Reviewerly/exHarmony huggingface dataset:
Blockchain recall management in pharmaceutical industry

With the rapid development of the Internet and the Internet of Things, product recall, especially pharmaceutical recall, have attracted various stakeholders as a phase of product service. However, there are some the long-term problems on pharmaceutical recall, such as low effectiveness, seldom transparent, data can be tampered easily and so on. We have proposed a service system of pharmaceutical recall based on blockchain to solve these problems. The solution runs through the entire recall process that includes decision making, the OOS/OOT investigation, the action of working group and the Corrective and Preventative Action (CAPA). Through the management of recall, we are capable of shortening time, enhancing transparency and protecting the integrity of data in pharmaceutical recall.
Effects of the Blockchain Technology on Human Resources and Marketing: An Exploratory Study

The paper aims to highlight some effects of the Blockchain Technology on Human Resources and Marketing of the organizations in the twenty first century. Blockchain is considered to be a disruptive technology that will dramatically transform the way companies are organized, managed, and how they perform their businesses. Most authors expect that blockchain will have profound effects on the nature of companies: how are they funded and managed, how they create value, and how they perform basic functions such as marketing, accounting, and incentivizing people. In this article, we analyzed the existing literature, the experts' expectations regarding the blockchain and we conceptualized some implications for businesses, human resources management, and marketing.
HIBEY: Hide the Keyboard in Augmented Reality

Text input is a very challenging task in Augmented Reality (AR). On non-touch AR headsets, virtual keyboards are counter-intuitive and character keys are hard to locate inside the constrained screen real estate. In this paper, we present the design, implementation and evaluation of HIBEY, a text input system for smartglasses. HIBEY provides a fast, reliable, affordable, and easy-to-use text entry solution through vision-based freehand interactions. Supported by a probabilistic spatial model and a language model, a three-level holographic environment enables users to apply fast and continuous hand gesture to pick characters and predictive words in a keyboardless interface. Through the pilot study and a thorough evaluations lasting 8 days, we show that HIBEY leads to a mean text entry rate of 9.95 word per minute (WPM) with 96.06% accuracy, which is comparable to other state-of-the-art approaches. After 8 days, participants can achieve an average of 13.19 WPM. In addition, HIBEY only occupies 13.14% of the screen real estate at the edge region, which is 62.80% smaller than the default keyboard layout on Microsoft Hololens.
Justifying Recommendations through Aspect-based Sentiment Analysis of Users Reviews

In this paper we present a methodology to justify the suggestions generated by a recommendation algorithm through the identification of relevant and distinguishing characteristics of the recommended item, automatically extracted by mining users' reviews. Our approach relies on a combination ofnatural language processing and sentiment analysis techniques, and is based on the following steps: (1) a set of users' reviews discussing the recommended item is gathered and analyzed; (2) the distinguishing aspects that characterize the item are extracted and a ranking function is used to identify the most relevant ones; (3) excerpts of the reviews discussing such aspects are extracted and a natural language template is filled in through the aggregation of these sentences. This represents the final output of the algorithm, which is provided to the user as justification of the recommendation she received. In the experimental evaluation, we carried out a user study (N=296, 73.6% male) aiming to investigate the effectiveness of our methodology in two different domains, as movies and books. Results showed that our technique can provide users with rich and satisfying justifications. Moreover, our experiment also showed that the users prefer review-based justifications to other explanation strategies, and this finding further confirmed the effectiveness of the approach.
Transfer Convolutional Neural Network for Cross-Project Defect Prediction

Cross-project defect prediction (CPDP) is a practical solution that allows software defect prediction (SDP) to be used earlier in the software lifecycle. With the CPDP technique, the software defect predictor trained by labeled data of mature projects can be applied for the prediction task of a new project. Most previous CPDP approaches ignored the semantic information in the source code, and existing semantic-feature-based SDP methods do not take into account the data distribution divergence between projects. These limitations may weaken defect prediction performance. To solve these problems, we propose a novel approach, the transfer convolutional neural network (TCNN), to mine the transferable semantic (deep-learning (DL)-generated) features for CPDP tasks. Specifically, our approach first parses the source file into integer vectors as the network inputs. Next, to obtain the TCNN model, a matching layer is added into convolutional neural network where the hidden representations of the source and target project-specific data are embedded into a reproducing kernel Hilbert space for distribution matching. By simultaneously minimizing classification error and distribution divergence between projects, the constructed TCNN could extract the transferable DL-generated features. Finally, without losing the information contained in handcrafted features, we combine them with transferable DL-generated features to form the joint features for CPDP performing. Experiments based on 10 benchmark projects (with 90 pairs of CPDP tasks) showed that the proposed TCNN method is superior to the reference methods.
Deep LDA : A new way to topic model

