Reviewerly/exHarmony huggingface dataset:
A statistical method for detecting spatiotemporal co-occurrence patterns

Spatiotemporal co-occurrence patterns (STCOPs) are subsets of Boolean features whose instances frequently co-occur in both space and time. The detection of STCOPs is crucial to the investigation of the spatiotemporal interactions among different features. However, prevalent STCOPs reported by available methods do not necessarily indicate the statistically significant dependence among different features, which is likely to result in highly erroneous assessments in practice. To improve the reliability of results, this paper develops a statistical method to detect STCOPs and discern their statistical significance. The proposed method detects STCOPs against the null hypothesis that the spatiotemporal distributions of different features are independent of each other. To construct the null hypothesis, suitable spatiotemporal point-process models considering spatiotemporal autocorrelation are employed to model the distributions of different features. The performance of the proposed statistical method is assessed by synthetic experiments and a case study aimed at identifying crime patterns among multiple crime types in Portland City. The experimental results demonstrate that the proposed method is more effective for detecting meaningful STCOPs than the available alternative methods.
Eye Fixation Location Recommendation in Advanced Driver Assistance System

Recent research progress on the approach of visual attention modeling for mediated perception to advanced driver assistance system (ADAS) has drawn the attention of computer and human vision researchers. However, it is still debatable whether the actual driver’s eye fixation locations (EFLs) or the predicted EFLs which are calculated by computational visual attention models (CVAMs) are more reliable for safe driving under real-life driving conditions. We analyzed the suitability of the following two EFLs using ten typical categories of natural driving video clips: the EFLs of human drivers and the EFLs predicted by CVAMs. In the suitability analysis, we used the EFLs confirmed by two experienced drivers as the reference EFLs. We found that both approaches alone are not suitable for safe driving and EFL suitable for safe driving depends on the driving conditions. Based on this finding, we propose a novel strategy for recommending one of the EFLs to the driver for ADAS under predefined 10 real-life driving conditions. We propose to recommend one of the following 3 EFL modes for different driving conditions: driver’s EFL only, CVAM’s EFL only, and interchangeable EFL. In interchangeable EFL mode, driver’s EFL and CVAM’s EFL are interchangeable. The selection of two EFLs is a typical binary classification problem, so we apply support vector machines (SVMs) to solve this problem. We also provide a quantitative evaluation of the classifiers. The performance evaluation of the proposed recommendation method indicates that it is potentially useful to ADAS for future safe driving.
Mitigating the Ransomware Threat: A Protection Motivation Theory Approach

Ransomware has emerged as one of the biggest security threats to organizations and individuals alike. As technical solutions are developed the creators of ransomware are also improving the sophistication of such attacks. A combination of technical and behavioral measures is required to deal with this problem. This study investigates computer users’ motivation to adopt security measures against ransomware, using protection motivation theory (PMT) as a theoretical foundation. We conducted empirical research, using a survey methodology, collecting data from 118 respondents. Using partial least squares structural equation modelling our analysis provides support for several factors influencing protection motivation in this context. These include perceived threat severity and perceived threat vulnerability, mediated by fear. Self-efficacy is shown as a significant coping factor. Maladaptive rewards and response costs both have a significant negative influence on protection motivation. The results provide support for the use of fear appeals and PMT to influence protection motivation in the context of ransomware threats.
Exposing Social Data as Linked Data in Education

According to recent studies, the social interactions of users such as sharing, rating, and reviewing can improve the value of digital learning objects and resources on the web. Linked data techniques, on the other hand, make different kinds of data available and reusable for other applications on the web. Exposing (meta)data, especially with a complex structure, as resource description framework (RDF) requires an ontology to bring all the data types under one umbrella. In this article, the authors propose an ontology in which social activities of users are exposed as linked data by reusing existing vocabularies. The proposed ontology has been implemented in a federated open educational resources (OER) portal, in which they published ratings, shares, comments, and other social activities assigned to around 1,000 OERs. This exposure allows other datasets, including harvested repositories, to explore the exposed social data related to e-learning objects according to the users' social engagement.
Full View Optical Flow Estimation Leveraged From Light Field Superpixel

