Miracle-dz/UniFAR-FTUs huggingface dataset:
Electroencephalogram-Based Preference Prediction Using Deep Transfer Learning

Transfer learning is an approach in machine learning where a model that was built and trained on one task is re-purposed on a second task. The success of transfer learning in computer vision has motivated its use in neuroscience. Although common in image recognition, the use of transfer learning in EEG classification remains unexplored. Most EEG-based neuroscience studies depend on using traditional machine learning algorithms to answer a question, rather than on improving the algorithms. Developing algorithms for transfer learning for EEG can also assist with problems of low data availability in EEG classification. The primary objective of this study is to investigate EEG-based transfer learning and propose deep transfer learning models to transfer knowledge from emotion recognition to preference recognition to enhance the classification prediction accuracy. To the best of our knowledge, this is the first study demonstrating the effect of applying deep transfer learning between EEG-based emotion recognition and EEG-based preference detection. We propose different approaches for deep transfer learning models to detect preferences from EEG signals using the preprocessed DEAP dataset. Two types of features were extracted from EEG signals, namely the power spectral density and valence. We built three models of deep neural networks: basic without transfer learning, fine-tuning of deep transfer learning, and retraining of deep transfer learning. We compared the performance of deep transfer learning with those of deep neural networks and other conventional classification algorithms such as support vector machine, random forest, and k-nearest neighbor. Although the deep neural network classifiers achieved a high accuracy of greater than 87%, deep transfer learning achieved the highest accuracy result of 93%. The results demonstrate that although the proposed deep transfer learning approaches exhibit higher accuracy than the support vector machine and k-nearest neighbor classifiers, random forest achieves results similar to those of deep transfer learning.
POSTER: Distributed SSH Bruteforce Attack Detection with Flow Content Similarity and Login Failure Reputation

In this paper we propose a method to detect distributed bruteforcing by modeling failed login attempts as a Poisson probability distribution. We use content similarity between known SSH connection and flow characteristics of failed login attempts to attribute a flow to SSH application and subsequently either as failure or success. Using the failed login count in a window time, we label window as either normal or containing bruteforce attempts.
The Sealing Technology in Pipeline Repair Clamp

ABSTRACT Zhao, B.J.; Zhang, S.L. ; Li, T. ; Hao, J.L. , and Wang, T.Y. , 2020. The sealing technology in pipeline repair clamp. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 872–881. Coconut Creek (Florida), ISSN 0749-0208. In order to meet the repair requirements in the subsea pipeline, the present study envisages the design for the sealing structure of the subsea oil and gas pipeline repair clamp. Based on the uniform contact pressure distribution theory and the thick-walled cylinder theory, a theoretical calculation formula was established to calculate the contact pressure between the sealing ring and the pipeline. With a goal of employing a φ219 subsea oil and gas pipeline repair clamp, the finite element models were established and the finite element simulation calculations were carried out for different pressure and thickness sealing rings. Meanwhile, the models were calculated based on the theoretical calculation formula. Subsequently, the results of finite element simulation and the theoretical calculation formula were analysed. Based on the theoretical calculation and the finite element calculations, the acceptance criterion of the theoretical calculation was proposed. The study lays a reliable basis for the sealing design and manufacturing of subsea oil and gas pipeline clamp.
Urban Circle Structures and Regional Differences in China's Coastal Areas

ABSTRACT Xing, W.X. ; Liu, D.H. , and Li, F. , 2020. Urban circle structures and regional differences in China's coastal areas. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 554–565. Coconut Creek (Florida), ISSN 0749-0208. As a geographic element, oceans and seas exert a profound impact on regional social and economic development and change, and they are key factors that drive the formation and development of urban spatial structures in coastal areas. Based on the wide applicability of the “lake effect” theory to water economy research, this study analyzes the spatial structure characteristics of 735 counties in 11 provinces and municipalities in mainland China's coastal areas and the differences among three major coastal areas using buffer analysis and an extended Bass model. The results show that there is a notable urban circle structure in China's coastal areas under the influence of seas. China's coastal areas can be divided into three circular zones, namely, the core, key, and peripheral zones. The landward boundaries of core and key zones are 9 and 95 km away from the coastline, respectively. The landward boundary of the peripheral zone is the peripheral boundary of the study area. There is a significant difference in the spatial circle structure between the Northern, Central, and Southern coastal areas of China. In the Northern region, the core zone lacks a notable circle structure, whereas there is a notable dual-peak, dual-nuclei structure in the key and peripheral zones. The core zone width of the Central region is 15 km, and there is a dual-peak (high and low peaks), dual-nuclei structure in the key and peripheral zones. In the Southern region, the width of the core zone is 9 km, and there is a high-peak, single-nucleus structure in the key zone. The results of this research are helpful to clarify the position of different cities in regional development, and provide theoretical basis for land and sea resources allocation.
Hybrid Optimize Method for Multi-Objective Scheduling of Hydropower Stations with Coupled Power Generation, Navigation Demand and Ecological Benefit

