Miracle-dz/UniFAR-FTUs huggingface dataset:
FeSA: Automatic Federated Swarm Attestation on Dynamic Large-Scale IoT Devices

Swarm attestation, as an important branch of Remote Attestation (RA), enables a trusted party (verifier) to verify the security states of multiple devices (provers) in a large network (swarm) simultaneously via a challenge-response mechanism. However, swarm attestation suffers from significant redundancy overhead since all devices in the swarm need to be attested in each attestation round. Besides, it faces challenges such as verifier-impersonation Denial of Service (DoS) attacks, highly dynamic networks, transient & self-relocating malware, and Time-Of-Check-Time-Of-Use (TOCTOU) attacks. In this paper, considering not only the detection accuracy but also the privacy of swarm owners in real Internet of Things (IoT) scenarios, we propose an Automatic Federated Swarm Attestation scheme (FeSA). Under this scheme, we design a federated-learning-based automatic swarm attestation protocol that enables the verifiers to identify the suspicious devices by a neural network model and then attest them. To the best of our knowledge, this is the first scheme to apply a federated learning method to RA, ruling out the redundancy attestation rounds while preserving data privacy. The FeSA redesigns the interaction model of RA by a challenge-query mechanism to reduce the overhead of an individual device to a constant. In order to evaluate our scheme, we first set up a smart office environment with 12 types of smart IoT devices for real-world data collection up to 21 days. Based on the real dataset, we demonstrate that FeSA can indeed identify the compromised IoT devices while reducing redundancy. We further simulate large-scale swarms of up to 1,000,000 devices to validate the efficiency of FeSA in large-scale swarms. Last, the security analysis proves the ability of FeSA to resist various attacks.
Investigating the performance of scenario-based model predictive control of space heating in residential buildings

This paper investigates the performance of scenario-based model predictive control (SB-MPC) for space heating operation to address the inherent uncertainty of weather forecasts and predictions of occupancy. In contrast to existing reported studies, this study relied on a sophisticated meteorological model and a higher order Markov chain occupancy model to generate stochastic disturbance scenarios. When applying the SB-MPC scheme for energy-efficient operation, simulation results suggested a slight increase in energy consumption (from approx. 27.7 kWh/m2 to 28.0 kWh/m2) when using one and 100 disturbance scenarios, respectively, while thermal comfort violations were reduced significantly (from 60°Ch to 10°Ch). Furthermore, the SB-MPC scheme was tailored to provide demand response and thereby achieved cost savings of 16.1% and 13.1% compared to conventional proportional-integral control when considering one and 100 disturbance scenarios, respectively. Choosing the appropriate number of disturbance scenarios thus relies on a consideration of the trade-off between the acceptable thermal comfort violations and energy-related benefits.
Demonstration of Ultra-High-Q Silicon Microring Resonators for Nonlinear Integrated Photonics

A reflowing photoresist and oxidation smoothing process is used to fabricate ultra-high-Q silicon microring resonators based on multimode rib waveguides. Over a wide range of wavelengths near 1550 nm, the average Q-factor of a ring with 1.2-μm-wide waveguides reaches up to 1.17 × 106, with a waveguide loss of approximately 0.28 dB/cm. For a resonator with 1.5-μm-wide waveguides, the average Q-factor reaches 1.20 × 106, and the waveguide loss is 0.27 dB/cm. Moreover, we theoretically and experimentally show that a reduction in the waveguide loss significantly improves the conversion efficiency of four-wave mixing. A high four-wave mixing conversion efficiency of −17.0 dB is achieved at a pump power of 6.50 dBm.
Modeling and optimization of vlsi interconnects

