Miracle-dz/UniFAR-FTUs huggingface dataset:
Cross-Subject EEG Emotion Recognition Using Domain Adaptive Few-Shot Learning Networks

Due to the individual differences and nonstationary of EEG signals, it is difficult to classify EEG emotions with traditional machine methods, which assume that the training and testing set come from the same data distribution, but this assumption is usually not true in the EEG field, therefore the accuracy of emotion recognition is very poor. In this paper, a Single-Source Domain Adaptive Few-Shot Learning Networks (SDA-FSL) was proposed for cross-subject EEG emotion recognition. This is the first time that domain adaptation method with few-shot learning has been used in the field of EEG emotion recognition. A CBAM-based feature mapping module was designed to extract the common features of the two domains, and the domain adaptation module was used to align the data distribution of two domains. In addition, Prototypical Networks with instance-attention mechanism is introduced to preserve domain-specific information. The proposed method was evaluated on DEAP and SEED datasets in within-dataset and cross-dataset experiments under various N-way k-shot settings. Experimental results show that the performance of SDA-FSL outperforms other comparison methods and has superior generalization performance on cross-dataset experiments.
Design and Implementation for a Duplex Visible Light Communication Link

This paper presents the hardware design and implementation of a duplex visible light communication link. Our VLC link uses two wavelengths: visible light in the downlink and infrared light in the uplink. The prototype is compatible with a universal asynchronous receiver/transmitter and achieves a baud rate of 4 Mbps. The prototype uses light-emitting diodes used for lighting purposes, infrared light-emitting diodes used for infrared CMOS camera illumination, and other readily available components. The receivers use PIN photodiode detectors for the appropriate wavelength. The designed link can be used as a test-bed for media access control testing.
Whisper when under Attack: Delay Tolerant Fast-Forward Relays against Full-Duplex Jamming

In this work, we synthesise a cooperative strategy to mitigate jamming attacks by a full-duplex cognitive adversary. The full-duplex adversary is such that it jams a specific frequency band and measures the average energy level of the jammed band. We first analyse the state-of-art cooperative strategy to mitigate full-duplex cognitive adversary, wherein a full-duplex single-antenna helper node fast-forwards the victim's symbols to the base station. We then point out that these schemes are not robust when the helper node introduces processing delays. To circumvent this problem, we propose a relaying scheme, namely, the Semi-Coherent Delay Tolerant Fast-Forward Full-Duplex relaying scheme, wherein the full-duplex helper node uses multiple receive-antennas to listen to the victim's symbols, thereby reducing the dependency on the direct link to decode symbols. Through analytical and simulation results, we show that our scheme outperforms the state-of-art scheme and provides reliable communication between the victim and the base station even if the helper node introduces processing delays. Further, we also show that the victim completely engages the adversary to the jammed frequency band.
Model-Centric and Data-Centric Aspects of Active Learning for Deep Neural Networks

We study different aspects of active learning with deep neural networks in a consistent and unified way. i) We investigate incremental and cumulative training modes which specify how the newly labeled data are used for training. ii) We study active learning w.r. t. the model configurations such as the number of epochs and neurons as well as the choice of batch size. iii) We consider in detail the behavior of query strategies and their corresponding informativeness measures and accordingly propose more efficient querying procedures. iv) We perform statistical analyses, e.g., on actively learned classes and test error estimation, that reveal several insights about active learning. v) We investigate how active learning with neural networks can benefit from pseudo-labels as proxies for actual labels.
Nonlinear System Identification: Learning While Respecting Physical Models Using a Sequential Monte Carlo Method

The modern world contains an immense number of different and interacting systems, from the evolution of weather systems to variations in the stock market, autonomous vehicles interacting with their environment, and the spread of diseases. For society to function, it is essential to understand the behavior of the world so that informed decisions can be made that are based on likely future outcomes. For instance, consider the spread of a new disease such as COVID-19 coronavirus. It is of great importance to be able to predict the number of people that will be infected at different points in time to ensure that appropriate health-care facilities are available. It is also of interest to be able to make decisions based on accurate information to best attenuate the spread of disease. Moreover, understanding specific attributes of a disease—such as the incubation time and number of unreported cases—and how certain we are about this knowledge are also crucial.
Large Eddy Simulation of Flow and Heat Transfer in an Internal Cooling Duct With High Blockage Ratio 45° Staggered Ribs

