Reviewerly/exHarmony huggingface dataset:
Big Data

In the last three decades, big data has been applied to diverse fields, such as the government, international development, and education. It is only now that the US healthcare system has begun to explore its under-utilized data. Big data is not only referencing the quantity, but also the complexity, diversity, and relativity of the information. This information may be analyzed to reveal patterns, trends, and associations that may be applicable to the healthcare field. This information can be gathered through sources, such as EHRs, IRIS registry, and MIPS. Recognizing patterns would aid in predicting preventative measures for an increased holistic and personalized patient care. Although big data proves to have endless beneficial applications, it can bring into question the ownership of this information. Additionally, big data poses a risk for security breaches, and thus, precautionary measures will also be discussed. Ultimately, the emergence of big data creates an exhilarating frontier for healthcare with its unlimited possibilities.
Are We Ready to Play in the Cloud? Developing new Quality Certifications to Tackle Challenges of Cloud Gaming Services

Cloud services have already become an elementary part of our everyday lives. Nowadays even computer games are executed on powerful cloud servers. These cloud gaming services have many benefits for users, yet they have to fulfil high quality requirements to satisfy users' needs because even minimal performance losses are directly observable and worsen the gaming experience. Traditionally, cloud service providers undergo certifications to increase transparency and address users' quality concerns. However, existing certifications are not applicable in the context of cloud gaming to prove a high service quality, and it remains unclear which requirements a cloud gaming service has to fulfil. We conduct a thorough literature review to identify requirements for cloud gaming services as well as extensively review existing cloud service certifications. At the same time, we pay special attention to the interdependencies between the individual stages of the cloud service provisioning chain. With our study, we advance the understanding of cloud gaming services by providing a synthesis and discussion of requirements that providers need to fulfil to ensure user satisfaction. We guide future research as well as practitioners towards a new generation of cloud service certification for cloud gaming services.
Supervision of organizing data streaming in cloud environment

The supervision systems in information processing applications provide cloud supervision service by processing that data in the form of a torrent of continuous data and faster than those in traditionally stored databases, as these systems must be ready to handle the data set without compromising or affecting the speed and efficient carrying out of the system. In our research we illustrated this by exploiting the conception of immediate data, as the system can reducing the reaction time for most immediate in quarries whereas ensuring less consuming or waste bandwidth package. The study discussed the data supervision can be preserved disparity and that approximately data depict the procedures of the perilous system. The process of accessing the data will be an effective factor in determining the speed of interaction of the supervision operations, and the speed of access to the data set will help the system to work in real time when faced with a fatal error. On the other hand, the slow speed of another set may lead to more prompt consuming.
Scale Adaptive Target Tracking Algorithm Based on Correlation Filtering

Abstract Focusing on the issue that the kernel correlation filter tracking method can not accurately adapt to the target scale change, a scale estimation method is added under the kernel correlation filter tracking frame, and a scale adaptive target tracking algorithm is proposed. The algorithm firstly uses the kernel function to obtain the kernel correlation filter, and estimates the position of the target for the candidate sample detection; then uses the scale estimation method to detect the target scale to further determine the optimal scale of the target to be tracked in the current frame; Finally, according to the occlusion judgment mechanism, the parameters of the target performance model and the scale model are updated online. The experimental results show that compared with kernel correlation filtering algorithm, the proposed algorithm can accurately target tracking process to adapt to changes in the target scale, and the center position error reduced by an average of 4.39 pixels.
Master of Sheets: A Tale of Compromised Cloud Documents

As of 2014, a fifth of EU citizens relied on cloud accounts to store their documents according to a Eurostat report. Although useful, there are downsides to the use of cloud documents. They often accumulate sensitive information over time, including financial information. This makes them attractive targets to cybercriminals. To understand what happens to compromised cloud documents that contain financial information, we set up 100 fake payroll sheets comprising 1000 fake records of fictional individuals. We populated the sheets with traditional bank payment information, cryptocurrency details, and payment URLs. To lure cybercriminals and other visitors into visiting the sheets, we leaked links pointing to the sheets via paste sites. We collected data from the sheets for a month, during which we observed 235 accesses across 98 sheets. Two sheets were not opened. We also recorded 38 modifications in 7 sheets. We present detailed measurements and analysis of accesses, modifications, edits, and devices that visited payment URLs in the sheets. Contrary to our expectations, bank payment URLs received many more clicks than cryptocurrency payment URLs despite the popularity of cryptocurrencies and emerging blockchain technologies. On the other hand, sheets that contained cryptocurrency details recorded more modifications than sheets that contained traditional banking information. In summary, we present a comprehensive picture of what happens to compromised cloud spreadsheets.
ENVIRONMENTAL AND SOCIAL ECONOMIC CONSEQUENCES OF FOREST FIRES ON THE TERRITORY OF STARA ZAGORA REGION

