Reviewerly/exHarmony huggingface dataset:
Smartwatch Dynamics: A Novel Modality and Solution to Attacks on Cyber-behavioral Biometrics for Continuous Verification?

The cyber-behavioral biometric modalities such as keystroke dynamics, mouse dynamics, and touch screen dynamics have come under attacks of different forms in recent days. To address these attacks and other security issues, we present a novel concept of using smartwatch sensor data to continuously verify users in cyberspace and show its potential to be a new standalone cyber-behavioral biometric modality. For our experiments, smartwatch gyroscope and accelerometer data collected from 49 subjects while typing in desktop computer have been considered. We implemented six pattern matching classifiers to compare each verification attempt against the user profile. Experimental results comprising of 282,240 classification attempts show significantly high True Positive (TP) rates and extremely low False Positive (FP) rates with the highest achieved TP rate of 87.2% and lowest FP rate of 0.2%. With this level of accuracy and natural resiliency to attacks comes with physical biometric property as such in hand movement, we opine that smartwatch movement dynamics, besides being a new biometric trait, can be a solution to the security loopholes in existing cyber-behavioral biometric modalities for continuous verification.
A User Study on AR-assisted Industrial Assembly

The utilization of modern assistance system e.g. Augmented Reality (AR) has reached into industrial assembly scenarios. Beside the technical realization of AR assistance in the assembly scene the worker has to accept the new technology. Only both, user acceptance and technical user-interface design leads to an optimized overall system. Hence, this contribution gives a brief literature overview and analysis about AR acceptance and acceptance modeling. Then, a proprietary model for acceptance measurement is developed, which includes and synthesizes previous models (TAM and UTAUT) and simplifies them considerably for the purpose of industrial assembly. Following, a laboratory experiment is set-up in the FHWS c-Factory, which is a smart, IoT-based production environment. A survey and an assembly cycle time measurement is conducted to collect data to characterize AR assistance. The study participants assemble a toy truck once without and once with AR support. The evaluation shows that the mean assembly time decreases. The results show also, that AR is accepted by the participants supporting their work.
Classification of Benign or Malignant Tumor Using Machine Learning

Abstract The abnormal growth of the cell in the human body may leads to tumor. There are almost 100 types of cancer will affect the different parts of the human. The affected human may have symptoms like lump, abnormal bleeding, prolonged cough, weight loss etc. And depends on the part where the tumor formed. Breast cancer is one of the types among the 100 types of cancer and it is most commonly found on the female than the male. Breast cancer is a disease that caused due to the over growth of the cell in the breast area and they started to form lump over the breast. The people who are suffering from the breast cancer will have many emotional side effects and they are supped to undergo lot of pain in their day to day life. The most important problem for the health care people is earlier prediction. So, it can be rectified by employing machine learning algorithms in the prediction stage. In this paper the classification algorithms are used to classify whether the tumor is benign or malignant. The supervised learning algorithms of machine learning such as logistic regression, Support vector machine and K Nearest neighbour algorithm are usually used to analyse the tumour detection. Stacking ensemble method used in order to combine the entire three algorithms is proposed and the performance of algorithm is compared with the logistic regression, Support vector machine and K Nearest neighbour algorithm in order to get an efficient model for the classification.
Real-time method for traffic sign detection and recognition based on YOLOv3-tiny with multiscale feature extraction

As a part of Intelligent Transportation System (ITS), the vehicle traffic sign detection and recognition system have been paid more attention by Intelligent transportation researchers, the traffic sign detection and recognition algorithm based on convolution neural network has great advantages in expansibility and robustness, but it still has great optimization space inaccuracy, computation and storage space. In this paper, we design a multiscale feature fusion algorithm for traffic sign detection and recognition. In order to improve the accuracy of the network, the gaussian distribution characteristics are used in the loss function. The training and analysis of two neural networks with different feature scales and YOLOv3-tiny were carried out on the Tsinghua-Tencent open traffic sign dataset. The experimental results show that the detection and recognition of the targets by networks with multiple feature scales have improved significantly, and the recall and accuracy are 95.32% and 93.13% respectively. Finally, the algorithm of traffic sign detection and recognition is verified on the NVIDIA Jetson Tx2 platform and delivers 28 fps outstanding performances.
WITHDRAWN: An invisible based watermarking technique for biometric image authentication

