Reviewerly/exHarmony huggingface dataset:
On the Security of Face Recognition Terminals at Modern Airports

The inability of airports to absorb the increasing numbers of passengers year after year, makes the use of egates and face recognition terminals a promising solution to enhance the quality of service and security.Thus, over the past few years, many airports around the world have adopted facial recognition technology to facilitate verification and identification of passengers, for example for surveillance systems, and recently for face recognition terminals.We believe that there are still many challenges and questions about face recognition in the context of these huge security applications, concerning performance, security and privacy.We believe that these concerns must be deeply discussed.In this paper, we discuss the potential vulnerabilities of facial recognition, then we present and discuss the result of an adopted attack against security applications that compare the live face of a passenger with his/her stored face image in passport or visa.Our experimental results illustrate the ability of the presented trick attack to be a serious threat for such modern security applications.
FlexBoost: A Flexible Boosting Algorithm With Adaptive Loss Functions

Adaptive Boosting (AdaBoost) is a representative boosting algorithm that can build a strong classifier by optimally combining weak classifiers in such a way that subsequent weak classifiers are tweaked in favor of instances misclassified by previous classifiers. However, AdaBoost is known to be susceptible to overfitting problems due to the static nature of its weight-updating process. In this paper, we propose a new boosting algorithm, named FlexBoost (Flexible AdaBoost), that can enhance classification performance by employing adaptive loss functions, i.e., by adjusting the sensitivity of the conventional (exponential) loss function for each weak classifier. The performance benchmarks on 30 binary classification problems taken from the UCI and Kaggle datasets are presented to empirically validate the proposed algorithm.
DOCUMENTING HISTORICAL RESEARCH FOR A COLLECTION INFORMATION MODELLING. A PROPOSAL FOR A DIGITAL ASSET MANAGEMENT SYSTEM

Abstract. The paper describes part of the conceptual structure produced within the still ongoing project B.A.C.K. TO T.H.E. F.U.T.U.RE. (BIM Acquisition as Cultural Key TO Transfer Heritage of ancient Egypt For many Uses To many Users REplayed). The aim of the project was to use a semantic web infrastructure to describe archival research and tracking informations related to a hidden museum collection expedition models of Egyptian architecture partially stored in the depots of the Museo Egizio of Turin. The outcome will be an interactive web-presentation portal of high-resolution 3D models enriched by historical and archival set of content, from the digitization procedure applied to collection objects, to the digitization process of related data and information. The development of the collection documentation of the project illustrates how is crucial to declare the semantic description underlying narrative contents. Data about single collection objects were conceptually modelled using generalizable formulas already known by CIDOC-CRM community. The description of provenance of knowledge related to the historical investigation process was modelled using CRMinf extension, exploring the possibility of making beliefs based on the available documentation and validating the results of the assumptions made during the research.
Color image steganography in YCbCr space

Steganography is a best method for in secret communicating information during the transference of data. Images are an appropriate method that used in steganography can be used to protection the simple bits and pieces. Several systems, this one as color scale images steganography and grayscale images steganography, are used on color and store data in different techniques. These color images can have very big amounts of secret data, by using three main color modules. The different color modules, such as &#x0D; HSV-(hue, saturation, and value), RGB-(red, green, and blue), &#x0D; YCbCr-(luminance and chrominance), YUV, YIQ, etc. This paper uses unusual module to hide data: an adaptive procedure that can increase security ranks when hiding a top secret binary image in a RGB color image, which we implement the steganography in the YCbCr module space. We performed Exclusive-OR (XOR) procedures between the binary image and the RGB color image in the YCBCR module space. The converted byte stored in the 8-bit LSB is not the actual bytes; relatively, it is obtained by translation to another module space and applies the XOR procedure. This technique is practical to different groups of images. Moreover, we see that the adaptive technique ensures good results as the peak signal to noise ratio (PSNR) and stands for mean square error (MSE) are good. When the technique is compared with our previous works and other existing techniques, it is shown to be the best in both error and message capability. This technique is easy to model and simple to use and provides perfect security with unauthorized.
MLPV: Text Representation of Scientific Papers Based on Structural Information and Doc2vec

