Reviewerly/exHarmony huggingface dataset:
A novel proxy-oriented public auditing scheme for cloud-based medical cyber physical systems

Cloud-based medical cyber-physical system (MCPS) is attracting more and more attention, since it can break through the limitations of time, space and object, and allow doctors to make accurate telemedicine diagnosis for patients. However, since medical information is directly related to the safety of patients’ lives and property, it has become an urgent problem to ensure the integrity and availability of medical data. To solve the above problems, many integrity auditing schemes have been proposed in recent years. Unfortunately, most existing schemes have security flaws or poor performance. Very recently, Zhang et al. proposed an identity-based proxy-oriented auditing scheme for checking the integrity of medical data, and claimed that their scheme was secure. However, we find that their scheme fails to achieve its purpose since it is vulnerable to the user impersonation attack and misusing delegation attack. we propose a new proxy-oriented public auditing scheme to fix the security vulnerabilities. Security analysis and performance evaluation demonstrate that our proposed scheme greatly reduces the communication and computation costs while ensuring a novel security level. Compared with Zhang et al.’s scheme, our scheme is more suitable for applications in cloud-based MCPS.
Marine Vessel Re-Identification: A Large-Scale Dataset and Global-and-Local Fusion-Based Discriminative Feature Learning

A marine vessel re-identification system has to determine whether or not different images represent the same vessel. Accurate vessel re-identification improves onshore closed-circuit television monitoring in a vessel traffic services system as well as onboard surveillance of surrounding vessels. However, because ships are rigid bodies and the marine environment is harsh, the accurate re-identification of vessels at sea can be very difficult. We describe a marine vessel-re-identification framework, Global-and-Local Fusion-based Multi-view Feature Learning (GLF-MVFL), which is based on a combination of global and fine-grained local features. GLF-MVFL combines cross-entropy loss with our newly-developed orientation-guided quintuplet loss. We exploit intrinsic features of marine vessels to optimize multi-view representation learning for re-identification. GLF-MVFL uses ResNet-50 as the backbone network to extract features for simultaneous quintuple input. It detects and discriminates between features and estimates viewpoints to form a comprehensive re-identification framework. We created an annotated large-scale vessel retrieval dataset, VesselID-539, which contains images from viewpoints similar to those of an autonomous surface vessel, to use in evaluating the performance of the model. Extensive experiments and analysis of the results obtained from using VesselID-539 demonstrate that our approach significantly increases the accuracy of vessel re-identification and is more effective and robust for images from different viewpoints than other approaches.
Visual-based Autonomous Driving Deployment from a Stochastic and Uncertainty-aware Perspective

End-to-end visual-based imitation learning has been widely applied in autonomous driving. When deploying the trained visual-based driving policy, a deterministic command is usually directly applied without considering the uncertainty of the input data. Such kind of policies may bring dramatical damage when applied in the real world. In this paper, we follow the recent real-to-sim pipeline by translating the testing world image back to the training domain when using the trained policy. In the translating process, a stochastic generator is used to generate various images stylized under the training domain randomly or directionally. Based on those translated images, the trained uncertainty-aware imitation learning policy would output both the predicted action and the data uncertainty motivated by the aleatoric loss function. Through the uncertainty-aware imitation learning policy, we can easily choose the safest one with the lowest uncertainty among the generated images. Experiments in the Carla navigation benchmark show that our strategy outperforms previous methods, especially in dynamic environments.
Holonomic Quantum Computation in Surface Codes

It is well known that surface codes are favorable candidates toward realizing large-scale fault-tolerant quantum computation. While methods to reduce errors by surface codes have been well explored, it is still challenging to achieve small-enough errors as stated by the threshold theorem in practical experimental settings. This prompts the integration of surface codes and protected quantum operations to further mitigate the negative effect of errors, creating the challenge of executing protected surface codes. In this work, we propose one scheme to perform holonomic quantum operations in a physical system by use of auxiliary qubits. The system Hamiltonian describes a quantum spin model with adjustable coupling strength. Various noncommutable single-qubit and nontrivial two-qubit holonomic gates are applicable on the basis of the interaction between the auxiliary qubits and physical qubits. By considering a two-dimensional lattice of qubits, we explore the execution of surface codes. The utilization rate of physical qubits as data qubits is about $24.7\mathrm{%}$ in executing the protected surface codes. Our scheme is based on an experimentally achievable Hamiltonian and thus it brings us closer to realizing protected quantum operations on quantum-error-correction codes with a modest resource.
Always with Me

