Reviewerly/exHarmony huggingface dataset:
An Analysis of Effectiveness of Black-Box Web Application Scanners in Detection of Stored SQL Injection and Stored XSS Vulnerabilities

Black-box web application scanners are used to detect vulnerabilities in the web application without any knowledge of the source code. Recent research had shown their poor performance in detecting stored Cross-Site Scripting (XSS) and stored SQL Injection (SQLI). The detection efficiency of four black-box scanners on two testbeds, Wackopicko and Custom testbed Scanit (obtained from [5]), have been analyzed in this paper. The analysis showed that the scanners need to be improved for better detection of multi-step stored XSS and stored SQLI. This study involves the interaction between the selected scanners and the web application to measure their efficiency of inserting proper attack vectors in appropriate fields. The results of this research paper indicate that there is not much difference in terms of performance between open-source and commercial black-box scanners used in this research. However, it may depend on the policies and trust issues of the companies using them according to their needs. Some of the possible recommendations are provided to improve the detection rate of stored SQLI and stored XSS vulnerabilities in this paper. The study concludes that the state-of-the-art of automated black-box web application scanners in 2020 needs to be improved to detect stored XSS and stored SQLI more effectively.
Value creation in emerging technologies through text mining: the case of blockchain

As technology progresses, organisations must understand where to direct their value-creating efforts to achieve or sustain competitive advantage. This is even more true in the case of emerging technologies, where innovative activities often focus on achieving a technology promise while overlooking a set of technological, operational, organisational and user-related problems that must be overcome before the technology can fulfil this promise. Through an innovative application of text-mining, this paper develops a practical methodology to identify a range of problems related to a technological field in an unsupervised manner, that may benefit firms, researchers and policymakers. We apply the methodology to the field of blockchain and compare it to traditional literature reviews.
Sensuous futures: re-thinking the concept of trust in design anthropology

(2021). Sensuous futures: re-thinking the concept of trust in design anthropology. The Senses and Society: Vol. 16, The Ethnographic Palimpsest: Excursions in Paul Stoller's Sensory Poetics, pp. 193-202.
Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network

Clinical decision support assisted by prediction models usually faces the challenges of limited clinical data and a lack of labels when the model is developed with data from a single medical institution. Accordingly, research on multicenter clinical collaborative networks, which can provide external medical data, has received increasing attention. With the increasing availability of machine learning techniques such as transfer learning, leveraging large-scale patient data from multiple hospitals to build data-driven predictive models with clinical application potential provides an alternative solution to address the problem of limited patient data. A multicenter hybrid semi-supervised transfer learning model (MHSTL) is proposed in this study on the basis of unified common data model to ensure multicenter data standardized representation. Then the hospital-specific features, along with the co-occurrence features across domains, are aligned through a representation learning architecture that is built based on deep neural networks and the newly proposed neural decision forest model. In this process, limited patient data from the target hospital, both labeled and unlabeled, are incorporated during the feature adaptation process, thereby contributing to better model performance. Without patient-level data sharing, the proposed model learning strategy which overcomes feature misalignment and distribution divergence, enables the multi-source transfer learning process in the case of insufficient and unlabeled patient data at target hospital. The effectiveness of the proposed transfer learning model was evaluated on a collaborative research network of colorectal cancer patients in the US and China. The results demonstrate that the proposed model can achieve much better performance for predicting target risk with limited resources on patient data than baseline models . Better discrimination and calibration ability are also observed when sufficient labeled data are not available in the target hospital for prognosis prediction tasks . Further exploratory experiments show that the proposed approach exhibits good model generalizability regardless of the data heterogeneity. With the help of the SHapley Additive exPlanations for model interpretation, the effectiveness of incorporating hospital-specific features in the transfer learning model is shown. In this study, the proposed method can develop prediction models from multiple source hospitals and exhibit good performance by leveraging cross-domain hospital-specific feature information, therefore enhancing the model prediction when applied to single medical institution with limited patient data.
Research on Text Classification Based on BERT-BiGRU Model

Abstract Text classification is a typical application of natural language processing. At present, the most commonly used text classification method is deep learning. Meanwhile there are many difficulties in natural language processing, such as metaphor expression, semantic diversity and grammatical specificity. To solve these problems, this paper proposes the structure of BERT-BiGRU model. First, use the BERT model instead of the traditional word2vec model to represent the word vector, the word representation is calculated according to the context information, and it can be adjusted according to the meaning of word while the context information is fused. Secondly BiGRU model is attached to the BERT model, BiGRU model can extract the text information features from both directions at the same time. Multiple sets of experiments were set up and compared with the model proposed in this paper, according to the final experimental results, using the proposed BERT-BiGRU model for text classification, the final accuracy, recall and F1 score were all above 0.9. It shows that BERT-BiGRU model has good performance in the Chinese text classification task.
Elderly fall detection using IoT and image processing

