Reviewerly/exHarmony huggingface dataset:
A Unified Tensor Framework for Clustering and Simultaneous Reconstruction of Incomplete Imaging Data

Incomplete observations in the data are always troublesome to data clustering algorithms. In fact, most of the well-received techniques are not designed to encounter such imperative scenarios. Hence, clustering of images under incomplete samples is an inquisitive yet unaddressed area of research. Therefore, the aim of this article is to design a single-stage optimization procedure for clustering as well as simultaneous reconstruction of images without breaking the intrinsic spatial structure. The method employs the self-expressiveness property of submodules, and images are stacked as the lateral slices of a three-dimensional tensor. The proposed optimization method is designed to extract a sparse t -linear combination tensor with low multirank constraint, consisting of a unique set of linear coefficients in the form of mode-3 fibers and the spectral clustering is performed on these fibers. Simultaneously, the recovery of lost samples is accomplished by twisting the entire lateral slices of the data tensor and applying a low-rank approximation on each slice. The prominence of the proposed method lies in the simultaneous execution of data clustering and reconstruction of incomplete observations in a single step. Experimental results reveal the excellence of the proposed method over state-of-the-art clustering algorithms in the context of incomplete imaging data.
Personality2vec: Network Representation Learning for Personality

Online social networks have made tremendous progress in recent years. People generate a large amount of text information in which the linguistic and the semantic information could convey a great deal of information about the users' personality. Studies on personality analysis based on text information from online social networks have gradually increased in recent years. However, the previous works do not leverage the text information comprehensively and thus limits the prediction accuracy. In this paper, we propose personality2vec, a novel model that is based on network representation learning and makes full use of the semantic, the personality, and the structural information in users' texts. The model utilizes a new biased walk algorithm and an improved skip-gram algorithm to train on the network and eventually generates a personality vector for each user. We feed the personality vectors into regression algorithms to predict users' Big Five personality scores. Experimental results show that personality2vec outperforms seven popular methods on three personality datasets.
An Accurate, Robust Visual Odometry and Detail-Preserving Reconstruction System

Tracking and mapping functions in a monocular SLAM system remain active due to their challenging nature. In this paper, we propose a novel approach to perform the accurate and robust ego-motion estimation and provide the detail-preserving reconstruction in indoor environments. More specifically, we design a new algorithm called synchronous event measurement (SEM) to create event-based difference images (EDIs) so as to highlight frame-to-frame (F2F) difference. The observation indicates that F2F difference is highly correlated with the camera's motion change. We hereby feed EDIs into a deep convolutional neural network, in order to infer ego-motion of the camera. Subsequently, based on a monocular reconstruction framework (REMODE), we devise an algorithm named event region search or briefly ERS, to reduce possibility of mismatch on the depth estimation stage. Evaluations on a variety of datasets demonstrate the satisfactory performance of our proposed method: the ego-motion estimation is more accurate than some geometric based Visual Odometry (VO) and learning based approaches. The results are robust under extreme situations, such as brightness variation and motion blur. Meanwhile, our approach can provide more precise depth map with relatively rich textural information.
Item Recommendation for Word-of-Mouth Scenario in Social E-Commerce

Social commerce, which is different from traditional e-commerce where people purchase products via initiative searching or recommendations from the platform, transforms a social community into an inclusive place to do business by enabling people to share products with their friends. A user ( <i>sharer</i> ), can share a link of a product to their social-connected friends ( <i>receiver</i> ). Once a receiver purchases the product, the sharer can earn commission provided by the platform. To promote sales, the platform can also assist sharers by providing product candidates which are more likely to be purchased during the social sharing. We define this task of generating sharing suggestions as item recommendation for word-of-mouth scenario, and to the best of our knowledge, this is a new task that has never been explored. In this article, we propose a <i>TriM</i> (short for <b>Tri</b> ad based word-of- <b>M</b> outh recommendation) model that can capture both the sharer’s influence and the receiver’s interest at the same time, which are two significant factors that determine whether the receiver will buy the product or not. Furthermore, with joint learning on two parts of interaction data to address data sparsity issue, our proposed TriM-Joint further improves the recommendation performance. By conducting experiments, we show that our proposed models achieve the best results compared to state-of-the-art models with significant improvements by at least <inline-formula><tex-math notation="LaTeX">$7.4\% \sim 14.4\%$</tex-math></inline-formula> respectively.
Testing the Static-99R as a Global Screen for Risk of Sex Crime Recidivism in a Norwegian Routine Sample

