Reviewerly/exHarmony huggingface dataset:
Leveraging cloud computing for the semantic web: review and trends

Semantic and cloud computing technologies have become vital elements for developing and deploying solutions across diverse fields in computing. While they are independent of each other, they can be integrated in diverse ways for developing solutions and this has been significantly explored in recent times. With the migration of web-based data and applications to cloud platforms and the evolution of the web itself from a social, web 2.0 to a semantic, web 3.0 comes as the convergence of both technologies. While several concepts and implementations have been provided regarding interactions between the two technologies from existing research, without an explicit classification of the modes of interaction, it can be quite challenging to articulate the interaction modes; hence, building upon them can be a very daunting task. Hence, this research identifies and describes the modes of interaction between them. Furthermore, a "cloud-driven" interaction mode which focuses on fully maximising cloud computing characteristics and benefits for driving the semantic web is described, providing an approach for evolving the semantic web and delivering automated semantic annotation on a large scale to web applications.
Stronger Lower Bounds for Online ORAM

Oblivious RAM (ORAM), introduced in the context of software protection by Goldreich and Ostrovsky [JACM'96], aims at obfuscating the memory access pattern induced by a RAM computation. Ideally, the memory access pattern of an ORAM should be independent of the data being processed. Since the work of Goldreich and Ostrovsky, it was believed that there is an inherent $$ \varOmega (\log n) $$ bandwidth overhead in any ORAM working with memory of size n. Larsen and Nielsen [CRYPTO'18] were the first to give a general $$ \varOmega (\log n) $$ lower bound for any online ORAM, i.e., an ORAM that must process its inputs in an online manner. In this work, we revisit the lower bound of Larsen and Nielsen, which was proved under the assumption that the adversarial server knows exactly which server accesses correspond to which input operation. We give an $$\varOmega (\log n) $$ lower bound for the bandwidth overhead of any online ORAM even when the adversary has no access to this information. For many known constructions of ORAM this information is provided implicitly as each input operation induces an access sequence of roughly the same length. Thus, they are subject to the lower bound of Larsen and Nielsen. Our results rule out a broader class of constructions and specifically, they imply that obfuscating the boundaries between the input operations does not help in building a more efficient ORAM. As our main technical contribution and to handle the lack of structure, we study the properties of access graphs induced naturally by the memory access pattern of an ORAM computation. We identify a particular graph property that can be efficiently tested and that all access graphs of ORAM computation must satisfy with high probability. This property is reminiscent of the Larsen-Nielsen property but it is substantially less structured; that is, it is more generic.
Socioscope: I know who you are, a robo, human caller or service number

Telephony technologies (mobile, VoIP, and fixed) have potentially improved the way we communicate in our daily life and have been widely adopted for business and personal communications. At the same time, scammers, criminals, and fraudsters have also find the telephony network an attractive and affordable medium to target end-users with the advertisement, marketing of legal and illegal products, and bombard them with the huge volume of unwanted calls. These calls would not only trick call recipients into disclosing their private information such as credit card numbers, PIN code which can be used for financial fraud but also causes a lot of displeasure because of continuous ringing. The fraudsters, political campaigners can also use telephony systems to spread malicious information (hate political or religious messages) in real-time through audio or text messages, which have serious political and social consequences if malicious callers are not mitigated in a quick time. In this context, the identification of malicious callers would not only minimize telephony fraud but would also bring peace to the lives of individuals. One way to classifies users as a spammer or legitimate is to get feedback from the call recipients about their recent interactions with the caller, but these systems not only bring inconvenience to callees but also require changes in the system design. The call detail records extensively log the activities of users and can be used to categorize them as the spammer and non-spammer. In this paper, we utilize the information from the call detailed records and proposed a spam detection framework for the telephone network that identifies malicious callers by utilizing the social behavioral features of users within the network. To this extent, we first model the behavior of the users as the directed social graph and then analyze different features of the social graph i.e. the Relationship Network and Call patterns of users towards their peers. We then used these features along with the decision tree to classify callers into three classes i.e. human, spammer and call center. We analyzed the call record data-set consisting of more than 2 million users. We have conducted a detailed evaluation of our framework which demonstrates its effectiveness by achieving acceptable detection accuracy and extremely low false-positive rate. The performance results show that the spammers and call center numbers not only have a large number of non-repetitive calls but also have a large number of short duration calls. Similarly, on the other hand, the legitimate callers have a good number of repetitive calls and most of them interacted for a relatively long duration.
Optimal Recognition Model Based on Convolutional Neural Networks and Fuzzy Gravitational Search Algorithm Method

