered by advanced knowledge graphs. This paper explores the practical applications of Domain Specific Knowledge Graphs that allow for the extraction of information from trusted published and unpublished sources, to map the extracted information to an ontology defined in collaboration with sector experts, and to enable the public to go from single queries into ongoing conversations meeting their knowledge needs reliably. We focused on Social-Impact Funding, an area of need for over one million nonprofit organizations, foundations, government entities, social entrepreneurs, impact investors, and academic institutions in the US.
Secure Blockchain-based Storage with Meta-Key – Data Sharing Mechanism

The Blockchain mechanism is used for enriching the security of the data storage. The Meta-Key plays a vital role in Meta-Key Mechanism which employs a data-sharing mechanism in which a user encrypts their data using the blockchain mechanism. The encrypted keys include the private key and public key are used for designing a Meta-Key using blockchain architecture. It enriches the security of the stored key more safe and secure. The proposed Meta-Key mechanism is designed using the Block Chain mechanism. In the proposed system, the used keys are encrypted using blockchain in a continuous relational fashion. The continuous relational fashion generates a high complexity to the vulnerability of data security is being compromised. The re-encryption is also used for distributed networks. The overhead of encryption and traffic congestion is analyzed. It is found that the security of the data storage is improved while using the proposed Meta-Key mechanism and the size of the traffic congestion is the ignorable amount.
Unchain or Block the Hype? : Decision Drivers, Success Factors, and Perspectives for Blockchain Adoption

Cesare Pautasso: Let's open the discussion on the business level. Generally speaking, blockchains promise to fundamentally transform processes and accelerate the digital transformation, but what is the killer application for the blockchain?
Uncovering the Mystery of How Users Find and Use eBooks through Guerilla Usability Testing

This case study focuses on user research that was conducted to enhance our users' ebook discovery experience. We took an outward-facing approach by seeking the users' perspectives. We wanted to know: How do users find ebooks? How do they perceive multiple records for the same title? Would users find additional metadata useful, such as download availability, file format, and chapter titles? We coordinated guerilla usability testing in our library lobby, inspired by low-overhead, high-impact user experience work at other academic libraries. The resulting data gave us a much-needed new perspective of ebook usage that supplements what we already know from our day-to-day work.
Adapting Usability Metrics for a Socially Assistive, Kinesthetic, Mixed Reality Robot Tutoring Environment

The field of Socially Assistive Robot (SAR) tutoring has extensively explored both subjective and objective usability metrics for seated tablet-based human-robot interactions. As SAR tutoring introduces kinesthetic mixed reality environments where students can move around and physically manipulate virtual objects, usability metrics for such interactions need to be re-evaluated. This paper applies standard usability metrics from seated 2D interactions to a kinesthetic mixed reality environment and validates those metrics with post-interaction survey data. Using data from a pilot study ( $$n=9$$ ) conducted with a mixed reality SAR tutor, three commonly-used metrics of usability for seated 2D tutoring interfaces were collected: performance, manipulation time, and gaze. The strength of each usability metric was compared to subjective survey-based scores measured with the System Usability Scale (SUS). The results show that usability scores were correlated with the gaze metric but not with the manipulation time or performance metrics. The findings provide interesting implications for the design and evaluation of kinesthetic mixed reality robot tutoring environments.
Chinese Named Entity Recognition Based on BERT with Whole Word Masking

Named Entity Recognition (NER) is a basic task of natural language processing and an indispensable part of machine translation, knowledge mapping and other fields. In this paper, a fusion model of Chinese named entity recognition using BERT, Bidirectional LSTM (BiLSTM) and Conditional Random Field (CRF) is proposed. In this model, Chinese BERT generates word vectors as a word embedding model. Word vectors through BiLSTM can learn the word label distribution. Finally, the model uses Conditional Random Fields to make syntactic restrictions at the sentence level to get annotation sequences. In addition, we can use Whole Word Masking (wwm) instead of the original random mask in BERT's pre-training, which can effectively solve the problem that the word in Chinese NER is partly masked, so as to improve the performance of NER model. In this paper, BERT-wwm (BERT-wwm is the BERT that uses Whole-Word-Masking in pre training tasks), BERT, ELMo and Word2Vec are respectively used for comparative experiments to reflect the effect of bert-wwm in this fusion model. The results show that using Chinese BERT-wwm as the language representation model of NER model has better recognition ability.
The Capacity of Single-Server Weakly-Private Information Retrieval

