Reviewerly/exHarmony huggingface dataset:
Predicting Opportunities and Challenges Prior to Transitioning to Digital Pathology: An Interview Envisioning Study with 11 Pathologists

There is growing interest in implementing whole-slide imaging (WSI) for primary diagnosis. Ten subspecialized pathologists (2-39 years of experience) were interviewed from diverse subspecialty areas in a large academic medical center. Relevant semi-structured interview questions included image quality, workflow, and usability during clinical use. Analysis revealed that WSI implementation would raise some concerns: 1) delaying turn-around time for preliminary diagnoses as well as final reports, 2) delaying access to order ancillary tests, which could impact scheduling for some patients for follow-on surgery, 3) making it hard to track what has been reviewed and at what level of magnification, 4) potentially increase wrist pain from using a mouse or neck pain while viewing the monitor, and 5) increase vulnerability when a computer, monitor, or network goes down. However, WSI implementation would enable 1) increased flexibility for slide access remotely during nights, weekends, and work trips, 2) easier consults among peers and with mentors, 3) easier detection and recovery when images are routed to the wrong person, 4) creating digital libraries, including previous slides from a current patient, and 5) giving greater access to images for presentations, publications, and to other clinical personnel. Implementing digital slides and associated workflow will introduce many challenges and barriers. Studies such as the current one are much needed to explore the pathologist’s perspective on these workflow and implementation challenges prior to and during the installation of the digital pathology systems. These perspectives are important to understand in order to improve the experience of the pathologists and lab personnel as they interact with these systems.
Representing the spatiotemporal structure of visual events: Spatial and temporal frames of reference in working memory

All natural visual events exhibit a spatiotemporal structure, but our understanding of how temporal aspects are represented lags far behind our understanding of the role of space. Here, we present data indicating that a combination of both the spatial and the temporal structure of visual events provides frames of reference for storage in working memory. In a series of experiments, participants performed a change detection task, for which they memorized colours presented sequentially at different locations and with different stimulus onset asynchronies. Each item could be uniquely identified by its spatial or temporal coordinates, but neither spatial nor temporal information was task-relevant. The key manipulation was that of retrieval context: The test array was identical to the memory array in terms of either its entire spatiotemporal structure, or only its spatial or temporal components. Removing either spatial or temporal information at retrieval impaired performance, indicating that memory relied, at least to some degree, on the spatiotemporal structure in which items were initially perceived. Overall, more weight appeared to be assigned to the spatial structure, but there were pronounced individual differences in the relative weighting of spatial and temporal information, which were fairly consistent throughout and across sessions. Conceivably, these are indicative of stable individual preferences for coding in the spatial or temporal domain. We observed no substantial differences between a randomly interleaved and a blocked variation of retrieval context, suggesting that encoding in a spatiotemporal frame of reference was not strategically adjusted to task demands. However, the inter-item spacing of spatial and temporal structures influenced the relative weighting of spatial and temporal information. Favouring the frame of reference that is more widely spaced presumably facilitates item individuation and thereby access to representations. We propose that time may serve a similar function as space in the architecture of visual working memory.
IMPLEMENTASI LOGIKA FUZZY MAMDANI PADA SISTEM REKOMENDASI PERPANJANGAN KONTRAK KERJA KARYAWAN

Distributor companies in carrying out activities require better employees. In determining the good employees, often meet obstacles, usually the assessment is still objective, especially in terms of contract extension. In accordance with these needs the writer will create a system that can determine the extension of the contract to be more objective and accurate.&#x0D; In planning the system, he development method used is the waterfall method and the development tools used are flowcharts, data flow diagrams and the method used for prediction is to use the fuzzy method, the writer chose fuzzy mamdani to help solve predictions in this system. The fuzzy mamdani method uses several criteria or variables including Absence, Achievement, Long Work, Cooperation, Graduates, Age. For the application of the application system supporting the decision of a contract extension is Microsoft Visual Basic 6.0.&#x0D; The final results of this study are employees whose work contracts are extended by a value of 1.7. Imron Rusdi 1.5. Arif Hamdani 1.5. Suanda 4. Risman 2. Wardoyo 2..
Phase retrieval of a transparent object from intensity measurements

Surface characterization using a non-interferometric optical system is presented. The 3D phase of the object is extracted based on the Stokes vector estimation. Merits of the proposed system are that it is simple and robust.
Synopticon