Simple and low bandwidth communication on computers has been found to promote intimacy between couples. In this work, we further explore this minimal communication in the form of wearables. This pictorial presents an in-the-wild concept study of low bandwidth ambient wearable displays as a communication channel between couples. The goal is to understand the contexts in which the technology might be used and provide benefit. Our findings show that simple communications through a wearable device could provide an additional channel for communication. The wearable form factors also creates the feeling of being always connected. We highlight the importance and influence of form factors, contexts, and activities towards user experience. We discuss the opportunities this study opens for the future design of wearable ambient displays.
Quality Improvement for Exemplar-based Image Inpainting using a Modified Searching Mechanism

Digital image processing has a significant impact in different research areas including medical image processing, biometrics, image inpainting, object detection, information hiding, and image compression. Image inpainting is a science of reconstructing damaged parts of digital images and filling-in regions in which information are missing which has many potential applications such as repairing scratched images, removing unwanted objects, filling missing area, and repairing old images. In this paper, an image inpainting algorithm is developed based on exemplar, which is one of the most important and popular images inpainting technique, to fill-in missing area that caused either by removing unwanted objects, by image compression, by scratching image, or by image transformation through internet. In general, image inpainting consists of two main steps: The first one is the priority function. In this step, the algorithm decides to select which patch has the highest priority to be filled at the first. The second step is the searching mechanism to find the most similar patch to the selected highest priority patch to be inpainted. This paper concerns the second step and an improved searching mechanism is proposed to select the most similar patch. The proposed approach entails three steps: (1) Euclidean distance is used to find the similarity between the highest priority patches which need to be inpainted with each patch of the input image, (2) the position/location distance between those two patches is calculated, and (3) the resulted value from the first step is summed with the resulted value obtained from the second step. These steps are repeated until the last patch from the input image is checked. Finally, the smallest distance value obtained in step 3 is selected as the most similar patch. Experimental results demonstrated that the proposed approach gained a higher quality in terms of both objectives and subjective compared to other existing algorithms.
Experimental composable security decoy-state quantum key distribution using time-phase encoding

Quantum key distribution (QKD) promises provably secure communications. In order to improve the secret key rate, combining a biased basis choice with the decoy-state method is proposed. Concomitantly, there is a basis-independent detection efficiency condition, which usually cannot be satisfied in a practical system, such as the time-phase encoding. Fortunately, this flaw has been recently removed theoretically and experimentally in the four-intensity decoy-state BB84 QKD protocol using the fact that the expected yields of single-photon states prepared in two bases stay the same for a given measurement basis. However, the security proofs do not fully consider the finite-key effects for general attacks. In this work, we provide the rigorous finite-key security bounds in the universally composable framework for the four-intensity decoy-state BB84 QKD protocol. We build a time-phase encoding system with 200 MHz clock to implement this protocol, in which the real-time secret key rate is more than 60 kbps over 50 km single-mode fiber.
Text Data Security and Privacy in the Internet of Things: Threats, Challenges, and Future Directions

In our daily life, Internet-of-Things (IoT) is everywhere and used in many more beneficial functionalities. It is used in our homes, hospitals, fire prevention, and reporting and controlling the environmental changes. Data security is the crucial requirement for IoT since the number of recent technologies in different domains is increasing day by day. Various attempts have been made to cater the user’s demands for more security and privacy. However, a huge risk of security and privacy issues can arise among all those benefits. Digital document security and copyright protection are also important issues in IoT because they are distributed, reproduced, and disclosed with extensive use of communication technologies. The content of books, research papers, newspapers, legal documents, and web pages are based on plain text, and the ownership verification and authentication of such documents are essential. In the current domain of the Internet of Things, limited techniques are available for ownership verification and copyright protection. In the said perspective, this study includes the discussion about the approaches of text watermarking, IoT security challenges, IoT device limitations, and future research directions in the area of text watermarking.
Patient Questions and Physician Responses in a Chinese Health Q&amp;A Website: Content Analysis

