Reviewerly/exHarmony huggingface dataset:
Reverse Spatial Visual Top-$k$ Query

With the wide application of mobile Internet techniques an location-based services (LBS), massive multimedia data with geo-tags has been generated and collected. In this paper, we investigate a novel type of spatial query problem, named reverse spatial visual top-k query (RSVQk) that aims to retrieve a set of geo-images that have the query as one of the most relevant geo-images in both geographical proximity and visual similarity. Existing approaches for reverse top-k queries are not suitable to address this problem because they cannot effectively process unstructured data, such as image. To this end, firstly we propose the definition of RSVQ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> problem and introduce the similarity measurement. A novel hybrid index, named VR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Tree is designed, which is a combination of visual representation of geo-image and R-Tree. Besides, an extension of VR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Tree, called CVR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Tree is introduced and then we discuss the calculation of lower/upper bound, and then propose the optimization technique via CVR <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -Tree for further pruning. In addition, a search algorithm named RSVQk algorithm is developed to support the efficient RSVQ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> query. Comprehensive experiments are conducted on four geo-image datasets, and the results illustrate that our approach can address the RSVQk problem effectively and efficiently.
Facial Expression Recognition Using Robust Local Directional Strength Pattern Features and Recurrent Neural Network

This work proposes a novel facial expression recognition approach to contribute to better human-machine interactions. To do that, edge features in facial expression images are combined with a recurrent neural network (RNN) to classify different facial expressions. Robust edge features are first obtained by using Local Directional Strength Pattern (LDSP) and applied with RNN. This LDSP-RNN approach achieves superior recognition performance than other conventional approaches on a randomly distributed training and testing datasets obtained from a public dataset. The proposed approach should be useful for various practical applications such as a robot analyzing and understanding different human emotions from facial expressions based on robotic vision.
Comprehensive complexity metric for data warehouse multidimensional model understandability

Data warehouse quality can be determined during the initial phases of data warehouse development by quantifying the structural complexity of multidimensional models using metrics. The structural complexity of a multidimensional model is guided by its elements, types, and relationships among those elements. So far, most of the researchers have dealt with metrics based on various elements (facts, dimensions, dimensional hierarchies, and hierarchy levels) existing in these models. However, not much consideration is given to different types of dimensions based on hierarchy types and different relationships among those elements. Therefore, this work proposes a comprehensive complexity metric for measuring multidimensional model complexity by taking into account various elements, their types and the relationships among the elements at various levels of granularity in these models. The theoretical validation of the proposed metric using the property-based framework given by Briand et al. characterises it as a complexity measure. Furthermore, the empirical study, employing statistical techniques (correlation and multinomial regression), on 26 multidimensional models and 20 subjects proved that the authors’ proposed metric is strongly correlated with multidimensional model understandability. Hence, this metric can be considered as a good predictor for data warehouse multidimensional model understandability.
smoothROCtime: an R package for time-dependent ROC curve estimation

The receiver operating characteristic (ROC) curve has become one of the most used tools for analyzing the diagnostic capacity of continuous biomarkers. When the studied outcome is a time-dependent variable two main generalizations have been proposed, based on properly extensions of the sensitivity and the specificity. Different procedures have been suggested for their estimation mainly under the presence of right censorship. Most of them have been implemented, as well, in diverse types of software, including R packages. This work focuses on the R implementation for the smooth estimation of time-dependent ROC curves. The theoretical connection between them through the joint distribution function of the biomarker and time-to-event variables prompts an approximation method: considered estimators are based on the bivariate kernel density estimator for the joint density function of the bidimensional variable (Marker, Time-to-event). The use of the package is illustrated with two real-world examples.
An Authority Management Framework Based on Fabric and IPFS in Traceability Systems

