Reviewerly/exHarmony huggingface dataset:
A Wild Velvet Fork Appears! Inclusive Blockchain Protocol Changes in Practice

The loosely defined terms hard fork and soft fork have established themselves as descriptors of different classes of upgrade mechanisms for the underlying consensus rules of (proof-of-work) blockchains. Recently, a novel approach termed velvet fork, which expands upon the concept of a soft fork, was outlined in [22]. Specifically, velvet forks intend to avoid the possibility of disagreement by a change of rules through rendering modifications to the protocol backward compatible and inclusive to legacy blocks. We present an overview and definitions of these different upgrade mechanisms and outline their relationships. Hereby, we expose examples where velvet forks or similar constructions are already actively employed in Bitcoin and other cryptocurrencies. Furthermore, we expand upon the concept of velvet forks by proposing possible applications and discuss potentially arising security implications.
Preliminary Evaluation of Two Digital Image Processing Strategies for Head-Mounted Magnification for Low Vision Patients

In an observational clinical outcome study, we tested the effectiveness and use of the combination of two innovative approaches to magnification: a virtual bioptic telescope and a virtual projection screen, implemented with digital image processing in a head-mounted display (HMD) equipped with a high-resolution video camera and head trackers.We recruited 30 participants with best-corrected visual acuity <20/100 in the better-seeing eye and bilateral central scotomas. Participants were trained on the HMD system, then completed a 7- to 10-day in-home trial. The Activity Inventory was administered before and after the home trial to measure the effect of system use on self-reported visual function. A simulator sickness questionnaire (SSQ) and a system-use survey were administered. Rasch analysis was used to assess outcomes.Significant improvements were seen in functional ability measures estimated from goal difficulty ratings (Cohen's d = 0.79, P < 0.001), and reading (d = 1.28, P < 0.001) and visual information (d = 1.11, P < 0.001) tasks. There was no improvement in patient-reported visual motor function or mobility. One participant had moderately severe discomfort symptoms after SSQ item calibration. The average patient rating of the system's use was 7.14/10.Use of the system resulted in functional vision improvements in reading and visual information processing. Lack of improvement in mobility and visual motor function is most likely due to limited field of view, poor depth perception, and lack of binocular disparity.We determine if these new image processing approaches to magnification are beneficial to low vision patients performing everyday activities.
Large-Scale Twin Parametric Support Vector Machine Using Pinball Loss Function

Traditional hinge loss function-based large-scale support vector machine (SVM) algorithms tend to perform poorly in the presence of noise, especially when the model is trained incrementally. In this paper, we propose an efficient stochastic quasi-Newton method-based twin parametric SVM using the pinball loss function (termed as SQN-PTWSVM), which is efficient and more robust to the presence of noise when compared to conventional hinge loss SVM for large-scale data scenarios. To establish the theoretical convergence of the method, a modified version of SQN-PTWSVM, termed as SQN-SPTWSVM, has also been proposed. It overcomes the poor convergence issue faced by stochastic gradient twin SVM thus resulting in a faster and reliable model. In SQN-SPTWSVM, the hyperplanes obtained are stable enough to handle noise and resampling issues that occur frequently in stochastic learning scenarios, leading to better generalization ability of the classifier. The proposed method has been extended to nonlinear scenarios as well. Moreover, batch versions of the proposed algorithms have also been introduced which significantly reduce the training time and memory requirement of SQN-PTWSVM and SQN-SPTWSVM. The experimental results on several benchmark datasets and activity recognition applications have shown that the performance of our method is better than the existing classifiers in terms of speed and accuracy.
New perspectives on gray sheep behavior in E-commerce recommendations

With the exponential rise in the size of data being generated, personalization based on recommender systems has become an important aspect of digital marketing strategy of E-Commerce companies. Recommender systems also help these companies in cross-selling, up-selling and to increase the customer loyalty. However, presence of certain users, known as gray sheep users, with eccentric taste, minimizes the overall efficiency of the recommender systems. Hence, their identification and removal from the computation system is critical for more efficient recommendations. This work presents psychographic models-based approaches for gray sheep user identification with improved performance. It also studies gray sheep behavior across different domains and contexts, apart from introducing the idea of gray sheep items.
Cloud service recommendation based on unstructured textual information

