Reviewerly/exHarmony huggingface dataset:
Fog-to-MultiCloud Cooperative Ehealth Data Management With Application-Aware Secure Deduplication

The healthcare industry faces challenges regarding the security and efficiency of data management for patient health records (PHRs). We propose SafePHR: a secure and efficient medical data service with application aware deduplication in fog-to-multicloud encrypted storage. It introduces a fog-to-multicloud cooperative storage model, which combines low-latency and high-safety local fog with unlimited capacity and built-in disaster recovery in remote multi-cloud to enhance eHealth data management. It also provides an application aware secure deduplication scheme to improve the data traffic and space efficiency of cloud storage for encrypted PHRs using variants of convergent encryption. Then, it further enhances the resiliency and security of cloud storage by striping encrypted data across multiple cloud vendors with a fault-tolerant coding scheme. Compared with the state-of-the-art of cloud-assisted eHealth systems, our experiments demonstrate that SafePHR can ensure data confidentiality of deduplication, achieve a competitive low cloud storage space overhead, and improve system performance with high fault-tolerance capability.
Unsupervised anomaly detection ensembles using item response theory

Ensemble learning combines many algorithms or models to obtain better predictive performance. Ensembles have produced the winning algorithm in competitions such as the Netflix Prize. They are used in climate modeling and relied upon to make daily forecasts. Constructing an ensemble from a heterogeneous set of unsupervised anomaly detection methods presents challenges because the class labels or the ground truth is unknown. Thus, traditional ensemble techniques that use the class labels cannot be used for this task. We use Item Response Theory (IRT) – a class of models used in educational psychometrics – to construct an unsupervised anomaly detection ensemble. IRT’s latent trait computation lends itself to anomaly detection because the latent trait can be used to uncover the hidden ground truth. Using a novel IRT mapping to the anomaly detection problem, we construct an ensemble that can downplay noisy, non-discriminatory methods and accentuate sharper methods. We demonstrate the effectiveness of the IRT ensemble using two real data repositories and show that it outperforms other ensemble techniques. We find that the IRT ensemble performs well even if the set of anomaly detection methods have low correlation values.
Quality of Healthcare Services in Focus: The Role of Knowledge Transfer, Hierarchical Organizational Structure and Trust

This paper explores the role of knowledge transfer, hierarchical organisational structure, and trust as important organisational factors that influence knowledge management practices on the quality of healthcare services.Survey data from 151 healthcare employees collected in Montenegro in 2019 was used.Applying hierarchical linear regression, we found a significant and positive relationship between knowledge transfer and quality of healthcare services, and a significant and negative relationship between hierarchical organisational structure and quality of healthcare services.Trust acts as a moderator in the relationship between knowledge transfer and quality of healthcare services and between hierarchical organisational structure and quality of healthcare services.We provide empirical support for the influence of knowledge transfer, hierarchical organisational structure, and trust on the quality of healthcare services provided by Montenegrin healthcare employees.Our results offer theoretical and practical implications derived from this research study.
Multi-User Collusion-Resistant Searchable Encryption with Optimal Search Time

The continued development of cloud computing requires technologies that protect users' data privacy even from the cloud providers themselves. Multi-user searchable encryption is one such kind of technology. It allows a data owner to selectively enable users to perform keyword searches over her encrypted documents that are stored at a cloud server. For privacy purposes, it is important to limit what an adversarial server can infer about the encrypted documents, even if it colludes with some of the users. Clearly, in this case it can learn the content of documents shared with this subset of "corrupted" users, however, it is important to ensure that this collusion does not reveal information about parts of the dataset that are only shared with the remaining "uncorrupted" users via cross-user leakage. In this work, we propose three novel multi-user searchable encryption schemes for this setting that achieve different trade-offs between performance and leakage. Compared to previous ones, our first two schemes are the first to achieve asymptotically optimal search time. Our third scheme achieves minimal user storage and forward privacy with respect to document sharing, but slightly slower search performance. We formally prove the security of our schemes under reasonable assumptions. Moreover, we implement and evaluate their performance both on a single machine and over WAN. Our experimental results are encouraging, e.g., the search computation time is in the order of a few milliseconds.
Indifferent or Enthusiastic? Virtual Audiences Animation and Perception in Virtual Reality