Biometric image authentication is an important and challenging area of research. It involves an analysis of the candidate's biometrics for security purposes. Exposure to such sensitive information can be a threat to an individual's identity and system security. In this paper, we have proposed an invisible watermarking scheme to protect biometric information from harmful attacks. Initially, the technique segments the host image into blocks using edge entropy and then selects the appropriate band coefficients using 2D-DWT transformation. The adaptive histogram technique is used to embed the watermarking image properly. Finally, to retrieve the original watermark, a reversible data hiding approach (RDH) and visual cryptography technique (VC) is used that provides safety and security to biometric images. The proposed method was tested on two datasets, namely, CASIA-v5 and UBIRIS-v1 and is evaluated on various measuring metrics such as PSNR, NC, and BER. Further, the proposed technique is analyzed and compared with different traditionally implemented watermarking techniques based on PSNR and NC values. Results show that the presented method outperforms the other methods by achieving best in class results and highest PSNR (62.52, 61.25) and NC values (0.95, 0.94).
A gradient‐type iterative method for impulse noise removal

Abstract Image denoising is a typical inverse problem and is hard to be solved. Fortunately, a powerful two‐phase method for restoring images corrupted by high‐level impulse noise has been proposed. The key point of the method is the computational efficiency of the second phase which requires the minimization of a smooth objective function defined on the terms of edge‐preserving potential function. In this paper, we propose an effective three‐term conjugate gradient method to restore the corrupted images in the second phase of two‐phase method. An attractive feature of the proposed method is that the search direction satisfies the sufficient descent property at each iteration without any line search. The global convergence is established for general smooth functions under the Armijo‐type line search. Preliminary numerical results are reported to indicate that the proposed method to be used for impulse noise removal is promising.
Integrating Text Embedding with Traditional NLP Features for Clinical Relation Extraction

Recently, text embedding techniques such as Word2Vec and BERT have produced state-of-the-art results in a wide variety of NLP tasks.As a result, traditional NLP features frequently used in Information Extraction (IE) such as POS tags, dependency relations and semantic types have received less attention.In this paper, we investigate whether traditional NLP features can be combined with word and sentence embeddings to improve relation extraction.We have explored diverse feature sets and different neural network architectures and evaluated our models on a benchmark clinical text dataset.Our new models significantly outperformed all the baselines on the same dataset.
Phased-Guard: Multi-Phase Machine Learning Framework for Detection and Identification of Zero-Day Microarchitectural Side-Channel Attacks

Microarchitectural Side-Channel Attacks (SCAs) have emerged recently to compromise the security of computer systems by exploiting the existing processors' hardware vulnerabilities. In order to detect such attacks, prior studies have proposed the deployment of low-level features captured from built-in Hardware Performance Counter (HPC) registers in modern microprocessors to implement accurate Machine Learning (ML)-based SCAs detectors. Though effective, such attack detection techniques have mainly focused on binary classification models offering limited insights on identifying the type of attacks. In addition, while existing SCAs detectors required prior knowledge of attacks applications to detect the pattern of side-channel attacks using a variety of microarchitectural features, detecting unknown (zero-day) SCAs at run-time using the available HPCs remains a major challenge. In response, in this work we first identify the most important HPC features for SCA detection using an effective feature reduction method. Next, we propose Phased-Guard, a two-level machine learning-based framework to accurately detect and classify both known and unknown attacks at run-time using the most prominent low-level features. In the first level (SCA Detection), Phased-Guard using a binary classification model detects the existence of SCAs on the target system by determining the critical scenarios including system under attack and system under no attack. In the second level (SCA Identification) to further enhance the security against side-channel attacks, Phased-Guard deploys a multiclass classification model to identify the type of SCA applications. The experimental results indicate that Phased-Guard by monitoring only the victim applications' microarchitectural HPCs data, achieves up to 98 % attack detection accuracy and 99.5% SCA identification accuracy significantly outperforming the state-of-the-art solutions by up to 82 % in zero-day attack detection at the cost of only 4% performance overhead for monitoring.
Vanishing point detection with direct and transposed fast Hough transform inside the neural network

In this paper, we suggest a new neural network architecture for vanishing point detection in images. The key element is the use of the direct and transposed Fast Hough Transforms separated by convolutional layer blocks with standard activation functions. It allows us to get the answer in the coordinates of the input image at the output of the network and thus to calculate the coordinates of the vanishing point by simply selecting the maximum. Besides, it was proved that calculation of the transposed Fast Hough Transform can be performed using the direct one. The use of integral operators enables the neural network to rely on global rectilinear features in the image, and so it is ideal for detecting vanishing points. To demonstrate the effectiveness of the proposed architecture, we use a set of images from a DVR and show its superiority over existing methods. Note, in addition, that the proposed neural network architecture essentially repeats the process of direct and back projection used, for example, in computed tomography.
An Approach of Crossover Service Goal Convergence and Conflicts Resolution

