Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Parameter Identification Method for a 2(3HUS+S) Parallel Manipulator

To improve the control accuracy of a dynamic controller, the dynamic parameters should be identified. A method for identifying the dynamic parameters of a 2(3HUS+S) parallel manipulator, which only has rotational degrees of freedom, was investigated in this study. Based on the principle of virtual work, a dynamic model was established. In addition, a model for identifying dynamic parameters was established based on the matrix transformation principle. Based on the structure and movement characteristics of a parallel manipulator, an identification strategy of the dynamic parameters was designed using disassembly measurement and system identification methods. The identification strategy was used to identify the dynamic parameters through a motion track design under different conditions, which improves the accuracy of the identification results of a single dynamic parameter. The least squares method was used to improve the identification accuracy of the experiment data. Furthermore, by comparing the confidence level of the driving torque predictive value before and after identification, the accuracy and effectiveness of the method for identifying dynamic parameters were verified.
Uncertainty-Aware Prognosis via Deep Gaussian Process

The task of predicting how long a certain industrial asset will be able to operate within its nominal specifications is called Remaining Useful Life (RUL) estimation. Efficient methods of performing this task promise to drastically transform the world of industrial maintenance, paving the way for the so-called Industry 4.0 revolution. Given the abundance of data resulting from the advent of the digitalization era, Machine Learning (ML) models are the ideal candidates for tackling the RUL estimation problem in a fully data-driven fashion. However, given the safety-critical nature of maintenance operations on industrial assets, it’s crucial that such ML-based methods be designed such that their levels of transparency and reliability are maximized. Modern ML algorithms, however, are often employed as black-box methods, which do not provide any clue regarding the confidence level associated with their output. In this paper, we address this limitation by investigating the performance of a recently proposed class of algorithms, Deep Gaussian Processes, which provide uncertainty estimates associated with their RUL prediction, yet retain the expressive power of modern ML techniques. Contrary to standard approaches to uncertainty quantification, such methods scale favourably with the size of the available datasets, allowing their usage in the “big data” setting. We perform a thorough evaluation and comparison of several variants of DGPs applied to RUL predictions. The performance of the algorithms is evaluated on the NASA N-CMAPSS (New Commercial Modular Aero-Propulsion System Simulation) dataset for aircraft engines. The results show that the proposed methods are able to yield very accurate RUL predictions along with sensible uncertainty estimates, providing more reliable solutions for (safety-critical) real-life industrial applications.
Biomedical Sensing Using Quantum Radars Based on Josephson Parametric Amplifiers

We discuss the feasibility of using microwave quantum radars in biomedical sensing applications, with a heavy focus on the equipment needed to operate such radars. A recent microwave quantum radar experiment used a device called a Josephson parametric amplifier (JPA) to generate quantum entangled signals. Since JPAs or JPA-like devices are expected to be used in most quantum radar implementations in the foreseeable future, we examine the operating requirements of JPAs. We find that the most important requirement for operating a JPA is the need for cryogenic cooling. This is easily accommodated in medical environments such as hospitals, so it is natural to consider whether quantum radars could work for biomedical sensing. We then find that, in fact, many of the strengths of quantum radars are naturally suited to medical contexts. We therefore conclude that the case for using JPA-based quantum radars for medical sensing is a strong one.
A YOLO-Based Approach for Aedes Aegypti Larvae Classification and Detection

Mosquitoes are small, flying insects that can be found almost anywhere in the world. Although most of these creatures are just considered a nuisance, a few of its species can be vectors of some of the deadliest viruses and diseases that can greatly affect living organisms-animals and people alike. Aedes aegypti species, for instance, are deemed as the primary vector of the infamous dengue virus that has been around for a long time. With this, there have been many studies, mostly only with limited success, in different fields of learning on how to control and suppress both the mosquitoes and the diseases they carry. While some of these studies and research experiments are geared towards the use of modern technology such as machine learning, deep learning, and image processing, there are still a lot of strategies and methods that are yet to be discovered to improve present-day approaches and attain better results that are more sustainable and efficient. And so, the main goal of this study is to aid in the early suppression and prevention of the primary vector of dengue by creating a system implemented with the You Only Look Once (YOLO) algorithm that can detect and classify whether a mosquito larva is an Aedes aegypti species or not. Results showed that the system was able to correctly recognize most of the larval samples subjected to testing, with a root mean square error of 0.45 and 0.77 for Aedes and Non-Aedes larvae respectively.
Body Core Temperature Estimation Using New Compartment Model With Vital Data From Wearable Devices