Synopticon is a collection of tools for managing complex, multi-sensory data streams when conducting behavioral research in simulators or on the road. Synopticon's functionality includes automatic gaze object detection, multi-camera synchronization, camera-sensor synchronization (e.g. physiological sensors), camera-simulator synchronization, and support for computer vision and machine learning.
Computationally Efficient Learning of Quality Controlled Word Embeddings for Natural Language Processing

Deep learning (DL) has been used for many natural language processing (NLP) tasks due to its superior performance as compared to traditional machine learning approaches. In DL models for NLP, words are represented using word embeddings, which capture both semantic and syntactic information in text. However, 90-95% of the DL trainable parameters are associated with the word embeddings, resulting in a large storage or memory footprint. Therefore, reducing the number of word embedding parameters is critical, especially with the increase of vocabulary size. In this work, we propose a novel approximate word embeddings approach for convolutional neural networks (CNNs) used for text classification tasks. The proposed approach significantly reduces the number of model trainable parameters without noticeably sacrificing in computing performance accuracy. Compared to other techniques, our proposed word embeddings technique does not require modifications to the DL model architecture. We evaluate the performance of the the proposed word embeddings on three classification tasks using two datasets, composed of Yelp and Amazon reviews. The results show that the proposed method can reduce the number of word embeddings parameters by 98% and 99% for the Yelp and Amazon datasets respectively, with no drop in computing accuracy.
Stage-GAN with Semantic Maps for Large-scale Image Super-resolution

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs.However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration.In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors.We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process.In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN.In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN.In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details.Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements.For scale factor ×8, our method performs favorably against other methods in terms of gradients similarity.
Decision Support System for Badan Ekonomi Kreatif Indonesia

Badan Ekonomi Kreatif Indonesia or Bekraf is an institution formed by the government to support the creative industry which consists of 16 fields: architecture; interior design; visual communication design; product design; movies, animations and videos; photography; craft; culinary; music; fashion, application and game developer; publishing; advertising; television and radio; performing Arts; and fine arts. In its implementation, this agency provides assistance to communities throughout Indonesia to develop their creative efforts. The Indonesian government provides full support for the creative industry. The government provides assistance to the creative industry for facilitation of revitalizing physical infrastructure or funding and other guidance and assistance. To get this assistance the community must submit a proposal that will be assessed for its feasibility The proposal received will be assessed for its feasibility by considering several predetermined criteria Therefore, we create a system that can help management make the decision to provide assistance to those who need assistance. The result is a decision making model that helps management to deliver its assistance.
Unsupervised Basis Function Adaptation for Reinforcement Learning

When using reinforcement learning (RL) algorithms it is common, given a large state space, to introduce some form of approximation architecture for the value function (VF). The exact form of this architecture can have a significant effect on an agent's performance, however, and determining a suitable approximation architecture can often be a highly complex task. Consequently there is currently interest among researchers in the potential for allowing RL algorithms to adaptively generate (i.e. to learn) approximation architectures. One relatively unexplored method of adapting approximation architectures involves using feedback regarding the frequency with which an agent has visited certain states to guide which areas of the state space to approximate with greater detail. In this article we will: (a) informally discuss the potential advantages offered by such methods; (b) introduce a new algorithm based on such methods which adapts a state aggregation approximation architecture online and is designed for use in conjunction with SARSA; (c) provide theoretical results, in a policy evaluation setting, regarding this particular algorithm's complexity, convergence properties and potential to reduce VF error; and finally (d) test experimentally the extent to which this algorithm can improve performance given a number of different test problems. Taken together our results suggest that our algorithm (and potentially such methods more generally) can provide a versatile and computationally lightweight means of significantly boosting RL performance given suitable conditions which are commonly encountered in practice.
BLOCKCHAIN IN FINANCIAL SERVICES

Banks and financial institutions have been spending significant sums to explore potential applications of blockchain technology. The financial services industry is among those areas predicted to benefit the most from blockchain use in the years to come. This chapter investigates this prediction after providing a primer on blockchain. It analyses the potential of blockchain to increase efficiencies and to transform business processes in a number of areas within the industry such as the trade life-cycle of securities and of derivatives, payments, traditional financing arrangements, asset management, insurance and corporate governance. It also examines new currencies and cryptoassets, phenomena ultimately enabled by the use of blockchain technology.
Detection and classification of power quality events using empirical wavelet transform and error minimised extreme learning machine

The main purpose of this paper is to detect the power quality events (PQEs) by empirical wavelet transform (EWT) and classify by error minimised extreme learning machine (EMELM). Empirical wavelet transform (EWT) is used to analyse the non-stationary power quality event signals by multi-resolution analysis (MRA). Here, the disturbance energy index feature vector of different electric power supply signals have been acquired by applying the EWT on all the spectral components and to analyse the overall efficiency of the proposed method on both ideal and noisy environments, three types of PQ event data sets are constructed by accumulating the noise of 25, 35 and 45 dB. Extreme learning machine (ELM) is an advanced and efficient classifier, which is implemented to recognise the single as well as multiple PQ fault classes. Based on very high performance under ideal and noisy environment, the new EWT-EMELM method can be implemented in real electrical power systems. The feasibility of proposed method is tested by simulation to verify its cogency.
AR-VIS: Augmented Reality Interactive Visualization Environment for Exploring Dynamic Scientific Data

The AR Vis project is a general-purpose interactive data visualization platform for collaborative interaction with scientific data. The platform is designed for augmented reality displays of data supporting multi-user interaction and simulations. Methods developed include a procedural pipeline for data culling, modeling, visualization, and porting to multiuser augmented reality. A prototype interactive visualization application demonstrates the system via visualization and simulation of magnetic fields. The magnetic field visualizations are attached to physical objects or embedded in the environment. The invisible magnetic fields are transformed into tangible models of nano and geospatial scales magnetic phenomena accessible to a user’s full body (embodied) interaction. The project seeks to make a significant contribution to scientific visualization. Extending beyond the cognitive impact of traditional scientific visualization, the goal of the AR Vis platform is to additionally leverage human perception and spatial cognition and make data patterns tangible, manipulable and more accessible. In supporting augmented information cognition in scientists and learners, AR Vis design supports data discovery and learning. The prototype implementation uses physics data modeling of the invisible and largely intangible forces of magnetism across different scales. The project yields both a prototype platform and develops a data visualization pipeline. Both demonstrate a substantial and concrete implementation and demonstration of AR Vis techniques.
Optimizing Resource Allocation in Hierarchically Distributed Data Centers

The current networks infrastructure needs to support the rapidly increasing data traffic. Sophisticated planning approaches must be adopted by the operators so the high number of applications can be managed efficiently. In this work, a resource provisioning model for hierarchically distributed data centers is proposed using Integer Linear Programming (ILP). The objective is to increase the efficiency in the use of computational resources and decrease the overhead in network links. Results show that the model is able to efficiently accommodate 20% more applications when compared to the First-Fit approach.
Named Entity Recognition and Aspect based Sentiment Analysis

Twitter is a microblogging website where people can share their feelings quickly and spontaneously by sending a tweets limited by 280 characters.You can directly address a tweet to someone by adding the target sign "@" or participate to a topic by adding an hashtag "#" to your tweet.Here specific hashtag (#) based tweets are downloaded using tweepy and they are cleansed for removal of irrelevant data then Entity Recognition is performed using the NER Algorithm which specifies different entities belonging to that tweet eg person, place, organization, etc. and finally sentiment analysis is performed where we analyze the general sentiment that can either be positive, negative or neutral at the entity level.
Visualizing evidence for Alzheimer's disease in deep neural networks trained on structural MRI data

Deep neural networks have led to state-of-the-art results in many medical imaging tasks including Alzheimer's disease (AD) detection based on structural magnetic resonance imaging (MRI) data. However, the network decisions are often perceived as being highly non-transparent, making it difficult to apply these algorithms in clinical routine. In this study, we propose using layer-wise relevance propagation (LRP) to visualize convolutional neural network decisions for AD based on MRI data. Similarly to other visualization methods, LRP produces a heatmap in the input space indicating the importance/relevance of each voxel contributing to the final classification outcome. In contrast to susceptibility maps produced by guided backpropagation (Which change in voxels would change the outcome most?), the LRP method is able to directly highlight positive contributions to the network classification in the input space. In particular, we show that (1) the LRP method is very specific for individuals (Why does this person have AD?) with high inter-patient variability, (2) there is very little relevance for AD in healthy controls and (3) areas that exhibit a lot of relevance correlate well with what is known from literature. To quantify the latter, we compute size-corrected metrics of the summed relevance per brain area, e.g., relevance density or relevance gain. Although these metrics produce very individual fingerprints of relevance patterns for AD patients, a lot of importance is put on areas in the temporal lobe including the hippocampus. After discussing several limitations such as sensitivity toward the underlying model and computation parameters, we conclude that LRP might have a high potential to assist clinicians in explaining neural network decisions for diagnosing AD (and potentially other diseases) based on structural MRI data.
Robust RLS Wiener Fixed-Lag Smoother for Discrete-Time Stochastic Systems with Uncertain Parameters