Background Since the turn of this century, the internet has become an invaluable resource for people seeking health information and answers to health-related queries. Health question and answer websites have grown in popularity in recent years as a means for patients to obtain health information from medical professionals. For patients suffering from chronic illnesses, it is vital that health care providers become better acquainted with patients’ information needs and learn how they express them in text format. Objective The aims of this study were to: (1) explore whether patients can accurately and adequately express their information needs on health question and answer websites, (2) identify what types of problems are of most concern to those suffering from chronic illnesses, and (3) determine the relationship between question characteristics and the number of answers received. Methods Questions were collected from a leading Chinese health question and answer website called “All questions will be answered” in January 2018. We focused on questions relating to diabetes and hepatitis, including those that were free and those that were financially rewarded. Content analysis was completed on a total of 7068 (diabetes) and 6685 (hepatitis) textual questions. Correlations between the characteristics of questions (number of words per question, value of reward) and the number of answers received were evaluated using linear regression analysis. Results The majority of patients are able to accurately express their problem in text format, while some patients may require minor social support. The questions posted were related to three main topics: (1) prevention and examination, (2) diagnosis, and (3) treatment. Patients with diabetes were most concerned with the treatment received, whereas patients with hepatitis focused on the diagnosis results. The number of words per question and the value of the reward were negatively correlated with the number of answers. The number of words per question and the value of the reward were negatively correlated with the number of answers. Conclusions This study provides valuable insights into the ability of patients suffering from chronic illnesses to make an understandable request on health question and answer websites. Health topics relating to diabetes and hepatitis were classified to address the health information needs of chronically ill patients. Furthermore, identification of the factors affecting the number of answers received per question can help users of these websites to better frame their questions to obtain more valuable answers.
A Novel Interpretable Computer-Aided Diagnosis System of Thyroid Nodules on Ultrasound Based on Clinical Experience

Computer-aided diagnosis systems (CADs) present valuable second opinions to radiologists in diagnosis. Many studies on thyroid nodules have proposed various CADs to provide a binary result, benignity or malignancy, for doctors, ignoring interpretability of more ultrasonic features that could be more useful. We develop an interpretable CADs (iCADS) that utilizes deep-learning networks' classification power and interpretability potential of clinical guidelines, like TIRADS, a well-established scale for thyroid nodules. iCADS incorporates a main neural-networks model and six neural-network based interpreters. The outputs of the six interpreters are compared with TIRADS guidelines and the matched result will form a report, more than a benignity or malignancy result, for radiologists. Clinical images of 16,946 thyroid nodules from 5,885 patients were used to train the proposed iCADS. An extra experimental data set containing 501 images were used to test the performance of the model. For better illustrating the assistant ability of iCADS, we also recruited ten junior radiologists to make diagnosis decisions with or without the help of different versions of iCADS. The experiments demonstrated that iCADS can largely improve junior radiologists diagnosis with the help of interpreter strategy. These experiments are also the very first attempt to evaluate the effect of interpretability of deep-learning based CADs in clinical practice. Comparison experiments with other deep-learning based CADs and traditional CADs indicated that the interpreter strategy can easily be combined to other intelligent CADs without the loss of performance. The framework of iCADS can also inspire more research on the development of CADs.
A survey on dragonfly algorithm and its applications in engineering

The dragonfly algorithm was developed in 2016. It is one of the algorithms used by researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large-scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.
Pemanfaatan Bot Telegram sebagai Media Informasi Akademik di STMIK Hang Tuah Pekanbaru