ABSTRACT Shen, Q. ; Mo, L. , and Liu, G.J., 2020. Hybrid optimize method for multi-objective scheduling of hydropower stations with coupled power generation, navigation demand and ecological benefit. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 379–384. Coconut Creek (Florida), ISSN 0749-0208. As power station dispatching faces higher comprehensive utilization requirements, power generation benefit cannot be ignored for river basins with navigation and ecology requirements. In this paper, the Shuibuya Hydropower Station is adopted as the engineering background, and a multi-objective optimization model with power generation, navigation and ecology as the objectives is constructed. The RSEA algorithm is adopted to obtain a scheme set. By comparing the typical schemes, changes in power generation have less impact on navigation and ecology. The optimized benefits can be sacrificed at the expense of a small part of the power generation. In the model calculation, a large weight coefficient should be allocated to the power generation, and the optimal solution set is still obtained. The result can provide reference for how to balance multi-target weight coefficients of similar hydropower stations.
Predicting Antidepressant Citalopram Treatment Response via Changes in Brain Functional Connectivity After Acute Intravenous Challenge

Introduction: The early and therapy-specific prediction of treatment success in major depressive disorder is of paramount importance due to high lifetime prevalence, and heterogeneity of response to standard medication and symptom expression. Hence, this study assessed the predictability of long-term antidepressant effects of escitalopram based on the short-term influence of citalopram on functional connectivity. Methods: Twenty nine subjects suffering from major depression were scanned twice with resting-state functional magnetic resonance imaging under the influence of intravenous citalopram and placebo in a randomized, double-blinded cross-over fashion. Symptom factors were identified for the Hamilton depression rating scale (HAM-D) and Beck's depression inventory (BDI) taken before and after a median of seven weeks of escitalopram therapy. Predictors were calculated from whole-brain functional connectivity, fed into robust regression models, and cross-validated. Results: Significant predictive power could be demonstrated for one HAM-D factor describing insomnia and the total score (r = 0.45–0.55). Remission and response could furthermore be predicted with an area under the receiver operating characteristic curve of 0.73 and 0.68, respectively. Functional regions with high influence on the predictor were located especially in the ventral attention, fronto-parietal, and default mode networks. Conclusion: It was shown that medication-specific antidepressant symptom improvements can be predicted using functional connectivity measured during acute pharmacological challenge as an easily assessable imaging marker. The regions with high influence have previously been related to major depression as well as the response to selective serotonin reuptake inhibitors, corroborating the advantages of the current approach of focusing on treatment-specific symptom improvements.
PathAFL: Path-Coverage Assisted Fuzzing

Fuzzing is an effective method to find software bugs and vulnerabilities. One of the most useful techniques is the coverage-guided fuzzing, whose key element is the tracing code coverage information. Existing coverage-guided fuzzers generally use the the number of basic blocks or edges explored to measure code coverage. Path-coverage can provide more accurate coverage information than basic block and edge coverage. However, the number of paths grows exponentially as the size of a program increases. It is almost impossible to trace all the paths of a real-world application. In this paper, we propose a fuzzing solution named PathAFL, which assists a fuzzer by path identification. It can effectively identify and utilize the important h-path, which is a new path but whose edges have all been touched previously. First, PathAFL only inserts one assembly instruction to AFL's original code to calculate the path hash, and uses a selective instrumentation strategy to reduce the tracing granularity of an execution path. Second, we design a fast filtering algorithm to choose higher weight paths from a large number of h-paths and add them to the seed queue. Third, both the seed selection algorithm and the power schedule are implemented based on the path weight. Finally we implemented PathAFL based on the popular fuzzer AFL and evaluated it on 10 well-fuzzed benchmark programs. In 24 hours, PathAFL explored 38% more paths and 9.3% more edges than AFL. Compared with CollAFL-x, the number is 25% and 5.9% correspondingly. Moreover, PathAFL found the more bugs on the LAVA-M dataset, even four unlisted bugs. The results show that PathAFL outperforms the previous fuzzers in terms of both code coverage and bug discovery. In well-tested programs, PathAFL found 8 new security bugs with 6 CVEs assigned.
Efficient streaming of 3D maps for unmanned navigation: 3D tiling the navigable space and eliminating occluded geometry