In this paper, we present a virtual audience simulation system for Virtual Reality (VR). The system implements an audience perception model controlling the nonverbal behaviors of virtual spectators, such as facial expressions or postures. Groups of virtual spectators are animated by a set of nonverbal behavior rules representing a particular audience attitude (e.g., indifferent or enthusiastic). Each rule specifies a nonverbal behavior category: posture , head movement , facial expression and gaze direction as well as three parameters: type , frequency and proportion . In a first user-study, we asked participants to pretend to be a speaker in VR and then create sets of nonverbal behaviour parameters to simulate different attitudes. Participants manipulated the nonverbal behaviours of single virtual spectator to match a specific levels of engagement and opinion toward them. In a second user-study, we used these parameters to design different types of virtual audiences with our nonverbal behavior rules and evaluated their perceptions. Our results demonstrate our system’s ability to create virtual audiences with three types of different perceived attitudes: indifferent , critical , enthusiastic . The analysis of the results also lead to a set of recommendations and guidelines regarding attitudes and expressions for future design of audiences for VR therapy and training applications.
Unsupervised Graph Alignment with Wasserstein Distance Discriminator

Graph alignment aims to identify node correspondence across multiple graphs, with significant implications in various domains. As supervision information is often not available, unsupervised methods have attracted a surge of research interest recently. Most of existing unsupervised methods assume that corresponding nodes should have similar local structure, which, however, often does not hold. Meanwhile, rich node attributes are often available and have shown to be effective in alleviating the above local topology inconsistency issue. Motivated by the success of graph convolution networks (GCNs) in fusing network and node attributes for various learning tasks, we aim to tackle the graph alignment problem on the basis of GCNs. However, directly grafting GCNs to graph alignment is often infeasible due to multi-faceted challenges. To bridge the gap, we propose a novel unsupervised graph alignment framework WAlign. We first develop a lightweight GCN architecture to capture both local and global graph patterns and their inherent correlations with node attributes. Then we prove that in the embedding space, obtaining optimal alignment results is equivalent to minimizing the Wasserstein distance between embeddings of nodes from different graphs. Towards this, we propose a novel Wasserstein distance discriminator to identify candidate node correspondence pairs for updating node embeddings. The whole process acts like a two-player game, and in the end, we obtain discriminative embeddings that are suitable for the alignment task. Extensive experiments on both synthetic and real-world datasets validate the effectiveness and efficiency of the proposed framework WAlign.
Attention-guided image captioning with adaptive global and local feature fusion

Although attention mechanisms are exploited widely in encoder-decoder neural network-based image captioning framework, the relation between the selection of salient image regions and the supervision of spatial information on local and global representation learning was overlooked, thereby degrading captioning performance. Consequently, we propose an image captioning scheme based on adaptive spatial information attention (ASIA), extracting a sequence of spatial information of salient objects in a local image region or an entire image. Specifically, in the encoding stage, we extract the object-level visual features of salient objects and their spatial bounding-box. We obtain the global feature maps of an entire image, which are fused with local features and the fused features are fed into the LSTM-based language decoder. In the decoding stage, our adaptive attention mechanism dynamically selects the corresponding image regions specified by an image description. Extensive experiments conducted on two datasets demonstrate the effectiveness of the proposed method.
Vehicle Identification from Traffic Video Surveillance Using YOLOv4

Traffic Video Surveillance has emerged as an important tool in our lives. The ability to detect & follow vehicles, both individual & public transport ones, is extremely useful in numerous applications such as the safety of the drivers, preventing & monitoring road accidents etc. Using continuous video stream from CCTV cameras from all over the world, this project deals with the concept of Vehicle Detection with the support of Computer Vision algorithm in real-time frame. The proposed framework capitalizes on YOLOv4 to achieve faster object detection in real time and using the proposed dataset it was tested on various conditions such as rain, low visibility, daylight, snow, and night. Pre-Processing steps of the dataset is done in the first phase. After the first phase, Various types of Vehicle images are captured. In the final phase, depending on the properties computed or calculated in the earlier phases, the Precision, Mean Average Precision (map) and Average Intersection Over Union (IoU) of the model with which it can recognize the various types of Vehicles were found. This model can further help in the development of a framework for Vehicular accident detection in real time.
Structure-Preserving and Color-Restoring Up-Sampling for Single Low-Light Image

Single image super-resolution (SR), as a basic computer vision task, has been widely studied. However, existing single image SR methods, applied in images with normal light, perform poorly on low-light images. To address this limitation, a method of structure-preserving and color-restoring up-sampling for single low-light image is proposed. Theoretical and experimental analysis indicates that existing methods suffer negative effects due to the suppressed color and the weakened texture in low-light image. Therefore, combined with the Retinex theory, the single low-light image up-sampling model is established for the first time, which avoids the conflict between color and texture by distinguishing reflectance and illumination. Further, we develop a structure-preserving and color-restoring up-sampling network for single low-light image SR. In the network, the reflectance and illumination components are obtained by decomposing the observed image, and then up-sampling of reflectance and enhancement of illumination are performed to complete the primary SR. In addition, the gradient information is reconstructed and fused into the up-sampling process to further enrich the SR texture. Experiments demonstrate that our method obtains competitive qualitative and quantitative evaluation on the produced dataset and real-world images.
OntoReq: An Ontology Focused Collective Knowledge Approach for Requirement Traceability Modelling