The traceability system is significant to consumers because it can protect the health of consumers and the stability of society. With the development of blockchain technology, it is concerned by researchers of the traceability system because of its immutability and consensus mechanism. For guaranteeing the confidentiality of core data, it is necessary to control data permissions in the supply chain. However, the existing blockchain authority management frameworks mostly concentrate on the medical field and the Internet of Things (IoT). They have to take a long time to reach consensus and store lots of data in the chain. The most important is that the number of transactions submitted per second has always been a bottleneck. In this paper, we proposed an authority management framework used to control the authority of different roles in the traceability system. It pays attention to store small amounts of data on the chain, optimize the consensus rate, and increase the number of transactions per second (TPS). We design a simple experiment to contrast the performance of our framework and the MedRec [1] framework, and the results show that our framework’s consensus rate and TPS are better than the MedRec [1].
Real-time abnormal light curve detection based on a Gated Recurrent Unit network

Abstract Targeting the problem of high real-time requirements in astronomical data processing, this paper proposes a real-time early warning model for light curves based on a Gated Recurrent Unit (GRU) network. Using the memory function of the GRU network, a prediction model of the light curve is established, and the model is trained using the collected light curve data, so that the model can predict a star magnitude value for the next moment based on historical star magnitude data. In this paper,we calculate the difference between the model prediction value and the actual observation value and set a threshold. If the difference exceeds the set threshold, the observation value at the next moment is considered to be an abnormal value, and a warning is given. Astronomers can carry out further certification based on the early warning and in combinationwith other means of observation. Themethod proposed in this paper can be applied to real-time observations in time domain astronomy.
Performance of Finite Precision on Discrete Chaotic Map Based on a Feedback Shift Register

The scintillating technological improvements have changed the process of communication in all parts of the world. Shopping, banking, instant communication, and so on can be operated online and do not care about the linkage of those Internet communications. Because of simple chaotic structure, discrete nature, less arithmetic computation, and high complexity, low-dimensional chaotic systems such as a logistic map and tent map are more attractive than a high-dimensional chaotic system. To overcome the disadvantages of a low-dimensional chaotic map with a finite precision in chaos-based application, the chaotic map with a feedback shift register (CMFSR) is proposed. The related properties and performance associated with CMFSR are analysed. The relationship among the precision of the system, the architecture, and the performance are discussed. The experiments about new pseudorandom number generator (PRNG) based on CMFSR show that our scheme is simple, secure, and easy to accomplish. Experiments show that the proposed architecture of CMFSR is secure for random number generation.
FPGA Based Video Transcoding System with 2K-4K Super-Resolution Conversion

We present a FPGA-based system supporting video stream transcoding with 2k full high-definition (FHD) video to 4k ultra high-definition (UHD) video super- resolution(SR) conversion. Our system focuses on building a functional pipeline with convolutional neural network (CNN) accelerator and real-time video codec unit for converting H.264 video stream to H.265/HEVC video stream. The overall video processing system can be used as an important plug-in module in the video streaming network to improve the video stream service quality.
Health Monitoring and Auto-Scaling RabbitMQ Queues within the Smart Home System

Smart home solutions rely on their cloud components to provide various administrative functionalities and advanced data processing features, such as alarms, historical data storage, notifications, etc. As the complexity of a cloud system increases, its components need monitoring in order to detect anomalies like system unavailability, unusual load, memory overload, and similar. The monitoring tools collect and store system metrics, which are important not only for anomaly detection, but also for root cause analysis and capacity planing. When an anomaly is detected, an alarm of some sort is typically sent to the system administrator. Usually, human interaction is needed in order to remove the anomaly. In this paper we present a software tool that monitors messages between cloud components of the existing smart home system. This tool aims to provide autonomous reaction of the system to potential problems, and enable auto-scaling, without human interference.
Expert System to Diagnose Gastritis Disease Using the Certainty Factor Method

Gastritis is a condition when the stomach lining becomes irritated, inflamed or eroded. Based on the period of development of symptoms, the gastritis is divided into two, namely acute (developing quickly and suddenly) and chronic (developing slowly). Symptoms that commonly appear are pain, discomfort in the upper abdomen, nausea, vomiting, and indigestion. If the condition is considered erosive gastritis, the patient will experience black stools or vomit blood. Most people do not understand the types of diseases that people suffer. This is because people do not have the existing knowledge about a disease. People who suffer from disease generally rely on doctors to deal with diseases that people suffer. But for people who are less costly and also difficult to go to hospitals far from their homes, this is a big obstacle. Therefore researchers recommend a system that can help people to be able to diagnose gastritis. The system that the researcher uses is an expert system. However, to diagnose gastritis, a method is needed that can be applied to expert systems. Therefore, researchers propose the Certainty Factor method. By using Visual Basic 2010 programming language and storage using SQL Server 2008 databases, it can be created an application Expert System to Diagnose Gastritis Disease Using Certainty Factor Method.
Explaining the Performance of Black Box Regression Models