The Static-99R has been recommended for use as a first global screen for sorting out sex-convicted persons who are in need of further risk assessment. This study investigated the Static-99R's predictive validity based on a nonselected Norwegian sample (n = 858) of persons released from prison after having served a sex crime sentence. After a mean observation period of 2,183 days, 3.4% (n = 29) had recidivated to a new sex offense. A higher number of recidivists were found among those with higher Static-99R total scores. The predictive contribution from each of the ten Static-99R risk items was investigated using standard logistic regression, proportional hazard regression, and random forest classification algorithm. The overall results indicate that the Static-99R is relevant as a risk screen in a Norwegian context, providing similar results concerning predictive accuracy as previous studies.
Risk-Based Multi-Attribute Decision-Making for Normal Cloud Model Considering Pre-Evaluation Information

An uncertain multi-attribute decision-making (MADM) problem is studied based on cloud models.Cloud models, referring to fuzziness and randomness, are utilized to depict evaluation and pre-evaluation information which can reflect the future development performance of alternatives.Because of bounded rationality, decision maker's (DM) risk attitudes should be considered when facing uncertainty.Thus, a behavioral MADM (BMADM) method is proposed by considering DM's risk attitudes and preevaluation.First, a distance measure for normal cloud models is developed with consideration of both DM's risk preferences and random distribution, aiming at making full use of information.Second, as a basis of applying prospect theory, positive ideal reference point is set by considering both evaluation and pre-evaluation information from three aspects: risk-averse, risk-neutral, and risk-seeking preference coefficients, in which the sign of distance is not necessary to determine.The third element is the establishment of an optimization model for handling incomplete attribute weights, following which is to obtain the ranking of alternatives.The final phase is the application of the proposed method to one case, along with sensitivity and comparison analyses, as a means of illustrating the applicability and feasibility of the new method.
Initial coin offerings and their initial returns

We empirically examine the initial returns of Initial Coin Offerings (ICOs) and show that ICO underpricing is enormous, which implies that cryptocurrency markets are inefficient. Moreover, we find that having a short offering phase, not holding a presale, a precisely written whitepaper, and the creation of an independent blockchain all have a positive impact on ICOs' initial returns. Our results also suggest that the driving factor behind initial returns is the movement of the cryptocurrency markets, measured by both Bitcoin and Ethereum returns. In addition, whether or not the jurisdiction has cryptocurrency regulations is an influential indicator. ICOs that belong to the high-tech services and platform industries have higher initial returns. Conventional financial assets, such as the stock market and gold, have a positive influence on ICOs' initial returns.
A stakeholder framework for evaluating the ‐ilities of autonomous behaviors in complex adaptive systems

Abstract Complex adaptive systems (CAS) exhibit emergent behaviors caused by the nonlinear actions between individual components within the system and their environment. These emergent behaviors are difficult to predict and measure making it very challenging to initially design from the start. Developing CASs, such as an autonomous swarming unmanned system, is a relevant key task today for solving hard problems with limited resources in a demanding economy. Swarming unmanned systems are valued by their unique capabilities, but how do stakeholders ensure their needs are considered during development? Certain stakeholders have interests that go beyond the initial delivery of the system, yet developers seem to focus efforts on designing autonomous behaviors to satisfy immediate mission needs. To close this gap, we propose a stakeholder focused framework for evaluating CASs over a full system life cycle for swarming unmanned systems. A goal‐oriented requirements engineering modeling methodology, Knowledge Acquisition in AutOmated Specification, is used to map requirements between stakeholder goals and autonomous behaviors. Behaviors are analyzed in an agent‐based simulation using NetLogo for quantifying stakeholder‐driven performance qualities (‐ilities). A case study was performed for a swarm of unmanned surface vessels called wave gliders to evaluate performance over the entire system life cycle.
A unified framework for cloud security transparency and audit

The paradigm of cloud computing has elevated IT to new heights by offering the elasticity to match customer needs, while also reducing capital expenditure on procuring IT infrastructure. Despite the apparent benefits provided by cloud computing, organisations are slow in embracing the technology due to numerous issues that are associated with the lack of security transparency such as trust and accountability. Several contributions have been proposed to address these issues. However, most of the contributions have not provided a definite method by which security transparency can be achieved based on user requirements, and particularly, by probing or auditing cloud service providers. In this paper, we propose a framework for addressing a pressing challenge of cloud security transparency. Our approach includes a process and a supporting auditing tool for vetting cloud service providers and enabling security transparency based on predefined user requirements. The paper builds on our previous work on security transparency framework by incorporating an implementation process. In addition, we have developed a Security Transparency and Audit Tool through which users can collect and analyze evidence from cloud service providers for determining conformity to requirements, as well as for the specification of remedial actions. The tool is designed to be a supplementary component of the proposed framework that enables continuous probing and vetting of cloud provider meets user requirements, thereby enhancing security transparency. The work is novel in its approach because it consolidates various elements to provide a simplified method for organizations to attain security transparency. We also believe that the contributions are significant towards solving the issues and challenges of cloud security transparency in general.
Classifying Intelligence in Machines: A Taxonomy of Intelligent Control

