Reviewerly/exHarmony huggingface dataset:
Occlusion Resilient Adversarial Attack for Person Re-identification

Deep learning-based person re-identification (Re-ID) methods have achieved the significant performance of matching person images across camera views, which plays an important role in the construction of the smart city. Recent works of adversarial attacks have explored the serious vulnerability of deep Re-ID systems. However, existing attacks are performed with idealized conditions and ignore the real-world environments, such as occlusion caused by walking habits. In this paper, we propose a two-stage method to perform an occlusion resilient adversarial attack for better evaluation of deep Re-ID systems. Specifically, we construct the occlusion template from the observation and statics of pedestrian walking habits. Then, we design a partition training strategy for a better combination of occluded and exposed adversarial patches. During the training, we introduce contextual loss to penalize the semantic distance of attacked images with the same identity. The extensive experiments on Market1501 demonstrate the performance of our method.
Improvement of Association Algorithm and Its Application in Audit Data Mining

Data mining has become a relatively modern platform for information retrieval. The efficient data mining techniques can increase the reliability and accuracy of internal auditing for the various community even while lowering audit risk. Existing audit data mining approaches lack significant identification of hidden connections and interactions in bid data platforms. Hence, this study extends the literature survey on the signification of audit data mining in multiple applications. This survey identifies the scope of improved association algorithms in audit data mining, a rule-based machine learning approach to determine the exciting relationship among variables in large audit datasets. Therefore, a Conceptual Framework of Improved Association Algorithm (CFiAA) and its application in audit data mining is proposed. This study examines the strengths and weaknesses of the proposed CFiAA in audit mining. The proposed model has been trained using an audit data set and validates with various audit datasets. Finally, this paper presents the comparative analysis of the proposal to show its highest performance related to existing models. Thus, CFiAA scores the performance ratio of 94.5%, accuracy ratio of 92.4%, an efficiency ratio of 92.5%, F1 measure of 91.8%, error rate 32.5%, prediction ratio of 93.7%, and the precision ratio of 92.5% compared to existing models.
Timing-based side-channel attack and mitigation on PCIe connected distributed embedded systems

PCIe connected peripheral devices are increasingly deployed in distributed embedded systems. For example, a GPU accelerator connected with a host CPU via PCIe interconnect brings massive performance improvement for artificial intelligence applications. These peripheral devices benefit from the shared memory of the host CPU for performance gains, but sharing the host CPU resources brings security challenges. The shared PCIe interconnect hardware of the host CPU can be exploited to create a timing-based information leakage side-channel between multiple connected peripheral devices that are isolated at the software level. This paper proposes an attack setup that consists of GPU and FPGA peripheral devices accessing their data from the host CPU’s main memory. Both covert communication and information leakage attacks are demonstrated at a throughput rate of 13.02 kbps. A temporal isolation-based mitigation scheme is proposed that utilizes time-division multiplexing between the peripheral devices to mitigate the attacks. The paper primarily focuses on demonstrating the security context of the proposed attack and mitigation.
Secure COVID-19 Treatment with Blockchain and IoT-Based Framework

The COVID pandemic has opened the eyes of numerous nations about their medical services framework. The short blast and uncontrolled overall spread of COVID-19 show the constraints of existing medical services frameworks to handle general wellbeing crises conveniently. The disease and demise numbers detailed by World Health Organization (WHO) about this pandemic is an expanding danger to the lives of individuals and the financial matters of nations. The greatest challenge that most governments are experiencing is the absence of a precise mechanism to recognize obscure contaminated cases and anticipate the disease danger of COVID-19 infection. Numerous countries have been utilizing a scope of devices to battle the pandemic, looking for data about development, checking just as the releasing the private data of the occupants. This research paper plans to help tainted individuals with care utilizing the Internet of Things (IoT) and blockchain innovations. From one viewpoint, blockchain can battle pandemics by empowering early discovery of the cases, securing client protection, and guaranteeing a compacted clinical flexibly chain during the pandemic attack. Then again, IoT-based medical services accumulate valuable data, give advanced knowledge through indications and practices, permit far-off checking, and essentially give individuals better self-assurance and medical services. A proposed layer consists of four-layer architecture utilizing IoT and blockchain to identify and predict people to be COVID-19. This idea provides a framework for patients with COVID-19 irresistible infection and perceives medical problems and determinations. The proposed approach is anticipated to deliver a robust framework ready to help governments, healthcare specialists, and residents make basic choices concerning disease recognition, disease forecast, and disease avoidance.
Review of Blockchain-Based Energy Trading Models

