Reviewerly/exHarmony huggingface dataset:
Toward a document‐centered ontological theory for information architecture in corporations

Abstract The beginning of the 21st century attested to the first movements toward information architecture (IA), originating from the field of library and information science (LIS). IA is acknowledged as an important meta‐discipline concerned with the design, implementation, and maintenance of digital information spaces. Despite the relevance of IA, there is little research about the subject within LIS, and still less if one considers initiatives for creating a theory for IA. In this article, we provide a theory for IA and describe the resources needed to create it through ontological models. We also choose the “document” as the key entity for such theory, contemplating kinds of documents that not only serve to register information, but also create claims and obligations in society. To achieve our goals, we provide a background for subtheories from LIS and from Applied Ontology. As a result, we present some basic theory for IA in the form of a formal framework to represent corporations in which IA activities take place, acknowledging that our approach is de facto a subset of IA we call the enterprise information architecture (EAI) approach. By doing this, we highlight the effects that documents cause within corporations in the scope of EIA.
Comparing the Effectiveness of Query-Document Clusterings Using the QDSM and Cosine Similarity

Typically, approaches based on query clustering in IR only take the keywords that belong to the queries into consideration, with the aim of calculating the similarity among them. In search engines, one of the factors that affects precision is the short lengths of queries. One way to tackle this problem is through the use of the context. Aiming to consider the context of queries, several measures have been proposed to calculate their inter-document similarity, which are known as Query-Sensitive Similarity Measure (QSSM). This paper presents a new QSSM called Query-Document Similarity Measure (QDSM), which is used to build clusters of similar search results. The goal of this measure is to provide clusters of better quality (i.e., clusters with more relevant documents) taking into account the context in which the queries are submitted. To achieve this goal, several experiments were carried out using four well-known algorithms in Information Retrieval (IR) along with the AOL Query Logs Dataset (AOL). Final results using Bisection K-Means and Average link show that the clusters built with QDSM provide better effectiveness than the clusters built with Cosine Similarity (Sc).
DMap: A Distributed Blockchain-based Framework for Online Mapping in Smart City

Smart cities are growing significantly due to the growth of smart connected vehicles and Internet of Things (IoT) where a wide range of devices are connected to share data. Online mapping is one of the fundamental services offered in smart cities which enables the vehicle owners to find shortest or fastest direction toward a destination thus reduces travel cost and air pollution. However, existing online mapping services rely on centralized servers, e.g., Google, which collect data from users to offer service. This method is unlikely to scale with growth in the smart city participants and introduces privacy concerns and data wall where data of the users is managed by big companies. To address these challenges, in this paper we introduce DMap, a blockchain-based platform where the users can share data in an anonymous distributed manner with service providers. To the best of our knowledge, DMap is the first distributed blockchain-based solution for online mapping. To improve the scalability of the blockchain, we propose to use edge-computing along with blockchain. To protect against malicious vehicles that may inject false data, we define a reputation system where the collected data of the vehicles is verified by monitoring the neighbor data. We introduce data marketplace where users can trade their data to address data wall challenge.
Paywall Policy Learning in Digital News Media

Subscription-based online newspapers usually offer non-subscribed users a certain number of free articles in a period of time, and then directs them to a page (called paywall) asking for subscription. This approach (also known as metered or fixed paywall) does not consider the user's reading history nor the articles that the user may read in the future, and consequently, it may disengage many potential subscribers. To that end, we propose adaptive paywall mechanisms to make optimal paywall decisions (i.e., showing the article or the paywall) by balancing the benefit of showing the article against that of presenting the paywall. We define the notions of utility and cost which are used to define an objective function for the optimal paywall decision problem. We propose the Lookahead policy (LAP) and QPaywall policy (QP) as two data-driven approaches to solve the adaptive paywall problem. While the LAP method makes paywall decisions on the fly by simulating trajectories of article requests using Monte Carlo sampling, the QP approach is based on reinforcement learning and learns a neural network-based action-value (Q) function for this purpose. We compare advantages of the proposed approaches and discuss the practical considerations of using them in a real environment. Empirical studies on a real dataset from a major newspaper in Canada show that the proposed methods outperform several baseline approaches in terms of various business objectives.
Impacts of process audit review and control efforts on software project outcomes