The quest to create machines that can solve problems as humans do leads us to intelligent control. This field encompasses control systems that can adapt to changes and learn to improve their actions—traits typically associated with human intelligence. In this work we seek to determine how intelligent these classes of control systems are by quantifying their level of adaptability and learning. First we describe the stages of development towards intelligent control and present a definition based on literature. Based on the key elements of this definition, we propose a novel taxonomy of intelligent control methods, which assesses the extent to which they handle uncertainties in three areas: the environment, the controller, and the goals. This taxonomy is applicable to a variety of robotic and other autonomous systems, which we demonstrate through several examples of intelligent control methods and their classifications. Looking at the spread of classifications based on this taxonomy can help researchers identify where control systems can be made more intelligent.
AW-SDRLSE: Adaptive Weighting and Scalable Distance Regularized Level Set Evolution for Lymphoma Segmentation on PET Images

Accurate lymphoma segmentation on Positron Emission Tomography (PET) images is of great importance for medical diagnoses, such as for distinguishing benign and malignant. To this end, this paper proposes an adaptive weighting and scalable distance regularized level set evolution (AW-SDRLSE) method for delineating lymphoma boundaries on 2D PET slices. There are three important characteristics with respect to AW-SDRLSE: 1) A scalable distance regularization term is proposed and a parameter <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> can control the contour's convergence rate and precision in theory. 2) A novel dynamic annular mask is proposed to calculate mean intensities of local interior and exterior regions and further define the region energy term. 3) As the level set method is sensitive to parameters, we thus propose an adaptive weighting strategy for the length and area energy terms using local region intensity and boundary direction information. AW-SDRLSE is evaluated on 90 cases of real PET data with a mean Dice coefficient of 0.8796. Comparative results demonstrate the accuracy and robustness of AW-SDRLSE as well as its performance advantages as compared with related level set methods. In addition, experimental results indicate that AW-SDRLSE can be a fine segmentation method for improving the lymphoma segmentation results obtained by deep learning (DL) methods significantly.
Traceability Blockchain Prototype for Regulated Manufacturing Industries

Blockchain emerged as a peer-to-peer trust platform for trading virtual currencies and evolved to be used for different problems including supply chain provenance. Due to stringent requirements of safety, regulated manufacturing and service industries such as aerospace, healthcare, and transportation require regulated traceability for parts, from source to the last customer, with detailed information requirements for each handover and operation. In this research, we analyzed the current traceability problem and list use cases of a traceability blockchain platform. A prototype platform is developed for the aerospace industry where every single part is required to have source and path traces recorded by certified supply chain actors. We evaluate the efficiency benefits of the platform in terms of duration and address future research topics.
Improved estimation of global solar radiation over rugged terrains by the disaggregation of Satellite Applications Facility on Land Surface Analysis data (LSA SAF)

Abstract This paper presents a new method to predict global solar radiation over irregular terrain, named Estimation of global solar RADiation (ERAD). The method is based on the disaggregation of Satellite Applications Facility on Land Surface Analysis (LSA SAF) data using a digital elevation model and is applied in Italy with a time step of 1 min and a spatial resolution of 200 m. A quantitative assessment of ERAD is performed in comparison with three other standard methods (Mountain Microclimate Simulation Model [MTCLIM], LSA SAF and Copernicus Atmosphere Monitoring Service [CAMS]) using measurements taken in 43 stations located in Italy or in the surrounding countries, in the years 2005–2016. Such assessment concerns the irradiance incoming on a horizontal surface, which is measured by ground radiation sensors and is summarized by means of four accuracy statistics (i.e. mean absolute error [MAE], root mean square error [RMSE], coefficient of determination [ R 2 ] and mean bias error [MBE]). Overall, the average daily global solar radiation estimates obtained by ERAD have RMSE and R 2 about 25 W·m −2 and 0.943, respectively. These statistics are similar to those of LSA SAF and better than those of CAMS and, above all, MTCLIM. The bias analysis by elevation ranges shows a slight ERAD overestimation over plains and hills and a slight underestimation over mountains. An additional qualitative assessment shows how the ERAD radiation estimates are more spatially detailed than those of the other methods and are redistributed on inclined surfaces consistently with expectations.
Keyframe Extraction From Laparoscopic Videos via Diverse and Weighted Dictionary Selection