Renewable energies (REs) are trending as the technology to improve its efficacy moving at a fast pace. REs has been utilized in many domestic and industrial premises as a surplus energy. Trading of this surplus energy within a local market scenario, can help in meeting the demand requirements and can outlook a new scope of energy trading. This requires high security and transparency among the stake holders. Among various technologies, blockchain can be proved as a promising platform for a secure and transparent transaction. This paper comprises an extensive review of different blockchain based energy trading model and discusses various research scopes for mitigating of certain issues with ongoing research models to gain higher efficiency.
Vulnerabilities Space and the Superiority of Hackers

This work affirms the idea of comprehensive control over vulnerabilities in ICT systems. A new category related to the cardinality of vulnerabilities space is introduced and justified, referring to the comprehensive consideration of all vulnerabilities in the security of an ICT system. A new model demonstrating the reasons for the superiority of hackers against cyber defenders is presented. It is based on the new proposed category vulnerabilities space. The model reveals the reason for the superiority of hackers against cybersecurity teams. The paper contains analysis and systematization of main organizational and technological differences in the activities of defenders and attackers that amplify the position of hackers.
World monies or money-worlds: A new perspective on cryptocurrencies and their moneyness

Abstract This essay makes the case that current debates about the ‘moneyness’ of Bitcoin and other cryptocurrencies are occurring at the incorrect scale. Rather than being some form of transnational digital money to be used alongside or compete with national fiat currencies, I argue that, instead, each cryptocurrency represents its own self-contained ‘money-world’. Put differently, a cryptocurrency is the uniquely specified unit of account and medium of exchange within the socio-technical bounds of its own blockchain. This new perspective can open new lines of intellectual dialogue and inform better policy choices for regulating cryptocurrencies.
Variable-Speed Traveling Salesman Problem for Vehicles with Curvature Constrained Trajectories

This paper presents a novel approach to the multigoal trajectory planning for vehicles with curvature-constrained trajectories such as fixed-wing aircraft. In the existing formulation called the Dubins Traveling Salesman Problem (DTSP), the vehicle speed is assumed to be constant over the whole trajectory, and that does not allow adaptation of the turning radius of the trajectory between the target locations. It does not support optimization of the overall flight time of the multi-goal trajectory by exploiting higher speeds for longer turning radii. Therefore, we propose a novel problem formulation called the Variable-Speed Traveling Salesman Problem (VS-TSP) that employs time-efficient trajectories with variable speed based on a generalization of the Dubins vehicle model, allowing multiple turning radii and change of the forward speed of the vehicle. The VS-TSP allows the vehicle to slow down if high maneuverability is necessary and speed up if high-speed turns with a large radius are beneficial to the overall tour cost. Based on the evaluation results for Cessna 172 aircraft model, the proposed VNS-based algorithm with variable speed provides up to about 20 % faster trajectories than a solution of the DTSP with a single turning radius.
Careless Participants Are Essential for Our Phishing Study: Understanding the Impact of Screening Methods

Online surveys using crowdsourcing services have been widely adopted in academic research projects aimed at understanding human perception and behavior. Because there is a concern that online surveys may include dishonest or careless responses by crowdworkers who perform a large number of tasks, or responses by bots, several screening methods have been proposed to discard such low-quality responses. However, in security research, especially in phishing research where the attention of participants is considered to influence the results, the elimination of careless responses may lead to the removal of participants who should be included in the research. In this study, we address the following research question: "Does the adoption of existing screening methods bias the results of security surveys?" Using Amazon Mechanical Turk and Prolific Academic, two popular crowdsourcing platforms used in online surveys, we conducted online user studies (N = 600) on security knowledge, security behavior, and phishing email detection performance to elucidate the influence of screening methods on the results. The obtained results indicate that the adoption of the instructional manipulation check (IMC) screening method triggers bias in the demographics of the participants, as well as differences in the results of phishing email detection performance. In addition, the degree of these differences depends on the crowdsourcing platform. We also demonstrated that it is non-trivial to determine the correlation between screening methods and factors that can influence the results of a survey on security behavior. These findings suggest that caution should be exercised when applying screening methods such as attention checks and IMC in studies where the extent of user attention could have a significant impact on the results.
Method of non-invasive parameters for predicting the probability of early in-hospital death of patients in intensive care unit

