Reviewerly/exHarmony huggingface dataset:
Exploring Twitter use and services of academic innovation centers

This study examined the Twitter streams and websites of 36 university innovation centers and identified 14 service categories the centers offered. Exploring the present Twitter use practices of innovation centers and the services the centers provide can inform the design and planning of service offerings at new innovation centers and support training for center staff in the use of this social media platform. In addition, existing innovation centers can benchmark their service offerings against those services. Furthermore, mapping the services the innovation centers offer to the activities in an innovation workflow model can help center managers optimize the information architecture of their websites and resource guides. In this way, students can easily be informed about the help and resources available for each activity or phase of the innovation process. A comparison of the tweet categories identified in the present study with those of academic libraries assembled in a previous study revealed significant overlap, but some differences as well. In contrast to the Twitter accounts of academic libraries, the Twitter accounts of innovation centers did not tweet about their information services even if they offered them. Innovation centers also did not use Twitter to provide Q&A services to their users. Furthermore, innovation centers tweeted not only about the technological resources they provided, but also about the human resources they recruited to serve as student mentors and advisors. Finally, technology use was more mediated in innovation centers than in libraries, and some centers offered their users fee-based assistance from professionals with their 3D design and printing tasks.
A deep learning approach for length of stay prediction in clinical settings from medical records

Deep neural networks are becoming an increasingly popular solution for predictive modeling using electronic health records because of their capability of learning complex patterns and behaviors from large volumes of patient records.In this paper, we have applied an autoencoded deep neural network algorithm aimed at identifying short(0-7 days) and long stays (>7 days) in hospital based on patient admission records, demographics, diagnosis codes and chart events.We validated our approach using the de-identified MIMIC-III dataset.This proposed Autoencoder+DNN model shows that the two classes are separable with 73.2% accuracy based upon ICD-9 and demographics features.Once vital chart events data such as body temperature, blood pressure, heart rate information available after 24 hour of admission is added to the model, the classification accuracy is increased up to 77.7%.Our results showed a better performance when compared to a baseline random forest model.
Resource-Aware Optimization of DNNs for Embedded Applications

Despite their outstanding success in solving complex computer vision problems, Deep Neural Networks (DNNs) still require high-performance hardware for real-time inference. Therefore they are not applicable to low-cost embedded hardware, where memory resources, computational performance and power consumption are restricted. Furthermore, current approaches of fitting neural networks to embedded hardware are time consuming, inducing slow development cycles. To address these drawbacks and satisfy the demands of embedded hardware, this paper proposes a computationally efficient magnitude-based pruning scheme, based on a half-interval search, combined with effective weight sharing, fixed-point quantization, and lossless compression. The proposed solution can be utilized to generate an optimized model, either with respect to memory demand or execution time. For instance, the memory demand of LeNet is compressed about 385×. VGG16 is pruned by about 14.5×, whilst its computational costs are reduced by about 1.6× for a CPU-based application and 4.8× for an FPGA one.
Identifying Security Bug Reports Based Solely on Report Titles and Noisy Data

Identifying security bug reports (SBRs) is a vital step in the software development life-cycle. In supervised machine learning based approaches, it is usual to assume that entire bug reports are available for training and that their labels are noise free. To the best of our knowledge, this is the first study to show that accurate label prediction is possible for SBRs even when solely the title is available and in the presence of label noise.
Toward Making Unsupervised Graph Hashing Discriminative

Recently, hashing has attracted much attention in visual information retrieval due to its low storage cost and fast query speed. The goal of hashing is to map original high-dimensional data into a low-dimensional binary-code space where the similar data points are assigned similar hash codes and dissimilar points are far away from each other. Existing unsupervised hashing methods mainly focus on recovering the pairwise similarity of the original data in hash space, but do not take specific measures to make the generated binary codes to be discriminative. To address this problem, this paper proposes a novel unsupervised hashing method, named “Discriminative Unsupervised Graph Hashing” (DUGH), which takes both similarity and dissimilarity of original data into consideration to learn discriminative binary codes. In particular, a probabilistic model is utilized to learn the encoding of original data in low-dimensional space, which models the original neighbor structure through both positive and negative edges in the KNN graph and then maximizes the likelihood of observing these edges. To efficiently and accurately measure the neighbor structure for largescale datasets, we propose an effective KNN graph construction algorithm based on the random projection tree and neighbor exploring techniques. The experimental results on one synthetic dataset and four typical real-world image datasets demonstrate that the proposed method significantly outperforms the state-of-the-art unsupervised hashing methods.
Hybrid advanced player selection strategy based population search for global optimization

