Miracle-dz/UniFAR-FTUs huggingface dataset:
An Intersection Attack on the CirclePIN Smartwatch Authentication Mechanism

We present a thorough security analysis of a recent smartwatch authentication mechanism called CirclePIN, which was considered resilient to several attacks, including shoulder surfing and video recording. This mechanism avoids the direct entry of the personal identification number (PIN) by using consecutive screens of random colors that fool the attacker. We disclose a vulnerability in CirclePIN inherent to the way in which the users match the random colors to their PINs’ digits and we illustrate how to exploit it with an intersection attack. This attack uses the information extracted from multiple video recordings of legitimate authentication sessions. We prove that it has a high probability of revealing the user PIN with only three video recordings and always succeeds with five. Our proof is twofold. We formulate the theoretical probability of success for the attack as a function of the number of available video recordings. Then, we validate this formula with a simulation of a large number of attacks to compute their experimental probability of success. In our estimation, manual information extraction takes around 1 min per exploitable video recording. So, a complete intersection attack is cost effective in terms of time, as it lasts 5 min or less.
Decentralized Fuzzy Control Based Trajectory Tracking of a Robotic Manipulator

The study aims to control a robotic manipulator to achieve the trajectory-tracking of its end-effector. To the objective, a decentralized fuzzy control scheme is proposed. The desired motion trajectories including circular and cardioid shapes of the end-effector are planned such that the corresponding joint limits are mapped to the feasible working space by using the projective inverse kinematics. In the meantime, a common PID control is utilized to compare control performance with the proposed decentralized fuzzy control. Experimental results directly validate that the proposed decentralized fuzzy controller is capable of tracking desired angles of each joint and further achieves trajectory tracking of the end-effector. In addition, applying the proposed decentralized fuzzy control scheme can lead to better performance compared to the common PID control.
Blocking the Apelin Receptor (APJ) Attenuates TNBS-Induced Colitis in Rats

Objective: The apelinergic system, consisting of apelin, ELABELA, and the apelin receptor (APJ), has a wide range of roles in physiological and pathophysiological processes in tissues. The effects of increased apelin and APJ as an indicator of damage in inflammatory conditions or as a compensatory mechanism are not fully clear in inflammatory bowel disease (IBD). This study was designed to assess the role of APJ in 2,4,6-trinitrobenzene sulfonic acid (TNBS)-induced colitis model. Methods: Colitis in adult male Wistar rats were induced by intrarectally administered TNBS (30 mg b.w. in 50% ethanol). While the control group was treated with only saline to the colon, the TNBS+F13A and F13A groups received the APJ antagonist F13A (30 µg/kg/day, i.v.) for 3 days, starting immediately after TNBS or saline administration, respectively. Results: A decrease in body weight and an increase in colon weight/length ratio and stool consistency score were observed in the TNBS group. TNBS caused an increase in the myeloperoxidase (MPO) activity and the number of proinflammatory cytokines (TNF-α, IL-1β, and IL-6), as well as apelin production, leading to mucosal ulceration, necrosis, and submucosal edema in the colon. While F13A administration to the control did not cause any change in the colon, F13A administration immediately after TNBS greatly reduced the effects of TNBS. Conclusion: APJ is involved in the development of damage in colitis induced by TNBS. F13A reduces the level of damage, inflammatory cell infiltration, and MPO enzyme activity. APJ may be a therapeutic target in IBD.
Effect Sacubitril/Valsartan in patients with Acute Myocardial Infarction (AMI): a systematic review and meta-analysis