The widespread usage of Machine Learning and Data Mining models in several key areas of our societies has raised serious concerns in terms of accountability and ability to justify and interpret the decisions of these models. This is even more relevant when models are too complex and often regarded as black boxes. In this paper we present several tools designed to help in understanding and explaining the reasons for the observed predictive performance of black box regression models. We describe, evaluate and propose several variants of Error Dependence Plots. These plots provide a visual display of the expected relationship between the prediction error of any model and the values of a predictor variable. They allow the end user to understand what to expect from the models given some concrete values of the predictor variables. These tools allow more accurate explanations on the conditions that may lead to some failures of the models. Moreover, our proposed extensions also provide a multivariate perspective of this analysis, and the ability to compare the behaviour of multiple models under different conditions. This comparative analysis empowers the end user with the ability to have a case-based analysis of the risks associated with different models, and thus select the model with lower expected risk for each test case, or even decide not to use any model because the expected error is unacceptable.
Sentiment Analysis Application on Twitter for E-reputation

The Internet has become the number one means of communication in the world using emails and social networks, it contains a large amount of data. Every Internet user can express himself freely and give his opinions and feelings, on any subject including companies. Those last ones are interested in improving their images, by analyzing the opinions presented in tweets or comments, and identifying weaknesses to correct them later. This paper focuses on a new topic that is E-reputation or electronic reputation, where we will analyze the tweets on Twitter of a mobile operator in Algeria "Djezzy". The study is divided into two parts, the first part is the pre-treatment where we eliminate noise and keep only the necessary information, the second part is using Machine Learning techniques and algorithms to detect the company's reputation between Internet users, based on the three languages: French, English and Arabic.
Insurance Block: A Blockchain Credit Transaction Authentication Scheme Based on Homomorphic Encryption

The data transaction in insurance has problems in terms of low security, high management cost and difficulty in supervision. This paper proposes a trustable blockchain based transaction authentication scheme based on homomorphic encryption. By considering the features of distributed authority management in insurance business, a shared insurance account book is realized as well as the data consensus, smart contracts and special chained storage structures. The experimental results show that the proposed scheme has high security and effectiveness.
A Predictive Model of Users’ Behavior and Values of Smart Energy Meters Using PLS-SEM

A smart energy metering system is an IoT device that connects several electrical household devices and record, monitor, estimate, control in-house energy consumption in a real-time basis. Although smart energy meters have great capabilities, this technology is still in infancy stages in many developing countries, and little is known about what perceived values are associated with smart meters from residents' perspectives. Therefore, this research aimed to fill this gap by examining the impact of six different types of perceived values on residents' intentions to use smart meters in UAE. The study followed a quantitative approach by gathering 266 survey responses which were tested by using Partial Least Squares-Structural Equation Modeling (PLS-SEM). The statistical results genuinely indicated that perceived epistemic values, environmental values, emotional values, and convenience values can significantly impact residents' intention to use smart meter, whereas social values and monetary values found to have no significant impact on their intentions to use this technology. Theoretical and practical implications are indicated, and directions of future research are specified afterwards.
Optical cryptosystem model based on the keyspace transformation

The existing optical encryption schemes always use the same cryptosystem for both encryption and decryption processes. This fact makes the cryptosystem vulnerable to some specific attacks. A new model of optical cryptosystem based on keyspace transformation is proposed to avoid this security risk. The designed cryptosystem following the principles of this model can be very safe. The senders and receivers use different keys for encryption and decryption processes, respectively. This proposed model includes an encryption subsystem, a decryption subsystem, and a keyspace transformation method. Some specific schemes following the principles of the proposed model are given as instances for studying the proposed model. The validity, generality, collision-resistant property, and robustness of the proposed model are discussed. The proposed model provides a new way of designing optical cryptosystems.
Privacy Analysis of Format-Preserving Data-Masking Techniques