Academic Information System is a means of supporting academic activities in STMIK HangTuah Pekanbaru by utilizing web-based technology. However, the application of webtechnology on Academic information System in STMIK Hang Tuah Pekanbaru can notalways run as desired, sometimes the information system cannot be accessed due to technicalconstraints or other factors. Based on these problems, an application was created that couldrun on instant messages and could be used via smartphones or other devices as mediainformation for students, namely utilization of telegram bot based on PHP and MySQL asacademic information at STMIK Hang Tuah Pekanbaru. This telegram bot is made using theWebhook method and can run on all operating systems that have Telegram Messengerinstalled. This bot is able to provide academic information including lecture schedules, grades,filling in krs, short semester submissions and research submissions, it is hoped that thetelegram bot will make it easier for students to get academic information through smartphonedevices that can be accessed anytime.
Comparison of Single-Trace and Multiple-Trace Polarity Determination for Surface Microseismic Data Using Deep Learning

Abstract For surface microseismic monitoring, determination of the P-wave first-motion polarity is important because (1) it has been widely used to determine focal mechanisms and (2) the location accuracy of the diffraction-stack-based method is improved greatly using polarization correction. The convolutional neural network (CNN) is a form of deep learning algorithm that can be applied to predict the polarity of a seismogram automatically. However, the existing network designed for polarity detection utilizes only individual trace information. In this study, we design a multitrace-based CNN (MT-CNN) architecture using several neighbor traces combined as training samples, which could utilize the polarity information of neighbor sensors in the surface microseismic array. We use 17,227 field seismograms with labeled polarities to train two different neural networks that predict the polarities by a single trace or by multiple traces. The performance of the test set and field example of two CNN architectures shows that the MT-CNN significantly produces fewer polarity prediction errors and leads to more accurate focal mechanism solutions for microseismic events.
Effects of voluntary heart rate control on user engagement and agency in a virtual reality game

It has been demonstrated that virtual reality (VR) exposure can affect the subjective experience of different situations, cognitive capabilities or behavior. It is known that there is a link between a person’s physiological state and their psychological self-report and user experience. As an immersive experience can affect users’ physiological data, it is possible to adapt and enhance the content of a virtual environment in real-time base on physiological data feedback (biofeedback). With the rapid evolution of the physiological monitoring technologies, it is now possible to exploit different modalities of biofeedback, in a cheap and non-cumbersome manner, and study how they can affect user experience. While most of the studies involving physiological data use it as a measuring tool, we want to study its impact when direct and voluntary physiological control becomes a mean of interaction. To do so, we created a two-parts protocol. The first part was designed to categorize the participants on their heart rate control competency. In the second part of the study, we immersed our participants in a VR experience where they must control their heart rate to interact with the elements in the game. The results were analyzed based on the competency distribution. We observed consistent results between our competency scale and the participants’ control of the biofeedback game mechanic. We also found that our direct biofeedback mechanic is highly engaging. We observed that it generated a strong feeling of agency, which is linked with users’ level of heart rate control. We highlighted the richness of biofeedback as a direct game mechanic, prompting interesting perspective for personalized immersive experiences.
Blockchain Empowers Social Resistance and Terrorism Through Decentralized Autonomous Organizations

The invention of the Internet has changed the way social resistance, revolutionary movements and terror groups are organized with new features such as loose network organization, netwars, social media campaigns, and lone wolf attacks. This article argues that blockchain technology will lead to more far-reaching changes in the organization of resistance to authority. Blockchain is a distributed ledger that records transactions using a consensus protocol, and when it meets objective conditions, it also enables smart contracts that execute transactions. Blockchain technology is not only a system for transferring value, but also it is a trustless system in which strangers can cooperate without the need for having to trust each other, as computer code governs their interactions. Blockchain will not only allow resistance/ terror organizations to easily receive donations globally, to have assets that a government can easily confiscate, and to disseminate censorship-resistant propaganda, but more importantly, to operate and cooperate across the world in a truly leaderless, coordinated, and highly decentralized fashion. Governments will need to be more proactive in the area of blockchain technology to mitigate some of the dangers to political stability that may emerge from it.