Miracle-dz/UniFAR-FTUs huggingface dataset:
WSAD: An Unsupervised Web Session Anomaly Detection Method

servers in the Internet are vulnerable to Web attacks, to detect Web attacks, a commonly used method is to detect anomalies in the request parameters by making regular-expression-based matching rules for the parameters based on known security threats. However, such methods cannot detect unknown anomalies well and they can also be easily bypassed by using techniques like transcoding. Moreover, existing anomaly detection methods are usually based on a single HTTP request, which is easy to ignore the attack behavior within a period of time, such as brute-force password cracking attack. In this paper, we propose an unsupervised W eb S ession A nomaly D etection method called WSAD. WSAD uses ten features of web session to perform anomaly detection. After extracting the ten features, WSAD uses the DBSCAN algorithm to cluster the features of each session and outputs the outliers found in the clustering process as anomalies. We evaluate the performance of WSAD on several datasets from multiple real websites of a company. The results indicate that WSAD could detect malicious behaviors that could not be detected by Web Application Firewall, and it almost has no false positives.
A Machine Learning-Based Model for Early Stage Detection of Diabetes

Diabetes is an omnipresent immedicable disorder that occurs when the pancreas can’t produce enough insulin, or the body can’t utilize the insulin decently. As persistent cleanse of this disease is not attainable, early diagnosis and taking indispensable steps to combat this disease and lessen the probability of deadly diseases. Several researchers have developed innumerable prediction models based on Machine Learning (ML) techniques to classify diabetic patients. Nevertheless, due to the non-linear nature, having redundant features, and imbalanced class issues in the medical dataset, accurate prediction is always a vigorous task. We took an earlystage diabetes risk prediction dataset and applied various Feature Transformation (FT) methods, including Robust (RS) Scaler, Standard Scaler (SS), and Min-Max Scaler (MS). In addition, several classification methods were implemented on these transformed datasets and found Extra Trees (ET) showed the best performance on the z-score transformed dataset. Moreover, different Feature Selection Techniques (FST’s) were employed on the z-score transformed dataset and again performed classification task using ET and found that the Pearson Correlation Coefficient (PCC) was able to pick up the most relevant features. This suggests that it may be possible to utilize this model for classifying diabetic patients at an early stage.
Review of Research and Development of Supernumerary Robotic Limbs

Supernumerary robotic limbs (SRLs) are a new type of wearable human auxiliary equipment, which is currently a hot research topic in the world. SRLs have broad applications in many fields, and will provide a reference and technical support for the realization of human-robot collaboration and integration, while playing an important role in improving social security and public services. In this paper, representative SRLs are summarized from the aspects of related literature analysis, research status, ontology structure design, control and driving, sensing and perception, and application fields. This paper also analyzes and summarizes the current technical challenges faced by SRLs, and reviews development progress and key technologies, thus giving a prospect of future technical development trends.
Subject-Transfer Approach based on Convolutional Neural Network for the SSSEP-BCIs

Nowadays, pattern recognition or machine learning-based various approaches have been applied to the steady-state somatosensory evoked potential (SSSEP)-based brain-computer interfaces (BCIs) for improving the accuracy of classifying the user's selective attention to vibration stimulation. However, the accuracy still needs to be improved for applying to a real-world environment. In this paper, the subject-transfer strategy based on the convolutional neural network (CNN) was investigated, if it can improve classification accuracy within SSSEP-based BCIs. In our experiment, for the subject-transferring, the supportive subject's data and target subject's data were used to training the common spatial pattern (CSP) for feature extraction and CNN for classification. The SSSEPs were acquired by selective attention to one of the vibration motors (left-hand and right-hand). The experimental results based on 10-fold cross-validation indicate that the subject-transfer method showed approximately 5% higher accuracies than the traditional self-training method, which is training the CSP and CNN using only the target subject's data. Based on these results, we can confirm that the subject-transfer strategy can contribute to improving the accuracy of the SSSEP-based BCIs for real-world applications.
Peripheral View of IoT based Miniature Devices Security Paradigm

Tunnel approach to the security and privacy aspects of communication networks has been an issue since the inception of networking technologies. Neither the technology nor the regulatory and legal frame works proactively play a significant role towards addressing the ever escalating security challenges. As we have move to ubiquitous computing paradigm where information secrecy and privacy is coupled with new challenges of human to machine and machine to machine interfaces, a transformational model for security should be visited. This research is attempted to highlight the peripheral view of IoT based miniature device security paradigm with focus on standardization, regulations, user adaptation, software and applications, low computing resources and power consumption, human to machine interface and privacy.
Phase Transition in previous Motor Imagery affects Efficiency of Motor Imagery based Brain-computer Interface