In this paper, we present a full view optical flow estimation method for plenoptic imaging. Our method employs the structure delivered by the four-dimensional light field over multiple views making use of superpixels. These superpixels are four dimensional in nature and can be used to represent the objects in the scene as a set of slanted-planes in three-dimensional space so as to recover a piecewise rigid depth estimate. Taking advantage of these superpixels and the corresponding slanted planes, we recover the optical flow and depth maps by using a two-step optimization scheme where the flow is propagated from the central view to the other views in the imagery. We illustrate the utility of our method for depth and flow estimation making use of a dataset of synthetically generated image sequences and real-world imagery captured using a Lytro Illum camera. We also compare our results with those yielded by a number of alternatives elsewhere in the literature.
Thermal face segmentation based on circular shortest path

Recently, research on thermal infrared human face analysis grows rapidly. However, due to the low contrast and limited information, human face in thermal infrared images is difficult to segment precisely. To overcome these shortcomings of thermal images, we propose an improved circular shortest path method in this paper. In our method, the proposed gradient based cost function enhances the gradient information and extracts detail of the original image. In addition, we propose a shape constraint by using ellipse derivative in the cost function. The constraint helps the contour to conform to the real human face. Moreover, the proposed certainty penalty term and straight path penalty term restrain the effect of the local minima regions and improve the robustness of our method. Our method could effectively extract the precise human face contour and thus segment the complete human face. Experimental results show that our method performs well for thermal infrared face segmentation, in both visual and quantitative ways.
Smart-Grid-Aware Load Regulation of Multiple Datacenters towards the Variable Generation of Renewable Energy

Recently, as renewable and distributed power sources boost, many such resources are integrated into the smart grid as a clean energy input. However, since the generation of renewable energy is intermittent and unstable, the smart grid needs to regulate the load to maintain stability after integrating the renewable energy source. At the same time, with the development of cloud computing, large-scale datacenters are becoming potentially controllable loads for the smart grid due to their high energy consumption. In this paper, we propose an appropriate approach to dynamically adjust the datacenter load to balance the unstable renewable energy input into the grid. This could meet the demand response requirements by taking advantage of the variable power consumption of datacenters. We have examined the scenarios of one or more datacenters being integrated into the grid and adopted a stochastic algorithm to solve the problem we established. The experimental results illustrated that the dynamic load management of multiple datacenters could help the smart grid to reduce losses and thus save operational costs. Besides, we also analyzed the impact of the flexibility and the delay of datacenter actions, which could be applied to more general scenarios in realistic environments. Furthermore, considering the impact of the action delay, we employed a forecasting method to predict renewable energy generation in advance to eliminate the extra losses brought by the delay as much as possible. By predicting solar power generation, the improved results showed that the proposed method was effective and feasible under both sunny and cloudy/rainy/snowy weather conditions.
Extended Non-local Feature for Visual Saliency Detection in Low Contrast Images

Saliency detection model can substantially facilitate a wide range of applications. Conventional saliency detection models primarily rely on high level features from deep learning and hand-crafted low-level image features. However, they may face great challenges in nighttime scenario, due to the lack of well-defined feature to represent saliency information in low contrast images. This paper proposes a saliency detection model for nighttime scene. This model is capable of extracting non-local feature that is jointly learned with local features under a unified deep learning framework. The key idea of the proposed model is to hierarchically introduce non-local module with local contrast processing blocks, aiming to provide robust representation of saliency information towards low contrast images with low signal-to-noise ratio property. Besides, both nighttime and daytime images are utilized in training to provide complementary information. Extensive experiments have been conducted on five challenging datasets and our nighttime image dataset to evaluate the performance of the proposed model.
DWT and QR Code Based Watermarking for Document DRM

This paper presents a digital rights protection scheme for every type of document presented as an image, by using steps that use cryptography and watermarking. The entities involved in this process are two: the owner of the document that owns its digital rights and a generic user who can download or view a watermarked version of the original document. The watermarked version contains a QR code that is repeatedly inserted, and scrambled, by the document rights owner, into the frequency components of the image, thus producing the watermarked image. The QR code contains a signed ID that uniquely identifies every users using the system. The schema, a non-blind type, achieves good perceptive quality and fair robustness using the third level of the Discrete Wavelet Transform. The experimental results show that by inserting several occurrences of a scrambled QR code we get an approach that is quite resistant to JPEG compression, rotation, cropping, and salt and pepper noise.
New and improved search algorithms and precise analysis of their average-case complexity

