Reviewerly/exHarmony huggingface dataset:
Lattice based Ring Signature Scheme for Secure Cloud-based EMR Sharing

Advances in medical science and technology continue to drive the development of e-healthcare, with Electronic Medical Records (EMRs) consisting of medical information such as patient consultation records and bodily data being gradually popularized in the form of cloud sharing. The sharing of EMR effectively enhances the effectiveness and reliability of healthcare services. However, the highly sensitive EMRs are susceptible to tampering and misuse during the EMR sharing, which poses a tremendous threat to patients&#x2019; privacy and the security of e-health. Further, EMRs are extremely vulnerable to quantum computing attacks during the process of sharing in cloud, which will be the most significant bottleneck in the advancement of e-healthcare systems. To tackle the foregoing issues, we present a lattice-based ring signature scheme for secure EMR sharing on the cloud. To begin with, we implement EMR sharing with medical cloud. Then, we set a lattice-based ring signature scheme to ensure user anonymity, EMR and signature unforgeability, while resisting quantum attacks. Further, we require all EMR-related users to perform multiparty confirmation signatures on the EMR. In conlusion, the security analysis and performance evaluation indicate that our scheme guarantees correctness, anonymity, and unforgeability, while outperforming other existing schemes.
A Lightweight CNN for Large-scale Chinese Character Recognition

The ancient Chinese characters appear in various historical documents and poetry. People tend to use optical character recognition tools to understand these uncommon characters. The current Chinese text recognition interface is restricted to a limited character set, such as GB2312-80 and GB18010-2005 standard. However, the newest HanYu Dictionary contains over 55K characters, much more than the commonly-used character set. This work proposes a compact deep network (HYD-CNet) composed of depthwise separable convolutional blocks and co-ordinate attention mechanism to recognize the ancient Chinese characters. It can achieve efficient retrieval and low-storage need for large-scale character recognition on mobile devices. We build a Chinese character database (HYDDB) using the HanYu Dictionary to evaluate the model performance, containing 55,360 character images. The experiment demonstrates that the proposed HYD-CNet has fewer model parameters at a similar accuracy to mainstream lightweight CNNs.
Selective face de-identification scheme using multiple face recognition and classification techniques

Social media is rapidly growing through platforms that provide live-streaming service, video sharing service, etc. But, as the social media grows, the cases where faces of people in the media are indiscreetly exposed are also increasing. Personal sensitive information may be included in a photo and video, and representative data that can identify an individual is face. So, various face de-identification methods have been proposed. However, most face de-identification methods have tried to obfuscate every face in an image or video and tend to focus on minimizing the loss of facial features due to obfuscation for utilizing the rest of facial features as data except for features that can identify individuals. Therefore, those methods are not adequate for face de-identification in social media service because main characters' faces like media producers, creators or actors need to be exposed on purpose, whereas the other targets' faces like pedestrians or crowd should not be exposed in the videos. Selectively obfuscating targets' faces by classifying all faces in the video into exposing targets and obfuscating targets is necessary for improving the privacy in social media service. In this paper, we propose a selective face de-identification system that exposes main characters' faces and obfuscates the other targets' faces by recognizing and classifying all faces appeared in a video. Our system detects and tracks multiple faces using a deep learning-based face recognition, and classifies main characters' faces and the obfuscating targets' faces, and finally de-identifies only the faces of the obfuscating targets on purpose. We also propose an efficient pipelining scheme for batch processing of face de-identification that rearranges the order of faces to be identified in each frame by matching faces existing in continuous frames of a video.
Writer-Independent Offline Signature Verification using LBP and NN

Verification of a signature is a problematic research topic since two or more people’s sign may appear to be identical, but a person’s sign may differ depending on the state. The purpose of this research is to examine how effectively an Artificial-Neural-Network and a Local-Binary-Pattern feature set can be combined to construct a Writer-Independent-Offline-Signature verification arrangement. The system’s performance is assessed using two signature datasets, each with 260 and 100 writers. Authentic signatures of a person, as well as skilled-forgery, nonskilled-forgery, and random-forgery signs, are used to test the performance of the developed system, and authentic signatures, as well as skilled-forgery, nonskilled-forgery, and random-forgery signs, are taken into account in the development of the desired system. In this study, a false-acceptance-rate of 21.00 percent, 11.00 percent, and 1.00 percent was obtained for skilled-forgery signs, nonskilled-forgery signs, and random-forgery signs, respectively, while a false-rejection-rate of 0.00 percent was obtained for 11 reference signatures using a database of 260 writer’s signatures.
Virtual Reality &amp; Augmented Reality: A way to Digital Transformation of Customer Engagement

