Reviewerly/exHarmony huggingface dataset:
Physiological Big Data Mining Through Machine Learning and Wireless Sensor Networks

With the improvement of living standards, the requirements for medical care and daily healthcare quality have become higher and higher. However, the traditional medical diagnosis mode cannot provide patients with all-round, real-time, and accurate health status. With the aggravation of the aging population, the scale of physiological data will increase in a blowout manner. The traditional medical diagnosis model for monitoring, which is based at the central hospital, has been unable to meet the current real-time monitoring needs for families and individuals. In order to solve this issue, this paper establishes a wireless sensor network based medical platform, which implements sleep monitoring by mining electroencephalogram signals. The wireless sensor network-based medical platform adopts the end-edge-cloud architecture. The experiments and simulations show the effectiveness of the proposed end-edge-cloud architecture-based medical platform.
Registering Particle Data Sets Using a Rotation and Translation Invariant Nearest-Neighbor Algorithm

It can be useful to register (or align) two sets of particle data measured from the same physical sample. However, if the two data sets were collected at different translational or rotational offsets, finding the optimal registration can be a challenge. We will present an algorithm that efficiently determines the rotation and translational offset that best registers (in a least-squares sense) the corresponding particles in two or more data sets measured from the same sample. This algorithm can be used to merge two data sets that have been collected on overlapping but otherwise distinct regions on the sample. Alternatively, it can be used to overlay data sets that have been collected on the same sample area to compare replicate data for quality control and measurement efficiency purposes.
Adaptive hash code balancing for remote sensing image retrieval

Remote sensing image retrieval (RSIR) refers to finding images from an image database that contain the same instance as the query image, which is an essential task in remote sensing applications. Traditional depth-based hashing algorithms usually convert the image library into a hash matrix with a specified number of digits. On the one hand, the quality of hash matrices generated by traditional methods is low and cannot guarantee good clustering between pictures of the same class. On the other hand, the ability to extract features using backbone networks must be improved. This paper proposes a deep hashing model called adaptive hash code balancing (AHCB) to solve two existing problems. The model introduces a balanced binary method to maximize the hash value entropy so that the generated hash has better clustering. Graph convolutional networks(GCNs) are introduced to automatically detect relevant data points in the graph, perform back-propagation, and propagate the updated network feedback to the feature extraction layer to improve the ability to extract features. It enables the model to learn the intrinsic data structure between remote sensing images. Experimental results on three public datasets show that the proposed method outperforms the current state-of-the-art deep hashing remote sensing image retrieval algorithms by a large margin.
Implementation of ID-based Audit Protocols to Enhance Security and Productivity

Cloud storage has gained increasing popularity, as it helps cloud users arbitrarily store and access the related outsourced data.Numerous public audit buildings have been presented to ensure data transparency.However, modern developments have mostly been constructed on the public key infrastructure.To achieve data integrity, the auditor must first authenticate the legality of the public key certificate, which adds to an immense workload for the auditor, in order to ensure that data integrity is accomplished.The data facilities anticipate that the storage data quality should be regularly tracked to minimize disruption to the saved data in order to maintain the intactness of the stored data on the remote server.One of the main problems for individuals, though, is how to detect data integrity on a term where people have a backup of local files.Meanwhile, a system is often unlikely for a source-limited person to perform a data integrity inspection if the overall data file is retrieved.In this work, a stable and effective ID-based auditing setting that uses machine learning techniques is proposed to improve productivity and enhance the protection of ID-based audit protocols.The study tackles the issue of confidentiality and reliability in the public audit framework focused on identity.The idea has already been proved safe; its safety is very relevant to the traditional presumption of the Computational Diffie-Hellman security assumption.
An Anti-Physical Attack Scheme of ARX Lightweight Algorithms for IoT Applications