The falls due to accidents or loss of consciousness in older people can turn out to be fatal if the required assistance is not received with in time. The fear of falling puts constraints on the free movement of the elderly and forces them to always be accompanied by a caretaker. This paper deals with the problem to seek help as and when required rather than always being watched over. The key contribution of this paper belongs to its novelty to detect elderly fall and even to cross validate it(that increase the trust for the model). To find the fall, algorithm fallows 2-way approach. In first way the sensors collect all the data related to fall such as Net acceleration of body, Pitch and Roll, based upon these values various activity is identified. Secondly, to further increases the trust over the model (remove false positive scenarios), this paper has also used image processing. Here, the images of various finger sign made by elderly person are processed at the time of fall. Thus, our work in this paper (Algorithm for fall detection using IoT and Image Processing(AFDI2) works over all the possible conditions and gives 99% accuracy mostly in all the possible conditions whereas SVM model only able to reach an accuracy of 97%, 96%, 98% while walking, walking downstairs and walking upstairs.
optNet-50: An Optimized Residual Neural Network Architecture of Deep Learning for Driver's Distraction

Over the last few decades, human facial recognition has gained significant popularity in areas ranging from surveillance, tracking, and access control to more recent developments in advertising, disease diagnosis, assistance to people requiring special needs and drivers' distraction detection. This study proposes a modified deep learning neural network architecture using Openpose library for a two-category problem of distraction detection. Openpose library, detects the human face and draws 43 points on face skeleton, which is given to the deep network along with the input images. The proposed approach attains an accuracy of 98% on publicly available `State Farm Distracted Driver Detection' dataset and outperforms existing state-of-the-art deep Residual Network architectures including ResNet-50 and ResNet-101.
A Bayesian Regularized Approach to Exploratory Factor Analysis in One Step

This research proposes a one-step Bayesian regularized approach to exploratory factor analysis (EFA) with an unknown number of factors. The proposed Bayesian regularized exploratory factor analysis (BREFA) model builds on the idea of bi-level Bayesian sparse group selection and can produce exact zero estimates at both the factor and loading levels. It can distinguish true factors from spurious factors and provide estimations of model and tuning parameters simultaneously. In addition to achieving model simplicity at both the factor and item levels, the approach provides interval estimates that can be used for significance testing, making it capable of addressing both uncorrelated and correlated factors. The Bayesian hierarchical formulation is implemented using Markov chain Monte Carlo estimation with the multivariate spike and slab priors and posterior median estimator. Based on simulated and real data analysis, BREFA demonstrates clear advantages or flexibility compared with traditional and Bayesian EFA, in terms of factor extraction, parameter estimation, and model interpretation.
The role of human resource managers in the promotion of hotel sector as a brand in Jordanian hotel industry - a cross-sectional study

This study assesses the impact of human resource managers on the effectiveness of hotel branding with reference to Jordanian hotel industry. The study followed a cross-sectional design in which data was collected through a questionnaire-based survey. Subsequently, the collected data were analysed using descriptive and multiple regression analysis. Findings of the study revealed that job satisfaction of the employee, employee effectiveness, employee selection, and employee retention strongly and significantly impact the brand promotion and are the focus of interest of HR managers. The results concluded that effective HR policies and practices can ensure improved performance of the hotel sector.
Tampering Detection Approach of Arabic-Text Based on Contents Interrelationship

Text information depends primarily on natural languages processing. Improving the security and usability of text information shared through the public internet has therefore been the most demanding problem facing researchers.... | Find, read and cite all the research you need on Tech Science Press
Deniable-Based Privacy-Preserving Authentication Against Location Leakage in Edge Computing

