Reviewerly/exHarmony huggingface dataset:
Emergency Alert to Safeguard the Visually Impaired Novice Using Internet of Things

Recognizing a crisis with help of sensors and controls the situation by announcing them to the Real world, that worry calamity group makes suitable move to protect the costless. Methods/Analysis: Internet of things is system that controls the interconnected things which are implanted with sensors, programming that empowers to gather and trade information. These physical articles are controlled remotely with sufficient system foundations. The absence of help administrations can make outwardly impeded individuals excessively reliant on their families, which keep them from being financially dynamic and socially included. IoT can offer individuals with handicaps the help and supports greatly to accomplish a decent personal satisfaction and enables them to get appropriate instruction without dangers. Findings: For this reason the proposed engineering of web of things is to screen the vehicle by utilizing Raspberry controller and it set forth numerous highlights to recognize the purpose behind happening accidents and furthermore protects the outwardly weakened victims from the accidents or dangers. Applications/Improvements: Distinctive application things are taken into issues the tip goal to indicate the collaboration of the segments of internet of things. While the proposed work will be a keen ready framework to different components without human mediation. Basic difficulties have been distinguished and tended to particular.
Real Time Multiple Object Tracking using Deep Features and Localization Information

In this paper we propose a tracking by detection method using a dissimilarity measure calculated based on the location and the appearance information of the object. These dissimilarity values are used in Hungarian Algorithm [1] in the data association step for track identity assignment. We make use of YOLO [2] deep learning based object detector in the detection step from camera image feed. Location measure is calculated using the predicted object location and bounding box, while the appearance measure is from the last feature layer from the detection network. Main focus in this work is to propose a tracking framework that can be used in real time automated vehicle guiding applications, by striking a balance between computational complexity and tracking accuracy. Therefore, we make use of the deep features available from detection framework rather than calculating a new appearance measure during the tracking step. The method proposed is very efficient and enables to achieve speeds up to 500+ frames per second (fps) in KITTI [3] tracking benchmark while achieving state-of-the-art results.
Linear Regression Correlation Filter: An Application to Face Recognition

This paper proposes a novel method of designing a correlation filter for frequency domain pattern recognition. The proposed correlation filter is designed with linear regression technique and termed as linear regression correlation filter. The design methodology of linear regression correlation filter is completely different from standard correlation filter design techniques. The proposed linear regression correlation filter is estimated or predicted from a linear subspace of weak classifiers. The proposed filter is evaluated on standard benchmark database and promising results are reported.
Recursion Schemes in Coq

Program calculation, a programming technique to derive efficient programs from naive ones by program transformation, is challenging for program optimization. Tesson et al. have shown that Coq, a popular proof assistant, provides a cost-effective way to implement a powerful system for verifying correctness of program transformations, but their applications are limited to list functions in the Theory of Lists. In this paper, we propose an easy-to-use Coq library to prove more advanced calculation rules in Coq for various recursion schemes, which capture recursive programs on an arbitrary algebraic datatype. We prove all the lemmas and theorems about recursion schemes in Coq including histomorphisms and futumorphisms proposed by Uustalu et al. Our library can be used to obtain certified runnable programs from their definitions written with recursion schemes in Coq scripts. We demonstrate a certified runnable program for the Fibonacci numbers and unbounded knapsack problem from their histomorphic definitions.
On Non-Completeness and G-Equivariance

With the growing threat of the side-channel attack (SCA) to the cryptographic algorithm’s implementations, the masking method has become one of the most promising SCA countermeasures for securely implementing, for example, block ciphers. The basic principle of the masking method is that if the sensitive variable (which, by definition, depends on sensitive information) is split into some random variables and they are manipulated in a secure manner, then the relationship between the random variables and the corresponding side-channel information may look independent from the outside world. However, after the introduction of the glitch attack, there has been a lot of concern about the security of the masking method itself. And, to mitigate the threat of the glitch attack, the threshold implementation (TI) and G-equivariant gates were independently introduced as countermeasures. In this paper, we consider the main notions of two such independent glitch attack’s countermeasures, say, non-completeness and G-equivariance, and investigate their relationship. The contribution of this paper is three-fold. First, we show that the widely-circulated proof that the non-complete TI with uniform inputs guarantees the security against the 1st order DPA even in the presence of glitches is not satisfactory. Next, using the extended notion of G-equivariance to the higher-order setting, we prove that non-completeness implies G-equivariance, which, in turn, means that the non-complete TI with uniform inputs has resistance against the glitch attack. Thirdly, we prove that the set of non-complete gates is a proper subset of the set of G-equivariant gates by showing there is a gate that is G-equivariant but not non-complete.
Intrinsic Face Image Decomposition from RGB Images with Depth Cues