Ground operations in urban environments present a considerable risk to dismounted and mobile tactical teams. Special operations, counterterrorism, and other missions require forces to operate in complex, densely occluded urban environments. In these situations, an adversary can often exploit in-depth local knowledge of the 3D structure of the environment to establish optimal cover, maintain concealed ring positions, and channelize the attacking force into exposed ingress routes. Small Unmanned Aerial Systems (UAS) operating at low altitude and indoors could potentially provide real-time situational awareness in these conditions but are constrained by the limited availability of GPS in these environments and by a lack of detailed 3D map data required for precision sensor-based navigation in complex environments. This paper, based on our prior work in1 introduces first responder and militaristic applications for an open- source framework for the rapid processing, exploitation, and dissemination of 3D mapping and navigational data at the tactical edge of the network. The proposed protocol is called 3D Tiles Nav which includes certain navigation cell and visibility metadata in order to improve navigational performance and autonomy in complex environments. The navigation cell data structures organizes the low airspace (below 400ft) in which 3D Tiles NAV data is embedded. The resulting data organization supports efficient data delivery, especially in densely occluded (e.g. low altitude and ground level) use cases. In addition, the resulting metadata fuses information about the visible and navigable structure of the operating environment to provide a unique informational framework for planning and conducting missions in densely occluded, high-threat environments. We demonstrate through experiments the extraction of planar features from point cloud data and illustrate through simulations the reduction network, compute, storage, and sensor requirements made possible using our approach.
Image-Based High-Throughput Detection and Phenotype Evaluation Method for Multiple Lettuce Varieties

The yield and quality of fresh lettuce can be determined from the growth rate and color of individual plants. Manual assessment and phenotyping for hundreds of varieties of lettuce is very time consuming and labor intensive. In this study, we utilized a “Sensor-to-Plant” greenhouse phenotyping platform to periodically capture top-view images of lettuce, and datasets of over 2000 plants from 500 lettuce varieties were thus captured at eight time points during vegetative growth. Here, we present a novel object detection–semantic segmentation–phenotyping method based on convolutional neural networks (CNNs) to conduct non-invasive and high-throughput phenotyping of the growth and development status of multiple lettuce varieties. Multistage CNN models for object detection and semantic segmentation were integrated to bridge the gap between image capture and plant phenotyping. An object detection model was used to detect and identify each pot from the sequence of images with 99.82% accuracy, semantic segmentation model was utilized to segment and identify each lettuce plant with a 97.65% F1 score, and a phenotyping pipeline was utilized to extract a total of 15 static traits (related to geometry and color) of each lettuce plant. Furthermore, the dynamic traits (growth and accumulation rates) were calculated based on the changing curves of static traits at eight growth points. The correlation and descriptive ability of these static and dynamic traits were carefully evaluated for the interpretability of traits related to digital biomass and quality of lettuce, and the observed accumulation rates of static straits more accurately reflected the growth status of lettuce plants. Finally, we validated the application of image-based high-throughput phenotyping through geometric measurement and color grading for a wide range of lettuce varieties. The proposed method can be extended to crops such as maize, wheat, and soybean as a non-invasive means of phenotype evaluation and identification.
Sugarcane (Saccharum officinarum L.) Top Extract Ameliorates Cognitive Decline in Senescence Model SAMP8 Mice: Modulation of Neural Development and Energy Metabolism

Age-related biological alterations in brain function increase the risk of mild cognitive impairment and dementia, a global problem exacerbated by aging populations in developed nations. Limited pharmacological therapies have resulted in attention turning to the promising role of medicinal plants and dietary supplements in the treatment and prevention of dementia. Sugarcane (Saccharum officinarum L.) top, largely considered as a by-product because of its low sugar content, in fact contains the most abundant amounts of antioxidant polyphenols relative to the rest of the plant. Given the numerous epidemiological studies on the effects of polyphenols on cognitive function, in this study, we analyzed polyphenolic constituents of sugarcane top and examined the effect of sugarcane top ethanolic extract (STEE) on a range of central nervous system functions in vitro and in vivo. Orally administrated STEE rescued spatial learning and memory deficit in the senescence-accelerated mouse prone 8 (SAMP8) mice, a non-transgenic strain that spontaneously develops a multisystemic aging phenotype including pathological features of Alzheimer’s disease. This could be correlated with an increased number of hippocampal newborn neurons and restoration of cortical monoamine levels in STEE-fed SAMP8 mice. Global genomic analysis by microarray in cerebral cortices showed multiple potential mechanisms for the cognitive improvement. Gene set enrichment analysis (GSEA) revealed biological processes such as neurogenesis, neuron differentiation, and neuron development were significantly enriched in STEE-fed mice brain compared to non-treated SAMP8 mice. Furthermore, STEE treatment significantly regulated genes involved in neurotrophin signaling, glucose metabolism, and neural development in mice brain. Our in vitro results suggest that STEE treatment enhances the metabolic activity of neuronal cells promoting glucose metabolism with significant upregulation of genes, namely PGK1, PGAM1, PKM, and PC. STEE also stimulated proliferation of human neural stem cells (hNSCs), regulated bHLH factor expression and induced neuronal differentiation and astrocytic process lengthening. Altogether, our findings suggest the potential of STEE as a dietary intervention, with promising implications as a novel nutraceutical for cognitive health.
Model Reference Predictive Adaptive Control for Large-Scale Soft Robots