Text representation is the key for text processing.Scientific papers have significant structural features.The different internal components, mainly including titles, abstracts, keywords, main texts, etc., embody different degrees of importance.In addition, the external structural features of scientific papers, such as topics and authors, also have certain value for analysis of scientific papers.However, most of the traditional analysis methods of scientific papers are based on the analysis of keyword co-occurrence and citation links, which only consider partial information.There is a lack of research on the textual information and external structural information of scientific papers, which has led to the inability to deeply explore the inherent laws of scientific papers.Therefore, this paper proposes Multi-Layers Paragraph Vector (MLPV), a text representing method for scientific papers based on Doc2vec and structural information of scientific papers including both internal and external structures, and constructs five text representation models: PV-NO, PV-TOP, PV-TAKM, MLPV and MLPV-PSO.The results show that the effect of the MLPV model is much better than the PV-NO, PV-TOP and PV-TAKM models.The average accuracy of MLPV model is much more stable and higher, reaching 91.71%, which proves its validity.On the basis of the MLPV model, the accuracy of the optimized MLPV-PSO model is 3.33% higher than MLPV model which proves the effectiveness of the optimization algorithm.
DRONE BASED NEAR REAL-TIME HUMAN DETECTION WITH GEOGRAPHIC LOCALIZATION

Abstract. Detection of humans, e.g. for search and rescue operations has been enabled by the availability of compact, easy to use cameras and drones. On the other hand, aerial photogrammetry techniques for inspection applications allow for precise geographic localization and the generation of an overview orthomosaic and 3D terrain model. The proposed solution is based on nadir drone imagery and combines both deep learning and photogrammetric algorithms to detect people and position them with geographical coordinates on an overview orthomosaic and 3D terrain map. The drone image processing chain is fully automated and near real-time and therefore allows search and rescue teams to operate more efficiently in difficult to reach areas.
Dual-Ouroboros: An improvement of the McNie scheme

McNie [8] is a code-based public key encryption scheme submitted to the NIST Post-Quantum Cryptography standardization [10] as a candidate. In this paper, we present Dual-Ouroboros, an improvement of McNie, which can be seen as a dual version of the Ouroboros-R protocol [1], another candidate to the NIST competition. This new improved protocol permits, first, to avoid an attack proposed by Gaborit [7] and second permits to benefit from a reduction security to a standard problem (as the original Ouroboros protocol).
Virtual Trial Room

Augmented reality is the technology that expands our physical world, adding layers of digital information onto it.Augmented reality adds digital element to live view by using cameras or sensors.The main aim of this work is to develop virtual trial room using augmented reality which allows user to try on virtual clothes.Kinect sensor is used to calculate the effective distance between the sensor and the user who is standing in front of it.Based on this distance we can analyze how the clothes will look on user.This application improves present augmented based posing and gives full view of 3D image rather than 2D image.
Data Infrastructure for Machine Learning

Data quality is critical for effective machine learning, and this makes data a first-class citizen in the context of machine learning, on par with algorithms, software, and infrastructure.As a result, machine-learning platforms need to support data analysis and validation in a principled manner, throughout the lifecycle of the machine learning process.This paper reviews the data infrastructure we built at Google to address these challenges in the context of large-scale production machine learning pipelines.
Usability Evaluation of SMS-Based System: Basis for Systems Development

The objective of the study is to evaluate the usability of SMS-Based Auto-Reply Inquiry System for Grades and Accounts. This descriptive study used a three-part questionnaire which was administered by the researcher. The usability questionnaire adapted from the USE Questionnaire used a 5-point scale composed of 30 statements. Convenience sampling was used choosing the respondents from the total population of 149 students of the CICS during the second semester of school year 2018-2019. A total of 45 questionnaires were retrieved and were used in the usability analysis. The usability evaluation results yielded a grand mean of 4.54 (Strongly Agree) showing that the SMS-based inquiry system is easy to learn, easy to use, useful, and satisfying. The results of this study show that pre-coded keywords are confusing for beginners but can be learned and familiarized by the users as they continue use the system. Further investigation should be conducted to prove that ease of use, ease of learning, and usefulness have direct impacts to user satisfaction.
Adaptive RSA Encryption Algorithm for Smart Grid

Abstract Information security has been paid more and more attention as it is a high-weight factor that affects many aspects in our life. Many technologies have been introduced to improve information security. Encryption algorithm is one of the most frequently used method. There are many encryption algorithms. RSA algorithm, which uses asymmetric encryption method, is a secure encryption algorithms. RSA’s encryption effect is pretty good due to difficulty in cracking its private key. Smart grid is the upgrade of power grid, which is based on integrated, high-speed two-way communication network. In smart grid, there are lots of data to be transferred along the network. In order to ensure that the data are not stolen or tampered with, it is necessary to encrypt the data during transferring. However, it is ineffective to use conventional RSA algorithm directly in smart grid. To solve the problem, we propose an adaptive RSA algorithm to generate primes, which modifies the application scope of RSA algorithm, so that the algorithm can be successfully applied in smart grid. According to the time limit of the encryption algorithm, the prime number is generated adaptively, so that the encryption time of the algorithm is reasonably controlled. The data is encrypted by generating the public key and the private key used for encryption through the generated prime number. The experiments verified the effectiveness of the algorithm.
Algebraic dependence in generating functions and expansion complexity

