Reviewerly/exHarmony huggingface dataset:
A data mining system for predicting solar global spectral irradiance. Performance assessment in the spectral response ranges of thin-film photovoltaic modules

Knowing the spectral distribution of solar radiation is required to estimate the performance of photovoltaic modules, especially for thin-film modules. This is not a trivial problem due to the large number of environmental factors that affect this distribution as solar radiation passes through the atmosphere. The use of techniques of artificial intelligence and data mining can help in the development of models to address this problem. A system based on these techniques is proposed to predict the solar global spectral irradiance requiring only a few meteorological variables as inputs. The evaluation of the proposed system has been carried out for different wavelengths taking into account the spectral response of different technologies of thin-film photovoltaic modules. The errors in predicting solar global spectral irradiance for wavelengths that range between 350 and 900 nm and air mass lower than 2.1 are smaller than 7% on clear-sky days and than 16% for cloudy days, which is a significant improvement on other proposed models. Moreover, an open access implementation of the developed system is available at the URI: http://fvred1.ctima.uma.es. It could be useful for engineers and companies in the fields of the environment and renewable energies.
Entropic uncertainty relations and the measurement range problem, with consequences for high-dimensional quantum key distribution

The measurement range problem, where one cannot determine the data outside the range of the detector, limits the characterization of entanglement in high-dimensional quantum systems when employing, among other tools from information theory, the entropic uncertainty relations. Practically, the measurement range problem weakens the security of entanglement-based large-alphabet quantum key distribution (QKD) employing degrees of freedom including time-frequency or electric field quadrature. We present a modified entropic uncertainty relation that circumvents the measurement range problem under certain conditions, and apply it to well-known QKD protocols. For time-frequency QKD, although our bound is an improvement, we find that high channel loss poses a problem for its feasibility. In continuous variable QKD, we find our bound provides a quantitative way to monitor for saturation attacks.
Probabilistic Reasoning for Unique Role Recognition Based on the Fusion of Semantic-Interaction and Spatio-Temporal Features

This paper deals with the problem of recognizing the unique role in dynamic environments. Different from social roles, the unique role refers to those who are unusual in their carrying items or movements in the scene. In this paper, we propose a hierarchical probabilistic reasoning method that relates spatial relationships between interested objects and humans with their temporal changes to recognize the unique individual. Two observation models, Object Existence Model (OEM) and Human Action Model (HAM), are established to support role inference by analyzing the corresponding semantic-interaction features and spatio-temporal features. Then, OEM and HAM results of each person are compared with the overall distribution in the scene, respectively. Finally, we can determine the role through the fusion of two observation models. Experiments are conducted in both indoor and outdoor environments concerning different settings, degrees of clutter, and occlusions. The results show that the proposed method can adapt to a variety of scenarios and outperforms other methods on accuracy and robustness, moreover, exhibiting stable performance even in complex scenes.
Set operations over compressed binary relations

Binary relations are commonly used to represent relationships between real-world objects. Classical representations for binary relations can be very space-consuming when the set of elements is large. In these cases, compressed representations, such as the k2-tree, have proven to be a competitive solution, as they are efficient in time while consuming very little space. Moreover, k2-trees can successfully represent both sparse and dense binary relations, using different variants of the technique. In this paper, we propose and evaluate algorithms to efficiently perform set operations over binary relations represented using k2-trees. More specifically, we present algorithms for computing the union, intersection, difference, symmetric difference, and complement of binary relations. Thus, this work extends the functionality of the different variants of the k2-tree representation for binary relations. Our algorithms are computed directly over the compressed representation, without requiring previous decompression, and generate the result in compressed form. The experimental evaluation shows that they are efficient in terms of space and time, compared with different baselines where the binary relations are represented in plain form or require a previous decompression to perform the set operation.
Quantifying the Amount of Visual Information Used by Neural Caption Generators

This paper addresses the sensitivity of neural image caption generators to their visual input. A sensitivity analysis and omission analysis based on image foils is reported, showing that the extent to which image captioning architectures retain and are sensitive to visual information varies depending on the type of word being generated and the position in the caption as a whole. We motivate this work in the context of broader goals in the field to achieve more explainability in AI.
Linear codes associated to skew-symmetric determinantal varieties