Laparoscopic videos have been increasingly acquired for various purposes including surgical training and quality assurance, due to the wide adoption of laparoscopy in minimally invasive surgeries. However, it is very time consuming to view a large amount of laparoscopic videos, which prevents the values of laparoscopic video archives from being well exploited. In this paper, a dictionary selection based video summarization method is proposed to effectively extract keyframes for fast access of laparoscopic videos. Firstly, unlike the low-level feature used in most existing summarization methods, deep features are extracted from a convolutional neural network to effectively represent video frames. Secondly, based on such a deep representation, laparoscopic video summarization is formulated as a diverse and weighted dictionary selection model, in which image quality is taken into account to select high quality keyframes, and a diversity regularization term is added to reduce redundancy among the selected keyframes. Finally, an iterative algorithm with a rapid convergence rate is designed for model optimization, and the convergence of the proposed method is also analyzed. Experimental results on a recently released laparoscopic dataset demonstrate the clear superiority of the proposed methods. The proposed method can facilitate the access of key information in surgeries, training of junior clinicians, explanations to patients, and archive of case files.
Evaluation of Generative Adversarial Network Performance Based on Direct Analysis of Generated Images

Recently, a number of papers have addressed the theory and applications of the Generative Adversarial Network (GAN) in various fields of image processing. Fewer studies, however, have directly evaluated GAN outputs. Those that have been conducted focused on using classification performance and statistical metrics. In this paper, we consider a fundamental way to evaluate GANs by directly analyzing the images they generate, instead of using them as inputs to other classifiers. We consider an ideal GAN according to three aspects: 1) Creativity: non-duplication of the real images. 2) Inheritance: generated images should have the same style, which retains key features of the real images. 3) Diversity: generated images are different from each other. Based on the three aspects, we have designed the Creativity-Inheritance-Diversity (CID) index to evaluate GAN performance. We compared our proposed measures with three commonly used GAN evaluation methods: Inception Score (IS), Fréchet Inception Distance (FID) and 1-Nearest Neighbor classifier (1NNC). In addition, we discuss how the evaluation could help us deepen our understanding of GANs and improve their performance.
TissueWand, a Rapid Histopathology Annotation Tool

Recent advancements in machine learning (ML) bring great possibilities for the development of tools to assist with diagnostic tasks within histopathology. However, these approaches typically require a large amount of ground truth training data in the form of image annotations made by human experts. As such annotation work is a very time-consuming task, there is a great need for tools that can assist in this process, saving time while not sacrificing annotation quality.In an iterative design process, we developed TissueWand - an interactive tool designed for efficient annotation of gigapixel-sized histopathological images, not being constrained to a predefined annotation task.Several findings regarding appropriate interaction concepts were made, where a key design component was semi-automation based on rapid interaction feedback in a local region. In a user study, the resulting tool was shown to cause substantial speed-up compared to manual work while maintaining quality.The TissueWand tool shows promise to replace manual methods for early stages of dataset curation where no task-specific ML model yet exists to aid the effort.
RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift

Semi-supervised learning (SSL) tries to improve performance with the use of massive unlabeled data, which typically works in an offline manner with two assumptions. i) Data distribution is static; ii) Data storage overhead is unlimited. In many online tasks, however, none of the above assumptions is valid. For example, in online image classification, a large amount of unlabeled images increases sharply, which makes it difficult to store them in full; meanwhile, the content of unlabeled images changes constantly, and it is no longer suitable to assume a fixed distribution. We call such a novel setting Resource Constrained SSL under Distribution Shift (or Record for short) and to our best knowledge, it has not been thoroughly studied yet. This paper presents a systemic solution Record consisting of three sub-steps, that is, distribution tracking, sample selection and model updating. Specifically, we propose an effective method to track the distribution changes and locate distribution shifted samples. A novel influence-based approach is used to select the most influential samples for the distribution change based on resource constraints. Finally, we free up memory to put the latest unlabeled data with its pseudo-label for the next distribution tracking. Extensive empirical results confirm the effectiveness of our scheme. In the case of diverse and unknown distribution shifts, our solution is consistently and clearly better than many baseline and SOTA methods along with the memory budget and in some cases it can even approximate the performance of oracle.