Due to the growth of moveable and personified devices, along with extremely interactive network greatly influences the way customers interact. The landscape of customer engagement is progressing and it is turning towards the novel types of mixed reality experiences. As countless firms starting to use Augmented Reality (AR) and Virtual Reality (VR) for value-added customer experiences, particularly throughout offline and online purchase rides, there is a mounting need to increase understanding of the exceptional benefits of the application of AR & VR for creative activities in the marketing perspective. Brands have touched technoid customers and transformed their spending habits by providing a new way to explore the products and enhancing the digital shopping experience with simulated storefronts. Nonetheless, despite these encouraging developments, current research and reports recommend that there is a cost and experience involved in deploying the AR & VR, also the mangers lack visions into how AR might increase customer engagement in different industries and facilitate more enduring customer experiences. As research on this budding area is inadequate, our aim is to cultivate this novel topic in the technology stream and report the knowledge gaps. The paper aims to determine conceptual and managerial inferences, as well as also addresses the scope for future researches.
Visualization and Analysis of Populations

In any sector, Big Data has changed the manner in which information is dealt with, handled, and utilized. In this paper the Python Programming Language is used to visualize and analyse data. Visualization techniques can aid in the visual or graphical representation of data relationships. The data must be analysed to produce a satisfactory result. The outcome can be used to make a decision. In this paper, the most fundamental procedures of data analysis, such as cleaning, transforming, and modelling data, are briefly discussed, with a focus on exploratory data analysis of a current dataset and uncovering bits of knowledge. Using several Python libraries and functions, some graphical examination of the information from the dataset will be shown.
Approaches Towards AI-Based Recommender System

Recommender Systems (RS) have application in all areas where digital information is generating at high rate. They are used to filter large volume of digital data containing user preferences, interests and behaviour patterns for products and services. Today artificial intelligence has achieved rapid development in automation, computation, knowledge-engineering, information retrieval and processing. Techniques such as fuzzy logic, neural networks, natural language processing, transfer learning, machine learning are also contributing to enhance the performance, accuracy of recommendation system and reducing problems like cold-start, data-sparsity, privacy and scalability. This paper explains the development stages and different categories of recommender system. This paper further explores the challenges and recent trends in building recommendation system.
Keeping the Baddies Out and the Bridge Calm: Embedded Authentication for Maritime Networks

Integrated bridges of today’s vessels are complex and distributed maritime systems that interconnect versatile electronic equipment. However, digitized vessels have long ceased to be isolated systems and are thus increasingly vulnerable to cyber attacks. In this context, integrity and authentication of the communication onboard is crucial. Therefore, we introduce MARMAC, a low-cost solution to retrofit authentication of nautical communication. MARMAC is based on symmetric cryptography and extends the prevalent IEC61162-450 protocol enabling a backward-compatible solution which mitigates common attacks. Using a specific gatekeeper approach, MARMAC can prevent unauthenticated messages from being processed that could otherwise affect the nautical situational awareness on the bridge. Our approach is evaluated using real network traffic in a laboratory testbed with low-cost hardware, highlighting its feasibility and potential to secure existing maritime systems.
Cloud Infrastructure-as-a-Service Testbed Implementation using OpenStack

Cloud computing offers ubiquitous, on-demand services over the Internet as a utility, providing users configurable resources, infrastructure scaling, and minimal upfront cost. Yet, it faces many challenges in network topologies, network security, and resource scheduling. Hence, testbeds are used to test different solutions based on these challenges. Testbeds are a realistic setup of a particular hardware-software environment where applications and frameworks can be studied for functional performance and operational efficiency. The most common tool for creating testbeds is OpenStack, as it is open-sourced, scalable, and flexible. This work organizes the recent developments in the domain and implements an OpenStack testbed for evaluating its applicability. The findings of this review suggest that OpenStack supports the development of testbeds for enterprises and independent researchers by offering varying levels of configuration and modularity to satisfy specific requirements. Metric collection tools such as the Ceilometer can facilitate planning, protection, and preventative measures.
Digital Gardening with a Forest Atlas

