Reviewerly/exHarmony huggingface dataset:
Fast and effective biomedical named entity recognition using temporal convolutional network with conditional random field

&lt;abstract&gt; &lt;p&gt;Biomedical named entity recognition (Bio-NER) is the prerequisite for mining knowledge from biomedical texts. The state-of-the-art models for Bio-NER are mostly based on bidirectional long short-term memory (BiLSTM) and bidirectional encoder representations from transformers (BERT) models. However, both BiLSTM and BERT models are extremely computationally intensive. To this end, this paper proposes a temporal convolutional network (TCN) with a conditional random field (TCN-CRF) layer for Bio-NER. The model uses TCN to extract features, which are then decoded by the CRF to obtain the final result. We improve the original TCN model by fusing the features extracted by convolution kernel with different sizes to enhance the performance of Bio-NER. We compared our model with five deep learning models on the GENIA and CoNLL-2003 datasets. The experimental results show that our model can achieve comparative performance with much less training time. The implemented code has been made available to the research community.&lt;/p&gt; &lt;/abstract&gt;
Augmenting Physics Education with Haptic and Visual Feedback

For educational purposes, virtual reality (VR) is often used to produce life-like experiences. However, the use of haptic feedback in educational practices for science and mathematics remained understudied. Haptically-enabled science simulations (HESSs) enable students to physically experience the concepts being taught via haptic feedback. We present a study on the effect of a buoyancy HESS designed to aid in the understanding of basic physics concepts. We hypothesize that introducing both visual and haptic feedback of the underlying simulated forces will improve participants understanding. We investigate this hypothesis with a 2 (haptics: yes, no) $\times 2$ (visuals: yes, no) between subjects design user study, where all participants were randomly assigned to one of the four conditions. Participants were given a pre-test of buoyancy knowledge, then instructed to interact with the buoyancy simulation, then given a post-test of buoyancy knowledge. The present study is still in the process of data collection, with 40 out of 60 participants. Preliminary results highlight a significant improvement in performance of participants in the haptic-and-visual condition, while no significant differences were observed in other conditions.
DIVA: Domain Invariant Variational Autoencoder.

We consider the problem of domain generalization, namely, how to learn representations given data from a set of domains that generalize to data from a previously unseen domain. We propose the domain invariant VAE (DIVA), a generative model that tackles this problem by learning three independent latent subspaces, one for the class, one for the domain and one for the object itself. In addition, we highlight that due to the generative nature of our model we can also incorporate unlabeled data from known or previously unseen domains. This property is highly desirable in fields like medical imaging where labeled data is scarce. We experimentally evaluate our model on the rotated MNIST benchmark where we show that (i) the learned subspaces are indeed complementary to each other, (ii) we improve upon recent works on this task and (iii) incorporating unlabelled data can boost the performance even further. © Deep Generative Models for Highly Structured Data, DGS@ICLR 2019 Workshop.All right reserved.
Design cues for tobacco communication: Heuristic interpretations and usability of online health information about harmful chemicals

Many people have a poor understanding of the numerous chemicals in tobacco products that cause severe health harms. The US government must display a list of these harmful chemicals for the public. Online disclosures are one promising solution, but evidence is needed for effective design strategies to encourage interpretation and use of information as intended. To examine the impact of website designs for the activation of heuristics and usability perceptions, a national probability sample of US adolescents and adults (n = 1441) was randomized in a 3 (chemical format) × 2 (webpage layout) between-subjects online experiment. Chemicals were displayed as names only, with a visual risk indicator, or with numerical ranges. Layouts displayed health harms at the top of the webpage separate from chemicals or the chemicals grouped by associated health harms. Participants viewed a webpage and reported activated heuristics, usability (perceived ease of use and usefulness), and intentions to use the website. Displaying risk indicators increased website usability by encouraging users to rely on colors to interpret the risk of the chemicals (all p < .01). Website designs that grouped chemicals with harms allowed users to link the chemicals to harms they cause and increased perceived usability and intentions to use the website (all p < .001). Assessing heuristics gives insights for how US adolescents and adults interpret chemical information and the impact of design strategies on usability. Public disclosures of chemicals in tobacco products could be optimized with color-coded risk indicators and layouts placing chemicals near the harms they cause.
Picometer-differential twice-exposed element for three-dimensional measurement with extremely long depth of field

