Reviewerly/exHarmony huggingface dataset:
Future pHealth Ecosystem-Holistic View on Privacy and Trust

Modern pHealth is an emerging approach to collecting and using personal health information (PHI) for personalized healthcare and personalized health management. For its products and services, it deploys advanced technologies such as sensors, actuators, computers, mobile phones, etc. Researchers have shown that today's networked information systems, such as pHealth ecosystems, miss appropriate privacy solutions, and trust is only an illusion. In the future, the situation will be even more challenging because pHealth ecosystems will be highly distributed, dynamic, increasingly autonomous, and multi-stakeholder, with the ability to monitor the person's regular life, movements, emotions, and health-related behavior in real time. In this paper, the authors demonstrate that privacy and trust in ecosystems are system-level problems that need a holistic, system-focused solution. To make future pHealth ethically acceptable, privacy-enabled, and trustworthy, the authors have developed a conceptual five-level privacy and trust model as well as a formula that describes the impact of privacy and trust factors on the level of privacy and trust. Furthermore, the authors have analyzed privacy and trust challenges and possible solutions at each level of the model. Based on the analysis performed, a proposal for future ethically acceptable, trustworthy, and privacy-enabled pHealth is developed. The solution combines privacy as personal property and trust as legally binding fiducial duty approaches and uses a blockchain-based smart contract agreement to store people's privacy and trust requirements and service providers' promises.
Authorship Identification of Binary and Disassembled Codes Using NLP Methods

This article is part of a series aimed at determining the authorship of source codes. Analyzing binary code is a crucial aspect of cybersecurity, software development, and computer forensics, particularly in identifying malware authors. Any program is machine code, which can be disassembled using specialized tools and analyzed for authorship identification, similar to natural language text using Natural Language Processing methods. We propose an ensemble of fastText, support vector machine (SVM), and the authors’ hybrid neural network developed in previous works in this research. The improved methodology was evaluated using a dataset of source codes written in C and C++ languages collected from GitHub and Google Code Jam. The collected source codes were compiled into executable programs and then disassembled using reverse engineering tools. The average accuracy of author identification for disassembled codes using the improved methodology exceeds 0.90. Additionally, the methodology was tested on the source codes, achieving an average accuracy of 0.96 in simple cases and over 0.85 in complex cases. These results validate the effectiveness of the developed methodology and its applicability to solving cybersecurity challenges.
Backdoor Attack against Face Sketch Synthesis

Deep neural networks (DNNs) are easily exposed to backdoor threats when training with poisoned training samples. Models using backdoor attack have normal performance for benign samples, and possess poor performance for poisoned samples manipulated with pre-defined trigger patterns. Currently, research on backdoor attacks focuses on image classification and object detection. In this article, we investigated backdoor attacks in facial sketch synthesis, which can be beneficial for many applications, such as animation production and assisting police in searching for suspects. Specifically, we propose a simple yet effective poison-only backdoor attack suitable for generation tasks. We demonstrate that when the backdoor is integrated into the target model via our attack, it can mislead the model to synthesize unacceptable sketches of any photos stamped with the trigger patterns. Extensive experiments are executed on the benchmark datasets. Specifically, the light strokes devised by our backdoor attack strategy can significantly decrease the perceptual quality. However, the FSIM score of light strokes is 68.21% on the CUFS dataset and the FSIM scores of pseudo-sketches generated by FCN, cGAN, and MDAL are 69.35%, 71.53%, and 72.75%, respectively. There is no big difference, which proves the effectiveness of the proposed backdoor attack method.
Physically Plausible Realistic Grip-Lift Interaction Based on Hand Kinematics in VR

Immersive technology, refers to various novel ways of creating and interacting with applications and experiences, e.g., virtual reality (VR), has been used in various simulations and training where preparing real/physical settings is not ideal or possible, or where the use of virtual contents is otherwise beneficial. Realizing realistic interactions with virtual content is crucial for a quality experience and the effectiveness of such simulation and training. In this paper, we propose a kinematics-based realistic hand interaction method to enable a physically plausible grip-lifting experience in VR. The method reflects three kinematic characteristics of the hand: the force at contact points, finger flexion, and the speed of hand/finger motion, and we developed a grip-lift interaction prototype using the proposed method. To examine the sense of realism and hand poses during the grip-lift interaction, we conducted a human subjects experiment using the prototype, resulting in positive effects on the perceived realism and usefulness of the interaction. Grip-lifting is a fundamental interaction technique that is involved in most embodied interaction scenarios. Our method would contribute to the design and development of realistic virtual experiences, of which we will discuss the implications and potential based on our findings.
Adapting Geo-Indistinguishability for Privacy-Preserving Collection of Medical Microdata