Background: Acute Myocardial Infarction (AMI) is a type of coronary heart disease with high morbidity and mortality. The renin-angiotensin-aldosterone system (RAAS) is an important endocrine system in controlling blood pressure and cardiovascular homeostasis and is responsible for AMI. In patients with AMI or a heart attack, sacubitril/valsartan has been shown to reduce the risk of heart failure and death. This study aims to evaluate the effect of sacubitril/valsartan in AMI patients. Methods: In order to evaluate the effects of sacubitril/valsartan in patients with AMI, three databases, including PubMed, Cochrane Library, and the ClinicalTrials website, were searched. The final search was performed in January 2023. The meta‑analysis was subsequently performed with Revman 5.4 software and a total of three studies were included. Results: The results of the main outcomes showed that sacubitril/valsartan has no significant reduction in major adverse cardiovascular and cerebrovascular events (MACCEs) odds ratio (OR), 0.70; 95% confidence interval (CI), 0.38‑1.30; P=0.26] and incidence of acute heart failure (AHF) (OR, 0.52; 95% CI, 0.24‑1.15; P=0.11). The secondary outcomes showed no effect of sacubitril/valsartan to increase left ventricular remodeling. Conclusion: In conclusion, the present meta‑analysis revealed that sacubitril/valsartan could not effectively reduce the incidence of MACCEs and AHF in patients with AMI. In the future, a meta‑analysis study will be designed with many studies to reduce the incidence of MACCEs and AHF effectively.
Fish and Wildlife Issues Related to the Use of Lead Fishing Gear

There is growing concern about the amount of lead that is deposited into our environment by various means. Federal laws have addressed what appear to be the most common pathways. The manufacture of paint with high levels of lead was banned in 1978, and leaded gasoline was banned in the mid-1980s. But problems persist, and a recent New York Times article (January 17, 2006) states that about 25% of our nation's children are still exposed to lead in their homes and more than 400,000 each year are found to have amounts of lead in their bodies that are hazardous to normal mental and physical development. Workers in the metals trades are still at risk – as recently as 1998, over 320,000 workers in the U.S. were exposed to lead (Needleman, 2004). Another more recent area of concern is the lead deposited into the environment from hunters and fishers in the form of lead shot and lead fishing tackle. This paper looks at the possible environmental effects of this metal, focusing on lost fishing tackle. Although many different species of birds, reptiles, and small mammals are known to have died from ingesting lead, studies have shown that birds are very susceptible to lead poisoning because the grinding action of their gizzards releases the toxic metal directly into their bloodstream. Loons are the birds most likely to ingest lead fishing tackle, and one lead sinker or lead jig can kill a loon. Several countries have enacted laws banning or limiting the use of lead fishing tackle. Several U.S. states also have passed laws, often based limiting the use of small lead sinkers or jigs that are more likely to be swallowed.
Microwave Substrate Integrated Waveguide Power Amplifier using Complementary Split Ring Resonators

This paper proposes a substrate integrated waveguide (SIW) power amplifier (PA). By using the SIW technology, the PA has advantages of low radiation loss and high capability of integration with other planar components on the same substrate. In this paper, a new configuration of resonator is used in input/output matching circuit, which is complementary split ring resonator (CSRR). The CSRR has benefits of high selectivity, compact size, and feasibility for etching on the SIW. The method of designing the SIW matching circuit is that the resonant frequency of the first/last resonator is detuned to transform a real impedance of 50 Ω to a complex impedance. The cosimulation results show that the SIW PA achieves a maximum power added efficiency (PAE) of 57.05%, an output power $P_{\mathrm{out}}$ of 37.43 dBm, and a power gain G of 13.43 dB at 6 GHz.
Adversarial Testing: A Novel On-Line Testing Method for Deep Learning Processors

Deep neural networks have shown outstanding performance on complex tasks. Recently, various researches have been developed to pursue fast and energy-efficient deep learning accelerators. However, devices may suffer from hard defects and hardware variability during its lifetime, which poses severe challenges to deep learning accelerators. To protect edge deep learning accelerators from fault-induced failures, we leverage the adversarial deep learning technique to tailor a lightweight online fault detection method for neural network accelerator chips. The proposed Adversarial Testing scheme (AT) is a function-level testing method outcompeting conventional test in several ways: negligible run-time overhead, super sensitivity to subtle hardware variations, which reduces chip over-kills and also the unnecessary diagnosis operations. The evaluation results show that AT can accurately detect fault occurrence and ensure the normal use of deep learning accelerator during its lifetime.
Thermotolerance of tomato plants grafted onto wild relative rootstocks

