Miracle-dz/UniFAR-FTUs huggingface dataset:
Detectability of Denial-of-Service Attacks on Arbitrarily Varying Classical-Quantum Channels

Communication systems are subject to adversarial attacks since malevolent adversaries might harm and disrupt legitimate transmissions intentionally. Of particular interest in this paper are so-called denial-of-service (DoS) attacks in which the jammer completely prevents any transmission. Arbitrarily varying classical-quantum channels, providing a suitable model to capture the jamming attacks of interest, are studied. Algorithmic detection frameworks are developed based on Turing machines and also Blum-Shub-Smale (BSS) machines, where the latter can process and store arbitrary real numbers. It is shown that Turing machines are not capable of detecting DoS attacks. However, BSS machines are capable thereof implying that real number signal processing enables the algorithmic detection of DoS attacks.
Approximate Trace Reconstruction: Algorithms

We introduce approximate trace reconstruction, a relaxed version of the trace reconstruction problem. Here, instead of learning a binary string perfectly from noisy samples, as in the original trace reconstruction problem, the goal is to output a string that is close in edit distance to the original string using few traces. We present several algorithms that can approximately reconstruct strings that belong to certain classes, where the estimate is within $n$ / polylog (n) edit distance and where we only use polylog (n) traces (or sometimes just a single trace). These classes contain strings that require a linear number of traces for exact reconstruction and that are quite different from a typical random string. From a technical point of view, our algorithms approximately reconstruct consecutive substrings of the unknown string by aligning dense regions of traces and using a run of a suitable length to approximate each region. A full version of this paper is accessible at: https://arxiv.org/abs/2012.06713. pdf
Perceived Listening Difficulty in the Classroom, Not Measured Noise Levels, Is Associated With Fatigue in Children With and Without Hearing Loss.

Purpose The purpose of this study was to examine if classroom noise levels and perceived listening difficulty were related to fatigue reported by children with and without hearing loss. Method Measures of classroom noise and reports of classroom listening difficulty were obtained from 79 children (ages 6-12 years) at two time points on two different school days. Forty-four children had mild to moderately severe hearing loss in at least one ear. Multiple regression analyses were conducted to evaluate if measured noise levels, perceived listening difficulty, hearing status, language abilities, or grade level would predict self-reported fatigue ratings measured using the Pediatric Quality of Life Inventory Multidimensional Fatigue Scale. Results Higher perceived listening difficulty was the only predictor variable that was associated with greater self-reported fatigue. Conclusions Measured classroom noise levels showed no systematic relationship with fatigue ratings, suggesting that actual classroom noise levels do not contribute to increased reports of subjective fatigue. Instead, perceived challenges with listening appears to be an important factor for consideration in future work examining listening-related fatigue in children with and without hearing loss.
A decision framework for estimating the cost of marine plastic pollution interventions

Marine plastic pollution has emerged as one of the most pressing environmental challenges of our time. Although there has been a surge in global investment for implementing interventions to mitigate plastic pollution, there has been little attention given to the cost of these interventions. We developed a decision support framework to identify the economic, social, and ecological costs and benefits of plastic pollution interventions for different sectors and stakeholders. We calculated net cost as a function of six cost and benefit categories with the following equation: cost of implementing an intervention (direct, indirect, and nonmonetary costs) minus recovered costs and benefits (monetary and nonmonetary) produced by the interventions. We applied our framework to two quantitative case studies (a solid waste management plan and a trash interceptor) and four comparative case studies, evaluating the costs of beach cleanups and waste‐to‐energy plants in various contexts, to identify factors that influence the costs of plastic pollution interventions. The socioeconomic context of implementation, the spatial scale of implementation, and the time scale of evaluation all influence costs and the distribution of costs across stakeholders. Our framework provides an approach to estimate and compare the costs of a range of interventions across sociopolitical and economic contexts.
Parallel and Pipelining design of SLAM Feature Detection Algorithm in Hardware

Simultaneous Localization and Mapping (SLAM) is a system used to achieve autonomous positioning and navigation. Feature detection is an important part of a SLAM system as fast and robust image matching is required for the task. A typical feature detection algorithm called Speeded-Up Robust Features (SURF) is used in a robot SLAM system with Moving Object Detection (MOD). This paper describes a modified feature detection algorithm based on Field Programmable Gate Array (FPGA) hardware. The paper focuses on implementing the software algorithm on a hardware platform. The advantage of the parallel and pipelining design of FPGA is fully applied to highly improve the performance and efficiency of the system. By using the FPGA hardware platform, the algorithm can also be implemented easily in an FPGA-based SLAM system afterward to finally use for System-On-Chip (SoC) applications.
The architectural divergence problem in security and privacy of eHealth IoT product lines