There is need for software-based applications in every domain and software developers offer high customizability to their client; it is very much important to deliver a defect free product that matches all the specification of the client and must pass all the testcases. So, to do this systematically, a requirement traceability matrix is used. Requirement traceability matrix is set of documents that consist of all the requirements and the test cases and the track of relation between the customer’s requirements for the system. Manually creating a Requirement traceability matrix is a very time-consuming job and are always prone to errors. There is a lot of methods that are proposed in order to create a efficient traceability matrix using Language processing and machine learning techniques but these approaches require lot of data and does not take care of the real world knowledge and lead to errors. This paper proposes a knowledge engineering-based architecture to this problem with the use of ontology, machine learning and optimization algorithm which produces a dependability and steadiness of 97% and 95% respectively and the performance of the proposed model is compared with baseline approaches.
SQL Injection: Classification and Prevention

With the world moving towards digitalization, more applications and servers are online hosted on the internet, more number of vulnerabilities came out which directly affects an individual and an organization financially and in terms of reputation too. Out of those many vulnerabilities such as Injection, Deserialization, Cross site scripting and more. Injection stand top as the most critical vulnerability found in the web application. Injection itself is a broad vulnerability as it further consists of SQL Injection, Command injection, LDAP Injection, No-SQL Injection etc. In this paper we have reviewed SQL Injection, different types of SQL injection attacks, their causes and remediation to comprehend this attack.
Mask wearing detection method based on SSD-Mask algorithm

With the spread of COVID-19 all over the world, under the background of normalized epidemic prevention and control, proper wearing and using of masks can effectively filter virus pathogen particles. In order to reduce the infection rate of ordinary people and improve the efficiency of monitoring people wearing masks in public places, this paper improves a method of face mask wearing detection in natural scenes. On the basis of SSD algorithm, SSD-Mask introduces a channel attention mechanism to improve the ability of the model to express salient features. At the same time, the information of different feature levels is fully utilized, and the loss function is optimized. The final experimental results show that the algorithm can effectively achieve the goal of face recognition and mask detection.
A family of projective two-weight linear codes

Projective two-weight linear codes are closely related to finite projective spaces and strongly regular graphs. In this paper, a family of q-ary two-weight linear codes including two subfamilies of projective codes is presented, where q is a prime power. The parameters of both the codes and their duals are excellent. As applications, the codes are used to derive strongly regular graphs with new parameters and secret sharing schemes with interesting access structures.
Design, fabrication and characterization of a micro-fabricated stacked-wafer segmented ion trap with two X-junctions

Abstract We describe the implementation of a three-dimensional Paul ion trap fabricated from a stack of precision-machined silica glass wafers, which incorporates a pair of junctions for two-dimensional ion transport. The trap has 142 dedicated electrodes which can be used to define multiple potential wells in which strings of ions can be held. By supplying time-varying potentials, this also allows for transport and re-configuration of ion strings. We describe the design, simulation, fabrication and packaging of the trap, including explorations of different parameter regimes and possible optimizations and design choices. We give results of initial testing of the trap, including measurements of heating rates and junction transport.
Hardware Implementation of a Takagi-Sugeno Neuro-Fuzzy System Optimized by a Population Algorithm

Abstract Over the last several decades, neuro-fuzzy systems (NFS) have been widely analyzed and described in the literature because of their many advantages. They can model the uncertainty characteristic of human reasoning and the possibility of a universal approximation. These properties allow, for example, for the implementation of nonlinear control and modeling systems of better quality than would be possible with the use of classical methods. However, according to the authors, the number of NFS applications deployed so far is not large enough. This is because the implementation of NFS on typical digital platforms, such as, for example, microcontrollers, has not led to sufficiently high performance. On the other hand, the world literature describes many cases of NFS hardware implementation in programmable gate arrays (FPGAs) offering sufficiently high performance. Unfortunately, the complexity and cost of such systems were so high that the solutions were not very successful. This paper proposes a method of the hardware implementation of MRBF-TS systems. Such systems are created by modifying a subclass of Takagi-Sugeno (TS) fuzzy-neural structures, i.e. the NFS group functionally equivalent to networks with radial basis functions (RBF). The structure of the MRBF-TS is designed to be well suited to the implementation on an FPGA. Thanks to this, it is possible to obtain both very high computing efficiency and high accuracy with relatively low consumption of hardware resources. This paper describes both, the method of implementing MRBFTS type structures on the FPGA and the method of designing such structures based on the population algorithm. The described solution allows for the implementation of control or modeling systems, the implementation of which was impossible so far due to technical or economic reasons.
Bipartite Dynamic Representations for Abuse Detection

