Reviewerly/exHarmony huggingface dataset:
User Authentication in Big Data

Managing security and privacy of big data is a serious concern for the data centers. In big data, data is collected from various sources, protecting privacy for such large amount of data raises issues related to authentication. User authentication and access to data from multiple locations need to be controlled and secured. In specific, mobile users and social network users share more and more personal data through their devices. There are many cryptography techniques or protocols, which are currently in use to solve the problem of authentication. This paper proposes an image-based password generation method for authentication of users in mobile data center. The proposed research uses concepts of soft dipole representation of the image to compute unique password for users and steganographic technique for transferring password securely to the users.
Fault Analysis of Wind Turbine Gearbox Based on Vague Set and Fault Tree

Aiming at the high failure rate of gearbox-main components of wind turbine and the limitation of single diagnosis method, this paper proposes a fault diagnosis method based on the generalized set of the fuzzy set-the vague set, which combines the vague set and the fault tree. It is introduced into the fault tree model. The triangular Vague set defined on [0], [1] is used to describe the normal, failure and abnormal behavior of bottom events. Based on the fault tree model of the vague set, the qualitative and quantitative analysis of system faults is made. The vague fuzzy operator is used to calculate the importance of each bottom event and judge the weak links of the system. Finally, a wind power gearbox fault example is simulated. The simulation results show that the proposed method is more flexible and practical than the fuzzy fault tree model.
Design of the Architecture of Computer-Aided Reading System for College English

With the development of the English teaching theories and computer technologies, there are many kinds of the English teaching software. At the same time, the research on these kinds of software is increasing. Most of the researches on these kinds of the software focuses on the English majors, while the research on the non-English majors’ learning software has only begun in recent years, and there are only a few. However, the number of the English learning of non-English majors is large, involving a wide range of disciplines and benefiting a lot. Therefore, it should be the focus of the urgent development and research of the English learning software.
An Advance on Variable Elimination with Applications to Tensor-Based Computation

We present new results on the classical algorithm of variable elimination, which underlies many algorithms including for probabilistic inference. The results relate to exploiting functional dependencies, allowing one to perform inference and learning efficiently on models that have very large treewidth. The highlight of the advance is that it works with standard (dense) factors, without the need for sparse factors or techniques based on knowledge compilation that are commonly utilized. This is significant as it permits a direct implementation of the improved variable elimination algorithm using tensors and their operations, leading to extremely efficient implementations especially when learning model parameters. Moreover, the proposed technique does not require knowledge of the specific functional dependencies, only that they exist, so can be used when learning these dependencies. We illustrate the efficacy of our proposed algorithm by compiling Bayesian network queries into tensor graphs and then learning their parameters from labeled data using a standard tool for tensor computation.
A Novel Image Steganography Using Multiple LSB Substitution and Pixel Randomization Using Stern-Brocot Sequence

Steganography is a technology of hiding confidential information within regularly used data files such as image, audio, video files. There have been several research works on the image steganography. Most of the works used only single LSB or multiple LSB of RGB components of each pixel. However, using only one LSB of the color components is an obstacle to convey much information. Again, if the pixels are not selected randomly, then the pixels used in steganography can be predicted very easily, thereby exposing the hidden information. In this paper, we have proposed a novel approach to inject information into an image in such a way that it is very difficult (almost impossible) for others (except sender and receiver) to detect and extract the hidden information within an image. We have encrypted our secret message before embedding it within an image. Pixels of the image have been selected in a randomized way using Stern-Brocot Sequence. Multiple LSBs of colors components (RGB) are used while inserting the encrypted message into the randomly selected pixels. We have implemented our proposed algorithm and showed our experimental results on the basis of PSNR and MSE for the stego images. Our results show that the quality of the images changes with different size of our secret messages and the quality of the stego image does not change significantly after applying our proposed algorithm. We have successfully extracted our secret messages from the stego images at the receiver side. Our proposed algorithm of image steganography can be useful for establishing a secret communication over a public communication using that public communication. As our proposed algorithm provides two step protection layer, it is quite impossible to extract the actual secret message from the image.
Nonlinear classifiers for ranking problems based on kernelized SVM.