In the era of the Fourth Industrial Revolution, the increasing demand for data collection and sharing for analysis purposes has raised concerns regarding privacy violations. Protecting individual privacy during the collection and dissemination of sensitive information has emerged as a critical concern. In this paper, we propose a privacy-preserving framework for collecting users’ medical microdata, utilizing geo-indistinguishability (Geo-I), a concept based on well-known differential privacy. We adapt Geo-I, originally designed for protecting location information privacy, to collect medical microdata while minimizing the reduction in data utility. To mitigate the reduction in data utility caused by the perturbation mechanism of Geo-I, we propose a novel data perturbation technique that utilizes the prior distribution information of the data being collected. The proposed framework enables the collection of perturbed microdata with a distribution similar to that of the original dataset, even in scenarios that demand high levels of privacy protection, typically requiring significant perturbations to the original data. We evaluate the performance of our proposed algorithms using real-world data and demonstrate that our approach significantly outperforms existing methods, ensuring user privacy while preserving data utility in medical data collection.
Research on Dynamic Path Exploration and Planning Method of Quadrotor Unmanned Aerial Vehicle in Complex Environment

As a research hotspot in the field of mobile robots in recent years, UAV(Unmanned Aerial Vehicle) has been improving its autonomous performance. Compared with traditional UAV, UAV has many advantages, such as vertical lifting, strong maneuverability and convenient operation. However, the dynamic path planning of quadrotor UAV, as a typical autonomous ability of mobile robot, has always been the research focus in the field of UAV. In this regard, this paper will try to study the dynamic path exploration planning method of quadrotor UAV in complex environment. In the complex environment model, the comparative simulation from the aspects of calculation time, calculation complexity and success rate shows that the selection of high-order polynomials can ensure high-order continuity, but it will also increase the calculation time and complexity. Compared with CF algorithm, the calculation time in this paper is 57% lower on average and the success rate is 17.6% higher on average, which shows that this algorithm is more suitable for online flight path planning of quadrotor. Comparatively speaking, the success rate of the other two methods decreases with the increase of environmental complexity, and the overall calculation time is longer.
NativeVRF: A Simplified Decentralized Random Number Generator on EVM Blockchains

Smart contracts refer to small programs that run in a decentralized blockchain infrastructure. The blockchain system is trustless, and the determination of common variables is done by consensus between peers. Developing applications that require generating random variables becomes significantly challenging—for instance, lotteries, games, and random assignments. Many random number generators (RNGs) for smart contracts have been developed for the decentralized environment. The methods can be classified into three categories: on-chain RNG, Verifiable Random Function (VRF), and the Commit–reveal scheme. Although the existing methods offer different strengths and weaknesses, none achieves the three important requirements for an ideal RNG solution: security, applicability, and cost efficiency. This paper proposes a novel RNG approach called Native VRF, which offers application development simplicity and cost efficiency while maintaining strong RNG security properties. Experimental results show that Native VRF has the same security properties as the widely used RNG methods, i.e., Randao and Chainlink VRF. On top of that, our work offers a much simpler setup process and lower hardware resources and developer expertise requirements. Most importantly, the proposed Native VRF is compatible with all Ethereum virtual machine (EVM) blockchains, contributing to the overall growth of the blockchain ecosystem.
Review of Recent Distillation Studies

Knowledge distillation has gained a lot of interest in recent years because it allows for compressing a large deep neural network (teacher DNN) into a smaller DNN (student DNN), while maintaining its accuracy. Recent improvements have been made to knowledge distillation. One such improvement is the teaching assistant distillation method. This method involves introducing an intermediate "teaching assistant" model between the teacher and student. The teaching assistant is first trained to mimic the teacher, and then the student is trained to mimic the teaching assistant. This multi-step process can improve student performance. Another improvement to knowledge distillation is curriculum distillation. This method involves gradually training the student by exposing it to increasingly difficult concepts over time, similar to curriculum learning in humans. This process can help the student learn in a more stable and consistent manner. Finally, there is the mask distillation method. Here, the student is trained to specifically mimic the attention mechanisms learned by the teacher, not just the overall output of the teacher DNN. These improvements help to enhance the knowledge distillation process and enable the creation of more efficient DNNs.
High Dimensional Data Differential Privacy Protection Publishing Method Based on Association Analysis