Motivation: Finding the optimum solution of real world complex optimization problem needs efficient solver system. Meta-heuristic like swarm, evolutionary or physics based methods essentially form an expert system which may strive for the search of optimum solution. However, these methods have their own merits and demerits. To supersede the performance of existing systems in context of real time and advanced problems and to propose new intelligent method having an optimal balance between exploration and exploitation phase have become a center of focus of the expert system design. Therefore, being inspired from such a scenario, a hybrid advanced player selection strategy based population search (HAPS-PS) technique is proposed and evaluated in this paper in context of modern applications. Methods: Sport is a multi-functional activity where different learning methods like cooperative, competitive and self-learning variants along with interactive environmental features help to improve the performance of the players. The properties of playing activity and the thematic learning process of sports, along with some features like, chaotic map based initialization, pool based topological structure selection, integration of blended laplacian operator etc. contributes in the design of HAPS-PS. Also a novel mechanism of team reformation through elimination of comparatively poor players and recruitment of new talents is employed. It provides diversification and regulates the search agents in a promising direction. Results: We have comprehensively evaluated the performance of the HAPS-PS by applying it on standard benchmark problems and on various shifted rotated, hybrid and composite problems of CEC2013 and CEC2014. Also some of the competitive problems of CEC 2017 are tested using the proposed method and they are also compared with other state-of-the-art algorithm to validate the efficacy of the system. Further, tests are carried out on some real-world problems from CEC2011 which includes applications related to parameter optimization of frequency-modulated (FM) sound wave, minimization of molecular potential energy of Lennard-Jones cluster, controlling the chemical reactors, and evaluation of the inter atomic tersoff potentials for covalent systems of silicon. The proposed method is also applied on spread spectrum radar polyphase code design problem as a part of real world application. The comparative results validate the effectiveness and efficiency of HAPS-PS.
The geochemistry of Au–Ag minerals and base‐metal sulphides as indicators for gold precipitation: Case study of the Shihu gold deposit, central North China Craton

The Shihu quartz vein‐type gold deposit, the largest gold deposit in the central North China Craton, occurs within wall rocks of Neoarchean TTG (tonalite–trondhjemite–granodiorite) gneiss and shows a close relationship with the early Cretaceous Mapeng granitoid pluton and various intermediate–mafic dykes. The occurrence, morphology, composition, and precipitation mechanism of the Au–Ag minerals are short of research, which is the purpose of this study. The primary mineral assemblage of orebody contains quartz, calcite, and base‐metal sulphides including pyrite, galena, sphalerite, chalcopyrite, and minor pyrrhotite and enargite. The Au–Ag minerals can be classified into three distinct types, namely, (a) quartz‐hosted, (b) pyrite‐hosted, and (c) base‐metal sulphidehosted, corresponding to formation sequence. Electron microprobe analysis indicates that these Au–Ag minerals show fineness (=Au/[Au + Ag] × 1,000) ranging from 976 to 366 with an average of 667 and are rich in Bi, Te, and Hg while deficient of Cu, As, and Se. Bismuth and tellurium in the Au–Ag minerals show a linear relationship with Au, whereas in the coexisted sulphides are almost below detection limits. We suggest that Bi and Te fundamentally influenced the transportation, enrichment, and precipitation of gold as scavengers to concentrate and deposit gold at the location with an “appropriate distance” from magma. Phase analyses of native bismuth and Bi–Te minerals included in pyrite, reported for the first time in Shihu Gold deposit, imply that the gold precipitation witnessed the ore‐forming fluid evolution with log f Te 2 decreasing from approximately −11 to approximately −15 and log f S 2 increasing from approximately −13 to more than −11. Mineralogical mapping with the fineness of Au–Ag minerals (fineness contours) suggest that the ore‐forming fluids came from the deep earth beneath the north of the mining area with excellent gold potential, which has been verified by later drilling.
Transforming Convolutional Neural Network to an Interpretable Classifier

For machine learning based methods to be accepted in practice, these methods should be either directly or indirectly interpretable or should at least provide a reliable estimate of a confidence degree about their prediction. This is of the importance primarily in critical domains like medicine where the decision should be clearly justified. Although deep neural networks achieve remarkable performance in almost any computer vision task, due to their complexity, their predictions remain difficult to interpret and explain. An interpretable classifier does not only provide the prediction but also a kind of evidence or explanation. In this paper, we present an effective way of transforming a sufficiently well trained convolutional neural network (98.50% accuracy on MNIST dataset, which contains small images of digits) to an interpretable classifier. We use hidden activations of training observations as their descriptors. Next, for every unseen observation, we use these descriptors to identify three most similar training samples based on cosine distance of their hidden activations. Using this approach, we are able not only to predict the class of an unknown observation, but also to justify the prediction by providing three most similar training observations in exchange to a slightly decreased accuracy (98.12%).
High-dimensional distributed semantic spaces for utterances