Many classification problems focus on maximizing the performance only on the samples with the highest relevance instead of all samples. As an example, we can mention ranking problems, accuracy at the top or search engines where only the top few queries matter. In our previous work, we derived a general framework including several classes of these linear classification problems. In this paper, we extend the framework to nonlinear classifiers. Utilizing a similarity to SVM, we dualize the problems, add kernels and propose a componentwise dual ascent method. This allows us to perform one iteration in less than 20 milliseconds on relatively large datasets such as FashionMNIST.
Two-Stage Fully Convolutional Networks for Stroke Recovery of Handwritten Chinese Character

In this paper, we propose a method to recover strokes from offline handwritten Chinese characters. The proposed method employs a fully convolutional network (FCN) to estimate the writing order of connected components in offline Chinese character images and a multi-task FCN to estimate the writing order and directions of strokes in each connected component. Online dataset CASIA-OLHWDB1.0 from the CASIA database is hired as the training set. Because the network produces discontinuous strokes, we refine the estimated writing orders using a graph cut (GC), in which the estimated directions are used for calculation of smoothness term. Experimental results with test dataset of CASIA-OLHWDB1.0tst demonstrate the effectiveness of our method.
GDPR Compliance: Proposed Technical and Organizational Measures for Cloud Providers

The process of GDPR compliance for cloud computing environments may turn out to be a demanding process in terms of the technical, organizational and procedural measures that should be adopted. This paper identifies the requirements and the appropriate countermeasures for GDPR compliance in cloud environments. Furthermore, it describes the necessary GDPR related roles and separates the requirements and measures in accordance to the cloud architecture (IAAS, PAAS, SAAS).
R2CNN: Recurrent Residual Convolutional Neural Network on FPGA

Over the past years, feed-forward convolutional neural networks (CNNs) have evolved from a simple feed-forward architecture to deep and residual (skip-connection) architectures, demonstrating increasingly higher object categorization accuracy and increasingly better explanatory power of both neural and behavioral responses. However, from the neuroscientist point of view, the relationship between such deep architectures and the ventral visual pathway is incomplete. For example, current state-of-the-art CNNs appear to be too complex (e.g., now over 100 layers for ResNet) compared with the relatively shallow cortical hierarchy (4-8 layers). We introduce new CNNs with shallow recurrent architectures and skip connections requiring fewer parameters. With higher accuracy for classification, we propose an architecture for recurrent residual convolutional neural network (R2CNN) on FPGA, which efficiently utilizes on-chip memory bandwidth. We propose an Output-Kernel- Input-Parallel (OKIP) convolution circuit for a recurrent residual convolution stage. We implement the inference hardware on a Xilinx ZCU104 evaluation board with high-level synthesis. Our R2CNN accelerator achieves top-5 accuracy of 90.08% on ImageNet bench- mark, which has higher accuracy than conventional FPGA implementations.
The Shape of Patterns Tells More

For image processing applications, an initial step is usually extracting features from the target image. Those features can be lines, curves, circles, circular arcs and other shapes. The Hough transform is a reliable and widely used method for straight line and circle detection, especially when the image is noisy. However, techniques of Hough transform for detecting lines and circles are different; when detecting circles it usually requires a three-dimensional parameter space while detecting straight lines only requires two. Higher dimensional parameter transforms suffer from high storage and computational requirements. However, in the two dimensional Hough transform space, straight lines and circles yield patterns with different shapes. By analysing the shape of patterns within the Hough transform space it is possible to reconstruct the circles in image space. This paper proposes a new circle detection method based on analysing the pattern shapes within a two-dimensional line Hough transform space. This method has been evaluated by a simulation of detecting multiple circles and a group of real-world images. From the evaluation our method shows ability for detecting multiple circles in an image with mild noise.
Hardware Transactional Memory as Anti-analysis Technique for Software Protectors

Software protectors aim to shield executable file against reversing and cracking, by implementing sophisticated mechanisms capable of hiding real binary code and by inserting, inside an executable file, pieces of control code created to reconstruct software to its original state only during execution. This process of hiding and restoring protected bytes is composed by complex code fragments which perform all essential operations, keeping in general a constant structure for all protected software. The goal of this paper is to enhance security of these types of software against crackers, using Hardware Transactional Memories (HTM) by exploiting their features in a way for which they were not designed for. The proposed methodology offers a good level of defence of the protector core part by implementing new specific checks which use hardware processor features, ensuring reliability and a good level of performance with respect to different software implementations and which can be inserted inside a just present chain of checks, enhancing whole programs protection.
Data Integrity Authentication Techniques in Cloud Computing: A Survey