In order to solve the problem of privacy disclosure when publishing high-dimensional data and to protect the privacy of frequent itemsets in association rules, a high-dimensional data publishing method based on frequent itemsets of association rules (PDP Growth) is proposed. This method, in a distributed framework, utilizes rough set theory to improve the mining of association rules. It optimizes association analysis while reducing the dimensionality of high-dimensional data, eliminating more redundant attributes, and obtaining more concise frequent itemsets, and uses the exponential mechanism to protect the differential privacy of the simplest frequent itemset obtained, and effectively protects the privacy of the frequent itemset by adding Laplace noise to its support. The theory validates that the method satisfies the requirement of differential privacy protection. Experiments on multiple datasets show that this method can improve the efficiency of high-dimensional data mining and meet the privacy protection. Finally, the association analysis results that meet the requirements are published.
The automatic evaluation system based on open source hardware of endurance running in senior high school entrance physical examination

Based on open-source hardware, a set of automatic evaluation system of endurance running items was designed, which aims at the problem that the hand timing of endurance running items is not accurate enough. The system can be applied to the daily physical education training as well as the test every school year and Senior High School Entrance Physical Examination. The system is divided into basic version and test version. The basic version is mainly composed of UNO, LCD screen, voice module, clock module, rotary potentiometer, infrared beam module, etc. The test version is mainly composed of ESP32-CAM and human body infrared sensor. In Arduino IDE, C language program is written to achieve the design and development of the whole system. The system uses variable and array flexibly to record various rating tables and marking patterns, and the system test data is better than manual timing. The whole system is simple and practical. The future improvement direction of the system is to further optimize the system and add the function of automatic circle recording.
Two-Branch Underwater Image Enhancement and Original Resolution Information Optimization Strategy in Ocean Observation

In complex marine environments, underwater images often suffer from color distortion, blur, and poor visibility. Existing underwater image enhancement methods predominantly rely on the U-net structure, which assigns the same weight to different resolution information. However, this approach lacks the ability to extract sufficient detailed information, resulting in problems such as blurred details and color distortion. We propose a two-branch underwater image enhancement method with an optimized original resolution information strategy to address this limitation. Our method comprises a feature enhancement subnetwork (FEnet) and an original resolution subnetwork (ORSnet). FEnet extracts multi-resolution information and utilizes an adaptive feature selection module to enhance global features in different dimensions. The enhanced features are then fed into ORSnet as complementary features, which extract local enhancement features at the original image scale to achieve semantically consistent and visually superior enhancement effects. Experimental results on the UIEB dataset demonstrate that our method achieves the best performance compared to the state-of-the-art methods. Furthermore, through comprehensive application testing, we have validated the superiority of our proposed method in feature extraction and enhancement compared to other end-to-end underwater image enhancement methods.
Entanglement Distillation Optimization Using Fuzzy Relations for Quantum State Tomography

Practical entanglement distillation is a critical component in quantum information theory. Entanglement distillation is often utilized for designing quantum computer networks and quantum repeaters. The practical entanglement distillation problem is formulated as a bilevel optimization problem. A fuzzy formulation is introduced to estimate the quantum state (density matrix) from pseudo-likelihood functions (i.e., quantum state tomography). A scale-independent relationship between fuzzy relations in terms of the pseudo-likelihood functions is obtained. The entanglement distillation optimization problem is solved using the combined coupled map lattice and dual annealing approach. Comparative analysis of the results is then conducted against a standard dual annealing algorithmic implementation.
Medium-Term Horizon Time Photovoltaic Power Generation Prediction for an Island Zone

This article presents a Johansen test assessing the predetermined long-term relationships of datasets from a photovoltaic (PV) plant to predict the power output of an island zone. The goal was to use Johansen’s model to predict the PV power generation in the island of Mauritius. In this article, time series using an on-site measurement dataset have been used to design an original prediction model, the Johansen model for PV power output. This model is trained to predict random monthly, weekly, and daily PV power outputs in different seasons and years. The experimental results demonstrate that the Johansen model is a powerful medium-term predicting tool.
iMouse: Augmentative Communication with Patients Having Neuro-Locomotor Disabilities Using Simplified Morse Code

