Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantum Tunneling PUF: A Chip Fingerprint for Hardware Security

As there are more things connected, more attacks through the internet occur. To protect the connected devices from being hacked, the security of each device becomes very important. The root of trust, which provides the secure storage of the secret key and the operation of security services, such as confidentiality, integrity, and authenticity, is the most important element in the chip to safeguard the chip and the operation of the system. A quantum tunneling PUF (Physically Unclonable Functions) can be formed by using the oxide variations during manufacturer processes. The quantum tunneling PUF stores the quantum signature inside the ultra-thin oxide, which is random, unique, stable, reliable, radiation-hardened, and non-traceable, such that it can play the role of fingerprint of the chip. The PUF-based hardware Root of Trust provides high security with high reliability whilst retaining a low cost for the applications in IoT, AI, autonomous driving, block-chains and 5G.
Social Recommendation Combining Implicit Information and Rating Bias

In recent years, more and more recommendation algorithm considers social information. However, in the existing social recommendation algorithm focused on the rating prediction task, the user's rating bias and the item's rating bias are often not considered. Besides, the existing social recommendation algorithms usually fail to deal with the sparse problem of social information. Concerning the problem of ignores rating bias and social information sparsity in the social recommendation, this paper proposes a novel recommendation model that combines implicit information and rating bias based on the traditional matrix factorization model. The social information used includes explicit social information and implicit social information generated by collaborative user network embedding methods. Experimental results on two real public datasets show that compared with other recommendation models, the proposed model reduces 1.13% to 10.09% and 3.14% to 8.38% respectively in the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE). It shows that our proposed recommendation algorithm has a better recommendation effect.
Evaluation of the Effect of Moisture on Dielectric Properties of Ester Liquids

Four different types of insulation liquids have been investigated: synthetic ester MIDEL 7131, natural esters MIDEL 1204, MIDEL 1215, as well as Nytro Gemini X mineral oil as reference material. The liquids were subjected to different humidity conditions (32% and 75% respectively) and subsequently characterized by means of AC breakdown, DC conductivity, dielectric spectroscopy (for permittivity and loss tangent) as well as KF titration. The effect of moisture on the loss tangent was minimal in ester liquids, even when saturated, while the temperature dependence of esters was more pronounced than that of mineral oil. The expectation was that synthetic esters would show improved AC breakdown results, but this was not the case. Natural ester MIDEL 1215 showed the best breakdown strength, also outperforming the mineral oil, while the synthetic ester had significantly reduced Weibull scale and shape parameters. Contrary to expectations, natural ester MIDEL 1215 performed significantly better than the synthetic ester in terms of AC breakdown strength and temperature dependence of conductivity and loss factor. Synthetic ester was also found to be much more dependent on moisture content than the natural ester.
An Agent-Based Approach for Dynamic Scheduling in Hybrid Flow Shops

Today, manufacturing enterprises must respond quickly to the ever-changing market environment in order to survive. At the same time, dynamic disturbances in production sites such as machine failures, reworking caused by quality problems, and changes of processing time and personnel will also have negative impacts on the effectiveness of the original plan. In order to ensure the feasibility of scheduling algorithms in highly dynamic environment, this paper presents agent-based rescheduling negotiation mechanisms with five dynamic disturbances, and verifies its feasibility through simulations. A multi-agent system structure with quick responses and simple communication networks is designed according to characteristics of hybrid flow shops, with which the processing of jobs can be easily traced back.
Human Cognitive Features to Define Correlation Between Depression and Internet Gaming Disorder

This paper explores relationship between depression and internet gaming disorder (IGD) symptoms using electroencephalography (EEG) signals recordings. With the advancement of technology and penetration of Internet in human's life, gaming community also has shifted to online platform like massively multiplayer online role-playing games (MMORPGs) instead of physical games, causing multiple health hazards including behavioral and physical anomalies. Diagnosis of IGD is an arduous task using subjective assessments and need neurological methods to be used in identification. The umbrella of symptoms for IGD overlap with other neurological and behavioral disorders like depression, attention deficit hyperactivity disorder (ADHD), obsessive-compulsive disorder (OCD), thus can share the methods for their identification. In this paper, we determine the power spectral density (SPD) of EEG recording for depression subjects. Rigorous experiments are conducted to analyze the proposed system. Experimental results reveal the coherence between the power for the beta band for depression and IGD subjects. The results suggest that due to the comorbidity of IGD and depression correlation are cohesively predictive.
A 20.8-Gbps dual-carrier wireless communication link in 220-GHz band