Cloud computing is a visible or feasible solution to IT industry. It is famed among business owners because it removes the need for users to plan ahead of initiating business. It let the users free to start from scratch and increase their resources only when there is a growth in the business. Though, regardless of the truth that cloud computing offers vast opportunities to enterprises, users need to put their data over the remotely located cloud servers and lose their control over data after storing on cloud. Consequently, security and integrity of data that users put over the cloud are the foremost concern of cloud computing. Occasionally, cloud service provider (CSP) can tamper or delete the data which is not used by the users from a long time or engaged large memory space. So, there is a demand to verify the data from time to time, to maintain its integrity. Examining data to check the correctness of data is called data integrity. To conquer the problem of data integrity, various techniques are proposed underneath different systems and privacy models. This paper covers various data integrity proving techniques.
Cyber Security: Advice from the Front Line

Abstract Joseph Rose is a Senior Security Architect for a large financial institution. Johanna Hamilton AMBCS asks him about the challenges of cyber security for the decade and why humans pose more problems than quantum.
3D Scene Reconstruction of Vision Information for Mobile Robot Applications

Three dimensional scene reconstruction from stereo image pairs plays an important role in different engineering applications such as computer vision, computer graphics, medical diagnosis, animation and more particularly in mobile robot mapping. For 3D scene reconstruction triangulation method is considered as an efficient tool which relies on camera intrinsic/extrinsic calibration parameters and a disparity map. In practice, the image points in the left and right image plane cannot be measured with accuracy due to geometric distortion (for example lens distortion), dispersed intensity, etc. However, the projection rays do not intersect in 3D space and also may not intersect with the point in the 3D space. In this study, an optimised mid-point triangulation method is proposed to reduce an error in the reconstructed data. An iterative refinement process is introduced in this approach to improve accuracy in the depth measurement of a stereoscopic system. The performance of the method is validated by extensive experimentation and the obtained 3D reconstruction data demonstrates better accuracy than the conventional methods.
Early detection of cognitive impairment of elders using wearable sensors

Worldwide, the population of dementia patients is increasing rapidly. As per the findings of experts, most cases diagnosed with mild cognitive impairment (MCI) have the highest risk of developing Alzheimer's disease (AD). The objective of this chapter is to monitor and evaluate the activities of daily living (ADLs) of MCI patients using noninvasive wearable sensors. The proposed work assists in predicting cognitive decline in early stages and helps clinical experts to provide proper treatment to prevent its progression to further stages. The proposed work comprises two modules: the Activity Recognition (AR) module and the Abnormal Activity Detection (AAD) module. The data from a wearable sensor unit is processed using the AR module to detect ADLs of an MCI patient. The AR module uses ensemble machine learning techniques to detect an MCI patient's activities. Stochastic gradient boosting detects the ADL with an improved classification accuracy of 84.75%. The AAD module receives input from the AR module and executes the abnormal activity detection algorithm to identify whether the detected ADL is normal or abnormal.
Telehomecare System Design with Interface Web

Abstract – Introduced by the Ontario Telemedicine Network, the telehomecare program provides monitoring of patient parameters and training sessions. Telehomecare is a sub-field of telehealth that influences the delivery of health services to patients who use telecommunications technology and is often accelerated with long-distance patient distance in realtime. Telehomecare offers improved access to home care at lower costs and promises unique opportunities for home care patient empowerment and improved care outcomes. The telehomecare method that is designed is that there are various sensors that are used to find out biomedical data, the data is then converted by the sensor to a certain amount and sent to the process section, after processing the data is sent to the webserver with an intermediate wifi module that was previously connected to the internet, on the webserver the measured data can be observed through the website. The main purpose of making this telehomecare system is to make health monitoring tools that can be easily accessed through the website, used in patients who have conditions must be under intensive supervision at home. Data monitored is body temperature, pulse, body position using input from temperature sensors, pulsemeter sensors and accelerometer sensors. In this tool using microcontrollers, namely Arduino Uno and Esp 8266 W-iFi module (NodeMCU) which have been programmed to send sensors input data to the web server data sent to the web server will be displayed in a website interface. The database used as a webserver on this system is Firebase, because it is a realtime database. The results obtained from this study, for reading the desired health parameters can be read, were temperature sensor readings that reached 99.29%, and pulsemeter sensor readings which reached 99.178%.&#x0D; &#x0D; Keywords : Telehomecare, Website, Arduino Uno, NodeMCU, Webserver.
Robust Pedestrian Detection: Faster Deployments with Fusion of Models