Numerical predictions of a hydrodynamic and thermally developed turbulent flow are presented for a unit period of a stationary duct with square ribs aligned at 45° to the main flow direction. The rib height to channel hydraulic diameter (e/Dh ) is 0.375 and the rib pitch to rib height (P/e) is 10. The domain under consideration is a rectangular passage of aspect ratio 1:2.5 with 45° ribs on the top and bottom walls arranged in a staggered fashion. The computations are carried out for a bulk Re of 27,000. The rib geometry introduces a strong secondary flow along the rib. A large helical vortex develops behind the rib which breaks down before it reaches the outer wall. This results in higher heat transfer at the inner wall as compared to the outer wall, which is in contrast to the trend observed in a square channel with low blockage ribs. In a square duct with low blockage ribs the secondary flow has two counter-rotating cells which do not change direction through the channel. However in this case only one rotating cell is observed in this case, which changes direction as it passes over successive ribs. The average friction and the heat transfer augmentation ratios are consistent with the experimental results [1], predicting values within 15% of the measured quantities. © 2005 ASME
HGNA-HTI: Heterogeneous graph neural network with attention mechanism for prediction of herb-target interactions

Herb-target interactions (HTIs) prediction plays a key role in exploring the mechanism of Traditional Chinese Medicine (TCM). There have been many studies on the prediction of drug-target interactions. However, these methods cannot be directly applied to TCM, which has the characteristics of multiple-component, multiple-target and multiple-pathway. Although network-based methods began to be used to study the prediction of HTIs, the aggregate information from the high-order neighborhood on the heterogeneous herb-target network is underutilized. Therefore, this paper proposes a Heterogeneous Graph Neural Network with Attention Mechanism for Prediction of Herb-Target Interactions (HGNA-HTI). Specifically, based on the heterogeneous herb-target graph, HGNA-HTI uses attention mechanism to give high attention values to important nodes and edges based on different types of meta relations, and applies message passing process to incorporate information from different types of links. Moreover, HGNA-HTI aggregates the above information to extract the semantic information and high-level structure of the heterogeneous herb-target graph to obtain the final feature representation to enhance the ability for prediction of HTIs. The experiment results on two datasets, show that the HGNA-HTI model is better than state-of-the-art approaches.
Automated Monitoring For Construction Productivity Recognition

Comparing to other sectors, the construction sector suffers from low productivity, and it is not improving over time due to the unique nature of construction projects. It is believed that construction productivity cannot be improved without efficient monitoring and measuring, and this is crucial for project success. There are many limitations for the traditional construction productivity monitoring practices like time and cost consuming and error-prone. Although a lot of studies have been implemented to eliminate these limitations, a gap still exists in the automated monitoring of construction productivity. This study proposes an automated monitoring model for indoor productivity recognition in construction projects. The model will provide an instant evaluation of the project productivity which will enhance the optimum utilization of the project resources. The proposed model will be developed by first generating a baseline for the activities state which will be represented as baseline state model. Then the as-built model will be generated. Preliminary experimentation was performed on selected images where the number of tiles and bricks was obtained. The experimentation was performed using Open-Source Computer Vision Library (OpenCV). Preliminary results depict that by using the proposed model the automated monitoring of productivity is achievable. Although, there is a need of dedicated efforts for improvement and development of technique for more effective and efficient results.
Rootkits on smart phones: attacks, implications, and energy-aware defense techniques

OF THE THESIS Rootkits on Smart Phones: Attacks, Implications, and Energy-Aware Defense Techniques by Jeffrey Earl Bickford Thesis Director: Vinod Ganapathy and Liviu Iftode Smart phones are increasingly being equipped with operating systems that compare in complexity with those on desktop computers. This trend makes smart phone operating systems vulnerable to many of the same threats as desktop operating systems. In this dissertation, we focus on the threat posed by smart phone rootkits. Rootkits are malware that stealthily modify operating system code and data to achieve malicious goals, and have long been a problem for desktops. We use four example rootkits to show that smart phones are just as vulnerable to rootkits as desktop operating systems. However, the ubiquity of smart phones and the unique interfaces that they expose, such as voice, GPS and battery, make the social consequences of rootkits particularly devastating. The rapid growth of mobile malware and the four rootkit attacks developed, necessitates the presence of robust malware detectors on mobile devices. However, running malware detectors on mobile devices may drain their battery, causing users to disable these protection mechanisms to save power. This dissertation studies the security versus energy tradeoffs for a particularly challenging class of malware detectors, namely rootkit detectors. We investigate the security versus energy tradeoffs along two axes: attack surface and malware scanning frequency, for both code and data based rootkit detectors. Our findings, based on a real implementation on a mobile handheld device, reveal that protecting against code-driven attacks
Predicting the next stage of China’s household sustainable energy consumption research based on a comparative knowledge graph analysis between China and foreign countries