The accuracy of optical three-dimensional (3D) shape measurement is always influenced by the defocusing of a projection or imaging system. In this paper, a novel optical element made by picometer-differential twice-exposed holography, called a picometer comb, is proposed to generate the projection pattern for 3D shape measurement. Two interference fields with picometer-scale period difference are recorded on a substrate to fabricate the picometer comb by twice-exposed laser holography; this element reconstructs the diffraction field, which is essentially the interference between the holograms of two object waves with a slight angle. This picometer comb has the advantage of the generation of a stable light field distribution with extremely long depth of field and small divergence angle. We demonstrate that this diffraction field provides a solution for non-defocusing 3D shape measurement.
The inefficiencies of bitcoins in developing countries

Cryptocurrency is an emerging phenomenon set to revolutionize the financial industry. It utilizes blockchain technology, which ensures data is immutable, transparent and reliable. Cryptocurrency is present in different forms like Blockchain and is present in most parts of the world. Developed nations have a significant interest in technology and have put a measure to investigate while developing nations are also following suit. The global interest in cryptocurrency is associated with the fact that the movement of money will be traceable and it will minimize activities such as money laundering and corruption. The paper analyzes a selected period of six years to establish the efficiency of the Blockchain in the markets. A graph and tables are essential in generating more data for interpretation of the events. The tests used in the analysis are the Ljung-box, R/S Hurst, BDS, Runs, AVR test, and Bartels tests. All the tests demonstrated inefficiencies in the Bitcoin trend, especially in the full sample and slightly low inefficiency in the later subsample. The conclusion made is that bitcoin will improve its efficiency over time as it is an emerging industry that needs to grow. Developing countries experience higher inefficiency combined with the prevalent policy challenges and issues like corruption and unemployment.
Technical and methodological problems of formation of the educational space of digital university

The digital transformation of the economy and the information society affects all aspects of human activity, both in his personal life and in professional and social activities. One of the important aspects of the ongoing global changes is the transformation in education. This is a multifactorial problem that concerns not only technical, technological, methodological solutions, but also serious changes in the mental sphere, both for teachers and students. And here it is necessary to take into account the age of the students, and their preparedness for using various infocommunication solutions in the organization of education, and the readiness of relevant educational and methodological materials, and so on and so forth.Currently, there are a large number of examples of the implementation of infocommunication technologies in education at various levels, starting with preschool education. However, in this article we will focus on considering what seems to be effective in teaching at the level of higher education. This allows us to count on the appropriate level of students’ competencies in terms of computer skills and readiness for more independent mastering of the discipline. At the same time, we note that the problems studied and the proposed solutions can be used at other levels of training with appropriate refinement and adaptation.
Analysis of Chat Conversations between Two Semesters after the Implementation of New Electronic Services at an Academic Health Sciences Center

Library chat queries changed between the Spring 2018 and Spring 2019 semesters. During the Fall 2018 semester, the Library implemented two major services. Researchers saw trends associated with these services reflected in chats. Analysis examined how the frequency of controlled terms applied to chat transcripts changed before and after the services’ implementation. During the Spring 2018 semester, 411 substantive chat conversations occurred. During the Spring 2019 semester, 237 substantive chat conversations occurred. The terms associated with the new services showed a dramatic decline in frequency. An association is strongly suggested between changes in chat frequency and new services.
Skeleton-based STIP feature and discriminant sparse coding for human action recognition

Purpose To find a successful human action recognition system (HAR) for the unmanned environments. Design/methodology/approach This paper describes the key technology of an efficient HAR system. In this paper, the advancements for three key steps of the HAR system are presented to improve the accuracy of the existing HAR systems. The key steps are feature extraction, feature descriptor and action classification, which are implemented and analyzed. The usage of the implemented HAR system in the self-driving car is summarized. Finally, the results of the HAR system and other existing action recognition systems are compared. Findings This paper exhibits the proposed modification and improvements in the HAR system, namely the skeleton-based spatiotemporal interest points (STIP) feature and the improved discriminative sparse descriptor for the identified feature and the linear action classification. Research limitations/implications The experiments are carried out on captured benchmark data sets and need to be analyzed in a real-time environment. Practical implications The middleware support between the proposed HAR system and the self-driven car system provides several other challenging opportunities in research. Social implications The authors’ work provides the way to go a step ahead in machine vision especially in self-driving cars. Originality/value The method for extracting the new feature and constructing an improved discriminative sparse feature descriptor has been introduced.
SMART–JaCaMo: An Organisation-Based Team for the Multi-Agent Programming Contest

