Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Preprocessing Methodology for DNN-Based Intrusion Detection

Intrusion Detection System (IDS) plays a vital role in cybersecurity. One major challenge in state-of-the-art IDS is that the Advanced Persistent Threats (APTs) cannot be well detected. Deep Neural Networks (DNNs) extract latent features of input data, which is considered to be able to capture intrinsic property of APTs, to detect APTs. However, DNN is not well used in APT detection. In this paper, a DNN model is utilized for intrusion detection and a novel method of preprocessing is proposed to improve the performance of the detection algorithms. We consider the statistical characteristics of training set to be the statistical characteristics of test set and detection set. Experiment results show that our preprocessing methodology achieves a better performance in terms of accuracy, recall and F1 Score than traditional preprocessing methods, which means a better detection for APTs.
Advanced TSGL-EEGNet for Motor Imagery EEG-Based Brain-Computer Interfaces

Deep learning technology is rapidly spreading in recent years and has been extensive attempts in the field of Brain-Computer Interface (BCI). Though the accuracy of Motor Imagery (MI) BCI systems based on the deep learning have been greatly improved compared with some traditional algorithms, it is still a big problem to clearly interpret the deep learning models. To address the issues, this work first introduces a popular deep learning model EEGNet and compares it with the traditional algorithm Filter-Bank Common Spatial Pattern (FBCSP). After that, this work considers that the 1-D convolution of EEGNet can be explained by a special Discrete Wavelet Transform (DWT), and the depthwise convolution of EEGNet is similar to the Common Spatial Pattern (CSP) algorithm. Therefore, this work improves the EEGNet by using the algorithm Temporary Constrained Sparse Group Lasso (TCSGL) to enhance its performance. The proposed model TSGL-EEGNet is tested on the BCI Competition IV 2a and BCI Competition III IIIa datasets that both are 4-classes classification MI tasks. The testing results show that the proposed model has achieved 78.96% (0.7194) average classification accuracy (kappa) on the dataset BCI Competition IV 2a, which are greater than EEGNet, C2CM, MB3DCNN, SS-MEMDBF and FBCSP, especially on insensitive subjects. The proposed model has also achieved 85.30% (0.8040) average classification accuracy (kappa) on the dataset BCI Competition III IIIa, which are greater than the EEGNet, MFTFS et al. At last, this work uses average-validation and stacking to further enhance the effect of the model. The 4-classes classification average accuracy rates reach 81.34% and 88.89%, and the kappas reach 0.7511 and 0.8519 on dataset BCI Competition IV 2a and BCI Competition III IIIa, respectively. Additionally, this work also uses the Grad-CAM to visualize the frequency and spatial features that are learned by the neural network.
Coherent Chirp Generation by Narrowband Transceiver Chips for ToF Indoor Localization

We present an approach for indoor localization of radio nodes using commercial off-the-shelf (COTS), low-cost, low-power, transceiver chips, found in many internet of things (IoT) applications. As such chips are typically narrowband by design, suitable localization schemes are narrowband as well, e.g., based on received signal strength indication (RSSI) or angle of arrival (AoA) measurements. Naturally, these techniques are prone to multipath fading in indoor scenarios. To circumvent this, we show that for a typical transceiver chip it is possible to generate reproducible frequency-chirped wideband signals with deterministic behavior, suitable for time of flight (ToF) localization. Despite the simplicity of the deployed radio, it is possible to predict the phase of this waveform in order to use a signal model for filtering at the receiver side. In an exemplary implementation based on a Texas Instruments CC2510 chip, we achieve an overall signal bandwidth of up to 80 MHz, centered at 2.44 GHz. With a synchronized receiver infrastructure in a simple scenario, an absolute localization error below 1 m in 90% of the cases can be reached even with a fast, sub-optimal time difference of arrival (TDoA) multilateration algorithm.
Optimized Task Offloading Policy in Given Sequence in Mobile Edge Computing