By using the knowledge graph tool, this paper compared the development course and content evolution trend of the scientific research on household sustainable energy consumption in China and abroad, and then based on Shneider's theory of scientific evolution, the current evolution stage was judged and the future innovative direction of relevant scientific research in China was pointed out. It was found that the overall scientific research of household sustainable energy consumption has evolved to its third stage, that is, the stage of rapid accumulation of knowledge and sustainability enhancement. Despite the late start, the gap between China's research progress and that of other advanced regions is narrowing. And the main path of sustainable transition of household energy consumption is becoming clear, namely, to develop sustainable buildings, use renewable energy and change residents’ energy consumption behavior. The current research of Chinese scholars should return to the basic point of how to guide the change of household energy consumption behavior according to China’s national conditions.
Combating Fake Cyber Threat Intelligence using Provenance in Cybersecurity Knowledge Graphs

Today there is a significant amount of fake cybersecurity related intelligence on the internet. To filter out such information, we build a system to capture the provenance information and represent it along with the captured Cyber Threat Intelligence (CTI). In the cybersecurity domain, such CTI is stored in Cybersecurity Knowledge Graphs (CKG). We enhance the exiting CKG model to incorporate intelligence provenance and fuse provenance graphs with CKG. This process includes modifying traditional approaches to entity and relation extraction. CTI data is considered vital in securing our cyberspace. Knowledge graphs containing CTI information along with its provenance can provide expertise to dependent Artificial Intelligence (AI) systems and human analysts.
Differential Identification of Prodromal Stages of Alzheimer’s Disease Using Tissue Probability Map (TPM) based Network

A lot of efforts have been made by researchers for easy detection of the prodromal phase of Alzheimer’s disease (AD) and other dementia to enable curative measures. Among the leading approaches that show promising results is the use of complex network theory on neuroimaging data such as functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), magnetoencephalogram (MEG), electroencephalogram (EEG) etc. However, exploring the network theory using the tissue probability Map (TPM) of magnetic resonance imaging (MRI) data has been quite unexplored. Therefore, in this paper, we developed the generalized improved multiscale permutation entropy (GIMPE) for the computation of complexity of grey matter (GM) TPM for all the considered region of interests (ROIs). In order to formulate a well defined network, the vectors, GIMPEs of all ROIs are taken as nodes and the edges between the nodes are defined by the Euclidean distance between the corresponding vectors (GIMPEs). The validation of our approach on MRI data accentuates the importance of the proposed approach as well as the significance of TPM based brain networks for the discrimination and differential diagnosis of normal aging, prodromal phase and the later phase of AD.
Heart Rate Monitoring Using PPG With Smartphone Camera

Methods of estimating heart rate without the use of sensor devices provides essential benefits in both the medical field as well as the other computing applications. Smartphones are the handiest devices available to everyone today. By using videos of fingertip captured with smartphone camera, heart rate (HR) can be estimated using the photoplethysmography (PPG) technique. It is based on tracking subtle color changes on the skin owing to cardiovascular activities. These color changes are invisible to the human eye but can be detected by digital cameras. The method is divided into three main steps: first, reading the video frames and processing them to obtain the PPG data, next, extracting the Blood Volume Pulse (BVP) signal, and finally, estimating the HR from the signal. In this project, the color intensity of the skin pixels is used, and filters are applied to eliminate the noise and retain only the pulses of interest. The extracted signal is fed into a convolutional regression neural network which outputs the estimated HR. The results obtained are compared with the ground truth HR obtained by using a contact PPG sensor. We obtained a Mean Absolute Error (MAE) of 7.01 beats per minute (bpm) and an error percentage of 8.3% on test data.
Mild respiratory SARS-CoV-2 infection can cause multi-lineage cellular dysregulation and myelin loss in the brain

Survivors of Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) infection frequently experience lingering neurological symptoms, including impairment in attention, concentration, speed of information processing and memory. This long-COVID cognitive syndrome shares many features with the syndrome of cancer therapy-related cognitive impairment (CRCI). Neuroinflammation, particularly microglial reactivity and consequent dysregulation of hippocampal neurogenesis and oligodendrocyte lineage cells, is central to CRCI. We hypothesized that similar cellular mechanisms may contribute to the persistent neurological symptoms associated with even mild SARS-CoV-2 respiratory infection. Here, we explored neuroinflammation caused by mild respiratory SARS-CoV-2 infection – without neuroinvasion - and effects on hippocampal neurogenesis and the oligodendroglial lineage. Using a mouse model of mild respiratory SARS-CoV-2 infection induced by intranasal SARS-CoV-2 delivery, we found white matter-selective microglial reactivity, a pattern observed in CRCI. Human brain tissue from 9 individuals with COVID-19 or SARS-CoV-2 infection exhibits the same pattern of prominent white matter-selective microglial reactivity. In mice, pro-inflammatory CSF cytokines/chemokines were elevated for at least 7-weeks post-infection; among the chemokines demonstrating persistent elevation is CCL11, which is associated with impairments in neurogenesis and cognitive function. Humans experiencing long-COVID with cognitive symptoms (48 subjects) similarly demonstrate elevated CCL11 levels compared to those with long-COVID who lack cognitive symptoms (15 subjects). Impaired hippocampal neurogenesis, decreased oligodendrocytes and myelin loss in subcortical white matter were evident at 1 week, and persisted until at least 7 weeks, following mild respiratory SARS-CoV-2 infection in mice. Taken together, the findings presented here illustrate striking similarities between neuropathophysiology after cancer therapy and after SARS-CoV-2 infection, and elucidate cellular deficits that may contribute to lasting neurological symptoms following even mild SARS-CoV-2 infection.
Comparing Ransomware using TLSH and @DisCo Analysis Frameworks