With the successful demonstration of terahertz (THz) high-speed wireless data transmission, the THz frequencies are now becoming a worth candidate for post-5G wireless communications. On the other hand, to bring THz communications a step closer to real scenario application, solving high data rate realtime transmission is also an important issue. This paper describes a 220-GHz solid-state dual-carrier wireless link whose maximum transmission real-time data rates are 20.8 Gbps (10.4 Gbps per channel). By aggregating two carrier signals in the THz band, the contradiction between high real-time data rate communication and low sampling rate analog-to-digital (ADC) and digital-to-analog converter (DAC) is alleviated. The transmitting and receiving front-ends consist of 220-GHz diplexers, 220-GHz sub-harmonic mixers based on anti-parallel Schottky barrier diodes, G-band low-noise amplifiers (LNA), WR-4.3 band high-gain Cassegrain antennas, high data rates dual-DAC and —ADC baseband platform and other components. The low-density parity-check (LDPC) encoding is also realized to improve the bit error rate (BER) of the received signal. Modulated signals are centered at 214.4 GHz and 220.6 GHz with −11.9 dBm and −13.4 dBm output power for channel 1 and 2, respectively. This link is demonstrated to achieve 20.8-Gbps real-time data transmission using 16-QAM modulation over a distance of 1030 m. The measured signal to noise ratio (SNR) is 17.3 dB and 16.5 dB, the corresponding BER is 8.6e-7 and 3.8e-7, respectively. Furthermore, 4K video transmission is also carried out which is clear and free of stutter. The successful transmission of aggregated channels in this wireless link shows the great potential of THz communication for future wireless high-rate real-time data transmission applications.
Prediction of Typical Flue Gas Pollutants from Municipal Solid Waste Incineration Plants

With rapid population growth and urbanization, municipal solid waste (MSW) generation rates are rising around the world. Incineration is considered as an effective way to deal with the growing demand for MSW disposal. The prediction of concentrations of pollutants generated from MSW incineration plants is a powerful support for waste incineration process control, which aims to reduce pollutant emissions. This paper proposes a two-stage prediction method based on the long short-term memory (LSTM) network to forecast typical flue gas pollutants of an MSW incineration plant in South China. In the first stage, an LSTM-based classification model is utilized to determine whether the pollutants are at a low level, and over-sampling strategy is applied to deal with the class-imbalance problem. At the second stage, an LSTM-based prediction model is built to further estimate the amounts of pollutants. Besides, the sliding average method is used to process the multi-scale raw data. Experiment results proved the effectiveness of the proposed method and indicated how different inputs affect the forecasting of pollutant emissions.
β1-subunit of BK channels regulates arterial wall [Ca2+] and diameter in mouse cerebral arteries

Mice with a disrupted β1(BKβ1)-subunit of the large-conductance Ca2+-activated K+ (BK) channel gene develop systemic hypertension and cardiac hypertrophy, which is likely caused by uncoupling of Ca2+ sparks to BK channels in arterial smooth muscle cells. However, little is known about the physiological levels of global intracellular Ca2+ concentration ([Ca2+]i) and its regulation by Ca2+ sparks and BK channel subunits. We utilized a BKβ1 knockout C57BL/6 mouse model and studied the effects of inhibitors of ryanodine receptor and BK channels on the global [Ca2+]i and diameter of small cerebral arteries pressurized to 60 mmHg. Ryanodine (10 μM) or iberiotoxin (100 nM) increased [Ca2+]i by ∼75 nM and constricted +/+ BKβ1 wild-type arteries (pressurized to 60 mmHg) with myogenic tone by ∼10 μm. In contrast, ryanodine (10 μM) or iberiotoxin (100 nM) had no significant effect on [Ca2+]i and diameter of −/− BKβ1-pressurized (60 mmHg) arteries. These results are consistent with the idea that Ca2+ sparks in arterial smooth muscle cells limit myogenic tone through activation of BK channels. The activation of BK channels by Ca2+ sparks reduces the voltage-dependent Ca2+ influx and [Ca2+]i through tonic hyperpolarization. Deletion of BKβ1 disrupts this negative feedback mechanism, leading to increased arterial tone through an increase in global [Ca2+]i.
Information Diffusion Prediction via Dynamic Graph Neural Networks

Information diffusion prediction studies how information items spread in the social network. Previous works either concentrate on leveraging a single diffusion sequence once a time or using the social relationships among users. However, diffusion sequence as a simple chain structure cannot fully capture complex interactions between users. And the diffusion paths of all messages can construct a dynamic diffusion graph. In this paper, we propose a new dynamic structural-temporal graph neural network(DySTGNN) to jointly utilize the structural pattern of the social network and the temporal feature underlying the diffusion graph. What's more, the experimental results have shown that DySTGNN significantly outperforms many recent competitive works on three real-world datasets, which validate the effectiveness of our methods.
Optimizing Process Discovery Quality Criteria and Model Measurements using Receiver Operating Characteristic Analysis and Infrequent Inductive Miner