This study examines the potential of using stage-wise process audit review and control (ARC) efforts in estimating overall effort and defects in a software project. Using archival data from 49 software projects that were based on the waterfall methodology, and obtained from a CMMI level 5 organisation, the authors found that higher ARC efforts at the requirement and build phases of a project were associated with an increase in overall project effort. Further, higher ARC efforts at the design and build phases were correlated with an increase in the number of defects delivered to the client at the end of the project. The predictive ability of the authors’ effort models was very high, with mean errors in the range of 3–4% of the overall project effort. They found that the mean ARC effort in their sample was 0.15 h/FP for the requirements stage, 0.31 h/FP for the design stage and 0.67 h/FP for the build stage. A project where the ARC effort for any of the three stages exceeded the respective benchmark, could be a candidate for deeper inspection to prevent excess effort or defects. Organisations can use their own data to develop similar benchmarks for their software project portfolios.
Deep Compression with Low Rank and Sparse Integrated Decomposition

This work aims to compress the deep neural networks (DNNs) by learning the low rank and sparsity characteristics of weight filters. Most of the compression models consider the low rank or sparse property of weights independently, however, they cannot capture the features accurately and thus become inefficient. It is revealed that the low rank component of the weights also tends to be sparse, therefore, in this work, we propose an extreme neural network compression method by embedding sparsifying operations after the low rank decomposition. In addition, a global sparse component is proposed to compensate the performance loss of the compressed model. Experiments are also included to prove the benefits of the proposed algorithm.
Enhanced Initialization with Multi-Stage Learning for Robust Visual Tracking

Visual tracking is the task of estimating the trajectory of a target given its first location in a video. Existing tracking-by-detection approaches build trackers on binary classifiers. However, these approaches are sometimes not discriminative enough to distinguish the target from the background, which impedes the performance under challenging conditions. In this paper, we demonstrate that conventional initializing strategies make the model insufficiently trained. To make full use of the information contained in the first frame, we propose an enhanced scheme of model initialization. In the scheme, several kinds of negative samples are heuristically defined and a multi-stage learning mechanism is adopted to make the model initialization more efficient and stable. Validation experiment demonstrates the effectiveness of the mechanism. Experiments on benchmark datasets show that the proposed method improves the results of the baseline work and achieves state-of-the-art performance.
Gaze-based video games for assessment of attention outside of the lab

Advances in remote therapeutics also demand simultaneous development of remote assessment tools. Here, we report on a comparison of gamified assessments that we developed for assessing three components of the broad construct of attention, that will be used as part of an attention training suite designed to be used outside of the laboratory.
A Note on the Invisibility and Anonymity of Undeniable Signature Schemes

Undeniable signature is a special featured digital signature which can only be verified with the help of the signer. Undeniable signature should satisfy invisibility which implies the inability of a user to determine the validity of a message and signature pair as introduced by Chaum et al. Galbraith and Mao later proposed the notion of anonymity which implies the infeasibility to determine which user has issued the signature. They also proved that the notions of invisibility and anonymity are equivalent when the signers possess the same signature space, such that if an undeniable signature possesses invisibility, then it also possesses anonymity, and vice versa. In this paper, we show that in contradiction to the equivalency result established by Galbraith and Mao, there exist some undeniable signature schemes that possess invisibility but not anonymity. This motivates us to find out whether there is a limitation on Galbraith and Mao’s equivalency result or the schemes are actually flawed. Our analysis shows that the anonymity property requires all signers to possess the same signature space but the invisibility property does not. This conforms to the equivalency result and implies that an undeniable signature scheme can be invisible but not anonymous if the signers possess the different signature spaces. Our result invalidates two past cryptanalysis on undeniable signature schemes.
Incomplete multi-view spectral clustering

Multi-view clustering algorithms mostly apply to data without incomplete instances. However, in real-world applications, representations for the same instance are probably absent from several but not all views. This incompleteness disables traditional multi-view clustering methods from grouping in complete multi-view data. Recently, multi-view clustering methods on incomplete data have been proposed, and the existing methods have two limitations. One is that most methods were developed for incomplete datasets only with two views. The other is that most methods were incapable of grouping data with complex distributions. In this paper, we propose a novel incomplete multi-view clustering algorithm named IMSVC, in which we adopt spectral analysis to supervise the common representation extracted from all the views. Firstly, IMVSC constructs a bipartite graph for each view. By introducing an instance-view indicator matrix to indicate whether a representation exists in a view or not, we calculate the edge weights of bipartite graph based on the point-to-point similarity. Secondly, IMVSC constructs the multi-view relationship by guiding the multiple views to share the same instance partitioning. Finally, we create a novel iterative method to optimize IMVSC. Experimental results show sound performance of the proposed algorithm on several incomplete datasets.
An Image Segmentation Method by Multi-scale Local Thresholding Based on Class Uncertainty Theory