Modern malware indicators utilized by the current top threat feeds are easily bypassed and generated through enigmatic methods, leading to a lack of detection capabilities for cyber defenders. Static hash-based algorithms such as MD5 or SHA generate indicators that are rendered obsolete by modifying a single byte of the source file. Conversely, fuzzy hash-based algorithms such as SSDEEP and TLSH are more robust to alterations of source information; however, these methods often utilize context boundaries that are hard to define or not based on meaningful information. In previous work, a custom binary analysis tool was created called @DisCo. In this study, four current ransomware campaigns were analyzed using TLSH fuzzy hashing and the @DisCo tool. While TLSH works on the binary level of the entire program, @DisCo works at an intermediate function level. The results from each analysis method were compared to provide validation between the two as well as introduce a narrative for using combinations of these types of methods for the creation of stronger indicators of compromise.
Visualization of SpoVAEa Protein Dynamics in Dormant Spores of Bacillus cereus and Dynamic Changes in Their Germinosomes and SpoVAEa during Germination

Bacillus cereus is commonly present in soil and infects humans via contaminated food. In this study, we used B. cereus spores to investigate the movement of the spore-specific inner membrane (IM) channel protein SpoVAEa, the interaction between SpoVAEa and the germinosome scaffold protein GerD, and the dynamics of germinosomes with GerR and GerD in spore germination. ABSTRACT Bacillus cereus spores, like most Bacillus spores, can survive for years and germinate when their surroundings become suitable, and germination proteins play an important role in the initiation of germination. Because germinated spores lose the extreme resistance of dormant spores, information on the function of germination proteins could be useful in developing new strategies to control B. cereus spores. Prior work has shown that (i) the channel protein SpoVAEa exhibits high-frequency movement in the outer leaflet of the inner membrane (IM) in dormant B. subtilis spores and (ii) the formation of the foci termed germinosomes between two germination proteins, the germinant receptor GerR and the scaffold protein GerD, in developing B. cereus spores is slower than foci formation by GerR and GerD individually. However, the movement dynamics of SpoVAEa in B. cereus spores, and the behavior of the germinosome upon B. cereus spore germination, are not known. In this study, we found that SpoVAEa fluorescent foci in dormant B. cereus spores move on the IM, but slower than in B. subtilis spores, and they likely co-localize transiently with GerD-mScarlet-I in the germinosome. Our results further indicate that (i) the expression of GerR-SGFP2 and SpoVAEa-SGFP2 with GerD-mScarlet-I from a plasmid leads to more heterogeneity and lower efficiency of spore germination in B. cereus, and (ii) germinosome foci observed by Fluorescence resonance energy transfer (FRET) between GerR-SGFP2 and GerD-mScarlet-I can be lost soon after the spore-phase transition. However, this is not always the case, as some GerR-SGFP2 and GerD-mScarlet-I foci continued to exist, co-localize, and even show a weak FRET signal. These data highlight the heterogeneous behavior of spore germination protein complexes and indicate that some complexes may persist beyond the initiation of germination. IMPORTANCE Bacillus cereus is commonly present in soil and infects humans via contaminated food. In this study, we used B. cereus spores to investigate the movement of the spore-specific inner membrane (IM) channel protein SpoVAEa, the interaction between SpoVAEa and the germinosome scaffold protein GerD, and the dynamics of germinosomes with GerR and GerD in spore germination. Our results expand upon observations of interactions between specific B. cereus spore germination proteins, in particular the GerR germinant receptor A, B, and C subunits and GerD, as well as those between SpoVAEa and GerD. The approaches used in this work could also be used to examine the interactions between GerD and SpoVAEa and other germination proteins in spores of other Bacillus species.