With increasing heat-wave frequency, the prevention and public awareness of heat-related illnesses has become an essential topic. In the standard for heat strain and stress, empirical guidelines to prevent excess core temperature rise above 1 °C have been prescribed for workers. However, measuring core temperature change in our daily life or working place is not straightforward. The estimation of core temperature from measured vital signals in a non-invasive manner is thus essential for the management of heat stress or strain. Here, we propose an estimation method for core temperature change by a simplified thermodynamics model with the measured heart rate and ambient conditions (temperature and relative humidity). Our proposed model is based on a two-layer two-compartment model with tuned parameters, which were derived from comparison between the computations using high-resolution anatomical human body model. Our model exhibited good agreement with the measured core temperature rise; the computed and measured core temperature rise for the naked trial were 0.54 °C and 0.53 °C, whereas those for the clothed trial were 0.70 °C, and 0.71 °C, respectively. Furthermore, our compartment model with vital data measured from a wearable device achieved good estimation in real time for field measurement in addition to computational replication with a previous study.
Influences of EWOM in Social Media on Consumer's Purchase Intention on Online Video Streaming

Electronic Word of Mouth (eWOM) is considered as one of marketing tools moreover in the digital business environment like online video streaming. This research investigates how eWOM can influence consumer’s purchase intention with online video streaming. The conceptual framework developed with the IAM by considering eWOM quality, eWOM quantity, eWOM credibility, Needs of information, Information usefulness, Information adoption and Purchase intention. Based on questionnaires collected from 163 respondent’s eWOM quality, eWOM credibility, and Needs of Information are proven to influence information usefulness and finally influence the purchase intention even though eWOM quantity is not influential.
Machine Learning-based Channel Tracking for Next-Generation 5G Communication System

The use of millimeter-wave (mmWave) frequencies is a promising technology for meeting the ever-growing data traffic in next-generation wireless communications. A major challenge of mmWave communications is the high path loss. To overcome this issue, mmWave systems adopt beamforming techniques, which require robust channel estimation and tracking algorithms to maintain an adequate quality of service. In this study, we propose the machine learning-based channel tracking algorithm for vehicular mmWave communications. In this paper, we propose a long short-term memory (LSTM)-based channel tracking algorithm for vehicle-to-infrastructure mmWave communications. The bidirectional LSTM is leveraged to track the channel. Simulation results demonstrate that the proposed algorithm efficiently tracks the mmWave channel with negligible training overhead.
Low-Noise InGaAs/InP Single-Photon Avalanche Diodes for Fiber-Based and Free-Space Applications

We present the design and the experimental characterization of a new InGaAs/InP single-photon avalanche diode (SPAD), with two different diameters: i) a 10 μm device, suitable for optical fiber-based quantum applications; ii) a 25 μm one, more appropriate for free-space applications. Compared to a previous generation, we improved the design of the double zinc diffusion and optimized the layer structure. We achieved low dark count rate, around 1 kcps and 4 kcps at 225 K and 5 V excess bias for 10 μm and 25 μm devices, respectively, and down to few tens of counts per seconds at 175 K for the 10 μm detector. At 5 V excess bias and 225 K temperature, both devices also show a high photon detection efficiency (33% at 1064 nm, 31% at 1310 nm and 25% at 1550 nm for the 10 μm SPAD). Afterpulsing has been measured with a custom readout integrated circuit, achieving very low probability values. Timing jitter is comparable to previous-generation devices.
Measurement of Momentum and Recoil Impulse of Plasma Jet of Vacuum Arc Thruster

To measure the mechanical momentum of a plasma jet, the deflection of a collector suspended on long threads in front of the jet was analyzed (ballistic pendulum method). Alternatively, the recoil impulse in frequency mode of arc operation was measured with a specially designed thrust balance. The dependence of the vacuum arc plasma jet momentum and recoil impulse on the arc current amplitude was studied in the current range from 10 to 800 A. The experiment shows that at a relatively low arc current (<100 A), the specific reaction force is close to <inline-formula> <tex-math notation="LaTeX">$150~\mu \text{N}$ </tex-math></inline-formula>/A, which is consistent with the known data; with a further increase in the arc current (>100–150 A), the specific reaction force increases and can significantly exceed the above mentioned value. In addition, the specific reaction force strongly increases when the axial magnetic field is applied to the discharge cell. The experiments performed with two cathode materials are also compared. The material presented in the article is of interest for fundamental and methodological issues as well as for applications primarily to the creation and testing of vacuum arc based micro-thrusters (<inline-formula> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula>VAT) for small/ultra-small satellites.
Minerva- An Efficient Risk-Limiting Ballot Polling Audit