Weakly-private information retrieval (WPIR) is a variant of the private information retrieval problem in which a user wants to efficiently retrieve a file stored across a set of servers while tolerating some information leakage on the identity of the requested file to the servers. In this paper, we consider WPIR from a single-server database where the information leakage is measured in terms of the mutual information (MI) or maximal leakage (MaxL) privacy metrics. In particular, we establish a connection between the WPIR problem and rate-distortion theory, and fully characterize the optimal tradeoff between the download cost and the allowed information leakage under the MI and MaxL metrics, settling the single-server WPIR capacity.
Retracted: Evaluation of user concentration in ubiquitous and cognitive artificial intelligence‐assisted English online guiding system integrating face and eye movement detection

Summary In recent years, artificial intelligence technology has made significant breakthroughs. Big data, cloud computing, speech recognition, deep learning, and so on have become new hot spots after the Internet of things. The rapid development of artificial intelligence technology promotes the innovation of foreign language teaching ideas and learning methods. The new ecology and new paradigm of foreign language guiding must be reconstructed. The biggest difference between the Internet of things and the Internet is that the Internet of things is directly connected with all kinds of sensors. It does not need people to input information through the keyboard but automatically obtain information and carry out automatic processing. The deep integration of smart classroom and English online teaching will help to promote the reform and innovation of English teaching and provide a new way for the development of English online teaching. In this paper, we analyze the fusion of face recognition technology and eye tracking system and analyze the user focus evaluation theory in the Internet of things environment. Based on the above technology, this paper proposes an online English teaching user focus evaluation system in the context of artificial intelligence.
Joint Learning of Binary Classifiers and Pairwise Label Correlations for Multi-label Image Classification

Typical approaches to multi-label image classification learn a binary classifier for each label, including the binary cross-entropy (CE) loss function in convolutional neural network (CNN) based methods, which learns each label with independent binary logistic regression at the output layer. While these approaches have achieved sufficient success due to the strong learning capability of CNN, further improvements are limited for their neglect of the data imbalance and failure in exploiting explicit label correlations. In this paper, we deal with this problem by jointly learning the binary classifiers and pairwise label correlations (JBP) in an end-to-end manner. For pairwise learning, we introduce the strategy of online hard sample mining to focus on distinguishing confusing label pairs. we also investigate an imbalance aware cross-entropy (ICE) loss function by incorporating cost-sensitivity into the existing cross-entropy loss. Experiments on three popular datasets demonstrate the effectiveness of our proposed method.
Horus: An Interference-Aware Resource Manager for Deep Learning Systems

Deep Learning (DL) models are deployed as jobs within machines containing GPUs. These DL systems - ranging from a singular GPU device to machine clusters - require state-of-the-art resource management to increase resource utilization and job throughput. While it has been identified that co-location - multiple jobs co-located within the same GPU - is an effective means to achieve this, such co-location incurs performance interference that directly debilitates DL training and inference performance. Existing approaches to mitigate interference require resource intensive and time consuming kernel profiling ill-suited for runtime scheduling decisions. Current DL system resource management are not designed to deal with these problems. This paper proposes Horus, an interference-aware resource manager for DL systems. Instead of leveraging expensive kernel-profiling, our approach estimates job resource utilization and co-location patterns to determine effective DL job placement to minimize likelihood of interference, as well as improve system resource utilization and makespan. Our analysis shows that interference cause up to 3.2x DL job slowdown. We integrated our approach within the Kubernetes resource manager, and conduct experiments in a DL cluster by training 2,500 DL jobs using 13 different models types. Results demonstrate that Horus is able to outperform other DL resource managers by up to 61.5% for resource utilization and 33.6% for makespan.
Evaluating performance of agglomerative clustering for extended NMF