The Multi-Agent Programming Contest in 2018 expanded upon the Agents in the City scenario used in the 2016 and 2017 editions of the contest. In this scenario two teams compete to score points by building and attacking wells using realistic city maps from OpenStreetMap. Wells are the main addition to the new version of the scenario; they cost money to build and generate score overtime but can be dismantled by agents from the other team. This, along with other additions, made it a significantly more complex scenario than before. In this paper, we describe the strategies used by our team, highlighting our adaptations and new additions from our participation in the previous years. We have fully explored the use of all three programming dimensions (agent, environment, and organisation) available in JaCaMo, the multi-agent system development platform that we used to implement our team. Our agents were able to dynamically switch between organisational roles, allowing them to promptly respond to changes in the environment and different opponent strategies. We were the highest-scoring team in the contest and our multi-agent system turned out to be stable and robust in solving the difficult problems posed by the contest scenario.
ARTICONF: Towards a Smart Social Media Ecosystem in a Blockchain Federated Environment

The ARTICONF project funded by the European Horizon 2020 program addresses issues of trust, time-criticality and democratisation for a new generation of federated infrastructure, to fulfil the privacy, robustness, and autonomy related promises critical in proprietary social media platforms. It aims to: (1) simplify the creation of open and agile social media ecosystem with trusted participation using a two stage permissioned blockchain; (2) automatically detect interest groups and communities using graph anonymization techniques for decentralised and tokenized decision-making and reasoning; (3) elastically autoscale time-critical social media applications through an adaptive orchestrated Cloud edge-based infrastructure meeting application runtime requirements; and (4) enhance monetary inclusion in collaborative models through cognition and knowledge supply chains. We summarize the initial envisaged architecture of the ARTICONF ecosystem, the industrial pilot use cases for validating it, and the planned innovations compared to related other European research projects.
Internet of Thing for the Commando Robot Command System

Abstract In the current digitalization era, and keep abreast of the industrial 4.0 technological revolution. Wireless communication is now developing very rapidly because of its very efficient use. One of the uses of short-range wireless technology that is widely used is a device that can be integrated with other devices. Communication between robots is the process of delivering information to do commands that are controlled remotely. Communication between robots is an important component in interactions in robots and their environment. In the robotic system, comanndo robots to convey commands to find out the location of the source, for example detecting bombs, detecting metals and detecting suspicious gases, then the commando robot can move to follow orders determined by its master. So that there is a form of communication between robots wirelessly, and this is what is called the internet of things. This form of communication will be a command to the robot commando to follow the movement of the source of detection. The advantage of communication between robots is efficiency in completing the task of knitting. This commando robot uses the Arduino software programming application, so it can make it easier in making commando robots based on the Internet of things.
Mineralogy and lithology of the Upper Cretaceous Niobrara Formation determined by hyperspectral core imaging

Sections of the Upper Cretaceous (Coniacian to Campanian) Niobrara Formation in two cores from Kansas and Colorado, the Amoco Rebecca Bounds and USGS Portland 1, respectively, were examined by hyperspectral core imaging and analysis. A spectral imaging system combining high-resolution photography (50 μm), 3D laser profiling (20 μm), and near-visible + short-wave infrared reflectance spectroscopy (wavelengths from 450 to 2500 nm, 500 μm pixel size) was applied to these cores to provide spectral and textural data facilitating creation of continuous mineral and lithology class maps. In addition, compositing of pixel-based results to group pixels to create mineralogical and lithological logs (0.5-ft resolution) was performed to facilitate comparisons to other geochemical datasets. The results show general correspondence in trends identified by previous geochemistry studies, with some exceptions due to instrumental limitations related to low reflectance of some rock intervals and the limited range of infrared wavelengths examined. This study provides a cursory overview of an extensive dataset meant to demonstrate the utility of hyperspectral core scanning to studies of mudrocks in petroleum systems as well as the kinds of information this technique can provide for detailed examination of stratigraphic features in sedimentary systems more generally.
Drone-Based Face Recognition Using Deep Learning

The next phase of facial recognition research is continuing to finding ways of improving the accuracy rates of models when input images are taken from less than ideal angles and distances, from lower quality images, and from images that do not show much facial information of the person. In this paper, we attempted to use convolutional neural network (CNN) models to accomplish these tasks and attain an improved top accuracy. In this study, we compared three different deep learning models: VGG16, VGG19, and InceptionResNetV2; when testing them in several different facial recognition tasks, using the DroneFace dataset. We used three of the most accurate CNNs, when tested by using the ImageNet database, in an attempt to show that they can be used to achieve high drone face recognition accuracy. After applying these three CNNs to the image dataset used in the study, we compared the accuracy achieved by using each deep learning model in order to see which model was best able to handle and interpret images presented it, when the images provided are taken from a drone. Specifically, we tested how the heights at which the images were taken, by the drone, affected the accuracy of the model at detecting who the photographs were taken of. We attempted to achieve this by training the model at large heights and testing at low heights, by training the model at large heights and testing at low heights, and by training on a random set of 80% of photographs of all subjects and testing on the remaining 20% of photographs of all subjects.
From Federated Learning to Fog Learning: Towards Large-Scale Distributed Machine Learning in Heterogeneous Wireless Networks