Evidence-based elections aim to produce trustworthy and compelling evidence of the correctness of election outcomes, enabling the detection of problems with high probability. They require a well-curated voter-veriﬁed paper trail, compliance audits, and a rigorous tabulation audit of the election outcome, known as a risk-limiting audit (RLA). This paper focuses on ballot polling RLAs which can require that a very large sample of ballots be drawn. The main ballot polling RLA in use today, BRAVO, is designed for use when single ballots are drawn at random and a decision regarding whether to stop the audit or draw another ballot is taken after each ballot draw. But in practice, ballot polling audits draw many ballots in a single round before determining whether to stop. Direct application of B RAVO to large rounds results in considerable inefﬁciency. We present M INERVA , a risk-limiting audit that addresses this problem. When compared to the B RAVO stopping rule being applied at the end of the round, for a ﬁrst-round with 90% stopping probability, M INERVA halves the number of ballots required across all state margins in the 2020 US Presidential election. When compared to the B RAVO stopping rule being applied after examination of individual ballots, M INERVA reduces the number of ballots by about a quarter. M INERVA requires that round sizes are predetermined; this does not appear to be a drawback for large ﬁrst rounds which have been typical choices for election ofﬁcials. Ballot-polling audits are the leading option in most states. M INERVA
Explanation Beats Context: The Effect of Timing & Rationales on Users' Runtime Permission Decisions

Current mobile platforms leave it up to the app developer to decide when to request permissions (timing) and whether to provide explanations why and how users’ private data are accessed (rationales) . Given these liberties, it is important to understand how developers should use timing and ratio-nales to effectively assist users in their permission decisions. While guidelines and recommendations for developers exist, no study has systematically investigated the actual inﬂu-ence of timing, rationales, and their combinations on users’ decision-making process. In this work, we conducted a comparative online study with 473 participants who were asked to interact with mockup apps drawn from a pool of 120 variations of 30 apps. The study design was guided by devel-opers’ current permission request practices derived from a dynamic analysis of the top apps on Google Play . Our results show that there is a clear interplay between timing and rationales on users’ permission decisions and the evaluation of their decisions, making the effect of rationales stronger when shown upfront and limiting the effect of timing when rationales are present. We therefore suggest adaptation to the available guidelines. We also ﬁnd that permission decisions depend on the individuality of users, indicating that there is no one-ﬁts-all permission request strategy, upon we suggest better individual support and outline one possible solution.
"Whether it's moral is a whole other story": Consumer perspectives on privacy regulations and corporate data practices

Privacy laws govern the collection, use, and disclosure of personal information by businesses. Through an online survey with 300 participants and a follow-up interview with 32 participants, we investigate Canadians’ awareness of their privacy rights and how businesses manage their personal information. Further, we explore how Canadians respond to hypotheti-cal privacy violations using ten scenarios adapted from real cases. Our participants are generally aware of having privacy rights but have insufﬁcient knowledge and resources to ex-ercise those rights properly. Participants did not necessarily equate compliance with the law as sufﬁcient for ethical conduct. Through our analysis, we identiﬁed a “moral code” that consumers rely on to assess privacy violations based on the core moral values of trust, transparency, control, and access. We investigated perspectives on their rights and corporate data practices relating to their digital through a with and followed-up with 32 The studies explored general privacy perceptions and self-reported knowledge of
Risky Business? Investigating the Security Practices of Vendors on an Online Anonymous Market using Ground-Truth Data

Cybercriminal entrepreneurs on online anonymous markets rely on security mechanisms to thwart investigators in at-tributing their illicit activities. Earlier work indicates that – despite the high-risk criminal context – cybercriminals may turn to poor security practices due to competing business incentives. This claim has not yet been supported through empirical, quantitative analysis on ground-truth data. In this paper, we investigate the security practices on Hansa Market (2015-2017) and measure the prevalence of poor security practices across the vendor population ( n = 1 , 733). We create ‘vendor types’ based on latent proﬁle analysis, clustering vendors that are similar regarding their experience, activity on other markets, and the amount of physical and digital items sold. We then analyze how these types of vendors differ in their security practices. To that end, we capture their password strength and password uniqueness, 2FA usage, PGP adoption and key strength, PGP-key reuse and the traceability of their cash-out. We ﬁnd that insecure practices are prevalent across all types of vendors. Yet, between them large differences exist. Rather counter-intuitively, Hansa Market vendors that sell digital items – like stolen credit cards or malware – resort to insecure practices more often than vendors selling drugs. We discuss possible explanations, including that vendors of illicit digital items may perceive their risk to be lower than vendors of illicit physical items.
The Hijackers Guide To The Galaxy: Off-Path Taking Over Internet Resources