Pedestrian detection has a wide range of real-world critical applications including security and management of emergency scenarios. In critical applications, detection recall and precision are both essential to ensure the correct detection of all pedestrians. The development and deployment of object detection vision-based models is a time-consuming task, depending on long training and fine-tuning processes to achieve top performance. We propose an alternative approach, based on a fusion of pre-trained off-the-shelf state-of-the-art object detection models, and exploit base model divergences to quickly deploy robust ensembles with improved performance. Our approach promotes model reuse and does not require additional learning algorithms, making it suitable for rapid deployments of critical systems. Experimental results, conducted on PASCAL VOC07 test dataset, reveal mean average precision (mAP) improvements over base detection models, regardless of the set of models selected. Improvements in mAP were observed starting from just two detection models and reached 3.53% for a fusion of four detection models, resulting in an absolute fusion mAP of 83.65%. Moreover, the hyperparameters of our ensemble model may be adjusted to set an appropriate tradeoff between precision and recall to fit different recall and precision application requirements.
Sparse Block Regression (SBR) for Big Data with Categorical Variables

Categorical variables are nominal variables that classify observations by groups. The treatment of categorical variables in regression is a well-studied yet vital problem, with the most popular solution to perform a one hot encoding. However, challenges arise if a categorical variable has millions of levels. It will cause the memory needed for the computation far exceeds the total available memory in a given computer system or even a computer cluster. Thus, it is fair to state that one hot encoding approach has its limitations when a categorical variable has a large number of levels. The common workaround is the sparse matrix approach because it requires much fewer resources to cache the dummy variables. However, existing sparse matrix approaches are still not sufficient to handle extreme cases when a categorical variable has millions of levels. For instance, the number of subnets in network traffic analyses can easily exceeds tens of millions. In this paper, we proposed an innovative approach called sparse block regression (SBR) to address this challenge. SBR constructs a sparse block matrix using sufficient statistics. The benefits include but not limited to: 1) overcome the memory barrier issue caused by one hot encoding, 2) obtain multiple models with a single scan of data stored in the secondary storage; and 3) update the models with simple matrix operations. The study compared proposed SBR against conventional sparse matrix approaches. The experiments proved that SBR can efficiently and accurately solve the regression problem with large category number. Compared to the sparse matrix approach, SBR saved 90% memory in size during the computation.
Walking Distance Perception in Jakarta MRT Station Area*

The interest of the community, especially MRT users to walk at the beginning or end of the journey to the MRT station is relatively low.The purpose of this study was to recognize the effect of changed preference attributes that affect the likelihood and distance of someone who will choose to walk compared to BRT and motorcycle taxi modes at the beginning or end of their journey.Data obtained using stated preference techniques and were analyzed by discrete choice analysis using a binomial logit model approach with 2 schemes, walking with BRT and walking with motorcycle taxi.The walking distance perception between men and women will choose to walk compared to others alternative mode when the distance to MRT stations are 629 meters and 593 meters respectively and the walking distance perception, when compared to the area between the office area and residence area are 689 meters and 547 meters. Kerwords: perception,
Goal Model Maintenance

Goal models, and in particular, those specified in the form of AND-OR graphs, are often specified without regard for the context in which these goal models are used. In this chapter, we offer techniques for making goal models context-sensitive. We do this in two ways. First, we provide a means for representing and analysing the collateral effects of achieving a goal. A collateral effect is a collection of state changes (in the world in which we wish to pursue the achievement of the goals specified in the goal model) which are not desired but are nonetheless necessary to achieve the goal(s) of interest. If the goal is to achieve a condition p and the only two options available to an organization to achieve (these are thus OR-refined subgoals) is via the sub-goal p AND q or the sub-goal p AND r, then q and r are collateral effects (or collateral post-conditions).
Learned Lessons from Implementing an Android Client for the Cloud Signature Consortium API

Advanced electronic signatures are the main security mechanism used for assuring authentication, integrity and non-repudiation of electronic documents. Digitization on a large scale requires secure and flexible electronic signature systems. In E.U., the use of remote qualified electronic signatures has considerably increased after the adoption of the Regulation (EU) No 910/2014 (“eIDAS”). Thanks to the new legislative measures, owning a physical device to create a qualified electronic signature in no longer mandatory, so the user experience has been considerably improved. However, the full potential of remote qualified electronic signatures has not been reached yet. Our work supports the adoption of the remote digital signature in various fields by implementing an Android application that can apply qualified electronic signatures. To assure interoperability, the client-server communication follows a standard protocol: the Cloud Signature Consortium API. The main advantage of our approach is that the Android application is able to sign using certificates issued by different Trust Service Providers. This paper will analyze the current situation and will present the main challenges encountered when designing and developing a digital signature application that uses remote qualified digital certificates as well as the learned lessons that could be of tremendous help for others activating in this field.