Miracle-dz/UniFAR-FTUs huggingface dataset:
Gait Phase Recognition Based on Air-pressure Mechanomyogram and Sensor Fusion

Gait phase recognition can improve the control of lower limb exoskeleton robot and promote human-machine collaboration. One of the current mainstream methods is to use electromyogram (EMG) to recognize gait phase, but the EMG signal has shortcomings such as weak signal, difficult to wear, easy to be affected by noise and sweat. Therefore, we designed a novel air-pressure mechanomyograph (PMMG) sensor, and further made a wearable PMMG-based sensing system composed of PMMG-based thighrings and inertial measurement units (IMUs). In order to improve the performance of gait phase recognition, we used five popular machine learning algorithms to fuse the data from PMMG-based thighrings and IMUs. We recruited three experimental subjects and constructed two datasets for different walking conditions: constant speed walking and variable speed walking. Experimental results show that the proposed PMMG sensor is effective, and the gait phase recognition accuracy reached 96.25% by using only the PMMG-based thighrings. In addition, we found that the performance of multi-modal sensor fusion is better than that of single-modal sensor fusion through three comparative experiments. Among the five machine learning algorithms, the SVM fusion model got the highest average accuracy of 98.82%.
Powerline Energy Harvesting Circuit with a Desaturation Controller for a Magnetic Core

The proposed powerline energy harvesting circuit aims to prevent saturation of a magnetic core, resulting increase of the harvested energy. The proposed magnetic field energy harvester (MFEH) has two secondary coils, the original one to harvest energy from the primary powerline and the additional one to desaturate the magnetic core. When the magnetic core is saturated by the magnetic field generated by the powerline, current starts to flow through the additional coil to desaturate the core. The desaturation controller is composed of a current sensor and a microcontroller unit (MCU) with associated switches. Experimental results show that the proposed circuit harvests 42.7 mW under powerline current of 25 A in rms. The circuit increases the amount of harvested power by 5.2 mW or 13.7 % through desaturation of the core.
Power Management for Energy Harvesting in IoT – A Brief Review of Requirements and Innovations

The Internet of Things (IoT) requires billions of wireless sensor network (WSN) nodes that rely on energy harvesting from ambient sources to recharge their limited-capacity batteries and achieve energy-autonomous operation. However, IoT is an evolving field with a vast number of applications that utilize different standards and protocols. Hence, power specifications for energy harvesting and WSN power management circuits vary and it can be difficult for circuit designers and researchers to identify the appropriate power specifications to target in their designs. This paper presents a brief guide on ambient power levels available for harvesting in low-power IoT applications as well as the power levels consumed by WSNs in various IoT communication protocols. It also presents a short review of various innovations in power management for low-power WSNs that enable harvesting with high power conversion efficiency. This work can be used as a resource by circuit designers researching power management for energy-harvested WSN nodes.
This paper is included in the Proceedings of the Seventeenth Symposium on Usable Privacy and Security.

Browser extensions enrich users’ browsing experience, e.g., by blocking unwanted advertisements on websites. To perform these functions, users must grant certain permissions during the installation process. These permissions, however, give very limited information about the fact that they allow the extension to access user’s personal data and browsing behaviour, posing security and privacy risks. To understand users’ awareness of these privileges and the associated threats, we conducted an online survey with 353 participants, focusing on users’ attitude, knowledge, and preference towards extensions’ permission requests. We found that users report interest in seeking information, trust the developers but do little to protect their data. They have limited knowledge about the technical abilities of browser extensions and prefer permission statements that evoke a clear mental model. Based on our ﬁndings we derive recommendations for the improvement of browser extension permission dialogues through clear language, technical improvements and distinct responsibilities.
Having Your Cake and Eating It: An Analysis of Concession-Abuse-as-a-Service

Concession Abuse as a Service (CAaaS) is a growing scam service in underground forums that defrauds online retailers through the systematic abuse of their return policies (via social engineering) and the exploitation of loopholes in company protocols. Timely detection of such scams is difﬁcult as they are fueled by an extensive suite of criminal services, such as credential theft, document forgery, and fake shipments. Ultimately, the scam enables malicious actors to steal arbitrary goods from merchants with minimal investment. In this paper, we perform in-depth manual and automated analysis of public and private messages from four large underground forums to identify the malicious actors involved in CAaaS, carefully study the operation of the scam, and deﬁne attributes to ﬁngerprint the scam and inform mitigation strategies. Additionally, we surveyed users to evaluate their attitudes toward these mitigations and understand the factors that merchants should consider before implementing these strategies. We ﬁnd that the scam is easy to scale—and can bypass traditional anti-fraud efforts—and thus poses a notable threat to online retailers.
PhishPrint: Evading Phishing Detection Crawlers by Prior Profiling