Edge computing provides cloud services at the edge of the network for Internet of Things (IoT) devices. It aims to address low latency of the network and alleviates data processing of the cloud. This “cloud-edge-device” paradigm brings convenience as well as challenges for location-privacy protection of the IoT. In the edge computing environment, the fixed edge equipment supplies computing services for adjacent IoT devices. Therefore, edge computing suffers location leakage as the connection and authentication records imply the location of IoT devices. This article focuses on the location awareness in the edge computing environment. We adopt the “deniability” of authentication to prevent location leakage when IoT devices connect to the edge nodes. In our solution, an efficient deniable authentication based on a two-user ring signature is constructed. The robustness of authentication makes the fixed edge equipment accept the legal end devices. Besides, the deniability of authentication cannot convince any third party that the fact of this authentication occurred as communication transcript is no longer an evidence for this connection. Therefore, it handles the inherent location risk in edge computing. Compared to efficient deniable authentications, our protocol saves 10.728% and 14.696% computational cost, respectively.
Unified smart home resource access along with authentication using Blockchain technology

Since the last decade, Smart home systems achieved popularity due to their increase in comfort and improvement in quality life. Smart homes with the integration of Internet of Things devices by embedding intelligence into sensors and actuators, data, and services can be communicated efficiently and in a more secure manner. In a Smart home environment, there can be several users who will need to access services from various Smart home devices at any point in time either individually or concurrently. In an Internet of Things, it is possible to process and exchange data without human intervention. Therefore, because of this full autonomy, these entities need to recognize, authenticate entities and ensure the integrity of their exchanged data. In a conventional smart home system, every device has to authenticate separately using credentials. Such authentication is not required in a trusted environment. The same account can be shared across different devices and when shared, the authentication process can be reduced to a consent-based process. In this research, assuming that there are multiple smart devices connected to each other and to the cloud via the hub. Also, there can be multiple sets of users accessing the same set of smart devices and related services through different accounts. The problem with conventional authentication is no common authentication or authorization system to avoid redundant logins. In this regard, Blockchain has been considered for its potential to provide a framework for decentralized-based applications based on the concept of Smart Contracts. By combining the power of Blockchain technology and public-key cryptography it is possible to ensure secure authentication and identification of people and devices. This work avoids the duplicate authentication for accessing the same service and does the communication in a limited latency. Most of the important security goals such as authentication, authorization, confidentiality, and integrity are met.
Shape-Matching GAN++: Scale Controllable Dynamic Artistic Text Style Transfer

Dynamic artistic text style transfer aims to migrate the style in terms of both the appearance and motion patterns from a reference style video to the target text to create artistic text animation. Recent researches have improved the usability of transfer models by introducing texture control. However, it remains an important open challenge to investigate the control of the stylistic degree with respect to shape deformation. In this paper, we explore a new problem of dynamic artistic text style transfer with glyph stylistic degree control. The key idea is to build multi-scale glyph-style shape mappings through a novel bidirectional shape matching framework. Following this idea, we first introduce a scale-ware Shape-Matching GAN to learn such mappings to simultaneously model the style shape features at multiple scales and transfer them onto the target glyph. Furthermore, an advanced Shape-Matching GAN++ is proposed to animate a static text image based on the reference style video. Our Shape-Matching GAN++ characterizes the short-term consistency of motion patterns via shape matchings within consecutive frames, which are propagated to achieve effective long-term consistency. Experiments show that the proposed method outperforms previous state-of-the-arts both qualitatively and quantitatively, and generate high-quality and controllable artistic text.
Smart Education as Empowerment: Outlining Veteran Teachers’ Training to Promote Digital Migration

Within the enhancement of technology and its ongoing integration into formal education setting, learning environments have been challenged to operationalize and arrange systems that engage pedagogy and technology together. The nature of this ongoing transformation is closely related to the paradigms that reign in the twenty-first century, in a scenario of what is now called a Fourth Industrial Revolution. School, despite losing its monopoly on knowledge diffusion, still plays a central role in educating new generations, therefore, it holds key responsibility in addressing contemporary logics of learning, living and becoming a citizen. Amidst the course of change and the ultimate calls for innovation in education, we encounter veteran teachers, professionals with a long teaching history, whose challenges include becoming familiar with new devices in order to fulfil their work demands. In this article, we then explore how central veteran teachers are for the progression to a smarter education scenario, through debating a training carried out in Portugal with 38 teachers from pre-school and k-12, aimed at promoting their digital migration. Data strengthen ties regarding teachers' perceptions and attitudes in relation to technology and consequent resource on it as pedagogical tools. Also, the overall discussion of the training provides clues on how teacher-oriented actions might address their identity if meaningful output is desired, in order to support a real change of practice.
Shooting Labels: 3D Semantic Labeling by Virtual Reality