In this paper we propose the optimization of a convolutional neural network (CNN) using the Fuzzy Gravitational Search Algorithm method (FGSA). The FGSA is inspired in extension of the Gravitational Search Algorithm (GSA) using fuzzy logic and this method is used to obtain the number of images per block that will enter in the training phase. The optimized CNN is applied for pattern recognition using the 10 handwritten numbers of the MINIST database. The model of the CNN model presented in this paper can be applied for any recognition or image classification application. In addition, the recognition rate achieved with the CNN optimized by the FGSA was compared against the results obtained with the non-optimized CNN.
Robust optimal aiming strategies in central receiver systems

A central receiver system is a power plant that consists of a receiver mounted atop of a central tower and a field of adjustable mirrors called heliostats. The heliostats concentrate solar radiation onto the receiver where a fluid is heated to produce electricity in a conventional thermodynamic cycle. Aiming strategies are used to assign each heliostat to an individual aim point on the receiver such that a given flux distribution on the receiver surface is reached. As uncertainties in the tracking of the heliostats exist, aiming strategies are applied that use large safety margins to avoid dangerously high flux concentrations on the receiver. This approach leads to an inefficient use of the power plant and thus economical losses. In this paper, we consider advanced methods to include these uncertainties into the design of efficient aiming strategies. To this end, we present a mixed-integer linear programming (MILP) formulation for the optimization of aiming strategies based on Γ-robustness. In a case study, we show that the Γ-robust optimization approach yields solutions with strong objective values and thus economical benefits while maintaining a high degree of safety. Compared to non-robust solutions, the Γ-robust solutions achieve better objective values while guaranteeing the same safety.
Multifractal-based lacunarity analysis of trabecular bone in radiography

This study presents textural characterization techniques for effective osteoporosis diagnosis using bone radiograph images. The automatic classification of osteoporosis and healthy (control) cases using bone radiograph images in this work presents a major challenge as the images show no visual differences for both cases. The proposed work utilizes multifractals to characterize the trabecular bone texture in the radiographs. Initially, Holder exponents are computed, then Hausdorff dimensions are determined, which quantify the global regularity of the pixels. Finally, lacunarity is computed from the Hausdorff dimensions. Performance metrics show that estimating lacunarity from the Hausdorff dimensions, rather than the input image, directly helps in achieving better textural characterization of bone radiographs, leading to better performance in osteoporosis classification. The proposed lacunarity-based trabecular bone textural characterization method is compared with other multifractal-based methods for trabecular bone textural characterization, such as box-counting and regularization dimensions. The proposed method is also evaluated with the textural characterization of a bone radiograph challenge dataset to demonstrate its effectiveness compared to the other methods used in the challenge.
Open-Source ANSS Quake Monitoring System Software

Abstract ANSS stands for the Advanced National Seismic System of the U.S.A., and ANSS Quake Monitoring System (AQMS) is the earthquake management system (EMS) that most of its member regional seismic networks (RSNs) use. AQMS is based on Earthworm, but instead of storing files on disk, it uses a relational database with replication capability to store pick, amplitude, waveform, and event parameters. The replicated database and other features of AQMS make it a fully redundant system. A graphical user interface written in Java, Jiggle, is used to review automatically generated picks and event solutions, relocate events, and recalculate magnitudes. Add-on mechanisms to produce various postearthquake products such as ShakeMaps and focal mechanisms are available as well. It provides a configurable automatic alarming and notification system. The Pacific Northwest Seismic Network, one of the Tier 1 ANSS RSNs, has modified AQMS to be compatible with a freely available, capable, open-source database system, PostgreSQL, and is running this version successfully in production. The AQMS Software Working Group has moved the software from a subversion repository server hosted at the California Institute of Technology to a public repository at gitlab.com. The drawback of AQMS as a whole is that it is complex to fully configure and comprehend. Nevertheless, the fact that it is very capable, documented, and now free to use, might make it an attractive EMS choice for many seismic networks.
A new mixed‐effects mixture model for constrained longitudinal data