Security companies often use web crawlers to detect phishing and other social engineering attack websites. We built a novel, scalable, low-cost framework named PhishPrint to enable the evaluation of such web security crawlers against multiple cloaking attacks. PhishPrint is unique in that it completely avoids the use of any simulated phishing sites and blocklisting measurements. Instead, it uses web pages with benign content to proﬁle security crawlers. We used PhishPrint to evaluate 23 security crawlers including highly ubiquitous services such as Google Safe Browsing and Microsoft Outlook e-mail scanners. Our 70-day evaluation found several previously unknown cloaking weaknesses across the crawler ecosystem. In particular, we show that all the crawlers’ browsers are either not supporting advanced ﬁngerprinting related web APIs (such as Canvas API) or are severely lacking in ﬁngerprint diversity thus exposing them to new ﬁngerprinting-based cloaking attacks. We conﬁrmed the practical impact of our ﬁndings by deploying 20 evasive phishing web pages that exploit the found weaknesses. 18 of the pages managed to survive indeﬁnitely despite aggressive self-reporting of the pages to all crawlers. We conﬁrmed the speciﬁcity of these attack vectors with 1150 volunteers as well as 467K web users. We also proposed countermeasures that all crawlers should take up in terms of both their crawling and reporting infrastructure. We have relayed the found weaknesses to all entities through an elaborate vulnerability disclosure process that resulted in some remedial actions as well as multiple vulnerability rewards.
Cross-VM and Cross-Processor Covert Channels Exploiting Processor Idle Power Management

To achieve power-efﬁcient computing, processors engage idle power management mechanisms to turn on/off idle components according to the dynamics of the workload. A pro-cessor’s hardware components are classiﬁed and managed through the core and the uncore. The uncore is the supporting hardware shared by the cores, hence the decision of turning it on/off depends on the cores’ activities. Such dependency implies a covert channel threat in multi-core platforms. Specifically, the power status of the uncore reﬂects the workload pattern of the active core, and it can be probed by any process running on the processor. This allows the process to infer the workload information of the active core. We show this covert channel can work across processors and violate VM isolation. We validate the channel in in-house testbeds as well as proprietary cloud servers.
Data Transmission Evaluation and Allocation Mechanism of the Optimal Routing Path: An Asynchronous Advantage Actor-Critic (A3C) Approach

The delay tolerant networks (DTN), which have special features, differ from the traditional networks and always encounter frequent disruptions in the process of transmission. In order to transmit data in DTN, lots of routing algorithms have been proposed, like “Minimum Expected Delay,” “Earliest Delivery,” and “Epidemic,” but all the above algorithms have not taken into account the buffer management and memory usage. With the development of intelligent algorithms, Deep Reinforcement Learning (DRL) algorithm can better adapt to the above network transmission. In this paper, we firstly build optimal models based on different scenarios so as to jointly consider the behaviors and the buffer of the communication nodes, aiming to ameliorate the process of the data transmission; then, we applied the Deep Q-learning Network (DQN) and Advantage Actor-Critic (A3C) approaches in different scenarios, intending to obtain end-to-end optimal paths of services and improve the transmission performance. In the end, we compared algorithms over different parameters and find that the models build in different scenarios can achieve 30% end-to-end delay decline and 80% throughput improvement, which show that our algorithms applied in are effective and the results are reliable.
Cooperative Offloading in D2D-Enabled Three-Tier MEC Networks for IoT

Mobile/multi-access edge computing (MEC) takes advantage of its proximity to end-users, which greatly reduces the transmission delay of task offloading compared to mobile cloud computing (MCC). Offloading computing tasks to edge servers with a certain amount of computing ability can also reduce the computing delay. Meanwhile, device-to-device (D2D) cooperation can help to process small-scale delay-sensitive tasks to further decrease the delay of tasks. But where to offload the computing tasks is a critical issue. In this article, we integrate MEC and D2D cooperation techniques to optimize the offloading decisions and resource allocation problem in D2D-enabled three-tier MEC networks for Internet of Things (IoT). Mobile devices (MDs), edge clouds, and central cloud data center (DC) make up these three-tier MEC networks. They cooperate with each other to finish the offloading tasks. Each task can be processed by MD itself or its neighboring MDs at device tier, by edge servers at edge tier, or by remote cloud servers at cloud tier. Under the maximum energy cost constraints, we formulate the cooperative offloading problem into a mixed-integer nonlinear problem aiming to minimize the total delay of tasks. We utilize the alternating direction method of multipliers (ADMM) to speed up the computing process. The proposed scheme decomposes the complicated problem into 3 smaller subproblems, which are solved in a parallel fashion. Finally, we compare our proposal with D2D and MEC networks in simulations. Numerical results validate that the proposed D2D-enabled MEC networks for IoT can significantly enhance the computing abilities and reduce the total delay of tasks.
Security Obstacles and Motivations for Small Businesses from a CISO's Perspective