A electroencephalography (EEG) based braincomputer interface (BCI) provides a communication channel to operate the external environment by decoding brain patterns. Movement-Related Cortical Potentials (MRCPs) are one particular type of EEG pattern during movement of peripheral limbs. The performance of motor imagery (MI) is related to pattern of MRCPs by planning simulation. In resent decade, MI-based BCI have shown potential as its performance significantly improved. In this study, the feasibility of selected-based method was proposed by compared with conventional method. The detection accuracy overall performances were $72.42\pm 3.12\%$. When a top 97.8% trial is selected, overall performance improved approximately 3.15% compared to baseline. When MI were analyzed in non-selected trials, C3 and C4 channel showed no different aspects in left and right class respectively. The brain state was changed after the cue appeared, and these power of delta band appeared in all subjects. The performance of classification was improved by rejecting trials with no difference between the state before and after cue.
A brain—computer interface-based meal—assist robot control system

Brain-computer interface (BCI) research has evolved enough to be applied for everyday use. Assistance devices have been developed to aid those who are disabled and aging. Many meal–assist robots are already in the market. However, the previous meal–assist robot has been deemed inadequate as users require help to use it. In this study, we developed a BCI-based meal–assist robot system in which the start of a meal is triggered by an eye blink, the selection of a food is based on steady-state visual evoked potential and the next meal is triggered by an electromyogram.
HRRP Data Augmentation Using Generative Adversarial Networks

In radar automatic target recognition, high resolution range profile (HRRP) can promise satisfactory performance by deep learning when the training samples are affluent. Actually, it is difficult to acquire HRRP samples in battlefield environment. An approach using generative adversarial network (GAN) to augment HRRP data is proposed to deal with the lack of data. There are four GAN models adopted to explore the effect of data augmentation: deep convolutional GAN (DCGAN), Auxiliary Classifier GAN (ACGAN), Least Squares Conditional GAN (LSCGAN) and Wasserstein Conditional GAN (WCGAN). The experimental results show that ACGAN is more suitable than other models in HRRP data augmentation.
Predicting Stock Closing Price After COVID-19 Based on Sentiment Analysis and LSTM

Stock market prediction remains a challenging problem in the economic field because of its highly stochastic nature which is compounded by the uncertainty that the COVID-19 pandemic presents. To address the volatility and noise, we utilize a deep learning stock market prediction model based on investors’ sentiments, which allows us to better understand the direction the market will shift, improving the model prediction accuracy. We adopt an LSTM recurrent neural network because of its advantages in analyzing time-series data through its memory function and utilize an attention mechanism to focus the model on critical information. Experimental data demonstrate that utilizing the investors’ emotional tendencies and the attention mechanism can help LSTM to predict closing prices despite the uncertainty of the pandemic. Accurate predictions will allow shareholders and investors to understand market forces and emphasize sustainable investing and development.
Graduates Profile Mapping based on Job Vacancy Information Clustering

Nowadays, an industry’s expectation that’s often not fulfilled by job applicants require companies to actively cooperate with universities, one of the reasons is through employees that is considered to have good performance to find talents within their alma mater. This research aims to analyze job vacancy information uploaded by graduates for juniors in their university that can be mapped into a graduate’s profile and evaluation materials in making a curriculum. Collected job vacancy information from several communication media are generally unstructured data which requires it to be preprocessed first through a data mining convention to produce several terms ready to be processed, continued with implementation of TF-IDF, feature extraction using PCA, and grouping using k-Means algorithm. The clustering analysis found 3 job clusters i.e. developer, teacher and researcher/lecturer as job vacancies that frequently shared by graduates. This result obtained from clustering analysis using 10 words as a minimum document frequency based on Elbow Method and Silhouette Coefficient analysis.
Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form Surfaces