AbstractAbstractProbabilistic topic models like Latent Semantic Indexing (LSI), Latent Dirichlet Allocation (LDA) and Biterm Topic Model (BTM) have been successfully implemented and used in many areas like movie reviews, recommender systems and text summarization etc. These models however become computationally heavy if tested on humongous corpus. Keeping in view the vide acceptability of Deep Neural network based machine learning, this research proposes two deep neural network variants (2NN DeepLDA and 3NN DeepLDA) of existing topic modeling technique Latent Dirichlet Allocation (LDA) with specific aim to handle large corpuses with less computational efforts. Two proposed models (2NN DeepLDA and 3NN DeepLDA) are used to mimic the statistical process of Latent Dirichlet Allocation. Reuters-21578 dataset has been used in the study. Results computed from LDA are compared with the proposed models (2NN DeepLDA and 3NN DeepLDA) using Support Vector Machine (SVM) classifier. Proposed models have shown significant accuracy besides computational effectiveness in comparison to traditional LDA.Keywords: Deep LearningLDASVM ClassifierTopic ModellingKerasTensorflow
Development of Highly Homogenous Quantum Dot Micropillar Arrays for Optical Reservoir Computing

Neuromorphic computing has received considerable attention as promising alternatives to classical von Neumann computing architectures.An attractive concept in this field is reservoir computing which is based on coupled non-linear elements to enable for instance ultra-fast pattern recognition.We focus on the development of microlasers in a dense regular array for the implementation of photonic reservoir computing based on the diffractive coupling.The coupling relies on injection locking of microlasers and sets stringent requirements on the spectral homogeneity of the array, which needs to be on the order of the achievable locking range.We realize GaAs/AlGaAs micropillar arrays with InGaAs quantum dots as active medium.To achieve the high spectral homogeneity on the order of 100 µeV, as determined by injection locking experiments, the emission energy of each individual micropillar is adjusted to compensate for local inhomogeneities of order ∼1.3 meV in the underlying microcavity structure.The realized micropillar arrays have a spectral inhomogeneity as low as 190 µeV for an 8 × 8 array and down to 118 µeV for a 5 × 5 sub-array.The arrays have high potential to enable the implementation of powerful photonic reservoir computing, which can be extended to a reservoir of hundreds of microlasers in the future.
Analyzing and Supporting Adaptation of Online Code Examples

Developers often resort to online Q&A forums such as Stack Overflow (SO) for filling their programming needs. Although code examples on those forums are good starting points, they are often incomplete and inadequate for developers' local program contexts; adaptation of those examples is necessary to integrate them to production code. As a consequence, the process of adapting online code examples is done over and over again, by multiple developers independently. Our work extensively studies these adaptations and variations, serving as the basis for a tool that helps integrate these online code examples in a target context in an interactive manner. We perform a large-scale empirical study about the nature and extent of adaptations and variations of SO snippets. We construct a comprehensive dataset linking SO posts to GitHub counterparts based on clone detection, time stamp analysis, and explicit URL references. We then qualitatively inspect 400 SO examples and their GitHub counterparts and develop a taxonomy of 24 adaptation types. Using this taxonomy, we build an automated adaptation analysis technique on top of GumTree to classify the entire dataset into these types. We build a Chrome extension called ExampleStack that automatically lifts an adaptation-aware template from each SO example and its GitHub counterparts to identify hot spots where most changes happen. A user study with sixteen programmers shows that seeing the commonalities and variations in similar GitHub counterparts increases their confidence about the given SO example, and helps them grasp a more comprehensive view about how to reuse the example differently and avoid common pitfalls.
The critical radius in sampling-based motion planning

We develop a new analysis of sampling-based motion planning in Euclidean space with uniform random sampling, which significantly improves upon the celebrated result of Karaman and Frazzoli and subsequent work. In particular, we prove the existence of a critical connection radius proportional to [Formula: see text] for n samples and d dimensions: below this value the planner is guaranteed to fail (similarly shown by Karaman and Frazzoli). More importantly, for larger radius values the planner is asymptotically (near-)optimal. Furthermore, our analysis yields an explicit lower bound of [Formula: see text] on the probability of success. A practical implication of our work is that asymptotic (near-)optimality is achieved when each sample is connected to only [Formula: see text] neighbors. This is in stark contrast to previous work that requires [Formula: see text] connections, which are induced by a radius of order [Formula: see text]. Our analysis applies to the probabilistic roadmap method (PRM), as well as a variety of “PRM-based” planners, including RRG, FMT*, and BTT. Continuum percolation plays an important role in our proofs. Lastly, we develop similar theory for all the aforementioned planners when constructed with deterministic samples, which are then sparsified in a randomized fashion. We believe that this new model, and its analysis, is interesting in its own right.
Encoder-Decoder Residual Network for Real Super-Resolution