This article presents the dynamics of ground and canopy forest fires outbreaks on the territory of Stara Zagora region in the period of 1990-2016. We have also made a detailed analysis of the fires with the causes for their occurrence, the burnt area, damages, the number of firefighters and equipment used to extinguish fires by the Regional Directorate of Forestry – Stara Zagora in 2016. It was found that the total number of fires during the studied period was 821, as the largest number was registered in 2001 (115 fires), and the lowest – in 2014 (3 fires). There were three times more ground fires than canopy fires for the whole period of study. The total burnt area was 204 710 decares. This indicator reached its peak in 2007 (52 310 decares), and its lowest point in 2014 (7 decares). The damages inflicted by forest fires on the Regional Directorate of Forestry –Stara Zagora in 2016 amounted to 434 116 BGN. In the same year, the firefighters involved were 449 state forestry employees, 347 fire safety employees, 30 military servants and 154 volunteers. The number of equipment used was: 156 from the state forestries, 109 from the fire safety services, 6 heavy vehicles and 128 mobile devices. In conclusion, in the past 15 years, Bulgaria, and Stara Zagora in particular, have become a high risk territory for forest fire outbreaks, which requires an entirely new level of firefighting practices.
Prediction of hourly solar radiation using fuzzy clustering and linguistic modifiers

Solar energy is subject to large temporal and spatial fluctuations. These fluctuations require predicting the share of solar energy and its requirements in energy supply systems for optimal use. These predictions form the basis for various solar management options such as storage and control. However, before predicting the production of solar systems, it is necessary to focus on predicting solar radiation. The global prediction of solar radiation is divided into two broad categories of prediction methods: cloud images associated with physical models and automated learning models. In this paper we present a new leaning MAMDANI fuzzy rules based system FRLC (Fuzzy Rule Learning through Clustering) for solar radiation prediction with meteorological data. FRLC based on linguistic modifiers and fuzzy clustering is compared to the most accurate machine learning algorithms such as multilayer feed-forward neural network, radial basis function neural network, support vector regression, and adaptive neuro-fuzzy inference system. FRLC outperforms all algorithms at interpretability level by offering a linguistic knowledge base to the experts of the domain.
An Effective and Efficient Constrained Ward’s Hierarchical Agglomerative Clustering Method

Constraints-based hierarchical clustering (HC) has emerged as an important improvement over the existing clustering algorithms. Triple-wise relative constraints are suitable to be applicable for HC, enabling the derivation of a cluster hierarchy instead of a flat partition. This paper proposes Constrained Ward’s Hierarchical Agglomerative Clustering algorithm (CWHAC). It is a novel variation of Ward’s hierarchical agglomerative clustering method based on the ideas of triple-wise relative constraints. The algorithm is proposed based on the ultra-metric transformation of the dissimilarity matrix which exploits the triple-wise relative constraints as background knowledge to create a new metric for data similarity. IPoptim and UltraTran methods are introduced to address the triple-wise relative constraints to modify and update the similarity metric for the proposed algorithm. This study addresses the issue of non-satisfaction of triple-wise relative constraints with HC to improve the effectiveness of CWHAC by addressing the issue of constraint violation and redundancy. Furthermore, this paper presents three computational optimization strategies for generating constraints to enhance the efficiency of CWHAC for massive data sets. The proposed algorithm is validated using seven benchmark UCI datasets in terms of F-Score for effectiveness and execution time for efficiency by varying the proportion of constraints. Experimental results demonstrate the improvements made by the proposed algorithm in comparison to the existing Ward’s Hierarchical Clustering algorithm, based constraints and unsupervised HC. Finally, Mann-Witney test is performed to prove the significant improvement demonstrated by the proposed algorithm.
Navigation of a quadratic potential with ellipsoidal obstacles

Given a convex quadratic potential of which its minimum is the agent’s goal and a Euclidean space populated with ellipsoidal obstacles, one can construct a Rimon–Koditschek (RK) artificial potential to navigate. Its negative gradient attracts the agent toward the goal and repels the agent away from the boundary of the obstacles. This is a popular approach to navigation problems since it can be implemented with local spatial information that is acquired during operation time. However, navigation is only successful in situations where the obstacles are not too eccentric (flat). This paper proposes a modification to gradient dynamics that allows successful navigation of an environment with a quadratic cost and ellipsoidal obstacles regardless of their eccentricity. This is accomplished by altering gradient dynamics with a Hessian correction that is intended to imitate worlds with spherical obstacles in which RK potentials are known to work. The resulting dynamics simplify by the quadratic form of the obstacles. Convergence to the goal and obstacle avoidance is established from almost every initial position (up to a set of measure one) in the free space, with mild conditions on the location of the target. Results are corroborated empirically with numerical simulations.
Multi-robot Coverage Using Voronoi Partitioning Based on Geodesic Distance