In the era of big data, tasks with complex computation emerge in large numbers. It is hard to solve them properly when the tasks are in a given sequence. So we should find a way to utilize the local CPU and the edge server on the premise of keeping the task order. We consider a task sequence in a user device and a base station that can receive offloaded task to solve. We find out the optimized offloading policy by cutting the task sequence directed acyclic graph (DAG) into basic units. The power and frequency allocated to every task can be also gained at the same time. Then we can perfectly know how to deal with the task chain. We choose to solve the original problem by decomposing it into sub-problems according with different scenarios. We show that our algorithm achieves the least consumed time among the compared algorithms. Simulations are performed to verify the proposed algorithm.
Ultralow-Power Double Vertical Junction Microdisk Modulators

One of the most critical elements in a data center transceiver is the optoelectronic modulator. In a silicon-based optoelectronic modulator, the ultimate performance depends on the orientation and positioning of its pn junction(s). In comparison to a conventional, lateral pn junction, a vertical pn junction improves energy efficiency by having a stronger overlap with the resonant optical signal. Adding to this design philosophy, we present two new designs of vertical junction modulators: (i) a double vertical junction, which doubles the interaction length, and (ii) a staggered double vertical junction, which further increases the interaction length by adding a lateral component to the double junction design. In this work, we provide a comprehensive numerical comparison of microring/disk modulators between the conventional, lateral pn junction and our vertical pn junction designs. We demonstrate how our new double vertical junction configurations offer the most promising power performance characteristics, with an energy-efficiency improvement by over a factor of 4. Our work may pave the way for new, energy-efficient silicon modulators in next-generation information processing and communications.
Holistic Adaptive Multi-Model Predictive Control for the Path Following of 4WID Autonomous Vehicles

This paper presents a novel adaptive multiple-model predictive control (MPC) scheme for Four-Wheel Independent Drive (4WID) autonomous vehicles with a holistic structure. Firstly, the combined vehicle-path model is established. To ensure the real-time performance of MPC, the coupling relationships in the control output and the longitudinal/lateral motions are well decoupled, in this way a linear integrated model is utilized as the internal model of the controller. Then, the holistic MPC is proposed to acquire the steering angle and the force on each corner. Based on the advantages of the proposed structure, a weight adaptive mechanism is introduced to improve the handling ability of the controller to various driving conditions, especially some extreme conditions. For the uncertainties in tire cornering stiffness, a multiple-model adaptive law is designed with its convergence proved by Lyapunov theory. Numerical results based on Carsim-Simulink co-simulation platform demonstrate the effectiveness and superiority of the proposed control method in both normal and extreme driving conditions.
The Research of Ontology-based Digital Twin Machine Tool Modeling

As a core manufacturing resource, CNC machine tool acts a pivotal part in the new trend of smart manufacturing. Aiming at the intelligent demands of CNC machine tool, this paper proposes a digital twin modeling framework for CNC machine tool. Furthermore, a semantic model of CNC machine tool based on ontology is established to solve the issues of multi-source heterogeneous data fusion and insufficient semantic information, which contributes to condition monitoring service to fully perceive the running status of machine tool.
Target Fast Reconstruction of Real Aperture Radar Using Data Extrapolation-Based Parallel Iterative Adaptive Approach

Real aperture radar (RAR) usually sweeps a wide sector to continuously observe the scenario of interest. Because its angular resolution is limited by the size of the antenna aperture, target reconstruction methods are widely applied to obtain super-resolution radar images. However, the wide-sector processing mode suffers from high operational complexity because of the high-dimensional matrix inversion. Even worse, for the targets located at the scene edge, its echo data are received less than half beamwidth. The incomplete echo data will lead to the deformation of the reconstructed targets by existing reconstruction methods. To overcome the two problems, a data extrapolation-based parallel iterative adaptive approach is proposed to fast reconstruct the targets in the whole sector without the distortion at the scene edge. First, the echo model of RAR is repaired to remedy the model error. Then, based on the correlation of the echo data within one beamwidth, an autoregressive model is adopted to extrapolate the data of the missing half beamwidth. Finally, a parallel iterative adaptive approach method is proposed to efficiently recover the targets by exploiting the regular characteristics of the repaired steering matrix. Simulations and experimental data are applied to verify the proposed method.
Balancing Algorithm and Active Shutter Improved ROV Monitoring and Control Platform Using Mobile Phone