With the rapid growth of cloud service platform, it is more and more difficult for users to discover cloud services satisfying their personalized demands. The unstructured textual information, such as the service descriptive texts and service tags, contain rich features of cloud services and are useful for cloud service platforms to construct personalized service recommendation. This paper proposes a two-stage model for cloud service recommendation by integrating the information of service descriptive texts and service tags. In the first stage, we propose a Hierarchical Dirichlet Processes (HDP) model to cluster cloud services into an optimal number of clusters based on descriptive texts. In the second stage, we propose a Personalized PageRank algorithm based on service tags to rank and recommend cloud services in each cluster. Our experiments on a real data set show that the proposed two-stage model can segment cloud services well and obtain accurate recommendation results compared with the baseline methods.
Feasibility of using a low-cost head-mounted augmented reality device in the operating room

Augmented reality (AR) has great potential for improving image-guided neurosurgical procedures, but until recently, hardware was mostly custom-made and difficult to distribute. Currently, commercially available low-cost AR devices offer great potential for neurosurgery, but reports on technical feasibility are lacking. The goal of this pilot study is to evaluate the feasibility of using a low-cost commercially available head-mounted holographic AR device (the Microsoft Hololens) in the operating room. The Hololens is operated by performing specific hand gestures, which are recognized by the built-in camera of the device. This would allow the neurosurgeon to control the device "touch free" even while wearing a sterile surgical outfit.The Hololens was tested in an operating room under two lighting conditions (general background theatre lighting only; and general background theatre lighting and operating lights) and wearing different surgical gloves (both bright and dark). All required hand gestures were performed, and voice recognition was evaluated against background noise consisting of two nurses talking at conversational speech level.Wearing comfort was sufficient, with and without regular glasses. All gestures were correctly classified regardless of lighting conditions or the sort of sterile gloves. Voice recognition was good. The visibility of the holograms was good if the device was configured to use high brightness for display.We demonstrate that using a commercially available low-cost head-mounted holographic AR device is feasible in a sterile surgical setting, under different lighting conditions and using different surgical gloves. Given the availability of freely available software for application development, neurosurgery can benefit from new opportunities for image-guided surgery.
Ellipsoidal Fusion Estimation for Multisensor Dynamic Systems With Bounded Noises

This paper considers the set membership fusion estimation problem for the general multisensor dynamic systems with unknown but bounded noises. In order to achieve the ellipsoidal fusion formulae, we divide the set membership filter problem into two steps: the prediction step and the fusion update step. The proposed method has the following nice properties. First, each step can be converted into a semidefinite programming problem, which can be efficiently computed. Second, part-analytical formulae of the shape matrix and the center of the bounding ellipsoid can be derived by using a decoupled technique, which can significantly reduce the computational complexity. Moreover, the relationship between the fusion center and local sensors can be clearly revealed based on the ellipsoidal fusion formula. Finally, it is interesting that the ellipsoidal fusion formula is similar in form to the classic mean-squared error filter fusion, although they are of the different optimization frameworks. However, the fusion weights of the information matrix provided by the different sensors are different and the optimal fusion weights can be calculated by solving a convex optimization problem. A typical numerical example in target tracking demonstrates the effectiveness of the proposed centralized and distributed fusion formula.
Mind the Mining

In this paper we revisit the mining strategies in Proof of Work based cryptocurrencies and propose two strategies, which we call smart and smarter mining, that in many cases strictly dominate honest mining. In contrast to other known attacks, such as selfish mining, which induce zero-sum games among the miners, the strategies proposed in this paper increase miners' profit by reducing their variable costs (i.e., electricity). Moreover, the proposed strategies are viable for much smaller miners than previously known attacks and, surprisingly, an attack launched by one miner can be profitable for all other miners as well.
DOE-based structured-light method for accurate 3D sensing

This paper presents a compact and accurate three-dimensional (3D) sensing system that employs a diffraction optical element as a projection device. Compared with the conventional laser speckle-based 3D sensing methods, a gridline pattern is utilized instead of a dot pattern. The proposed pattern is designed according to a pseudorandom coding scheme, and eight geometrical elements are embedded into the grid cells to form a unique codeword for each defined grid-point. By extracting the grid-points with the proposed feature detector, a topological graph is established to separate each pattern element. A convolutional neural network is trained for robust identification of the projected pattern elements. Finally, a codeword-correction procedure is applied to refine the decoding results. Using the proposed system-calibration method, accurate 3D reconstruction can be realized for the decoded grid-points. The measurement of the planarity and step distance has an absolute mean error of only 0.2–0.3 mm, indicating that it is far more accurate than the measurement using classical laser speckle-based 3D sensors. To demonstrate robustness of the proposed decoding algorithms, targets with plentiful color and texture are used. The results show that most of the grid-points can be robustly detected and that complex surfaces such as human faces and bodies can be precisely reconstructed.
First Effort at Constructing a High-Density Photosynthetically Active Radiation Dataset during 1961–2014 in China