In this article we consider linear codes coming from skew-symmetric determinantal varieties, which are defined by the vanishing of minors of a certain fixed size in the space of skew-symmetric matrices. In odd characteristic, the minimum distances of these codes are determined and a recursive formula for the weight of a general codeword in these codes is given.
The literature review of action recognition in traffic context

With the development of science and technology and the progress of computing level, the research field based on video is getting more and more attention. Video understanding is a hot and challenging topic in computer vision area. Human action recognition means to automatically analyze the ongoing actions from an unknown video or image sequence and classify them correctly. Action recognition technology is widely used in many areas, including patient monitoring system, human-computer interaction, intelligent video surveillance, virtual reality, intelligent security and other aspects. In addition, video retrieval based on content and intelligent image compression have broad application prospects, among which many methods of action recognition are used. With the increasing demand for technology, there are still some challenges of action recognition in traffic context. In this paper, we briefly review the development process of action recognition technology, and classify some relevant methods, then we summarize the overall development trend of this technology. Finally, we also discussed the practical significance of it in the future in traffic context.
The Diagnosing Behaviour of Intelligent Tutoring Systems

Intelligent Tutoring Systems (ITSs) determine the quality of student responses by means of a diagnostic process, and use this information for providing feedback and determining a student's progress. This paper studies how ITSs diagnose student responses. In a systematic literature review we compare the diagnostic processes of 40 ITSs in various domains. We investigate what kinds of diagnoses are performed and how they are obtained, and how the processes compare across domains. The analysis identifies eight aspects that ITSs diagnose: correctness, difference, redundancy, type of error, common error, order, preference, and time. All ITSs diagnose correctness of a step. Mathematics tutors diagnose common errors more often than programming tutors, and programming tutors diagnose type of error more often than mathematics tutors. We discuss a general model for representing diagnostic processes.
Changing Formal and Informal Learning Practices Using Smartphones

Smartphones have afforded women opportunities to overcome some of the constraints they face in the informal sector. The culture of traditional learning for women in the marketplace refers to sharing common standardized practices of learning from each other, conducting business, communicating, and making money. Sharing information, knowledge, and experiences is already embedded in the culture of the informal sector therefore a network connection through smartphones will bring a new light of opportunities to the learning environment. Using a case study of market women in Ghana, the authors of this chapter focus on these women's experiences learning with video animation in smartphones and predict how they will envision a new way of learning that combines the formal and informal learning with easy capabilities such as visualization, simulation, technical proficiency, and accessibility to information.
Quantitative study of fast non-local means-based denoising filter in chest X-ray imaging with lung nodule using three-dimensional printing

In chest radiography, a solitary pulmonary nodule, which may be a precursor of lung cancer, is a frequently detected finding. However, as the image quality is deteriorated owing to the increase in the noise, lung cancer screening studies revealed that the likelihood of finding a nodule is lower than those of other modalities. This study quantitatively evaluates three widely used filters (median, Wiener, and total variation) and a newly proposed filter (fast non-local means (FNLM)), which reduce image noise. Images of a phantom with lung nodules, obtained from a patient using the 3D printing technology, were acquired at the chest anterior–posterior, lateral, and posterior–anterior positions. To evaluate their denoising performance, normalized noise power spectrum, contrast to noise ratio and coefficient of variation were used. In the quantitative evaluation of the overall image, the proposed FNLM filter exhibited the best image performance. In the quantitative evaluation of the nodule image, the FNLM filter, which exhibits outstanding denoising performance and time efficiency, can be employed. Therefore, with the use of the FNLM filter in chest radiography, the detection probability of a nodule, which can be a precursor of lung cancer, is increased, and the cancer can be prevented even with a lower dose.
Checking unscented information fusion algorithm for autonomous navigation vehicles

For high speed vehicles, the dimensions of the observed information from the sensors are not always appropriate, and the quality of the information differs. To enhance the accuracy of the terminal autonomous navigation for high speed vehicle, A checking unscented information filters (CUIF) algorithm is proposed based on the principle of EIF(extended information fusion). CUIF adopts distributed structure instead of centralized one, While the sub-algorithm of Unscented Kalman filter (UKF) is used instead of Extended Kalman filter (EKF). The improved algorithm performs real-time system state compensation for the delayed information acquired from the sensors, so that problems such as single sensor signal delay, packet loss could be resolved. Simulation results indicate that the CUIF algorithm is 50% more accurate than the UIF and EIF in terms of positioning precision, while both consistency and robustness are improved in global terms.
Probably bounded suboptimal heuristic search