This paper, by combining the robust recursive least-squares (RLS) Wiener filter and the RLS Wiener fixed-lag smoothing algorithm, proposes the robust RLS Wiener fixed-lag smoothing algorithm. In the robust estimation problem, it is assumed that the system and observation matrices include some uncertain parameters. With the observations generated by the state-space model including the uncertain parameters, the robust RLS Wiener fixed-lag smoother estimates the signal recursively as the time advances. Both the signal and the degraded signal processes are fitted to the finite order auto-regressive (AR) models. The robust RLS Wiener fixed-lag smoother uses the following information. (1) The covariance function of the state for the degraded signal. (2) The cross-covariance function of the state for the signal with the state for the degraded signal. (3) The observation matrices for the signal and the degraded signal. (4) The system matrices for the signal and the degraded signal. (5) The variance of the white observation noise. A numerical simulation example shows that the robust RLS Wiener fixed-lag smoother, proposed in this paper, is superior in estimation accuracy to the H-infinity RLS Wiener fixed-point smoother and the RLS Wiener fixed-lag smoother. In the appendix, by using MAXIMA and MATLAB, the derivation method of the coefficients, used in the robust RLS Wiener fixed-lag smoothing algorithm, is shown.
Learning a Neural-network-based Representation for Open Set Recognition

In this paper, we present a neural network based representation for the Open Set Recognition problem.In this representation instances from the same class are close to each other while instances from different classes are further apart.When used for Open Set Recognition tasks, evaluated on three datasets from two different domains, the proposed approach results in a statistically significant improvement compared to other approaches.
Thirteen years of SysML: a systematic mapping study

The OMG standard Systems Modeling Language (SysML) has been on the market for about thirteen years. This standard is an extended subset of UML providing a graphical modeling language for designing complex systems by considering software as well as hardware parts. Over the period of thirteen years, many publications have covered various aspects of SysML in different research fields. The aim of this paper is to conduct a systematic mapping study about SysML to identify the different categories of papers, (i) to get an overview of existing research topics and groups, (ii) to identify whether there are any publication trends, and (iii) to uncover possible missing links. We followed the guidelines for conducting a systematic mapping study by Petersen et al. (Inf Softw Technol 64:1–18, 2015) to analyze SysML publications from 2005 to 2017. Our analysis revealed the following main findings: (i) there is a growing scientific interest in SysML in the last years particularly in the research field of Software Engineering, (ii) SysML is mostly used in the design or validation phase, rather than in the implementation phase, (iii) the most commonly used diagram types are the SysML-specific requirement diagram, parametric diagram, and block diagram, together with the activity diagram and state machine diagram known from UML, (iv) SysML is a specific UML profile mostly used in systems engineering; however, the language has to be customized to accommodate domain-specific aspects, (v) related to collaborations for SysML research over the world, there are more individual research groups than large international networks. This study provides a solid basis for classifying existing approaches for SysML. Researchers can use our results (i) for identifying open research issues, (ii) for a better understanding of the state of the art, and (iii) as a reference for finding specific approaches about SysML.
Monocular 3D Object Detection with Decoupled Structured Polygon Estimation and Height-Guided Depth Estimation

Monocular 3D object detection task aims to predict the 3D bounding boxes of objects based on monocular RGB images. Since the location recovery in 3D space is quite difficult on account of absence of depth information, this paper proposes a novel unified framework which decomposes the detection problem into a structured polygon prediction task and a depth recovery task. Different from the widely studied 2D bounding boxes, the proposed novel structured polygon in the 2D image consists of several projected surfaces of the target object. Compared to the widely-used 3D bounding box proposals, it is shown to be a better representation for 3D detection. In order to inversely project the predicted 2D structured polygon to a cuboid in the 3D physical world, the following depth recovery task uses the object height prior to complete the inverse projection transformation with the given camera projection matrix. Moreover, a fine-grained 3D box refinement scheme is proposed to further rectify the 3D detection results. Experiments are conducted on the challenging KITTI benchmark, in which our method achieves state-of-the-art detection accuracy.