Abstract Photosynthetically active radiation (PAR) is a key factor for vegetation growth and climate change. Different types of PAR models, including four physically based models and eight artificial intelligence (AI) models, were proposed for predicting daily PAR. Multiyear daily meteorological parameters observed at 29 Chinese Ecosystem Research Network (CERN) stations and 2474 Chinese Meteorological Administration (CMA) stations across China were used for testing, validating, and comparing the above models. The optimized back propagation (BP) neural network based on the mind evolutionary algorithm (MEA-BP) was the model with highest accuracy and strongest robustness. The correlation coefficient R , mean absolute bias error (MAE), and RMSE for MEA-BP were 0.986, 0.302 MJ m −2 day −1 and 0.393 MJ m −2 day −1 , respectively. Then, a high-density PAR dataset was constructed for the first time using the MEA-BP model at 2474 CMA stations of China. A quality control process and homogenization test (using RHtestsV4) for the PAR dataset were further conducted. This high-density PAR dataset would benefit many climate and ecological studies.
Extraction of Geriatric Syndromes From Electronic Health Record Clinical Notes: Assessment of Statistical Natural Language Processing Methods

Geriatric syndromes in older adults are associated with adverse outcomes. However, despite being reported in clinical notes, these syndromes are often poorly captured by diagnostic codes in the structured fields of electronic health records (EHRs) or administrative records.We aim to automatically determine if a patient has any geriatric syndromes by mining the free text of associated EHR clinical notes. We assessed which statistical natural language processing (NLP) techniques are most effective.We applied conditional random fields (CRFs), a widely used machine learning algorithm, to identify each of 10 geriatric syndrome constructs in a clinical note. We assessed three sets of features and attributes for CRF operations: a base set, enhanced token, and contextual features. We trained the CRF on 3901 manually annotated notes from 85 patients, tuned the CRF on a validation set of 50 patients, and evaluated it on 50 held-out test patients. These notes were from a group of US Medicare patients over 65 years of age enrolled in a Medicare Advantage Health Maintenance Organization and cared for by a large group practice in Massachusetts.A final feature set was formed through comprehensive feature ablation experiments. The final CRF model performed well at patient-level determination (macroaverage F1=0.834, microaverage F1=0.851); however, performance varied by construct. For example, at phrase-partial evaluation, the CRF model worked well on constructs such as absence of fecal control (F1=0.857) and vision impairment (F1=0.798) but poorly on malnutrition (F1=0.155), weight loss (F1=0.394), and severe urinary control issues (F1=0.532). Errors were primarily due to previously unobserved words (ie, out-of-vocabulary) and a lack of context.This study shows that statistical NLP can be used to identify geriatric syndromes from EHR-extracted clinical notes. This creates new opportunities to identify patients with geriatric syndromes and study their health outcomes.
Exposure Measurement and Fusion via Adaptive Multiscale Edge-Preserving Smoothing

Natural environments usually have a larger dynamic range than the dynamic range that can be acquired by an optical camera with a single shot. In this paper, we propose a multiexposure fusion method that effectively fuses in a direct manner differently exposed images of a high dynamic range scene into a high-quality image. First, we present a developed joint weight by considering the exposure level measurement of the local and global luminance components of the input images. Second, we introduce a designed multiscale edge-preserving smoothing (MEPS) model for direct representing the weight maps. Third, two scale-aware factors for the MEPS model are adaptively determined without manual interference to obtain an optimal representation effect for each scale of the weight maps. The proposed adaptive MEPS model does not require Gaussian filtering steps to first smoothen the weight maps. It significantly reduces spatial artifacts in the fused image. We compare the proposed method with eight existing methods on 30 sequences from two databases with different characteristics. The experimental results indicate that the proposed approach achieves better imaging performance than the existing state-of-the-art methods, on both quantitative and qualitative evaluation. Moreover, it maintains a high computational efficiency.
Using Blockchains to Implement Distributed Measuring Systems

In recent years, measuring instruments have become quite complex due to the integration of embedded systems and software components and the increasing aggregation of new features. Consequently, metrological regulation and control require more efforts from notified bodies, becoming slower and more expensive. In this paper, we evaluate the use of blockchains as a resource to overcome such challenges. We start with a conceptual model for implementing measuring instruments in a distributed blockchain-based architecture and compare it with traditional measuring instruments and distributed measuring models discussed in previous works. We also made a security analysis, demonstrating that blockchain-based measuring systems can impact the way measuring instruments are used in consumer relations while improving security and simplifying metrological regulation and control. We implement a vehicle speed measuring system using the Hyperledger Fabric blockchain platform. We evaluate the security and performance of our blockchain-based measuring system by executing tests with data from real speed meter sensors. The results are promising and validate the feasibility of our idea. Finally, we point out the main challenges related to our approach, suggesting alternatives and potential issues to be addressed by future works.
Robust Model-Based Reliability Approach to Tackle Shilling Attacks in Collaborative Filtering Recommender Systems