The Internet of Things (IoT) seamlessly becomes integrated into many aspects of daily life, and in the case of healthcare, it arises in the shape of eHealth IoT systems. Evidently, the design of such systems must apply best practices when it comes to security and privacy, in addition to ensuring compliance with various national and international regulations. When it comes to the required functionality, commonalities and variations can effectively be managed in a product line approach that involves deriving specific application architecture variants from a common reference architecture. This paper illustrates and discusses a specific problem encountered in the establishment of a software product-line in this specific context: the adoption of systematic security and privacy threat modeling and risk assessment approaches introduces a variation space that is very difficult to capture in a proactive product-line approach. One of the main causes for this is that threat assessment itself suffers from the problem of threat explosion, i.e. combinatorial explosions of threats that have to be investigated and systematically mitigated. The highlighted divergence of the security and privacy threats across architectural variants is illustrated in the specific case of an industry IoT-based e-health software product line.
Use of electronic health records to manage tobacco screening and treatment in rural primary care.

PURPOSE Electronic health records (EHRs) can facilitate primary care providers' (PCPs) use of best practices in addressing tobacco dependence. It is unknown whether rural PCPs reap the same benefits as their urban counterparts when employing EHRs for this purpose. Our study examines this issue. METHODS This cross-sectional investigation based on the 2012-2015 National Ambulatory Medical Care Survey used chi-square tests and adjusted logistic regression models to explore how rurality and use of tobacco-related EHR functions were related to smoking status documentation (SSD) and cessation treatment at adult primary care visits. FINDINGS SSD rates were similar in visits to rural- and urban-based PCPs (88.2% rural-based vs 81.1% urban-based, P = .5819). Use of EHRs for SSD was associated with higher SSD odds at visits to both rural- and urban-based PCPs, but this increase was greater for visits to rural-based PCPs (428% vs 220% urban-based, P = .0443). Rates of cessation treatment at smokers' visits were low in rural and urban contexts (19.3% rural vs 19.6% urban, P = .9430). Odds of cessation treatment were 68% higher where EHRs were used to remind PCPs of treatment guidelines (P = .001), with no rural-urban difference in the size of the increase. Access to EHRs with tobacco-related functions was similar across rural and urban practices. CONCLUSIONS Rural-based PCPs were at least as successful as urban-based PCPs in leveraging EHRs to enhance tobacco-related services. Even where EHRs are used, opportunities exist to expand cessation treatment in rural primary care.
Vulture Abundance and Habitat Association Following Major Hurricane Disturbance in the Tropical Dry Forest of Western Mexico

Abstract. Scavenger birds are common species frequently associated with open habitats, but sympatric vulture species have differing natural history traits that may influence seasonal habitat relationships and responses to natural disturbances, such as hurricanes. We evaluated seasonality and habitat relationships of Black Vultures (Coragyps atratus) and Turkey Vultures (Cathartes aura) at sites within and outside the path of maximum winds of Hurricane Patricia in western Mexico. We used 1.5-km line transects to survey vultures during dry and rainy seasons in four habitats of the tropical dry forest biome over a 2-yr period after hurricane landfall. We measured habitat structure along each transect using the point-centered quarter method, and determined landscape composition within a 3000-ha circular plot centered around the midpoint of each transect. Vulture abundance was influenced by habitat structure variables of open areas, but at sites affected by hurricane disturbance vulture abundance was positively associated with increased cover of wetlands and agricultural land. Both species of vultures had low abundance in forests, and Black Vultures had significantly higher abundance in agricultural land and wetlands. Season significantly influenced vulture abundance by habitat: agricultural land and wetlands had higher vulture abundance in the rainy season, but forests had higher abundance in the dry season. Disturbance by a major hurricane induced species-specific changes in spatial-temporal abundance of vultures: the Black Vulture in particular had higher abundance in all habitats except wetlands during the dry season, and greater abundance in the first months after hurricane landfall. Hence, although vultures occur predominantly in open areas, they show species-specific habitat associations and responses to hurricane disturbance.
Air Expulsion Analysis of an Industrial Air Valve Using CFD

CFD (Computational Fluid Dynamic) analysis is a modern technique to estimate and determine fluid characteristics. CFD uses the Navier-Stokes equation to solve complex fluid behavior along with turbulence models such as k-Ɛ which is well known for solving engineering and scientific problems. The objective of this paper is to determine the performance of an industrial component in the pipeline "Bermad Air Valve" where this valve plays a key role in releasing and intaking air volume in a pressurized system. The air valve has three different positions, fully open, partially closed, and fully closed. The procedure started with preparing the geometry, meshing, and boundary condition setup for a fully and 99% closed valve. The results were compared with the experimental air valve test along with CFD analysis done previously in an experimental article. The final data gathered from the valve outlet shows that CFD is an effective engineering tool to predict fluid behavior.
Closed-Loop Position Control of a Self-Sensing 3-DoF Origami Module With Pneumatic Actuators