To achieve timely and accurate prediction of the death probability of intensive care unit (ICU) patients without the invasive physiological parameters of professional medical staff and patients, a new method for predicting the death probability of ICU patients based on Extreme Gradient Boosting (XGBoost) machine learning is proposed. 15 non-invasive physiological parameters all from Medical Information Mart for Intensive Care III (MIMIC III) database were used in this study, and a total of 151 characteristic variables were extracted. And based on the XGBoost machine learning method, the ten-fold cross-validation method and the cost-sensitive classification threshold adjustment method are used to optimize the model parameters to establish the optimal prediction model of patient death probability. In the case of using the entire feature set, the optimal prediction results of the XGBoost machine learning model are 0.858 for accuracy (ACC) and 0.941 for area under receiver operating characteristic curve (AUC). Better than traditional scoring methods (the ACC and AUC of the optimal prediction results of the traditional scoring method are 0.736 and 0.816, respectively). Compared with the traditional scoring system, the machine learning algorithm proposed in this study uses the patient's non-invasive physiological parameters, and can provide a more accurate prediction of the death probability of ICU patients in a timely manner without medical professionals and invasive physiological parameters. This makes it possible for the death probability of ICU patients’ prediction to be widely used and timely and appropriate clinical decision-making possible.
Participatory Design

This book introduces Participatory Design to researchers and students in Human–Computer Interaction (HCI). Grounded in four strong commitments, the book discusses why and how Participatory Design is i
QueryNet: Querying neural networks for lightweight specialized models

Despite the active research on deep learning these days, no existing works attempt to query neural networks for a specific task so that we can quickly obtain a model specialized for the queried task. This paper presents the first study on the problem of querying neural networks, aiming to efficiently find a lightweight model for any on-demand sub-task supported by a large and generic neural network. This problem is well motivated by the fact that such a lightweight model is particularly suitable for deployment in commodity mobile devices with limited computing resources. In this paper, we propose QueryNet, a framework of queriable neural networks, which is based on our class-aware channel pruning technique and the proposed method of merging multiple tiny networks for producing a specialized model for the task. Through the extensive experiments, we show that QueryNet can generate a lightweight neural network for a given task up to 3.25 times faster than learning a specialized model from scratch, and the resulting specialized neural network carries up to 37 times less parameters than a pretrained network, and even shows a slightly better accuracy than that of the pretrained model.
Generalization error of GAN from the discriminator’s perspective

The generative adversarial network (GAN) is a well-known model for learning high-dimensional distributions, but the mechanism for its generalization ability is not understood. In particular, GAN is vulnerable to the memorization phenomenon, the eventual convergence to the empirical distribution. We consider a simplified GAN model with the generator replaced by a density and analyze how the discriminator contributes to generalization. We show that with early stopping, the generalization error measured by Wasserstein metric escapes from the curse of dimensionality, despite that in the long term, memorization is inevitable. In addition, we present a hardness of learning result for WGAN.
Seamless Temporal Gait Evaluation during Walking and Running Using Two IMU Sensors

In this study, we proposed a framework for extracting gait events and extensive temporal features, seamlessly, during walking and running on a treadmill by constructing a finite state machine (FSM) transition rules based on two IMU sensors attached to the back of the shoes. Detailed innerclass states were defined to recognize the double support phase on walking gait and the double flight phase on running gait. Further, an in-depth speed-based analysis of temporal gait features can be performed for each tested speed with an automatic speed change detection algorithm based on the moving average filter applied to motion intensity data. The results have demonstrated that the FSM can accurately distinguish walking gait and running gait while also extract a detailed gait phase, respectively. This finding may contribute to a more flexible gait analysis where a change in speed or transition from walk to run can be anticipated and recognized accordingly.
Deep low-rank feature learning and encoding for cross-age face recognition

• A robust deep low-rank feature learning and encoding model for cross-age face recognition is proposed. • A manifold-constrained low-rank approximation algorithm is proposed to learn clean low-rank feature matrix for representation. • A locality-constrained feature encoding framework is proposed to represent the cross-age face images of the same identity by similar codewords. • The proposed method outperforms other state-of-the-art methods in terms of recognition rate. Cross-age face recognition (CAFR) is a challenging task, due to significant intra-personal variations. Furthermore, the training and testing data may contain random noise components. To address these issues, this paper proposes a deep low-rank feature learning and encoding method. Firstly, our method employs manifold learning in the low-rank optimization, which preserves the global and local structure of the data samples, while learning the clean low-rank features. Secondly, we encode the low-rank features using our locality-constrained feature encoding method, which learns an age-insensitive codebook from training data, and enables the intra-class samples to share the same local bases in a codebook. In the testing stage, the gallery and probe features are encoded by the learned codebook, which represents the images of the same identity by similar codewords for recognition. Furthermore, the periocular region of human faces is investigated for CAFR. Extensive experiments on five datasets demonstrate the effectiveness of our method.
An analysis of data breaches in the U.S. healthcare industry: diversity, trends, and risk profiling