As very large scale integrated circuits move into the era of deep-submicron technology and gigahertz clock frequency, the system performance has increasingly become dominated by the interconnect delay. Contributions of this dissertation include the following three related aspects for the design of high-performance interconnects: interconnect modeling, interconnect optimization, and optimization theory. Concerning interconnect modeling, we study the two extraction problems to compute the capacitance and inductance values respectively from complex 3-dimensional interconnect structures. For each problem, we first propose and validate “foundations” that can be used to reduce the problem complexity, then present a simple yet accurate methodology directly based on the foundations. The resulting extraction methodologies are applicable to both IC and MCM/PCB designs, and have been used in the industry for real designs. Concerning interconnect optimization, we formulate and solve the following three problems: (1) The multi-source wire sizing (MSWS) problem. It determines the optimal widths for all wire segments to minimize the delay along a given routing tree with multiple sources. (2) The simultaneous transistor and interconnect sizing (STIS) problem using the accurate device model. It assigns optimal wire widths to interconnects and optimal sizes to transistors to minimize the delay for multiple critical paths, where a path may contain multiple routing trees and related devices. (3) The global interconnect sizing and spacing (GISS) problem. Given the topology for multiple routing trees, the GISS problem finds the wire sizing and spacing solution simultaneously for all trees, with consideration of coupling capacitance between them. We reveal interesting properties and present efficient algorithms for all those problems. Experiments show that our formulations and algorithms have achieved significant reduction of the interconnect delay. As to the optimization theory, we formulate three classes of optimization problems: the simple, monotonically constrained, and bounded CH-problems. We reveal that the dominance property holds for those CH-problems under different types of local-refinement operations. This property immediately leads to an efficient polynomial-time algorithm, which provides a unified solution to a number of interconnect optimization problems, including the MSWS, STIS, and GISS problems.
COMPARATIVE ANALYSIS OF MARKET OVERREACTION IN INDONESIA AND SINGAPORE STOCK EXCHANGE 2016-2019

This study aims to determine whether there is a market overreaction phenomenon in the Indonesia Stock Exchange which is classified as an emerging market and the Singapore Stock Exchange which is classified as a developed market. This research was conducted in a weekly period during 2016-2019. This study uses sample included in the LQ-45 index for the Indonesia Stock Exchange and the top 30 market cap for the Singapore Stock Exchange. This research found that there was a market overreaction in the Indonesia Stock Exchange, especially the loser portofolio which experienced the strongest reversal. Meanwhile, the significance value in the one sample t-test for the average cumulative abnormal return difference value is not significance. While the results of research on the Singapore Stock Exchange found no market overreaction phenomenon as indicated by a negative and insignificance average cumulative abnormal return difference. The result showed that the Indonesia Stock Exchange has not been efficient where investors tend to overreact in responding to information while investors on the Singapore Stock Exchange are rational.
Anonymous Jamming Detection in 5G with Bayesian Network Model Based Inference Analysis

Jamming and intrusion detection are some of the most important research domains in 5G that aim to maintain use-case reliability, prevent degradation of user experience, and avoid severe infrastructure failure or denial of service in mission-critical applications. This paper introduces an anonymous jamming detection model for 5G and beyond based on critical signal parameters collected from the radio access and core network’s protocol stacks on a 5G testbed. The introduced system leverages both supervised and unsupervised learning to detect jamming with high-accuracy in real time, and allows for robust detection of unknown jamming types. Based on the given types of jamming, supervised instantaneous detection models reach an Area Under the Curve (AUC) within a range of 0.964 to 1 as compared to temporal-based long short-term memory (LSTM) models that reach AUC within a range of 0.923 to 1. The need for data annotation effort and the required knowledge of a vocabulary of known jamming limits the usage of the introduced supervised learning-based approach. To mitigate this issue, an unsupervised auto-encoder-based anomaly detection is also presented. The introduced unsupervised approach has an AUC of 0.987 with training samples collected without any jamming or interference and shows resistance to adversarial training samples within certain percentage. To retain transparency and allow domain knowledge injection, a Bayesian network model based causation analysis is further introduced.
Complete mitochondrial genome of oriental pratincole Glareola maldivarum (Charadriiformes: Glareolidae) and its phylogenetic relationship