As a pre-step of reconstructing face attributes technology, the quality of face intrinsic image decomposition result has a direct impact on the sub-operations of reconstructing face attributes detail. There are two challenging problems with the intrinsic face image decomposition methods which are the quality of face-base intrinsic image, and the details of the shading image. In this study a new image model for intrinsic face image decomposition from RGB images with depth cues is proposed to produce high quality results even with simple constraints. The proposed model consists of three main steps: face cropping operation, processing the RGB color normalization, and the super-pixel segmentation. The face image is first cropped to get face area, then a color normalization process for the cropped face image is used to normalize RGB pixels, and finally the super-pixel segmentation based on mean shift algorithm is applied which has a good performance on reduce artifact and shading image’s detail retention. To evaluate the proposed model, both qualitative and quantitative assessments be used. The qualitative assessment is based on human subjective visual standards to compare the intrinsic images results, and the quantitative assessment is based on the data analyze of the image information entropy. Qualitative and quantitative results both demonstrate that the performance of the proposed model is better than other techniques in the field of intrinsic face image decomposition.
The Minimum Completions and Covers of Symmetric, Hankel Symmetric, and Centrosymmetric Doubly Substochastic Matrices

In this paper, we investigate and describe all the minimal completions and covers of the symmetric, Hankel symmetric, and centrosymmetric doubly substochastic matrices, respectively.
Designing the Application of Security Text Messages Into Audio Files Using Data Encryption Standard (DES) Algorithms Using the End Of File (EOF) Method

Abstract There are several ways to deal with security issues of confidential data sent via the internet, including using cryptographic techniques and steganography. Steganography is the science and art of concealing information / messages in a media in such a way that its existence is not detected by other parties who are not entitled to the information. Instead, cryptography disguises the meaning of a message, but does not hide that there is a message because the file looks suspicious. The Steganography technique used here was End Of File (EOF). The EOF technique described how to add data or files at the end of the image file. For this technique, the size of data or files that will be hidden could be larger than the size of the image file. The hidden data will be inserted at the end of the file so that it will not affect the image. This steganography application is also equipped with a cryptographic function Data Encryption Standard (DES) at the time of insertion of data that functions as a generator code and encrypts data, so that the security of a data in the file is more protected and protected from those who are not entitled to know the data.
Numerical Comparison of some Crossover in Genetic Algorithm for Two Dimensional Global Minimization Problem using C++

In this paper we simulate the comparison of numerical results of the different crossover method in genetics algorithm for solving global minimization problem. In this paper, the objective function is for 2 two variables function. The genetics algorithm is run using C++ program and implemented to several benchmark test functions of global optimization. The performance of each crossover method is analyse according to the numerical results, which show that certain crossover method is better for certain benchmark test function.
Study on Precursor Anomaly Data Recognition and Prediction of L-IAZPSO-SVM Algorithm

The traditional seismic data anomaly identification method usually adopts a simple threshold method to determine the data collected by the device outside the set normal monitoring threshold as abnormal data, and the abnormal data cannot be accurately identified. This paper adopts a method to improve the traditional anomaly recognition L-IAZPSO-SVM method to improve the accuracy of abnormal data recognition.In this paper, the earthquake precursor data [1] provided by China Seismic Network is used as the input index, and the improved PSO (Particle Swarm Optimization) [2] is used to optimize the SVM(support vector machine) [3] training to realize the identification and prediction of abnormal data. First Using Cluster analysis and PCA(Principal Component Analysis) [4] make the data preprocessed, and then Itô Lemma [5] is used to optimize the PSO initialization process to improve the particle variability and make the initial particles diverse. The adaptive speed update method is used to improve the problem that the algorithm is easy to fall into the local optimal solution in the later stage of the algorithm. The Ziggurat algorithm [6] is used to increase the number of particles and solve the problem of imbalance between the global search capability and the local search capability of the system. The improved algorithm solves the problem that the traditional SVM method cannot determine the number of support vectors, the system fitting fuzzy problem and improves the algorithm's abnormal recognition rate and prediction rate. Compared with the traditional PSO-SVM method, other improved PSO-SVM methods, and other anomaly data detection algorithms, the system's running speed, global search capability, operational stability, and abnormal data recognition rate prediction rate are improved.
DIC‐measurement during tensile testing and its approach for the FEM‐simulation of "Die‐Less‐Hydroforming"