In this paper, we propose a novel and convenient Remote Operated Vehicle (ROV) monitoring and control platform based on Virtual Reality (VR) system and virtual rocker control system. In general, conventional monitoring and control platforms for ROV is bulky and lack of real-world interaction. To overcome these problems, we design a portable monitor and control platform that offers 3D visualization and control interface based on mobile phone. Besides, to improve the immersion perception of mobile phone VR platform, an algorithm based on Kalman filter is proposed to improve the accuracy of controlling data. Active shutter technology is also applied to enhance the image resolution in VR visualization.
Fabricating Thin-Film Transistors on Plastic Substrates Using Spin Etching and Device Transfer

This work presents a novel method for transferring thin-film transistors (TFTs) from a Si wafer to another flexible plastic substrate. First, high-performance poly-Si TFTs were fabricated on a 1.5 μm thick SiO 2 on a Si wafer and then attached to a flexible plastic substrate by optical adhesive. Next, spin-etching was utilized to remove the backside Si, using SiO 2 as a stopping layer. A qualitative model was established to explain the relationship between the chemical flow rate/rotation speed to the etching rate and the uniformity of Si removal. The Si etching rate exceeded 200 μm/min while the Si to SiO 2 selectivity of 250 was maintained given optimized spin-etching parameters. Substrate bonding and Si spin-etching steps caused no degradation or yield loss as compared to the electrical characteristics before transference. Additionally, extrinsic stress only weakly affected the properties of poly-Si resistors and TFTs on a flexible plastic substrate.
Dynamic deuterium NMR in liquid crystalline solvents: Ring inversion of cyclohexane‐d12

The application of deuterium NMR spectroscopy of molecules dissolved in liquid crystalline solvents to study intramolecular dynamic processes is demonstrated using the ring inversion kinetics of cyclohexane‐d12. The spectra of C6D12 dissolved in phase V and in hexyloxyazoxybenzene were studied over the temperature range −36 to +115 °C, and were found to exhibit a very pronounced line shape variation with temperature. At low temperatures (<−10 °C) the spectrum consists of two symmetric doublets due to the axial and equatorial sites. The doublet splittings are due to the quadrupole interactions of the corresponding deuterons. Upon increasing the temperature the lines broaden until finally the spectrum transforms into a single doublet with a spacing corresponding to the mean quadrupole splitting of the axial and equatorial deuterons. In an Appendix the line shape theory for two interacting nuclei of spin I = 1 which undergo mutual exchange is presented, and this theory is used to quantitatively interpret the.
The relationship between hemogram parameters with clinical progress in COVID-19 patients

Objective: In the present study, we aimed to show the relationship between the clinical characteristics age, gender, hospitalization time values and hemogram parameters of COVID-19 patients. Methods: Total 70 patients who were diagnosed with COVID-19 between April 2020 and June 2020 in a secondary hospital and discharged with healing were retrospectively examined in terms of demographic data, epidemiological properties and hemogram parameters. Results: The mean age of 70 patients included in the study is 43.8± 17.2 (range, 17 to 87) years; 28 (40%) were female and 42 (60%) were male. The mean hospitalization time of the patients was 6.33 ± 3.05 (range, 1 to 15) days. We observed a significant difference between eosinophil count (p = 0.05) and platelet distribution width (PDW) values (p = 0.032) according to the duration of hospitalization. There was no significant difference between the clinical progress and blood values in general. However, when patients with and without fever were compared, a significant difference was found for mean platelet volume (MPV) (p=0.035) values. Similarly, a statistically significant difference was found between hemoglobin (p = 0.046) and eosinophil number (p = 0.010) when male and female patients were compared. Conclusion: The relationship between clinical progress and hemogram parameters in patients diagnosed with COVID-19 may be significant for the evaluation of prognosis.
CHARACTERISING HAZARDOUS SUBSTANCES IN THE UK MARINE ENVIRONMENT. ICES CM 2000/S:11