Image segmentation is one of the most important core technologies in the field of image processing and computer vision, which has lots of applications specifically in medical image analysis. Due to medical imaging mechanism, medical images usually suffer from heavy noises, uneven intensity distribution and fuzzy boundaries between biological tissues. That makes classical segmentation methods based on global or local thresholding difficult to get accurate results. This paper proposes an image segmentation method by multi-scale local thresholding based on class uncertainty theory. Firstly, the original image is divided into a set of sub-regions with different scales by using a multi-layer pyramid structure. Secondly, the energy function with inequality constraints is constructed based on class uncertainty and region uniformity. Then by an iterative process, the optimal local threshold for each sub-region are calculated by an optimization algorithm for each layer until the final optimal threshold mask is determined for image segmentation. Experimental results verify that the proposed method can effectively eliminate the interferences caused by image noises, intensity unevenness and fuzzy boundaries, and preserve details of object structure while segmenting the object from the background. Segmentation results demonstrate the superior performance of the proposed method by comparison with supervised range-constrained thresholding methods (RCOtsu), classical global/local Otsu method, and the minimization of homogeneity- and uncertainty-based energy method (MHUE).
Cryptography by Synchronization of Hopfield Neural Networks that Simulate Chaotic Signals Generated by the Human Body

In this work, an asymmetric cryptography method for information security was developed, inspired by the fact that the human body generates chaotic signals, and these signals can be used to create sequences of random numbers. Encryption circuit was implemented in a Reconfigurable Hardware (FPGA). To encode and decode an image, the chaotic synchronization between two dynamic systems, such as Hopfield neural networks (HNNs), was used to simulate chaotic signals. The notion of Homotopy, an argument of topological nature, was used for the synchronization. The results show efficiency when compared to state of the art, in terms of image correlation, histogram analysis and hardware implementation.
Multi-scale Attentive Residual Network for Single Image Deraining

Removing rain streaks from a single image is extremely challenging since the appearance of rain streaks in shapes, scales and densities is ever changing. Therefore, we propose a novel end-to-end two- stage multi-scale attentive residual network that is both location-aware and density-aware, in order to preferably remove various rain streaks. Specifically, in the first stage, a multi-scale progressive attention sub- network is designed to automatically locate the distribution of diverse rain streaks and further to guide the following deraining. Then the second stage with the guidance of the attention map generated in the former stage aims to efficiently remove various rain streaks. To aggregate the characteristics of rain streaks with different scales and densities, we construct a multi-scale residual sub-network in which dilated convolution and residual learning are used to combine these features. As a result, these two sub-networks make up the whole network, and accomplish the process of joint detection and removal of diverse rain streaks fairly well. Extensive experiments on both synthetic and real-world rainy images demonstrate that our proposed method significantly outperforms several recent state-of-the-art approaches.
Cyber Forensic Lab Setup and Its Requirement

The year 1978 was the year when the first computer-related crime took place; it was alteration or deletion of data. The day-by-day level and intensity of cybercrime has strengthened and is getting stronger in nature in the current era. So, to achieve accuracy during the investigation, an intensive investigation environment or lab is needed. This will help the investigation team in various ways. More advanced techniques and tools are used in a current age lab setup, and solutions to forensically examine a variety of digital devices apart from computers are made.
Accuracy Improvement of Fashion Style Estimation with Attention Control of a Classifier

Computer vision has begun to be applied to fashion applications owing to the remarkable development of image recognition technology and deep learning. Fashion style estimation, the focus of this paper, is a hot topic as it provides useful information and services to consumers. This paper aims to enhance the accuracy of fashion style estimation by improving the classifier. To control the attention of a classifier, two different approaches are followed: human detection after segmentation with the aim of improving on previous schemes and enhancing the visual attention model (VAM). Experiments were conducted using the Hipster Wars dataset to train a final SVM-based classifier and the WEARStyle dataset to construct a feature extractor. The results using SSD-based detection after PSPNet-based segmentation, VAM with ICNet, and VAM with PSPNet showed classification accuracies of 83.8%, 84.7%, and 85.1%, respectively. All these outperformed VAM with full convolutional network (FCN), which resulted in a classification accuracy of 83.0% under the same experimental conditions. The results indicate that classification accuracy can be improved upon when the attention of the classifier is controlled.
An Autonomous Planning Method for UAV Based on Behavior-Conditional Model

With the rapid development of information technology and intelligent technology, the future environment of battlefield is being complex and changeable. The traditional use way of UAV based on pre-planning is difficult to meet the basic needs, especially to deal with battlefield emergencies. In order to solve this problem, this paper takes the application of Rule-based Modeling Method in autonomous mission planning of UAV as the research object, and simplifies it into Behavior- Conditional Model by using Rule-based Modeling Method. Through the analysis and modeling of Behavior-Conditional Rules, detailed rules describing various common task patterns are established, and task operation behavior rules and priority decision rules of task objectives are formulated. In summary, an autonomous decision-making method based on rule reasoning is formed. Finally, the method of random generation of threat sources is used to simulate the actual combat environment. By evaluating the effects of rule-based and irregular deduction, simulation experiments verify the feasibility and effectiveness of the Rule-based Modeling Method, and the experimental results are satisfactory.
Learning Optical Flow via Deformable Convolution and Feature Pyramid Networks

Convolutional neural networks have powerful learning capabilities which can simulate various complex mapping relationships, so they are widely used in various fields of computer vision. Nowadays, learning optical flow based on CNN has achieved remarkable results. However, optical flow learning approaches still require further research and development. Firstly, the large displacement problem in the traditional methods still exists in optical flow learning approaches. What's more, the development of optical flow learning approaches are limited to a certain extent because of convolutional neural networks lacks the ability to handle geometric transformations, which result from their fixed geometry structure. In order to solve above problems, we introduce the deformation convolution into the optical flow estimation network, which can adaptively adjust the shape of the receptive field to learn different levels and different types of motion information. In addition, the feature pyramid is also integrated into the network to further enhance the learning ability of optical flow learning network. The experimental results surpass many state-of-the-art algorithms in the sintel dataset, which proves the effectiveness of our approach.
An Optimal, Generative Model for Estimating Multi-Label Probabilistic Maps

Multi-label probabilistic maps, a.k.a. probabilistic segmentations, parameterize a population of intimately co-existing anatomical shapes and are useful for various medical imaging applications, such as segmentation, anatomical atlases, shape analysis, and consensus generation. Existing methods to estimate probabilistic segmentations rely on ad hoc intermediate representations (e.g., average of Gaussian-smoothed label maps and smoothed signed distance maps) that do not necessarily conform to the underlying generative process. Generative modeling of such maps could help discover as well as aide in the statistical analysis of sub-groups in a population via clustering and mixture modeling techniques. In this paper, we propose an estimation of multi-label probabilistic maps and showcase their favorable performance for modeling anatomical shapes such as the left atrium of the human heart and brain structures. The proposed formulation relies on a constrained optimization in the natural parameter space of the exponential family form of categorical distributions. A smoothness prior provides generalizability in the model and helps achieve greater performance in modeling tasks for unseen samples. We demonstrate and compare the effectiveness of the proposed method for Bayesian image segmentation, multi-atlas segmentation, and shape-based clustering.
Gradient-Based Differential Privacy Optimizer for Deep Learning Model Using Collaborative Training Mode

Deep learning model based on artificial neural network is one of the greatest pushers to realize intelligence of information system unprecedentedly. However, the risk of leaking user data privacy by attacking deep learning model exists in the training process, especially when multi-users concurrently utilize the service of cloud. Motivated by this observation, in order to protect the privacy of deep learning model, this paper proposes a gradient-based differential privacy optimizer using collaborative training mode based on CPU-GPUs hybrid system in cloud. In gradient-based differential privacy optimizer, random sampling, gradient tailoring, gradient-based random perturbation, and advanced privacy budget statistics together guarantee the usability and privacy of the model. Specifically, the effects of privacy budget, noise scale, and privacy deviation parameters and their combinations on accuracy of model are experimentally studied in this paper. To further improve the training efficiency of model, the CPU-GPUs hybrid system is explored as a collaborative training mode for gradient-based differential privacy optimizer. The experimental results indicate that using publicly available dataset MINIST and implemented in TensorFlow is proved to be feasible and efficient. Specifically, our implementation and experiments demonstrate that we can obtain approximately 96% of accuracy under a modest privacy budget. Furthermore, we can achieve up to 20%-30% speed-up ratio in the training process based on gradient-based differential privacy optimizer.
User–Robot Interaction for Safe Navigation of a Quadrotor

SUMMARY This paper introduces an intuitive and safe command approach for a quadrotor, where inertial and muscular gestures are used for semi-autonomous flight. A bracelet composed of gyroscopes, accelerometers, and electromyographic sensors is used to detect user gestures, then an algorithm is proposed to interpret the signals as flight commands. The main goal is to provide a wearable, easy-to-handle human–machine interface for users a to safely command this kind of vehicles, even for inexpert operators. Safety measures are incorporated in the scheme to further enhance the user’s experience. Experimental tests are performed to validate the proposal.