The lightweight encryption algorithm based on Add-Rotation-XOR (ARX) operation has attracted much attention due to its high software affinity and fast operation speed.However, lacking an effective defense scheme for physical attacks limits the applications of the ARX algorithm.The critical challenge is how to weaken the direct dependence between the physical information and the secret key of the algorithm at a low cost.This study attempts to explore how to improve its physical security in practical application scenarios by analyzing the masking countermeasures of ARX algorithms and the leakage causes.Firstly, we specify a hierarchical security framework by quantitatively evaluating the indicators based on side-channel attacks.Then, optimize the masking algorithm to achieve a trade-off balance by leveraging the software-based local masking strategies and non-full-round masking strategies.Finally, refactor the assembly instruction to improve the leaks by exploring the leakage cause at assembly instruction.To illustrate the feasibility of the proposed scheme, we further conducted a case study by designing a software-based masking method for Chaskey.The experimental results show that the proposed method can effectively weaken the impact of physical attacks.
Technology Cannot Fix the Privacy Crisis

Recent decades have produced a wide variety of technical tools for privacy protection. However, the hopes placed in these tools are often inflated. Overestimating the protective effect of privacy-enhancing technologies can be dangerous and may lead to a false sense of security. This chapter stresses that the following aspects need to be born in mind when assessing the real impact of technical privacy safeguards: (1) Data controllers’ reluctance to apply self-limiting privacy safeguards, (2) the loophole created by the notice-and-choice approach to privacy protection, (3) technical challenges and limitations, (4) the limited scope of protection that privacy-enhancing technologies can offer. Also, many problems associated with the processing of personal data are not primarily of technical nature but involve fundamental social, ethical, and political questions. There is no doubt that technical tools play a crucial part in protecting people’s privacy today. They are necessary but not sufficient. To meaningfully protect people against the dangers resulting from modern data processing, strong regulation is needed as well.
Development of a Decision Support System for the Diagnosis of Clinical Diseases

This article discusses the decision support system used in medical practice, which makes it possible to facilitate the process of making a diagnosis and prescribing treatment. The principle of operation of the decision support system is stated. A decision support system (DSS) is a computer automated system, the purpose of which is to help people who make decisions in difficult conditions for a complete and objective analysis of subject activity. The decision support system is designed to support multicriteria decisions in a complex information environment. At the same time, multi-criteria are understood as the fact that the results of decisions made are evaluated not by one, but by the totality of many indicators (criteria) considered simultaneously. Information complexity is determined by the need to take into account a large amount of data, the processing of which is practically impossible without the help of modern computer technology. Under these conditions, the number of possible solutions, as a rule, is very large, and the choice of the best of the solutions, without a comprehensive analysis, can lead to gross errors. Thus, in this paper, we consider the solution to the problem of a large information load on specialists and the design of the system being developed.
Lattice-Based Ordered Multisignature for Industrial IoT

In this paper lattice-based ordered multisignature was proposed. The proposed signature uses the lattice’s basis delegation mechanism and provides not only data authenticity, but also the signature generation chain verification, which can be applied in various systems with a hierarchical structure, including the industrial Internet of Things. A security analysis of this scheme was carried out, in which 5 attack scenarios were considered, taking into account the attacker’s various capabilities. Methods for improving the scheme security are proposed. Ways for improving the scheme performance are also suggested. To conduct performance testing, two scheme versions were launched: with and without optimization. It was found that when using the optimized variant, the time of parameters generation increases, but signature generation and verification time and the total time for all algorithms decreases. This research can be used as a starting point for further study of the lattice’s basis delegation use to constructing ordered multisignature schemes.
An investigation of class diagram builder system from the translation of BPMN and database

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Farida Utami, Syafira Nurilhaq Maulidya, Bilal Hidayaturrohman, Nadlir Mubarak, Muhammad Zege Zain, Indra Kharisma Raharjana, Badrus Zaman; An investigation of class diagram builder system from the translation of BPMN and database. AIP Conference Proceedings 25 January 2023; 2554 (1): 040006. https://doi.org/10.1063/5.0103679 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Mapping regencies and cities in East Java related food potential using the K-means method

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation M. Fariz Fadillah Mardianto, Suliyanto, Faried Effendy, Ayuning Dwis Cahyasari, Chaerobby Fakhri Fauzaan Purwoko, Netha Aliffia, Antonio Nikolas Manuel Bonar Simamora; Mapping regencies and cities in East Java related food potential using the K-means method. AIP Conference Proceedings 25 January 2023; 2554 (1): 030013. https://doi.org/10.1063/5.0103807 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Detection of Acute Myeloid Leukemia from Peripheral Blood Smear Images Using Transfer Learning in Modified CNN Architectures