In order to assess the risk posed by hazardous substances present in the marine environment, investigations are required to characterise the most toxicologically significant substances present. This task can represent finding the proverbial needle in a haystack, since, in many industrially impacted areas, estuarine sediments and surface waters contain a complex mixture of natural and synthetic substances. To help the process, toxicity identification evaluation (TIE) procedures have been developed to isolate and characterise the biologically active compounds present. The TIE procedures have been combined with an acute bioassay using a marine copepod (Tisbe battagliai), mutagenic assay (Mutatox ) and an in vitro yeast-based screen for oestrogenic activity. A wide range of compounds, including pesticides, surfactant metabolites, natural steroids and industrial chemicals, have been identified and used to inform monitoring programmes and risk assessments.
Using Interactive Multiobjective Optimization to Design a Permanent Magnet Synchronous Generator

We consider an analytical model of a permanent magnet synchronous generator and formulate a mixed-integer constrained multiobjective optimization problem with six objective functions. We demonstrate the usefulness of solving such a problem by applying an interactive multiobjective optimization method called NIMBUS. In the NIMBUS method, a decision is iteratively involved in the optimization process and directs the solution process in order to ﬁnd her/his most preferred Pareto optimal solution for the problem. We also employ a commonly used noninteractive evolutionary multiobjective optimization method NSGA-II to generate a set of solutions that approximates the Pareto set and demonstrate the advantages of using an interactive method. This study is the ﬁrst one to consider an interactive approach for the design of a permanent magnet synchronous generator. Thus, we promote the further usage of interactive multiobjective optimization methods in the design. Further, we see that these methods could also be very useful in the teaching of electrical machines.
Forecasting of Significant Wave Height Based on Gated Recurrent Unit Network in the Taiwan Strait and Its Adjacent Waters

Significant wave height (SWH) forecasting is a key process for offshore and costal engineering. However, accurate prediction of the SWH is quite challenging due to the randomness and fluctuation features of waves. This paper employs a novel deep learning method, the gated recurrent unit network (GRU), to forecast SWH with lead times of 3, 6, 12 and 24 h. The data sets used in this study include the wind speed of the past 3 h and the current SWH as inputs, which were obtained from six buoy stations in the Taiwan Strait and its adjacent waters. The GRU results are compared with those of back propagation neural network (BP), extreme learning machine (ELM), support vector machine (SVM), and random forest (RF). Although the error indices of the six stations are different, the general performance of GRU is satisfactory, with a faster forecasting speed, smaller volatility and better adaptability. Using buoy station 46714D as an example, the root mean square error (RMSE) predicted by GRU reaches 0.234, 0.299, 0.371, and 0.479 with lead times of 3, 6, 12, and 24 h, respectively.
Different dielectric breakdown mechanisms for RF-MgO and naturally oxidized MgO

We investigate the voltage breakdown of the spin transfer torque magnetic random access memory (STT-MRAM) with perpendicular magnetic tunnel junctions (pMTJs). Different breakdown behaviors are observed for RF-MgO pMTJs and naturally oxidized MgO pMTJs. While the time-to-failure body distribution of the naturally oxidized MgO follows the Weibull distribution, that of RF-MgO follows the lognormal distribution. This result suggests distinctly different dielectric breakdown mechanisms for naturally oxidized MgO and RF-MgO. For low failure probability, the progressive voltage breakdown of RF-MgO (associated with the lognormal distribution) results in an order-of-magnitude reliability improvement over the abrupt breakdown of the naturally oxidized MgO. We show that RF-MgO is suitable for perpendicular STT-MRAM applications.
Study of antioxidant activity of liposomal forms of quercetin and curcumin in ischemic heart disease