As healthcare information technology (HIT) advances, major stakeholders in the healthcare industry such as healthcare providers, health plan organizations, and business associates are generating and exchanging enormous amounts of patient data throughout the healthcare supply chain. Along with the explosive growth of electronic patient data, these stakeholders have experienced an increasing number of data breaches. Despite the significant consequences of the data breaches such as the loss of client privacy, regulatory penalty, and financial loss, there is a lack of studies on sector level trend analysis and risk profiling. This study develops a diversity index that can be used to compare the distribution of data breaches between three sectors of the healthcare industry. To enhance the understanding of the data breaches, this study utilizes a temporal aggregation of the data breaches, analyzes data breach risks, and develops a data-driven risk profile. The findings of this study can be used to improve the cybersecurity management of healthcare organizations.
Research on Similar Animal Classification Based on CNN Algorithm

Abstract Animal image classification with CNN (convolutional neural network) is commonly investigated in aera of image recogniation and classification, but major studies focus on species pictures classification with obvious distinctions. For example, CNN is usually employed to distinghish images between dogs and cats. This article puts the effort on similar animal images classification by applying simple 2D CNN via python. It focus on the binary classification for snub-nosed monkeys and normal monkeys. This distinguishment is hard to be done manually in a short time. For constructing complete convolutional neural network, some preparations are done in advance, such as the database construction and preprocess. The database is constructed by python crawler (downloading from google images), with 800 and 200 images for each class respectively as train data and test data. The pre-work includes image resizing, decoding and standardization. After that, the model is trained and then tested for verifying the model reliability. The training accuracy is 96.67% without any abnormality. On the basis of successful training, the test accuracy almost coincides with train accuracy in each 50 generations and plots in a graph. It indicates similar trends and results for them in the whole process. Because of this, CNN model in the study can help people identify rare animals in time and then people can effectively protect them. Therefore, CNN will be helpful in field of animal conservation, especially for rare species.
Instantaneous cross-correlation function type of WD based LFM signals analysis via output SNR inequality modeling

Abstract Linear canonical transform (LCT) is a powerful tool for improving the detection accuracy of the conventional Wigner distribution (WD). However, the LCT free parameters embedded increase computational complexity. Recently, the instantaneous cross-correlation function type of WD (ICFWD), a specific WD relevant to the LCT, has shown to be an outcome of the tradeoff between detection accuracy and computational complexity. In this paper, the ICFWD is applied to detect noisy single component and bi-component linear frequency-modulated (LFM) signals through the output signal-to-noise ratio (SNR) inequality modeling and solving with respect to the ICFWD and WD. The expectation-based output SNR inequality model between the ICFWD and WD on a pure deterministic signal added with a zero-mean random noise is proposed. The solutions of the inequality model in regard to single component and bi-component LFM signals corrupted with additive zero-mean stationary noise are obtained respectively. The detection accuracy of ICFWD with that of the closed-form ICFWD (CICFWD), the affine characteristic Wigner distribution (ACWD), the kernel function Wigner distribution (KFWD), the convolution representation Wigner distribution (CRWD) and the classical WD is compared. It also compares the computing speed of ICFWD with that of CICFWD, ACWD, KFWD and CRWD.
A Blockchain-based Architecture for Secure IoT-based Health Monitoring Systems

Although IoT technology brings significant benefits to the healthcare sector and can play a noteworthy role in improving citizens' quality of life by enabling IoT-based health monitoring systems, it also raises many security challenges. Conventional security mechanisms are inadequate to secure IoT-based health monitoring systems as they have high resource requirements, in terms of computational power and energy consumption, and thus they cannot be afforded by the resource-constrained IoT nodes of these systems. On the other hand, blockchain is a promising technology that can be used to enhance the security of IoT-based health monitoring systems due to its decentralized and autonomous nature. Therefore, in this paper, we propose a blockchain architecture, based on the Hyperledger Fabric platform, for securing IoT-based health monitoring systems in a more lightweight manner as Hyperledger Fabric does not apply the consensus protocol of Proof of Work (PoW) that cannot be afforded by IoT devices.