Abstract Digital Image Correlation (DIC)‐measurement is an optical measurement method which enables contactless and area‐wide continuous acquisition of 3D‐geometry, deformation and surface strain during experimental investigations. Within Die‐Less‐Hydroforming, an increasing innovative forming technology, see [1] and [2], the DIC‐measurement may be used for two tasks at once: The forming process of Die‐Less‐Hydroforming can be investigated continuously within the scope of the classical application of DIC in terms of geometry and deformation acquisition. However, this paper focuses on the use of DIC during the uniaxial tensile test of sheet metal used for manufacturing the double‐layered Die‐Less‐Hydroforming‐blank. Using DIC during the tensile test facilitates to measure the current cross‐section area continuously which allows the determination of a true stress‐true plastic strain‐curve. This curve is required as input for FEM‐forming‐simulation to achieve significant results. But with conventional testing methods, for the case of flat tensile test specimens, it cannot be determined beyond uniform strain A g . Therefore, the “true” values beyond A g are often estimated by mathematical approximations.
A Secured Predictive Analytics Using Genetic Algorithm and Evolution Strategies

In the banking sector, the major challenge will be retaining customers. Different banks will be offering various schemes to attract new customers and retain existing customers. The details about the customers will be provided by various features like account number, credit score, balance, credit card usage, salary deposited, and so on. Thus, in this work an attempt is made to identify the churning rate of the possible customers leaving the organization by using genetic algorithm. The outcome of the work may be used by the banks to take measures to reduce churning rates of the possible customers in leaving the respective bank. Modern cyber security attacks have surely played with the effects of the users. Cryptography is one such technique to create certainty, authentication, integrity, availability, confidentiality, and identification of user data can be maintained and security and privacy of data can be provided to the user. The detailed study on identity-based encryption removes the need for certificates.
Integration of Camera and Inertial Measurement Unit for Entire Human Robot Interaction Using Machine Learning Algorithm

Importance of robots in industrial applications is a well-known fact. There are many tasks in industries, which can't be done alone by a robot or alone by a worker. Therefore, there is a need to establish a reliable and safe interaction environment between robots and workers. To do so, some information about the worker should be conveyed to the robot. This article focuses on industrial Human-Robot Interaction. For a safe and efficient Human-Robot Interaction, a robot needs to know about worker's position, posture and gesture. This paper proposes integration of an Inertial Measurement Unit (IMU) and a 3D camera as an image sensor to eliminate each other drawbacks and applies machine learning algorithm to detect postures and gestures of worker. Some experimental results during the interaction with heavy-duty robot will be presented.
Protecting Secrets of Persistent Systems with Volatility

The volatility of main memory and CPU caches is an important implicit protection mechanism for sensitive data: in-memory data gets erased if memory modules are disconnected from power supply. Persistent systems, on the other hand, cannot rely on volatility and without further measures their secrets can be easily retrieved by physical access. In this paper, we present Volatility, a system which protects secrets stored in persistent memory. This system provides mechanisms which turn persistent sub-systems into volatile ones by the use of AMD Secure Memory Encryption (SME), a new extension of AMD CPUs which provides encryption of main memory at the page granularity. Volatility protects secrets at two levels: it offers fine-grained memory encryption inside the kernel, where only information considered as sensitive is secured, and per-process memory encryption, which encrypts selected user space programs. Besides storing subsystems in an encrypted form, all relevant input and output paths, e.g. managed by the kernel, are protected as well. Our evaluation of Volatility demonstrates that the proposed protection mechanism does not impact the system performance, while protecting against strong adversaries.
Multi-scale saliency using local gradient and global colour features

In this paper, the issue of scale is addressed in the context of salient object detection. To date, many single scale models have been proposed for detecting salient objects within a scene. Scale is a fundamental problem within image processing, and therefore, multiple scale techniques are investigated and evaluated, as well the presentation of a novel multi-scale saliency model. The proposed model is compared with two state-of-the-art multi-scale saliency algorithms and qualitatively evaluated with respect to algorithmic accuracy and efficiency on the publicly available MSRA10K salient object dataset.
Monitoring the Virtual Reality Influence on Cognitive Tasks Performance in Healthy Individuals

Today, the everyday life of almost every person includes not only the real environment but also the virtual reality, which has many manifestations and reflections in modern life despite the fact that the influence of virtual reality on the human body has not yet been studied. In current pilot study, the effect of virtual reality technologies on the cognitive ability of a person was studied and the possibility of monitoring this effect in real time was tested. To obtain reliable data of the virtual reality influence, the protocol of the experiment was created to include mental arithmetic, task of finding a path and logical problems. Before the test, five subjects were trained and the quality of their cognitive performance was evaluated. These results showed that those, who tried the virtual environment under different stress factors for the first time, responded more clearly than people who were often immersed in the virtual environment, which confirms the theory of “learning”. In current study, symptoms such as nausea, headache and disorientation were not observed. There were no documented strong cognitive deviations before and after immersion in the virtual environment. Further research should incorporate additional real-time stress-recording technologies to refine the obtained data.
Cost-Minimizing Online Algorithms for Geo-Distributed Data Analytics