Heat stress is a major environmental constraint limiting tomato production. Tomato wild relatives Solanum pennellii and S. peruvianum are known for their drought tolerance but their heat stress responses have been less investigated, especially when used as rootstocks for grafting. This study aimed to evaluate the physiological and biochemical heat stress responses of tomato seedlings grafted onto a commercial ‘Maxifort’ and wild relative S. pennellii and S. peruvianum rootstocks. ‘Celebrity’ and ‘Arkansas Traveler’ tomato scion cultivars, previously characterized as heat-tolerant and heat-sensitive, respectively, were grafted onto the rootstocks or self-grafted as controls. Grafted seedlings were transplanted into 10-cm pots and placed in growth chambers set at high (38/30°C, day/night) and optimal (26/19°C) temperatures for 21 days during the vegetative stage. Under heat stress, S. peruvianum-grafted tomato seedlings had an increased leaf proline content and total non-enzymatic antioxidant capacity in both leaves and roots. Additionally, S. peruvianum-grafted plants showed more heat-tolerant responses, evidenced by their increase in multiple leaf antioxidant enzyme activities (superoxide dismutase, catalase and peroxidase) compared to self-grafted and ‘Maxifort’-grafted plants. S. pennellii-grafted plants had similar or higher activities in all antioxidant enzymes than other treatments at optimal temperature conditions but significantly lower activities under heat stress conditions, an indication of heat sensitivity. Both S. pennellii and S. peruvianum-grafted plants had higher leaf chlorophyll content, chlorophyll fluorescence and net photosynthetic rate under heat stress, while their plant growth was significantly lower than self-grafted and ‘Maxifort’-grafted plants possibly from graft incompatibility. Root abscisic acid (ABA) contents were higher in ‘Maxifort’ and S. peruvianum rootstocks, but no ABA-induced antioxidant activities were detected in either leaves or roots. In conclusion, the wild relative rootstock S. peruvianum was effective in enhancing the thermotolerance of scion tomato seedlings, showing potential as a breeding material for the introgression of heat-tolerant traits in interspecific tomato rootstocks.
When Lyapunov Drift meets DRL: Energy Efficient Resource Allocation for IoT Data Collecting

In this study, we explore the problem of optimizing throughput while maintaining energy efficiency in an Internet of Things (IoT) data collection system. The system is characterized by unique data generation rates from each IoT device, and the details of these rates are uncertain. We propose a Lyapunov drift-based deep reinforcement learning framework (LDRL) to handle mixed integer programming with uncertain arrival rates, aiming to maximise the average throughput. To demonstrate the effectiveness of our framework, we compare it to throughput-oriented and energy-oriented frameworks through a series of simulations. The simulation results show that our framework strikes an impressive balance between throughput and energy efficiency, achieving high average throughput while maintaining a moderate energy consumption rate, and we also observe that it maintains performance under a certain level of rapidly changing network environments.
Temporal and Type Correlation in Digital Phenotyping for Bipolar Disorder State Prediction Using Multitask Self-Supervised Learning

Bipolar disorder is a prevalent mental illness characterized by a high relapse rate. In this study, we propose an early warning system that utilizes digital phenotyping to collect various data points from bipolar patients, including location information, self-assessment scales, daily mood reports, sleep patterns, and multimedia records, through a mobile application. These collected data are utilized to develop a predictive model for assessing the risk state of bipolar disorder. Compared to traditional recurrence prediction methods, this study incorporates medical records, medication data, and emergency records, as suggested by medical professionals, to define the five states of bipolar disorder, leading to enhanced accuracy. To account for data type correlation and temporal correlation, we employ a multitask self-supervised learning mechanism. The proposed method is trained on a Gated recurrent unit and demonstrates an improved prediction accuracy of 88.2% on the collected test data, as compared to the baseline accuracy of 85.1%. These findings highlight the significant importance of considering data type and temporal correlations in digital phenotyping for predicting the state of bipolar disorder.
MARKER REGISTRATION FOR INVERSE KINEMATIC MODELS OF THE UPPER LIMB: IMPORTANT CONSIDERATIONS FOR THE SPORT SCIENTIST