Abstract Oriental Pratincole Glareola maldivarum is a widely distributed water bird species of the Charadriiformes group across Eurasia. In this study, we reveal the mitochondrial genome sequence of the Oriental Pratincole for the first time. The length of the mitochondrial genome is 18,422 bp and contains 2 ribosomal RNA genes, 13 protein coding genes, 22 transfer RNA genes, and 2 D-loop sequences. We further provide a phylogenetic tree showing relationships among Oriental Pratincole and other Charadriiformes species.
Analysis of Machine-Sided Influences during Clinching

Mechanical joining techniques like clinching are standard joining techniques for processing aluminum and steel alloys in the automotive car body manufacturing. When using conventional methods, joints will have a high quality after a finally tool check on a specific joining press system. However, if the press system during manufacturing will changed, it can occur that joints get other quality values e.g. smaller interlock. The reason for that has multiple influences,this paper considers especially the press-sided ones. With optical measurements of press deformation and punch speed during real joining processes, new 3D-halfsymmetric simulation models were built up, which take into account of press-side behavior such as joining velocity and angular as well as lateral misalignment of the joining tools during clinching process. Sensitivity analyses identifies significant influencing variables. On the base of this, equations of quality changes can be determined. Finally, this allows better prediction of the modification about joint quality after a press change from system A to B or C during manufacturing.
Feedback Linearization Control in Photovoltaic Module Integrated Converters

The strive to increase the energy yield of photovoltaic (PV) power systems has made PV module integrated dc–dc converters (dc-MICs) a reality of modern PV plants. These converters regulate their input voltage, and their dynamic behavior is heavily influenced by the non-linear characteristic of the PV module. The regulation of the PV module voltage and average inductor current by means of a linear cascaded controller is a popular control technique, simplifying the converter dynamics, and providing inherent current limiting; however, it is prone to instability depending on the interaction between the PV source and the interfacing converter, as well as the value of the controller parameters. These factors present a clear challenge for control design; moreover, the converter transient response undesirably depends on the PV module operating point. In order to solve these issues, while maintaining regulation of PV module voltage and average inductor current, this paper proposes to adopt a non-linear controller designed with the feedback linearization control (FLC) technique. The control laws are derived and implemented in a non-inverting buck–boost dc module integrated converter, as this is a favorite topology for the PV interfacing application. A digitally controlled converter prototype is built and used to obtain experimental results, where the FLC technique is compared with a linear cascaded control technique. The results confirm the superior performance of the presented FLC technique, which is robust and able to regulate the converter input voltage with fast and consistent dynamics, regardless of the PV module or load operating conditions.
AlGaN Quantum Disk Nanorods with Efficient UV-B Emission Grown on Si(111) Using Molecular Beam Epitaxy

AlGaN nanorods have attracted increasing amounts of attention for use in ultraviolet (UV) optoelectronic devices. Here, self-assembled AlGaN nanorods with embedding quantum disks (Qdisks) were grown on Si(111) using plasma-assisted molecular beam epitaxy (PA-MBE). The morphology and quantum construction of the nanorods were investigated and well-oriented and nearly defect-free nanorods were shown to have a high density of about 2 × 1010 cm−2. By controlling the substrate temperature and Al/Ga ratio, the emission wavelengths of the nanorods could be adjusted from 276 nm to 330 nm. By optimizing the structures and growth parameters of the Qdisks, a high internal quantum efficiency (IQE) of the AlGaN Qdisk nanorods of up to 77% was obtained at 305 nm, which also exhibited a shift in the small emission wavelength peak with respect to the increasing temperatures during the PL measurements.
Effects of Temperature on Deep Drawing of an Aluminum Alloy for Different Yield Criteria and Hardening Models