Real single image super-resolution is a challenging task to restore lost information and attenuate noise from images mixed unknown degradations complicatedly. Classic single image super-resolution, aims to enhance the resolution of bicubically degraded images, has recently obtained great success via deep learning. However, these existing methods do not perform well for real single image super-resolution. In this paper, we propose an Encoder-Decoder Residual Network (EDRN) for real single image super-resolution. We adopt an encoder-decoder structure to encode highly effective features and embed the coarse-to-fine method. The coarse-to-fine structure can gradually restore lost information and reduce noise effects. We empirically rethink and discuss the usage of batch normalization. Compared with state-of-the-art methods in classic single image super-resolution, our EDRN can efficiently restore the corresponding high-resolution image from a degraded input image. Our EDRN achieved the 9th place for PSNR and top 5 for SSIM in the final result of NTIRE 2019 Real Super-resolution Challenge. The source code and the trained model are available at https://github.com/yyknight/NTIRE2019_EDRN.
Dynamic Group-Oriented Provable Data Possession in the Cloud

As an important security property of cloud storage, data integrity has not been sufficiently studied under the multi-writer model, where a group of users work on shared files collaboratively and any group member can update the data by modification, insertion, and deletion operations. Existing works under such multi-writer model would bring large storage cost to the third-party verifiers. Furthermore, to the best of our knowledge, none of the existing works for shared files supports fully dynamic operations, which implies that users cannot freely perform the update operations. In this paper, we propose the first public auditing scheme for shared data that supports fully dynamic operations and achieves constant storage cost for the verifiers. Our scheme, named PRAYS, is boosted by a new paradigm for remote data integrity checking. To implement the new paradigm, we proposed a specially designed authenticated structure, called blockless Merkle tree, and a novel cryptographic primitive, called permission-based signature. Extensive evaluation demonstrates that PRAYS is as efficient as the existing less-functional solutions. We believe that PRAYS is an important step towards designing practical multi-writer cloud storage systems.
Revisiting Assumptions for Website Fingerprinting Attacks

Most privacy-conscious users utilize HTTPS and an anonymity network such as Tor to mask source and destination IP addresses. It has been shown that encrypted and anonymized network traffic traces can still leak information through a type of attack called a website fingerprinting (WF) attack. The adversary records the network traffic and is only able to observe the number of incoming and outgoing messages, the size of each message, and the time difference between messages. In previous work, the effectiveness of website fingerprinting has been shown to have an accuracy of over 90% when using Tor as the anonymity network. Thus, an Internet Service Provider can successfully identify the websites its users are visiting. One main concern about website fingerprinting is its practicality.
Lattice-based Cryptography for IoT in A Quantum World: Are We Ready?

The impending realization of scalable quantum computers has led to active research in Post Quantum Cryptography (PQC). The challenge is harder for embedded IoT (edge) devices, due to their pervasive diffusion in today's world as well as their stricter resources (tight area and energy budgets). Amongst various classes of quantum-resistant cryptography schemes, Lattice- based Cryptography (LBC) is emerging as one of the most viable, almost half of the `survivors' of second round of the NIST's PQC competition are lattice-based in construction. This paper surveys the practicality of deployment of these schemes. In this context, the state-of-the-art LBC implementations on the constrained devices (including low-power FPGAs and embedded microprocessors), leading in terms of low-power footprint, small area, compact bandwidth requirements and high performance is fairly evaluated and bench-marked. The work concludes by identifying a suite of some favorite LBC schemes in terms of various IoT critical performance bench-marks.
What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks?