The scaling of rigid-linked skeletal models is an important consideration for researchers looking to calculate joint angles via inverse kinematics (IK). It has been suggested (Dunne et al., 2013) that registering marker positions with known kinematics during scaling can improve the accuracy of IK derived lower limb joint angles during gait. The purpose of this manuscript was to determine if registering marker positions with known joint kinematics can improve the accuracy and reliability of time varying IK derived elbow flexion/extension (FE) estimates during cricket bowling. Registering marker positions and joint kinematics (MKR) resulted in improved accuracy than marker positions only (MR) (RMSE = 8.9° v 25.1°) when compared with known DK derived elbow angles. The inter-tester reliability of MKR model elbow extension range was also superior (ICC = 0.626 v 0.318).
Self-pacing ameliorates recall deficit when listening to vocoded discourse: a cochlear implant simulation

Introduction In spite of its apparent ease, comprehension of spoken discourse represents a complex linguistic and cognitive operation. The difficulty of such an operation can increase when the speech is degraded, as is the case with cochlear implant users. However, the additional challenges imposed by degraded speech may be mitigated to some extent by the linguistic context and pace of presentation. Methods An experiment is reported in which young adults with age-normal hearing recalled discourse passages heard with clear speech or with noise-band vocoding used to simulate the sound of speech produced by a cochlear implant. Passages were varied in inter-word predictability and presented either without interruption or in a self-pacing format that allowed the listener to control the rate at which the information was delivered. Results Results showed that discourse heard with clear speech was better recalled than discourse heard with vocoded speech, discourse with a higher average inter-word predictability was better recalled than discourse with a lower average inter-word predictability, and self-paced passages were recalled better than those heard without interruption. Of special interest was the semantic hierarchy effect: the tendency for listeners to show better recall for main ideas than mid-level information or detail from a passage as an index of listeners’ ability to understand the meaning of a passage. The data revealed a significant effect of inter-word predictability, in that passages with lower predictability had an attenuated semantic hierarchy effect relative to higher-predictability passages. Discussion Results are discussed in terms of broadening cochlear implant outcome measures beyond current clinical measures that focus on single-word and sentence repetition.
Density functional theory calculation for carrier scattering at 4H-SiC(0001)/SiO2 interface

Electronic structure and charrier scattering property of the 4H-SiC(0001)/SiO2 interface are investigated by the density functional theory calculations. It is found that the bonding structure with SiO2 affects the electronic structure of the interface. In addition, there are two conduction channels at the interface. The carriers flowing the channel immediately below the interface are significantly scattered at the step edge and those flowing the channel 5 Å away from the interface are insensitive to the existence of the step edges. These results indicate that on-current will increase when the on-axis SiC substrates are used for metal-oxide-semiconductor field-effect transistors because the density of the step edges is low at the on-axis substrates.
Earthquake Density Measurement using Automatic Clustering