Acute myeloid leukemia (AML), the most fatal hematological malignancy, is characterized by immature leukocyte proliferation in the bone marrow and peripheral blood. Conventional diagnosis of AML, performed by trained examiners using microscopic images of a peripheral blood smear, is a time-consuming and tedious process. Considering these issues, this study proposes a transfer learning-based approach for the accurate detection of immature leukocytes to diagnose AML. At first, the data was resized and transformed at the pre-processing stage. Then augmentation was performed on training data. Finally, the pre-trained convolutional neural network architectures were used with transfer learning. Transfer learning through modified AlexNet, ResNet50, DenseNet161, and VGG-16 were used to detect immature leukocytes. After model training and validation using different parameters, models with the best parameters were applied to the test set. Among other models, modified AlexNet achieved 96.52% accuracy, 94.94% AUC and an average recall, precision, and F1-score of 97.00%, 97.00%, and 97.00%, respectively. The investigative results of this study demonstrate that the proposed approach can aid the diagnosis of AML through an efficient screening of immature leukocytes.
Research on Vehicle-Road Environment Perception Technology

Autonomous driving technology can assist people to drive vehicles without even needing human participation to control vehicles, which can effectively reduce traffic accidents caused by human factors. The vehicle-road environment perception technology based on target detection is the core technology in autonomous driving. Aiming at the problems that the excessive amount of image information leads to too long target detection time, the existing automatic driving target detection model has many training parameters, and the amount of point cloud data directly collected by Lidar is too large, we propose a vehicle-road environment perception system based on deep learning, which realizes real-time vehicle detection, multi-target detection, and collision detection. The experimental results show that the system achieves good detection results.
Research on Food Production Date Inkjet Defect Detection System Based on Deep Learning

In order to solve the above problems and improve the production efficiency of enterprises, combined with machine vision, this paper designs and implements a food production date inkjet defect detection system based on deep learning. The system can quickly and accurately judge whether there are defects such as missing printing and missing printing on the production date inkjet code on the food package, and eliminate the problem food in time. The experimental results show that the system can accurately identify the defective products with printing code on the production date, and achieve the expected detection effect.
3D Semantic Scene Perception Using Distributed Smart Edge Sensors

We present a system for 3D semantic scene perception consisting of a network of distributed smart edge sensors. The sensor nodes are based on an embedded CNN inference accelerator and RGB-D and thermal cameras. Efficient vision CNN models for object detection, semantic segmentation, and human pose estimation run on-device in real time. 2D human keypoint estimations, augmented with the RGB-D depth estimate, as well as semantically annotated point clouds are streamed from the sensors to a central backend, where multiple viewpoints are fused into an allocentric 3D semantic scene model. As the image interpretation is computed locally, only semantic information is sent over the network. The raw images remain on the sensor boards, significantly reducing the required bandwidth, and mitigating privacy risks for the observed persons. We evaluate the proposed system in challenging real-world multi-person scenes in our lab. The proposed perception system provides a complete scene view containing semantically annotated 3D geometry and estimates 3D poses of multiple persons in real time.
Agile 8D Problem-Solving Framework in a Renewable Energy Sector

Quality management (QM) has evolved due to the current market needs, where innovation and flexibility are essential. In this sense, there is a tendency to combine and integrate different methodologies with QM, namely, the integration of agile approaches given their synergies. This work is supported by the proposition that QM can benefit from agile methods, improving the organization management facing current challenges. Supported by an action research strategy, it describes a case study in a renewable energy organization aiming to understand how agile practices can be adapted to QM, specifically in problem-solving methods. This research also presents theoretical contributions as it provides a framework named “Agile 8D problem-solving,” resulting from integrating the Scrum framework with the 8D methodology. However, it also has practical implications for organizations and QM professionals once it hands over a framework composed of guidelines for implementing more collaborative and efficient problem-solving methods.
Post-facto Misrecognition Filter Based on Resumable Interruptions for Coping with Real World Uncertainty in the Development of Reactive Robotic Behaviors