Abusive behavior in online retail websites and communities threatens the experience of regular community members. Such behavior often takes place within a complex, dynamic, and large-scale network of users interacting with items. Detecting abuse is challenging due to the scarcity of labeled abuse instances and complexity of combining temporal and network patterns while operating at a massive scale. Previous approaches to dynamic graph modeling either do not scale, do not effectively generalize from a few labeled instances, or compromise performance for scalability. Here we present BiDyn, a general method to detect abusive behavior in dynamic bipartite networks at scale, while generalizing from limited training labels. BiDyn develops an efficient hybrid RNN-GNN architecture trained via a novel stacked ensemble training scheme. We also propose a novel pre-training framework for dynamic graphs that helps to achieve superior performance at scale. Our approach outperforms recent large-scale dynamic graph baselines in an abuse classification task by up to 14% AUROC while requiring 10x less memory per training batch in both open and proprietary datasets.
Look Before You Leap: Secure Connection Bootstrapping for 5G Networks to Defend Against Fake Base-Stations

The lack of authentication protection for bootstrapping messages broadcast by base-stations makes impossible for devices to differentiate between a legitimate and a fake base-station. This vulnerability has been widely acknowledged, but not yet fixed and thus enables law-enforcement agencies, motivated adversaries and nation-states to carry out attacks against targeted users. Although 5G cellular protocols have been enhanced to prevent some of these attacks, the root vulnerability for fake base-stations still exists. In this paper, we propose an efficient broadcast authentication protocol based on a hierarchical identity-based signature scheme, Schnorr-HIBS, which addresses the root cause of the fake base-station problem with minimal computation and communication overhead. We implement and evaluate our proposed protocol using off-the-shelf software-defined radios and open-source libraries. We also provide a comprehensive quantitative and qualitative comparison between our scheme and other candidate solutions for 5G base-station authentication proposed by 3GPP. Our proposed protocol achieves at least a 6x speedup in terms of end-to-end cryptographic delay and a communication cost reduction of 31% over other 3GPP proposals.
Event-Based Microservices With Apache Kafka Streams: A Real-Time Vehicle Detection System Based on Type, Color, and Speed Attributes

The work presented in this paper proposes a novel approach to tracking a specific vehicle over the video streams published by the collaborating traffic surveillance cameras.In recent years, smart, effective transportation systems and intelligent traffic management applications are among the topics that have been given importance by various institutions.Developing a scalable, fault-tolerant, and resilient traffic monitoring system that retrieves video chunks with the desired query is challenging.For these challenging problems, stream processing and data retrieval systems have been developed over the years.However, there are still existing shortcomings between users and retrieval systems.This paper investigates the problem of retrieving video chunks by key-value query based on publish/subscribe model.Thus, we propose a hybrid of an asynchronous and synchronous communication mechanism for the Event-Based Microservice framework.We aim to develop generic techniques for better utilization of existing platforms.In the proposed framework, (i) first of all, microservices detect vehicles and extract their type, color, and speed features, and stored them in the metadata repository.(ii) Microservices publish each feature as events (iii) Other microservices self-join subscribe to those events, which leads to more events being published by combing all the possibilities: typecolor, type-speed, color-speed, and type-color-speed.Finally, (iv) the system visualizes the query result and system status in real-time.When the user has selected color or/and a type or/and a speed feature, the system will return the best-matched vehicles without re-processing the videos.Experimental results show that our proposed system filters messages in real-time and supports easy integration of new microservices with the existing system.
Personalized dynamic evaluation technology of online education quality management based on artificial intelligence big data

Limited by the difficulty of management, the quality of online education is far worse than the quality of classroom teaching. In order to improve the effect of online education quality management, according to the actual needs of online teaching, this article builds personalized dynamic evaluation technology based on artificial intelligence big data technology and uses evaluation as the center of teaching. Each link in the teaching is for the generation and development of evaluation, and the teaching plan takes the learning objectives as the basis for the entire teaching process. Moreover, this research combines the needs analysis to build each functional module and build a model framework on this basis. In addition, in order to verify the performance of the model, this article conducts model analysis through practical teaching methods. The research results show that the model constructed in this paper has good performance.