The government and earthquake associations have recorded the seismic data in spatial-temporal usually used to measure the earthquake intensity, but such information has not been processed to obtain the earthquake density. This condition makes it difficult to map all the risk, so it creates the lack of participatory development to the earthquake areas that have a high density. This research proposes a new approach for measuring the density of earthquake and performing automatic clustering with valley tracing method to detect the number of group spatial regions automatically from analysis of cluster moving variances. The density is obtained with a new approach that involves the area and the amount of data on clusters. This system follows the steps: (1) display the spatial-temporal earthquake dataset into a map, (2) create vector space data consist of temporal, spatial, and magnitude, (3) detect number of cluster with valley tracing method on automatic clustering, (4) calculate the earthquake density, and (5) display special temporal visualization with the cluster density measurement result. To perform the proposed idea, this system is examined with an experimental study for a series of quake about Indonesia and Japan during the last 50 years from ANSS catalog.
Design of a Dog-type Social Robot to Support Children's Reading Activities and Development of a Touch Sensor Module for Users' Touch Interaction

“Read to a Dog” is a program operated in the United Kingdom, the United States, and New Zealand to induce children's interest in reading through children's reading to dogs. This program is a type of animal-assisted therapy (AAT) that uses pets to heal psychological problems, and it can reduce reading rejection and induce interest in reading by providing an environment where children can read confidently and feel comfortable. This study aims to develop a dog-type robot that can be operated in the library based on the research contents of the corresponding “Read to a Dog”. The purpose of this study is to stimulate children's interest in reading through a reading activity support robot, while giving them with an emotional experience of artificial intelligence through robots. Through robot use environment analysis and user analysis, the appearance shape of the robot is designed and the service requirements are defined. In addition, for touch interaction for emotional communication between children and the dog-type robot, this paper proposes a touch sensor technology using a pressure sensor and Micro Electro Mechanical Systems (MEMS) microphone, which can reduce hardware complexity and detect a wide range of touch of a robot.
Peak Demand Management and Voltage Regulation Using Coordinated Virtual Power Plant Controls

The aggregation of distributed energy resources (DERs) enables them to provide various grid services as a virtual power plant (VPP). Utilities use enterprise control solutions, such as advanced distribution management systems (ADMS) and distributed energy resource management systems (DERMS), to efficiently integrate DERs and realize the benefits of a VPP. These control solutions can complement each other to offer additional benefits. This paper evaluates the coordinated operation of an ADMS and a DERMS that collectively implements a VPP to provide peak demand reduction and voltage regulation through the simulation of an actual distribution feeder. A commercial ADMS reduces the peak demand through conservation voltage reduction (CVR). A prototype DERMS dispatches residential battery energy storage systems (BESS) based on real-time optimal power flow to provide additional peak demand reduction. The DERMS also maintains voltage regulation across the feeder by controlling both residential batteries and rooftop PV systems. The results from the controller-hardware-in-the-loop (CHIL) real-time simulations conducted in a realistic laboratory environment show that the coordinated operation of the ADMS and the DERMS effectively achieves peak demand reduction while enforcing voltage regulation across the feeder. Specifically, the ADMS dynamic voltage regulation (DVR) application and DERMS working together achieved a peak demand reduction of nearly 500 kW, whereas the ADMS DVR application alone obtained a reduction of approximately 100 kW. The DERMS VPP control in this work relies on the residential BESS for the demand reduction; the demand reduction accomplished depends on the BESS capacity available in the distribution system.
Evaluation of neural response recorded using scalp EEG in virtual reality environment

With the recent development of virtual reality technology, sensory information in virtual space could be a candidate for experimental stimuli in the field of cognitive neuroscience. Specifically, auditory and tactile stimuli, which is spatiotemporally synchronized to visual information of the space and body delivered using a head-mounted display (HMD), can be presented in controlled way using headphones, tactile stimulators (e.g. Kanayama et al., 2019), etc. During such a sensory stimulus presentation, we could measure any neuronal responses, and realize cognitive neuroscience study in the experimental setting impossible in real world. Such a method involves fixing the HMD to participant's head and using the actuator near participant's body, which suggests possible contamination of greater noise to recorded neuronal responses compared to that in experiments without virtual reality. In the case of high-density EEG measurements on the scalp, there should be interference to the electrodes from the fixation band of HMD to the participant's head, electromyogram (EMG) noise induced by participant's head movements, and noise emitted by the HMD device itself.