In this study, the influences of temperature variation in deep drawing process are investigatedby changing the temperatures of the whole blank, a part of the blank, the punch and die fordifferent yield criteria and hardening models. Von-Mises criterion with isotropic, kinematic and combinedhardening and, Hill48 and Yld2003 yield criteria with isotropic hardening are considered toform the constitutive relations. Circular, square and complex shaped parts made of AA5754 materialare used in the numerical analyses. The local heating simulations are conducted for circular blanksand the drawability of the parts are evaluated by using the Johnson-Cook failure criteria. Hot formingand quenching and local heating analyses are also carried out for a complex shaped part. The resultsobtained by the finite element analyses for different constitutive equations are compared with eachother and experimental results according to thickness strain distribution, punch force variation andrim shape of the deformed blanks.
Measuring the Tree Height of Picea crassifolia in Alpine Mountain Forests in Northwest China Based on UAV-LiDAR

Forests in alpine mountainous regions are sensitive to global climate change. Accurate measurement of tree height is essential for forest aboveground biomass estimation. Unmanned aerial vehicle light detection and ranging (UAV-LiDAR) in tree height estimation has been extensively used in forestry inventories. This study investigated the influence of varying flight heights and point cloud densities on the extraction of tree height, and four flight heights (i.e., 85, 115, 145, and 175 m) were set in three Picea crassifolia plots in the Qilian Mountains. After point cloud data were classified, tree height was extracted from a canopy height model (CHM) on the basis of the individual tree segmentation. Through comparison with ground measurements, the tree height estimations of different flight heights and point cloud densities were analyzed. The results indicated that (1) with a flight height of 85 m, the tree height estimation achieved the highest accuracy (R2 = 0.75, RMSE = 2.65), and the lowest accuracy occurred at a height of 175 m (R2 = 0.65, RMSE = 3.00). (2) The accuracy of the tree height estimation decreased as the point cloud density decreased. The accuracies of tree height estimation from low-point cloud density (R2 = 0.70, RMSE = 2.75) and medium density (R2 = 0.69, RMSE = 2.80) were comparable. (3) Tree height was slightly underestimated in most cases when CHM-based segmentation methods were used. Consequently, a flight height of 145 m was more applicable for maintaining tree height estimation accuracy and assuring the safety of UAVs flying in alpine mountain regions. A point cloud density of 125–185 pts/m2 can guarantee tree height estimation accuracy. The results of this study could potentially improve tree height estimation and provide available UAV-LiDAR flight parameters in alpine mountainous regions in Northwest China.
Fabrication and Characterization of Carbon Nanotube Random Network Transistors

A solution-processable technique combining drop-casting and shadow-mask deposition was developed for the fabrication of field-effect transistors based on carbon nanotube random networks. The proposed technique, which is easy, fast, and reliable, allows direct control of network density and thus of the transistor characteristics. A systematic study has been carried out on the factors influencing the transistor performance, which provides novel knowledge and guidance for future device optimization.
Audio and Image Cross-Modal Intelligence via a 10TOPS/W 22nm SoC with Back-Propagation and Dynamic Power Gating

We present an ultra-low-power multimedia signal processor (MMSP) SoC that integrates a versatile deep neural network (DNN) engine with audio and image signal processing accelerators for cross-modal IoT intelligence. The proposed MMSP features 2MB MRAM to store all DNN weights on-chip with an energy-efficient dataflow using an MRAM-cache and dynamic power gating. The SoC achieves up to 3-10 TOPS/W peak energy efficiency and consumes only 0.25-3.84 mW. Being the first to demonstrate CNN, GAN, and back-propagation (BP) on a single accelerator SoC for cross-modal fusion, it outperforms state-of-the-art DNN processors by 1.4 - 4.5× in energy efficiency.
Effects of Climate Change on the Habitat of the Leopard (Panthera pardus) in the Liupanshan National Nature Reserve of China