Past work has shown model predictive control (MPC) to be an effective strategy for controlling continuum joint soft robots using basic lumped-parameter models. However, the inaccuracies of these models often mean that an integral control scheme must be combined with MPC. In this paper we present a novel dynamic model formulation for continuum joint soft robots that is more accurate than previous models yet remains tractable for fast MPC. This model is based on a piecewise constant curvature (PCC) assumption and a relatively new kinematic representation that allows for computationally efficient state prediction. However, due to the difficulty in determining model parameters (e.g., inertias, damping, and spring effects) as well as effects common in continuum joint soft robots (hysteresis, complex pressure dynamics, etc.), we submit that regardless of the model selected, most model-based controllers of continuum joint soft robots would benefit from online model adaptation. Therefore, in this paper we also present a form of adaptive model predictive control based on model reference adaptive control (MRAC). We show that like MRAC, model reference predictive adaptive control (MRPAC) is able to compensate for “parameter mismatch" such as unknown inertia values. Our experiments also show that like MPC, MRPAC is robust to “structure mismatch” such as unmodeled disturbance forces not represented in the form of the adaptive regressor model. Experiments in simulation and hardware show that MRPAC outperforms individual MPC and MRAC.
A Purely Model-Based Approach to Object Pose and Size Estimation With Electric Sense

In the 50s, biologists discovered that some electric fish is capable of discriminating the pose as well as the electric and geometric properties of surrounding objects by navigating and measuring the distortions of a self-generated electric field. In this article, we address the challenging issue of ellipsoidal objects pose and size estimation for underwater robots equipped with artificial electric sense. Unlike current methods, the approach can estimate both the position and size in parallel with a single straight trajectory. Neither multipolarization nor reactive self-alignment control are necessary to locate the object. The approach is a purely model-based heuristic that selects the best ellipsoid parameters among a set of potential candidates. It is based on a set of four electric measurements recorded at several positions along the robot trajectory along which the displacement is measured. The efficiency of the method is assessed over numerous experiments with different objects, several positions, and orientations, and two different kinds of water (fresh and salt water). Despite some model simplifications and experimental errors, location and size estimation errors are on average below $\text{1}\,$cm and $\text{15}\%$, respectively, while offering promising perspectives for real-time computation.
Business Process Management Standardization in Operation Support System: Case Study of Fulfillment and Assurance Process in an Indonesian Telecommunication Company

Companies should have great capabilities to scale up the business capacity and great agility to adapt with fast business change. Scalability and agility do not always related to complex IT system. The study background is based on the condition of a telecommunication company that has various products and services, so the company should adopt standardized process in order to keep organization activities still maintainable to operate. The purpose of this study is to propose on developing method of Business Process Management Standardization based on current process conditions problem The method of this study is referenced to eTOM (Enhanced Telecom Operational Map) Framework decomposition. The study result provides a simulation to detailing business function based on eTOM as foundation to establish Business Process Management that suitable with the condition of the company. In this case study, the standardized BPM of fulfilment and assurance in Operation Support System are developed to solve the two main problems, such as the longer time to market when configuring services and the redundancy functions between modules.
A $4\times 4$ MIMO dual-band antenna design for agile radiation pattern 5G tablet phone

The design of a dual-band $4\times 4$ MIMO (Multiple Input Multiple Output) antenna system that can be reconfigured into a radiation pattern is presented in this paper. A compact and low profile PIFA antenna occupying a volume of $18\times 5.5\times 4.5\ {m}^{3}$ constitute the system reference. The dual-band operation (2.6 / 3.6 GHz) of the reference antenna is obtained by using the slot insertion technique at the level of the radiating element. The antenna system is designed for a mobile tablet of future 5G networks. The states of agility in radiation pattern are obtained by excitation combination of antenna couple with a phase shift of 90 ° for the LTE2600 band (Low Band (LB)) and 180 ° for the LTE3600 band (High Band (HB)). The proposed system covers a band of 190 MHz and a band of 700 MHz at — 10 dB respectively in the LB and the HB. It also offers a minimum gain of 5 dB and 7 dB respectively in the low and high band. The proposed system has 88 per cent efficiency at 2.6 GHz and 97 per cent efficiency at 3.6 GHz.