In this paper, we consider the searching problem over ordered sequences. It is well known that Binary Search (BS) algorithm solves this problem with very efficient complexity, namely with the complexity θ(log2n). The developments of the BS algorithm, such as Ternary Search (TS) algorithm do not improve the efficiency. The rapid increase in the amount of data has made the search problem more important than in the past. And this made it important to reduce average number of comparisons in cases where the asymptotic improvement is not achieved. In this paper, we identify and analyze an implementation issue of BS. Depending on the location of the conditional operators, we classify two different implementations for BS which are widely used in the literature. We call these two implementations weak and correct implementations. We calculate precise number of comparisons in average case for both implementations. Moreover, we transform the TS algorithm into an improved ternary search (ITS) algorithm. We also propose a new Binary–Quaternary Search (BQS) algorithm by using a novel dividing strategy. We prove that an average number of comparisons for both presented algorithms ITS and BQS is less than for the case of correct implementation of the BS algorithm. We also provide the experimental results.
Filter-in-Filter: Low Cost CNN Improvement by Sub-filter Parameter Sharing

Increasing the number of parameters seems to have improved convolutional neural networks, e.g. increasing the depth or width of the networks. In this paper, we propose a scheme to improve CNNs by deriving the six sub-filters from a filter, which share parameters among them and enhance the expressibility of the filter. We first defined the sub-filters of a filter, and by visualizing a well-trained CNN, we verified that these sub-filters could recognize multiple meaningful patterns with different visual characteristics, even when the filter containing them was not activated. These findings revealed that the filter has the potential to recognize multiple patterns. Inspired by these findings, we proposed the filter-in-filter (FIF) scheme to enhance the expressibility of a filter, by making full use of its sub-filters to recognize multiple meaningful sub-patterns. We verified the effectiveness of FIF on three image classification benchmark datasets, namely Tiny ImageNet, CIFAR-100 and ImageNet. Our experimental results showed that our models achieved consistent improvement over the base CNNs on the benchmark datasets, e.g. AlexNet and VGG16 using FIF achieved approximately 1% improvement on ImageNet. The sub-filters share the parameters and most of the computational cost with the filter containing them; therefore, FIF does not increase the number of parameters and increases the computational cost only slightly.
The Optimally Designed Variational Autoencoder Networks for Clustering and Recovery of Incomplete Multimedia Data

Clustering analysis of massive data in wireless multimedia sensor networks (WMSN) has become a hot topic. However, most data clustering algorithms have difficulty in obtaining latent nonlinear correlations of data features, resulting in a low clustering accuracy. In addition, it is difficult to extract features from missing or corrupted data, so incomplete data are widely used in practical work. In this paper, the optimally designed variational autoencoder networks is proposed for extracting features of incomplete data and using high-order fuzzy c-means algorithm (HOFCM) to improve cluster performance of incomplete data. Specifically, the feature extraction model is improved by using variational autoencoder to learn the feature of incomplete data. To capture nonlinear correlations in different heterogeneous data patterns, tensor based fuzzy c-means algorithm is used to cluster low-dimensional features. The tensor distance is used as the distance measure to capture the unknown correlations of data as much as possible. Finally, in the case that the clustering results are obtained, the missing data can be restored by using the low-dimensional features. Experiments on real datasets show that the proposed algorithm not only can improve the clustering performance of incomplete data effectively, but also can fill in missing features and get better data reconstruction results.
Possibilistic compositions and state functions: application to the order promising process for perishables