With the increasingly complex requirements of users in modern society, it is difficult to meet the complex goals of users by services from a single domain, and thus crossover service convergence, which aims to provide deep service integration and innovation across boundaries of industries, organizations, and value chains becomes an urgent need. Goal convergence is the first step to guide the process of crossover service convergence. However, there is currently no systematic theory and method to address this problem. In this paper, we propose a method to support goal convergence of crossover services. Firstly, two goal models from different domains are converged according to new user requirements. We then detect inconsistencies of goal models and resolve potential conflicts. We have developed a goal model convergence tool to help requirements analysts perform goal convergence in a visualized way. A case study is presented to show the feasibility of the proposed method.
Conceptual Software Engineering Applied to Movie Scripts and Stories

According to researchers, a large proportion of research software (software on which researchers rely) is fragile and the source of numerous problems that plague computational science. This study introduces another application of software engineering tools, conceptual modeling, which can be applied to other fields of research. One way to strengthen the relationship between software engineering and other fields is to develop a good way to perform conceptual modeling that is capable of addressing the peculiarities of these fields of study. This study concentrates on humanities and social sciences, which are usually considered “softer” and further away from abstractions and (abstract) machines. Specifically, we focus on conceptual modeling as a software engineering tool (e.g., UML) in the area of stories and movie scripts. Researchers in the humanities and social sciences might not use the same degree of formalization that engineers do, but they still find conceptual modeling useful. Current modeling techniques (e.g., UML) fail in this task because they are geared toward the creation of software systems. Similar Conceptual Modeling Language (e.g., ConML) has been proposed with the humanities and social sciences in mind and, as claimed, can be used to model “anything.” This study is a venture in this direction, where a software modeling technique, Thinging Machine (TM), is applied to movie scripts and stories. The paper presents a novel approach to developing diagrammatic static/dynamic models of movie scripts and stories. The TM model diagram serves as a neutral and independent representation for narrative discourse and can be used as a communication instrument among participants. It is based on the notion of thing/machine (or thimac). Things and events are defined as what can be created, released, transferred, received, accepted and processed. Machines are what create, process, release, transfer and receive things. The examples presented include examples from Propp’s model of fairytales; the railway children and an actual movie script seem to point to the viability of the approach
A Color Image Authentication Scheme With Grayscale Invariance

Color image authentication is a method that allows the detection of tampered regions on images. Existing related works evaluated their performance based on visual quality and detection capability of the marked image, but the issue of grayscale invariance was ignored. However, many applications in image processing required the color image to first convert into a grayscale image before any further post-processing such as edge detection, color masking in Photoshop, and the display of e-ink. If the resulting image and the original image are different in their grayscale value, they might produce different outcomes after the post-processing. Therefore, how to maintain unchanged of the grayscale value has become an important issue. This paper presents a grayscale-invariance color image authentication technique. We proposed to embed the authentication code into two of three primary color channels and adjust the remaining one to remedy the distortion of grayscale value. The experimental results showed that, when embedding each four-bit authentication code into two color channels, the visual quality of the marked images has achieved an average 33.26 dB of peak signal-to-noise ratio (PSNR) while providing a satisfactory detectability.
MYSTIKO: Cloud-Mediated, Private, Federated Gradient Descent

Federated learning enables multiple, distributed participants (potentially on different clouds) to collaborate and train machine/deep learning models by sharing parameters/gradients. However, sharing gradients, instead of centralizing data, may not be as private as one would expect. Reverse engineering attacks [1], [2] on plaintext gradients have been demonstrated to be practically feasible. Existing solutions for differentially private federated learning, while promising, lead to less accurate models and require nontrivial hyperparameter tuning. In this paper, we examine the use of additive homomorphic encryption (specifically the Paillier cipher) to design secure federated gradient descent techniques that (i) do not require addition of statistical noise or hyperparameter tuning, (ii) does not alter the accuracy or utility of the final model, (iii) ensure that the plaintext model parameters/gradients of a participant are never revealed to any other participant or third party coordinator involved in the federated learning job, (iv) minimize the trust placed in any third party coordinator and (v) are efficient, with minimal overhead, and cost effective.
Graph interpolating activation improves both natural and robust accuracies in data-efficient deep learning