World is full of data. Data is ubiquitous and massive. This data is in its raw form and is mined to extract advantageous information. Data is coming from various industries and may contain very sensitive data also. So, securing this data before data mining is very important such that it does not reveal any sensitive information and still extract useful information. Data privacy is attained by data perturbation using the Extended NMF method. The perturbed data is then used to extract the knowledge by Data Mining. Mining can be done by clustering which organizes objects into groups of members which have some similarity amongst them. These groups are called clusters which contain objects which are similar to each other but would be dissimilar to other objects from different clusters. Hierarchical Clustering is a method which organizes the data into a tree of clusters. This tree is called Dendrogram and can be formed in bottom-up (agglomerative) manner or top-down (divisive) manner. Distance between the clusters plays a very important role in the agglomerative method. Distance measure can be Euclidean and Manhattan. Based on the distance, various types of agglomerative methods exist: complete-linkage, average-linkage, single-linkage and Ward’s- linkage. In this paper, the authors have compared different types of agglomerative methods based on various criteria and identified the best method amongst these with Extended NMF.
Recent Advances in Multimodal Educational Data Mining in K-12 Education

Recently we have seen a rapid rise in the amount of education data available through the digitization of education. This huge amount of education data usually exhibits in a mixture form of images, videos, speech, texts, etc. It is crucial to consider data from different modalities to build successful applications in AI in education (AIED). This tutorial targets AI researchers and practitioners who are interested in applying state-of-the-art multimodal machine learning techniques to tackle some of the hard-core AIED tasks. These include tasks such as automatic short answer grading, student assessment, class quality assurance, knowledge tracing, etc.
A Novel Multi-Task Learning Framework for Semi-Supervised Semantic Parsing

While sequence-to-sequence (seq2seq) models on semantic parsing have demonstrated significant performance, the need for large amounts of labeled data still hinders the application of this technology to resource-poor domains. In this work, we work on alleviating data scarcity in semantic parsing. We propose a semi-supervised semantic parsing methods by exploiting unlabeled natural utterances in a novel multi-task learning framework. Two strategies are proposed. The first one takes entity sequences as training targets to improve the representations of encoder and reduce entity-mistakes in prediction. The second one extends Mean Teacher to seq2seq model and generates more target-side data to improve the generalizability of decoder network. Experiments demonstrate that our proposed methods significantly outperform the supervised baseline and achieve more impressive improvement than previous methods.
Pattern Mining Algorithms

In this chapter, we first look at patterns with their relevance of discovery to business. We then do a survey and evaluation, in terms of advantages and disadvantages, of different mining algorithms that are suited for both traditional and big data sources. These algorithms include those designed for both sequential and closed sequential pattern mining for both the sequential and parallel processing environments.
Manifold-based Test Generation for Image Classifiers

Neural networks used for image classification tasks in critical applications must be tested with sufficient realistic data to assure their correctness. This raises two challenges: first, an adequate subset of the data points must be carefully chosen to inspire confidence, and second, the implicit requirements must be meaningfully extrapolated to data points beyond those in the explicit training set. This paper proposes a novel framework to address these challenges. Our approach is based on the premise that patterns in a large input data space can be effectively captured in a smaller manifold space, from which similar yet novel test cases-both the input and the label-can be sampled and generated. A variant of Conditional Variational Autoencoder (CVAE) is used for capturing this manifold with a generative function, and a search technique is applied on this manifold space to efficiently find fault-revealing inputs. Experiments show that this approach enables generation of thousands of realistic yet fault-revealing test cases efficiently even for well-trained models.
Online Message Passing-based Inference in the Hierarchical Gaussian Filter

