Miracle-dz/UniFAR-FTUs huggingface dataset:
Edge-Computing Deep Learning-Based Computer Vision Systems

Computer vision has become ubiquitous in today’s society, with applications ranging from medical imaging to visual diagnostics to aerial monitoring to self-driving vehicles and many more. Common to many of these applications are visual perception systems which consist of classification, localization, detection, and segmentation components, just to name a few. Recently, the development of deep neural networks (DNN) have led to great advancements in pushing state-of-the-art performance in each of these areas. Unlike traditional computer vision algorithms, DNNs have the ability to generalize features previously hand-crafted by engineers specific to the application; this assumption models the human visual system’s ability to generalize its surroundings. Moreover, convolutional neural networks (CNN) have been shown to not only match, but exceed performance of traditional computer vision algorithms as the filters of the network are able to learn "important" features present in the data. In this research we aim to develop numerous applications including visual warehouse diagnostics and shipping yard managements systems, aerial monitoring and tracking from the perspective of the drone, perception system model for an autonomous vehicle, and vehicle re-identification for surveillance and security. The deep learning models developed for each application attempt to match or exceed state-of-the-art performance in both accuracy and inference time; however, this is typically a trade-off when designing a network where one or the other can be maximized. We investigate numerous object-detection architectures including Faster R-CNN [1, 2], SSD [3], YOLO [4, 5], and a few other variations in an attempt to determine the best architecture for each application. We constrain performance metrics to only investigate inference times rather than training times as none of the optimizations performed in this research have any effect on training time. Further, we will also investigate re-identification of vehicles as a separate application add-on to the object-detection pipeline. Re-identification will allow for a more robust representation of the data while leveraging
High-Resolution Topography of Titan Adapting the Delay/Doppler Algorithm to the Cassini RADAR Altimeter Data

The Cassini RADAR altimeter has provided broad-scale surface topography data for Saturn’s largest moon Titan. Herein, we adapt the delay/Doppler algorithm to take into account Cassini geometries and antenna mispointing usually occurring during hyperbolic Titan flybys. The proposed algorithm allows up to tenfold improvement in the along-track resolution. Preliminary results are provided that show how the improved topography presented herein can advance our understanding of Titan’s surface characteristics.
The Budget of Macrobenthic Reworked Organic Carbon: A Modeling Case Study of the North Sea

The importance of macrobenthos in benthic‐pelagic coupling and early diagenesis of organic carbon has long been recognized but has not been quantified at a regional scale. By using the southern North Sea as an exemplary area we present a modeling attempt to quantify the budget of total organic carbon (TOC) reworked by macrobenthos in seafloor surface sediments. Vertical profiles in sediments collected in the field indicate a significant but nonlinear correlation between TOC and macrobenthic biomass. A mechanistic model is used to resolve the bidirectional interaction between TOC and macrobenthos. A novelty of this model is that bioturbation is resolved dynamically depending on variations in local food resource and macrobenthic biomass. The model is coupled to 3‐D hydrodynamic‐biogeochemical simulations to hindcast the mutual dependence between sedimentary TOC and macrobenthos from 1948 to 2015. Agreement with field data reveals a satisfactory model performance. Our simulations show that the preservation of TOC in the North Sea sediments is determined not only by pelagic conditions (hydrodynamic regime and primary production) but also by the vertical distribution of TOC, bioturbation intensity, and the vertical positioning of macrobenthos. Macrobenthos annually ingest 20–35% and in addition vertically diffuse 11–22% of the total budget of TOC in the uppermost 30‐cm sediments in the southern North Sea. This result indicates a central role of benthic animals in modulating the organic carbon cycling at the sediment‐water interface of continental margins.
STYX: A Hierarchical Key Management System for Elastic Content Delivery Networks on Public Clouds

Hosting content delivery networks (CDNs) on clouds has the potential to improve the performance as resources and caches can be placed closer to subscribers. However, avoiding data leakage over an untrusted public cloud is critical, especially for sensitive data such as the SSL private key. The popular Keyless SSL solution allows content owners to retain on-premise custody of SSL private keys on their own key servers, but this solution likely causes performance bottlenecks and impedes the elasticity of CDNs. This paper describes a novel key management system, named STYX, for transmitting trusted data over untrusted channels and storing them on untrusted platforms. STYX accomplishes secure key provisioning for CDN scale-out and the key is securely protected with full revocation rights for CDN scale-in. STYX is implemented as a three-phase hierarchical key management scheme by leveraging Intel Software Guard Extensions (SGX) and QuickAssist Technology (QAT). Furthermore, STYX supports CDN services by integrating Nginx as the SSL termination proxy and the popular Redis/Memcached/Apache as backend caching engines. The performance evaluation shows that STYX significantly outperforms the native HTTPS servers on the CDN node due to QAT acceleration, providing up to a 5x enhancement in throughput and a 50 percent reduction in latency.
Decoding high level influences on facial expression recognition