Improving the accuracy and robustness of deep neural nets (DNNs) and adapting them to small training data are primary tasks in deep learning (DL) research. In this paper, we replace the output activation function of DNNs, typically the data-agnostic softmax function, with a graph Laplacian-based high-dimensional interpolating function which, in the continuum limit, converges to the solution of a Laplace–Beltrami equation on a high-dimensional manifold. Furthermore, we propose end-to-end training and testing algorithms for this new architecture. The proposed DNN with graph interpolating activation integrates the advantages of both deep learning and manifold learning. Compared to the conventional DNNs with the softmax function as output activation, the new framework demonstrates the following major advantages: First, it is better applicable to data-efficient learning in which we train high capacity DNNs without using a large number of training data. Second, it remarkably improves both natural accuracy on the clean images and robust accuracy on the adversarial images crafted by both white-box and black-box adversarial attacks. Third, it is a natural choice for semi-supervised learning. This paper is a significant extension of our earlier work published in NeurIPS, 2018. For reproducibility, the code is available at https://github.com/BaoWangMath/DNN-DataDependentActivation .
Adversarial Signal Denoising with Encoder-Decoder Networks

The presence of noise is common in signal processing regardless the signal type. Deep neural networks have shown good performance in noise removal, especially on the image domain. In this work, we consider deep neural networks as a denoising tool where our focus is on one dimensional signals. We introduce an encoder-decoder architecture to denoise signals, represented by a sequence of measurements. Instead of relying only on the standard reconstruction error to train the encoder-decoder network, we treat the task of denoising as distribution alignment between the clean and noisy signals. Then, we propose an adversarial learning formulation where the goal is to align the clean and noisy signal latent representation given that both signals pass through the encoder. In our approach, the discriminator has the role of detecting whether the latent representation comes from clean or noisy signals. We evaluate on electrocardiogram and motion signal denoising; and show better performance than learning-based and non-learning approaches.
Evaluating usefulness, ease of use and usability of an UML-based Software Product Line Tool

Software Product Line (SPL) is a software development approach that systematically applies reuse of artifacts in a specific domain. In the last years, the industry has increasingly required the support of tools for most SPL life cycle activities, targeting feature models and related diagrams, variability management and SPL specific products configuration. However, existing literature does not present any tools with native support to UML-based SPLs. In addition, relying on manipulating XMI files for general-purpose UML tools for such SPLs takes significant effort, and it is time-consuming and error-prone. In this scenario, we developed SMartyModeling, with support to UML stereotype-based variability management. To evolve our tool, we evaluated it throughout a survey answered by 37 participants. We adopted questions from the Technology Acceptance Model (TAM) and the System Usability Scale (SUS). We organized it into three sections of Likert-scaled questions for usefulness, ease of use, and usability. A last section consisted of open questions focused on positive and negative aspects and an overview of the evalaution. SMartyModeling was well evaluated in relation to usefulness, ease of use, and usability. We analyzed and interpreted the respondents quotes using correlation techniques and open and axial coding. The analysis of open questions allowed us a direct identification of points to improve the tool, its limitations and positive aspects.
COVID-19’s impact on digitalization of education: incorporating visual vocabulary learning application to foster vocabulary knowledge

Purpose COVID-19’s attack on the system of education left the learning of many students in limbo. For minimizing this effect, the current study sought to probe how Mobile-Assisted Language Learning (MALL) can facilitate English as a Foreign Language (EFL) learners’ vocabulary knowledge. In so doing, a quasi-experimental pretest-posttest research design was adopted to check the effect of a newly developed application (i.e. visual vocabulary learning, hereafter VVL) in an EFL classroom. Design/methodology/approach To complying with the objective, a number of intermediate students ( N = 40) were considered to participate in the study. They were assigned into a visual learning group (VLG) and a nonvisual learning group (NVLG) comprising 20 students in each. The VLG was directed to learn vocabulary by a VVL, and the NVG, a conventional method was employed for vocabulary instruction. An independent samples t -test was run to screen the efficacy of the intervention. Findings The finding revealed that participants in the VLG outperformed significantly in the posttest. The finding is beneficial for EFL teachers, syllabus designers, and material developers to take advantage of mobile applications in teaching vocabulary. Originality/value This study was conducted during COVID 19 between two groups in Iran.
Unequal Dimension Track-to-Track Fusion Approaches Using Covariance Intersection

The fusion of tracks with different state spaces is referred to as heterogeneous track-to-track fusion (HT2TF). In this paper, we present novel approaches for HT2TF using Covariance Intersection (CI). The underlying idea is to augment the low dimensional track. We investigate whether the augmentation approaches proposed for mode mixing in the Interacting Multiple Model (IMM) algorithm can also be employed for the CI. As the augmentation influences the CI optimization, we compare different optimization variants. Finally, we evaluate the presented approaches for collective perception.