In 2012, Diem introduced a new figure of merit for cryptographic sequences called expansion complexity. Recently, a series of paper has been published for analysis of expansion complexity and for testing sequences in terms of this new measure of randomness. In this paper, we continue this analysis. First we study the expansion complexity in terms of the Gröbner basis of the underlying polynomial ideal. Next, we prove bounds on the expansion complexity for random sequences. Finally, we study the expansion complexity of sequences defined by differential equations, including the inversive generator.
Intermobility of barium, strontium, and lead in chloride and sulfate leach solutions

Production of radionuclide-free copper concentrates is dependent on understanding and controlling the deportment of daughter radionuclides (RNs) produced from 238U decay, specifically 226Ra, 210Pb, and 210Po. Sulfuric acid leaching is currently employed in the Olympic Dam processing plant (South Australia) to remove U and fluorine from copper concentrates prior to smelting but does not adequately remove the aforementioned RN. Due to chemical similarities between lead and alkaline earth metals (including Ra), two sets of experiments were designed to understand solution interactions between Sr, Ba, and Pb at various conditions. Nanoscale secondary ion mass spectrometry (NanoSIMS) isotopic spatial distribution maps and laser ablation inductively coupled-plasma mass spectrometry transects were performed on laboratory-grown crystals of baryte, celestite, and anglesite which had been exposed to different solutions under different pH and reaction time conditions. Analysis of experimental products reveals three uptake mechanisms: overgrowth of nearly pure SrSO4 and PbSO4 on baryte; incorporation of minor of Pb and Ba into celestite due to diffusion; and extensive replacement of Pb by Sr (and less extensive replacement of Pb by Ba) in anglesite via coupled dissolution-reprecipitation reactions. The presence of H2SO4 either enhanced or inhibited these reactions. Kinetic modelling supports the experimental results, showing potential for extrapolating the (Sr, Ba, Pb)SO4 system to encompass RaSO4. Direct observation of grain-scale element distributions by nanoSIMS aids understanding of the controlling conditions and mechanisms of replacement that may be critical steps for Pb and Ra removal from concentrates by allowing construction of a cationic replacement scenario targeting Pb or Ra, or ideally all insoluble sulfates. Experimental results provide a foundation for further investigation of RN uptake during minerals processing, especially during acid leaching. The new evidence enhances understanding of micro- to nanoscale chemical interactions and not only aids determination of where radionuclides reside during each processing stage but also guides development of flowsheets targeting their removal.
Identify High-Impact Bug Reports by Combining the Data Reduction and Imbalanced Learning Strategies

As software systems become increasingly large, the logic becomes more complex, resulting in a large number of bug reports being submitted to the bug repository daily. Due to tight schedules and limited human resources, developers may not have enough time to inspect all the bugs. Thus, they often concentrate on the bugs that have large impacts. However, there are two main challenges limiting the automation technology that would help developers to become aware of high-impact bug reports early, namely, low quality and class distribution imbalance. To address these two challenges, we propose an approach to identify high-impact bug reports that combines the data reduction and imbalanced learning strategies. In the data reduction phase, we combine feature selection with the instance selection method to build a small-scale and high-quality set of bug reports by removing the bug reports and words that are redundant or noninformative; in the imbalanced learning strategies phase, we handle the imbalanced distributions of bug reports through four imbalanced learning strategies. We experimentally verified that the method of combining the data reduction and imbalanced learning strategies could effectively identify high-impact bug reports.
Improving Students' Descriptive Text Writing by Using Writing in the Here and Now Strategy at the Tenth Grade Students of Vocational High School

The objective of the research is to find out whether or not the use of writing in the here and now strategy improve students' ability in writing descriptive text at class X of SMK Negeri 1 Mamuju. The method of this research was quantitative method and type of this research was pre-experimental with one group pre-test, treatment and post-test design. In this research, the researchers took the tenth grade of the second semester in which the total numbers of the population were 323, and the samples consist of 37 students. Based on the data analysis, the alternative hypothesis in this research was accepted because t-test value was more significant than t -table value (11.06 &gt; 1.688). The researchers conclude that Writing in Here and Now strategy can improve the Students' Writing Skill at Tenth Grade Students on the second semester of SMK Negeri 1 Mamuju in academic year 2017/2018.
Application of MES System in Offshore Oil and Gas Field Production Management

In order to solve information island problem of offshore oil and gas field production-related information system, including repetitive reporting and input of data, data isolation of central control system, inadequate follow-u... | Find, read and cite all the research you need on Tech Science Press
Research on pose point cloud matching error compensation method for confocal image assembly

Abstract High-precision matching of spatial poses is the key to ensuring the assembly performance of complex three-dimensional devices. The human eye cannot directly judge the three-dimensional pose of mesoscale devices, and high-precision observation instruments are needed to assist accurate judgment, ensuring smooth assembly. In this paper, based on the three-dimensional assembly system of micro-devices of confocal microscope, it is difficult to realize the problem of assembly alignment of spatial poses in the existing methods, this focuses on solving the problem of how to perform high-precision pose matching by using point cloud data measured through the confocal microscope.A new method for directly performing alignment of three-dimensional poses is proposed. The principle of point cloud registration ICP algorithm is studied, and the theory of point cloud axis hole registration theory is carried out.A neural network about the mapping relationship between the angle calculated by the RT matrix obtained by ICP algorithm and the theoretical angle is constructed. The comprehensive assembly accuracy of the whole method is analyzed to be 2.41μm. Finally, the actual experiment was carried out through the standard hole axis of 3μm for the single-edge gap and assembled successfully.
A Fast Image Deformity Correction Algorithm for Underwater Turbulent Image Distortion

An algorithm correcting distortion based on estimating the pixel shift is proposed for the degradation caused by underwater turbulence. The distorted image is restored and reconstructed by reference frame selection and two-dimensional pixel registration. A support vector machine-based kernel correlation filtering algorithm is proposed and applied to improve the speed and efficiency of the correction algorithm. In order to validate the algorithm, laboratory experiments on a controlled simulation system of turbulent water and field experiments in rivers and oceans are carried out, and the experimental results are compared with traditional, theoretical model-based and particle image velocimetry-based restoration and reconstruction algorithms. Using subjective visual evaluation, image distortion has been effectively suppressed; based on an objective performance statistical analysis, the measured values are better than the traditional and formerly studied restoration and reconstruction algorithms. The method proposed in this paper is also much faster than the other algorithms. It can be concluded that the proposed algorithm can effectively improve the de-distortion effect of the underwater turbulence degraded image, and provide potential techniques for the accurate operation of underwater target detection in real time.
A New Degradation Model for Imaging in Natural Water and Validation Through Image Recovery

Scattering and turbulence are the main factors affecting imaging in natural water environment. A new model for underwater turbulent degradation is obtained by calculating the scattering transfer factors including the intensity distribution of beam propagation, turbulent fluid medium and suspended particle. In order to verify the proposed model, a controlled laboratory simulation system of turbulent water is established, from which the degradation transfer factor is measured and compared by image restoration and reconstruction. The new model is also applied for field tests in natural ocean water. Experimental results show that based on the proposed model, effect of image restoration and reconstruction can be substantially improved, which proves the correctness and validity of the model.
Blockchain Security: Situational Crime Prevention Theory and Distributed Cyber Systems

The authors laid the groundwork for analyzing the crypto-economic incentives of interconnected blockchain networks and utilize situational crime prevention theory to explain how more secure systems can be developed. Blockchain networks utilize smaller blockchains (often called sidechains) to increase throughput in larger networks. Identified are several disadvantages to using sidechains that create critical exposures to the assets locked on them. Without security being provided by the mainchain in the form of validated exits, sidechains or statechannels which have a bridge or mainchain asset representations are at significant risk of attack. The inability to have a sufficiently high cost to attack the sidechain while mainchain assets can be withdrawn, along with the disconnect between the integrity of the sidechain and the value of the stolen assets are among the top disadvantages. The current study used a vulnerability analysis and theoretical mathematics based on situational crime prevention theory to highlight the attack vectors and prevention methods for these systems. Much of the analysis can be applied to any distributed system (e.g. blockchain network), particularly any supposedly trustless off-chain component. The equations developed in the current study will hold for any two chains that are bridged and pass value back and forth and provides evidence to suggest a public sidechain is likely not a viable option for scalability due to security concerns. Criminal strategies on blockchain networks in the digital realm are similar to criminal strategies in the physical realm; therefore, the application of criminology can lead to more efficient development and ultimately more effective security protocols.