This paper details a research project and design, the Smart Forests Atlas, which expands open-data research platforms toward pluralistic and participatory practices. Working with the under-examined practice of ‘digital gardening’, the project reconfigures the open-data research platform into a dynamic and participatory space for cultivating ideas and generating content. However, we found that digital gardening could risk reinscribing existing colonial and anthropocentric practices of gardening and digital archiving. To challenge these risks, we propose six complementary design qualities that inform the design of the Atlas and could influence other open-data research platforms. We developed these qualities from a design inquiry into open-data platforms, environmental atlases, and digital gardening practices, while designing and developing the Atlas together with a digital tools cooperative. We argue that transformed digital gardening practices could advance more pluralistic, participatory, and more-than-human approaches to open-data research platforms while contributing to epistemic justice.
Toward a Failures Model for Communication of Decentralized Applications with Blockchain Networks Applied in the Industrial Environment

Consensus algorithms seek to solve communication failures between nodes in a blockchain network. However, the communication failures that involve decentralized applications running on Industrial Internet of Things devices have not yet been addressed. Thus, this work aims to fill this research gap, presenting a failure model for communication between decentralized applications and the blockchain network. Initially, the failures are defined and classified; then countermeasures are presented to circumvent the failures; and at the end, the impact of the countermeasures on the performance of decentralized applications, smart contract, and the blockchain network is evaluated. Therefore, this work provides an analysis of the effects of failures and assists in the design of blockchain applications capable of tolerating certain failures and continuing to function.
Improved YOLOv5s Model for Vehicle Detection and Recognition

Vehicle detection and recognition is one of hotspots for intelligent transportation. The existing object detection and recognition methods have the problem of high accuracy for a single kind of object and low accuracy for multiple kinds of object. To solve this problem, an improved YOLOv5s-ATE network model is proposed in this paper. The coordinate attention (CA) is incorporate in YOLOv5s-ATE. Furthermore, SPPF is added to the neck of the model, and can effectively improve the accuracy of multiple object detection and recognition. When the trained network model is evaluated on VOC validation set, the mean accuracy of YOLOv5s-ATE mAP@0.5 and mAP@0.5:0.95 are improved. The proposed YOLOV5-ATE model can effectively carry out target detection and recognition, which improves the theoretical research and technical support for vehicle detection and recognition.
An Improved Waste Detection and Classification Model Based on YOLOV5

The improvement in people's lives has resulted in a significant rise in the amount of household garbage created on a daily basis, to the point where waste separation can no longer be disregarded, especially for the series of problems: manual waste classification is time-consuming and laborious, and human waste classification errors are caused by a lack of knowledge reserve related to waste classification. To address these issues, we propose a waste classification network YOLO-CG optimized on the basis of YOLOV5 network structure in campus scene. Firstly, YOLO-CG draws lessons from the optimization idea of Transformer performance improvement by stacking the ConvNeXt Blocks in the ratio of 3:3:9:3 as backbone, adding the big size kernel and other adjustments, upgrading the mean average precision (mAP) of the network model by 5%. Then, to maintain the original accuracy while reducing the number of parameters, a computationally reduced cheap operation is introduced, which employs a simple 3 * 3 convolution to achieve a low-cost acquisition of redundant feature maps, resulting in a reduction of 12% in parameter count while also increasing the mAP. Both theoretical analysis and experiments demonstrate the effectiveness of the improved network model.
Falling for Social Engineering: A Qualitative Analysis of Social Engineering Policy Recommendations

The current study examines recommendations for addressing organizational members who fall prey to social engineering as a matter of organizational policy through a qualitative analysis of interviews with organizational IT security administrators, IT security auditors, and social engineers. The results of this analysis indicate that participants had an aversion to punitive approaches to security failures. Instead, they tended to favor education as a more pragmatic and humane solution. Others argued that emphasis should be given to rewarding positive security behavior over punishing lapses. Few participants contended that there is a time and place for harsher sanctions but generally only for repeat offenders. Implications for security policy, deterrence theory, and reintegrative shaming theory are considered.
A Systematic Review of Distributed Deep Learning Frameworks for Big Data

Traditional Machine Learning and Deep Learning techniques (data acquisition, preparation, model training and evaluation) take a lot of computational resources and time to produce even a simple prediction model, especially when implemented on a single machine. Intuitively, the demand for computational requirements is higher in case of management of Big Data and training of complex models. Thus, a paradigm shift from a single machine to a BD-oriented approach is required for making traditional Machine Learning and Deep Learning techniques fit to Big Data. In particular, it emerges the need for developing and deploying Big Data Analytics Infrastructures on cluster of machines. In this context, main features and principles of Distributed Deep Learning frameworks are here discussed. The main contribution of this paper is a systematic review of proposed solutions, aimed at investigating under a unifying lens their foundational elements, functional features and capabilities, despite the inherent literature fragmentation. To this, we conducted a literature search in Scopus and Google Scholar. This review also compares Distributed Deep Learning approaches according to more technical facets: implemented of parallelism techniques, supported hardware, model parameters sharing modalities, computation modalities for stochastic gradient descent and compatibility with other frameworks.
Multi-Geometric Reasoning Network for Insulator Defect Detection of Electric Transmission Lines