Modern enterprises often manage geographically distributed datacenters around the globe. In such environment, datasets are naturally collected and stored in different data centers and were later queried for complex analytics. In this paper, we study the Wide-Area Data Analytics problem, which aims to efficiently control data movements and achieve low latency for overall queries processing, both constrained by limited and expensive network resources across datacenters. Previous papers focus on offline settings of single analytical queries and do not consider time in optimizing system performance, and therefore ignores the dynamics of data and task placement in terms of inter-DC bandwidth utilization. In this paper, we consider the online setting and formulate a cost-minimizing optimization problem over time for arbitrary Directed Acyclic Graph query processing. Considering dynamics of network resource usage, we developed two online algorithms, Online Switch Resist (OSR) and Most Fixed Horizon Control (MFHC) with good competitive ratios. We performed extensive simulations and comparative studies using the TPC-CH benchmark and verified the efficacy of proposed algorithms. The algorithm we proposed is better than the existing algorithm, and its performance approximates the theoretical optimal value.
Post-Quantum Pseudorandom Functions from Mersenne Primes

Pseudorandom functions (PRFs) serve as a fundamental cryptographic primitive that is essential for encryption, identification and authentication. The concept of PRFs first formalized by Goldreich, Goldwasser, and Micali (JACM 1986), and their construction is based on length-doubling pseudorandom generators (PRGs) by using the tree-extention technique. Subsequently, Naor and Reingold proposed a construction based on synthesizers (JACM 2004) which can be instantiated from factoring and the Diffie-Hellman assumption. Recently, some efficient constructions were proposed in the post-quantum background. Banerjee, Peikert, and Rosen (Eurocrypt 2012) constructed relatively more efficient PRFs based on “learning with error” (LWE). Soon afterwards, Yu and Steinberger (Eurocrypt 2016) proposed two efficient constructions of randomized PRFs (with public coin as a parameter) from “learning parity with noise” (LPN). In this paper, we construct standard and randomized PRFs via Mersenne prime assumptions which were proposed by Aggarwal et al. (Crypto 2018) as new post-quantum candidate hardness assumptions. In contrast with Yu and Steinberger’s constructions, our first construction could have the same parameters to their second construction but not needs extra public coin and our second construction has a smaller public coin and key size comparing with their first construction.
Dissecting Widening: Separating Termination from Information

Widening ensures or accelerates convergence of a program analysis, and sometimes contributes a guarantee of soundness that would otherwise be absent. In this paper we propose a generalised view of widening, in which widening operates on values that are not necessarily elements of the given abstract domain, although they must be in a correspondence, the details of which we spell out. We show that the new view generalizes the traditional view, and that at least three distinct advantages flow from the generalization. First, it gives a handle on “compositional safety”, the problem of creating widening operators for product domains. Second, it adds a degree of flexibility, allowing us to define variants of widening, such as delayed widening, without resorting to intrusive surgery on an underlying fixpoint engine. Third, it adds a degree of robustness, by making it difficult for an analysis implementor to make certain subtle (syntactic vs semantic) category mistakes. The paper supports these claims with examples. Our proposal has been implemented in a state-of-the-art abstract interpreter, and we briefly report on the changes that the revised view necessitated.
Gel edasi: Ethosomal gel using cow’s blood as a supportive of healing diabetic foot ulcer

Abstract Diabetes mellitus (DM) is a chronic carbohydrate metabolic disorder. Diabetic foot injury is one of the chronic complications of people with diabetes mellitus because it can cause disability and even cause death. Cow blood waste is a waste that can disturb the environment. Components of the chemical elements contained in cow blood include nitrogen, phosphorus, potassium, organic carbon, erythrocytes and leukocytes which function in the body’s defense by fighting infections so that cow blood waste is one alternative solution in the treatment of diabetic feet at a price affordable. Ethosome gel is a gel preparation that has the advantage of penetrating into the skin layer faster and significantly increasing the value of transdermal preparation flux. Based on the results of the study, the desired platelets from cow blood were obtained after sampling from the Makassar city slaughterhouse. The platelets obtained were then weighed as much as 0.5 g, 1 g, and 1.5 g to make a gel formula with a carbopol base of 1%. The gel that has been made is then evaluated like a homogeneous and clear gel physical examination. The pH obtained in formulas 1, 2 and 3 is 6.43; 7.00; and 6.80. The formula 1 has a viscosity of 47,342.6 cps and the formula 2 has a viscosity of 45,678.5 cps while formula 3 has viscosity 35,456.7 cps. In the stability test the results obtained were that the preparation did not experience changes in color and odor.