In this paper, we propose the concepts of the composition of possibilistic variables and state functions. While in conventional compositional data analysis, the interdependent components of a deterministic vector must add up to a specific quantity, we consider such components as possibilistic variables. The concept of state function is intended to describe the state of a dynamic variable over time. If a state function is used to model decay in time, it is called the ageing function. We present a practical implementation of our concepts through the development of a model for a supply chain planning problem, specifically the order promising process for perishables. We use the composition of possibilistic variables to model the existence of different non-homogeneous products in a lot (sub-lots with lack of homogeneity in the product), and the ageing function to establish a shelf life-based pricing policy. To maintain a reasonable complexity and computational efficiency, we propose the procedure to obtain an equivalent interval representation based on α-cuts, allowing to include both concepts by means of linear mathematical programming. Practical experiments were conducted based on data of a Spanish supply chain dedicated to pack and distribute oranges and tangerines. The results validated the functionality of both, the compositions of possibilistic variables and ageing functions, showing also a very good performance in terms of the interpretation of a real problem with a good computational performance.
A corpus platform of Indonesian academic language

<h2>Abstract</h2> The Indonesian language is the language of education and the language to unify the 701 ethnic languages in Indonesia. To document this language and to determine how it is used in academic texts, a corpus database needs to be collected together with a corpus platform to explore the corpus. This paper presents the features and usage of the first and freely available corpus platform of Indonesian Academic language. The corpus was compiled from over five million word tokens comprising articles from nationally accredited journals and theses from reputable universities. The main features of the software are context, collocate, and frequency. The corpus platform will be an essential resource for linguists, lexicographers, and teachers.
Citation.js: a format-independent, modular bibliography tool for the browser and command line

Given the vast number of standards and formats for bibliographical data, any program working with bibliographies and citations has to be able to interpret such data. This paper describes the development of Citation.js (https://citation.js.org/), a tool to parse and format according to those standards. The program follows modern guidelines for software in general and JavaScript in specific, such as version control, source code analysis, integration testing and semantic versioning.The result is an extensible tool that has already seen adaption in a variety of sources and use cases: as part of a server-side page generator of a publishing platform, as part of a local extensible document generator, and as part of an in-browser converter of extracted references. Use cases range from transforming a list of DOIs or Wikidata identifiers into a BibTeX file on the command line, to displaying RIS references on a webpage with added Altmetric badges to generating "How to cite this" sections on a blog. The accuracy of conversions is currently 27% for properties and 60% for types on average and a typical initialization takes 120 ms in browsers and 1 s with Node.js on the command line.Citation.js is a library supporting various formats of bibliographic information in a broad selection of use cases and environments. Given the support for plugins, more formats can be added with relative ease.
Smart Contract This! An Assessment of the Contractual Landscape and the Herculean Challenges it Currently Presents for “Self-executing” Contracts

The widespread use of “self-executingSelf-executing ” contracts is now only a question of time. For certain standard contract types with simple and well understood provisions, little international variation, no intermediation and short execution periods, that time is now as the change is already happening (examples are digital rights management and various banking applications.). It is the other more complex, more entrenched, and less agile sectors of the global economy which are the focus of this paper. Here there will be hurdles to overcome and more time needed for implementation—it will be a difficult task. The arrival of this new technologyTechnology presents important questions about the future of contracting, as well as about traditional legal practiceLegal practice within both legal departments and law firmsLaw firms , calling for a new quality of cooperation between businessBusiness and their lawyersLawyers . Given that the natural reaction to change is resistance, that companies are having ever-greater challenges navigating international regulation and that as a result legal department within companies tend to exert a strong influence out of line with the number of employees they include, the power of institutional resistance to delay adoption of the change will be considerable. This chapter will seek to add a dose of realism to the techno-optimists in the late adolescence of the 21st century for whom the change is so far advanced it is practically finished. In this chapter it is largely assumed that readers understand how Distributed Ledger TechnologyDistributed Ledger Technology (DLT) works, and the principal focus will be on the contractual challenges standing in the way of the implementation of “self-executingSelf-executing contracts,” to which it will also offer some solutions. The original idea contained in this chapter is to embark on a well-planned Digital Contract OptimizationDigital contract optimization journey, supported by new technologies, as a means to manage various risks associated with algorithmically driven processes. This chapter will address the question of the institutional legal mindset as a potential delaying factor and will present a Darwinian argument to explain that change is inevitable and will be radical in terms of the new demands on lawyersLawyers .
Hierarchical graphs for coarse-to-fine error tolerant matching

During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting).