Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of coma etiology using convolutional neural networks and long-short term memory networks

Coma can be caused by different health conditions. Sometimes, patients are admitted to intensive care unit (ICU) without the cause of the coma being known. Knowing the coma etiology of a patient is very important for prognosis and treatment. Classification of electroencephalogram (EEG) signals by deep learning is proposed to help predict the coma etiology of ICU patients. EEG is a cheap noninvasive technique that can be used for the diagnostics and evaluation of neurological diseases. The objective is to classify coma etiology into one of four categories: Traumatic Brain Injury (TBI), Metabolic Coma, Stroke, and Other. A deep learning model based on convolutional neural networks (CNNs) is proposed to classify the EEG signals, using information from two different sources: i) intermediate layers of CNN + long-short term memory network (LSTM); ii) additional features from patients and statistical measures extracted from EEG signals. Outputs of the LSTM and additional features are inserted as additional inputs to the first dense layer of the CNN. The proposed model was compared to six other approaches, some of which incorporated additional features from patients or statistical measures from EEG signals, while others did not. Experimental results show that inserting patient information, like age and genre, as input to the first dense layer improve the predictive performance of the classification model. Moreover, this work suggests new possibilities to assist physicians in the detection of the coma etiology, especially those in small and far health units.
Online Trajectory Anomaly Detection Based on Intention Orientation

Trajectory anomaly detection has become increasingly important in many fields. However, most existing methods do not support online detection, and have limited performance in multi-source/multi-destination environments. To address these challenges, we propose an unsupervised method named intention orientation based trajectory anomaly detection (IO-TAD) which detects anomalies by inferring their underlying intentions in an online manner. IO-TAD is also robust in multi-source/multi-destination environments. To achieve this, we leverage Inverse Reinforcement Learning and recover the reward functions as a representation of the agents' intentions. Then we use the inferred value functions to quantify the agents' intention orientation pattern. The intuition behind our method is that different normal agents take different routes, or have different intentions, but the ways they achieve their intentions are similar. In other words, the values of normal trajectories tend to be monotonically increasing, while anomalous behaviour is non-monotonic. Our experiments show IO-TAD can achieve good online anomaly detection performance with up to 18.1% improvement in $F_{1}$ score over the comparable state-of-the-art methods.
Assessing and Improving the Quality of Generated Tests in the Context of Maintenance Tasks

Maintenance tasks often rely on failing test cases, highlighting the importance of well-designed tests for their success. While automatically generated tests can provide higher code coverage and detect faults, it is unclear whether they can be effective in guiding maintenance tasks or if developers fully accept them. In our recent work, we presented the results of a series of empirical studies that evaluated the practical support of generated tests. Our studies with 126 developers showed that automatically generated tests can effectively identify faults during maintenance tasks. Developers were equally effective in creating bug fixes when using manually-written, Evosuite, and Randoop tests. However, developers perceived generated tests as not well-designed and preferred refactored versions of Randoop tests. We plan to enhance Evosuite tests and propose an approach/tool that assesses the quality of generated tests and automatically enhances them. Our research may impact the design and use of generated tests in the context of maintenance tasks.
A Survey of Conversational Agents and Their Applications for Self-Management of Chronic Conditions

Conversational agents have gained their ground in our daily life and various domains including healthcare. Chronic condition self-management is one of the promising healthcare areas in which conversational agents demonstrate significant potential to contribute to alleviating healthcare burdens from chronic conditions. This survey paper introduces and outlines types of conversational agents, their generic architecture and workflow, the implemented technologies, and their application to chronic condition self-management.
The Effects of Elasticity and Geometry on Cell Migration and Polarization

Title of Dissertation: THE EFFECTS OF ELASTICITY AND GEOMETRY ON CELL MIGRATION AND POLARIZATION Katrina Adlerz, Doctor of Philosophy, 2017 Dissertation directed by: Associate Professor Dr. Helim Aranda-Espinoza, Fischell Department of Bioengineering Cell migration is a crucial process in the development and maintenance of the human body. Migration is also involved in a number of pathologies. In atherosclerosis, for example, immune cells migrate to the site of inflammation and contribute to the progression of the disease. In cancer, cells migrate out of the primary tumor through the body to metastasize at distant sites creating deadly secondary tumors. In all of these examples, cells confront and must adapt to a broad range of extracellular environments. Two important properties that cells encounter in the body are the elasticity of the environment and confinement. A better understanding of how a cell responds to these parameters would offer insights into the progression of diseases like cancer and atherosclerosis. Much cell migration research, however, has focused on cells moving on flat stiff substrates, like a glass culture dish. Therefore, in this dissertation, we investigated the effects of substrate elasticity and confinement on cell polarization and migration. First, macrophage behavior was studied on substrates of different stiffness. We found that macrophages are mechanosensitive and respond with changes in area, proliferation, and migration. To further investigate cell migration in response to stiffness we focused on polarization, the first step in directed cell migration, and found that the position of the centrosome, an organelle indicating polarity, was dependent on substrate elasticity. Micropatterned one-dimensional lines and a microfluidic device were used to study the effect of confinement on cell polarization and migration. We discovered that the centrosome position for cells migrating on lines is different than in two-dimensional migration and we also show the importance of microtubule polymerization forces in maintaining centrosome position. We used a microfluidic device to mimic the three-dimensional confinement cells encounter in the body. Under increased confinement, the centrosome position is more similar to migration on lines than on flat surfaces and is maintained even when cells change directions. These results demonstrate how the elasticity and confinement of a cell’s microenvironment affect cell polarization and migration. These results are important to further understand the role of these parameters in the progression of diseases like atherosclerosis and cancer. THE EFFECTS OF ELASTICITY AND GEOMETRY ON CELL MIGRATION AND POLARIZATION
The Case for Virtual PLC-enabled Honeypot Design

Programmable logic controllers (PLCs) are essential components of Industrial Control System (ICS) in acting as a practical link between the cyber and physical worlds. In recent years, we have seen an increase in attacks targeting PLCs. Honeypot for PLCs, as an effective technique to gather attacker information and attack tactics, is limited in vendor-specific implementation, configuration, extensibility, and scalability. With the emergence of virtual PLCs, this paper introduces a honeypot, named PLCHoney, to overcome the existing challenges in a cost-effective approach. We designed and implemented PLCHoney with a proxy profiler, dockerized virtual PLCs, a physical process simulator, and a security analysis engine. PLCHoney was able to correctly simulate responses to various internet requests and tested effectively on a network of virtualized traffic light applications. We enabled further security analysis with a dataset containing PLC I/O status, collected with and without attacks. We envision that PLCHoney paves the avenue for the future development of PLC-based honeypots.
Characterizing the surface fluctuation of an epitaxial wafer by using a Shack-Hartmann wave-front sensor

By using a light-emitting diode as the probing light source and a Shack-Hartmann wave-front sensor to execute a relative measurement, we present a simple and sensitive method for measuring surface fluctuation of a nominally flat sample. We used an epitaxial wafer for test. The reflected wave front from the surface of the sample was first calibrated to be a planar surface. The surface fluctuation of the test sample could be estimated from the increment on the variance of the wave-front surface to its regression plane after the sample had been shifted by a small distance by using the Bienaymé formula.
Security is Readily to Interpret: Quantitative Feature Analysis for Botnet Encrypted Malicious Traffic

Nowadays, there are two problems in the network traffic analysis. One is that it’s difficult to detect the encrypted threat traffic resulting in the ineffective analysis and detection of attack characteristics of complex network such as botnet traffic. The other is that machine learning(ML) is featured by high false alarm rate, long training period and demanding data accuracy. In this work, the data packet was deeply analyzed through the data packet analysis method including the analysis of its header and content so as to realize the analysis of network traffic’s characteristics. The CTU-13 and MCFP data sets were dimension-reduced by the t-SNE algorithm, and the 2-dimensional space was mapped so that the distribution boundary of the data was observed and judged. Through eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), Random Forest, Logistic Regression and other machine learning algorithms, we detected the encrypted traffic and found that XGBoost and Random Forest were better for threatening dense flows. On the basis of feature analysis, we selected all, DNS, HTTP and deleted 20/30/40 importance features for ranking, and quantitatively explained the impact of each feature on threat dense flow classification. Through conducting the performed recursive feature elimination with XGBoost algorithm, this paper obtained the classification ranking and then concluded that the influence degree of overall characteristics was very uneven with the first six characteristics led by average of duration occupying a large weight. By obtaining the SHAP evaluation data of the selected all, DNS, HTTP, and deleted 20/30/40 importance features, this paper quantitatively explained the impact of each feature on the classification of threat dense traffic. In addition, this paper also carried out the impact verification of the importance characteristics. Because machine learning, including deep learning, is difficult to interpret. And to ensure the security of cyberspace, we make predictions all the time — to determine whether extensive encrypted traffic is malicious. It is important to interpret the quantitative influencing factors of the predictive model. This paper proposed the influence of features on the classification of encrypted malicious traffic by quantitative methods-SQEITF.
FACEE: Framework for Automating CNN Explainability Evaluation

Convolutional Neural Networks (CNNs) are used mainly for image classification because of their high accuracy and fast performance. Due to their complexity, their functionality is like a black box to the human user. Hence, this black box functionality may classify an image based on the wrong features, which can lead to severe consequences in critical applications, such as disease detection. Therefore, explainer methods provide the users with the reasoning behind each classification. However, selecting well-matching pairs of CNN models and explainers is challenging.This paper proposes a framework for automated explainability evaluation in CNN models, which follows a quantitative approach for assessing the model and explainer pairs. The proposed framework supersedes the previous attempts towards an explainability evaluation framework by replacing the time-consuming qualitative measure with a unified quantitative metric. This quantitative approach allows the users to assess several models and explainer pairs and compare the results based on two aspects on the one hand, selecting the most prominent model/explainer pair or, on the other hand, making compromises for better real-time performance. The framework is applied to a defect detection problem for printed circuit board assembly(PCBA) automatic optical inspection (AOI). The results are analyzed for thirteen built-in CNN models and ten built-in explainer methods. The results demonstrate the superiority of the "CoAtNet0" and "Grad-Cam selfmatch" pair with the 55.38% Explanation Score (ES) metric. The authors also provide a discussion on other criteria in the selection of a prominent pair using the proposed novel FACEE framework.
Continual Learning for Sentence Representations Using Conceptors

Distributed representations of sentences have become ubiquitous in natural language processing tasks. In this paper, we consider a continual learning scenario for sentence representations: Given a sequence of corpora, we aim to optimize the sentence encoder with respect to the new corpus while maintaining its accuracy on the old corpora. To address this problem, we propose to initialize sentence encoders with the help of corpus-independent features, and then sequentially update sentence encoders using Boolean operations of conceptor matrices to learn corpus-dependent features. We evaluate our approach on semantic textual similarity tasks and show that our proposed sentence encoder can continually learn features from new corpora while retaining its competence on previously encountered corpora.
A Picture is Worth 1,000 Millimeters: Combining Vision and Wi-Fi to Improve Localization

In the past, researchers designed, deployed, and evaluated Wi-Fi based localization techniques in order to locate users and devices without adding extra or costly infrastructure. However, as infrastructure deployments change, one must reexamine the role of Wi-Fi localization. Today, cameras are becoming increasingly deployed, and therefore this work examines how contextual and vision data obtained from cameras can be integrated with Wi-Fi localization techniques. We present an approach called CALM that works on commodity APs and cameras. Our approach contains several contributions: a camera line fitting technique to restrict the search space of candidate locations, single AP and camera localization via a deprojection scheme inspired from 3D cameras, simple and robust AP weighting that analyzes the context of users via the camera, and a new virtual camera methodology to scale analysis. We motivate our scheme by analyzing real camera and AP topologies from a major vendor. Our evaluation over 9 rooms and 102,300 wireless readings shows CALM can obtain decimeter-level accuracy, improving performance over previous Wi-Fi techniques like FTM by $2.7\times$ and SpotFi by $2.3\times$.
A Survey on Task Partitioning and Scheduling for Vehicular Edge Computing

Vehicle edge computing (VEC) has become an important research field in recent years. In VEC, computation offloading moves computationally intensive tasks from resource-constrained devices to the network edge, it provides service closer to the end-users. By processing tasks with abundant idle resources at the network edge, low-latency demands for some tasks can be met. However, the mobility and uncertainty of vehicles pose significant challenges to vehicle computation offloading. This paper focuses on the decision-making process of vehicle computation offloading, specifically task partitioning and scheduling decisions. This paper summarizes some hot problems and solutions, including latency optimization, reliability optimization, energy efficiency optimization, cost optimization, and mobility support. This study will help researchers discover important features of vehicle computation offloading and find the most suitable scheme to solve the vehicle offloading problem in different scenarios.
Guiding Directed Fuzzing with Feasibility

Directed fuzzing is a useful technique that can confirm bugs found by static analysis, reproduce existing bugs, and efficiently test code changes. A general mechanism in directed fuzzing is to calculate the distance between the current progress and the target, and use that as a feedback to guide the directed fuzzing. A fundamental problem with existing distance calculation is that it is feasibility-unaware. For instance, it always assumes that the two branches of an if statement have equal feasibility, which is likely not true in real-world programs and would inevitablly incur significant biases in the directed fuzzing. In this work, we propose feasibility-aware directed fuzzing, AFLGopher. Our new feasibility-aware distance calculation provides precise feedback to guide directed fuzzing to reach targets efficiently. We propose new techniques to address challenges of the feasibility prediction. Our new classification method allows to predict the feasibility of all branches based on limited traces, and our runtime feasibility-updating mechanism gradually improves the prediction precision. We implement AFLGopher, and the evaluation results show that AFLGopher uses less time to reach the bugs, compared to the state of the art.
A Novel Score-CAM based Denoiser for Spectrographic Signature Extraction without Ground Truth

Sonar based audio classification techniques are a growing area of research in the field of underwater acoustics. Usually, underwater noise picked up by passive sonar transducers contains all types of signals that travel through the ocean and is transformed into spectrographic images. As a result, the corresponding spectrograms intended to display the temporal-frequency data of a certain object often include the tonal regions of abundant extraneous noise that can effectively interfere with a ‘contact’. So, a majority of spectrographic samples extracted from underwater audio signals are rendered unusable due to their clutter and lack the required indistinguishability between different objects. With limited clean true data for supervised training, creating classification models for these audio signals is severely bottlenecked. This paper derives several new techniques to combat this problem by developing a novel Score-CAM based denoiser to extract an object's signature from noisy spectrographic data without being given any ground truth data. In particular, this paper proposes a novel generative adversarial network architecture for learning and producing spectrographic training data in similar distributions to low-feature spectrogram inputs. In addition, this paper also a generalizable class activation mapping based denoiser for different distributions of acoustic data, even real-world data distributions. Utilizing these novel architectures and proposed denoising techniques, these experiments demonstrate state-of-the-art noise reduction accuracy and improved classification accuracy than current audio classification standards. As such, this approach has applications not only to audio data but for countless data distributions used all around the world for machine learning.
Memory-virtualizing and -devirtualizing VM Migration with Private Virtual Memory

Recently, Infrastructure-as-a-Service clouds provide virtual machines (VMs) with a large amount of memory. Such large-memory VMs can be migrated to other hosts, but it is costly to always preserve hosts with sufficient memory as the destinations of VM migration. Instead, memory-virtualizing VM migration using virtual memory is possible, but the performance of VM migration and migrated VMs largely degrades because traditional virtual memory causes excessive paging during and after VM migration. This paper proposes VMemDirect, which achieves efficient memory-virtualizing VM migration. VMemDirect provides private virtual memory with private swap space on an NVMe SSD for each VM. Then, it directly transfers the memory data of a VM to either physical memory or private swap space to completely avoid paging during VM migration. In addition, VMemDirect provides efficient memory-devirtualizing VM migration for VMs running on private virtual memory. To optimize the performance of migrated VMs, it uses a more accurate and efficient LRU approximation using chunk queues and performs asynchronous paging. We showed that VMemDirect dramatically improved the performance of VM migration and migrated VMs using large VMs.
Ultrathin Wideband Printed Antenna for FR-II NR n257, n258 and n261, for 5G Wireless Applications

This manuscript presents the design and simulation of a wideband antenna for millimetre-wave (mmWave) 5G wireless communication. The antenna is designed using Rogers RT5880 substrate of thickness 0.254 mm and with dielectric constant 2.2 to cover the 5G FR-II New Radio (NR) bands n257, n258, and n261. Initially, a narrowband antenna is designed by using mathematical equations and then the structure is modified by incorporating a partial ground plane (PGP) with a slot and inserting a circular tapered slot in the patch to achieve a wide bandwidth. The optimization is performed by using CST Microwave Studio and Ansys HFSS software, and the results are validated. The proposed antenna depicts an effective impedance bandwidth of 17.13 GHz, ranging from 19.5 GHz to 36.63 GHz with a fractional bandwidth of 61%. The characteristics achieved are adequate such as the value of the VSWR and the minimum to maximum radiation efficiency ranges from 96% to 99%. This research provides a promising solution for a wideband antenna designed for mmWave 5G wireless communication.
Repr2Seq: A Data-to-Text Generation Model for Time Series

Data-to-text generation takes structured data as input and produces text that sufficiently describes the data as output. Recently, it has received a lot of attention from both research field and industry. However, as a critical data form, time series is less discussed in this domain. This paper proposes Repr2Seq, a data-to-text generation model for time series. To better capture the structure and core information of time series, Repr2Seq obtains representation vectors using time series representation learning methods, which are then fed into a neural network-based model to generate text sequences. To demonstrate the effectiveness of Repr2Seq, a dataset consisting of stock price series and corresponding comments is proposed. Experiments show that Repr2Seq achieves significant improvement over standard approaches and leads to satisfactory results. We also conduct experiments to investigate the effect of hyperparameters on the model and detect performance improvements in various settings.