In this paper we propose a resumable interruption framework for robotic applications which allows to “filter” misrecognition signals after their occurrence. Handling misrecognition is essential for deploying reactive systems into the real world, since being over-reactive to detection errors can lead to livelocks and stagnation. For example, constantly interrupting and resuming a picking task due to misrecognition can make the robot alternate between pre-grasping and grasping motions, without ever achieving the task. Our solution is based on resumable interruptions, continuing interrupted procedures from the exact preemption point if similar execution requests are received shortly after a cancellation order. This acts as a post-facto misrecognition filter, which stabilizes execution and ensures task completion. Compared with standard filtering, the post-facto approach allows to deliver signals faster and recover from misrecognition longer. The proposed system is verified through real robot experiments in dynamic and static environments.
Programming Neuromorphics Using the Neural Engineering Framework

As neuromorphic hardware begins to emerge as a viable target platform for artificial intelligence (AI) applications, there is a need for tools and software that can effectively compile a variety of AI models onto such hardware. Nengo ( http://nengo.ai ) is an ecosystem of software designed to fill this need with a suite of tools for creating, training, deploying, and visualizing neural networks for various hardware backends, including CPUs, GPUs, FPGAs, microcontrollers, and neuromorphic hardware. While backpropagation-based methods are powerful and fully supported in Nengo, there is also a need for frameworks that are capable of efficiently mapping dynamical systems onto such hardware while best utilizing its computational resources. The neural engineering framework (NEF) is one such method that is supported by Nengo. Most prominently, Nengo and the NEF have been used to engineer the world’s largest functional model of the human brain. In addition, as a particularly efficient approach to training neural networks for neuromorphics, the NEF has been ported to several neuromorphic platforms. In this chapter, we discuss the mathematical foundations of the NEF and a number of its extensions and review several recent applications that use Nengo to build models for neuromorphic hardware. We focus in-depth on a particular class of dynamic neural networks, Legendre Memory Units (LMUs), which have demonstrated advantages over state-of-the-art approaches in deep learning with respect to energy efficiency, training time, and accuracy.
Adversarial Examples Protect Your Privacy on Speech Enhancement System

Speech is easily leaked imperceptibly.When people use their phones, the personal voice assistant is constantly listening and waiting to be activated.Private content in speech may be maliciously extracted through automatic speech recognition (ASR) technology by some applications on phone devices.To guarantee that the recognized speech content is accurate, speech enhancement technology is used to denoise the input speech.Speech enhancement technology has developed rapidly along with deep neural networks (DNNs), but adversarial examples can cause DNNs to fail.Considering that the vulnerability of DNN can be used to protect the privacy in speech.In this work, we propose an adversarial method to degrade speech enhancement systems, which can prevent the malicious extraction of private information in speech.Experimental results show that the generated enhanced adversarial examples can be removed most content of the target speech or replaced with target speech content by speech enhancement.The word error rate (WER) between the enhanced original example and enhanced adversarial example recognition result can reach 89.0%.WER of target attack between enhanced adversarial example and target example is low at 33.75%.The adversarial perturbation in the adversarial example can bring much more change than itself.The rate of difference between two enhanced examples and adversarial perturbation can reach more than 1.4430.Meanwhile, the transferability between different speech enhancement models is also investigated.The low transferability of the method can be used to ensure the content in the adversarial example is not damaged, the useful information can be extracted by the friendly ASR.This work can prevent the malicious extraction of speech.
Effect of shade and cover pore on development of BSF prepupa

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Wisje Lusia Toar, Ivonne Maria Untu, Geertruida Assa, Heidy Jultje Manangkot, Laurentius Rumokoy; Effect of shade and cover pore on development of BSF prepupa. AIP Conference Proceedings 24 January 2023; 2586 (1): 040009. https://doi.org/10.1063/5.0111973 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
Privacy in the Era of 5G, IoT, Big Data, and Machine Learning

We have today a number of technologies that, when combined, can support unprecedented applications and significantly enhance existing applications. These technologies include 5G cellular networks, big data, machine learning, and the Internet of Things (IoT). The combination of those technologies allows us to: a) increase our capacity for pervasive, fine-grained, and continuous data gathering and for the effective and efficient processing of these data (even with real-time guarantees); b) generate knowledge from data and continuously evolve this knowledge, thus supporting recommendation systems and decision processes—also accompanied by suitable explanations; and c) make devices, control systems, and cyberphysical systems intelligent and autonomous.