Internet resources form the basic fabric of the digital society. They provide the fundamental platform for digital services and assets, e.g., for critical infrastructures, ﬁnancial services, government. Whoever controls that fabric effectively controls the digital society. In this work we demonstrate that the current practices of Internet resources management, of IP addresses, domains, certiﬁcates and virtual platforms are insecure. Over long periods of time adversaries can maintain control over Internet resources which they do not own and perform stealthy manipulations, leading to devastating attacks. We show that network adversaries can take over and manipulate at least 68% of the assigned IPv4 address space as well as 31% of the top Alexa domains. We demonstrate such attacks by hijacking the accounts associated with the digital resources. For hijacking the accounts we launch off-path DNS cache poisoning attacks, to redirect the password recovery link to the adversarial hosts. We then demonstrate that the adversaries can manipulate the resources associated with these accounts. We ﬁnd all the tested providers vulnerable to our attacks. We recommend mitigations for blocking the attacks that we present in this work. Nevertheless, the countermeasures cannot solve the fundamental problem - the management of the Internet resources should be revised to ensure that applying transactions cannot be done so easily and stealthily as is currently possible.
Location Privacy Protection Scheme for LBS in IoT

The widespread use of Internet of Things (IoT) technology has promoted location-based service (LBS) applications. Users can enjoy various conveniences brought by LBS by providing location information to LBS. However, it also brings potential privacy threats to location information. Location data that contains private information is often transmitted among IoT networks in LBS, and such privacy information should be protected. In order to solve the problem of location privacy leakage in LBS, a location privacy protection scheme based on k -anonymity is proposed in this paper, in which the Geohash coding model and Voronoi graph are used as grid division principles. We adopt the client-server-to-user (CS2U) model to protect the user’s location data on the client side and the server side, respectively. On the client side, the Geohash algorithm is proposed, which converts the user’s location coordinates into a Geohash code of the corresponding length. On the server side, the Geohash code generated by the user is inserted into the prefix tree, the prefix tree is used to find the nearest neighbors according to the characteristics of the coded similar prefixes, and the Voronoi diagram is used to divide the area units to complete the pruning. Then, using the Geohash coding model and the Voronoi diagram grid division principle, the G-V anonymity algorithm is proposed to find k neighbors in an anonymous area so that the user’s location data meets the k -anonymity requirement in the area unit, thereby achieving anonymity protection of location privacy. Theoretical analysis and experimental results show that our method is effective in terms of privacy and data quality while reducing the time of data anonymity.
Replication: Effects of Media on the Mental Models of Technical Users

Media has been observed to inﬂuence users’ mental models in several domains. It was recently demonstrated that ﬁctional television and movies have a strong inﬂuence on non-technical end users’ mental models of security. We extended this study to explore its effect on 23 participants with technical backgrounds, given that misconceptions amongst this group could have important organisational impacts or could inﬂuence other non-technical end users. Our qualitative analysis reveals that technical participants sourced their mental models from both their academic or professional lives and from different forms of media (like news, cinema, forums, and social media). They were capable of identifying unrealistic depictions of hacking in the provided video clips and most could offer simplistic explanations about why these were problematic. We found that they generally had more nuanced understanding of the issues than non-technical end users, but they were not immune to misinformation from mass media.
Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America

While anthropogenic climate change poses a risk to freshwater resources across the globe through increases in evapotranspiration and temperature, it is essential to quantify the risks at local scales in response to projected trends in both freshwater supply and demand. In this study, we use empirical modeling to estimate the risks of municipal water shortages across North America by assessing the effects of climate change on streamflow and urban water demand. In addition, we aim to quantify uncertainties in both supply and demand predictions. Using streamflow data from both the US and Canada, we first cluster 4,290 streamflow gauges based on hydrograph similarity and geographical location. We develop a set of multiple linear regression (MLR) models, as a simplified analog to a distributed hydrological model, with minimum input data requirements. These MLR models are calibrated to simulate streamflow for the 1993–2012 period using the ERA5 climate reanalysis data. The models are then used to predict streamflow for the 2080–2099 period in response to two climate scenarios (RCP4.5 and RCP8.5) from five global climate models. Another set of MLR models are constructed to project seasonal changes in municipal water consumption for the clustered domains. The models are calibrated against collected data on urban water use from 47 cities across the study region. For both streamflow and water use, we quantified uncertainties in our predictions using stochastic weather generators and Monte Carlo methods. Our study shows the strong predictive power of the MLR models for simulating both streamflow regimes (Kling-Gupta efficiency >0.5) and urban water use (correlation coefficient ≈0.7) in most regions. Under the RCP4.5 (RCP8.5) emissions scenario, the West Coast, the Southwest, and the Deep South (South-Central US and the Deep South) have the highest risk of municipal water shortages. Across the whole domain, the risk is the highest in the summer season when demand is high. We find that the uncertainty in projected changes to the water demand is substantially lower than the uncertainty in the projected changes to the supply. Regions with the highest risk of water shortages should begin to investigate mitigation and adaptation strategies.