Fig. 1: Virtual Reality view of a 3D reconstruction from KITTI sequence.Left image: RGB visualization of the virtual world.Middle image: label palette and empty
An autonomic decision tree‐based and deadline‐constraint resource provisioning in cloud applications

Abstract Cloud computing provides a set of resources and services for customers on the Internet on demand and based on a pay as you go model. Cloud providers are looking to decrease costs and increase profits. Therefore, resource management and provisioning are very important for cloud providers. Automated scaling can be used to provide resources for user requests. Auto‐scaling can decrease the total operational costs for providers, although it does have its own cost and time overheads. In this paper, a new solution is presented for resource provisioning on multi‐layered cloud applications based on MAPE‐K loop. A weighted ensemble prediction model is proposed to estimate the resources utilization in each cloud layer. In addition, accuracy of the model and a regularization technique are used to regulate the weights of the models in the proposed hybrid prediction model. Furthermore, a decision tree‐based algorithm is presented to analyze status of the resources to make scaling decision. In addition, we propose a resource allocation algorithm that is based on Virtual Machine priority and request deadline in order to allocate requests on suitable resources. The experimental results indicate that the proposed algorithm has the best performance among its counterparts.
Zero-shot learning with self-supervision by shuffling semantic embeddings

Zero-shot learning and self-supervised learning have been widely studied due to the advantage of performing representation learning in a data shortage situation efficiently. However, few studies consider zero-shot learning using semantic embeddings (e.g., CNN features or attributes) and self-supervision simultaneously. The reason is that most zero-shot learning works employ vector-level semantic embeddings. However, most self-supervision studies only consider image-level domains, so a novel self-supervision method for vector-level CNN features is needed. We propose a simple way to shuffle semantic embeddings. Furthermore, we propose a method to enrich feature representation and improve zero-shot learning performance effectively. We show that our model outperforms current state-of-the-art methods on the large-scale ImageNet 21K and the small-scale CUB and SUN datasets.
Class center-based firefly algorithm for handling missing data

Abstract A significant advancement that occurs during the data cleaning stage is estimating missing data. Studies have shown that improper data handling leads to inaccurate analysis. Furthermore, most studies indicate the occurrence of missing data irrespective of the correlation between attributes. However, an adaptive search procedure helps to determine the estimates of the missing data when correlations between attributes are considered in the process. Firefly Algorithm (FA) implements an adaptive search procedure in the imputation of the missing data by determining the estimated value closest to others' value. Therefore, this study proposes a class center-based adaptive approach model for retrieving missing data by considering the attribute correlation in the imputation process (C3-FA). The result showed that the class center-based firefly algorithm (FA) is an efficient technique for obtaining the actual value in handling missing data with the Pearson correlation coefficient ( r ) and root mean squared error (RMSE) close to 1 and 0, respectively. In addition, the proposed method has the ability to maintain the true distribution of data values. This is indicated by the Kolmogorov–Smirnov test, which stated that the value of D KS for most attributes in the dataset is generally closer to 0. Furthermore, the accuracy evaluation results using three classifiers showed that the proposed method produces good accuracy.
Alphabetical Gesture Recognition of American Sign Language using E-Voice Smart Glove

E-Voice a Smart Glove designed to be an interpreter between deaf-mute individuals and the normal public. Gesture translation into speech and textural form is carried out through this device. The smart glove is embedded with modern and technologically advanced sensors to make the overall prototype lightweight and easy to carry. Gesture translation is performed by idealizing the standard ASL template. Speaking disability of mute person is removed through E Voice glove by translating gestures into speech and display form. The main features of the prototype include a gesture recognizer that is portable and reduces the necessity of learning the sign language gestures for normal people. The objectivity of developing an SL prototype is to remove social racism between deaf-mute and normal communities with the involvement of a smart gesture translation glove.
Algorithm-Aware Neural Network Based Image Compression for High-Speed Imaging

In wearable AR/VR systems, data transmission between cameras and central processors can account for a significant portion of total system power, particularly in high framerate applications. Thus, it becomes necessary to compress video streams to reduce the cost of data transmission. In this paper we present a CNN-based compression scheme for such vision systems. We demonstrate that, unlike conventional compression techniques, our method can be tuned for specific machine vision applications. This enables increased compression for a given application performance target. We present results for Detectron2 Keypoint Detection and compare the performance and computational complexity of our method to existing compression schemes, such as H.264. We created a new high-framerate dataset which represents common scenarios for wearable AR/VR devices.