To address the challenges in the unmanned system-based intelligent inspection of electric transmission line insulators, this paper proposed a multi-geometric reasoning network (MGRN) to accurately detect insulator geometric defects based on aerial images with complex backgrounds and different scales. The spatial geometric reasoning sub-module (SGR) was developed to represent the spatial location relationship of defects. The appearance geometric reasoning sub-module (AGR) and the parallel feature transformation (PFT) sub-module were adopted to obtain the appearance geometric features from the real samples. These multi-geometric features can be fused with the original visual features to identify and locate the insulator defects. The proposed solution is assessed through experiments against the existing solutions and the numerical results indicate that it can significantly improve the detection accuracy of multiple insulator defects using the aerial images.
CFRV: A Decentralized Control-Flow Attestation Schema Using Mutual Secret Sharing

Control-flow attestation (CFA) is a mechanism that securely logs software execution paths running on remote devices. It can detect whether a device is being control-flow hijacked by launching a challenge-response process. In the growing landscape of the Internet of Things, more and more peer devices need to communicate to share sensed data and conduct inter-operations without the involvement of a trusted center. Toward the scalability of CFA mechanisms and mitigating the single-point failure, it is important to design a decentralized CFA schema. This paper proposed a decentralized schema (CFRV) to verify the control flow on remote devices. Moreover, it introduces a token (asymmetric secret slices) into peer devices to make the attestation process mutual. In this case, CFRV can mitigate a particular kind of man-in-the-middle attack called response defraud. We built our prototype toolbox on Raspberry-Pi to formulate our proof of concept. In our evaluation, CFRV protects the verification process from malicious verifiers and the man-in-the-middle attack. The proposed mechanism can also limit the PKI (Public Key Infrastructure) usage to a single stage to save the peer devices' computational cost. Compared to related decentralized schemes, the cryptographic operation's duration is reduced by 40%.
Research on Data Security Communication Scheme of Heterogeneous Swarm Robotics System in Emergency Scenarios

In emergency scenarios where the on-site information is completely lacking or the original environmental state has been completely changed, autonomous and mobile swarm robotics are used to quickly build a rescue support system to ensure the safety of follow-up rescuers and improve rescue efficiency. To address the data security problem caused by the complex and changeable topology of the heterogeneous swarm robotics network in the process of building the rescue support system, this paper introduced a decentralized data security communication scheme for heterogeneous swarm robotics. First, we built a decentralized network topology model by using base robot, communication robotics, and business robotics, and it can ensure the stability of the system. Moreover, based on the decentralized network topology model, we designed a storage model using the master-slave blockchain method. The master chain is composed of base robot and communication robotics, which mainly store the digests of robot data in multiple slave chains to reach the global data consensus of the system. The slave chains are composed of business robotics and communication robotics, which mainly store all data on the slave chains to reach the local data consensus of the system. The whole data storage system adopts the Delegated Proof of Stake consensus mechanism to elect proxy nodes to participate in the data consensus tasks in the system and to ensure the data consistency of each robot node in the decentralized network. Additionally, a prototype of the heterogeneous swarm robotics system based on the master-slave chains is constructed to verify the effectiveness of the proposed model. The experimental results show that the scheme effectively solves the data security problem caused by the unstable communication link of the heterogeneous swarm robotics system.
Use of Spherical and Cartesian Features for Learning and Recognition of the Static Mexican Sign Language Alphabet

The automatic recognition of sign language is very important to allow for communication by hearing impaired people. The purpose of this study is to develop a method of recognizing the static Mexican Sign Language (MSL) alphabet. In contrast to other MSL recognition methods, which require a controlled background and permit changes only in 2D space, our method only requires indoor conditions and allows for variations in the 3D pose. We present an innovative method that can learn the shape of each of the 21 letters from examples. Before learning, each example in the training set is normalized in the 3D pose using principal component analysis. The input data are created with a 3D sensor. Our method generates three types of features to represent each shape. When applied to a dataset acquired in our laboratory, an accuracy of 100% was obtained. The features used by our method have a clear, intuitive geometric interpretation.