Generating process models that reflect close behavioral resemblance to the actual process Standard Operating Procedure (SOP) in process mining can be challenging without taking the four quality criteria of process discovery into account. The four quality criteria, i.e. fitness, precision, generalization, and simplicity, should be well balanced in order to produce proper process models which are aligned to the real-life executions. This paper proposes a method to optimize process discovery quality criteria (PDQC) by implementing different thresholds and analyzing calculation results using Receiver Operating Characteristic (ROC) curve and Infrequent Inductive Miner algorithm. This paper sets up two experiments with different scenarios to measure the calculations of quality criteria and the quality of generated models. The experiments compare two SOPs to the process models discovered by Infrequent Inductive Miner algorithm; hence the SOPs serve as references to determine the generated models quality. The purpose of applying two different scenarios in the experiments is to discover how well the Infrequent Inductive Miner thresholds can produce predictive models under these two different scenarios circumstances. This paper has been successful in predicting the best-fit model in reference to the SOPs by optimizing the four quality criteria of process discovery using ROC thresholds settings and by using infrequent inductive miner algorithm for models generation, and also in improving the accuracy of models measurements. The accuracy rate of the prediction model from Experiment 1 is 83%, while Experiment 2 yields an accuracy rate of 88%. The most optimal threshold settings to generate the best model in this paper are threshold 0.4 in Experiment 1 and threshold 0.5 in Experiment 2.
HDQGF:Heterogeneous Data Quality Guarantee Framework Based on Deep Learning

Although user-generated data on the Internet contains rich information, many approaches cannot effectively guarantee data quality for data analysis from raw data. In recent years, many researches on data quality guarantee using machine learning have shown that enhancing data quality is conducive to improve the accuracy of analysis results. But the existing approaches only consider the single dimension and neglect the fusion of heterogeneous data, such as images or social graphs. To consider this element and address the above issue, we leverage the deep learning technique to guarantee the data quality by using the heterogeneous data. Our framework is named HDQGF, which is an end-to-end approach using a combination of multiple networks to fuse heterogeneous information. In order to verify the effectiveness of the model, we designed related experiments on three real datasets. According to the experimental results, our model HDQGF can enhance the performance by improving the data quality.
Humanoid Loco-Manipulations Pattern Generation and Stabilization Control

In order for a humanoid robot to perform loco-manipulation such as moving an object while walking, it is necessary to account for sustained or alternating external forces other than ground-feet reaction, resulting from humanoid-object contact interactions. In this letter, we propose a bipedal control strategy for humanoid loco-manipulation that can cope with such external forces. First, the basic formulas of the bipedal dynamics, i.e., linear inverted pendulum mode and divergent component of motion, are derived, taking into account the effects of external manipulation forces. Then, we propose a pattern generator to plan center of mass trajectories consistent with the reference trajectory of the manipulation forces, and a stabilizer to compensate for the error between desired and actual manipulation forces. The effectiveness of our controller is assessed both in simulation and loco-manipulation experiments with real humanoid robots.
A 500V SOI-LIGBT With Multiple Deep-Oxide Trenches For Fast Turn-OFF

A 500V Silicon-on-Insulator Lateral Insulated Gate Bipolar Transistor (SOI-LIGBT) is proposed in this paper. The device features multiple deep-oxide trenches (MDOT) placed in the silicon region. Two trenches (T1 and T2) are placed in the N-drift region while one trench (T3) locates at the emitter side and shorted with the P+ emitter. T1 and T2 assist in sustaining the electric potential from the collector, which enables the SOI-LIGBT to reduce its drift region length. At the same breakdown voltage of 560V, the drift region length is reduced from 47μm for the conventional SOI-LIGBT to 21. 9μm for the proposed SOI-LIGBT. Due to the small-sized drift region, decreased number of stored carriers in the drift region during the turnoff process is realized. T3 acts as a vertical field plate and can accelerate the depletion of the N-drift region during the turn-off process. The proposed MDOT SOI-LIGBT achieves a turn-off loss (EOFF) 45.7% lower than the conventional SOI-LIGBT at on-state voltage drop (VON) of 1.6V.
IoT Resiliency through Edge-located Container-based Virtualization and SDN

Despite the falling cost of electronics, the adoption IoT systems in households (“smart homes”) is hampered by their strict reliance on stable and reliable Internet connection, which is not readily available in all places around the world. This reliance is greater still in sensor-actuator setups that are comprised of products from several manufacturers or ecosystems, since the packets have to traverse and be processed by different cloud-based servers. In this paper, we propose a resilient IoT system which enables household IoT systems to remain functional even in the absence of an Internet connection. The system utilizes container-based virtualization in locally hosting cloud server functionality and software-defined networking (SDN) in hiding the packet redirection and providing a seamless network experience for the IoT devices. The system was built and its performance profiled as it utilized different hardware for its container-based virtualization host.
Conversational Agents for Elderly Interaction