Quercetin and curcumin are plant polyphenolic antioxidants with proven pharmacological efficacy. Their use is, however, limited due to low bioavailability and oral administration route. The encapsulation of lipophilic compounds in liposomes enables to increase their bioavailability and to create an injectable form. The present study aimed to comparatively investigate the antioxidant activity of a complex liposomal preparation containing two lipophilic antioxidants (quercetin and curcumin) and their monopreparations in liposomal forms. This study was conducted on Wistar line rats with experimental model of ischemic heart disease. Oxidative stress markers such as total antioxidant activity, malondialdehyde, and peroxidized proteins were analyzed in blood serum and cardiac tissue. Ischemic heart disease is accompanied with lipid peroxidation and changes in the activity of the antioxidant system. The obtained results demonstrated the antioxidant activity of monopreparations of curcumin and quercetin and their complex in liposomal forms. Quercetin and curcumin showed different antioxidant activities in terms of oxidative stress markers. The complex of the two antioxidants showed the synergistic effect of their lipophilic compounds in liposomal forms, which led to the normalization of test parameters according to the level of the control sample.
Boosting of photocatalytic hydrogen evolution via chlorine doping of polymeric carbon nitride

Chlorine is found to be a suitable element for the modification of polymeric carbon nitride properties towards an efficient visible-light photocatalytic activity. In this study, chlorine-doped polymeric carbon nitride (Cl-PCN) has been examined as a photocatalyst in the hydrogen evolution reaction. The following aspects were found to enhance the photocatalytic efficiency of Cl-PCN: (i) unique location of Cl atoms at the interlayers of PCN instead of on its π-conjugated planes, (ii) slight bandgap narrowing, (iii) lower recombination rate of the electron–hole pairs, (iv) improved photogenerated charge transport and separation, and (v) higher reducing ability of the photogenerated electrons. The above factors affected the 4.4-fold enhancement of the photocatalytic efficiency in hydrogen evolution in comparison to the pristine catalyst.
Monads à la Mode

Suppose that we would like to implement call/cc in the pure (side-effect free) λ-calculus. To achieve this, we first explicitly expose continuations in our programs by converting each term in our language into continuation-passing style. Continuation-passing style is a set of transformations to the λ-calculus that provides access to continuations. Figure 1 presents rules for translating the λ-calculus into continuationpassing style. The transformation works by simply walking the grammar, converting each expression on the left into the corresponding expression on the right and then recurring. After the transformation is performed on a λ-expression, continuations are explicitly exposed in the language. Adding features such as call/cc, let/cc, throw, and abort are now possible (call/cc is included in Figure 1 and the rest are left as an exercise for the reader); before this transformation, a language implementer would have to provide such functions without explicitly exposing continuations to the user—similar to how implementations of Scheme provide call/cc as an system-level feature.
Digital Ink vs. Physical ink User Perception Limits when Writing with a Digital Pen

Despite the spread of tablet computers (or “tablets”), people still take notes with physical pens on paper. Yet, tablets are a good tradeoff between laptop computers and physical paper. They are lighter and more suitable for complex annotation than computers, and they do not suffer from the limitations of physical paper: tablets are dynamic, they allow easy duplication, distribution and archival. This work is an attempt to understand why paper is till preferred to tablets when it comes to taking notes. We worked with the hypothesis that the technical performances of digital pens are not good enough to replace physical pens. Our approach was to create an experimental high performances digital pen and selectively degrade various aspect of our pen. We ran two user studies to measure the effect of these degradations on the users of the pen. In a subjective experiment, we collected the general feelings of participants when writing with a physical pen on paper, a commercial tablet and our device. Contrary to our expectations, we discovered that the smooth contact of a plastic nib on a glass display was not a hindrance. In an objective experiment, we revealed the thresholds of human perception on various technical characteristics of our digital pen. In particular, we showed that current commercial tablets do not satisfy the requirements of human perception in term of offset and latency. This work provides new knowledge on digital pens requirements, which should contribute to the ultimate goal of providing usable digital services to the task of note taking.