This letter presents closed-loop position control of a pneumatically actuated modular robotic platform “pneumagami” that can be stacked to enlarge work and design space for wearable applications. The module is a 3 degrees of freedom (DoF) parallel robot with two rotational and one translational motion, which is actuated by three antagonistic pneumatic pouch motor pairs attached to three leg joints. To control the pouch motors, we utilize miniature proportional valves. As for the sensing, we introduce a novel embedded resistive sensor mechanism utilizing rotary-to-translational transmission. The sensor's transmission is modeled and verified by experiments. Furthermore, we study analytic forward and inverse kinematic models of the pneumagami module. Utilizing the models, we design a closed-loop feedback controller to track two different trajectories. The experimental results show that the module follows the desired trajectories successfully. Thus, we report that the proposed pneumagami modules can be utilized for achieving a controllable robotic third arm with higher DoFs and range of motion (RoM) when connected in series.
Language-agnostic speech anger identification

Following the constantly increasing adoption of affective computing based solutions, this paper investigates the feasibility of multilingual anger identification. To this end, we formed such a corpus by suitably combining seven different datasets representing five different languages, i.e. English, German, Italian, Urdu, and Persian. After analyzing the diverse characteristics of the datasets, we designed four classification algorithms, namely Support Vector Machine, Decision Tree-based Bagging scheme, Convolutional Neural Network, and Convolutional Recurrent Neural Network. Such classification mechanisms are trained on appropriate features extracted from time and/or frequency domains, while speech data have been balanced considering every diverse characteristic incorporated in the datasets (language, sex, acted, etc.). Our findings render multilingual anger identification feasible since the proposed audio pattern recognition methodology based on Mel-spectrograms and CRNN achieved quite satisfactory identification rates.
Impact of communication delay and temporal sensitivity on perceived workload and teleoperation performance

As teleoperated robotic units operate in remote and distant environments such as outer space, the communication latency impacts the operator’s performance. Yet, the delay impact on teleoperation performance and mental workload has scarcely been evaluated. Human temporal sensitivity appears to modulate the impact of latency on operators’ performance but no joint assessment of temporal sensitivity and subjective workload has been reported. In this study, we assess the relationship between the impact of communication delay on teleoperation performance, mental workload, and operators’ temporal sensitivity. Sixteen participants completed two online tasks: a duration reproduction task in which they were asked to reproduce the duration of previously presented visual stimuli, and an egocentric maze navigation task which required participants to escape a static maze, under an input latency of 0, 400, and 3000 ms. Completion time, move count, and error rate were recorded for each trial, along with perceived workload using the NASA-TLX questionnaire. The results showed that performance was significantly deteriorated by an increase in communication delay. Moreover, participants’ self-rated performance decreased with a larger communication delay, while their reported frustration, effort, and mental demands significantly increased. Interestingly, a possible effect of the temporal sensitivity profile on teleoperation performance - number of moves - was found, with a reduced number of moves for sensitive participants compared to insensitive ones, following a speed/accuracy trade-off (yet not significant). Hence, different operators’ strategies were uncovered, depending on their temporal sensitivity profile, to mitigate the impact of communication delay on the mission outcome.
A Localizability Estimation Method for Mobile Robots Based on 3D Point Cloud Feature

This paper introduces the theoretical analysis and derivation of localizability estimation for mobile robots based on point cloud observation. Combined with the observation model, the pre-established point cloud map is firstly clustered and segmented with a newly proposed method considering multiple constraints on depth map. Then localizability is set to be equal to the strength of the constraints associated with 3D point cloud features. In addition, based on the method of using information matrix theory, this paper integrates the Fisher's information matrix and point cloud features to estimate localizability.
‘#hellobrother needs to trend’: methodological reflections on the digital and emotional afterlife of mediated violence