Finding an optimal solution to a search problem is often desirable, but can be too difficult in many cases. A common approach in such cases is to try to find a solution whose suboptimality is bounded, where a parameter ϵ defines how far from optimal a solution can be while still being acceptable. A scarcely studied alternative is to try to find a solution that is probably optimal , where a parameter δ defines the confidence required in the solution's optimality. This paper explores this option and introduces the concept of a probably bounded-suboptimal search (pBS search) algorithm. Such a search algorithm accepts two parameters, ϵ and δ , and outputs a solution that with probability at least 1 − δ costs at most 1 + ϵ times the optimal solution. A general algorithmic framework for pBS search algorithms is proposed. Several instances of this framework are described and analyzed theoretically and experimentally on a range of search domains. Results show that pBS search algorithms are often faster than a state-of-the-art bounded-suboptimal search algorithm. This shows in practice that finding solutions that satisfy a given suboptimality bound with high probability can be done faster than finding solutions that satisfy the same suboptimality bound with certainty.
Research on cloud computing task scheduling algorithm based on particle swarm optimization

In the field of cloud computing, Particle swarm optimization (PSO) is an important intelligent algorithm for solving the task scheduling problem, and has been rapidly developed. In order to improve the overall optimization ability, and get a low cost optimization solution, this paper proposes an im proved particle swarm optimization (IPSO) algorithm based on the adaptive inertia weight and random factor correlation. Simulation results show that under the same conditions, IPSO algorithm is less than the sequential scheduling algorithm, the greedy algorithm, the correlation particle swarm optimization (CPSO) algorithm and the new adaptive inertia weight based particle swarm optimization (NewPSO) algorithm in terms of cost consumption (including time cost and virtual machine cost).
Show and Tell in the Loop: Cross-Modal Circular Correlation Learning

Multimedia data with various modalities, such as image and text, are huge in quantity but have inconsistent distribution and representation. Many works have been done to break the boundary between image and text to measure their correlation. However, they focus on either the transformation to common subspace or the unidirectional generation from one to another individually, which cannot fully explore their interactions. It is noted that the bidirectional generation between image and text not only can provide complementary hints and mutually boost to learn cross-modal correlation but also cross-modal correlation learning can feed back to give comprehensive clues for promoting the cross-modal generation process. Therefore, we have the motivation that information transmission between image and text should be treated as a circular process, which aims to fully understand their latent correlation, and further realize cross-modal generation to produce both realistic images and text descriptions in a unified framework. In this paper, we propose the cross-modal circular correlation learning approach to perform both cross-modal correlation learning and generation simultaneously through an efficient circular learning training procedure. First, we propose the cross-modal circular learning model to perform an image-to-text caption and text-to-image synthesis circularly and learn common representation as a round-trip bridge, which can realize efficient interactions to fully exploit latent cross-modal correlations. Second, a unified bidirectional framework is proposed to conduct cross-modal mutual generation and is trained in an efficient circular process to enhance the generative ability of common representation, which can feed back circularly to further promote cross-modal correlation learning. In summary, we simultaneously perform cross-modal retrieval, image-to-text caption, and text-to-image synthesis in a unified framework with the circular learning process, which has high scalability and generality to realize universal cognition on the cross-modal data. We conduct extensive experiments to not only evaluate the correlation performance by cross-modal retrieval but also to show the generation effectiveness of both image caption and synthesis on the MS-COCO dataset.
Salient object detection via reliable boundary seeds and saliency refinement

Salient object detection can identify the most distinctive objects in a scene. In this study, a novel graph‐based approach is proposed to detect a salient object via reliable boundary seeds and saliency refinement. A natural image is firstly mapped to a graph with superpixels as nodes. Saliency information is then diffused over the graph using seeds. For the reason that the boundary nodes may contain salient nodes, it is not appropriate to use all boundary nodes as the background seeds. Therefore, a boundary saliency measurement is proposed to obtain more accurate background seeds. After that, the information of background seeds is diffused by a two‐stage scheme. A background‐based map and a foreground‐based map are generated based on the two‐stage scheme. Furthermore, in order to enhance the detection accuracy, a refinement model is presented to fuse the information of background‐based and foreground‐based maps. Experiments on seven public datasets show the proposed algorithm out‐performs the state‐of‐the‐art salient object detection algorithms.
A Framework for Preserving the Privacy of Online Users Against XSS Worms on Online Social Network