Abstract High-dimensional distributed semantic spaces have proven useful and effective for aggregating and processing visual, auditory and lexical information for many tasks related to human-generated data. Human language makes use of a large and varying number of features, lexical and constructional items as well as contextual and discourse-specific data of various types, which all interact to represent various aspects of communicative information. Some of these features are mostly local and useful for the organisation of, for example, argument structure of a predication; others are persistent over the course of a discourse and necessary for achieving a reasonable level of understanding of the content. This paper describes a model for high-dimensional representation for utterance and text-level data including features such as constructions or contextual data, based on a mathematically principled and behaviourally plausible approach to representing linguistic information. The implementation of the representation is a straightforward extension of Random Indexing models previously used for lexical linguistic items. The paper shows how the implementedmodel is able to represent a broad range of linguistic features in a common integral framework of fixed dimensionality, which is computationally habitable, and which is suitable as a bridge between symbolic representations such as dependency analysis and continuous representations used, for example, in classifiers or further machine-learning approaches. This is achieved with operations on vectors that constitute a powerful computational algebra, accompanied with an associative memory for the vectors. The paper provides a technical overview of the framework and a worked through implemented example of how it can be applied to various types of linguistic features.
Self-position awareness-based presence and interaction in virtual reality

The awareness of self-position, which refers to the location of our cyclopean eye, is essential to the experience of being immersed and for interacting with a virtual environment. Currently, individual variability in the cyclopean eye location is not considered when presenting virtual reality content. The effect of the cyclopean eye shift was analyzed with theoretical and experimental methods, and it was found that human vision is sensitive to the visual direction offset caused by the cyclopean eye shift. To compensate for the cyclopean eye shift, an original model is proposed with shifting the 3D camera system accordingly. The proposed method is expected to make all individuals with different cyclopean eye shifts perceive the virtual environment from the same viewpoint as the designer desired and may achieve more accurate interaction.
Verifying effectful Haskell programs in Coq

We show how various Haskell language features that are related to ambient effects can be modeled in Coq. For this purpose we build on previous work that demonstrates how to reason about existing Haskell programs by translating them into monadic Coq programs. A model of Haskell programs in Coq that is polymorphic over an arbitrary monad results in non-strictly positive types when transforming recursive data types likes lists. Such non-strictly positive types are not accepted by Coq's termination checker. Therefore, instead of a model that is generic over any monad, the approach we build on uses a specific monad instance, namely the free monad in combination with containers, to model various kinds of effects. This model allows effect-generic proofs.
Visually Lossless Compression of Dental Images

It has become a standard practice to use images in medicine. Due to increasing resolution, their size often occurs to be large and, then, necessity to compress them efficiently before storage and/or transmission arises. Since compression ratio of lossless compression is frequently limited, one has to apply lossy compression. Then, a problem of providing an acceptable quality to retain diagnostic value of compressed data appears. This paper deals with analyzing opportunities to perform this in non- iterative way for dental medical images for two versions of a coder based on discrete cosine transform (DCT) - AGU and AGU-M. It is demonstrated that mean squared error (MSE) and MSE modified with taking into account peculiarities of human vision system (MSEHVS) of distortions due to lossy compression can be predicted before starting compression itself. Then, a desired quantization step (QS) for AGU or scaling factor (SF) for AGU-M can be adjusted to provide a desired quality. Regression uses statistics of alternating current (AC) DCT coefficients calculated in 300...500 8x8 pixel blocks to predict output metrics using fitting curves in preliminary obtained scatter-plots.
BTEL: A Binary Tree Encoding Approach for Visual Localization

Visual localization algorithms have achieved significant improvements in performance thanks to recent advances in camera technology and vision-based techniques. However, there remains one critical caveat: all current approaches that are based on image retrieval currently scale at best linearly with the size of the environment with respect to both storage, and consequentially in most approaches, query time. This limitation severely curtails the capability of autonomous systems in a wide range of compute, power, storage, size, weight or cost constrained applications such as drones. In this work, we present a novel binary tree encoding approach for visual localization which can serve as an alternative for existing quantization and indexing techniques. The proposed tree structure allows us to derive a compressed training scheme that achieves sub-linearity in both required storage and inference time. The encoding memory can be easily configured to satisfy different storage constraints. Moreover, our approach is amenable to an optional sequence filtering mechanism to further improve the localization results, while maintaining the same amount of storage. Our system is entirely agnostic to the front-end descriptors, allowing it to be used on top of recent state-of-the-art image representations. Experimental results show that the proposed method significantly outperforms state-of-the-art approaches under limited storage constraints.
Assessment of a novel, smartglass-based control device for electrically powered wheelchairs

Background A variety of conditions can lead to reduced ambulation and the need of an electrically powered wheelchair (EPW). Some people are limited in their ability to use any of the available control devices for EPWs.Objective To assess safety and maneuverability of a new smartglass-based head control device (Munevo DRIVE©).Methods Participants drove four indoor test courses with their own control device (or with a hand joystick in case of pedestrians) and with the new smartglass control device. A penalty was added for every driving error and the time of the best attempt was compared between control devices. Minimal driving errors were measured as a secondary outcome. In addition, participants filled in questionnaires to assess their subjective impressions.Results Nine EPW users and five non-disabled persons were tested in this single center pilot trial. As anticipated, participants were slower using the smartglass-based control device (in median 25.0%). Notably though, the minimal amount of driving errors was equal between groups. One adverse event occurred (collision with consecutive swelling of the ankle).Conclusions Smartglass control enables safe maneuverability for people with various diseases.Implications for rehabilitationMunevo DRIVE© is a novel, smartglass-based, head control device for electrically powered wheelchairs.Smartglass control is slower as hand joystick control but enables safe maneuverability.Smartglass control is an alternative for people with impaired or lost upper limb function, for example, those currently using chin joysticks or similar devices.
An Industrial Analysis Technology About Occupational Adaptability and Association Rules in Social Networks

With the enormous growth of the content and users of social network, user portrait based on social network have been widely used in industrial applications such as artificial intelligence, recommendation systems, etc. The issue has been extensively studied, and most of them have focused on user attribute mining and behavior prediction. However, there is little research on the intrinsic relationship between big data-based interests and skills and occupational adaptability. To clarify this issue, in this article, we first collect a large amount of user data from LinkedIn. Then, we filter high-frequency interests and skills, and explore relevant characteristics of interests and skills through relevant analytical methods, found that there are many association rules. Next, the users are grouped according to the characteristics of occupational adaptability, and the relationship between the association rules of interests and skills and user adaptability is studied. Finally, designed an occupational adaptive classifier based on association rules. This article reveals the connections and dependencies between human interests and professional skills, as well as the impact of these association rules on user career adaptability, also discusses the prospects of these results in industrial applications. We hope that our research results will provide a solid theoretical foundation for other areas of research and industrial applications, such as career adaptability judgment, interest training, career development, career recommendation, etc.
A Probabilistic Multi-Agent System Architecture for Reasoning in Smart Homes

Uncertainty is inevitable in ambient assisted living (AAL) environments as sensors may read inaccurate data or due to the existence of unobserved variables for privacy reasons. Furthermore, the dynamic nature of the home environment and vague human communications may result in ambiguous, incomplete and inconsistent contextual information, which ultimately lead the smart home system into uncertainty. This paper aims to tackle some of these challenges, in particular, uncertainty due to vague human communication and missing information in ambient environments. For this, we proposed a probabilistic multi-agent system architecture for reasoning in smart homes by utilizing the notion of multiagent systems (MAS) technologies and probabilistic logic programming techniques. Accordingly, this study shows how the probabilistic reasoning technique enables the agents to reason under uncertainty. Furthermore, it discusses how the intelligent agents enhance their decision-making process by exchanging information about missing data or unobservable variables using agent interaction protocols. In general, the study demonstrates that the combination of MAS technologies and probabilistic logic programming can help in building a reasoning system, which is capable of performing well under vague inhabitant commands and missing information in a partially observable environment.
Spoofing PRNU Patterns of Iris Sensors while Preserving Iris Recognition

The principle of Photo Response Non-Uniformity (PRNU) is used to link an image with its source, i.e., the sensor that produced it. In this work, we investigate if it is possible to modify an iris image acquired using one sensor in order to spoof the PRNU noise pattern of a different sensor. In this regard, we develop an image perturbation routine that iteratively modifies blocks of pixels in the original iris image such that its PRNU pattern approaches that of a target sensor. Experiments indicate the efficacy of the proposed perturbation method in spoofing PRNU patterns present in an iris image whilst still retaining its biometric content.