Simple Summary Climate change affects animal populations by affecting their habitats. The leopard population has significantly decreased due to climate change and human disturbance. We studied the impact of climate change on leopard habitats using infrared camera technology in the Liupanshan National Nature Reserve of Jingyuan County, Ningxia Hui Autonomous Region, China, from July 2017 to October 2019. We captured 25 leopard distribution points and used the MAXENT model to predict and analyze the habitat. We studied the leopard’s suitable habitat area and distribution area under different geographical scales in the reserve. Changes in habitat area of leopards under three climate models in Guyuan in 2050 were also studied. We conclude that the current main factors affecting suitable leopard habitat area were vegetation cover and human disturbance. The most critical factor affecting future suitable habitat area is rainfall. Under the three climate models, the habitat area of the leopard decreased gradually because of an increase in carbon dioxide concentration. Abstract Climate change affects animal populations by affecting their habitats. The leopard population has significantly decreased due to climate change and human disturbance. We studied the impact of climate change on leopard habitats using infrared camera technology in the Liupanshan National Nature Reserve of Jingyuan County, Ningxia Hui Autonomous Region, China, from July 2017 to October 2019. We captured 25 leopard distribution points over 47,460 camera working days. We used the MAXENT model to predict and analyze the habitat. We studied the leopard’s suitable habitat area and distribution area under different geographical scales in the reserve. Changes in habitat area of leopards under the rcp2.6, rcp4.5, and rcp8.5 climate models in Guyuan in 2050 were also studied. We conclude that the current main factors affecting suitable leopard habitat area were vegetation cover and human disturbance. The most critical factor affecting future suitable habitat area is rainfall. Under the three climate models, the habitat area of the leopard decreased gradually because of an increase in carbon dioxide concentration. Through the prediction of the leopard’s distribution area in the Liupanshan Nature Reserve, we evaluated the scientific nature of the reserve, which is helpful for the restoration and protection of the wild leopard population.
Identifying job seekers’ perceptions of public officials in Korea using Q methodology

ABSTRACT Public service positions have long been one of the most sought-after jobs among the youth in the Republic of Korea. Individuals may possess mixed motives to take the national civil service exam, which cannot be explained by a single theory or framework. Public choice or public service motivation theory, for example, may not alone describe young Koreans’ desire to work for the government. Furthermore, it may be necessary to specifically consider the Korean culture to examine the complex nature of one’s motivation to become a public official. The study aims to categorize how the youth perceive bureaucrats and find reasons behind the craze for taking the national civil service exam, thus suggesting theoretical and practical implication. Using Q methodology, this study identified four distinct perceptions of Korean public officials: genuine public servers, rise-in-status pursuers, self-interest maximizers, and public policymakers. These conceptions contribute to understanding how young Koreans feature mixed motivations to choose the government as their employer and designing better policies to tackle the issue of youth removing themselves from the labor market to prepare for the national civil service exam.
Spatial-Temporal Edge User Allocation: An Expectation Confirmation Perspective Approach

As the 5th generation (5G) network develops and rolls out rapidly, user requests can be offloaded to nearby edge servers for processing. This alleviates the pressure on the network backhaul and the remote cloud. Nevertheless, the edge user allocation (EUA) problem, as one of the main research challenges in the 5G era, has become a major obstacle to ensuring users’ Quality of Experience (QoE) in the edge computing environment. Conventional EUA approaches, ranging from static global allocation model to online decision-making model, have ignored the long-term impact of the changes in users’ expectations on user-perceived Quality of Service (QoS). Additionally, most existing approaches have not taken into account the distance between an edge user and an edge server, which impacts the user’s data rate profoundly. In this paper, we tackle these challenges by formulating EUA problem as a spatial-temporal one (ST-EUA), which models distance-aware QoS based on the wireless transmission attenuation and models users’ QoE based on the Expectation Confirmation Theory (ECT). To find an appropriate solution for ST-EUA problem, we develop two fuzzy control-based approaches, namely FC and BFC, for on demand scenarios and batch processing scenarios, respectively. They can balance effectively the user consolidation and server load. We conduct extensive experiments based on two widely-used real-world datasets. The results demonstrate the superiority of our FC and BFC in effectiveness and efficiency over the baselines and state-of-the-art.