Social isolation and loneliness are problems faced by the elderly that might be aggravated due to quarantine during the coronavirus pandemic. This motivates a search demand for automatic technological solutions, such as chatbots, to address the problem, and establish a collaborative interaction between elderly and conversational agents. We analyze the use of conversational agents and the main types of personalization used during interacting with the elderly. We mapped 53 papers in 5 sources to answer 5 research questions that guided our study. Our findings show these solutions may have a positive impact on elderly's life.
DeepMIT: A Novel Malicious Insider Threat Detection Framework based on Recurrent Neural Network

Currently, more and more malicious insiders are making threats, and the detection of insider threats is becoming more challenging. The malicious insider often uses legitimate access privileges and mimic normal behaviors to evade detection, which is difficult to be detected via using traditional defensive solutions. In this paper, we propose DeepMIT, a malicious insider threat detection framework, which utilizes Recurrent Neural Network (RNN) to model user behaviors as time sequences and predict the probabilities of anomalies. This framework allows DeepMIT to continue learning, and the detections are made in real time, that is, the anomaly alerts are output as rapidly as data input. Also, our framework conducts further insight of the anomaly scores and provides the contributions to the scores and, thus, significantly helps the operators to understand anomaly scores and take further steps quickly(e.g. Block insider's activity). In addition, DeepMIT utilizes user-attributes (e.g. the personality of the user, the role of the user) as categorical features to identify the user's truly typical behavior, which help detect malicious insiders who mimic normal behaviors. Extensive experimental evaluations over a public insider threat dataset CERT (version 6.2) have demonstrated that DeepMIT has outperformed other existing malicious insider threat solutions.
Artificial Olfaction in the 21st Century

The human olfactory system remains one of the most challenging biological systems to replicate. Humans use it without thinking, where it can measure offer protection from harm and bring enjoyment in equal measure. It is the system’s real-time ability to detect and analyze complex odors that makes it difficult to replicate. The field of artificial olfaction has recruited and stimulated interdisciplinary research and commercial development for several applications that include malodor measurement, medical diagnostics, food and beverage quality, environment and security. Over the last century, innovative engineers and scientists have been focused on solving a range of problems associated with measurement and control of odor. The IEEE Sensors Journal has published Special Issues on olfaction in 2002 and 2012. Here we continue that coverage. In this article, we summarize early work in the <inline-formula> <tex-math notation="LaTeX">$20^{{\mathrm {th}}}$ </tex-math></inline-formula> Century that served as the foundation upon which we have been building our odor-monitoring instrumental and measurement systems. We then examine the current state of the art that has been achieved over the last two decades as we have transitioned into the <inline-formula> <tex-math notation="LaTeX">$21^{{\mathrm {st}}}$ </tex-math></inline-formula> Century. Much has been accomplished, but great progress is needed in sensor technology, system design, product manufacture and performance standards. In the final section, we predict levels of performance and ubiquitous applications that will be realized during in the mid to late <inline-formula> <tex-math notation="LaTeX">$21^{{\mathrm {st}}}$ </tex-math></inline-formula> Century.
Application of Machine Learning in Discharge Classification

Condition monitoring of electric insulators is essential to maintain reliable power transmission and distribution. This paper presents a study to use a commercial acoustic sensor along with different machine learning algorithms to classify different types of discharges in outdoor electric insulation systems. Support vector machine (SVM) based classifiers have been utilized to distinguish between five common electrical discharges that were generated under controlled conditions. The classification problem expands to include outdoor ceramic insulators with three defects i.e., a crack in the ceramic disc, surface pollution discharge, and corona near the insulator surface. For both controlled samples and ceramic insulators, an average recognition rate of more than 91 % has been achieved.
A Robust Optical Flow Tracking Method Based On Prediction Model for Visual-Inertial Odometry

Indirect method is widely used in the field of visual SLAM at present, and it can be divided into feature matching method and optical flow tracking method according to whether matching descriptors are needed in the tracking process. Compared to feature matching method, optical flow does not need to spend too much time on calculating and matching feature points, but it is easy to be affected by illumination changes and fast motion. In recent years, the visual-inertial fusion method improves the localization accuracy and robustness on the condition that the illumination is poor or image blur caused by fast motion. A robust optical flow tracking method based on prediction model for visual-inertial odometry is proposed in this letter. A new optical flow and brightness correction method based on a linear illumination model is used to deal with the large changes of illumination. We also proposed an initial optical flow prediction method with the pre-integration of IMU and reprojection model between image frames to make the optical flow more robust. The results of optical flow are also used to judge the image quality of specific areas to make sure the extracted feature points are more stable and suitable for tracking. Finally, the method is proved that can outperform other state-of-the-art VIO methods in experiments on the public benchmark even without loop closure.