Deep learning has demonstrated high accuracy for 3D object shape error modeling necessary to estimate dimensional and geometric quality defects in multi-station assembly systems (MAS). Increasingly, deep learning-driven Root Cause Analysis (RCA) is used for decision-making when planning corrective action of quality defects. However, given the current absence of scalability enabling models, training deep learning models for each individual MAS is exceedingly time-consuming as it requires large amounts of labelled data and multiple computational cycles. Additionally, understanding and interpreting how deep learning produces final predictions while quantifying various uncertainties also remains a fundamental challenge. In an effort to address these gaps, a novel closed-loop in-process (CLIP) diagnostic framework underpinned algorithm portfolio is proposed which simultaneously enhances scalability and interpretability of the current Bayesian deep learning approach, Object Shape Error Response (OSER), to isolate root cause(s) of quality defects in MAS. The OSER-MAS leverages a Bayesian 3D U-Net architecture integrated with Computer-Aided Engineering simulations to estimate root causes. The CLIP diagnostic framework shortens OSER-MAS model training time by developing: (i) closed-loop training to enable faster convergence for a single MAS by leveraging uncertainty estimates of the Bayesian 3D U-net model; and, (ii) transfer/continual learning-based scalability model to transmit meta-knowledge from the trained model to a new MAS resulting in convergence using comparatively less training samples. Additionally, CLIP increases the transparency for quality-related root cause predictions by developing interpretability model which is based on 3D Gradient-based Class Activation Maps (3D Grad-CAMs) and entails: (a) linking elements of MAS model with functional elements of the U-Net architecture; and, (b) relating features extracted by the architecture with elements of the MAS model and further with the object shape error patterns for root cause(s) that occur in MAS. Benchmarking studies are conducted using six automotive-MAS with varying complexities. Results highlight a reduction in training samples of up to 56% with a loss in performance of up to 2.1%.
Multivariate Time-Series Modeling for Forecasting Sintering Temperature in Rotary Kilns Using DCGNet

The sintering temperature (ST) is a critical index for condition monitoring and process control of coal-fired equipment and is widely used in the production of cement, aluminum, electricity, steel, and chemicals. The accurate prediction of the ST is important for control systems to anticipate tragedies. In this article, we propose a deep learning model for forecasting the ST using automatic spatiotemporal feature extraction from multivariate thermal time series. A hybrid deep neural network named deep convolutional neural network and gated recurrent unit network (DCGNet) is designed to extract multivariate coupling and nonlinear dynamic characteristics for forecasting the ST. DCGNet uses convolutional neural networks and gated recurrent unit (GRU) to extract the local spatial-temporal dependence patterns among the multivariates, and another parallel GRU using the historical ST data as input is incorporated to more accurately capture the dynamic characteristics of ST time series. Based on the real-world data, application results show that the proposed approach has high forecasting accuracy and robustness, thus having broad application prospects in industrial processes.
MTSAN: Multi-Task Semantic Attention Network for ADAS Applications

This paper presents a lightweight Multi-task Semantic Attention Network (MTSAN) to collectively deal with object detection as well as semantic segmentation aiding real-time applications of the Advanced Driver Assistance Systems (ADAS). This paper proposes a Semantic Attention Module (SAM) that introduces the semantic contextual clues from a segmentation subnet to guide a detection subnet. The SAM significantly boosts up the detection performance and computational cost by considerably decreasing the false alarm rate and it is completely independent of any other parameters. The experimental results show the effectiveness of each component of the network and demonstrate that the proposed MTSAN yields a better balance between accuracy and speed. Following the post-processing methods, the proposed module is tested and proved for its accuracy in the Lane Departure Warning System (LDWS) and Forward Collision Warning System (FCWS). In addition, the proposed lightweight network is deployable on low-power embedded devices to meet the requirements of the real-time applications yielding 10FPS @ 512 X 256 on NVIDIA Jetson Xavier and 15FPS @ 512 X 256 on Texas Instrument’s TDA2x.
Wideband Circularly Polarized Microstrip Patch Antenna With Multimode Resonance

A novel wideband circularly polarized microstrip patch antenna (MPA) with multimode resonance is proposed in this letter. The TM1/2,0 mode, the zeroth-order mode, and the TM1,0 mode are excited by inserting the shorting pins underneath the driven patch and the parasitic patch. Therefore, a wide impedance bandwidth is achieved under triple-mode resonance. By sequentially rotating the triple-mode elements, a circularly polarized MPA is developed with a four-feed network. Additionally, four metallic side walls are employed to obtain desirable radiation performances. For demonstration, a prototype of the proposed antenna is fabricated and measured. The measured −10 dB impedance bandwidth is 96% (1.53–4.39 GHz) and 3 dB axial ratio bandwidth is 86% (1.74–4.36 GHz). Meanwhile, a low profile with the height of 0. 037λ0 (λ0 is the wavelength in free space at the lowest operating frequency) is obtained. Such a wideband, low-profile, and circularly polarized antenna is very promising for modern wireless communication applications.
Advanced Persistent Threat Detection: A Survey

Advanced Persistent Threat is a very sophisticated targeted attack aimed at organizations. Several approaches have been proposed to detect APT. This paper defines an APT as an attack that has certain objectives to be achieved, and are performed by well-funded organizations, and is long term campaign. In this paper we have identified APT as a threat that follows a kill chain process. Intrusion detection and intrusion detection methods are summarized in this paper. Detection of an APT is a challenge. In this paper various detection methods used by researchers and the challenges in detecting APT is highlighted.
An MPR Set Selection Algorithm Based on Set Operation