Small businesses (SBs) are often ill-informed and under-resourced against increasing online threats. Chief Information Security Ofﬁcers (CISOs) have a key role in contextualizing trade-offs between competing costs and priorities for SB management. To explore the challenges CISOs face when guiding SBs towards improved security we conducted two interview studies. Firstly, an exploratory study with CISOs with SB experience to identify themes related to their work (n=8). Secondly, we reﬁned our methods and conducted broader structured interviews with a larger non-overlapping group of similarly qualiﬁed SB CISOs (n=19) to validate those themes and extend outcomes. We found CISOs conﬁrmed common observations that SBs are generally unprepared for online threats, and uninformed about issues such as insurance and regulation. We also found that despite perceived usability problems with language and formatting, the effectiveness of government-authored guidance (a key reference source for CISOs and SBs) was deemed on par with commercial resources. These observations yield recommendations for better formatting, prioritizing, and timing of security guidance for SBs, such as better tailoring checklists, investment suggestions, and scenario-based exercises.
On the Robust Communication Complexity of Bipartite Matching

We study the robust – à la Chakrabarti, Cormode, and McGregor [STOC’08] – communication complexity of the maximum bipartite matching problem. The edges of an adversarially chosen n -vertex bipartite graph G are partitioned randomly between Alice and Bob. Alice has to send a single message to Bob, using which Bob has to output an approximate maximum matching of G . We are particularly interested in understanding the best approximation ratio possible by protocols that use a near-optimal message size of n · polylog ( n ). The communication complexity of bipartite matching in this setting under an adversarial partitioning is well-understood. In their beautiful paper, Goel, Kapralov, and Khanna [SODA’12] gave a 2 / 3 -approximate protocol with O ( n ) communication and showed that this approximation is tight unless we allow more than a near-linear communication. The complexity of the robust version, i.e., with a random partitioning of the edges, however remains wide open. The best known protocol, implied by a very recent random-order streaming algorithm of the authors [ICALP’21], uses O ( n log n ) communication to obtain a ( 2 / 3 + ε 0 )-approximation for a constant ε 0 ∼ 10 − 14 . The best known lower bound, on the other hand, leaves open the possibility of all the way up to even a (1 − ε )-approximation using near-linear communication for constant ε > 0. In this work, we give a new protocol with a significantly better approximation. Particularly, our protocol achieves a 0 . 716 expected approximation using O ( n ) communication. This protocol is based on a new notion of distribution-dependent sparsifiers which give a natural way of sparsifying graphs sampled from a known distribution. We then show how to lift the assumption on knowing the graph’s distribution via minimax theorems. We believe this is a particularly powerful method of designing communication protocols and might find further applications.
Credibility Assessment of a Subject-Specific Mathematical Model of Blood Volume Kinetics for Prediction of Physiological Response to Hemorrhagic Shock and Fluid Resuscitation

Subject-specific mathematical models for prediction of physiological parameters such as blood volume, cardiac output, and blood pressure in response to hemorrhage have been developed. In silico studies using these models may provide an effective tool to generate pre-clinical safety evidence for medical devices and help reduce the size and scope of animal studies that are performed prior to initiation of human trials. To achieve such a goal, the credibility of the mathematical model must be established for the purpose of pre-clinical in silico testing. In this work, the credibility of a subject-specific mathematical model of blood volume kinetics intended to predict blood volume response to hemorrhage and fluid resuscitation during fluid therapy was evaluated. A workflow was used in which: (i) the foundational properties of the mathematical model such as structural identifiability were evaluated; (ii) practical identifiability was evaluated both pre- and post-calibration, with the pre-calibration results used to determine an optimal splitting of experimental data into calibration and validation datasets; (iii) uncertainty in model parameters and the experimental uncertainty were quantified for each subject; and (iv) the uncertainty was propagated through the blood volume kinetics model and its predictive capability was evaluated via validation tests. The mathematical model was found to be structurally identifiable. Pre-calibration identifiability analysis led to splitting the 180 min of time series data per subject into 50 and 130 min calibration and validation windows, respectively. The average root mean squared error of the mathematical model was 12.6% using the calibration window of (0 min, 50 min). Practical identifiability was established post-calibration after fixing one of the parameters to a nominal value. In the validation tests, 82 and 75% of the subject-specific mathematical models were able to correctly predict blood volume response when predictive capability was evaluated at 180 min and at the time when amount of infused fluid equals fluid loss.
Stepwise Weakening of Aeolian Activities During the Holocene in the Gannan Region, Eastern Tibetan Plateau