With the growing number of regulations and concerns regarding data privacy, there is an increasing need for protecting Personally Identifiable Information (PII). A widely-used approach to protect PII is to apply data-masking techniques in order to remove or hide the identities of the individuals referred to in the data under investigation. A particular class of data-masking techniques aims at preserving the format of the source data, so as to allow using encoded data where the corresponding source is expected, thereby minimising application changes to perform tasks such as statistical analysis or testing. Various encoding techniques are used to protect data privacy while preserving the format, including Format-Preserving Encryption (FPE) and masking out. Even though convenient, preserving the format of data might lead to re-identification attacks. In this paper, we discuss the vulnerabilities of data-masking techniques that preserve the format of data and analyse their security and privacy properties. We investigate two industrial datasets and quantify the potential data privacy leakage that could arise from using inappropriate data masking techniques.
Multiunit automotive perception framework: Synergy between AI and deterministic processing

Since neural networks were first introduced into automotive systems, safety has been a major concern. The prevailing safety standard in the automotive industry, ISO26262, does not fully define testing and verification methods for software based on deep learning. In this paper, we propose a multiunit perception framework that increases the determinism of automotive systems incorporating deep learning. Our approach relies on ASIL decomposition and algorithm diversification, which are enabled through the utilization of multiple low ASIL perception units and one high ASIL monitor unit. In addition to the framework concept, we specify how each component can be mapped to appropriate hardware and software platforms. The practical feasibility of the perception framework is demonstrated with a proof of concept prototype.
Gateway‐oriented two‐server password authenticated key exchange protocol for unmanned aerial vehicles in mobile edge computing

With the popularity of unmanned aerial vehicles (UAVs), more and more valuable data can be collected by UAVs. In order to balance the data usage and communication cost, the data can be preprocessed in UAVs rather than directly transmitting to the data centre in the edge computing paradigm. Users can obtain information of interest by accessing the data centre remotely by authenticating themselves to the data centre using the most pervasive password authentication method. Unfortunately, the data centre becomes the main attack target because it not only stores the data but also maintains the passwords of all the users. Aiming at protecting the data as well as the password in the UAV-enabled mobile edge computing environment, the authors combine the advantages of gateway-oriented password authenticated key exchange (PAKE) protocols and two-server PAKE protocols and put forward an efficient gateway-oriented two-server PAKE protocol. The security of the proposed protocol is given in the random oracle model. The performance comparison shows their proposal has comparable efficiency in computation and communication costs. Their protocol provides better protection to the password without sacrificing efficiency. Consequently, their protocol is more suitable for real applications in UAV-enabled mobile edge computing environment.
Investigation and Research on Vocational Education Teachers' Competencies Based on Smart Learning Environment

The development of vocational education in China has always been concerned and the teachers of vocational education are the main force in training applied works. Smart learning environment puts forward higher requirements for teachers' qualities, this article through the questionnaire survey method, carries on the analysis to the vocational education teacher's present situation. Starting from three aspects of teaching ability, professional ability and vocational analysis ability, the investigation content includes the way, motivation, content, demand and opportunity to improve the quality of vocational education teachers, so as to understand the current situation of the cultivation of vocational education teachers' quality and make the quality of vocational education teachers meet the requirements of smart learning environment.
Protecting Metadata of Access Indicator and Region of Interests for Image Files

With popularity of social network services, the security and privacy issues over shared contents receive many attentions. Besides, multimedia files have additional concerns of copyright violation or illegal usage to share over communication networks. For image file management, JPEG group develops new image file format to enhance security and privacy features. Adopting a box structure with different application markers, new standards for privacy and security provide a concept of replacement substituting a private part of the original image or metadata with an alternative public data. In this paper, we extend data protection features of new JPEG formats to remote access control as a metadata. By keeping location information of access control data as a metadata in image files, the image owner can allow or deny other’s data consumption regardless where the media file is. License issue also can be resolved by applying new access control schemes, and we present how new formats protect commercial image files against unauthorized accesses.