ABSTRACT In March 2019, the first ever act of terrorist violence in New Zealand was live-streamed on social media, making many social media users unwitting witnesses to the massacre on their devices. The Christchurch mosque attacks revealed a particular digital and emotional vulnerability embedded in the digital media infrastructure. The last words of the first victim soon transmorphed into #hellobrother that, as a digital artefact, participated in shaping the emotional landscape. Combining real-time digital media ethnography on Twitter with data science and computational tools, this multi-method study has two aims: first and foremost, to develop and apply new methodology for the study of unexpected, mediated events as they unfold in real time; second, to explore post-death digital artefacts through the concept of digital afterlife that we approach through two complementary perspectives, data afterlife (the technological) and data as afterlife (the emotional). Adopting a relational perspective, we further develop the concept, and highlight the constitutive role of data in the emotional dimension of digital afterlife arising from its capacity to enter affective arrangements. The methodological contributions include development of a conceptual and technological framework for conducting data science as ethnography and the introduction of Tweetboard, a novel artefact for investigating digital afterlife.
Distributed Interference Alignment for K-user Interference Channels via Deep Learning

In this paper, we develop a framework for an autoencoder based transmission strategy for achieving distributed interference alignment and optimal power allocation in a multiuser interference channel. The users in the interference channel have access to the local channel state information only. We compare the explicit schemes, such as MaxSINR [1], against the autoencoder schemes. We find that the MaxSINR schemes outperform the autoencoder networks which are either jointly or distributively trained from scratch. However, we find that the autoencoders which are pretrained with the beamforming vectors and the power allocation obtained from the explicit schemes outperform the explicit schemes when the interference gets stronger. The explicit schemes perform well as they are effective in choosing the set of users which are to be suppressed. The pretrained autoencoders benefit from this initialization, and also from the fact that end to end training can improve their performance even further. We showcase our performance comparison results for 5 user interference channels with different levels of interference.
Bilateral Symmetry Has No Effect on Stereoscopic Shape Judgments

A single experiment is reported that measured the apparent stereoscopic shapes of symmetric and asymmetric objects at different viewing distances. The symmetric stimuli were specifically designed to satisfy the minimal conditions for computing veridical shape from symmetry. That is to say, they depicted complex, bilaterally symmetric, plane-faced polyhedra whose symmetry planes were oriented at an angle of 45° relative to the line of sight. The asymmetric stimuli were distorted versions of the symmetric ones in which the 3D position of each vertex was randomly displaced. Prior theoretical analyses have shown that it is mathematically possible to compute the 3D shapes of symmetric stimuli under these conditions, but those algorithms are useless for asymmetric objects. The results revealed that the apparent shapes of both types of objects were expanded or compressed in depth as a function of viewing distance, in exactly the same way as has been reported in many other studies, and that the presence or absence of symmetry had no detectable effect on performance.
Influence of CTAB surfactant on structural and optical properties of CuS and CdS nanoparticles by hydrothermal route

ABSTRACT Metal sulphide CuS and CdS nanoparticles capped with Cetyltrimethylammonium bromide (CTAB) were synthesized by hydrothermal method. Structural, morphological, chemical composition, optical and luminescent properties were evaluated by different analytical techniques. X-ray diffraction (XRD) analysis of the CTAB capped metal sulfide nanoparticles reveals the formation of hexagonal structure. High-resolution transmission electron microscopy (HRTEM) images show that the morphology of the capped copper sulphide samples consists of hexagonal structure and capped cadmium has spherical shape and also confirms the crystalline nature of the particles with distinct lattice fringes. In FTIR spectroscopy, the composition of the CTAB capped CuS and CdS nanoparticles have been confirmed. The analysis of photoluminescence (PL) and optical transition show a red shift due to the reduction of band gap energy and it is attributed to the low defects and high crystallinity. The optical studies indicate that CuS and CdS nanoparticles with CTAB can be suitable for optoelectronic devices and photovoltaic applications.
Epidemic Contact Tracing With Campus WiFi Network and Smartphone-Based Pedestrian Dead Reckoning

Due to the rapid spreading of infectious disease COVID-19, numerous campus students are increasingly exposed to the dilemma and thus the provisioning of a safe environment becomes of vital importance. As a well-established approach, contact tracing could contain epidemic diseases spread. Since WiFi network could cover almost the whole campus and each student carries at least one WiFi capable device (i.e., smartphone), in this work, an epidemic contact tracing with campus WiFi network and smartphone-based pedestrian dead reckoning (PDR) is proposed, involving not only coarse-grained duration, but also fine-grained distance between students. First, students’ location distribution and duration are captured by non-perception WiFi network logs with highly flexibility. Then, the convolutional neural network (CNN) model is utilized to real-time recognize landmarks in PDR positioning trajectory, followed by the particle filter algorithm to fuse both the PDR positioning results and detected landmarks, thereby calibrating PDR cumulative error and calculating the social distance between students. Next, we analyze the contact degree between students by integrating duration and social distance. Finally, in a campus environment with an coverage of about 600m2, we simulate a COVID-19 case study to validate proposed approach, showing that the average positioning error is reduced from 3.23m to 2.77m.