Patients with amyotrophic lateral sclerosis (ALS), also known as Lou Gehrig’s disease, an incurable disease in which motor neurons are selectively destroyed, gradually lose their mobility as organ dysfunction begins, and eventually, patients find it challenging to make even minor movements and simple communications. To communicate with patients with quadriplegia, researchers have focused on movements of the eye, the only moving organ for patients with ALS, and they have investigated the detection of eyeblinks using brainwaves or cameras or other ways to select letters on a screen via eyeball movements based on eye-tracking cameras. However, brainwave-based techniques, which use the electrical signals of eye movements to determine patient’s intentions, are sensitive to noise, often resulting in the inaccurate identification of intent. Alternatively, a camera-based method that uses letter selection detects the movement of eye feature-points, and this method makes it easy to identify a patient’s intentions using a predefined decision-making process. However, it has long processing time and is prone to inaccuracy due to errors in either the Morse code implementation assigned to all alphabets or the sequential selection methods. Therefore, we have proposed iMouse-sMc, a simplified Morse code-based user interface model using an eye mouse for faster and easier communication with such patients. Furthermore, we improved the detection performance of the eye mouse by applying image contrast techniques to enable communication with patients even at night. To verify the excellent performance of the proposed eye mouse for a user interface, we conducted comparative experiments with existing camera-based communication models based on various words. The results revealed that the time of communication was reduced to 83 s and the intention recognition accuracy was improved by ~28.16%. Additionally, even in low-light environments, where existing models are unable to communicate with the patients due to difficulties with eye detection, the proposed model demonstrated its eye detection capability and proved that it can be used universally for communication with patients during the day and at night.
Combined Framework of Multicriteria Methods to Identify Quality Attributes in Augmented Reality Applications

This study proposes a combined framework of multicriteria decision methods to describe, prioritize, and group the quality attributes related to the user experience of augmented reality applications. The attributes were identified based on studies of high-impact repositories. A hierarchy of the identified attributes was built through the multicriteria decision methods Fuzzy Cognitive Maps and DEMATEL. Additionally, a statistical analysis of clusters was developed to determine the most relevant attributes and apply these results in academic and industrial contexts. The main contribution of this study was the categorization of user-experience quality attributes in augmented reality applications, as well as the grouping proposal. Usability, Satisfaction, Stimulation, Engagement, and Aesthetics were found to be among the most relevant attributes. After carrying out the multivariate analysis, two clusters were found with the largest grouping of attributes, oriented to security, representation, social interaction, aesthetics, ergonomics of the application, and its relationship with the user’s emotions. In conclusion, the combination of the three methods helped to identify the importance of the attributes in training processes. The holistic and detailed vision of the causal, impact, and similarity relationships between the 87 attributes analyzed were also considered. This framework will allow the generation of a baseline for the use of multicriteria methods in research into relevant aspects of Augmented Reality.
Adversarial Examples in Visual Object Tracking in Satellite Videos: Cross-Frame Momentum Accumulation for Adversarial Examples Generation

The visual object tracking technology of remote sensing images has important applications in areas with high safety performance such as national defense, homeland security, and intelligent transportation in smart cities. However, previous research demonstrates that adversarial examples pose a significant threat to remote sensing imagery. This article first explores the impact of adversarial examples in the field of visual object tracking in remote sensing imagery. We design a classification- and regression-based loss function for the popular Siamese RPN series of visual object tracking models and use the PGD gradient-based attack method to generate adversarial examples. Additionally, we consider the temporal consistency of video frames and design an adversarial examples attack method based on momentum continuation. We evaluate our method on the remote sensing visual object tracking datasets SatSOT and VISO and the traditional datasets OTB100 and UAV123. The experimental results show that our approach can effectively reduce the performance of the tracker.
To Study the Impact of AI on Business and Customer Services - A Case Study on Swiggy

In the food delivery sector, the usage of artificial intelligence (AI) technologies has grown significantly. An important player in the Indian market for online food ordering and delivery, Swiggy, has been looking into how AI could improve its business processes and customer support. This topic intends to analyze the research on artificial intelligence (AI) utilisation in the food delivery sector and investigate its possible uses for Swiggy. The paper covers several AI applications and their effects on many business elements, such as logistics optimization algorithms, NLP-based chat bots, and picture recognition-based recommendation systems. According to the study, Swiggy could boost customer service, increase delivery efficiency, and give users tailored recommendations if it used AI. However, it is essential to assure the moral use of AI and address any potential privacy and prejudice issues. The study comes to the conclusion that AI offers a lot of potential for enhancing Swiggy's business processes and customer service, which would eventually improve customer satisfaction and retention. To do the findings of this topic the sample size of 400 restaurants were taken into account to know the customer preference and the service provided by the restauramt partners in the Swiggy platform and to rectify there problems and issues faced by them in accepting the orders and their online presence in the Swiggy app so that there customer ratings and service can be increased.