This article presents a hybrid framework i.e. OXSSD (Online Social Network-Based XSS-Defender) that explores cross-site scripting (XSS) attack vectors at the vulnerable points in web applications of social networks. Initially, during training phase, it generates the views for each request and formulates the access control list (ACL) which encompasses all the privileges a view can have. It also ascertains all possible injection points for extracting malicious attack vectors. Secondly, during recognition phase, after action authentication XSS attack vectors are retrieved from the extracted injection points followed by the clustering of these attack vectors. Finally, it sanitizes the compressed clustered template in a context-aware manner. This context-aware sanitization ensures efficient and accurate alleviation of XSS attacks from the OSN-based web applications. The authors will evaluate the detection capability of OXSSD on a tested suite of real world OSN-based web applications (Humhub, Elgg, WordPress, Drupal and Joomla). The performance analysis revealed that OXSSD detects injection of illicit attack vectors with very low false positives, false negatives and acceptable performance overhead.
A fast video super resolution for facial image

Multi-frame super resolution has found various applications in different domains of machine vision such as remote sensing, object recognition, and security applications for the last two decades. Classic super resolution methods are not able to handle real word videos where different parts of scene undergo different motions. Most of recent methods in the literature address this problem but they suffer from time complexity. In this paper, we propose a fast method for super resolution of facial videos. Our proposed method provides less computational complexity in addition to handling videos having general motion patterns. These two benefits make the proposed method suitable for security purposes. In the proposed method, first we extract a number of key points from face in each video frame. Then, for each pixel in the reference frame the corresponding pixels in other frames are determined using triangular patches. Subsequently, the obtained solution is improved by minimizing an energy function considering both appearance and pixel displacements. Super resolved facial image is finally obtained by using information available in a small window around approximated location of the low resolution frames. The effectiveness of the proposed method has been demonstrated for real video sequences.
Dynamic Backfilling Algorithm to Increase Resource Utilization in Cloud Computing

In this article, the authors propose a novel backfilling-based task scheduling algorithm to schedule deadline-based tasks. The existing backfilling algorithm has some performance issues in comparison with the number of task scheduling in OpenNebula cloud platform. A lease could not be scheduled if it is not sorted with respect to its start time. In backfilling, a lease is selected in First Come First Serve (FCFS) to be backfilled from the queue in which some ideal resources can be found out and allocated to other leases. However, the scheduling performance is not better if there are similar types of leases to backfill. It requires a decision maker to resolve conflicts. The proposed approach schedules the number of tasks without any decision maker. An additional queue and the current time of the system is implemented to improve the scheduling performance. It performs quite satisfactorily in terms of number of a leases scheduling, and resource utilization. The performance result is compared with the existing backfilling algorithms.
A new selective segmentation model for texture images and applications to medical images

Segmentation of texture images is an active area of image processing. Selective segmentation is the process of extracting a region of interest (ROI) in the image. In this paper, we propose a new model for selective segmentation of texture images, by incorporating geometrical constraints after smoothing the texture in image. The proposed model uses L0 norm for smoothing of the texture and Badshah–Chen energy with local Gaussian kernel data fitting for selective segmentation. The proposed model is minimized to get gradient descent through Euler Lagrange's equation, which is then discretized through finite differences and the corresponding difference equation is solved by using additive operator splitting method. Experimental results of the proposed model are compared with the existing selective segmentation models and related models which are not based on selective segmentation. The model is further tested on medical images.
The role of inclusive development and military expenditure in modulating the effect of terrorism on governance

Purpose The purpose of this paper is to investigate the role of inclusive human development and military expenditure in modulating the effect of terrorism on governance. Design/methodology/approach It is based on 53 African countries for the period 1998–2012 and interactive generalised method of moments is employed. Six governance indicators from the World Bank and two terrorism variables are used, namely, domestic and transnational terrorism dynamics. Findings The following main findings are established. There is a negative net effect on governance (regulation quality and corruption-control) when inclusive human development is used to reduce terrorism. There is a positive net impact on governance (voice and accountability and rule of law) when military expenditure is used to reduce domestic terrorism. Originality/value The authors have complemented the sparse literature on the use of policy variables to mitigate the effect of policy syndromes on macroeconomic outcomes.