The present thesis explores the neural mechanisms underlying the recognition of emotion; the effect of high-level influences such as prior knowledge, task goals and the possible contribution of embodied simulation in facial expression recognition. The initial experiments (Chapters 2 & 3) investigate high-level processing that occurs when facial features are occluded in the recognition of facial expressions (visual route of recognition). This research examines the information about occluded facial features in early visual (V1-V3), face and emotion sensitive areas with fMRI, as well as the temporal dynamics of posterior brain regions in processing occluded facial features with EEG. MVPA reveals similar patterns of decoding across nonoverlapping samples of face information, suggesting the involvement of contextual influences beyond low-level processing (Chapter 2), as well as reliable decoding of facial expression (happy, fear and disgust) in conditions missing feature information (Chapters 2 & 3). This decoding, found 50-700ms, has three decoding phases, which potentially reveal the presence of feedforward and feedback processes (Chapter 3). These chapters also investigate the influence of task constraints, finding decoding differences between implicit and explicit processing conditions. Overall, this research helps understand how the brain deals with occluded stimuli; in keeping with accounts implying the rich role for top-down influences, such as predictive coding. The following experiment (Chapter 4) investigates embodiment in emotion recognition with fMRI; exploring shared representation in the perception and production of facial expression. MVPA reveals reliable expression decoding in the premotor brain regions across perception and production, demonstrating representational overlap across the sensory perception and motor production of expression. This tentatively supports strongly embodied simulation-based (nonvisual) theories. Collectively, the present research contributes to our knowledge of high-level influences in facial expression recognition, supporting the involvement of visual and non-visual routes to recognition, as well as providing further directions for future research.
Economic impact of the improvements on Fusarium head blight management in winter wheat in relation to modernization of agronomic practices

Ontario Ministry of Agriculture, Food, and Rural Affairs; Agriculture and Agri-Food Canada through the Growing Forward 2 Program; University of Guelph, Ridgetown Campus, Research Support Lab; Grain Farmers of Ontario; Food & Consumer Products of Canada; Canadian National Millers Association; Pepsico Foods and Nestle.
An Oscillator Ensemble Model of Sequence Learning

Learning and memorizing sequences of events is an important function of the human brain and the basis for forming expectations and making predictions. Learning is facilitated by repeating a sequence several times, causing rhythmic appearance of the individual sequence elements. This observation invites to consider the resulting multitude of rhythms as a spectral ‘fingerprint’ which characterizes the respective sequence. Here we explore the implications of this perspective by developing a neurobiologically plausible computational model which captures this ‘fingerprint’ by attuning an ensemble of neural oscillators. In our model, this attuning process is based on a number of oscillatory phenomena that have been observed in electrophysiological recordings of brain activity like synchronization, phase locking and reset as well as cross-frequency coupling. We compare the learning properties of the model with behavioral results from a study in human participants and observe good agreement of the errors for different levels of complexity of the sequence to be memorized. Finally, we suggest an extension of the model for processing sequences that extend over several sensory modalities.
DRAFT VERSION : Sketching and Ideation Activities for Situated Visualization Design

We report on findings from seven design workshops that used ideation and sketching activities to prototype new situated visualizations—representations of data that are displayed in proximity to the physical referents (such as people, objects, and locations) to which the data is related. Designing situated visualizations requires a fine-grained understanding of the context in which the visualizations are placed, as well as an exploration of different options for placement and form factors, which existing methods for visualization design do not account for. Focusing on small displays as a target platform, we reflect on our experiences of using a diverse range of sketching activities, materials, and prompts. Based on these observations, we identify challenges and opportunities for sketching and ideating situated visualizations. We also outline the space of design activities for situated visualization and highlight promising methods for both designers and researchers.
A comparison of clustering algorithms for face clustering

Video surveillance methods become increasingly widespread and popular in many organizations, including law enforcement, traffic control and residential applications. In particular, the police performs inves- tigations based on searching specific people in videos and in pictures. Because the number of such videos is increasing, manual examination of all frames becomes impossible. Some degree of automation is strongly needed. Face clustering is a method to group faces of people into clusters containing images of one single person. In the current study several clustering algorithms are described and applied on different datasets. The five clustering algorithms are: k-means, threshold clustering, mean shift, DBSCAN and Approximate Rank-Order. In the first experiments these clustering techniques are applied on subsets and the whole Labeled Faces in the Wild (LFW) dataset. Also a dataset containing faces of people appearing in videos of ISIS is tested to evaluate the performance of these clustering algorithms. The main finding is that threshold clustering shows the best performance in terms of the f-measure and amount of false positives. Also DBSCAN has shown good performance during our experiments and is considered as a good algorithm for face clustering. In addition it is discouraged to use k-means and for large datasets Approximate Rank-Order when clustering faces.
Short Communication: Antagonistic activity of bacteria isolated from coral Acropora sp of Karimunjawa Islands, Indonesia against Acroporid White Syndrome