Biomedical research often features continuous responses bounded by the interval [0, 1]. The well‐known beta regression model addresses the constrained nature of these data, while its augmented and mixed‐effects variants can address the presence of zeros and/or ones and longitudinal or clustered response values, respectively. However, these models are not robust to the presence of outliers and/or excessive number of observations near the tails. We propose a new augmented mixed‐effects regression model based on a special beta mixture distribution that is capable of handling these issues. Extensive simulation studies show the superiority of the proposed model to the models most often used in the literature. The proposed model is applied to two real datasets: one taken from a long‐term study of Parkinson's disease and the other taken from a study on reading accuracy.
OpenFACS: An Open Source FACS-Based 3D Face Animation System

We present OpenFACS, an open source FACS-based 3D face animation system. OpenFACS is a software that allows the simulation of realistic facial expressions through the manipulation of specific action units as defined in the Facial Action Coding System. OpenFACS has been developed together with an API which is suitable to generate real-time dynamic facial expressions for a three-dimensional character. It can be easily embedded in existing systems without any prior experience in computer graphics. In this note, we discuss the adopted face model, the implemented architecture and provide additional details of model dynamics. Finally, a validation experiment is proposed to assess the effectiveness of the model.
Heart Attack Detection in Colour Images Using Convolutional Neural Networks

Cardiovascular diseases are the leading cause of death worldwide. Therefore, getting help in time makes the difference between life and death. In many cases, help is not obtained in time when a person is alone and suffers a heart attack. This is mainly due to the fact that pain prevents him/her from asking for help. This article presents a novel proposal to identify people with an apparent heart attack in colour images by detecting characteristic postures of heart attack. The method of identifying infarcts makes use of convolutional neural networks. These have been trained with a specially prepared set of images that contain people simulating a heart attack. The promising results in the classification of infarcts show 91.75% accuracy and 92.85% sensitivity.
Personalized Micro-Service Recommendation System for Online News

In the era of artificial intelligence and high technology advance our life is dependent on them in every aspect. The dynamic environment forces us to plan our time with conscious and every minute is valuable. To help individuals and corporations see information that is only relevant to them, recommendation systems are in place. Popular platforms that such as Amazon, Ebay, Netflix, YouTube, make use of advanced recommendation systems to better serve the needed of their users. This research paper gives insight of building a microservice recommendation system for online news. Research in recommendation systems is mainly focused on improving user’s experience based mainly on personalization information, such as preferences, and searching history. To determine the initial preferences of a user an initial menu of topics/themes is provided for the user to choose from. In order to reflect as precise as possible the searching interests regarding news of user, all of his interactions are thoroughly recorded and in depth analyzed, based on advanced machine learning techniques, when adjusting the news topics, the user is interested for. Based on the aforementioned approach, a personalized recommendation system for online news has been developed. Existing techniques has been researched and evaluated to aid the decision about picking the best approach for the software to be implemented. Frameworks/technologies used for the development are Java 8, Spring boot, Spring MVC, Maven and MongoDB.
Extraction and analysis of microscopic traffic data in disordered heterogeneous traffic conditions

This paper discusses the development of a novel off-line vision-based system to obtain the naturalistic trajectory database of traffic streams from recorded video footages. The developed system uses a semi-automatic mechanism that provides manual interference for vehicle identification and classification and executes automated tracking of the identified vehicles. A trajectory database of typical disordered heterogeneous traffic stream was collected to evaluate the performance of the developed system. Results show that the developed system significantly enhances the process of trajectory data collection in such traffic conditions. The collected trajectory database is then used to investigate two crucial aspects that characterize the disordered heterogeneous traffic (i) interaction of different types of vehicles in the longitudinal and staggered following scenario, and (ii) lateral shift propensity of different types of vehicles. The analysis emphasizes the behavioral difference between different types of vehicles, and utility of the developed system to address the prevailing research gaps.
A Smart Contract Based Recommender System

Nowadays information available on the World Wide Web has reached unprecedented growth and it makes difficult for users to find the most relevant for them. In order to alleviate such issue, Recommender Systems (RSs) have been proposed to collect opinions and preferences about a set of items, process such preferences and build a personalized information access. While the most part of current RSs exploit centralized architecture to provide the service, in this manuscript we propose an alternative approach for building a general purpose RSs that provides to users with more transparent and decentralized rating strategy. Indeed, the proposed framework is built on top of a Distributed Ledger technology platform that runs without any centralized authority and it supports both decentralized ratings and ranking of different items. A preliminary evaluation on the Ethereum test network demonstrates the feasibility of the framework in terms of performance and cost.
Density peaks clustering based on circular partition and grid similarity