The flexibility and high-accuracy of Deep Neural Networks (DNNs) has transformed computer vision. But, the fact that we do not know when a specific DNN will work and when it will fail has resulted in a lack of trust. A clear example is self-driving cars; people are uncomfortable sitting in a car driven by algorithms that may fail under some unknown, unpredictable conditions. Interpretability and explainability approaches attempt to address this by uncovering what a DNN models, i.e., what each node (cell) in the network represents and what images are most likely to activate it. This can be used to generate, for example, adversarial attacks. But these approaches do not generally allow us to determine where a DNN will succeed or fail and why. i.e., does this learned representation generalize to unseen samples? Here, we derive a novel approach to define what it means to learn in deep networks, and how to use this knowledge to detect adversarial attacks. We show how this defines the ability of a network to generalize to unseen testing samples and, most importantly, why this is the case.
Bionic Face Sketch Generator

Face sketch synthesis is a crucial technique in digital entertainment. However, the existing face sketch synthesis approaches usually generate face sketches with coarse structures. The fine details on some facial components fail to be generated. In this paper, inspired by the artists during drawing face sketches, we propose a bionic face sketch generator. It includes three parts: 1) a coarse part; 2) a fine part; and 3) a finer part. The coarse part builds the facial structure of a sketch by a generative adversarial network in the U-Net. In the middle part, the noise produced by the coarse part is erased and the fine details on the important face components are generated via a probabilistic graphic model. To compensate for the fine sketch with distinctive edge and area of shadows and lights, we learn a mapping relationship at the high-frequency band by a convolutional neural network in the finer part. The experimental results show that the proposed bionic face sketch generator can synthesize the face sketch with more delicate and striking details, satisfy the requirement of users in the digital entertainment, and provide the students with the coarse, fine, and finer face sketch copies when learning sketches. Compared with the state-of-the-art methods, the proposed approach achieves better results in both visual effects and quantitative metrics.
Contextuality Test of the Nonclassicality of Variational Quantum Eigensolvers

Contextuality is an indicator of nonclassicality, and a resource for various quantum procedures. In this Letter, we use contextuality to evaluate the variational quantum eigensolver (VQE), one of the most promising tools for near-term quantum simulation. We present an efficiently computable test to determine whether or not the objective function for a VQE procedure is contextual. We apply this test to evaluate the contextuality of experimental implementations of VQE, and determine that several, but not all, fail this test of quantumness.
A Systems Approach to Project Stakeholder Management: Fuzzy Cognitive Map Modeling

Projects that make effective use of project stakeholder management (PSM) tend to run smoothly and be successful because stakeholders understand and agree with the project approaches and outcomes. Projects with ineffective stakeholder management, on the other hand, frequently experience delays and cost overruns or may even be terminated. To date, project teams have limited methodological support for PSM: Existing methods are dominantly static and internally focused, making it difficult to manage so-called external stakeholders, who are not under the authority of the project manager. This work aims to improve PSM practice by closing the methodological gap. We developed a novel decision-support methodology, based on Fuzzy Cognitive Map (FCM) modeling that leverages stakeholders’ public comments to anticipate the project’s impacts on them and to make conflicts between stakeholder interests and project objectives transparent. A demonstration of the method is provided using a single case—namely, a longitudinal case study at Bonneville Power Administration (BPA), a federal agency that provides power to the Pacific Northwest.
Congestion detection in pedestrian crowds using oscillation in motion trajectories

Identifying crowd congestion in high density pedestrian crowds is a challenging problem, with substantial interest for safety and security applications. We present a method to identify and localize congestion in high density crowd videos, based on the characteristic motion of individuals stuck in congestion. Pedestrians who cannot move freely due to congestion tend to undergo lateral oscillations. Using only motion features, extracted from optical flow and particle advection, we generate sampled trajectories and compute areas which show a pattern of increasing trajectory oscillation. A new and first ever data set including 15 different crowd scenes is introduced to better evaluate the accuracy of our method. Showing results on a diversity of challenging scenarios, we both qualitatively and quantitatively show that this approach provides accurate detection and excellent localization of congestion events.
Model Predictive Local Motion Planning With Boundary State Constrained Primitives

Motion primitives are frequently used to find valid local trajectories for mobile robots, especially in cases where fast replanning is required, but the onboard computational power is limited. In this letter, we present a practical framework for constructing motion primitives from boundary state constraints, and then using them for online planning. The primitives are offline constructed with either a boundary value problem solver or a controller. They are then approximated with a neural network for fast evaluation during online optimization. The references and nominal inputs are generated in a receding horizon fashion by solving a model predictive control problem in the continuous domain with either gradient-based or gradient-free techniques. The proposed approach is computationally efficient and has been tested on quadrotors in real flight experiments, including sensor-based navigation, flying through a complex three-dimensional environment, dynamic obstacle avoidance, and tracking moving references.