Abstract. Sabdono A, Trianto A, Radjasa OK, Wijayanti DP. 2019. Antagonistic activity of bacteria isolated from coral Acropora sp of Karimunjawa Islands, Indonesia against Acroporid White Syndrome. Biodiversitas 20: 1350-1355. Acroporid White Syndrome (AWS) disease, causing coral death and mortality, has come out as a serious threat to coral reefs in the Karimunjawa Islands, Java Sea. In the present study, the bacterial community associated with the Acroporid were assessed for their antagonistic activity against bacteria associated with AWS disease. A culture collection of 43 bacteria associated with coral Acroporid was established by plating on Zobell’s 2214E. Isolates were screened using the overlay method. The results showed that five isolates (11.62 %) were able to inhibit the growth of the AWS isolates. These isolates were tested further for their consistency of antagonistic activity by using disc-agar diffused method. Based on their consistency, AU5 isolate was selected for study at advance. Polyphasic and phylogenetic analysis were used to characterized AU5 isolate. The DNA sequence analyses demonstrated that this bacterial isolate affiliated with Firmicutes. The AU5 isolate showed high 16S rDNA sequence identity to Virgibacillus salarius strain muzPC213 (99%). The results demonstrated the existence of bacterial coral symbionts with AWS antipathogenic activity on the coral tissue, possibly performing as biocontrol agent to protect the coral Acroporids against AWS outbreaks.
CRISP - Distributed Intelligence for Distributed Energy Resources: Selected Publications from the CRISP project

The article discusses the behavior of the Distributed Generation (DG) in order to increase the EPS (Electric Power System) robustness. Special attention is paid to the DG influence during emergency states such as the propagation of cascading failures or other major events. In one hand, a high amount of DG can improve the system response to these disturbances but, on other hand, a massive DG insertion could endanger the system operation. The article proposes a methodology to define the robustness and risk of a system operating point through the evaluation of a robustness index. Furthermore, EPS robustness could be enhanced by new emergency strategies such as intentional islanding operation. This would require new ICT components (Information and Communication Technologies) in order to share and analyze the information and then, elaborate decisions to avoid total blackouts.
Indoor Multifloor Localization Method Based on WiFi Fingerprints and LDA

Indoor localization has elicited increasing attention because it has been widely used in indoor location-based services. At present, many complex scenarios for indoor localization require position estimation not only in single-floor environments but also in multifloor ones. However, existing works exhibit certain limitations in solving the problems that involve high computational complexity and floor localization accuracy. In this paper, a multifloor identification system based on WiFi fingerprint database is designed to address these issues. This floor identification system is divided into offline and online phases. In the offline phase, a localization fingerprint database is built based on WiFi nodes and a multifloor identification model is proposed based on linear discriminant analysis (LDA), called MA_LDA. In the online phase, the final floor number is determined, and the trained model is combined with the majority voting mechanism. After determining the floor number, an algorithm based on the k-nearest neighbor (KNN), called LL_KNN, is proposed to obtain the location information of a target on the floor. Real experiment results show that our system can identify the floor number by using only a little WiFi node fingerprint information rather than all the nodes to reduce the computational complexity. It works efficiently and achieves high fault-tolerance performance compared with existing approaches in locating targets in a multifloor environment.
Exploring the Chemical Space of Cytochrome P450 Inhibitors Using Integrated Physicochemical Parameters, Drug Efficiency Metrics and Decision Tree Models

The cytochrome P450s (CYPs) play a central role in the metabolism of various endogenous and exogenous compounds including drugs. CYPs are vulnerable to inhibition and induction which can lead to adverse drug reactions. Therefore, insights into the underlying mechanism of CYP450 inhibition and the estimation of overall CYP inhibitor properties might serve as valuable tools during the early phases of drug discovery. Herein, we present a large data set of inhibitors against five major metabolic CYPs (CYP1A2, CYP2C9, CYP2C19, CYP2D6 and CYP3A4) for the evaluation of important physicochemical properties and ligand efficiency metrics to define property trends across various activity levels (active, efficient and inactive). Decision tree models for CYP inhibition were developed with an accuracy >90% for both the training set and 10-folds cross validation. Overall, molecular weight (MW), hydrogen bond acceptors/donors (HBA/HBD) and lipophilicity (clogP/logPo/w) represent important physicochemical descriptors for CYP450 inhibitors. However, highly efficient CYP inhibitors show mean MW, HBA, HBD and logP values between 294.18–482.40,5.0–8.2,1–7.29 and 1.68–2.57, respectively. Our results might help in optimization of toxicological profiles associated with new chemical entities (NCEs), through a better understanding of inhibitor properties leading to CYP-mediated interactions.
Prevalence of Atypical Disorders and Associated Factors in Undergraduate Students of Prince of Songkla University, Songkhla, Thailand