In this paper we propose Geodesic-VPC, a “partition” and “cover” strategy for a multi-robot system using Voronoi partitioning based on geodesic distance metric in the place of the usual Euclidean distance. Each robot is responsible for covering the corresponding geodesic-Voronoi cell using a single-robot coverage strategy. The proposed partitioning scheme ensures that Voronoi cells are contiguous even in the presence of obstacles. We demonstrate that if the single-robot coverage strategy is capable of providing a complete and non-repetitive coverage, then the proposed Geodesic-VPC strategy provides a complete and non-repetitive coverage. We use spanning tree-based coverage algorithm as the underlying single-robot coverage strategy for the purpose of demonstration, though any existing single-robot coverage algorithm can be used.
Analysis of Abstention in the Elections to the Catalan Parliament by Means of Decision Trees

Democracies are based on political parties and election systems allowing voters to put the confidence in representers of these political parties to defend their interests. There are many studies analyzing the results of elections with the goal of (1) explaining the results, and (2) trying to predict what will happens in future elections. However no many attention has received the abstention, why voters do not use their right to elect representers? Commonly, abstention has not been too significant, however in last years it has been increased in many countries and it could be of great interest to analyze the causes. Studies about elections, both voting and abstention, are commonly based on statistical methods. The current paper is focused on analyzing the abstention based on symbolic learning methods (decision trees). Particularly, we are interested on identifying the groups of potential voters that have decided to abstain. We worked on data of the elections to Catalan Parliament held in 2015.
Knowledge-Aware Self-Attention Networks for Document Grounded Dialogue Generation

Dialogue systems have attracted more and more attention. Different from traditional open-domain conversational systems, document grounded dialogue generation aims to ground the semantics in a specified document and leverage contextual cues from dialogue history to generate on-topic and coherent responses, which is a renewed and challenging task. Some prior studies via neural sequence-to-sequence models have been conducted. However, they often treat the dialogue history and the given document independently while fail to model contextual dependence between them. To understand the dialogue better and respond more appropriately and informatively, we present a novel knowledge-aware self-attention approach for document grounded dialogue, called DialogTransformer. DialogTransformer can fully leverage the semantic knowledge from both the dialogue history and the given document to joint improve the content quality of generated responses. We conduct extensive experiments on the CMU-DoG benchmark dataset and the experimental results show that our approach outperforms several state-of-the-art models, which can generate more appropriate and informative responses.
A Tableaux Calculus for Default Intuitionistic Logic

We build a Default Logic variant on Intuitionistic Propositional Logic and develop a sound, complete, and terminating, tableaux calculus for it. We also present an implementation of the calculus. We motivate and illustrate the technical elements of our work with examples.
Is It Possible to Use Chatbot for the Chinese Word Segmentation?

A word is the smallest item in Natural Language Processing. However, there is no obvious boundary for Chinese words. How to segment Chinese words always obstructs Chinese researches and applications. Nowadays, a neural network model, Seq2Seq with LSTM, is well-known for translation or chatbot application. In this paper, we try to transform the Chinese word segmentation problem into a translation problem. And we utilized an open-source chatbot to simulate the translation task. In our experimental results, we can produce similar Chinese word segmentation results when we provide training data which is automatically generated from famous Chinese word segmentation services.
Hough Based Evolutions for Enhancing Structures in 3D Electron Microscopy

Connecting interrupted line-like structures is a frequent problem in image processing. Here we focus on the specific needs that occur in 3D biophysical data analysis in electron microscopy (EM). We introduce a powerful framework for connecting line-like structures in 3D data sets by combining a specific semilocal Hough transform with a directional evolution equation. The Hough transform allows to find the principal orientations of the local structures in a robust way, and the evolution equation is designed as a partial differential equation that smoothes along these principal orientations. We evaluate the performance of our method for enhancing structures in both synthetic and real-world EM data. In contrast to traditional structure tensor based methods such as coherence-enhancing diffusion, our method can handle the missing wedge problem in EM, also known as limited angle tomography problem. A modified version of our approach is also able to tackle the discontinuities created due to the contrast transfer function correction of EM images.
On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy

This paper proposes a collaborative filtering (CF) method using possibilistic clustering for spherical data based on Tsallis entropy. This study was motivated by a previous work, which showed that adopting fuzzy clustering for spherical data in CF tasks provided better recommendation accuracy than fuzzy clustering for categorical-multivariate data. Moreover, possibilistic clustering algorithms are naturally more robust to noise than fuzzy clustering. The results of experiments conducted on an artificial dataset and one real dataset indicate that the proposed method is better than the conventional methods in terms of recommendation accuracy.
SAF: Semantic Attention Fusion Mechanism for Pedestrian Detection

Benefiting from deep learning methods, pedestrian detection has witnessed a great progress in recent years. However, many pedestrian detectors are prone to detect background instances, especially under urban scenes, which results in plenty of false positive detections. In this paper, we propose a semantic attention fusion mechanism (SAF) to increase the discriminability of detector. The SAF includes two key components, attention modules and reverse fusion blocks. Different from previous attention mechanisms which use attention modules for re-weighting the top features of network directly, the outputs of our attention modules are fused by reverse fusion blocks from high level layers to low level layers step by step, which aims at generating strong semantic features for pedestrian detections. Experiments on CityPersons dataset demonstrate the effectiveness of our SAF.
Security and Privacy in the Internet of Things Using Blockchain Technology

The Internet of Things constitutes the latest paradigm shift in computing. We are soon going to be surrounded by billions of devices that will sense the real world and store produced data in the cloud. These data will probably be used in ways that the users will not be able to control, so their privacy violation risk will increase dramatically. In this paper a decentralized, peer to peer communication protocol for securing communication among devices of the Internet of Things is proposed. The protocol is based on the blockchain technology, which is adapted to satisfy the requirements of the IoT.
A Language-Independent Framework for Reasoning About Preferences for Declarative Problem Solving

Automated decision making often requires solving difficult and primarily NP-hard problems. In many AI applications (e.g., planning, robotics, recommender systems, etc.), users can assist decision making by specifying their preferences over some domain of interest. To take preferences into account, we take a model-theoretic approach to computationally hard problems with preferences. Computational problems are characterized as Model Expansion, that is, the logical task of expanding an input structure to satisfy a set of specifications. The uniformity of the model-theoretic approach allows us to combine computational problems with preferences regardless of the syntax of their specifications. We introduce a formalism to represent preferences of users associated with computationally hard problems. We introduce Prioritized Model Expansion, which is Model Expansion based on preferences. We investigate properties of Prioritized Model Expansion and conduct a thorough study of the impact of introducing preferences on the computational complexity of $$\varSigma _k^P$$ -complete Model Expansion problems. We also discuss how Prioritized Model Expansion is related to other preference-based declarative approaches, such as SAT with preferences and preference-based Logic Programming.
On Aggregation of Information in Timing Attacks

A key question for characterising a system's vulnerability against timing attacks is whether or not it allows an adversary to aggregate information about a secret over multiple timing measurements. Existing approaches for reasoning about this aggregate information rely on strong assumptions about the capabilities of the adversary in terms of measurement and computation, which is why they fall short in modelling, explaining, or synthesising real-world attacks against cryptosystems such as RSA or AES. In this paper we present a novel model for reasoning about information aggregation in timing attacks. The model is based on a novel abstraction of timing measurements that better captures the capabilities of real-world adversaries, and a notion of compositionality of programs that explains attacks by divide-and-conquer. Our model thus lifts important limiting assumptions made in prior work and enables us to give the first uniform explanation of high-profile timing attacks in the language of information-flow analysis.
Classifier Adaptive Fusion: Deep Learning for Robust Outdoor Vehicle Visual Tracking

Deep auto-encoder (DAE) models have been successfully used in object tracking due to its strong capability of feature representation. However, single deep auto-encoder model would not be robust enough to represent the appearance model of outdoor vehicle for its harsh working environment, such as illumination variation, occlusion, cluttered background and so on. In this paper, a novel multiple-DAE-based tracking approach, that is, classifier adaptive fusion for robust outdoor vehicle visual tracking approach is proposed under particle filter framework. Firstly, two deep auto-encoders are offline trained by gray-scale image and gradient image of the raw training images, respectively to obtain the stronger feature representation of gray-scale image and gradient image. Secondly, two classifiers are constructed using the encoder of the two well-trained deep auto-encoders and the output of the each classifier is used to compute the confidence of the corresponding particles. Finally, the confidence output of the two classifiers is fused and applied in online tracking, where, the fusion weight of the each classifier is computed according to the distribution of particles represented by different classifier. Extensive tracking experiments conducted on visual tracking benchmark (VTB) show that the proposed tracking algorithm outperforms 9 popular tracking algorithms in the challenge scenes of outdoor vehicle tracking such as illumination variation, occlusion, cluttered background and scale variation.