We address the problem of online state and parameter estimation in the Hierarchical Gaussian Filter (HGF), which is a multi-layer dynamic model with non-conjugate couplings between upper-layer hidden states and parameters of a lower layer. These non-conjugacies necessitate the approximation of marginalization and expectation integrals, while the online inference constraint renders batch learning and Monte Carlo sampling unsuitable. Here we formulate the problem as a message passing task on a factor graph and propose an online variational message passing-based state and parameter tracking algorithm, which uses Gaussian quadrature to deal with non-conjugacies. We present improved message update rules for all non-conjugate couplings, thus allowing a plug-in inference method for alternative models with equivalent non-conjugate layer couplings. The method is validated on a recorded time series of Bitcoin prices.
Scalable Graph Neural Networks with Deep Graph Library

Learning from graph and relational data plays a major role in many applications including social network analysis, marketing, e-commerce, information retrieval, knowledge modeling, medical and biological sciences, engineering, and others. In the last few years, Graph Neural Networks (GNNs) have emerged as a promising new supervised learning framework capable of bringing the power of deep representation learning to graph and relational data. This ever-growing body of research has shown that GNNs achieve state-of-the-art performance for problems such as link prediction, fraud detection, target-ligand binding activity prediction, knowledge-graph completion, and product recommendations. In practice, many of the real-world graphs are very large. It is urgent to have scalable solutions to train GNN on large graphs efficiently.
A Fingerprint Enhancement and Second-Order Markov Chain Based Malicious Encrypted Traffic Identification Scheme

Malicious encrypted traffic poses great threat to cyber security owing to encryption and the ability to bypass traditional traffic detection schemes. Malicious encrypted traffic identification is a challenging task and has attracted researchers' attention nowadays. Existing research way mainly extracts various statistical features of data-flow, which relies artificial experience heavily. To round the above problem. a fingerprint enhancement and second-order Markov chain based scheme is proposed in this paper, obtaining features more easily. Fingerprint enhancement is done to replace SSL fingerprint by refining data-flow's behavior. Then enhanced fingerprint is fed to second-order Markov chain to obtain dominating feature for identification model. To our best knowledge, this paper is the first one focusing on using fingerprint and second order Markov chain to simplify feature extraction. Finally, the proposed scheme is verified based on public dataset Stratosphere IPS.
Design and Testing of an Ultra-Light Weight Perching System for Sloped or Vertical Rough Surfaces on Mars

In this paper, we present the design, characterization, and functional demonstration of a perching system that enables a flying vehicle to land on rough sloped or vertical surfaces. Steep slopes are of particular scientific interest since they are often associated with geologically interesting features including sites of active modification (e.g. landslides/avalanches, slope streaks), exposed bedrock and/or ice, and as-yet unmodified young features (e.g. walls of fresh craters or polar pits that are actively expanding). However the steep nature of these sites makes access with traditional field robots difficult: ground vehicles are unable to traverse the steep terrain and aerial vehicles are limited by their flight time and an ability to operate near terrain. We propose to address these limitation by enabling the UAV to reliably perch on steep terrain to perform in situ measurements and collect samples. Perching also enables a solar powered UAV to traverse large terrain features such as the Valles Marineris that could not be covered in a single flight by repeatedly perching and recharging its batteries. The proposed perching system that is being developed consists of a microspine gripper, a compliant gripper to vehicle interface, and a flying vehicle equipped with an autonomy sensor suite. The system also includes perception and control algorithms that identify perching targets and execute the required perching maneuver. To date, the majority of the effort has focused on developing and characterizing the microspine gripper. The initial prototype weighs 100 g, is capable of securely grasping a range of natural surfaces, and successful grasps support loads of over 10 N. Refinement of the gripper, integrating and testing it on a UAV, measuring aerodynamic disturbances from wall effects, and developing the required perception and control algorithms is ongoing. This paper describes the overall architecture of our proposed system, the design of the gripper, and its performance during initial testing.