Contemporary network architectures are pushing computing tasks from the cloud towards the network edge, leveraging the increased processing capabilities of edge devices to meet rising user demands. Of particular importance are machine learning (ML) tasks, which are becoming ubiquitous in networked applications ranging from content recommendation systems to intelligent vehicular communications. Federated learning has emerged recently as a technique for training ML models by leveraging processing capabilities across the nodes that collect the data. There are several challenges with employing federated learning at the edge, however, due to the significant heterogeneity in compute and communication capabilities that exist across devices. To address this, we advocate a new learning paradigm called {fog learning which will intelligently distribute ML model training across the fog, the continuum of nodes from edge devices to cloud servers. Fog learning is inherently a multi-stage learning framework that breaks down the aggregations of heterogeneous local models across several layers and can leverage data offloading within each layer. Its hybrid learning paradigm transforms star network topologies used for parameter transfers in federated learning to more distributed topologies. We also discuss several open research directions for fog learning.
The Data Science Process: One Culture

We would like to congratulate Professor Brad Efron for the well-deserved honor of winning the 2019 International Statistics Prize, and for his thought-provoking paper discussing the diverging paths...
RACE: QoI-Aware Strategic Resource Allocation for Provisioning Se-aaS

In this paper, the problem of ensuring profitability for multiple sensor-owners in sensor-cloud, while satisfying the service requirements of end-users, is studied. In traditional sensor-cloud, Sensor-Cloud Service Provider (SCSP) solely dictates the service provisioning process. However, the SCSP cannot always ensure high profits for sensor-owners, who incur significant maintenance costs for their sensor-nodes. Contrarily, it is highly essential to meet the Quality-of-Information (QoI) requirements of end-users to ensure their service satisfaction. Existing works proposed a few node allocation schemes which neither consider the cost incurred by sensor-owners nor the QoI of sensed-data in sensor-cloud. To address this problem, a strategic resource allocation scheme, named RACE, is proposed, which introduces the participation of sensor-owners in the node allocation process. First, utility theory is used to calculate the optimum number of nodes to be allocated for a service. Thereafter, single leader multiple followers Stackelberg game is formulated to decide the number of nodes to be contributed by each sensor-owner and the price to be charged. Simulation-based experimental results reveal that, using RACE, the profits of the sensor-owners and those of the SCSP increase by 86.11–89.26 percent and 41.95–80.82 percent, respectively, as compared to the existing benchmark schemes, while considering that each sensor-node is capable of serving multiple applications simultaneously. Moreover, service availability in sensor-cloud increases by 31.70–96.96 percent using RACE.
Accelerated Stochastic Gradient-free and Projection-free Methods

In the paper, we propose a class of accelerated stochastic gradient-free and projection-free (a.k.a., zeroth-order Frank-Wolfe) methods to solve the constrained stochastic and finite-sum nonconvex optimization. Specifically, we propose an accelerated stochastic zeroth-order Frank-Wolfe (Acc-SZOFW) method based on the variance reduced technique of SPIDER/SpiderBoost and a novel momentum accelerated technique. Moreover, under some mild conditions, we prove that the Acc-SZOFW has the function query complexity of $O(d\sqrt{n}\epsilon^{-2})$ for finding an $\epsilon$-stationary point in the finite-sum problem, which improves the exiting best result by a factor of $O(\sqrt{n}\epsilon^{-2})$, and has the function query complexity of $O(d\epsilon^{-3})$ in the stochastic problem, which improves the exiting best result by a factor of $O(\epsilon^{-1})$. To relax the large batches required in the Acc-SZOFW, we further propose a novel accelerated stochastic zeroth-order Frank-Wolfe (Acc-SZOFW*) based on a new variance reduced technique of STORM, which still reaches the function query complexity of $O(d\epsilon^{-3})$ in the stochastic problem without relying on any large batches. In particular, we present an accelerated framework of the Frank-Wolfe methods based on the proposed momentum accelerated technique. The extensive experimental results on black-box adversarial attack and robust black-box classification demonstrate the efficiency of our algorithms.