Summary In density peaks clustering, its complexity for computing local density and relative distance of samples raises a scalability issue for processing large datasets. To address the issue, density peaks clustering based on circular partition and grid similarity has been proposed. The algorithm partitions the data space into circular grids, with each grid treated as a sample, for determining the number of clusters and searching for the density peaks; then, a new grid similarity is calculated to effectively assign unallocated grids. The proposed circular partition method effectively reduces the number of samples and the computational complexity. Extensive experiments have been conducted on several datasets with arbitrary shapes and scales, and the proposed method outperforms other density peaks clustering variants in terms of clustering accuracy and efficiency.
SME-Net: Sparse Motion Estimation for Parametric Video Prediction Through Reinforcement Learning

This paper leverages a classic prediction technique, known as parametric overlapped block motion compensation (POBMC), in a reinforcement learning framework for video prediction. Learning-based prediction methods with explicit motion models often suffer from having to estimate large numbers of motion parameters with artificial regularization. Inspired by the success of sparse motion-based prediction for video compression, we propose a parametric video prediction on a sparse motion field composed of few critical pixels and their motion vectors. The prediction is achieved by gradually refining the estimate of a future frame in iterative, discrete steps. Along the way, the identification of critical pixels and their motion estimation are addressed by two neural networks trained under a reinforcement learning setting. Our model achieves the state-of-the-art performance on CaltchPed, UCF101 and CIF datasets in one-step and multi-step prediction tests. It shows good generalization results and is able to learn well on small training data.
Vehicles Detection of Traffic Flow Video Using Deep Learning

The vehicle detection and tracking are important tasks in intelligent transportation system. The traditional methods of vehicle detection often cause the coarse-grained results due to suffering from the complex environments. YOLO is a pragmatic approach to multi-target detection with a simple and effective algorithm. This paper use YOLO to detect the moving vehicles and use a modified Kalman filter algorithm to dynamically track the detected vehicles, achieving overall competitive performance in day or night. The experimental results show the method is robust to occluding vehicles or congested roads and can obtain 92.11% average accuracy of vehicle counting at 2.55 fps speed.
Structure-Preserving and Re-randomizable RCCA-Secure Public Key Encryption and Its Applications

Re-randomizable RCCA-secure public key encryption (Rand-RCCA PKE) schemes reconcile the property of re-randomizability of the ciphertexts with the need of security against chosen-ciphertexts attacks. In this paper we give a new construction of a Rand-RCCA PKE scheme that is perfectly re-randomizable. Our construction is structure-preserving, can be instantiated over Type-3 pairing groups, and achieves better computation and communication efficiency than the state of the art perfectly re-randomizable schemes (e.g., Prabhakaran and Rosulek, CRYPTO'07). Next, we revive the Rand-RCCA notion showing new applications where our Rand-RCCA PKE scheme plays a fundamental part: (1) We show how to turn our scheme into a publicly-verifiable Rand-RCCA scheme; (2) We construct a malleable NIZK with a (variant of) simulation soundness that allows for re-randomizability; (3) We propose a new UC-secure Verifiable Mix-Net protocol that is secure in the common reference string model. Thanks to the structure-preserving property, all these applications are efficient. Notably, our Mix-Net protocol is the most efficient universally verifiable Mix-Net (without random oracle) where the CRS is an uniformly random string of size independent of the number of senders. The property is of the essence when such protocols are used in large scale.
What is a Tabby? Interpretable Model Decisions by Learning Attribute-Based Classification Criteria

State-of-the-art classification models are usually considered as black boxes since their decision processes are implicit to humans. On the contrary, human experts classify objects according to a set of explicit hierarchical criteria. For example, “tabby is a domestic cat with stripes, dots, or lines”, where tabby is defined by combining its superordinate category (domestic cat) and some certain attributes (e.g., has stripes). Inspired by this mechanism, we propose an interpretable Hierarchical Criteria Network (HCN) by additionally learning such criteria. To achieve this goal, images and semantic entities (e.g., taxonomies and attributes) are embedded into a common space, where each category can be represented by the linear combination of its superordinate category and a set of learned discriminative attributes. Specifically, a two-stream convolutional neural network (CNN) is elaborately devised, which embeds images and taxonomies with the two streams respectively. The model is trained by minimizing the prediction error of hierarchy labels on both streams. Extensive experiments on two widely studied datasets (CIFAR-100 and ILSVRC) demonstrate that HCN can learn meaningful attributes as well as reasonable and interpretable classification criteria. Therefore, the proposed method enables further human feedback for model correction as an additional benefit.