Objective: To quantify the prevalence of eating disorders and factors associated with eating disorders among undergraduate students in Prince of Songkla University, Hat Yai Campus.Material and Methods: This study was a cross sectional descriptive study using random sampling by proportionate accidental sampling. We used the Thai Eating Attitudes Test-26 (EAT-26) for collecting information about eating attitudes. Participants who had scores equal or higher than 12 (≥12) were assumed to have atypical eating attitudes and behaviors. We used the R and R studio program to analyze information. Multivariate logistic regression was used for correlation analysis.Results: In this study, we had completed questionnaires from 500 students (response rate 65.6%). The overall prevalence of atypical eating attitudes and behaviors in undergraduate students in Prince of Songkla University, Hat Yai Campus was 37.2%. We found that overweight body mass index (BMI) (BMI 23.00-24.99 kg/m2) and obesity BMI (≥25.00 kg/m2) were significantly more prevalent in students with atypical eating attitudes and behaviors than normal BMI (18.50-22.99 kg/m2), with odds ratios of 3.3 [95% confidence interval (CI)=1.8-6.2] and 3.7 (95% CI=1.9-6.9), respectively. However, multivariate logistic regression revealed no associations between atypical eating attitudes and behaviors, sex, target weight, biological disease, psychological disease, current medication(s) or faculty. Atypical eating attitudes and behaviors were significantly associated only with body mass index BMI. The overweight and obese BMI groups had significantly increased risks of 3.3 and 3.7 times of atypical eating attitudes and behaviors compared to the normal group, with 95% CIs of 1.8-6.2 and 1.9-6.9, respectively.Conclusion: From this study, overweight BMI and obesity BMI were significantly more prevalent in students with atypical eating attitudes and behaviors than normal BMI. BMI was the only factor significantly associated with atypical eating attitudes and behaviors.
Extreme Winds: Impacts of Hurricanes and Ramp Events on Wind Energy

Extreme fluctuations of wind cause damaging loads on wind turbines and pose significant difficulties in wind power forecasting. Increased global interest to move from conventional to renewable energy sources highlights the importance of successful wind energy integration into the electrical grid. Knowledge of extreme atmospheric phenomena that may affect wind turbines and the power they produce is critical to a wind farm’s success. This dissertation addresses two types of extreme events: extreme winds in the hurricane boundary layer, and forecasting challenges of rapid changes in wind speeds, or wind ramping events. First, this dissertation quantifies the extreme wind conditions that future offshore wind turbines, placed in hurricane-prone regions, could experience. The analyses include novel large-eddy simulations of major hurricanes evaluated with real hurricane reconnaissance flights. Investigations into turbulence characteristics, mean wind speeds, 3s gusts, wind veer, and wind shear at different locations of the hurricane provide first-ever estimates of how these variables could impact wind turbines. Comparisons of these variables to current wind turbine design standards suggest that modifications to the standards may be required to represent extreme hurricane conditions. Work presented herein also addresses the difficulty in predicting power ramp events, which are large and abrupt changes in wind power production. These ramp events
Improving AdaBoost-based Intrusion Detection System (IDS) Performance on CIC IDS 2017 Dataset

This paper considers the use of Synthetic Minority Oversampling Technique (SMOTE), Principal Component Analysis (PCA), and Ensemble Feature Selection (EFS) to improve the performance of AdaBoost-based Intrusion Detection System (IDS) on the latest and challenging CIC IDS 2017 Dataset [1]. Previous research [1] has proposed the use of AdaBoost classifier to cope with the new dataset. However, due to several problems such as imbalance of training data and inappropriate selection of classification methods, the performance is still inferior. In this research, we aim at constructing an improvement performance intrusion detection approach to handle the imbalance of training data, SMOTE is selected to tackle the problem. Moreover, Principal Component Analysis (PCA) and Ensemble Feature Selection (EFS) are applied as the feature selection to select important attributes from the new dataset. The evaluation results show that the proposed AdaBoost classifier using PCA and SMOTE yields Area Under the Receiver Operating Characteristic curve (AUROC) of 92% and the AdaBoost classifier using EFS and SMOTE produces an accuracy, precision, recall, and F1 Score of 81.83 %, 81.83%, 100%, and 90.01% respectively.