Aeolian sediments hold key information on aeolian history and past environmental changes. Aeolian desertification and extensive land degradation have seriously affected the eco-environment in the Gannan region on the eastern Tibetan Plateau. Understanding the history of aeolian activities can deepen our understanding of the impacts of climatic changes on aeolian activities in the future. This study uses a detailed chronology and multiple proxy analyses of a typical aeolian section in Maqu to reconstruct aeolian activities in the region during the Holocene. Our results showed that aeolian activities have occurred in the eastern Tibetan Plateau since the early Holocene. Magnetic susceptibility, grain size records, and paleosols formation indicated a trend of stepwise weakening in aeolian activities from the early Holocene to the present. The weakening of aeolian activities was divided into three stages: ∼10.0–8.0 ka BP, ∼8.0–4.0 ka BP, and ∼4.0 ka BP to the present. Paleosols were primarily formed after ∼8.0 ka BP, and episodically interrupted aeolian activities processes in the Gannan region. Aeolian activity may increase in the Gannan region as the climate gradually warms. Climatic changes and local hydrological conditions have jointly affected the history of aeolian activities in this region.
Managing Psoriatic Arthritis With Inflammatory Bowel Disease and/or Uveitis

Psoriatic arthritis (PsA) is a chronic inflammatory disease that presents with psoriasis (PsO), peripheral and axial arthropathy. The heterogeneity of disease presentation leads to the term “psoriatic disease (PsD)” which is thought to better encompass the range of clinical manifestations. PsA is associated with several comorbidities such as cardiovascular diseases, metabolic syndrome and other extra-articular manifestations including uveitis, and inflammatory bowel disease (IBD). While novel therapeutics are being developed following advances in our understanding of the pathogenesis of the disease, the diverse combinations of PsA with its various comorbidities still pose a clinical challenge in managing patients with PsA. This article reviews our current understanding of the pathogenesis of PsA and how various pathways in the pathogenesis lead to the two comorbid extra-articular manifestations – uveitis and IBD. We also review current evidence of treatment strategies in managing patients with PsA with comorbidities of uveitis and/or IBD.
PET Imaging of Neuroinflammation in Alzheimer’s Disease

Neuroinflammation play an important role in Alzheimer’s disease pathogenesis. Advances in molecular imaging using positron emission tomography have provided insights into the time course of neuroinflammation and its relation with Alzheimer’s disease central pathologies in patients and in animal disease models. Recent single-cell sequencing and transcriptomics indicate dynamic disease-associated microglia and astrocyte profiles in Alzheimer’s disease. Mitochondrial 18-kDa translocator protein is the most widely investigated target for neuroinflammation imaging. New generation of translocator protein tracers with improved performance have been developed and evaluated along with tau and amyloid imaging for assessing the disease progression in Alzheimer’s disease continuum. Given that translocator protein is not exclusively expressed in glia, alternative targets are under rapid development, such as monoamine oxidase B, matrix metalloproteinases, colony-stimulating factor 1 receptor, imidazoline-2 binding sites, cyclooxygenase, cannabinoid-2 receptor, purinergic P2X7 receptor, P2Y12 receptor, the fractalkine receptor, triggering receptor expressed on myeloid cells 2, and receptor for advanced glycation end products. Promising targets should demonstrate a higher specificity for cellular locations with exclusive expression in microglia or astrocyte and activation status (pro- or anti-inflammatory) with highly specific ligand to enable in vivo brain imaging. In this review, we summarised recent advances in the development of neuroinflammation imaging tracers and provided an outlook for promising targets in the future.
Physical Activity and Health Promotion in Esports and Gaming–Discussing Unique Opportunities for an Unprecedented Cultural Phenomenon

Due to long periods of sedentary behavior, and unhealthy diets gamers and esports players are at risk for numerous chronic diseases and all-cause mortality. Health research has started addressing the public health implications of the esports phenomenon, drawing a bleak picture of this megatrend. However, instead of just highlighting adverse public health implications of this trend, it is recommended to become involved in this phenomenon and positively influence it. Esports has an enormous potential for physical activity and health-promoting efforts, provides a context for broadly disseminating interventions, and offers new ways of gaining access to an often-neglected population. This paper presents: the potential of the esports phenomenon to promote physical activity, health, and well-being in gamers and esports players; the strategic and preventive solutions to ameliorate esports possible adverse health impacts; and the utilization of esports technology (streams, media platforms, exergames, etc.) as an innovative health promotion tool, especially reaching gamers and esports players with attractive and interactive interventions. This is to encourage systematic scientific research so that evidence-based guidelines and intervention strategies involving regular physical activity, healthy diet, and sleep hygiene for esports will be developed. The goal is to promote public health approaches that move toward a better integration of esports and gaming.