As the use of recommender systems becomes generalized in society, the interest in varying the orientation of their recommendations is increasing. There are shilling attacks' strategies that introduce malicious profiles in collaborative filtering recommender systems in order to promote the own products or services or to discredit those of the competition. Academic research against shilling attacks has been focused in statistical approaches to detect the unusual patterns in user ratings. Nowadays, there is a growing research area focused on the design of robust machine learning methods to neutralize the malicious profiles inserted into the system. This paper proposes an innovative robust method, based on matrix factorization, to neutralize the shilling attacks. Our method obtains the reliability value associated with each prediction of a user to an item. By monitoring the unusual reliability variations in the items prediction, we can avoid promoting the shilling predictions to the erroneous recommendations. This paper openly provides more than 13 000 individual experiments involving a wide range of attack strategies, both push, and nuke, in order to test the proposed approach. The results show that the proposed method is able to neutralize most of the existing attacks; its performance only decreases in the not relevant situations: when the attack size is not large enough to effectively affect the recommendations provided by the system.
Metamodel clone detection with SAMOS

Wider adoption of model-driven engineering leads to an abundance of models and metamodels in academic and industrial practice. One of the key techniques for the management and maintenance of such artifacts is model clone detection, where highly similar (meta-)models and (meta-)model fragments are mined from a typically large set of data. In this paper we have extended the SAMOS framework (Statistical Analysis of MOdelS) to clone detection, exemplified on Ecore metamodels. Our clone detection approach uses and extends the framework’s feature extraction, vector space model, natural language processing and clustering capabilities. We performed three extensive case studies to demonstrate its accuracy both quantitatively and qualitatively. We first compared the sensitivity and accuracy of SAMOS for metamodel changes through mutation and scenario analysis (which simulate clones) with those of NICAD-Ecore and MACH, tools for clone detection on Ecore and UML models respectively. We then compared the precision and recall of SAMOS and of NICAD-Ecore on a real dataset, consisting of conference management metamodels from the ATL Zoo. Finally we performed a repository-wide mining of metamodel clones from GitHub. We conclude that SAMOS stands out with its higher accuracy and yet considerable scalability for further large-scale clone detection and other empirical studies on metamodels and domain specific languages.
Technology Integration for Improved Performance: A Case Study in Digitization of Supply Chain with Integration of Internet of Things and Blockchain Technology

Supply chain networks are global, multi-modal platforms that are expected to facilitate seamless exchange of physical goods, information across multiple industries and enterprises and stakeholders. Current supply chain information systems are limited in term of providing validated, real-time asset specific business relevant information during its lifecycle. Only one or few stakeholders have information access privilege causing both information asymmetry and inefficiencies in the $35T global supply chain market. Emerging technologies such as IoT and Blockchain democratize "trustworthy" data availability and empower stakeholders to make the right decision at the right time in the most cost-effective manner. Digital supply chain integration is progressively becoming a competitive differentiation of enterprises. Organizations leading to this approach of digital supply chain are rapidly improving their asset utilization and enabling new databased services. This paper presents the idea about relevance of complementary technologies like IoT and Blockchain technology for complete digitization of supply chain. The business case of pallet renting vendor is taken to showcase the use of technology integration to improve efficiency of its supply chain and asset management.
CVTM: A Content-Venue-Aware Topic Model for Group Event Recommendation

Event recommendation is essential to help people find attractive events to attend, but it intrinsically faces cold-start problem. The previous studies exploit multiple contextual factors to overcome the cold-start problem in event recommendation. However, they do not consider the correlation among different contextual factors. Moreover, suggesting events for a group of users also has not been well studied. In this paper, we first discover the correlation between organizer and textual content, i.e., the events held by the same organizer tend to have more similar content. Based on this observation, we present a content-venue-aware topic model (CVTM) to capture group interests on an event from two perspectives: content and venue. The correlation between organizer and content is modeled in CVTM to alleviate the sparsity of textual content, and then we can further extract group interests on content of an event more accurately. Finally, a group event recommendation method using CVTM is proposed. We conduct comprehensive experiments to evaluate the recommendation performance of our model on two real-world datasets. The results demonstrate that the proposed model outperforms the state-of-the-art methods that suggest upcoming events for groups. Besides, CVTM can learn semantically coherent latent topics which are useful to explain recommendations.