In the OLSR protocol, MPR set is selected as the node of relay broadcast in the process of broadcast flooding, but in some cases the MPR set selected by traditional OLSR protocol is not optimal, which will lead to unnecessary data forwarding and waste of resources. Aiming at the deficiency of classical algorithm, a new algorithm of reverse thinking is proposed. By combining cyclic and set operations, invalid redundant nodes can be eliminated effectively. This algorithm not only achieves the effect of traditional OLSR protocol, but also has lower data overhead and higher efficiency than traditional OLSR protocol. Using the OPNET simulation tool, the MPR set selection algorithm is redefined. Simulation results show that the algorithm has a certain increase in the success rate of data transmission in the network.
Summarizing Online Product Reviews in Bengali Based on Similarity Using Sequence to Sequence RNNs

By text summarization, large texts or documents can be concise while preserving the context of the main document. This research work is focused on summarizing online product reviews in Bengali Language. By using online user feedback, future buyers will be able to take decisions whether to buy the product or not. However, it is very difficult for a buyer to decide by reading manually all the reviews. Machine learning techniques are available nowadays for text summarization which is a promising solution for extracting information from those user reviews. There are many well-known summarization tools for English language but in Bengali, there are very few and insufficient tools for text summarization. In this paper, a model has been for abstract Bangla text summarization on online product reviews using a Recurrent Neural Network(RNN). Long ShortTerm Memory (LSTM) and Sequence-to-Sequence (Seq2Seq) based RNN has been applied here. Experimented results underscore that the training loss is reduced to 0.0034 and able to generate a frequent predictive summary from original texts or documents. This research work also includes a Word Mover’s Distance(WMD) method which checks the similarity between human and machine summary. It is observed that, the applied WMD in this work for text similarity model has been able to achieve better performance than the Jaccard method.
Patient-Specific Modeling with Personalized Decision Paths

Predictive models can be useful in predicting patient outcomes under uncertainty. Many algorithms employ "population" methods, which optimize a single model to perform well on average over an entire population, but the model may perform poorly on some patients. Personalized methods optimize predictive performance for each patient by tailoring the model to the individual. We present a new personalized method based on decision trees: the Personalized Decision Path using a Bayesian score (PDP-Bay). Performance on eight synthetic, genomic, and clinical datasets was compared to that of decision trees and a previously described personalized decision path method in terms of area under the ROC curve (AUC) and expected calibration error (ECE). Model complexity was measured by average path length. The PDP-Bay model outperformed the decision tree in terms of both AUC and ECE. The results support the conclusion that personalization may achieve better predictive performance and produce simpler models than population approaches.
Implementation Artificial Neural Network on Microcontroller for Student Attention Level Monitoring Device Using EEG

The ability to concentrate is needed by students in the learning process. Also, the level of concentration required by each employee when they want to do various kinds of work. Monitoring and evaluating a person’s concentration conditions is not easy, one of which is obtained from the information signal Electroencephalography (EEG). This study aims to detect the condition of whether someone is concentrating when doing activities or not. The method used in this study is for Backpropagation Type Neural Networks. Inputs from Artificial Neural Networks are EEG waves with Theta, Low Beta, and High Beta types. The identification system outputs are two classes, namely the full concentration class and the low concentration class. The ANN model obtained through training is then implemented into the Arduino Microcontroller so that it can be used as portable device. So, the classification results obtained are suitable with the output.
Improved Drone Detection in FMCW Radar using SPC Technique (invited talk)

This paper introduces the stationary point concentration (SPC) technique, one of the state-of-the-art techniques for detecting small drones with frequency modulated continuous wave radars. A novel approach of the SPC technique is explained, and representative experimental results are shown. Finally, the promising potentials of the SPC technique are discussed.
Batch Mode Active Learning Based on Multi-Set Clustering

Batch mode active learning, where a batch of samples is simultaneously selected and labeled, is a challenging task. The challenge lies in how to maintain the informativeness and keep the diversity of selected samples concurrently. We propose a novel batch mode active learning that balances the informativeness and representativeness using multi-set clustering. Our method utilizes a sequential active learner to retain the informativeness by providing a ranking of unlabeled samples and constructing multiple informative sets for the subsequent clusterings. K-means clustering is used to minimize the redundancy among these samples and to improve the representativeness. Finally, the optimal batch chosen is the one minimizing the expected predictive variance on all the data. Our experimental results on a large number of benchmark datasets demonstrate excellent performance of the proposed method in comparison with current state-of-the-art batch mode active learning approaches.